diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2021-02-15 17:05:19 +0100 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2021-02-15 17:05:19 +0100 |
| commit | 11c7f26dd6c532d977d9875f3c16952c7bba37ca (patch) | |
| tree | 03393f3e63f1db688166e82fff7a8d16bb239154 | |
| parent | c4f327e62f19c0a3fc77a538f7cf0c2c619019d8 (diff) | |
| parent | a9427a09abdf7ce9aaeae7c7190f90c8f2e5ef52 (diff) | |
Merge branch 'master' into saemix
201 files changed, 1551 insertions, 1096 deletions
diff --git a/DESCRIPTION b/DESCRIPTION index 4a06ef2f..a47f0b74 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data -Version: 1.0.2.9000 -Date: 2021-02-13 +Version: 1.0.3.9000 +Date: 2021-02-15 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), email = "jranke@uni-bremen.de", @@ -1,4 +1,4 @@ -# mkin 1.0.2.9000 +# mkin 1.0.3.9000 ## General @@ -12,6 +12,10 @@ - 'saem': generic function to fit saemix models using 'saemix_model' and 'saemix_data', with a generator 'saem.mmkin', summary and plot methods +# mkin 1.0.3 + +- Review and update README, the 'Introduction to mkin' vignette and some of the help pages + # mkin 1.0.2 - 'mkinfit': Keep model names stored in 'mkinmod' objects, avoiding their loss in 'gmkin' diff --git a/R/sigma_twocomp.R b/R/sigma_twocomp.R index e7f4368b..eee742bb 100644 --- a/R/sigma_twocomp.R +++ b/R/sigma_twocomp.R @@ -23,6 +23,11 @@ #' #' Rocke, David M. and Lorenzato, Stefan (1995) A two-component model for #' measurement error in analytical chemistry. Technometrics 37(2), 176-184. +#' +#' Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical +#' Degradation Data. *Environments* 6(12) 124 +#' \doi{10.3390/environments6120124}. +#' #' @examples #' times <- c(0, 1, 3, 7, 14, 28, 60, 90, 120) #' d_pred <- data.frame(time = times, parent = 100 * exp(- 0.03 * times)) diff --git a/README.html b/README.html index c46cbc63..3f7e60d9 100644 --- a/README.html +++ b/README.html @@ -411,30 +411,44 @@ summary { </div> <div id="features" class="section level2"> <h2>Features</h2> +<div id="general" class="section level3"> +<h3>General</h3> <ul> <li>Highly flexible model specification using <a href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.</li> -<li>As of version 0.9-39, fitting of several models to several datasets, optionally in parallel, is supported, see for example <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</li> <li>Model solution (forward modelling) in the function <a href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li> -<li>If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">vignette <code>compiled_models</code></a>. The autogeneration of C code was inspired by the <a href="https://github.com/karlines/ccSolve"><code>ccSolve</code></a> package. Thanks to Karline Soetaert for her work on that.</li> -<li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice cooperation on this, especially the isometric log-ratio transformation that is now used for the formation fractions.</li> -<li>A side effect of this is that when parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which can not occur in a single experiment with a single defined radiolabel position.</li> <li>The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.</li> <li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.</li> <li>The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.</li> -<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.</li> -<li>Three different error models can be selected using the argument <code>error_model</code> to the <a href="https://pkgdown.jrwb.de/mkin/reference/mkinfit.html"><code>mkinfit</code></a> function.</li> <li>The ‘variance by variable’ error model which is often fitted using Iteratively Reweighted Least Squares (IRLS) should now be specified as <code>error_model = "obs"</code>.</li> -<li>A two-component error model similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke and Lorenzato</a> can be selected using the argument <code>error_model = "tc"</code>.</li> +</ul> +</div> +<div id="unique-in-mkin" class="section level3"> +<h3>Unique in mkin</h3> +<ul> +<li>Three different error models can be selected using the argument <code>error_model</code> to the <a href="https://pkgdown.jrwb.de/mkin/reference/mkinfit.html"><code>mkinfit</code></a> function. A two-component error model similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke and Lorenzato</a> can be selected using the argument <code>error_model = "tc"</code>.</li> +<li>Model comparisons using the Akaike Information Criterion (AIC) are supported which can also be used for non-constant variance. In such cases the FOCUS chi-squared error level is not meaningful.</li> +<li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution.</li> +<li>When parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which cannot occur in a single experiment with a single defined radiolabel position.</li> +<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model is equivalent to the DFOP model used by other tools for biphasic metabolite curves. However, the SFORB model has the advantage that there is a mechanistic interpretation of the model parameters.</li> <li>Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the <a href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">nlme.mmkin</a> method. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils).</li> </ul> </div> +<div id="performance" class="section level3"> +<h3>Performance</h3> +<ul> +<li>Parallel fitting of several models to several datasets is supported, see for example <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</li> +<li>If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">vignette <code>compiled_models</code></a>. The autogeneration of C code was inspired by the <a href="https://github.com/karlines/ccSolve"><code>ccSolve</code></a> package. Thanks to Karline Soetaert for her work on that.</li> +<li>Even if no compiler is installed, many degradation models still give <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html">very good performance</a>, as current versions of mkin also have <a href="https://jrwb.de/performance-improvements-mkin/">analytical solutions for some models with one metabolite</a>, and if SFO or SFORB are used for the parent compound, Eigenvalue based solutions of the degradation model are available.</li> +</ul> +</div> +</div> <div id="gui" class="section level2"> <h2>GUI</h2> <p>There is a graphical user interface that may be useful. Please refer to its <a href="https://pkgdown.jrwb.de/gmkin/">documentation page</a> for installation instructions and a manual.</p> </div> <div id="news" class="section level2"> <h2>News</h2> -<p>There is a ChangeLog, for the latest <a href="https://cran.r-project.org/package=mkin/news/news.html">CRAN release</a> and one for the <a href="https://github.com/jranke/mkin/blob/master/NEWS.md">github master branch</a>.</p> +<p>There is a list of changes for the latest <a href="https://cran.r-project.org/package=mkin/news/news.html">CRAN release</a> and one for each github branch, e.g. <a href="https://github.com/jranke/mkin/blob/master/NEWS.md">the main branch</a>.</p> </div> <div id="credits-and-historical-remarks" class="section level2"> <h2>Credits and historical remarks</h2> @@ -446,6 +460,16 @@ summary { <p>In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named KinGUII whose R code is based on <code>mkin</code>, but which added, among other refinements, a closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation of the variance for each of the observed variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, similar to what is available e.g. in the <code>FME</code> package.</p> <p>Somewhat in parallel, Syngenta has sponsored the development of an <code>mkin</code> and KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is more limited in the model formulation, but puts more weight on usability. CAKE is available for download from the <a href="https://www.tessella.com/showcase/computer-assisted-kinetic-evaluation">CAKE website</a>, where you can also find a zip archive of the R scripts derived from <code>mkin</code>, published under the GPL license.</p> <p>Finally, there is <a href="https://github.com/zhenglei-gao/KineticEval">KineticEval</a>, which contains a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well.</p> +<p>Thanks to René Lehmann, formerly working at the Umweltbundesamt, for the nice cooperation cooperation on parameter transformations, especially the isometric log-ratio transformation that is now used for formation fractions in case there are more than two transformation targets.</p> +<p>Many inspirations for improvements of mkin resulted from doing kinetic evaluations of degradation data for my clients while working at Harlan Laboratories and at Eurofins Regulatory AG, and now as an independent consultant.</p> +<p>Funding was received from the Umweltbundesamt in the course of the projects</p> +<ul> +<li>Grant Number 112407 (Testing and validation of modelling software as an alternative to ModelMaker 4.0, 2014-2015)</li> +<li>Project Number 56703 (Optimization of gmkin for routine use in the Umweltbundesamt, 2015)</li> +<li>Project Number 112407 (Testing the feasibility of using an error model according to Rocke and Lorenzato for more realistic parameter estimates in the kinetic evaluation of degradation data, 2018-2019)</li> +<li>Project Number 120667 (Development of objective criteria for the evaluation of the visual fit in the kinetic evaluation of degradation data, 2019-2020)</li> +<li>Project 146839 (Checking the feasibility of using mixed-effects models for the derivation of kinetic modelling parameters from degradation studies, 2020-2021)</li> +</ul> </div> <div id="references" class="section level2"> <h2>References</h2> @@ -45,40 +45,19 @@ version is found in the ['dev' subdirectory](https://pkgdown.jrwb.de/mkin/dev/). ## Features +### General + * Highly flexible model specification using [`mkinmod`](https://pkgdown.jrwb.de/mkin/reference/mkinmod.html), including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable. -* As of version 0.9-39, fitting of several models to several datasets, optionally in - parallel, is supported, see for example - [`plot.mmkin`](https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html). * Model solution (forward modelling) in the function [`mkinpredict`](https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html) is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the `deSolve` package (default is `lsoda`). -* If a C compiler is installed, the kinetic models are compiled from automatically - generated C code, see - [vignette `compiled_models`](https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html). - The autogeneration of C code was - inspired by the [`ccSolve`](https://github.com/karlines/ccSolve) package. Thanks - to Karline Soetaert for her work on that. -* By default, kinetic rate constants and kinetic formation fractions are - transformed internally using - [`transform_odeparms`](https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html) - so their estimators can more reasonably be expected to follow - a normal distribution. This has the side effect that no constraints - are needed in the optimisation. Thanks to René Lehmann for the nice - cooperation on this, especially the isometric log-ratio transformation - that is now used for the formation fractions. -* A side effect of this is that when parameter estimates are backtransformed - to match the model definition, confidence intervals calculated from - standard errors are also backtransformed to the correct scale, and will - not include meaningless values like negative rate constants or - formation fractions adding up to more than 1, which can not occur in - a single experiment with a single defined radiolabel position. * The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters. * Summary and plotting functions. The `summary` of an `mkinfit` object is in @@ -86,23 +65,59 @@ version is found in the ['dev' subdirectory](https://pkgdown.jrwb.de/mkin/dev/). approximately reproduce the fit with other tools. * The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable. -* When a metabolite decline phase is not described well by SFO kinetics, - SFORB kinetics can be used for the metabolite. -* Three different error models can be selected using the argument `error_model` - to the [`mkinfit`](https://pkgdown.jrwb.de/mkin/reference/mkinfit.html) - function. * The 'variance by variable' error model which is often fitted using Iteratively Reweighted Least Squares (IRLS) should now be specified as `error_model = "obs"`. -* A two-component error model similar to the one proposed by + +### Unique in mkin + +* Three different error models can be selected using the argument `error_model` + to the [`mkinfit`](https://pkgdown.jrwb.de/mkin/reference/mkinfit.html) + function. A two-component error model similar to the one proposed by [Rocke and Lorenzato](https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html) can be selected using the argument `error_model = "tc"`. +* Model comparisons using the Akaike Information Criterion (AIC) are supported which + can also be used for non-constant variance. In such cases the FOCUS + chi-squared error level is not meaningful. +* By default, kinetic rate constants and kinetic formation fractions are + transformed internally using + [`transform_odeparms`](https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html) + so their estimators can more reasonably be expected to follow + a normal distribution. +* When parameter estimates are backtransformed to match the model definition, + confidence intervals calculated from standard errors are also backtransformed + to the correct scale, and will not include meaningless values like negative + rate constants or formation fractions adding up to more than 1, which cannot + occur in a single experiment with a single defined radiolabel position. +* When a metabolite decline phase is not described well by SFO kinetics, + SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model + is equivalent to the DFOP model used by other tools for biphasic metabolite curves. + However, the SFORB model has the advantage that there is a mechanistic + interpretation of the model parameters. * Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the [nlme.mmkin](https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html) method. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils). +### Performance + +* Parallel fitting of several models to several datasets is supported, see for + example + [`plot.mmkin`](https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html). +* If a C compiler is installed, the kinetic models are compiled from automatically + generated C code, see + [vignette `compiled_models`](https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html). + The autogeneration of C code was + inspired by the [`ccSolve`](https://github.com/karlines/ccSolve) package. Thanks + to Karline Soetaert for her work on that. +* Even if no compiler is installed, many degradation models still give + [very good performance](https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html), + as current versions of mkin also have [analytical solutions for some models + with one metabolite](https://jrwb.de/performance-improvements-mkin/), and if + SFO or SFORB are used for the parent compound, Eigenvalue based solutions of + the degradation model are available. + ## GUI There is a graphical user interface that may be useful. Please @@ -111,8 +126,8 @@ for installation instructions and a manual. ## News -There is a ChangeLog, for the latest [CRAN release](https://cran.r-project.org/package=mkin/news/news.html) -and one for the [github master branch](https://github.com/jranke/mkin/blob/master/NEWS.md). +There is a list of changes for the latest [CRAN release](https://cran.r-project.org/package=mkin/news/news.html) +and one for each github branch, e.g. [the main branch](https://github.com/jranke/mkin/blob/master/NEWS.md). ## Credits and historical remarks @@ -167,6 +182,28 @@ Finally, there is a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well. +Thanks to René Lehmann, formerly working at the Umweltbundesamt, for the nice +cooperation cooperation on parameter transformations, especially the isometric +log-ratio transformation that is now used for formation fractions in case +there are more than two transformation targets. + +Many inspirations for improvements of mkin resulted from doing kinetic evaluations +of degradation data for my clients while working at Harlan Laboratories and +at Eurofins Regulatory AG, and now as an independent consultant. + +Funding was received from the Umweltbundesamt in the course of the projects + +- Grant Number 112407 (Testing and validation of modelling software as an alternative +to ModelMaker 4.0, 2014-2015) +- Project Number 56703 (Optimization of gmkin for routine use in the Umweltbundesamt, 2015) +- Project Number 112407 (Testing the feasibility of using an error model + according to Rocke and Lorenzato for more realistic parameter estimates in + the kinetic evaluation of degradation data, 2018-2019) +- Project Number 120667 (Development of objective criteria for the evaluation + of the visual fit in the kinetic evaluation of degradation data, 2019-2020) +- Project 146839 (Checking the feasibility of using mixed-effects models for + the derivation of kinetic modelling parameters from degradation studies, 2020-2021) + ## References <table> @@ -7,4 +7,3 @@ * checking for LF line-endings in source and make files and shell scripts * checking for empty or unneeded directories * building ‘mkin_1.0.2.9000.tar.gz’ - diff --git a/docs/404.html b/docs/404.html index f038fb95..48cba3d7 100644 --- a/docs/404.html +++ b/docs/404.html @@ -71,7 +71,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="https://pkgdown.jrwb.de/mkin/index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index 786b2c47..f3a56065 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -31,7 +31,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -94,13 +94,13 @@ - </header><div class="row"> + </header><script src="FOCUS_D_files/header-attrs-2.6/header-attrs.js"></script><script src="FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2021-02-03</h4> + <h4 class="date">Last change 31 January 2019 (rebuilt 2021-02-15)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/FOCUS_D.rmd"><code>vignettes/FOCUS_D.rmd</code></a></small> <div class="hidden name"><code>FOCUS_D.rmd</code></div> @@ -185,10 +185,10 @@ <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.0 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Wed Feb 3 17:32:13 2021 -## Date of summary: Wed Feb 3 17:32:14 2021 +## Date of fit: Mon Feb 15 13:46:23 2021 +## Date of summary: Mon Feb 15 13:46:23 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -196,7 +196,7 @@ ## ## Model predictions using solution type analytical ## -## Fitted using 401 model solutions performed in 0.161 s +## Fitted using 401 model solutions performed in 0.162 s ## ## Error model: Constant variance ## @@ -239,11 +239,11 @@ ## ## Parameter correlation: ## parent_0 log_k_parent log_k_m1 f_parent_qlogis sigma -## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.171e-06 -## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.481e-07 -## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.209e-07 +## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.172e-06 +## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.483e-07 +## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.205e-07 ## f_parent_qlogis -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 1.305e-06 -## sigma -1.171e-06 -8.481e-07 8.209e-07 1.305e-06 1.000e+00 +## sigma -1.172e-06 -8.483e-07 8.205e-07 1.305e-06 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. diff --git a/docs/articles/FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/articles/FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/FOCUS_D_files/figure-html/plot-1.png b/docs/articles/FOCUS_D_files/figure-html/plot-1.png Binary files differindex 5278038a..abf26715 100644 --- a/docs/articles/FOCUS_D_files/figure-html/plot-1.png +++ b/docs/articles/FOCUS_D_files/figure-html/plot-1.png diff --git a/docs/articles/FOCUS_D_files/header-attrs-2.6/header-attrs.js b/docs/articles/FOCUS_D_files/header-attrs-2.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/FOCUS_D_files/header-attrs-2.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html index 4c3bed4c..53e4e1d3 100644 --- a/docs/articles/FOCUS_L.html +++ b/docs/articles/FOCUS_L.html @@ -31,7 +31,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -94,13 +94,13 @@ - </header><div class="row"> + </header><script src="FOCUS_L_files/header-attrs-2.6/header-attrs.js"></script><script src="FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2021-02-03</h4> + <h4 class="date">Last change 17 November 2016 (rebuilt 2021-02-15)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/FOCUS_L.rmd"><code>vignettes/FOCUS_L.rmd</code></a></small> <div class="hidden name"><code>FOCUS_L.rmd</code></div> @@ -126,17 +126,17 @@ <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">m.L1.SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.0 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Wed Feb 3 17:32:16 2021 -## Date of summary: Wed Feb 3 17:32:16 2021 +## Date of fit: Mon Feb 15 13:46:25 2021 +## Date of summary: Mon Feb 15 13:46:25 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 133 model solutions performed in 0.031 s +## Fitted using 133 model solutions performed in 0.032 s ## ## Error model: Constant variance ## @@ -232,17 +232,17 @@ <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> -<pre><code>## mkin version used for fitting: 1.0.0 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Wed Feb 3 17:32:16 2021 -## Date of summary: Wed Feb 3 17:32:16 2021 +## Date of fit: Mon Feb 15 13:46:26 2021 +## Date of summary: Mon Feb 15 13:46:26 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 380 model solutions performed in 0.085 s +## Fitted using 369 model solutions performed in 0.083 s ## ## Error model: Constant variance ## @@ -270,22 +270,22 @@ ## ## Results: ## -## AIC BIC logLik -## 95.88778 99.44927 -43.94389 +## AIC BIC logLik +## 95.88781 99.44929 -43.9439 ## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 92.47 1.2820 89.720 95.220 -## log_alpha 16.92 NaN NaN NaN -## log_beta 19.26 NaN NaN NaN -## sigma 2.78 0.4501 1.814 3.745 +## log_alpha 13.78 NaN NaN NaN +## log_beta 16.13 NaN NaN NaN +## sigma 2.78 0.4598 1.794 3.766 ## ## Parameter correlation: -## parent_0 log_alpha log_beta sigma -## parent_0 1.000000 NaN NaN 0.002218 -## log_alpha NaN 1 NaN NaN -## log_beta NaN NaN 1 NaN -## sigma 0.002218 NaN NaN 1.000000 +## parent_0 log_alpha log_beta sigma +## parent_0 1.0000000 NaN NaN 0.0001671 +## log_alpha NaN 1 NaN NaN +## log_beta NaN NaN 1 NaN +## sigma 0.0001671 NaN NaN 1.0000000 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -293,9 +293,9 @@ ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 9.247e+01 NA NA 89.720 95.220 -## alpha 2.223e+07 NA NA NA NA -## beta 2.325e+08 NA NA NA NA -## sigma 2.780e+00 NA NA 1.814 3.745 +## alpha 9.658e+05 NA NA NA NA +## beta 1.010e+07 NA NA NA NA +## sigma 2.780e+00 NA NA 1.794 3.766 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -344,10 +344,10 @@ <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p> <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.0 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Wed Feb 3 17:32:17 2021 -## Date of summary: Wed Feb 3 17:32:17 2021 +## Date of fit: Mon Feb 15 13:46:26 2021 +## Date of summary: Mon Feb 15 13:46:26 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -389,10 +389,10 @@ ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09 -## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.617e-07 -## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.386e-07 -## sigma -7.436e-09 -1.617e-07 -1.386e-07 1.000e+00 +## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09 +## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.602e-07 +## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.372e-07 +## sigma -7.828e-09 -1.602e-07 -1.372e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -425,10 +425,10 @@ <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p> <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.0 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Wed Feb 3 17:32:17 2021 -## Date of summary: Wed Feb 3 17:32:17 2021 +## Date of fit: Mon Feb 15 13:46:27 2021 +## Date of summary: Mon Feb 15 13:46:27 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -437,7 +437,7 @@ ## ## Model predictions using solution type analytical ## -## Fitted using 581 model solutions performed in 0.133 s +## Fitted using 581 model solutions performed in 0.134 s ## ## Error model: Constant variance ## @@ -468,18 +468,18 @@ ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 93.950 9.998e-01 91.5900 96.3100 -## log_k1 3.117 1.929e+03 -4558.0000 4564.0000 +## log_k1 3.112 1.842e+03 -4353.0000 4359.0000 ## log_k2 -1.088 6.285e-02 -1.2370 -0.9394 ## g_qlogis -0.399 9.946e-02 -0.6342 -0.1638 ## sigma 1.414 2.886e-01 0.7314 2.0960 ## ## Parameter correlation: -## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 6.459e-07 9.147e-11 2.665e-01 8.413e-11 -## log_k1 6.459e-07 1.000e+00 1.061e-04 -2.087e-04 -9.802e-06 -## log_k2 9.147e-11 1.061e-04 1.000e+00 -7.903e-01 -2.429e-09 -## g_qlogis 2.665e-01 -2.087e-04 -7.903e-01 1.000e+00 4.049e-09 -## sigma 8.413e-11 -9.802e-06 -2.429e-09 4.049e-09 1.000e+00 +## parent_0 log_k1 log_k2 g_qlogis sigma +## parent_0 1.000e+00 6.783e-07 -3.390e-10 2.665e-01 -2.967e-10 +## log_k1 6.783e-07 1.000e+00 1.116e-04 -2.196e-04 -1.031e-05 +## log_k2 -3.390e-10 1.116e-04 1.000e+00 -7.903e-01 2.917e-09 +## g_qlogis 2.665e-01 -2.196e-04 -7.903e-01 1.000e+00 -4.408e-09 +## sigma -2.967e-10 -1.031e-05 2.917e-09 -4.408e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -487,7 +487,7 @@ ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 93.9500 9.397e+01 2.036e-12 91.5900 96.3100 -## k1 22.5800 5.303e-04 4.998e-01 0.0000 Inf +## k1 22.4800 5.553e-04 4.998e-01 0.0000 Inf ## k2 0.3369 1.591e+01 4.697e-07 0.2904 0.3909 ## g 0.4016 1.680e+01 3.238e-07 0.3466 0.4591 ## sigma 1.4140 4.899e+00 8.776e-04 0.7314 2.0960 @@ -499,7 +499,7 @@ ## ## Estimated disappearance times: ## DT50 DT90 DT50back DT50_k1 DT50_k2 -## parent 0.5335 5.311 1.599 0.0307 2.058</code></pre> +## parent 0.5335 5.311 1.599 0.03084 2.058</code></pre> <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p> </div> </div> @@ -531,10 +531,10 @@ <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> <div class="sourceCode" id="cb24"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.0 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Wed Feb 3 17:32:18 2021 -## Date of summary: Wed Feb 3 17:32:18 2021 +## Date of fit: Mon Feb 15 13:46:27 2021 +## Date of summary: Mon Feb 15 13:46:28 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -581,11 +581,11 @@ ## ## Parameter correlation: ## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.671e-08 -## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.148e-07 +## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.664e-08 +## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.147e-07 ## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 1.022e-06 -## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.929e-07 -## sigma -9.671e-08 7.148e-07 1.022e-06 -7.929e-07 1.000e+00 +## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.926e-07 +## sigma -9.664e-08 7.147e-07 1.022e-06 -7.926e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -644,17 +644,17 @@ <p>The <span class="math inline">\(\chi^2\)</span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.0 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Wed Feb 3 17:32:19 2021 -## Date of summary: Wed Feb 3 17:32:19 2021 +## Date of fit: Mon Feb 15 13:46:28 2021 +## Date of summary: Mon Feb 15 13:46:28 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 142 model solutions performed in 0.03 s +## Fitted using 142 model solutions performed in 0.031 s ## ## Error model: Constant variance ## @@ -709,17 +709,17 @@ ## parent 106 352</code></pre> <div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.0 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Wed Feb 3 17:32:19 2021 -## Date of summary: Wed Feb 3 17:32:19 2021 +## Date of fit: Mon Feb 15 13:46:28 2021 +## Date of summary: Mon Feb 15 13:46:28 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 224 model solutions performed in 0.046 s +## Fitted using 224 model solutions performed in 0.047 s ## ## Error model: Constant variance ## @@ -754,10 +754,10 @@ ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.456e-07 -## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.169e-08 -## log_beta -5.543e-01 9.889e-01 1.000e+00 4.910e-08 -## sigma -2.456e-07 2.169e-08 4.910e-08 1.000e+00 +## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07 +## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.478e-08 +## log_beta -5.543e-01 9.889e-01 1.000e+00 5.211e-08 +## sigma -2.468e-07 2.478e-08 5.211e-08 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -781,7 +781,7 @@ <div id="references" class="section level1 unnumbered"> <h1 class="hasAnchor"> <a href="#references" class="anchor"></a>References</h1> -<div id="refs" class="references"> +<div id="refs" class="references hanging-indent"> <div id="ref-ranke2014"> <p>Ranke, Johannes. 2014. “Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0.” Umweltbundesamt Projektnummer 27452.</p> </div> diff --git a/docs/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); 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We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/index.html b/docs/articles/index.html index 52770090..22753e8d 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -71,7 +71,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html index e240323f..6dbb093d 100644 --- a/docs/articles/mkin.html +++ b/docs/articles/mkin.html @@ -31,7 +31,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -94,13 +94,13 @@ - </header><div class="row"> + </header><script src="mkin_files/header-attrs-2.6/header-attrs.js"></script><script src="mkin_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2021-02-03</h4> + <h4 class="date">Last change 15 February 2021 (rebuilt 2021-02-15)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/mkin.rmd"><code>vignettes/mkin.rmd</code></a></small> <div class="hidden name"><code>mkin.rmd</code></div> @@ -148,28 +148,34 @@ <div id="background" class="section level1"> <h1 class="hasAnchor"> <a href="#background" class="anchor"></a>Background</h1> -<p>Many approaches are possible regarding the evaluation of chemical degradation data.</p> -<p>The <code>mkin</code> package <span class="citation">(Ranke 2019)</span> implements the approach recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe <span class="citation">(FOCUS Work Group on Degradation Kinetics 2006, 2014)</span> for simple decline data series, data series with transformation products, commonly termed metabolites, and for data series for more than one compartment. It is also possible to include back reactions, so equilibrium reactions and equilibrium partitioning can be specified, although this oftentimes leads to an overparameterisation of the model.</p> +<p>The <code>mkin</code> package <span class="citation">(Ranke 2021)</span> implements the approach to degradation kinetics recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe <span class="citation">(FOCUS Work Group on Degradation Kinetics 2006, 2014)</span>. It covers data series describing the decline of one compound, data series with transformation products (commonly termed metabolites) and data series for more than one compartment. It is possible to include back reactions. Therefore, equilibrium reactions and equilibrium partitioning can be specified, although this often leads to an overparameterisation of the model.</p> <p>When the first <code>mkin</code> code was published in 2010, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were KinGUI <span class="citation">(Schäfer et al. 2007)</span>, a MATLAB based tool with a graphical user interface that was specifically tailored to the task and included some output as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data.</p> -<p>The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>), where the code is still occasionally updated.</p> -<p>At that time, the R package <code>FME</code> (Flexible Modelling Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the relative standard deviation that has to be assumed for the residuals, such that the <span class="math inline">\(\chi^2\)</span> goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass using an significance level <span class="math inline">\(\alpha\)</span> of 0.05. This relative error, expressed as a percentage, is often termed <span class="math inline">\(\chi^2\)</span> error level or similar.</p> -<p>Also, <code>mkin</code> introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to <code>mkin</code> for the case of linear differential equations (<em>i.e.</em> where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases. This, however, has become somehow obsolete, as the use of compiled code described below gives even smaller execution times.</p> +<p>The ‘mkin’ code was first uploaded to the BerliOS development platform. When this was taken down, the version control history was imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>), where the code is still being updated.</p> +<p>At that time, the R package <code>FME</code> (Flexible Modelling Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the <span class="math inline">\(\chi^2\)</span> error level as defined in this guidance.</p> +<p>Also, <code>mkin</code> introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to <code>mkin</code> for the case of linear differential equations (<em>i.e.</em> where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases. This, has become somehow obsolete, as the use of compiled code described below gives even faster execution times.</p> <p>The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in <code>mkin</code> from the very beginning.</p> <div id="derived-software-tools" class="section level2"> <h2 class="hasAnchor"> <a href="#derived-software-tools" class="anchor"></a>Derived software tools</h2> -<p>Soon after the publication of <code>mkin</code>, two derived tools were published, namely KinGUII (available from Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.</p> -<p>CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.3 of CAKE release in March 2016 uses a basic scheme for up to six metabolites in a flexible arrangement, but does not support back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites.</p> +<p>Soon after the publication of <code>mkin</code>, two derived tools were published, namely KinGUII (developed at Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.</p> +<p>CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.4 of CAKE released in May 2020 uses a scheme for up to six metabolites in a flexible arrangement and supports biphasic modelling of metabolites, but does not support back-reactions (non-instantaneous equilibria).</p> <p>KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.</p> <p>A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named <code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin/">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html">manual</a> for further information.</p> <p>A comparison of scope, usability and numerical results obtained with these tools has been recently been published by <span class="citation">Ranke, Wöltjen, and Meinecke (2018)</span>.</p> </div> -<div id="recent-developments" class="section level2"> -<h2 class="hasAnchor"> -<a href="#recent-developments" class="anchor"></a>Recent developments</h2> -<p>Currently (July 2019), the main features available in <code>mkin</code> which are not present in KinGUII or CAKE, are the speed increase by using compiled code when a compiler is present, parallel model fitting on multicore machines using the <code>mmkin</code> function, and the estimation of parameter confidence intervals based on transformed parameters.</p> -<p>In addition, the possibility to use two alternative error models to constant variance have been integrated. The variance by variable error model introduced by <span class="citation">Gao et al. (2011)</span> has been available via an iteratively reweighted least squares (IRLS) procedure since mkin <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version 0.9-22</a>. With <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release 0.9.49.5</a>, the IRLS algorithm has been replaced by direct or step-wise maximisation of the likelihood function, which makes it possible not only to fit the variance by variable error model but also a <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component error model</a> inspired by error models developed in analytical chemistry.</p> </div> +<div id="unique-features" class="section level1"> +<h1 class="hasAnchor"> +<a href="#unique-features" class="anchor"></a>Unique features</h1> +<p>Currently, the main unique features available in <code>mkin</code> are</p> +<ul> +<li>the <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">speed increase</a> by using compiled code when a compiler is present,</li> +<li>parallel model fitting on multicore machines using the <a href="https://pkgdown.jrwb.de/mkin/reference/mmkin.html"><code>mmkin</code> function</a>,</li> +<li>the estimation of parameter confidence intervals based on transformed parameters (see below) and</li> +<li>the possibility to use the <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component error model</a> +</li> +</ul> +<p>The iteratively reweighted least squares fitting of different variances for each variable as introduced by <span class="citation">Gao et al. (2011)</span> has been available in mkin since <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version 0.9-22</a>. With <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release 0.9.49.5</a>, the IRLS algorithm has been complemented by direct or step-wise maximisation of the likelihood function, which makes it possible not only to fit the variance by variable error model but also a <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component error model</a> inspired by error models developed in analytical chemistry <span class="citation">(Ranke and Meinecke 2019)</span>.</p> </div> <div id="internal-parameter-transformations" class="section level1"> <h1 class="hasAnchor"> @@ -177,13 +183,13 @@ <p>For rate constants, the log transformation is used, as proposed by Bates and Watts <span class="citation">(1988, 77, 149)</span>. Approximate intervals are constructed for the transformed rate constants <span class="citation">(compare Bates and Watts 1988, 135)</span>, <em>i.e.</em> for their logarithms. Confidence intervals for the rate constants are then obtained using the appropriate backtransformation using the exponential function.</p> <p>In the first version of <code>mkin</code> allowing for specifying models using formation fractions, a home-made reparameterisation was used in order to ensure that the sum of formation fractions would not exceed unity.</p> <p>This method is still used in the current version of KinGUII (v2.1 from April 2014), with a modification that allows for fixing the pathway to sink to zero. CAKE uses penalties in the objective function in order to enforce this constraint.</p> -<p>In 2012, an alternative reparameterisation of the formation fractions was proposed together with René Lehmann <span class="citation">(Ranke and Lehmann 2012)</span>, based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter confidence intervals.</p> +<p>In 2012, an alternative reparameterisation of the formation fractions was proposed together with René Lehmann <span class="citation">(Ranke and Lehmann 2012)</span>, based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter confidence intervals. In the current version of mkin, a logit transformation is used for parameters that are bound between 0 and 1, such as the g parameter of the DFOP model.</p> <div id="confidence-intervals-based-on-transformed-parameters" class="section level2"> <h2 class="hasAnchor"> <a href="#confidence-intervals-based-on-transformed-parameters" class="anchor"></a>Confidence intervals based on transformed parameters</h2> <p>In the first attempt at providing improved parameter confidence intervals introduced to <code>mkin</code> in 2013, confidence intervals obtained from FME on the transformed parameters were simply all backtransformed one by one to yield asymmetric confidence intervals for the backtransformed parameters.</p> <p>However, while there is a 1:1 relation between the rate constants in the model and the transformed parameters fitted in the model, the parameters obtained by the isometric logratio transformation are calculated from the set of formation fractions that quantify the paths to each of the compounds formed from a specific parent compound, and no such 1:1 relation exists.</p> -<p>Therefore, parameter confidence intervals for formation fractions obtained with this method only appear valid for the case of a single transformation product, where only one formation fraction is to be estimated, directly corresponding to one component of the ilr transformed parameter.</p> +<p>Therefore, parameter confidence intervals for formation fractions obtained with this method only appear valid for the case of a single transformation product, where currently the logit transformation is used for the formation fraction.</p> <p>The confidence intervals obtained by backtransformation for the cases where a 1:1 relation between transformed and original parameter exist are considered by the author of this vignette to be more accurate than those obtained using a re-estimation of the Hessian matrix after backtransformation, as implemented in the FME package.</p> </div> <div id="parameter-t-test-based-on-untransformed-parameters" class="section level2"> @@ -198,36 +204,39 @@ <h1 class="hasAnchor"> <a href="#references" class="anchor"></a>References</h1> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references"> +<div id="refs" class="references hanging-indent"> <div id="ref-bates1988"> <p>Bates, D., and D. Watts. 1988. <em>Nonlinear Regression and Its Applications</em>. Wiley-Interscience.</p> </div> <div id="ref-FOCUS2006"> -<p>FOCUS Work Group on Degradation Kinetics. 2006. <em>Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration. Report of the Focus Work Group on Degradation Kinetics</em>. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<p>FOCUS Work Group on Degradation Kinetics. 2006. <em>Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration. Report of the Focus Work Group on Degradation Kinetics</em>. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> <div id="ref-FOCUSkinetics2014"> -<p>———. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<p>———. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> <div id="ref-gao11"> -<p>Gao, Z., J.W. Green, J. Vanderborght, and W. Schmitt. 2011. “Improving Uncertainty Analysis in Kinetic Evaluations Using Iteratively Reweighted Least Squares.” Journal. <em>Environmental Science and Technology</em> 45: 4429–37.</p> +<p>Gao, Z., J. W. Green, J. Vanderborght, and W. Schmitt. 2011. “Improving Uncertainty Analysis in Kinetic Evaluations Using Iteratively Reweighted Least Squares.” Journal. <em>Environmental Science and Technology</em> 45: 4429–37.</p> </div> <div id="ref-pkg:mkin"> -<p>Ranke, J. 2019. <em>‘mkin‘: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin" class="uri">https://CRAN.R-project.org/package=mkin</a>.</p> +<p>Ranke, J. 2021. <em>‘mkin‘: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin">https://CRAN.R-project.org/package=mkin</a>.</p> </div> <div id="ref-ranke2012"> <p>Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In <em>SETAC World 20-24 May</em>. Berlin.</p> </div> <div id="ref-ranke2015"> -<p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf" class="uri">http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf</a>.</p> +<p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf">http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf</a>.</p> +</div> +<div id="ref-ranke2019"> +<p>Ranke, Johannes, and Stefan Meinecke. 2019. “Error Models for the Kinetic Evaluation of Chemical Degradation Data.” <em>Environments</em> 6 (12). <a href="https://doi.org/10.3390/environments6120124">https://doi.org/10.3390/environments6120124</a>.</p> </div> <div id="ref-ranke2018"> -<p>Ranke, Johannes, Janina Wöltjen, and Stefan Meinecke. 2018. “Comparison of Software Tools for Kinetic Evaluation of Chemical Degradation Data.” <em>Environmental Sciences Europe</em> 30 (1): 17. <a href="https://doi.org/10.1186/s12302-018-0145-1" class="uri">https://doi.org/10.1186/s12302-018-0145-1</a>.</p> +<p>Ranke, Johannes, Janina Wöltjen, and Stefan Meinecke. 2018. “Comparison of Software Tools for Kinetic Evaluation of Chemical Degradation Data.” <em>Environmental Sciences Europe</em> 30 (1): 17. <a href="https://doi.org/10.1186/s12302-018-0145-1">https://doi.org/10.1186/s12302-018-0145-1</a>.</p> </div> <div id="ref-schaefer2007"> <p>Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. “KinGUI: A New Kinetic Software Tool for Evaluations According to FOCUS Degradation Kinetics.” In <em>Proceedings of the Xiii Symposium Pesticide Chemistry</em>, edited by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. Piacenza.</p> </div> <div id="ref-soetaert2010"> -<p>Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” <em>Journal of Statistical Software</em> 33 (3): 1–28. <a href="https://www.jstatsoft.org/v33/i03/" class="uri">https://www.jstatsoft.org/v33/i03/</a>.</p> +<p>Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” <em>Journal of Statistical Software</em> 33 (3): 1–28. <a href="https://www.jstatsoft.org/v33/i03/">https://www.jstatsoft.org/v33/i03/</a>.</p> </div> </div> </div> diff --git a/docs/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png Binary files differindex 8acd92af..bf38fdd7 100644 --- a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png +++ b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png diff --git a/docs/articles/mkin_files/header-attrs-2.6/header-attrs.js b/docs/articles/mkin_files/header-attrs-2.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/mkin_files/header-attrs-2.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/twa.html b/docs/articles/twa.html index c270659b..2e3d2f96 100644 --- a/docs/articles/twa.html +++ b/docs/articles/twa.html @@ -31,7 +31,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -94,13 +94,13 @@ - </header><div class="row"> + </header><script src="twa_files/header-attrs-2.6/header-attrs.js"></script><script src="twa_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Calculation of time weighted average concentrations with mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2021-02-03</h4> + <h4 class="date">Last change 18 September 2019 (rebuilt 2021-02-15)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/twa.rmd"><code>vignettes/twa.rmd</code></a></small> <div class="hidden name"><code>twa.rmd</code></div> @@ -141,9 +141,9 @@ \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) \]</span></p> <p>Note that a method for calculating maximum moving window time weighted average concentrations for a model fitted by ‘mkinfit’ or from parent decline model parameters is included in the <code><a href="../reference/max_twa_parent.html">max_twa_parent()</a></code> function. If the same is needed for metabolites, the function <code><a href="https://pkgdown.jrwb.de/pfm/reference/max_twa.html">pfm::max_twa()</a></code> from the ‘pfm’ package can be used.</p> -<div id="refs" class="references"> +<div id="refs" class="references hanging-indent"> <div id="ref-FOCUSkinetics2014"> -<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> </div> </div> diff --git a/docs/articles/twa_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/twa_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/articles/twa_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/twa_files/header-attrs-2.6/header-attrs.js b/docs/articles/twa_files/header-attrs-2.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/twa_files/header-attrs-2.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html index 8bdcbe30..57fc3545 100644 --- a/docs/articles/web_only/FOCUS_Z.html +++ b/docs/articles/web_only/FOCUS_Z.html @@ -31,7 +31,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -94,13 +94,13 @@ - </header><div class="row"> + </header><script src="FOCUS_Z_files/header-attrs-2.6/header-attrs.js"></script><script src="FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Example evaluation of FOCUS dataset Z</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2021-02-03</h4> + <h4 class="date">Last change 16 January 2018 (rebuilt 2021-02-15)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/FOCUS_Z.rmd"><code>vignettes/web_only/FOCUS_Z.rmd</code></a></small> <div class="hidden name"><code>FOCUS_Z.rmd</code></div> @@ -237,12 +237,12 @@ <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></code></pre></div> <pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 96.839001 1.994273 48.5585 4.0276e-42 92.827060 100.850943 -## k_Z0 2.215367 0.118456 18.7021 1.0410e-23 1.989432 2.466960 -## k_Z1 0.478310 0.028258 16.9265 6.2430e-22 0.424712 0.538673 -## k_Z2 0.451628 0.042139 10.7176 1.6313e-14 0.374337 0.544877 -## k_Z3 0.058692 0.015245 3.8498 1.7806e-04 0.034806 0.098972 -## f_Z2_to_Z3 0.471498 0.058350 8.0805 9.6614e-11 0.357741 0.588294 +## Z0_0 96.838822 1.994274 48.5584 4.0280e-42 92.826981 100.850664 +## k_Z0 2.215393 0.118458 18.7019 1.0413e-23 1.989456 2.466989 +## k_Z1 0.478305 0.028258 16.9266 6.2418e-22 0.424708 0.538666 +## k_Z2 0.451627 0.042139 10.7176 1.6314e-14 0.374339 0.544872 +## k_Z3 0.058692 0.015245 3.8499 1.7803e-04 0.034808 0.098965 +## f_Z2_to_Z3 0.471502 0.058351 8.0805 9.6608e-11 0.357769 0.588274 ## sigma 3.984431 0.383402 10.3923 4.5575e-14 3.213126 4.755736</code></pre> <div class="sourceCode" id="cb36"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></code></pre></div> @@ -253,9 +253,9 @@ ## $distimes ## DT50 DT90 ## Z0 0.31288 1.0394 -## Z1 1.44916 4.8140 +## Z1 1.44917 4.8141 ## Z2 1.53478 5.0984 -## Z3 11.80983 39.2314</code></pre> +## Z3 11.80986 39.2315</code></pre> <p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p> </div> <div id="using-the-sforb-model" class="section level1"> @@ -351,13 +351,13 @@ ## ## $SFORB ## Z0_b1 Z0_b2 Z3_b1 Z3_b2 -## 2.4471371 0.0075126 0.0800070 0.0000000 +## 2.4471322 0.0075125 0.0800069 0.0000000 ## ## $distimes ## DT50 DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2 -## Z0 0.3043 1.1848 0.35666 0.28325 92.265 NA NA +## Z0 0.3043 1.1848 0.35666 0.28325 92.266 NA NA ## Z1 1.5148 5.0320 NA NA NA NA NA -## Z2 1.6414 5.4525 NA NA NA NA NA +## Z2 1.6414 5.4526 NA NA NA NA NA ## Z3 NA NA NA NA NA 8.6636 Inf</code></pre> <p>It is clear the degradation rate of Z3 towards the end of the experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the system of two differential equations representing the SFORB system for Z3, corresponding to the slower rate constant of the DFOP model) is reported to be infinity. However, this appears to be a feature of the data.</p> </div> @@ -365,9 +365,9 @@ <h1 class="hasAnchor"> <a href="#references" class="anchor"></a>References</h1> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references"> +<div id="refs" class="references hanging-indent"> <div id="ref-FOCUSkinetics2014"> -<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> </div> </div> diff --git a/docs/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); 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We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/web_only/NAFTA_examples.html b/docs/articles/web_only/NAFTA_examples.html index d3e23253..e79375b3 100644 --- a/docs/articles/web_only/NAFTA_examples.html +++ b/docs/articles/web_only/NAFTA_examples.html @@ -31,7 +31,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -94,13 +94,13 @@ - </header><div class="row"> + </header><script src="NAFTA_examples_files/header-attrs-2.6/header-attrs.js"></script><script src="NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2021-02-03</h4> + <h4 class="date">26 February 2019 (rebuilt 2021-02-15)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/NAFTA_examples.rmd"><code>vignettes/web_only/NAFTA_examples.rmd</code></a></small> <div class="hidden name"><code>NAFTA_examples.rmd</code></div> @@ -156,7 +156,7 @@ ## Estimate Pr(>t) Lower Upper ## parent_0 9.99e+01 1.41e-26 98.8116 101.0810 ## k1 2.67e-02 5.05e-06 0.0243 0.0295 -## k2 2.42e-12 5.00e-01 0.0000 Inf +## k2 2.26e-12 5.00e-01 0.0000 Inf ## g 6.47e-01 3.67e-06 0.6248 0.6677 ## sigma 1.27e+00 8.91e-06 0.8395 1.6929 ## @@ -165,7 +165,7 @@ ## DT50 DT90 DT50_rep ## SFO 67.7 2.25e+02 6.77e+01 ## IORE 58.2 1.07e+03 3.22e+02 -## DFOP 55.5 5.22e+11 2.86e+11 +## DFOP 55.5 5.59e+11 3.07e+11 ## ## Representative half-life: ## [1] 321.51</code></pre> @@ -207,7 +207,7 @@ ## Estimate Pr(>t) Lower Upper ## parent_0 9.84e+01 1.24e-27 97.8078 98.9187 ## k1 1.55e-02 4.10e-04 0.0143 0.0167 -## k2 1.10e-11 5.00e-01 0.0000 Inf +## k2 8.63e-12 5.00e-01 0.0000 Inf ## g 6.89e-01 2.92e-03 0.6626 0.7142 ## sigma 6.48e-01 2.38e-05 0.4147 0.8813 ## @@ -216,7 +216,7 @@ ## DT50 DT90 DT50_rep ## SFO 86.6 2.88e+02 8.66e+01 ## IORE 85.5 7.17e+02 2.16e+02 -## DFOP 83.6 1.03e+11 6.29e+10 +## DFOP 83.6 1.32e+11 8.04e+10 ## ## Representative half-life: ## [1] 215.87</code></pre> @@ -258,7 +258,7 @@ ## Estimate Pr(>t) Lower Upper ## parent_0 9.66e+01 1.57e-25 95.3476 97.8979 ## k1 2.55e-02 7.33e-06 0.0233 0.0278 -## k2 3.60e-11 5.00e-01 0.0000 Inf +## k2 3.22e-11 5.00e-01 0.0000 Inf ## g 8.61e-01 7.55e-06 0.8314 0.8867 ## sigma 1.46e+00 6.93e-06 0.9661 1.9483 ## @@ -267,7 +267,7 @@ ## DT50 DT90 DT50_rep ## SFO 38.6 1.28e+02 3.86e+01 ## IORE 34.0 1.77e+02 5.32e+01 -## DFOP 34.1 9.07e+09 1.93e+10 +## DFOP 34.1 1.01e+10 2.15e+10 ## ## Representative half-life: ## [1] 53.17</code></pre> @@ -309,7 +309,7 @@ ## Estimate Pr(>t) Lower Upper ## parent_0 9.89e+01 9.44e-49 95.4640 102.2573 ## k1 1.81e-02 1.75e-01 0.0116 0.0281 -## k2 2.89e-10 5.00e-01 0.0000 Inf +## k2 3.63e-10 5.00e-01 0.0000 Inf ## g 6.06e-01 2.19e-01 0.4826 0.7178 ## sigma 7.40e+00 2.97e-15 6.0201 8.7754 ## @@ -318,7 +318,7 @@ ## DT50 DT90 DT50_rep ## SFO 94.3 3.13e+02 9.43e+01 ## IORE 96.7 1.51e+03 4.55e+02 -## DFOP 96.4 4.75e+09 2.40e+09 +## DFOP 96.4 3.77e+09 1.91e+09 ## ## Representative half-life: ## [1] 454.55</code></pre> @@ -420,7 +420,7 @@ ## Estimate Pr(>t) Lower Upper ## parent_0 9.85e+01 2.54e-20 97.390 99.672 ## k1 1.38e-01 3.52e-05 0.131 0.146 -## k2 9.03e-13 5.00e-01 0.000 Inf +## k2 9.02e-13 5.00e-01 0.000 Inf ## g 6.52e-01 8.13e-06 0.642 0.661 ## sigma 7.88e-01 6.13e-02 0.481 1.095 ## @@ -429,7 +429,7 @@ ## DT50 DT90 DT50_rep ## SFO 16.9 5.63e+01 1.69e+01 ## IORE 11.6 3.37e+02 1.01e+02 -## DFOP 10.5 1.38e+12 7.67e+11 +## DFOP 10.5 1.38e+12 7.69e+11 ## ## Representative half-life: ## [1] 101.43</code></pre> @@ -441,15 +441,16 @@ <div class="sourceCode" id="cb37"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p9b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p9b-1.png" width="700"></p> -<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></code></pre></div> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -473,12 +474,12 @@ ## sigma 1.288 1.76e-04 0.7456 1.830 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 94.7123 NA 93.1355 96.2891 -## k1 0.0389 NA 0.0266 0.0569 -## k2 0.0389 NA 0.0255 0.0592 -## g 0.5256 NA NA NA -## sigma 1.5957 NA 0.9135 2.2779 +## Estimate Pr(>t) Lower Upper +## parent_0 94.7123 1.61e-16 93.1355 96.2891 +## k1 0.0389 1.08e-04 0.0266 0.0569 +## k2 0.0389 2.23e-04 0.0255 0.0592 +## g 0.5256 NaN NA NA +## sigma 1.5957 2.50e-04 0.9135 2.2779 ## ## ## DTx values: @@ -494,15 +495,18 @@ <div id="example-on-page-10" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-10" class="anchor"></a>Example on page 10</h2> -<div class="sourceCode" id="cb46"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p10</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p10"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> +<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is +## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb50"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb53"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p10-1.png" width="700"></p> -<div class="sourceCode" id="cb51"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb54"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></code></pre></div> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -528,9 +532,9 @@ ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 101.7315 1.41e-09 91.6534 111.8097 -## k1 0.0495 5.63e-03 0.0240 0.1020 -## k2 0.0495 1.93e-03 0.0272 0.0903 -## g 0.4487 NaN 0.0000 1.0000 +## k1 0.0495 6.58e-03 0.0303 0.0809 +## k2 0.0495 2.60e-03 0.0410 0.0598 +## g 0.4487 5.00e-01 NA NA ## sigma 8.0152 2.50e-04 4.5886 11.4418 ## ## @@ -551,14 +555,14 @@ <div id="example-on-page-11" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-11" class="anchor"></a>Example on page 11</h2> -<div class="sourceCode" id="cb53"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb56"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p11</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p11"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb56"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb59"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p11-1.png" width="700"></p> -<div class="sourceCode" id="cb57"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></code></pre></div> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -594,10 +598,10 @@ ## DT50 DT90 DT50_rep ## SFO 2.16e+02 7.18e+02 2.16e+02 ## IORE 9.73e+02 1.37e+08 4.11e+07 -## DFOP 3.07e+11 1.93e+12 6.97e+11 +## DFOP 3.07e+11 1.93e+12 6.98e+11 ## ## Representative half-life: -## [1] 41148171</code></pre> +## [1] 41148170</code></pre> <p>In this case, the DFOP fit reported for PestDF resulted in a negative value for the slower rate constant, which is not possible in mkin. The other results are in agreement.</p> </div> </div> @@ -608,19 +612,21 @@ <div id="example-on-page-12-upper-panel" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-12-upper-panel" class="anchor"></a>Example on page 12, upper panel</h2> -<div class="sourceCode" id="cb59"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb62"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p12a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> <pre><code>## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance -## matrix - -## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance ## matrix</code></pre> +<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> +<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is +## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb63"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb70"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p12a-1.png" width="700"></p> -<div class="sourceCode" id="cb64"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb71"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></code></pre></div> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -644,12 +650,12 @@ ## sigma 3.965 NA NA NA ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 100.521 2.74e-10 NA NA -## k1 0.124 2.53e-05 NA NA -## k2 0.124 2.52e-02 NA NA -## g 0.793 5.00e-01 NA NA -## sigma 7.048 2.50e-04 NA NA +## Estimate Pr(>t) Lower Upper +## parent_0 100.521 2.74e-10 92.2366 108.805 +## k1 0.124 2.53e-05 0.0908 0.170 +## k2 0.124 2.52e-02 0.0456 0.339 +## g 0.793 NaN NA NA +## sigma 7.048 2.50e-04 4.0349 10.061 ## ## ## DTx values: @@ -664,20 +670,18 @@ <div id="example-on-page-12-lower-panel" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-12-lower-panel" class="anchor"></a>Example on page 12, lower panel</h2> -<div class="sourceCode" id="cb66"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p12b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> <pre><code>## Warning in qt(alpha/2, rdf): NaNs produced</code></pre> <pre><code>## Warning in qt(1 - alpha/2, rdf): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> +<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> +<pre><code>## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb74"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb80"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p12b-1.png" width="700"></p> -<div class="sourceCode" id="cb75"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb81"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></code></pre></div> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -702,11 +706,11 @@ ## ## $DFOP ## Estimate Pr(>t) Lower Upper -## parent_0 97.6840 NA NaN NaN -## k1 0.0589 NA NA NA -## k2 0.0589 NA NA NA -## g 0.6473 NA NA NA -## sigma 3.4323 NA NaN NaN +## parent_0 97.6840 NaN NaN NaN +## k1 0.0589 NaN NA NA +## k2 0.0589 NaN NA NA +## g 0.6473 NaN NA NA +## sigma 3.4323 NaN NaN NaN ## ## ## DTx values: @@ -721,18 +725,14 @@ <div id="example-on-page-13" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-13" class="anchor"></a>Example on page 13</h2> -<div class="sourceCode" id="cb77"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb83"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p13</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p13"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb83"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb86"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p13-1.png" width="700"></p> -<div class="sourceCode" id="cb84"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb87"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></code></pre></div> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -758,9 +758,9 @@ ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 92.73500 NA 8.95e+01 95.92118 -## k1 0.00258 NA 4.25e-04 0.01569 -## k2 0.00258 NA 1.76e-03 0.00379 -## g 0.16452 NA NA NA +## k1 0.00258 NA 4.14e-04 0.01611 +## k2 0.00258 NA 1.74e-03 0.00383 +## g 0.16452 NA 0.00e+00 1.00000 ## sigma 3.41172 NA 2.02e+00 4.79960 ## ## @@ -777,7 +777,7 @@ <div id="dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf" class="section level1"> <h1 class="hasAnchor"> <a href="#dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf" class="anchor"></a>DT50 not observed in the study and DFOP problems in PestDF</h1> -<div class="sourceCode" id="cb86"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb89"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p14</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p14"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> @@ -785,10 +785,10 @@ ## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb92"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb95"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p14-1.png" width="700"></p> -<div class="sourceCode" id="cb93"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb96"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></code></pre></div> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -815,7 +815,7 @@ ## Estimate Pr(>t) Lower Upper ## parent_0 1.00e+02 2.96e-28 99.40280 101.2768 ## k1 9.53e-03 1.20e-01 0.00638 0.0143 -## k2 5.33e-12 5.00e-01 0.00000 Inf +## k2 6.08e-12 5.00e-01 0.00000 Inf ## g 3.98e-01 2.19e-01 0.30481 0.4998 ## sigma 1.17e+00 7.68e-06 0.77406 1.5610 ## @@ -824,7 +824,7 @@ ## DT50 DT90 DT50_rep ## SFO 2.48e+02 8.25e+02 2.48e+02 ## IORE 4.34e+02 2.22e+04 6.70e+03 -## DFOP 3.48e+10 3.37e+11 1.30e+11 +## DFOP 3.05e+10 2.95e+11 1.14e+11 ## ## Representative half-life: ## [1] 6697.44</code></pre> @@ -833,14 +833,14 @@ <div id="n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero" class="section level1"> <h1 class="hasAnchor"> <a href="#n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero" class="anchor"></a>N is less than 1 and DFOP fraction parameter is below zero</h1> -<div class="sourceCode" id="cb95"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb98"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p15a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb98"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb101"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p15a-1.png" width="700"></p> -<div class="sourceCode" id="cb99"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb102"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></code></pre></div> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -864,12 +864,12 @@ ## sigma 3.105 1.78e-04 1.795 4.416 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 97.96751 NA 94.21913 101.7159 -## k1 0.00952 NA 0.00221 0.0411 -## k2 0.00952 NA 0.00626 0.0145 -## g 0.21241 NA 0.00000 1.0000 -## sigma 4.18778 NA 2.39747 5.9781 +## Estimate Pr(>t) Lower Upper +## parent_0 97.96751 2.85e-13 94.21913 101.7159 +## k1 0.00952 6.28e-02 0.00250 0.0363 +## k2 0.00952 1.27e-04 0.00646 0.0140 +## g 0.21241 5.00e-01 0.00000 1.0000 +## sigma 4.18778 2.50e-04 2.39747 5.9781 ## ## ## DTx values: @@ -880,7 +880,7 @@ ## ## Representative half-life: ## [1] 41.33</code></pre> -<div class="sourceCode" id="cb101"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb104"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p15b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> @@ -888,10 +888,10 @@ ## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb107"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb110"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p15b-1.png" width="700"></p> -<div class="sourceCode" id="cb108"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb111"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></code></pre></div> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -909,7 +909,7 @@ ## ## $IORE ## Estimate Pr(>t) Lower Upper -## parent_0 99.83 1.81e-16 97.51348 102.14 +## parent_0 99.83 1.81e-16 97.51349 102.14 ## k__iore_parent 0.38 3.22e-01 0.00352 41.05 ## N_parent 0.00 5.00e-01 -1.07696 1.08 ## sigma 2.21 2.57e-04 1.23245 3.19 @@ -936,16 +936,16 @@ <div id="the-dfop-fraction-parameter-is-greater-than-1" class="section level1"> <h1 class="hasAnchor"> <a href="#the-dfop-fraction-parameter-is-greater-than-1" class="anchor"></a>The DFOP fraction parameter is greater than 1</h1> -<div class="sourceCode" id="cb110"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb113"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p16</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p16"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The representative half-life of the IORE model is longer than the one corresponding</code></pre> <pre><code>## to the terminal degradation rate found with the DFOP model.</code></pre> <pre><code>## The representative half-life obtained from the DFOP model may be used</code></pre> -<div class="sourceCode" id="cb115"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb118"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p16-1.png" width="700"></p> -<div class="sourceCode" id="cb116"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb119"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></code></pre></div> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -996,7 +996,7 @@ <div id="references" class="section level1 unnumbered"> <h1 class="hasAnchor"> <a href="#references" class="anchor"></a>References</h1> -<div id="refs" class="references"> +<div id="refs" class="references hanging-indent"> <div id="ref-usepa2015"> <p>US EPA. 2015. “Standard Operating Procedure for Using the NAFTA Guidance to Calculate Representative Half-Life Values and Characterizing Pesticide Degradation.”</p> </div> diff --git a/docs/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + 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b/docs/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/header-attrs-2.6/header-attrs.js b/docs/articles/web_only/NAFTA_examples_files/header-attrs-2.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/web_only/NAFTA_examples_files/header-attrs-2.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html index e15358a0..d7b81f20 100644 --- a/docs/articles/web_only/benchmarks.html +++ b/docs/articles/web_only/benchmarks.html @@ -31,7 +31,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -94,13 +94,13 @@ - </header><div class="row"> + </header><script src="benchmarks_files/header-attrs-2.6/header-attrs.js"></script><script src="benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Benchmark timings for mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2021-02-03</h4> + <h4 class="date">Last change 13 May 2020 (rebuilt 2021-02-15)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/benchmarks.rmd"><code>vignettes/web_only/benchmarks.rmd</code></a></small> <div class="hidden name"><code>benchmarks.rmd</code></div> @@ -135,17 +135,11 @@ m1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span> <span class="va">t3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOMC_SFO</span>, <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span> <span class="va">t4</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOMC_SFO</span>, <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span>, - error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></code></pre></div> -<pre><code>## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): Optimisation did not converge: -## iteration limit reached without convergence (10) - -## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): Optimisation did not converge: -## iteration limit reached without convergence (10)</code></pre> -<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">t5</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOMC_SFO</span>, <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span>, + error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span> +<span class="va">t5</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOMC_SFO</span>, <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></code></pre></div> <p>Two metabolites, synthetic data:</p> -<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">m_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>, M1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>, M2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, @@ -172,7 +166,7 @@ error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span> <span class="va">t11</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">DFOP_par_c</span><span class="op">)</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></code></pre></div> -<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">mkin_benchmarks</span><span class="op">[</span><span class="va">system_string</span>, <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="st">"t"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">11</span><span class="op">)</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">t1</span>, <span class="va">t2</span>, <span class="va">t3</span>, <span class="va">t4</span>, <span class="va">t5</span>, <span class="va">t6</span>, <span class="va">t7</span>, <span class="va">t8</span>, <span class="va">t9</span>, <span class="va">t10</span>, <span class="va">t11</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/save.html">save</a></span><span class="op">(</span><span class="va">mkin_benchmarks</span>, file <span class="op">=</span> <span class="st">"~/git/mkin/vignettes/web_only/mkin_benchmarks.rda"</span><span class="op">)</span></code></pre></div> @@ -234,9 +228,9 @@ <td align="right">3.729</td> </tr> <tr class="odd"> -<td align="left">1.0.0</td> -<td align="right">1.770</td> -<td align="right">3.703</td> +<td align="left">1.0.3</td> +<td align="right">1.722</td> +<td align="right">3.419</td> </tr> </tbody> </table> @@ -302,10 +296,10 @@ <td align="right">2.810</td> </tr> <tr class="odd"> -<td align="left">1.0.0</td> -<td align="right">1.373</td> -<td align="right">7.127</td> -<td align="right">2.762</td> +<td align="left">1.0.3</td> +<td align="right">1.402</td> +<td align="right">6.343</td> +<td align="right">2.802</td> </tr> </tbody> </table> @@ -313,7 +307,7 @@ <div id="two-metabolites" class="section level3"> <h3 class="hasAnchor"> <a href="#two-metabolites" class="anchor"></a>Two metabolites</h3> -<p>Constant variance (t6 and t7), two-component error model (t8 and t9), and variance by variable (t10 and t11) for one model fitted to one dataset, i.e. one fit for each test.</p> +<p>Constant variance (t6 and t7), two-component error model (t8 and t9), and variance by variable (t10 and t11) for one model fitted to one dataset, i.e. one fit for each test.</p> <table class="table"> <thead><tr class="header"> <th align="left">mkin version</th> @@ -398,13 +392,13 @@ <td align="right">3.105</td> </tr> <tr class="odd"> -<td align="left">1.0.0</td> -<td align="right">0.775</td> -<td align="right">1.269</td> -<td align="right">1.467</td> -<td align="right">3.767</td> -<td align="right">1.919</td> -<td align="right">2.942</td> +<td align="left">1.0.3</td> +<td align="right">0.771</td> +<td align="right">1.251</td> +<td align="right">1.464</td> +<td align="right">3.074</td> +<td align="right">1.940</td> +<td align="right">2.831</td> </tr> </tbody> </table> diff --git a/docs/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js b/docs/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/web_only/compiled_models.html b/docs/articles/web_only/compiled_models.html index e45655b6..ce8d8481 100644 --- a/docs/articles/web_only/compiled_models.html +++ b/docs/articles/web_only/compiled_models.html @@ -31,7 +31,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -94,13 +94,13 @@ - </header><div class="row"> + </header><script src="compiled_models_files/header-attrs-2.6/header-attrs.js"></script><script src="compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Performance benefit by using compiled model definitions in mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2021-02-03</h4> + <h4 class="date">2021-02-15</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/compiled_models.rmd"><code>vignettes/web_only/compiled_models.rmd</code></a></small> <div class="hidden name"><code>compiled_models.rmd</code></div> @@ -159,9 +159,9 @@ <span class="op">}</span></code></pre></div> <pre><code>## test replications relative elapsed ## 4 analytical 1 1.000 0.181 -## 3 deSolve, compiled 1 1.818 0.329 -## 2 Eigenvalue based 1 2.061 0.373 -## 1 deSolve, not compiled 1 43.923 7.950</code></pre> +## 3 deSolve, compiled 1 1.812 0.328 +## 2 Eigenvalue based 1 2.088 0.378 +## 1 deSolve, not compiled 1 45.923 8.312</code></pre> <p>We see that using the compiled model is by more than a factor of 10 faster than using deSolve without compiled code.</p> </div> <div id="model-without-analytical-solution" class="section level2"> @@ -188,13 +188,13 @@ <span class="op">}</span></code></pre></div> <pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre><code>## test replications relative elapsed -## 2 deSolve, compiled 1 1.000 0.474 -## 1 deSolve, not compiled 1 30.179 14.305</code></pre> -<p>Here we get a performance benefit of a factor of 30 using the version of the differential equation model compiled from C code!</p> -<p>This vignette was built with mkin 1.0.0 on</p> +## 2 deSolve, compiled 1 1.000 0.486 +## 1 deSolve, not compiled 1 31.597 15.356</code></pre> +<p>Here we get a performance benefit of a factor of 32 using the version of the differential equation model compiled from C code!</p> +<p>This vignette was built with mkin 1.0.3 on</p> <pre><code>## R version 4.0.3 (2020-10-10) ## Platform: x86_64-pc-linux-gnu (64-bit) -## Running under: Debian GNU/Linux 10 (buster)</code></pre> +## Running under: Debian GNU/Linux bullseye/sid</code></pre> <pre><code>## CPU model: AMD Ryzen 7 1700 Eight-Core Processor</code></pre> </div> </div> diff --git a/docs/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js b/docs/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/authors.html b/docs/authors.html index e80c3930..61d29bf0 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -71,7 +71,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/index.html b/docs/index.html index 51571c81..88f118ea 100644 --- a/docs/index.html +++ b/docs/index.html @@ -37,7 +37,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -132,23 +132,40 @@ <div id="features" class="section level2"> <h2 class="hasAnchor"> <a href="#features" class="anchor"></a>Features</h2> +<div id="general" class="section level3"> +<h3 class="hasAnchor"> +<a href="#general" class="anchor"></a>General</h3> <ul> <li>Highly flexible model specification using <a href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.</li> -<li>As of version 0.9-39, fitting of several models to several datasets, optionally in parallel, is supported, see for example <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</li> <li>Model solution (forward modelling) in the function <a href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li> -<li>If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">vignette <code>compiled_models</code></a>. The autogeneration of C code was inspired by the <a href="https://github.com/karlines/ccSolve"><code>ccSolve</code></a> package. Thanks to Karline Soetaert for her work on that.</li> -<li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice cooperation on this, especially the isometric log-ratio transformation that is now used for the formation fractions.</li> -<li>A side effect of this is that when parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which can not occur in a single experiment with a single defined radiolabel position.</li> <li>The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.</li> <li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.</li> <li>The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.</li> -<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.</li> -<li>Three different error models can be selected using the argument <code>error_model</code> to the <a href="https://pkgdown.jrwb.de/mkin/reference/mkinfit.html"><code>mkinfit</code></a> function.</li> <li>The ‘variance by variable’ error model which is often fitted using Iteratively Reweighted Least Squares (IRLS) should now be specified as <code>error_model = "obs"</code>.</li> -<li>A two-component error model similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke and Lorenzato</a> can be selected using the argument <code>error_model = "tc"</code>.</li> +</ul> +</div> +<div id="unique-in-mkin" class="section level3"> +<h3 class="hasAnchor"> +<a href="#unique-in-mkin" class="anchor"></a>Unique in mkin</h3> +<ul> +<li>Three different error models can be selected using the argument <code>error_model</code> to the <a href="https://pkgdown.jrwb.de/mkin/reference/mkinfit.html"><code>mkinfit</code></a> function. A two-component error model similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke and Lorenzato</a> can be selected using the argument <code>error_model = "tc"</code>.</li> +<li>Model comparisons using the Akaike Information Criterion (AIC) are supported which can also be used for non-constant variance. In such cases the FOCUS chi-squared error level is not meaningful.</li> +<li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution.</li> +<li>When parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which cannot occur in a single experiment with a single defined radiolabel position.</li> +<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model is equivalent to the DFOP model used by other tools for biphasic metabolite curves. However, the SFORB model has the advantage that there is a mechanistic interpretation of the model parameters.</li> <li>Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the <a href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">nlme.mmkin</a> method. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils).</li> </ul> </div> +<div id="performance" class="section level3"> +<h3 class="hasAnchor"> +<a href="#performance" class="anchor"></a>Performance</h3> +<ul> +<li>Parallel fitting of several models to several datasets is supported, see for example <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</li> +<li>If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">vignette <code>compiled_models</code></a>. The autogeneration of C code was inspired by the <a href="https://github.com/karlines/ccSolve"><code>ccSolve</code></a> package. Thanks to Karline Soetaert for her work on that.</li> +<li>Even if no compiler is installed, many degradation models still give <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html">very good performance</a>, as current versions of mkin also have <a href="https://jrwb.de/performance-improvements-mkin/">analytical solutions for some models with one metabolite</a>, and if SFO or SFORB are used for the parent compound, Eigenvalue based solutions of the degradation model are available.</li> +</ul> +</div> +</div> <div id="gui" class="section level2"> <h2 class="hasAnchor"> <a href="#gui" class="anchor"></a>GUI</h2> @@ -157,7 +174,7 @@ <div id="news" class="section level2"> <h2 class="hasAnchor"> <a href="#news" class="anchor"></a>News</h2> -<p>There is a ChangeLog, for the latest <a href="https://cran.r-project.org/package=mkin/news/news.html">CRAN release</a> and one for the <a href="https://github.com/jranke/mkin/blob/master/NEWS.md">github master branch</a>.</p> +<p>There is a list of changes for the latest <a href="https://cran.r-project.org/package=mkin/news/news.html">CRAN release</a> and one for each github branch, e.g. <a href="https://github.com/jranke/mkin/blob/master/NEWS.md">the main branch</a>.</p> </div> <div id="credits-and-historical-remarks" class="section level2"> <h2 class="hasAnchor"> @@ -170,6 +187,16 @@ <p>In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named KinGUII whose R code is based on <code>mkin</code>, but which added, among other refinements, a closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation of the variance for each of the observed variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, similar to what is available e.g. in the <code>FME</code> package.</p> <p>Somewhat in parallel, Syngenta has sponsored the development of an <code>mkin</code> and KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is more limited in the model formulation, but puts more weight on usability. CAKE is available for download from the <a href="https://www.tessella.com/showcase/computer-assisted-kinetic-evaluation">CAKE website</a>, where you can also find a zip archive of the R scripts derived from <code>mkin</code>, published under the GPL license.</p> <p>Finally, there is <a href="https://github.com/zhenglei-gao/KineticEval">KineticEval</a>, which contains a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well.</p> +<p>Thanks to René Lehmann, formerly working at the Umweltbundesamt, for the nice cooperation cooperation on parameter transformations, especially the isometric log-ratio transformation that is now used for formation fractions in case there are more than two transformation targets.</p> +<p>Many inspirations for improvements of mkin resulted from doing kinetic evaluations of degradation data for my clients while working at Harlan Laboratories and at Eurofins Regulatory AG, and now as an independent consultant.</p> +<p>Funding was received from the Umweltbundesamt in the course of the projects</p> +<ul> +<li>Grant Number 112407 (Testing and validation of modelling software as an alternative to ModelMaker 4.0, 2014-2015)</li> +<li>Project Number 56703 (Optimization of gmkin for routine use in the Umweltbundesamt, 2015)</li> +<li>Project Number 112407 (Testing the feasibility of using an error model according to Rocke and Lorenzato for more realistic parameter estimates in the kinetic evaluation of degradation data, 2018-2019)</li> +<li>Project Number 120667 (Development of objective criteria for the evaluation of the visual fit in the kinetic evaluation of degradation data, 2019-2020)</li> +<li>Project 146839 (Checking the feasibility of using mixed-effects models for the derivation of kinetic modelling parameters from degradation studies, 2020-2021)</li> +</ul> </div> <div id="references" class="section level2"> <h2 class="hasAnchor"> diff --git a/docs/news/index.html b/docs/news/index.html index 766e6549..82fb92bd 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -71,7 +71,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -141,6 +141,14 @@ <small>Source: <a href='https://github.com/jranke/mkin/blob/master/NEWS.md'><code>NEWS.md</code></a></small> </div> + <div id="mkin-103" class="section level1"> +<h1 class="page-header" data-toc-text="1.0.3"> +<a href="#mkin-103" class="anchor"></a>mkin 1.0.3<small> Unreleased </small> +</h1> +<ul> +<li>Review and update README, the ‘Introduction to mkin’ vignette and some of the help pages</li> +</ul> +</div> <div id="mkin-102" class="section level1"> <h1 class="page-header" data-toc-text="1.0.2"> <a href="#mkin-102" class="anchor"></a>mkin 1.0.2<small> Unreleased </small> diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index f1fe0ec9..847dd4c5 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -10,7 +10,7 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-02-13T05:53Z +last_built: 2021-02-15T13:07Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/reference/AIC.mmkin.html b/docs/reference/AIC.mmkin.html index f54fd70f..fbc90802 100644 --- a/docs/reference/AIC.mmkin.html +++ b/docs/reference/AIC.mmkin.html @@ -73,7 +73,7 @@ same dataset." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -187,8 +187,7 @@ dataframe if there are several fits in the column).</p> <span class='va'>f</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"FOCUS A"</span> <span class='op'>=</span> <span class='va'>FOCUS_2006_A</span>, <span class='st'>"FOCUS C"</span> <span class='op'>=</span> <span class='va'>FOCUS_2006_C</span><span class='op'>)</span>, cores <span class='op'>=</span> <span class='fl'>1</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Optimisation did not converge:</span> -#> <span class='warning'>false convergence (8)</span></div><div class='input'> <span class='co'># We get a warning because the FOMC model does not converge for the</span> + <span class='co'># We get a warning because the FOMC model does not converge for the</span> <span class='co'># FOCUS A dataset, as it is well described by SFO</span> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span><span class='st'>"SFO"</span>, <span class='st'>"FOCUS A"</span><span class='op'>]</span><span class='op'>)</span> <span class='co'># We get a single number for a single fit</span> @@ -199,15 +198,15 @@ dataframe if there are several fits in the column).</p> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span>, <span class='st'>"FOCUS A"</span><span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> df AIC #> SFO 3 55.28197 -#> FOMC 4 57.28211 +#> FOMC 4 57.28222 #> DFOP 5 59.28197</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span>, <span class='st'>"FOCUS A"</span><span class='op'>]</span>, k <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span> <span class='co'># If we do not penalize additional parameters, we get nearly the same</span> </div><div class='output co'>#> df AIC #> SFO 3 49.28197 -#> FOMC 4 49.28211 +#> FOMC 4 49.28222 #> DFOP 5 49.28197</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>BIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span>, <span class='st'>"FOCUS A"</span><span class='op'>]</span><span class='op'>)</span> <span class='co'># Comparing the BIC gives a very similar picture</span> </div><div class='output co'>#> df BIC #> SFO 3 55.52030 -#> FOMC 4 57.59987 +#> FOMC 4 57.59999 #> DFOP 5 59.67918</div><div class='input'> <span class='co'># For FOCUS C, the more complex models fit better</span> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span>, <span class='st'>"FOCUS C"</span><span class='op'>]</span><span class='op'>)</span> diff --git a/docs/reference/CAKE_export.html b/docs/reference/CAKE_export.html index dcccca1b..05cd04a7 100644 --- a/docs/reference/CAKE_export.html +++ b/docs/reference/CAKE_export.html @@ -73,7 +73,7 @@ specified as well." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/D24_2014.html b/docs/reference/D24_2014.html index b78e73ef..e2d47f1b 100644 --- a/docs/reference/D24_2014.html +++ b/docs/reference/D24_2014.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/DFOP.solution.html b/docs/reference/DFOP.solution.html index cff29301..d04f98cc 100644 --- a/docs/reference/DFOP.solution.html +++ b/docs/reference/DFOP.solution.html @@ -73,7 +73,7 @@ two exponential decline functions." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html index 4e3cbe64..5a99cf1b 100644 --- a/docs/reference/Extract.mmkin.html +++ b/docs/reference/Extract.mmkin.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -220,7 +220,7 @@ either a list of mkinfit objects or a single mkinfit object.</p></td> <span class='op'>)</span> </div><div class='output co'>#> $par #> parent_0 log_alpha log_beta sigma -#> 99.666193 2.549849 5.050586 1.890202 +#> 99.666192 2.549850 5.050586 1.890202 #> #> $objective #> [1] 28.58291 diff --git a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html index d37fa5a5..ba1cb19a 100644 --- a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html +++ b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html @@ -76,7 +76,7 @@ in this fit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html index d3f7894c..cfb60536 100644 --- a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html @@ -76,7 +76,7 @@ in this fit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html index a18abad5..c90bcdbb 100644 --- a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html @@ -76,7 +76,7 @@ in this fit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html index e0fbc498..7b2747dc 100644 --- a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html @@ -76,7 +76,7 @@ in this fit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/FOCUS_2006_datasets.html b/docs/reference/FOCUS_2006_datasets.html index 543e24ad..7a39dcd0 100644 --- a/docs/reference/FOCUS_2006_datasets.html +++ b/docs/reference/FOCUS_2006_datasets.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/FOMC.solution.html b/docs/reference/FOMC.solution.html index c376227c..9136b882 100644 --- a/docs/reference/FOMC.solution.html +++ b/docs/reference/FOMC.solution.html @@ -73,7 +73,7 @@ a decreasing rate constant." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/HS.solution.html b/docs/reference/HS.solution.html index 8a90fec3..e3009bcc 100644 --- a/docs/reference/HS.solution.html +++ b/docs/reference/HS.solution.html @@ -73,7 +73,7 @@ between them." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/IORE.solution.html b/docs/reference/IORE.solution.html index 8e6a2792..f1a40d44 100644 --- a/docs/reference/IORE.solution.html +++ b/docs/reference/IORE.solution.html @@ -73,7 +73,7 @@ a concentration dependent rate constant." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/NAFTA_SOP_2015-1.png b/docs/reference/NAFTA_SOP_2015-1.png Binary files differindex 76d724f0..4f0d7833 100644 --- a/docs/reference/NAFTA_SOP_2015-1.png +++ b/docs/reference/NAFTA_SOP_2015-1.png diff --git a/docs/reference/NAFTA_SOP_2015.html b/docs/reference/NAFTA_SOP_2015.html index 301bf684..de9db9eb 100644 --- a/docs/reference/NAFTA_SOP_2015.html +++ b/docs/reference/NAFTA_SOP_2015.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/NAFTA_SOP_Attachment-1.png b/docs/reference/NAFTA_SOP_Attachment-1.png Binary files differindex eec3dd9b..9417685e 100644 --- a/docs/reference/NAFTA_SOP_Attachment-1.png +++ b/docs/reference/NAFTA_SOP_Attachment-1.png diff --git a/docs/reference/NAFTA_SOP_Attachment.html b/docs/reference/NAFTA_SOP_Attachment.html index 03a45bc3..0b063e2c 100644 --- a/docs/reference/NAFTA_SOP_Attachment.html +++ b/docs/reference/NAFTA_SOP_Attachment.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -193,7 +193,7 @@ #> Estimate Pr(>t) Lower Upper #> parent_0 9.99e+01 1.41e-26 98.8116 101.0810 #> k1 2.67e-02 5.05e-06 0.0243 0.0295 -#> k2 2.42e-12 5.00e-01 0.0000 Inf +#> k2 2.26e-12 5.00e-01 0.0000 Inf #> g 6.47e-01 3.67e-06 0.6248 0.6677 #> sigma 1.27e+00 8.91e-06 0.8395 1.6929 #> @@ -202,7 +202,7 @@ #> DT50 DT90 DT50_rep #> SFO 67.7 2.25e+02 6.77e+01 #> IORE 58.2 1.07e+03 3.22e+02 -#> DFOP 55.5 5.22e+11 2.86e+11 +#> DFOP 55.5 5.59e+11 3.07e+11 #> #> Representative half-life: #> [1] 321.51</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>nafta_att_p5a</span><span class='op'>)</span> diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png Binary files differindex 762a3dd4..7f498242 100644 --- a/docs/reference/Rplot001.png +++ b/docs/reference/Rplot001.png diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png Binary files differindex d24e35b4..54c31a3f 100644 --- a/docs/reference/Rplot002.png +++ b/docs/reference/Rplot002.png diff --git a/docs/reference/Rplot003.png b/docs/reference/Rplot003.png Binary files differindex e96adeb3..19198739 100644 --- a/docs/reference/Rplot003.png +++ b/docs/reference/Rplot003.png diff --git a/docs/reference/Rplot004.png b/docs/reference/Rplot004.png Binary files differindex 91058d4b..1028a9c4 100644 --- a/docs/reference/Rplot004.png +++ b/docs/reference/Rplot004.png diff --git a/docs/reference/Rplot005.png b/docs/reference/Rplot005.png Binary files differindex 91058d4b..aa844051 100644 --- a/docs/reference/Rplot005.png +++ b/docs/reference/Rplot005.png diff --git a/docs/reference/Rplot006.png b/docs/reference/Rplot006.png Binary files differindex 74f43dfa..81525882 100644 --- a/docs/reference/Rplot006.png +++ b/docs/reference/Rplot006.png diff --git a/docs/reference/Rplot007.png b/docs/reference/Rplot007.png Binary files differindex fce3b6ee..10b7455a 100644 --- a/docs/reference/Rplot007.png +++ b/docs/reference/Rplot007.png diff --git a/docs/reference/SFO.solution.html b/docs/reference/SFO.solution.html index 7e4fb237..826a0472 100644 --- a/docs/reference/SFO.solution.html +++ b/docs/reference/SFO.solution.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/SFORB.solution.html b/docs/reference/SFORB.solution.html index 7254c451..b2062e30 100644 --- a/docs/reference/SFORB.solution.html +++ b/docs/reference/SFORB.solution.html @@ -76,7 +76,7 @@ and no substance in the bound fraction." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/add_err-1.png b/docs/reference/add_err-1.png Binary files differindex 9d333cbb..9ba106db 100644 --- a/docs/reference/add_err-1.png +++ b/docs/reference/add_err-1.png diff --git a/docs/reference/add_err-3.png b/docs/reference/add_err-3.png Binary files differindex 07cf0032..493a761a 100644 --- a/docs/reference/add_err-3.png +++ b/docs/reference/add_err-3.png diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html index 2009aaf4..6fbecd14 100644 --- a/docs/reference/add_err.html +++ b/docs/reference/add_err.html @@ -74,7 +74,7 @@ may depend on the predicted value and is specified as a standard deviation." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/aw.html b/docs/reference/aw.html index 77ee9cef..6399b839 100644 --- a/docs/reference/aw.html +++ b/docs/reference/aw.html @@ -74,7 +74,7 @@ by Burnham and Anderson (2004)." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/confint.mkinfit.html b/docs/reference/confint.mkinfit.html index b71e039a..06e78459 100644 --- a/docs/reference/confint.mkinfit.html +++ b/docs/reference/confint.mkinfit.html @@ -79,7 +79,7 @@ method of Venzon and Moolgavkar (1988)." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -285,13 +285,13 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <span class='va'>f_d_1</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>FOCUS_2006_D</span>, <span class='va'>value</span> <span class='op'>!=</span> <span class='fl'>0</span><span class='op'>)</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span><span class='va'>ci_profile</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_1</span>, method <span class='op'>=</span> <span class='st'>"profile"</span>, cores <span class='op'>=</span> <span class='fl'>1</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> </div><div class='output co'>#> user system elapsed -#> 4.324 0.980 3.960 </div><div class='input'><span class='co'># Using more cores does not save much time here, as parent_0 takes up most of the time</span> +#> 4.255 1.029 3.937 </div><div class='input'><span class='co'># Using more cores does not save much time here, as parent_0 takes up most of the time</span> <span class='co'># If we additionally exclude parent_0 (the confidence of which is often of</span> <span class='co'># minor interest), we get a nice performance improvement if we use at least 4 cores</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span><span class='va'>ci_profile_no_parent_0</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_1</span>, method <span class='op'>=</span> <span class='st'>"profile"</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"k_parent_sink"</span>, <span class='st'>"k_parent_m1"</span>, <span class='st'>"k_m1_sink"</span>, <span class='st'>"sigma"</span><span class='op'>)</span>, cores <span class='op'>=</span> <span class='va'>n_cores</span><span class='op'>)</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='output co'>#> user system elapsed -#> 1.480 0.109 0.905 </div><div class='input'><span class='va'>ci_profile</span> +#> 1.469 0.092 0.911 </div><div class='input'><span class='va'>ci_profile</span> </div><div class='output co'>#> 2.5% 97.5% #> parent_0 96.456003640 1.027703e+02 #> k_parent_sink 0.040762501 5.549764e-02 @@ -349,14 +349,14 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, #> sigma 2.535612399 3.985263e+00</div><div class='input'><span class='va'>ci_quadratic_transformed_ff</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_2</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span><span class='op'>)</span> <span class='va'>ci_quadratic_transformed_ff</span> </div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.403833585 102.79311650 +#> parent_0 96.403833578 102.79311649 #> k_parent 0.090823771 0.10725430 #> k_m1 0.004012219 0.00689755 #> f_parent_to_m1 0.469118824 0.55959615 #> sigma 2.396089689 3.85491806</div><div class='input'><span class='va'>ci_quadratic_untransformed_ff</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_2</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span>, transformed <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span> <span class='va'>ci_quadratic_untransformed_ff</span> </div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.403833589 1.027931e+02 +#> parent_0 96.403833583 1.027931e+02 #> k_parent 0.090491913 1.069035e-01 #> k_m1 0.003835485 6.685823e-03 #> f_parent_to_m1 0.469113477 5.598387e-01 @@ -374,15 +374,15 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, #> f_parent_to_m1 TRUE FALSE #> sigma TRUE FALSE</div><div class='input'><span class='va'>rel_diffs_transformed_ff</span> </div><div class='output co'>#> 2.5% 97.5% -#> parent_0 0.0005408689 0.0002217234 +#> parent_0 0.0005408690 0.0002217233 #> k_parent 0.0009598532 0.0009001864 -#> k_m1 0.0307283044 0.0290588365 -#> f_parent_to_m1 0.0046881768 0.0027780063 +#> k_m1 0.0307283041 0.0290588361 +#> f_parent_to_m1 0.0046881769 0.0027780063 #> sigma 0.0550252516 0.0327066836</div><div class='input'><span class='va'>rel_diffs_untransformed_ff</span> </div><div class='output co'>#> 2.5% 97.5% -#> parent_0 0.0005408689 0.0002217233 -#> k_parent 0.0046102155 0.0023732281 -#> k_m1 0.0146740688 0.0025291817 +#> parent_0 0.0005408689 0.0002217232 +#> k_parent 0.0046102156 0.0023732281 +#> k_m1 0.0146740690 0.0025291820 #> f_parent_to_m1 0.0046995211 0.0023457712 #> sigma 0.0550252516 0.0327066836</div><div class='input'> <span class='co'># The profiling for the following fit does not finish in a reasonable time,</span> @@ -396,18 +396,18 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, error_model_algorithm <span class='op'>=</span> <span class='st'>"direct"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_tc_2</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span><span class='op'>)</span> </div><div class='output co'>#> 2.5% 97.5% -#> parent_0 94.596126334 106.19944007 -#> k_M1 0.037605408 0.04490759 -#> k_M2 0.008568739 0.01087675 -#> f_parent_to_M1 0.021463787 0.62023881 -#> f_parent_to_M2 0.015166531 0.37975349 -#> k1 0.273897467 0.33388084 -#> k2 0.018614555 0.02250379 -#> g 0.671943606 0.73583278 -#> sigma_low 0.251283766 0.83992113 -#> rsd_high 0.040411014 0.07662005</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_tc_2</span>, <span class='st'>"parent_0"</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span><span class='op'>)</span> +#> parent_0 94.596039609 106.19954892 +#> k_M1 0.037605368 0.04490762 +#> k_M2 0.008568731 0.01087676 +#> f_parent_to_M1 0.021462489 0.62023882 +#> f_parent_to_M2 0.015165617 0.37975348 +#> k1 0.273897348 0.33388101 +#> k2 0.018614554 0.02250378 +#> g 0.671943411 0.73583305 +#> sigma_low 0.251283495 0.83992077 +#> rsd_high 0.040411024 0.07662008</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_tc_2</span>, <span class='st'>"parent_0"</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span><span class='op'>)</span> </div><div class='output co'>#> 2.5% 97.5% -#> parent_0 94.59613 106.1994</div><div class='input'><span class='co'># }</span> +#> parent_0 94.59604 106.1995</div><div class='input'><span class='co'># }</span> </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> diff --git a/docs/reference/create_deg_func.html b/docs/reference/create_deg_func.html index ed49e37e..82583f1a 100644 --- a/docs/reference/create_deg_func.html +++ b/docs/reference/create_deg_func.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -182,7 +182,7 @@ replications <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'>Loading required package: rbenchmark</span></div><div class='output co'>#> test replications elapsed relative user.self sys.self user.child #> 1 analytical 2 0.389 1.000 0.389 0 0 -#> 2 deSolve 2 0.689 1.771 0.688 0 0 +#> 2 deSolve 2 0.690 1.774 0.690 0 0 #> sys.child #> 1 0 #> 2 0</div><div class='input'> <span class='va'>DFOP_SFO</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> @@ -193,8 +193,8 @@ deSolve <span class='op'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>DFOP_SFO</span>, <span class='va'>FOCUS_D</span>, solution_type <span class='op'>=</span> <span class='st'>"deSolve"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>, replications <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span> </div><div class='output co'>#> test replications elapsed relative user.self sys.self user.child -#> 1 analytical 2 0.825 1.000 0.825 0 0 -#> 2 deSolve 2 1.574 1.908 1.573 0 0 +#> 1 analytical 2 0.817 1.000 0.818 0 0 +#> 2 deSolve 2 1.591 1.947 1.591 0 0 #> sys.child #> 1 0 #> 2 0</div><div class='input'><span class='co'># }</span> diff --git a/docs/reference/dimethenamid_2018.html b/docs/reference/dimethenamid_2018.html index 435848c2..6845f74f 100644 --- a/docs/reference/dimethenamid_2018.html +++ b/docs/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/endpoints.html b/docs/reference/endpoints.html index e42ca9fe..584079d2 100644 --- a/docs/reference/endpoints.html +++ b/docs/reference/endpoints.html @@ -78,7 +78,7 @@ advantage that the SFORB model can also be used for metabolites." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/experimental_data_for_UBA-1.png b/docs/reference/experimental_data_for_UBA-1.png Binary files differindex 22f38cc2..33946ded 100644 --- a/docs/reference/experimental_data_for_UBA-1.png +++ b/docs/reference/experimental_data_for_UBA-1.png diff --git a/docs/reference/experimental_data_for_UBA.html b/docs/reference/experimental_data_for_UBA.html index 9333a34f..77f75678 100644 --- a/docs/reference/experimental_data_for_UBA.html +++ b/docs/reference/experimental_data_for_UBA.html @@ -100,7 +100,7 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/f_time_norm_focus.html b/docs/reference/f_time_norm_focus.html index 6917b4b3..aa494b27 100644 --- a/docs/reference/f_time_norm_focus.html +++ b/docs/reference/f_time_norm_focus.html @@ -73,7 +73,7 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/focus_soil_moisture.html b/docs/reference/focus_soil_moisture.html index f0c766da..61f235db 100644 --- a/docs/reference/focus_soil_moisture.html +++ b/docs/reference/focus_soil_moisture.html @@ -73,7 +73,7 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/get_deg_func.html b/docs/reference/get_deg_func.html index a0e341c9..44703806 100644 --- a/docs/reference/get_deg_func.html +++ b/docs/reference/get_deg_func.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/ilr.html b/docs/reference/ilr.html index b91ef055..671faf5d 100644 --- a/docs/reference/ilr.html +++ b/docs/reference/ilr.html @@ -73,7 +73,7 @@ transformations." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/index.html b/docs/reference/index.html index 7b1bc4f1..e21304b9 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -71,7 +71,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/loftest-3.png b/docs/reference/loftest-3.png Binary files differindex 43e6a00f..6afd084b 100644 --- a/docs/reference/loftest-3.png +++ b/docs/reference/loftest-3.png diff --git a/docs/reference/loftest-5.png b/docs/reference/loftest-5.png Binary files differindex cf7e5862..43460a65 100644 --- a/docs/reference/loftest-5.png +++ b/docs/reference/loftest-5.png diff --git a/docs/reference/loftest.html b/docs/reference/loftest.html index 614b8eea..6e72774e 100644 --- a/docs/reference/loftest.html +++ b/docs/reference/loftest.html @@ -75,7 +75,7 @@ lrtest.default from the lmtest package." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html index 8d984e55..9e5b4069 100644 --- a/docs/reference/logLik.mkinfit.html +++ b/docs/reference/logLik.mkinfit.html @@ -76,7 +76,7 @@ the error model." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/logistic.solution-2.png b/docs/reference/logistic.solution-2.png Binary files differindex 79bf3453..73e6436d 100644 --- a/docs/reference/logistic.solution-2.png +++ b/docs/reference/logistic.solution-2.png diff --git a/docs/reference/logistic.solution.html b/docs/reference/logistic.solution.html index 404344a3..d11e1b3c 100644 --- a/docs/reference/logistic.solution.html +++ b/docs/reference/logistic.solution.html @@ -73,7 +73,7 @@ an increasing rate constant, supposedly caused by microbial growth" /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -240,10 +240,10 @@ Version 1.1, 18 December 2014 <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span><span class='op'>(</span><span class='va'>m</span><span class='op'>)</span> </div><div class='img'><img src='logistic.solution-2.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>m</span><span class='op'>)</span><span class='op'>$</span><span class='va'>bpar</span> </div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower -#> parent_0 1.057896e+02 1.9023449703 55.610119 3.768361e-16 1.016451e+02 -#> kmax 6.398190e-02 0.0143201031 4.467978 3.841829e-04 3.929235e-02 +#> parent_0 1.057896e+02 1.9023449590 55.610120 3.768360e-16 1.016451e+02 +#> kmax 6.398190e-02 0.0143201029 4.467978 3.841828e-04 3.929235e-02 #> k0 1.612775e-04 0.0005866813 0.274898 3.940351e-01 5.846688e-08 -#> r 2.263946e-01 0.1718110715 1.317695 1.061044e-01 4.335843e-02 +#> r 2.263946e-01 0.1718110662 1.317695 1.061043e-01 4.335843e-02 #> sigma 5.332935e+00 0.9145907310 5.830952 4.036926e-05 3.340213e+00 #> Upper #> parent_0 109.9341588 diff --git a/docs/reference/lrtest.mkinfit.html b/docs/reference/lrtest.mkinfit.html index e39314d9..d670fb0c 100644 --- a/docs/reference/lrtest.mkinfit.html +++ b/docs/reference/lrtest.mkinfit.html @@ -76,7 +76,7 @@ and can be expressed by fixing the parameters of the other." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/max_twa_parent.html b/docs/reference/max_twa_parent.html index 2aa5d5f1..4d473893 100644 --- a/docs/reference/max_twa_parent.html +++ b/docs/reference/max_twa_parent.html @@ -78,7 +78,7 @@ soil section of the FOCUS guidance." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/mccall81_245T-1.png b/docs/reference/mccall81_245T-1.png Binary files differindex 58ae716a..91fe060e 100644 --- a/docs/reference/mccall81_245T-1.png +++ b/docs/reference/mccall81_245T-1.png diff --git a/docs/reference/mccall81_245T.html b/docs/reference/mccall81_245T.html index b99138a0..b7dca4a7 100644 --- a/docs/reference/mccall81_245T.html +++ b/docs/reference/mccall81_245T.html @@ -74,7 +74,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -181,30 +181,30 @@ <span class='va'>fit.1</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>mccall81_245T</span>, <span class='va'>soil</span> <span class='op'>==</span> <span class='st'>"Commerce"</span><span class='op'>)</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.1</span><span class='op'>)</span><span class='op'>$</span><span class='va'>bpar</span> </div><div class='output co'>#> Estimate se_notrans t value Pr(>t) -#> T245_0 1.038550e+02 2.1847074888 47.537272 4.472189e-18 +#> T245_0 1.038550e+02 2.1847074945 47.537272 4.472189e-18 #> k_T245 4.337042e-02 0.0018983965 22.845818 2.276911e-13 -#> k_phenol 4.050581e-01 0.2986993400 1.356073 9.756988e-02 -#> k_anisole 6.678742e-03 0.0008021439 8.326114 2.623176e-07 -#> f_T245_to_phenol 6.227599e-01 0.3985340295 1.562627 6.949412e-02 -#> f_phenol_to_anisole 1.000000e+00 0.6718439378 1.488441 7.867787e-02 -#> sigma 2.514628e+00 0.4907558750 5.123989 6.233156e-05 +#> k_phenol 4.050581e-01 0.2986993563 1.356073 9.756989e-02 +#> k_anisole 6.678742e-03 0.0008021439 8.326114 2.623177e-07 +#> f_T245_to_phenol 6.227599e-01 0.3985340558 1.562627 6.949413e-02 +#> f_phenol_to_anisole 1.000000e+00 0.6718439825 1.488441 7.867789e-02 +#> sigma 2.514628e+00 0.4907558883 5.123989 6.233157e-05 #> Lower Upper -#> T245_0 99.246061370 1.084640e+02 +#> T245_0 99.246061385 1.084640e+02 #> k_T245 0.039631621 4.746194e-02 -#> k_phenol 0.218013878 7.525762e-01 +#> k_phenol 0.218013879 7.525762e-01 #> k_anisole 0.005370739 8.305299e-03 -#> f_T245_to_phenol 0.547559083 6.924813e-01 +#> f_T245_to_phenol 0.547559081 6.924813e-01 #> f_phenol_to_anisole 0.000000000 1.000000e+00 #> sigma 1.706607296 3.322649e+00</div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>fit.1</span><span class='op'>)</span> </div><div class='output co'>#> $ff #> T245_phenol T245_sink phenol_anisole phenol_sink -#> 6.227599e-01 3.772401e-01 1.000000e+00 6.894640e-11 +#> 6.227599e-01 3.772401e-01 1.000000e+00 3.773626e-10 #> #> $distimes #> DT50 DT90 #> T245 15.982025 53.09114 #> phenol 1.711229 5.68458 -#> anisole 103.784092 344.76329 +#> anisole 103.784093 344.76329 #> </div><div class='input'> <span class='co'># formation fraction from phenol to anisol is practically 1. As we cannot</span> <span class='co'># fix formation fractions when using the ilr transformation, we can turn of</span> <span class='co'># the sink in the model generation</span> @@ -215,28 +215,28 @@ quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.2</span><span class='op'>)</span><span class='op'>$</span><span class='va'>bpar</span> </div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower -#> T245_0 1.038550e+02 2.1623653027 48.028439 4.993108e-19 99.271020526 -#> k_T245 4.337042e-02 0.0018343666 23.643268 3.573555e-14 0.039650977 -#> k_phenol 4.050582e-01 0.1177237248 3.440752 1.679252e-03 0.218746585 -#> k_anisole 6.678741e-03 0.0006829745 9.778903 1.872894e-08 0.005377083 -#> f_T245_to_phenol 6.227599e-01 0.0342197865 18.198825 2.039410e-12 0.547975628 +#> T245_0 1.038550e+02 2.1623653066 48.028439 4.993108e-19 99.271020284 +#> k_T245 4.337042e-02 0.0018343666 23.643268 3.573556e-14 0.039650976 +#> k_phenol 4.050582e-01 0.1177237473 3.440752 1.679254e-03 0.218746587 +#> k_anisole 6.678742e-03 0.0006829745 9.778903 1.872894e-08 0.005377083 +#> f_T245_to_phenol 6.227599e-01 0.0342197875 18.198824 2.039411e-12 0.547975637 #> sigma 2.514628e+00 0.3790944250 6.633250 2.875782e-06 1.710983655 #> Upper -#> T245_0 108.43904097 +#> T245_0 108.43904074 #> k_T245 0.04743877 -#> k_phenol 0.75005577 +#> k_phenol 0.75005585 #> k_anisole 0.00829550 -#> f_T245_to_phenol 0.69212306 +#> f_T245_to_phenol 0.69212308 #> sigma 3.31827222</div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>fit.1</span><span class='op'>)</span> </div><div class='output co'>#> $ff #> T245_phenol T245_sink phenol_anisole phenol_sink -#> 6.227599e-01 3.772401e-01 1.000000e+00 6.894640e-11 +#> 6.227599e-01 3.772401e-01 1.000000e+00 3.773626e-10 #> #> $distimes #> DT50 DT90 #> T245 15.982025 53.09114 #> phenol 1.711229 5.68458 -#> anisole 103.784092 344.76329 +#> anisole 103.784093 344.76329 #> </div><div class='input'> <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span><span class='op'>(</span><span class='va'>fit.2</span><span class='op'>)</span> </div><div class='img'><img src='mccall81_245T-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='co'># }</span> </div></pre> diff --git a/docs/reference/mixed-1.png b/docs/reference/mixed-1.png Binary files differindex 05beffc9..28a376f4 100644 --- a/docs/reference/mixed-1.png +++ b/docs/reference/mixed-1.png diff --git a/docs/reference/mixed.html b/docs/reference/mixed.html index f5429f8b..23d955e3 100644 --- a/docs/reference/mixed.html +++ b/docs/reference/mixed.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -235,18 +235,16 @@ #> Status of individual fits: #> #> dataset -#> model 1 2 3 4 5 6 7 8 -#> DFOP-SFO OK OK OK OK OK C OK OK +#> model 1 2 3 4 5 6 7 8 +#> DFOP-SFO OK OK OK OK OK OK OK OK #> #> OK: No warnings -#> C: Optimisation did not converge: -#> iteration limit reached without convergence (10) #> #> Mean fitted parameters: #> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 -#> 100.606304 -8.759216 -0.002001 -3.350539 -3.989549 +#> 100.674757 -8.761916 -0.004347 -3.348812 -3.986853 #> g_qlogis -#> -0.090353 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_mixed</span><span class='op'>)</span> +#> -0.087392 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_mixed</span><span class='op'>)</span> </div><div class='img'><img src='mixed-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> </div></pre> </div> diff --git a/docs/reference/mkin_long_to_wide.html b/docs/reference/mkin_long_to_wide.html index 7eca35de..c82da5dd 100644 --- a/docs/reference/mkin_long_to_wide.html +++ b/docs/reference/mkin_long_to_wide.html @@ -74,7 +74,7 @@ variable and several dependent variables as columns." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/mkin_wide_to_long.html b/docs/reference/mkin_wide_to_long.html index 5df8972f..15619fba 100644 --- a/docs/reference/mkin_wide_to_long.html +++ b/docs/reference/mkin_wide_to_long.html @@ -74,7 +74,7 @@ mkinfit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/mkinds.html b/docs/reference/mkinds.html index 543ea68d..5111a9e0 100644 --- a/docs/reference/mkinds.html +++ b/docs/reference/mkinds.html @@ -75,7 +75,7 @@ provided by this package come as mkinds objects nevertheless." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/mkindsg.html b/docs/reference/mkindsg.html index 74d3a26c..003e5e8f 100644 --- a/docs/reference/mkindsg.html +++ b/docs/reference/mkindsg.html @@ -75,7 +75,7 @@ dataset if no data are supplied." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/mkinerrmin.html b/docs/reference/mkinerrmin.html index 161eadca..f22b4350 100644 --- a/docs/reference/mkinerrmin.html +++ b/docs/reference/mkinerrmin.html @@ -73,7 +73,7 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/mkinerrplot.html b/docs/reference/mkinerrplot.html index 2509e4c1..361ce79a 100644 --- a/docs/reference/mkinerrplot.html +++ b/docs/reference/mkinerrplot.html @@ -76,7 +76,7 @@ using the argument show_errplot = TRUE." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/mkinfit-1.png b/docs/reference/mkinfit-1.png Binary files differindex bbc0ccb6..de2a90a9 100644 --- a/docs/reference/mkinfit-1.png +++ b/docs/reference/mkinfit-1.png diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html index 4d8aeb40..180f2ee7 100644 --- a/docs/reference/mkinfit.html +++ b/docs/reference/mkinfit.html @@ -80,7 +80,7 @@ likelihood function." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -431,10 +431,10 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 <span class='co'># Use shorthand notation for parent only degradation</span> <span class='va'>fit</span> <span class='op'><-</span> <span class='fu'>mkinfit</span><span class='op'>(</span><span class='st'>"FOMC"</span>, <span class='va'>FOCUS_2006_C</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -</div><div class='output co'>#> mkin version used for fitting: 1.0.0 +</div><div class='output co'>#> mkin version used for fitting: 1.0.3 #> R version used for fitting: 4.0.3 -#> Date of fit: Wed Feb 3 17:28:58 2021 -#> Date of summary: Wed Feb 3 17:28:58 2021 +#> Date of fit: Mon Feb 15 13:43:26 2021 +#> Date of summary: Mon Feb 15 13:43:26 2021 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -476,10 +476,10 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 #> #> Parameter correlation: #> parent_0 log_alpha log_beta sigma -#> parent_0 1.000e+00 -1.565e-01 -3.142e-01 4.758e-08 -#> log_alpha -1.565e-01 1.000e+00 9.564e-01 1.007e-07 -#> log_beta -3.142e-01 9.564e-01 1.000e+00 8.568e-08 -#> sigma 4.758e-08 1.007e-07 8.568e-08 1.000e+00 +#> parent_0 1.000e+00 -1.565e-01 -3.142e-01 4.772e-08 +#> log_alpha -1.565e-01 1.000e+00 9.564e-01 1.005e-07 +#> log_beta -3.142e-01 9.564e-01 1.000e+00 8.541e-08 +#> sigma 4.772e-08 1.005e-07 8.541e-08 1.000e+00 #> #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. @@ -548,7 +548,7 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span><span class='op'>(</span><span class='va'>fit.tc</span><span class='op'>)</span> </div><div class='output co'>#> parent_0 k_parent k_m1 f_parent_to_m1 sigma_low -#> 1.007343e+02 1.005562e-01 5.166712e-03 5.083933e-01 3.049884e-03 +#> 1.007343e+02 1.005562e-01 5.166712e-03 5.083933e-01 3.049883e-03 #> rsd_high #> 7.928118e-02 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>fit.tc</span><span class='op'>)</span> </div><div class='output co'>#> $ff @@ -575,9 +575,9 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 solution_type <span class='op'>=</span> <span class='st'>"analytical"</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>}</span> </div><div class='output co'>#> test relative elapsed -#> 3 analytical 1.000 0.542 -#> 1 deSolve_compiled 1.812 0.982 -#> 2 eigen 2.234 1.211</div><div class='input'><span class='co'># }</span> +#> 3 analytical 1.000 0.550 +#> 1 deSolve_compiled 1.731 0.952 +#> 2 eigen 2.662 1.464</div><div class='input'><span class='co'># }</span> <span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO</span> <span class='co'># \dontrun{</span> @@ -587,22 +587,21 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 </div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>fit.FOMC_SFO</span> <span class='op'><-</span> <span class='fu'>mkinfit</span><span class='op'>(</span><span class='va'>FOMC_SFO</span>, <span class='va'>FOCUS_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='co'># Again, we get a warning and try a more sophisticated error model</span> <span class='va'>fit.FOMC_SFO.tc</span> <span class='op'><-</span> <span class='fu'>mkinfit</span><span class='op'>(</span><span class='va'>FOMC_SFO</span>, <span class='va'>FOCUS_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Optimisation did not converge:</span> -#> <span class='warning'>iteration limit reached without convergence (10)</span></div><div class='input'><span class='co'># This model has a higher likelihood, but not significantly so</span> +<span class='co'># This model has a higher likelihood, but not significantly so</span> <span class='fu'><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></span><span class='op'>(</span><span class='va'>fit.tc</span>, <span class='va'>fit.FOMC_SFO.tc</span><span class='op'>)</span> </div><div class='output co'>#> Likelihood ratio test #> #> Model 1: FOMC_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model tc and fixed parameter(s) m1_0 #> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 7 -64.870 -#> 2 6 -64.983 -1 0.2259 0.6346</div><div class='input'><span class='co'># Also, the missing standard error for log_beta and the t-tests for alpha</span> +#> 1 7 -64.829 +#> 2 6 -64.983 -1 0.3075 0.5792</div><div class='input'><span class='co'># Also, the missing standard error for log_beta and the t-tests for alpha</span> <span class='co'># and beta indicate overparameterisation</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.FOMC_SFO.tc</span>, data <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: diag(.) had 0 or NA entries; non-finite result is doubtful</span></div><div class='output co'>#> mkin version used for fitting: 1.0.0 +</div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: diag(.) had 0 or NA entries; non-finite result is doubtful</span></div><div class='output co'>#> mkin version used for fitting: 1.0.3 #> R version used for fitting: 4.0.3 -#> Date of fit: Wed Feb 3 17:29:09 2021 -#> Date of summary: Wed Feb 3 17:29:09 2021 +#> Date of fit: Mon Feb 15 13:43:38 2021 +#> Date of summary: Mon Feb 15 13:43:38 2021 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -611,12 +610,12 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 #> #> Model predictions using solution type deSolve #> -#> Fitted using 4273 model solutions performed in 3.195 s +#> Fitted using 3729 model solutions performed in 2.802 s #> #> Error model: Two-component variance function #> #> Error model algorithm: d_3 -#> Three-step fitting yielded a higher likelihood than direct fitting +#> Direct fitting and three-step fitting yield approximately the same likelihood #> #> Starting values for parameters to be optimised: #> value type @@ -642,72 +641,67 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 #> value type #> m1_0 0 state #> -#> -#> Warning(s): -#> Optimisation did not converge: -#> iteration limit reached without convergence (10) -#> #> Results: #> -#> AIC BIC logLik -#> 143.7396 155.2027 -64.86982 +#> AIC BIC logLik +#> 143.658 155.1211 -64.82902 #> #> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 1.016e+02 1.90600 97.7400 105.5000 -#> log_k_m1 -5.285e+00 0.09286 -5.4740 -5.0950 -#> f_parent_qlogis 6.482e-04 0.06164 -0.1251 0.1264 -#> log_alpha 5.467e+00 NaN NaN NaN -#> log_beta 7.750e+00 NaN NaN NaN -#> sigma_low 0.000e+00 NaN NaN NaN -#> rsd_high 7.989e-02 NaN NaN NaN +#> Estimate Std. Error Lower Upper +#> parent_0 101.600000 2.6400000 96.240000 107.000000 +#> log_k_m1 -5.284000 0.0929100 -5.474000 -5.095000 +#> f_parent_qlogis 0.001426 0.0767000 -0.155000 0.157800 +#> log_alpha 5.522000 0.0077320 5.506000 5.538000 +#> log_beta 7.806000 NaN NaN NaN +#> sigma_low 0.002488 0.0002431 0.001992 0.002984 +#> rsd_high 0.079210 0.0093280 0.060180 0.098230 #> #> Parameter correlation: -#> parent_0 log_k_m1 f_parent_qlogis log_alpha log_beta -#> parent_0 1.0000000 -0.0002167 -0.6060 NaN NaN -#> log_k_m1 -0.0002167 1.0000000 0.5474 NaN NaN -#> f_parent_qlogis -0.6060320 0.5474423 1.0000 NaN NaN -#> log_alpha NaN NaN NaN 1 NaN -#> log_beta NaN NaN NaN NaN 1 -#> sigma_low NaN NaN NaN NaN NaN -#> rsd_high NaN NaN NaN NaN NaN -#> sigma_low rsd_high -#> parent_0 NaN NaN -#> log_k_m1 NaN NaN -#> f_parent_qlogis NaN NaN -#> log_alpha NaN NaN -#> log_beta NaN NaN -#> sigma_low 1 NaN -#> rsd_high NaN 1 +#> parent_0 log_k_m1 f_parent_qlogis log_alpha log_beta +#> parent_0 1.000000 -0.095226 -0.76678 0.70544 NaN +#> log_k_m1 -0.095226 1.000000 0.51432 -0.14387 NaN +#> f_parent_qlogis -0.766780 0.514321 1.00000 -0.61396 NaN +#> log_alpha 0.705444 -0.143872 -0.61396 1.00000 NaN +#> log_beta NaN NaN NaN NaN 1 +#> sigma_low 0.016073 0.001586 0.01548 5.87007 NaN +#> rsd_high 0.006626 -0.011700 -0.05357 0.04849 NaN +#> sigma_low rsd_high +#> parent_0 0.016073 0.006626 +#> log_k_m1 0.001586 -0.011700 +#> f_parent_qlogis 0.015476 -0.053566 +#> log_alpha 5.870075 0.048487 +#> log_beta NaN NaN +#> sigma_low 1.000000 -0.652558 +#> rsd_high -0.652558 1.000000 #> #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. #> t-test (unrealistically) based on the assumption of normal distribution #> for estimators of untransformed parameters. #> Estimate t value Pr(>t) Lower Upper -#> parent_0 1.016e+02 32.5400 7.812e-26 97.740000 1.055e+02 -#> k_m1 5.069e-03 10.0400 1.448e-11 0.004194 6.126e-03 -#> f_parent_to_m1 5.002e-01 20.7300 5.001e-20 0.468800 5.315e-01 -#> alpha 2.367e+02 0.6205 2.697e-01 NA NA -#> beta 2.322e+03 0.6114 2.727e-01 NA NA -#> sigma_low 0.000e+00 NaN NaN NaN NaN -#> rsd_high 7.989e-02 8.6630 4.393e-10 NaN NaN +#> parent_0 1.016e+02 32.7800 6.311e-26 9.624e+01 1.070e+02 +#> k_m1 5.072e-03 10.1200 1.216e-11 4.196e-03 6.130e-03 +#> f_parent_to_m1 5.004e-01 20.8300 4.317e-20 4.613e-01 5.394e-01 +#> alpha 2.502e+02 0.5624 2.889e-01 2.463e+02 2.542e+02 +#> beta 2.455e+03 0.5549 2.915e-01 NA NA +#> sigma_low 2.488e-03 0.4843 3.158e-01 1.992e-03 2.984e-03 +#> rsd_high 7.921e-02 8.4300 8.001e-10 6.018e-02 9.823e-02 #> #> FOCUS Chi2 error levels in percent: #> err.min n.optim df -#> All data 6.782 5 14 -#> parent 7.142 3 6 -#> m1 4.639 2 8 +#> All data 6.781 5 14 +#> parent 7.141 3 6 +#> m1 4.640 2 8 #> #> Resulting formation fractions: #> ff -#> parent_m1 0.5002 -#> parent_sink 0.4998 +#> parent_m1 0.5004 +#> parent_sink 0.4996 #> #> Estimated disappearance times: -#> DT50 DT90 DT50back -#> parent 6.81 22.7 6.833 -#> m1 136.74 454.2 NA</div><div class='input'> +#> DT50 DT90 DT50back +#> parent 6.812 22.7 6.834 +#> m1 136.661 454.0 NA</div><div class='input'> <span class='co'># We can easily use starting parameters from the parent only fit (only for illustration)</span> <span class='va'>fit.FOMC</span> <span class='op'>=</span> <span class='fu'>mkinfit</span><span class='op'>(</span><span class='st'>"FOMC"</span>, <span class='va'>FOCUS_2006_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> <span class='va'>fit.FOMC_SFO</span> <span class='op'><-</span> <span class='fu'>mkinfit</span><span class='op'>(</span><span class='va'>FOMC_SFO</span>, <span class='va'>FOCUS_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html index 43e5cc23..4ce9468a 100644 --- a/docs/reference/mkinmod.html +++ b/docs/reference/mkinmod.html @@ -78,7 +78,7 @@ mkinmod." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -348,7 +348,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> parent <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"m1"</span>, full_name <span class='op'>=</span> <span class='st'>"Test compound"</span><span class='op'>)</span>, m1 <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span>, full_name <span class='op'>=</span> <span class='st'>"Metabolite M1"</span><span class='op'>)</span>, name <span class='op'>=</span> <span class='st'>"SFO_SFO"</span>, dll_dir <span class='op'>=</span> <span class='va'>DLL_dir</span>, unload <span class='op'>=</span> <span class='cn'>TRUE</span>, overwrite <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Copied DLL from /tmp/Rtmp1BYo7R/file5c60502538f0.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span> +</div><div class='output co'>#> <span class='message'>Copied DLL from /tmp/RtmpiJ2M4Z/filee097a4a94a921.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span> <span class='fu'><a href='https://rdrr.io/r/base/readRDS.html'>saveRDS</a></span><span class='op'>(</span><span class='va'>SFO_SFO.2</span>, file <span class='op'>=</span> <span class='st'>"~/SFO_SFO.rds"</span><span class='op'>)</span> <span class='co'># Terminate the R session here if you would like to check, and then do</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://pkgdown.jrwb.de/mkin/'>mkin</a></span><span class='op'>)</span> @@ -397,7 +397,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> #> }) #> return(predicted) #> } -#> <environment: 0x55555caa9ee0></div><div class='input'> +#> <environment: 0x55555b0c2760></div><div class='input'> <span class='co'># If we have several parallel metabolites</span> <span class='co'># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span> <span class='va'>m_synth_DFOP_par</span> <span class='op'><-</span> <span class='fu'>mkinmod</span><span class='op'>(</span> diff --git a/docs/reference/mkinparplot-1.png b/docs/reference/mkinparplot-1.png Binary files differindex dcf3e4b5..c9ed49eb 100644 --- a/docs/reference/mkinparplot-1.png +++ b/docs/reference/mkinparplot-1.png diff --git a/docs/reference/mkinparplot.html b/docs/reference/mkinparplot.html index b4d11dcb..c298f5d2 100644 --- a/docs/reference/mkinparplot.html +++ b/docs/reference/mkinparplot.html @@ -73,7 +73,7 @@ mkinfit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -176,7 +176,8 @@ effect, namely to produce a plot.</p> phenol <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"anisole"</span><span class='op'>)</span><span class='op'>)</span>, anisole <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>model</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>mccall81_245T</span>, <span class='va'>soil</span> <span class='op'>==</span> <span class='st'>"Commerce"</span><span class='op'>)</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'>mkinparplot</span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='warning'>Warning: Optimisation did not converge:</span> +#> <span class='warning'>false convergence (8)</span></div><div class='input'><span class='fu'>mkinparplot</span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> </div><div class='img'><img src='mkinparplot-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> </div></pre> </div> diff --git a/docs/reference/mkinplot.html b/docs/reference/mkinplot.html index 1f0be544..b20a8c96 100644 --- a/docs/reference/mkinplot.html +++ b/docs/reference/mkinplot.html @@ -73,7 +73,7 @@ plot.mkinfit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html index 035a21f9..25e26419 100644 --- a/docs/reference/mkinpredict.html +++ b/docs/reference/mkinpredict.html @@ -74,7 +74,7 @@ kinetic parameters and initial values for the state variables." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -410,8 +410,8 @@ as these always return mapped output.</p></td> </div><div class='output co'>#> test relative elapsed #> 2 deSolve_compiled 1.0 0.005 #> 4 analytical 1.0 0.005 -#> 1 eigen 4.0 0.020 -#> 3 deSolve 44.6 0.223</div><div class='input'> +#> 1 eigen 4.4 0.022 +#> 3 deSolve 47.0 0.235</div><div class='input'> <span class='co'># \dontrun{</span> <span class='co'># Predict from a fitted model</span> <span class='va'>f</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO</span>, <span class='va'>FOCUS_2006_C</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> diff --git a/docs/reference/mkinresplot.html b/docs/reference/mkinresplot.html index 69517f36..04ff15b8 100644 --- a/docs/reference/mkinresplot.html +++ b/docs/reference/mkinresplot.html @@ -75,7 +75,7 @@ argument show_residuals = TRUE." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/mmkin-1.png b/docs/reference/mmkin-1.png Binary files differindex cce02aed..0db3379f 100644 --- a/docs/reference/mmkin-1.png +++ b/docs/reference/mmkin-1.png diff --git a/docs/reference/mmkin-2.png b/docs/reference/mmkin-2.png Binary files differindex 4f2529fa..024a9892 100644 --- a/docs/reference/mmkin-2.png +++ b/docs/reference/mmkin-2.png diff --git a/docs/reference/mmkin-3.png b/docs/reference/mmkin-3.png Binary files differindex fb5d4470..a23d7cb9 100644 --- a/docs/reference/mmkin-3.png +++ b/docs/reference/mmkin-3.png diff --git a/docs/reference/mmkin-4.png b/docs/reference/mmkin-4.png Binary files differindex 4f11753e..89975db5 100644 --- a/docs/reference/mmkin-4.png +++ b/docs/reference/mmkin-4.png diff --git a/docs/reference/mmkin-5.png b/docs/reference/mmkin-5.png Binary files differindex 5d653de9..a2f34983 100644 --- a/docs/reference/mmkin-5.png +++ b/docs/reference/mmkin-5.png diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html index 77f815da..c9800fe7 100644 --- a/docs/reference/mmkin.html +++ b/docs/reference/mmkin.html @@ -75,7 +75,7 @@ datasets specified in its first two arguments." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -234,19 +234,19 @@ plotting.</p></div> <span class='va'>time_default</span> </div><div class='output co'>#> user system elapsed -#> 4.634 0.317 1.280 </div><div class='input'><span class='va'>time_1</span> +#> 4.630 0.415 1.717 </div><div class='input'><span class='va'>time_1</span> </div><div class='output co'>#> user system elapsed -#> 5.249 0.016 5.267 </div><div class='input'> +#> 5.694 0.000 5.694 </div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>fits.0</span><span class='op'>[[</span><span class='st'>"SFO_lin"</span>, <span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> $ff #> parent_M1 parent_sink M1_M2 M1_sink -#> 0.7340478 0.2659522 0.7505687 0.2494313 +#> 0.7340481 0.2659519 0.7505683 0.2494317 #> #> $distimes #> DT50 DT90 #> parent 0.877769 2.915885 -#> M1 2.325746 7.725960 -#> M2 33.720083 112.015691 +#> M1 2.325744 7.725956 +#> M2 33.720100 112.015749 #> </div><div class='input'> <span class='co'># plot.mkinfit handles rows or columns of mmkin result objects</span> <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>fits.0</span><span class='op'>[</span><span class='fl'>1</span>, <span class='op'>]</span><span class='op'>)</span> @@ -273,12 +273,10 @@ plotting.</p></div> #> dataset #> model A B C D #> SFO OK OK OK OK -#> FOMC C OK OK OK +#> FOMC OK OK OK OK #> DFOP OK OK OK OK #> -#> OK: No warnings -#> C: Optimisation did not converge: -#> false convergence (8)</div><div class='input'><span class='co'># We get false convergence for the FOMC fit to FOCUS_2006_A because this</span> +#> OK: No warnings</div><div class='input'><span class='co'># We get false convergence for the FOMC fit to FOCUS_2006_A because this</span> <span class='co'># dataset is really SFO, and the FOMC fit is overparameterised</span> <span class='fu'>stopCluster</span><span class='op'>(</span><span class='va'>cl</span><span class='op'>)</span> <span class='co'># }</span> diff --git a/docs/reference/nafta-1.png b/docs/reference/nafta-1.png Binary files differindex 76d724f0..4f0d7833 100644 --- a/docs/reference/nafta-1.png +++ b/docs/reference/nafta-1.png diff --git a/docs/reference/nafta.html b/docs/reference/nafta.html index c6c1b173..29e03251 100644 --- a/docs/reference/nafta.html +++ b/docs/reference/nafta.html @@ -76,7 +76,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/nlme-1.png b/docs/reference/nlme-1.png Binary files differindex 82b952f7..728cc557 100644 --- a/docs/reference/nlme-1.png +++ b/docs/reference/nlme-1.png diff --git a/docs/reference/nlme-2.png b/docs/reference/nlme-2.png Binary files differindex 6bc121d1..e8167455 100644 --- a/docs/reference/nlme-2.png +++ b/docs/reference/nlme-2.png diff --git a/docs/reference/nlme.html b/docs/reference/nlme.html index f9e68b7f..7b0c6a97 100644 --- a/docs/reference/nlme.html +++ b/docs/reference/nlme.html @@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/nlme.mmkin-1.png b/docs/reference/nlme.mmkin-1.png Binary files differindex 546a3731..9186c135 100644 --- a/docs/reference/nlme.mmkin-1.png +++ b/docs/reference/nlme.mmkin-1.png diff --git a/docs/reference/nlme.mmkin-2.png b/docs/reference/nlme.mmkin-2.png Binary files differindex 7b5b4ab0..d395fe02 100644 --- a/docs/reference/nlme.mmkin-2.png +++ b/docs/reference/nlme.mmkin-2.png diff --git a/docs/reference/nlme.mmkin-3.png b/docs/reference/nlme.mmkin-3.png Binary files differindex 7c04df4b..40518a59 100644 --- a/docs/reference/nlme.mmkin-3.png +++ b/docs/reference/nlme.mmkin-3.png diff --git a/docs/reference/nlme.mmkin.html b/docs/reference/nlme.mmkin.html index 2e4f6337..189e34ef 100644 --- a/docs/reference/nlme.mmkin.html +++ b/docs/reference/nlme.mmkin.html @@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -157,7 +157,7 @@ have been obtained by fitting the same model to a list of datasets.</p> data <span class='op'>=</span> <span class='st'>"auto"</span>, fixed <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>as.list</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='fu'><a href='nlme_function.html'>mean_degparms</a></span><span class='op'>(</span><span class='va'>model</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>el</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/eval.html'>eval</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/parse.html'>parse</a></span><span class='op'>(</span>text <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='va'>el</span>, <span class='fl'>1</span>, sep <span class='op'>=</span> <span class='st'>"~"</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>, - random <span class='op'>=</span> <span class='fu'>pdDiag</span><span class='op'>(</span><span class='va'>fixed</span><span class='op'>)</span>, + random <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/pdDiag.html'>pdDiag</a></span><span class='op'>(</span><span class='va'>fixed</span><span class='op'>)</span>, <span class='va'>groups</span>, start <span class='op'>=</span> <span class='fu'><a href='nlme_function.html'>mean_degparms</a></span><span class='op'>(</span><span class='va'>model</span>, random <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>, correlation <span class='op'>=</span> <span class='cn'>NULL</span>, @@ -290,7 +290,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>anova</a></span><span class='op'>(</span><span class='va'>f_nlme_sfo</span>, <span class='va'>f_nlme_dfop</span><span class='op'>)</span> </div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio p-value #> f_nlme_sfo 1 5 625.0539 637.5529 -307.5269 -#> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9268 <.0001</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop</span><span class='op'>)</span> +#> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9269 <.0001</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop</span><span class='op'>)</span> </div><div class='output co'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: @@ -318,7 +318,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as </div><div class='img'><img src='nlme.mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop</span><span class='op'>)</span> </div><div class='output co'>#> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 10.79857 100.7937 30.34192 4.193937 43.85442 +#> parent 10.79857 100.7937 30.34193 4.193938 43.85443 #> </div><div class='input'> <span class='va'>ds_2</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>experimental_data_for_UBA_2019</span><span class='op'>[</span><span class='fl'>6</span><span class='op'>:</span><span class='fl'>10</span><span class='op'>]</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='va'>x</span><span class='op'>$</span><span class='va'>data</span><span class='op'>[</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span><span class='op'>)</span><span class='op'>]</span><span class='op'>)</span> @@ -350,8 +350,8 @@ methods that will automatically work on 'nlme.mmkin' objects, such as </div><div class='img'><img src='nlme.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>anova</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop_sfo</span>, <span class='va'>f_nlme_sfo_sfo</span><span class='op'>)</span> </div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio p-value -#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 -#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274 <.0001</div><div class='input'> +#> f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274 +#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3273 <.0001</div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>f_nlme_sfo_sfo</span><span class='op'>)</span> </div><div class='output co'>#> $ff #> parent_sink parent_A1 A1_sink @@ -364,12 +364,12 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> </div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop_sfo</span><span class='op'>)</span> </div><div class='output co'>#> $ff #> parent_A1 parent_sink -#> 0.2768574 0.7231426 +#> 0.2768575 0.7231425 #> #> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 11.07091 104.6320 31.49738 4.462384 46.20825 -#> A1 162.30523 539.1663 NA NA NA +#> parent 11.07091 104.6320 31.49737 4.462384 46.20825 +#> A1 162.30492 539.1653 NA NA NA #> </div><div class='input'> <span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/length.html'>length</a></span><span class='op'>(</span><span class='fu'>findFunction</span><span class='op'>(</span><span class='st'>"varConstProp"</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>></span> <span class='fl'>0</span><span class='op'>)</span> <span class='op'>{</span> <span class='co'># tc error model for nlme available</span> <span class='co'># Attempts to fit metabolite kinetics with the tc error model are possible,</span> @@ -396,7 +396,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Fixed effects: #> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> parent_0 log_k1 log_k2 g_qlogis -#> 94.04775 -1.82340 -4.16715 0.05685 +#> 94.04774 -1.82340 -4.16716 0.05686 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) @@ -410,7 +410,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Formula: ~fitted(.) #> Parameter estimates: #> const prop -#> 2.23224114 0.01262341 </div><div class='input'> +#> 2.23223147 0.01262395 </div><div class='input'> <span class='va'>f_2_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_2</span>, error_model <span class='op'>=</span> <span class='st'>"obs"</span><span class='op'>)</span> <span class='va'>f_nlme_sfo_sfo_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_2_obs</span><span class='op'>[</span><span class='st'>"SFO-SFO"</span>, <span class='op'>]</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_nlme_sfo_sfo_obs</span><span class='op'>)</span> @@ -442,7 +442,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Formula: ~1 | name #> Parameter estimates: #> parent A1 -#> 1.0000000 0.2050003 </div><div class='input'> <span class='va'>f_nlme_dfop_sfo_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_2_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, +#> 1.0000000 0.2049995 </div><div class='input'> <span class='va'>f_nlme_dfop_sfo_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_2_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, control <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>pnlsMaxIter <span class='op'>=</span> <span class='fl'>120</span>, tolerance <span class='op'>=</span> <span class='fl'>5e-4</span><span class='op'>)</span><span class='op'>)</span> <span class='va'>f_2_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_2</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> @@ -452,8 +452,8 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>anova</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop_sfo</span>, <span class='va'>f_nlme_dfop_sfo_obs</span><span class='op'>)</span> </div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio -#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 -#> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32089 +#> f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274 +#> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32093 #> p-value #> f_nlme_dfop_sfo #> f_nlme_dfop_sfo_obs <.0001</div><div class='input'> diff --git a/docs/reference/nobs.mkinfit.html b/docs/reference/nobs.mkinfit.html index dff8a285..6b9948c3 100644 --- a/docs/reference/nobs.mkinfit.html +++ b/docs/reference/nobs.mkinfit.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/parms.html b/docs/reference/parms.html index ab5888fa..e45d6a5c 100644 --- a/docs/reference/parms.html +++ b/docs/reference/parms.html @@ -74,7 +74,7 @@ considering the error structure that was assumed for the fit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -219,10 +219,10 @@ such matrices is returned.</p> #> #> $DFOP #> Dataset 7 -#> parent_0 91.058971597 +#> parent_0 91.058971589 #> k1 0.044946770 #> k2 0.002868336 -#> g 0.526942414 +#> g 0.526942415 #> sigma 2.221302196 #> </div><div class='input'><span class='fu'>parms</span><span class='op'>(</span><span class='va'>fits</span><span class='op'>)</span> </div><div class='output co'>#> $SFO @@ -233,17 +233,17 @@ such matrices is returned.</p> #> #> $FOMC #> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 -#> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481458 +#> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481459 #> alpha 1.338667 0.4967832 1.639099 1.074460 0.2805272 #> beta 13.033315 14.1451255 5.007077 4.397126 6.9052224 #> sigma 1.847671 1.9167519 1.066063 3.146056 1.6222778 #> #> $DFOP #> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 -#> parent_0 96.55213663 91.058971597 90.34509493 98.14858820 94.311323733 +#> parent_0 96.55213663 91.058971589 90.34509493 98.14858820 94.311323734 #> k1 0.21954588 0.044946770 0.41232288 0.31697588 0.080663857 #> k2 0.02957934 0.002868336 0.07581766 0.03260384 0.003425417 -#> g 0.44845068 0.526942414 0.66091967 0.65322767 0.342652880 +#> g 0.44845068 0.526942415 0.66091967 0.65322767 0.342652880 #> sigma 1.35690468 2.221302196 1.34169076 2.87159846 1.942067831 #> </div><div class='input'><span class='fu'>parms</span><span class='op'>(</span><span class='va'>fits</span>, transformed <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> $SFO diff --git a/docs/reference/plot.mixed.mmkin.html b/docs/reference/plot.mixed.mmkin.html index 46303c44..4b72a308 100644 --- a/docs/reference/plot.mixed.mmkin.html +++ b/docs/reference/plot.mixed.mmkin.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/plot.mkinfit-2.png b/docs/reference/plot.mkinfit-2.png Binary files differindex 376c812f..a11d1680 100644 --- a/docs/reference/plot.mkinfit-2.png +++ b/docs/reference/plot.mkinfit-2.png diff --git a/docs/reference/plot.mkinfit-5.png b/docs/reference/plot.mkinfit-5.png Binary files differindex bc44de88..6631aa68 100644 --- a/docs/reference/plot.mkinfit-5.png +++ b/docs/reference/plot.mkinfit-5.png diff --git a/docs/reference/plot.mkinfit-6.png b/docs/reference/plot.mkinfit-6.png Binary files differindex eb8cbd92..946b20c5 100644 --- a/docs/reference/plot.mkinfit-6.png +++ b/docs/reference/plot.mkinfit-6.png diff --git a/docs/reference/plot.mkinfit-7.png b/docs/reference/plot.mkinfit-7.png Binary files differindex 92a664f4..10807ea8 100644 --- a/docs/reference/plot.mkinfit-7.png +++ b/docs/reference/plot.mkinfit-7.png diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html index 1be0f9af..b80c672d 100644 --- a/docs/reference/plot.mkinfit.html +++ b/docs/reference/plot.mkinfit.html @@ -74,7 +74,7 @@ observed data together with the solution of the fitted model." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/plot.mmkin-1.png b/docs/reference/plot.mmkin-1.png Binary files differindex f12b7907..647dfb8a 100644 --- a/docs/reference/plot.mmkin-1.png +++ b/docs/reference/plot.mmkin-1.png diff --git a/docs/reference/plot.mmkin-2.png b/docs/reference/plot.mmkin-2.png Binary files differindex e3127554..1bc1c9db 100644 --- a/docs/reference/plot.mmkin-2.png +++ b/docs/reference/plot.mmkin-2.png diff --git a/docs/reference/plot.mmkin-3.png b/docs/reference/plot.mmkin-3.png Binary files differindex 5448976e..50d6ffac 100644 --- a/docs/reference/plot.mmkin-3.png +++ b/docs/reference/plot.mmkin-3.png diff --git a/docs/reference/plot.mmkin-4.png b/docs/reference/plot.mmkin-4.png Binary files differindex 9a25fc50..e049fa16 100644 --- a/docs/reference/plot.mmkin-4.png +++ b/docs/reference/plot.mmkin-4.png diff --git a/docs/reference/plot.mmkin-5.png b/docs/reference/plot.mmkin-5.png Binary files differindex 82b422b5..2421995b 100644 --- a/docs/reference/plot.mmkin-5.png +++ b/docs/reference/plot.mmkin-5.png diff --git a/docs/reference/plot.mmkin.html b/docs/reference/plot.mmkin.html index 4e9836ec..20f9033d 100644 --- a/docs/reference/plot.mmkin.html +++ b/docs/reference/plot.mmkin.html @@ -76,7 +76,7 @@ the fit of at least one model to the same dataset is shown." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/plot.nafta.html b/docs/reference/plot.nafta.html index 29cc984a..544ee5eb 100644 --- a/docs/reference/plot.nafta.html +++ b/docs/reference/plot.nafta.html @@ -73,7 +73,7 @@ function (SFO, then IORE, then DFOP)." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html index 2bfeb582..864c4ff9 100644 --- a/docs/reference/reexports.html +++ b/docs/reference/reexports.html @@ -79,7 +79,7 @@ below to see their documentation. </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/docs/reference/residuals.mkinfit.html b/docs/reference/residuals.mkinfit.html index db7c1a40..c5c2dcaf 100644 --- a/docs/reference/residuals.mkinfit.html +++ b/docs/reference/residuals.mkinfit.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -175,7 +175,7 @@ standard deviation obtained from the fitted error model?</p></td> </div><div class='output co'>#> [1] 0.09726374 -0.13912142 -0.15351210 0.73388322 -0.08657004 -0.93204702 #> [7] -0.03269080 1.45347823 -0.88423697</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/residuals.html'>residuals</a></span><span class='op'>(</span><span class='va'>f</span>, standardized <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> [1] 0.13969917 -0.19981904 -0.22048826 1.05407091 -0.12433989 -1.33869208 -#> [7] -0.04695354 2.08761977 -1.27002287</div></pre> +#> [7] -0.04695355 2.08761977 -1.27002287</div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> <nav id="toc" data-toggle="toc" class="sticky-top"> diff --git a/docs/reference/schaefer07_complex_case-1.png b/docs/reference/schaefer07_complex_case-1.png Binary files differindex be903641..96aab2dc 100644 --- a/docs/reference/schaefer07_complex_case-1.png +++ b/docs/reference/schaefer07_complex_case-1.png diff --git a/docs/reference/schaefer07_complex_case.html b/docs/reference/schaefer07_complex_case.html index 74f71f46..979e4c29 100644 --- a/docs/reference/schaefer07_complex_case.html +++ b/docs/reference/schaefer07_complex_case.html @@ -74,7 +74,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -186,15 +186,15 @@ </div><div class='img'><img src='schaefer07_complex_case-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> </div><div class='output co'>#> $ff #> parent_A1 parent_B1 parent_C1 parent_sink A1_A2 A1_sink -#> 0.3809620 0.1954665 0.4235715 0.0000000 0.4479662 0.5520338 +#> 0.3809620 0.1954667 0.4235713 0.0000000 0.4479619 0.5520381 #> #> $distimes #> DT50 DT90 #> parent 13.95078 46.34350 -#> A1 49.75343 165.27731 -#> B1 37.26912 123.80533 -#> C1 11.23131 37.30959 -#> A2 28.50569 94.69386 +#> A1 49.75342 165.27728 +#> B1 37.26908 123.80520 +#> C1 11.23131 37.30961 +#> A2 28.50624 94.69567 #> </div><div class='input'> <span class='co'># }</span> <span class='co'># Compare with the results obtained in the original publication</span> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>schaefer07_complex_results</span><span class='op'>)</span> diff --git a/docs/reference/sigma_twocomp.html b/docs/reference/sigma_twocomp.html index 46ca562a..397582f0 100644 --- a/docs/reference/sigma_twocomp.html +++ b/docs/reference/sigma_twocomp.html @@ -73,7 +73,7 @@ dependence of the measured value \(y\):" /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -188,6 +188,10 @@ Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry 24(11), 1895-1898.</p> <p>Rocke, David M. and Lorenzato, Stefan (1995) A two-component model for measurement error in analytical chemistry. Technometrics 37(2), 176-184.</p> +<p>Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical +Degradation Data. <em>Environments</em> 6(12) 124 +doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6120124</a> +.</p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><span class='va'>times</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>7</span>, <span class='fl'>14</span>, <span class='fl'>28</span>, <span class='fl'>60</span>, <span class='fl'>90</span>, <span class='fl'>120</span><span class='op'>)</span> diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html index 7be427bf..67a50216 100644 --- a/docs/reference/summary.mkinfit.html +++ b/docs/reference/summary.mkinfit.html @@ -76,7 +76,7 @@ values." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -236,17 +236,17 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span>, <span class='va'>FOCUS_2006_A</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> mkin version used for fitting: 1.0.0 +</div><div class='output co'>#> mkin version used for fitting: 1.0.3 #> R version used for fitting: 4.0.3 -#> Date of fit: Wed Feb 3 17:32:02 2021 -#> Date of summary: Wed Feb 3 17:32:02 2021 +#> Date of fit: Mon Feb 15 13:46:11 2021 +#> Date of summary: Mon Feb 15 13:46:11 2021 #> #> Equations: #> d_parent/dt = - k_parent * parent #> #> Model predictions using solution type analytical #> -#> Fitted using 131 model solutions performed in 0.028 s +#> Fitted using 131 model solutions performed in 0.029 s #> #> Error model: Constant variance #> diff --git a/docs/reference/summary.nlme.mmkin.html b/docs/reference/summary.nlme.mmkin.html index bc60b53e..d6840425 100644 --- a/docs/reference/summary.nlme.mmkin.html +++ b/docs/reference/summary.nlme.mmkin.html @@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -264,11 +264,11 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> </div><div class='output co'>#> <span class='warning'>Warning: Optimisation did not converge:</span> #> <span class='warning'>iteration limit reached without convergence (10)</span></div><div class='input'><span class='va'>f_nlme</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>)</span> </div><div class='output co'>#> <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_nlme</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> nlme version used for fitting: 3.1.151 -#> mkin version used for pre-fitting: 1.0.0 +</div><div class='output co'>#> nlme version used for fitting: 3.1.152 +#> mkin version used for pre-fitting: 1.0.3 #> R version used for fitting: 4.0.3 -#> Date of fit: Wed Feb 3 17:32:05 2021 -#> Date of summary: Wed Feb 3 17:32:05 2021 +#> Date of fit: Mon Feb 15 13:46:13 2021 +#> Date of summary: Mon Feb 15 13:46:13 2021 #> #> Equations: #> d_parent/dt = - k_parent * parent @@ -278,7 +278,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> #> #> Model predictions using solution type analytical #> -#> Fitted in 0.526 s using 4 iterations +#> Fitted in 0.553 s using 4 iterations #> #> Variance model: Two-component variance function #> @@ -307,19 +307,19 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> #> Formula: list(parent_0 ~ 1, log_k_parent ~ 1) #> Level: ds #> Structure: Diagonal -#> parent_0 log_k_parent Residual -#> StdDev: 6.91e-05 0.5863 1 +#> parent_0 log_k_parent Residual +#> StdDev: 6.924e-05 0.5863 1 #> #> Variance function: #> Structure: Constant plus proportion of variance covariate #> Formula: ~fitted(.) #> Parameter estimates: #> const prop -#> 0.0001206605 0.0789967776 +#> 0.0001208853 0.0789968036 #> #> Backtransformed parameters with asymmetric confidence intervals: #> lower est. upper -#> parent_0 99.370883 101.59243 103.81398 +#> parent_0 99.370882 101.59243 103.81398 #> k_parent 0.006923 0.01168 0.01972 #> #> Estimated disappearance times: @@ -330,68 +330,68 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> #> ds name time observed predicted residual std standardized #> ds 1 parent 0 104.1 101.592 2.50757 8.0255 0.312451 #> ds 1 parent 0 105.0 101.592 3.40757 8.0255 0.424594 -#> ds 1 parent 1 98.5 100.796 -2.29571 7.9625 -0.288314 +#> ds 1 parent 1 98.5 100.796 -2.29571 7.9625 -0.288313 #> ds 1 parent 1 96.1 100.796 -4.69571 7.9625 -0.589725 #> ds 1 parent 3 101.9 99.221 2.67904 7.8381 0.341796 -#> ds 1 parent 3 85.2 99.221 -14.02096 7.8381 -1.788813 +#> ds 1 parent 3 85.2 99.221 -14.02096 7.8381 -1.788812 #> ds 1 parent 7 99.1 96.145 2.95512 7.5951 0.389081 #> ds 1 parent 7 93.0 96.145 -3.14488 7.5951 -0.414065 -#> ds 1 parent 14 88.1 90.989 -2.88944 7.1879 -0.401988 +#> ds 1 parent 14 88.1 90.989 -2.88944 7.1879 -0.401987 #> ds 1 parent 14 84.1 90.989 -6.88944 7.1879 -0.958480 #> ds 1 parent 28 80.2 81.493 -1.29305 6.4377 -0.200857 -#> ds 1 parent 28 91.3 81.493 9.80695 6.4377 1.523365 +#> ds 1 parent 28 91.3 81.493 9.80695 6.4377 1.523364 #> ds 1 parent 60 65.1 63.344 1.75642 5.0039 0.351008 #> ds 1 parent 60 65.8 63.344 2.45642 5.0039 0.490898 -#> ds 1 parent 90 47.8 50.018 -2.21764 3.9512 -0.561253 +#> ds 1 parent 90 47.8 50.018 -2.21764 3.9512 -0.561252 #> ds 1 parent 90 53.5 50.018 3.48236 3.9512 0.881335 #> ds 1 parent 120 37.6 39.495 -1.89515 3.1200 -0.607423 #> ds 1 parent 120 39.3 39.495 -0.19515 3.1200 -0.062549 -#> ds 2 parent 0 107.9 101.592 6.30757 8.0255 0.785944 +#> ds 2 parent 0 107.9 101.592 6.30757 8.0255 0.785943 #> ds 2 parent 0 102.1 101.592 0.50757 8.0255 0.063245 -#> ds 2 parent 1 103.8 100.058 3.74159 7.9043 0.473362 -#> ds 2 parent 1 108.6 100.058 8.54159 7.9043 1.080627 +#> ds 2 parent 1 103.8 100.058 3.74159 7.9043 0.473361 +#> ds 2 parent 1 108.6 100.058 8.54159 7.9043 1.080626 #> ds 2 parent 3 91.0 97.060 -6.05952 7.6674 -0.790297 #> ds 2 parent 3 84.9 97.060 -12.15952 7.6674 -1.585874 -#> ds 2 parent 7 79.3 91.329 -12.02867 7.2147 -1.667252 -#> ds 2 parent 7 100.9 91.329 9.57133 7.2147 1.326648 +#> ds 2 parent 7 79.3 91.329 -12.02867 7.2147 -1.667251 +#> ds 2 parent 7 100.9 91.329 9.57133 7.2147 1.326647 #> ds 2 parent 14 77.3 82.102 -4.80185 6.4858 -0.740366 #> ds 2 parent 14 83.5 82.102 1.39815 6.4858 0.215571 #> ds 2 parent 28 66.8 66.351 0.44945 5.2415 0.085748 #> ds 2 parent 28 63.3 66.351 -3.05055 5.2415 -0.582002 #> ds 2 parent 60 40.8 40.775 0.02474 3.2211 0.007679 -#> ds 2 parent 60 44.8 40.775 4.02474 3.2211 1.249486 +#> ds 2 parent 60 44.8 40.775 4.02474 3.2211 1.249485 #> ds 2 parent 90 27.8 25.832 1.96762 2.0407 0.964198 #> ds 2 parent 90 27.0 25.832 1.16762 2.0407 0.572171 -#> ds 2 parent 120 15.2 16.366 -1.16561 1.2928 -0.901596 +#> ds 2 parent 120 15.2 16.366 -1.16561 1.2928 -0.901595 #> ds 2 parent 120 15.5 16.366 -0.86561 1.2928 -0.669547 #> ds 3 parent 0 97.7 101.592 -3.89243 8.0255 -0.485009 -#> ds 3 parent 0 88.2 101.592 -13.39243 8.0255 -1.668740 -#> ds 3 parent 1 109.9 99.218 10.68196 7.8379 1.362859 +#> ds 3 parent 0 88.2 101.592 -13.39243 8.0255 -1.668739 +#> ds 3 parent 1 109.9 99.218 10.68196 7.8379 1.362858 #> ds 3 parent 1 97.8 99.218 -1.41804 7.8379 -0.180921 #> ds 3 parent 3 100.5 94.634 5.86555 7.4758 0.784603 #> ds 3 parent 3 77.4 94.634 -17.23445 7.4758 -2.305360 -#> ds 3 parent 7 78.3 86.093 -7.79273 6.8010 -1.145813 -#> ds 3 parent 7 90.3 86.093 4.20727 6.8010 0.618621 -#> ds 3 parent 14 76.0 72.958 3.04222 5.7634 0.527849 -#> ds 3 parent 14 79.1 72.958 6.14222 5.7634 1.065723 +#> ds 3 parent 7 78.3 86.093 -7.79273 6.8011 -1.145813 +#> ds 3 parent 7 90.3 86.093 4.20727 6.8011 0.618620 +#> ds 3 parent 14 76.0 72.958 3.04222 5.7634 0.527848 +#> ds 3 parent 14 79.1 72.958 6.14222 5.7634 1.065722 #> ds 3 parent 28 46.0 52.394 -6.39404 4.1390 -1.544842 #> ds 3 parent 28 53.4 52.394 1.00596 4.1390 0.243046 #> ds 3 parent 60 25.1 24.582 0.51786 1.9419 0.266676 -#> ds 3 parent 60 21.4 24.582 -3.18214 1.9419 -1.638665 -#> ds 3 parent 90 11.0 12.092 -1.09202 0.9552 -1.143200 -#> ds 3 parent 90 14.2 12.092 2.10798 0.9552 2.206777 +#> ds 3 parent 60 21.4 24.582 -3.18214 1.9419 -1.638664 +#> ds 3 parent 90 11.0 12.092 -1.09202 0.9552 -1.143199 +#> ds 3 parent 90 14.2 12.092 2.10798 0.9552 2.206776 #> ds 3 parent 120 5.8 5.948 -0.14810 0.4699 -0.315178 #> ds 3 parent 120 6.1 5.948 0.15190 0.4699 0.323282 #> ds 4 parent 0 95.3 101.592 -6.29243 8.0255 -0.784057 -#> ds 4 parent 0 102.0 101.592 0.40757 8.0255 0.050785 +#> ds 4 parent 0 102.0 101.592 0.40757 8.0255 0.050784 #> ds 4 parent 1 104.4 101.125 3.27549 7.9885 0.410025 #> ds 4 parent 1 105.4 101.125 4.27549 7.9885 0.535205 #> ds 4 parent 3 113.7 100.195 13.50487 7.9151 1.706218 -#> ds 4 parent 3 82.3 100.195 -17.89513 7.9151 -2.260887 +#> ds 4 parent 3 82.3 100.195 -17.89513 7.9151 -2.260886 #> ds 4 parent 7 98.1 98.362 -0.26190 7.7703 -0.033706 #> ds 4 parent 7 87.8 98.362 -10.56190 7.7703 -1.359270 #> ds 4 parent 14 97.9 95.234 2.66590 7.5232 0.354357 -#> ds 4 parent 14 104.8 95.234 9.56590 7.5232 1.271522 +#> ds 4 parent 14 104.8 95.234 9.56590 7.5232 1.271521 #> ds 4 parent 28 85.0 89.274 -4.27372 7.0523 -0.606001 #> ds 4 parent 28 77.2 89.274 -12.07372 7.0523 -1.712017 #> ds 4 parent 60 82.2 77.013 5.18661 6.0838 0.852526 @@ -400,18 +400,18 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> #> ds 4 parent 90 61.7 67.053 -5.35308 5.2970 -1.010591 #> ds 4 parent 120 60.0 58.381 1.61905 4.6119 0.351058 #> ds 4 parent 120 56.4 58.381 -1.98095 4.6119 -0.429530 -#> ds 5 parent 0 92.6 101.592 -8.99243 8.0255 -1.120486 +#> ds 5 parent 0 92.6 101.592 -8.99243 8.0255 -1.120485 #> ds 5 parent 0 116.5 101.592 14.90757 8.0255 1.857531 #> ds 5 parent 1 108.0 99.914 8.08560 7.8929 1.024413 -#> ds 5 parent 1 104.9 99.914 4.98560 7.8929 0.631656 +#> ds 5 parent 1 104.9 99.914 4.98560 7.8929 0.631655 #> ds 5 parent 3 100.5 96.641 3.85898 7.6343 0.505477 -#> ds 5 parent 3 89.5 96.641 -7.14102 7.6343 -0.935383 +#> ds 5 parent 3 89.5 96.641 -7.14102 7.6343 -0.935382 #> ds 5 parent 7 91.7 90.412 1.28752 7.1423 0.180267 -#> ds 5 parent 7 95.1 90.412 4.68752 7.1423 0.656305 -#> ds 5 parent 14 82.2 80.463 1.73715 6.3563 0.273296 +#> ds 5 parent 7 95.1 90.412 4.68752 7.1423 0.656304 +#> ds 5 parent 14 82.2 80.463 1.73715 6.3563 0.273295 #> ds 5 parent 14 84.5 80.463 4.03715 6.3563 0.635141 #> ds 5 parent 28 60.5 63.728 -3.22788 5.0343 -0.641178 -#> ds 5 parent 28 72.8 63.728 9.07212 5.0343 1.802063 +#> ds 5 parent 28 72.8 63.728 9.07212 5.0343 1.802062 #> ds 5 parent 60 38.3 37.399 0.90061 2.9544 0.304835 #> ds 5 parent 60 40.7 37.399 3.30061 2.9544 1.117174 #> ds 5 parent 90 22.5 22.692 -0.19165 1.7926 -0.106913 diff --git a/docs/reference/synthetic_data_for_UBA_2014-1.png b/docs/reference/synthetic_data_for_UBA_2014-1.png Binary files differindex 4f11753e..89975db5 100644 --- a/docs/reference/synthetic_data_for_UBA_2014-1.png +++ b/docs/reference/synthetic_data_for_UBA_2014-1.png diff --git a/docs/reference/synthetic_data_for_UBA_2014.html b/docs/reference/synthetic_data_for_UBA_2014.html index af8bdcb2..c37b986e 100644 --- a/docs/reference/synthetic_data_for_UBA_2014.html +++ b/docs/reference/synthetic_data_for_UBA_2014.html @@ -87,7 +87,7 @@ Compare also the code in the example section to see the degradation models." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -297,10 +297,10 @@ Compare also the code in the example section to see the degradation models." /> quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> </div><div class='img'><img src='synthetic_data_for_UBA_2014-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -</div><div class='output co'>#> mkin version used for fitting: 1.0.0 +</div><div class='output co'>#> mkin version used for fitting: 1.0.3 #> R version used for fitting: 4.0.3 -#> Date of fit: Wed Feb 3 17:32:06 2021 -#> Date of summary: Wed Feb 3 17:32:06 2021 +#> Date of fit: Mon Feb 15 13:46:15 2021 +#> Date of summary: Mon Feb 15 13:46:15 2021 #> #> Equations: #> d_parent/dt = - k_parent * parent @@ -309,7 +309,7 @@ Compare also the code in the example section to see the degradation models." /> #> #> Model predictions using solution type deSolve #> -#> Fitted using 822 model solutions performed in 0.645 s +#> Fitted using 833 model solutions performed in 0.665 s #> #> Error model: Constant variance #> @@ -361,15 +361,15 @@ Compare also the code in the example section to see the degradation models." /> #> log_k_M2 2.819e-02 7.166e-02 -3.929e-01 1.000e+00 -2.658e-01 #> f_parent_qlogis -4.624e-01 -5.682e-01 7.478e-01 -2.658e-01 1.000e+00 #> f_M1_qlogis 1.614e-01 4.102e-01 -8.109e-01 5.419e-01 -8.605e-01 -#> sigma -7.941e-08 -9.143e-09 -1.268e-08 5.947e-08 5.657e-08 +#> sigma -2.900e-08 -8.030e-09 -2.741e-08 3.938e-08 -2.681e-08 #> f_M1_qlogis sigma -#> parent_0 1.614e-01 -7.941e-08 -#> log_k_parent 4.102e-01 -9.143e-09 -#> log_k_M1 -8.109e-01 -1.268e-08 -#> log_k_M2 5.419e-01 5.947e-08 -#> f_parent_qlogis -8.605e-01 5.657e-08 -#> f_M1_qlogis 1.000e+00 -2.382e-10 -#> sigma -2.382e-10 1.000e+00 +#> parent_0 1.614e-01 -2.900e-08 +#> log_k_parent 4.102e-01 -8.030e-09 +#> log_k_M1 -8.109e-01 -2.741e-08 +#> log_k_M2 5.419e-01 3.938e-08 +#> f_parent_qlogis -8.605e-01 -2.681e-08 +#> f_M1_qlogis 1.000e+00 4.971e-08 +#> sigma 4.971e-08 1.000e+00 #> #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. @@ -416,7 +416,7 @@ Compare also the code in the example section to see the degradation models." /> #> 7 parent 0.3 5.772e-01 -0.27717 #> 14 parent 3.5 3.264e-03 3.49674 #> 28 parent 3.2 1.045e-07 3.20000 -#> 90 parent 0.6 9.532e-10 0.60000 +#> 90 parent 0.6 9.530e-10 0.60000 #> 120 parent 3.5 -5.940e-10 3.50000 #> 1 M1 36.4 3.479e+01 1.61088 #> 1 M1 37.4 3.479e+01 2.61088 @@ -427,7 +427,7 @@ Compare also the code in the example section to see the degradation models." /> #> 14 M1 5.8 1.995e+00 3.80469 #> 14 M1 1.2 1.995e+00 -0.79531 #> 60 M1 0.5 2.111e-06 0.50000 -#> 90 M1 3.2 -9.671e-10 3.20000 +#> 90 M1 3.2 -9.670e-10 3.20000 #> 120 M1 1.5 7.670e-10 1.50000 #> 120 M1 0.6 7.670e-10 0.60000 #> 1 M2 4.8 4.455e+00 0.34517 diff --git a/docs/reference/test_data_from_UBA_2014-1.png b/docs/reference/test_data_from_UBA_2014-1.png Binary files differindex 168103ee..7bf0bd0f 100644 --- a/docs/reference/test_data_from_UBA_2014-1.png +++ b/docs/reference/test_data_from_UBA_2014-1.png diff --git a/docs/reference/test_data_from_UBA_2014-2.png b/docs/reference/test_data_from_UBA_2014-2.png Binary files differindex 68288aed..fc1f77e0 100644 --- a/docs/reference/test_data_from_UBA_2014-2.png +++ b/docs/reference/test_data_from_UBA_2014-2.png diff --git a/docs/reference/test_data_from_UBA_2014.html b/docs/reference/test_data_from_UBA_2014.html index eeaef9e0..c0056d45 100644 --- a/docs/reference/test_data_from_UBA_2014.html +++ b/docs/reference/test_data_from_UBA_2014.html @@ -73,7 +73,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -203,25 +203,25 @@ </div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span><span class='op'>(</span><span class='va'>f_soil</span>, lpos <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"bottomright"</span><span class='op'>)</span><span class='op'>)</span> </div><div class='img'><img src='test_data_from_UBA_2014-2.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_soil</span><span class='op'>)</span><span class='op'>$</span><span class='va'>bpar</span> </div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower -#> parent_0 76.55425649 0.859186399 89.1008710 1.113861e-26 74.755959406 +#> parent_0 76.55425650 0.859186399 89.1008710 1.113861e-26 74.755959418 #> k_parent 0.12081956 0.004601918 26.2541722 1.077359e-16 0.111561575 -#> k_M1 0.84258614 0.806159820 1.0451850 1.545267e-01 0.113779670 -#> k_M2 0.04210880 0.017083035 2.4649483 1.170188e-02 0.018013857 -#> k_M3 0.01122918 0.007245855 1.5497385 6.885052e-02 0.002909431 -#> f_parent_to_M1 0.32240200 0.240783909 1.3389682 9.819073e-02 NA -#> f_parent_to_M2 0.16099855 0.033691953 4.7785463 6.531137e-05 NA -#> f_M1_to_M3 0.27921507 0.269423745 1.0363417 1.565266e-01 0.022978220 -#> f_M2_to_M3 0.55641253 0.595119954 0.9349586 1.807707e-01 0.008002509 +#> k_M1 0.84258615 0.806160102 1.0451846 1.545268e-01 0.113779609 +#> k_M2 0.04210880 0.017083034 2.4649483 1.170188e-02 0.018013857 +#> k_M3 0.01122918 0.007245856 1.5497385 6.885052e-02 0.002909431 +#> f_parent_to_M1 0.32240200 0.240783943 1.3389680 9.819076e-02 NA +#> f_parent_to_M2 0.16099855 0.033691952 4.7785464 6.531136e-05 NA +#> f_M1_to_M3 0.27921507 0.269423780 1.0363416 1.565267e-01 0.022978205 +#> f_M2_to_M3 0.55641252 0.595119966 0.9349586 1.807707e-01 0.008002509 #> sigma 1.14005399 0.149696423 7.6157731 1.727024e-07 0.826735778 #> Upper -#> parent_0 78.35255357 +#> parent_0 78.35255358 #> k_parent 0.13084582 -#> k_M1 6.23970352 +#> k_M1 6.23970702 #> k_M2 0.09843260 #> k_M3 0.04333992 #> f_parent_to_M1 NA #> f_parent_to_M2 NA -#> f_M1_to_M3 0.86450768 +#> f_M1_to_M3 0.86450775 #> f_M2_to_M3 0.99489895 #> sigma 1.45337221</div><div class='input'> <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span><span class='op'>(</span><span class='va'>f_soil</span><span class='op'>)</span> </div><div class='output co'>#> err.min n.optim df diff --git a/docs/reference/transform_odeparms.html b/docs/reference/transform_odeparms.html index e2cb876b..c3c756f6 100644 --- a/docs/reference/transform_odeparms.html +++ b/docs/reference/transform_odeparms.html @@ -77,7 +77,7 @@ the ilr transformation is used." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> @@ -259,13 +259,13 @@ This is no problem for the internal use in <a href='mkinfit.html'>mkinfit</a>.</ <span class='va'>fit.2.s</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.2</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.2.s</span><span class='op'>$</span><span class='va'>par</span>, <span class='fl'>3</span><span class='op'>)</span> </div><div class='output co'>#> Estimate Std. Error Lower Upper -#> parent_0 99.59849 1.57022 96.40385 1.03e+02 +#> parent_0 99.59848 1.57022 96.40384 1.03e+02 #> k_parent_sink 0.04792 0.00365 0.04049 5.54e-02 #> k_parent_m1 0.05078 0.00205 0.04661 5.49e-02 #> k_m1_sink 0.00526 0.00070 0.00384 6.69e-03 #> sigma 3.12550 0.35852 2.39609 3.85e+00</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.2.s</span><span class='op'>$</span><span class='va'>bpar</span>, <span class='fl'>3</span><span class='op'>)</span> </div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower Upper -#> parent_0 99.59849 1.57022 63.43 2.30e-36 96.40385 1.03e+02 +#> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40384 1.03e+02 #> k_parent_sink 0.04792 0.00365 13.11 6.13e-15 0.04049 5.54e-02 #> k_parent_m1 0.05078 0.00205 24.80 3.27e-23 0.04661 5.49e-02 #> k_m1_sink 0.00526 0.00070 7.51 6.16e-09 0.00384 6.69e-03 diff --git a/docs/reference/update.mkinfit.html b/docs/reference/update.mkinfit.html index f7149d84..09050e53 100644 --- a/docs/reference/update.mkinfit.html +++ b/docs/reference/update.mkinfit.html @@ -75,7 +75,7 @@ override these starting values." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> </span> </div> diff --git a/man/sigma_twocomp.Rd b/man/sigma_twocomp.Rd index d205a2f7..e93254a5 100644 --- a/man/sigma_twocomp.Rd +++ b/man/sigma_twocomp.Rd @@ -59,4 +59,8 @@ Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry Rocke, David M. and Lorenzato, Stefan (1995) A two-component model for measurement error in analytical chemistry. Technometrics 37(2), 176-184. + +Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical +Degradation Data. \emph{Environments} 6(12) 124 +\doi{10.3390/environments6120124}. } @@ -31,7 +31,7 @@ Reason: Fitting with saemix takes around 10 minutes when using deSolve ✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.5 s] ✔ | 4 | Summary [0.1 s] ✔ | 1 | Summaries of old mkinfit objects -✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.3 s] +✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2 s] ✔ | 9 | Hypothesis tests [8.3 s] ✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.5 s] diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index 8ebd1047..f1f078db 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -11,10 +11,22 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-11-19" /> <title>Example evaluation of FOCUS Example Dataset D</title> +<script>// Pandoc 2.9 adds attributes on both header and div. 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940px; @@ -413,7 +387,7 @@ summary { <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-11-19</h4> +<h4 class="date">Last change 31 January 2019 (rebuilt 2021-02-15)</h4> </div> @@ -469,7 +443,7 @@ print(FOCUS_2006_D)</code></pre> <p>Next we specify the degradation model: The parent compound degrades with simple first-order kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics.</p> <p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p> <pre class="r"><code>SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>print(SFO_SFO$diffs)</code></pre> <pre><code>## parent ## "d_parent = - k_parent * parent" @@ -481,16 +455,16 @@ print(FOCUS_2006_D)</code></pre> ## of zero were removed from the data</code></pre> <p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p> <pre class="r"><code>plot_sep(fit, lpos = c("topright", "bottomright"))</code></pre> -<p><img 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hgBAJAHdwBBIerUqXP16tU33njjzp07ycnJgYGBpZsMDAy+/PLLadOmCSHMzMy2bNni5+c3bty4oKCgpk2bLlu27IMPPpAvcAAAnpfGjRsfO3ZM7ihqTlZWVq9evRITE7t27bpt2zZnZ+fSTYcPHx4xYsTevXu9vLwWLVokY5AAAMiFO4CgHO7u7rdu3dqzZ0/nzp1dXFxq1arVuHHjGTNmxMTESNWfUv369bt8+bKfn19GRsbYsWMHDhwoPSMGAICSfPjhh0eOHPHw8JA7kBqycuXKu3fvtm7d+vDhw2WrP0KIXr16HTp0SF9ff/ny5dHR0XJFCACAjCgAQVH09PQGDBjw22+/xcfHp6amRkRELFmypFwKKHFycjp8+PDGjRstLS337t3bqFGjDRs2lJSU1HzMAAA8Jx4eHm+//baVlZXcgdSQ7du3CyG++uorY2Pjils7dOgwaNCgwsLCXbt21XhoAADIjwIQdJq/v//Fixe7deuWmpo6duzYdu3ahYeHyx0UAACotuTk5KioqFq1ar322msP69O7d28hxLlz52owLgAAXhQUgKDrPDw8goKC9uzZ4+rqeu7cudatW3/88ccZGRlyxwUAAKohNTVVCOHg4KCn99D81snJqbQnAAC6hgIQIIQQAwYMuHbt2pQpU1Qq1Zo1axo0aPDNN98UFhbKHRcAAM/Y//3f/y1YsEDuKJ49BwcHIURiYmJxcfHD+sTExAghateuXXNhAQDwwqAABPw/lpaWK1euDA0N7dix44MHDyZNmtSkSZPdu3drNBq5QwMA4JlZsmTJmjVr5I7i2atVq1bTpk2zsrKCgoIe1mffvn1CiEc8IwYAgIJRAAL+R7Nmzf7444+DBw82adLk9u3bgwcPbtOmza+//ip3XAAA4DH8/f2FEFOnTq30Ue6DBw8eOnTIwsJi4MCBNR4aAADyowAEVKJ3796XLl3atGmTq6trSEhI165d27dv//PPP3M3EAAAL6zx48f7+vrevHmzU6dOly9fLm1Xq9Xr1q0bNGiQEGLBggWOjo7yxQgAgGwoAAGV09fXHzNmTGRk5NKlS+3t7c+cOePn59eiRYt9+/bxtngAAF5AxsbGBw8ebNSo0YULF3x9fdu3bz9mzJghQ4a4u7tPmDAhPz9/ypQpkydPljtMAADkQQEIeBRTU9NPP/307t27K1ascHFxuXDhwoABA5o2bbphw4bc3Fy5owMAAP+jbt26f//995QpU4yNjc+cOfP999/v2rUrLi6uYcOGAQEBK1eulDtAAABkQwEIeDxzc/OpU6dGRUWtW7fOw8Pj+vXrY8eOdXNzmz59+p07d+SODgCAanj11VdbtmwpdxTPkbW19cqVK5OTk48fP7558+Yffvjh4sWL169f79evn9yhAQAgJwpAQFUZGxuPGzfu5s2bu3fvbt++fVpa2vLly728vPr06XP8+HG1Wi13gAAAPN7Zs2ePHz8udxTPnZmZWffu3UePHj106NBmzZrJHQ4AAPKjAARUj4GBwcCBA0+fPh0aGjpy5EgjI6NDhw716NGjXr16n3/++c2bN+UOEAAAAACA8igAAU+oRYsWW7ZsiYmJ+fLLL729vePj47/88suGDRt26NBhw4YNKSkpcgcIAAAAAMD/QwEIeCoODg6fffbZjRs3Tp8+7e/vb2VlFRwcPHbsWCcnp7feemvz5s2pqalVOU5hYeHy5ctfeeWVunXrenl59ejR49ixY887eAAAAACAjqAABDwb7du337hxY2Ji4vbt29966y2VShUUFOTv7+/k5NSzZ88NGzbEx8c/bN/du3fb2NhMnz79n3/+iY2NvX379vHjx3v27Onl5fWIvQAAAAAAqCIKQMCzZGZmNnz48GPHjiUmJm7cuLFbt24ajebYsWNjx46tU6dOixYtZs+e/c8//5SUlJTusnbt2sGDB+fl5enr67dt23bixIn/+c9/PD09hRC3b99u0KBBTEyMfAMCAAAAACiBgdwBAMpkZ2fn7+/v7++fnJx86NChn3/++ZdffgkPDw8PD1+wYIGdnd0bb7zx5ptvenl5ffzxx0IIT0/Pv//+28HBofQIR48e7du3b15eXvv27WNjY+UbCgAAAABA63EHEPB82dvbjx49OjAwMDk5OSgo6KOPPvLw8EhJSdm3b9+4ceO6dOlSUlJibGz8+eefZ2RklN2xZ8+ev/76q0qliouL27p1q0zhAwAAAACUgAIQUEOMjY27deu2Zs2aqKioyMjI9evX9+/fX9pUUFDg7+/foEEDZ2fn/v37r1q16syZM3l5ea+99lqrVq2EEN98842ssQMAAAAAtBuPgAEy8PLy8vLyevvttwMCAlQq1fLly4ODg4ODg+/duxcYGBgYGCiEMDAw8PHxMTIyEkLcunWroKDA2NhY7sABAAAAAFqJAhAgmwcPHggh9PT0pk6dOnXqVCFEZGRkcHDw6dOnz58/HxERceHCBalnZmamhYVFo0aNXn755WbNmvn6+jZq1Khu3bpyRl8ZtVq9Y8eO7du3x8fHGxoaNmnSZPLkye3atZM7LgAAAADQdRSAANl4e3sLIdRqdXx8vKurqxCiQYMGDRo0GDlypBAiNzc3LCxsxowZZ86cMTAw0Gg0V65cuXLlys6dO6XdraysGjVq1LRp08aNG3t7e3t7e3t6esp4l9DRo0cHDx6cmZlZ2nL16tV9+/Y1adLk999/r127tlyBAQAAAAAoAAGyMTc3r1WrVlpa2rRp03bt2lVuq5mZWfv27a9fvy6E6Nu377Zt265evXrhwoWLFy9euXLl6tWrDx48+Oeff/7555/SXfT19evWrevl5VW/fn0PDw8PDw93d3cPDw97e/vnPZbt27ePHDlSo9GoVKqXXnrJx8cnPz//3Llz8fHxERERnp6eN2/edHFxed5hKIZarV62bFlAQMC9e/cMDAzc3d0nTpw4YMAAueMCAAAAoK10qwCUlJSUkJCQmJiYmJhYWFjo6urq5ubm6urq6Ogod2jQUe+///6yZcv27NkzfPjwnj17lts6dOjQ1NRUlUq1ZMkSU1PTVq1aSWtCS5KTkyMiIq5du3bt2rWbN2/eunXrzr9OnDhR9jgWFhZ169atW7dunTp13Nzc6tWr5+Tk5Orq6uzsbGdn9/SjiI+PHz16tEajcXZ2Pnv2bL169Uo3BQQEDBkyJCcnp23bttHR0U9/Ll2wb9++ESNG5OXllbbExMT8+eefU6ZM+f3336UbxwAAAACgWnSiAJSenr5q1apdu3bdvHmz0g5eXl7Dhg2bMmWKlZVVDccGHffll1/u2LEjMTHRz89v/Pjxixcvtra2FkJcv3592LBhoaGhQoj333/f09Oz4r729vYdO3bs2LFjaUtRUdHdu3cjIyNv37599+7d0npQRkZGRERERERExYOYmJg4Ozs7OzvXrl3bycnJ0dHRwcHB2dnZ/l92dnb6+vqPHsXw4cPVarWFhcXNmzctLCzKburfv3+tWrW6dOkSExOzbdu2ESNGPMEs6ZQNGzaMGzdOo9EYGBhIr4ErLCw8derUpUuXEhISfHx8wsPDmzZtKneYAAAAALSMSqPRyB3D83XmzJl33nlHWm23lPRmpcLCwrKNjo6Ohw8fbt26dY3GJ8T06dOXL1/u6uoaFxdXw6fGiyAxMdHX1/f+/ftCCJVKZWpqWlJSkp+fL23t3bv3wYMHn/IUGRkZsbGx0dHRsbGxcXFxMTEx8fHx9+7di4+Pz8jIeOzu9vb2tv+rVq1aNmV069atqKho/vz5M2bMkL5c5bRu3TokJKR58+ZhYWFPORZli42NdXd3Lykp8fT0DA0NtbGxKd0UFhbWsWPHnJwcW1vblJQUGYOELrOzs0tNTU1JSbG1tZU7Fmi9wMDA/v379+vXLyAgQO5YAAB4KCXlPwq/AygmJqZnz54ZGRnm5uZ9+/bt16+fr6+vnZ2dpaWlECIrKyslJeXatWs//fRTYGBgUlJS9+7dr1696uzsLHfg0CHOzs7R0dHjx4//8ccfCwsLc3NzpXY7O7uvvvpq1KhRT38Ka2tra2trHx+fiptyc3Pj4+OTkpLu37+fmJj44MGDpKSkpKSk5H+lpKRIPzz2LLNnz549e7axsbGVlZWlpaW1tbW5ubm5ubmVlZWenp4QIiIiYvHixSYmJhYWFubm5kZGRjY2NoaGhpaWlkZGRubm5sbGxmZmZiYmJqampk8/am30n//8p6SkxNraOiIiotx63i1atDh//ryPj09qaurSpUtnzJghV5AAAAAAtJHCC0BLly7NyMhwdHQ8fvy4r69vua2WlpaWlpbu7u49evSYOXNm165db968uWTJktWrV8sSLXSWiYnJli1bNm3adPbs2StXrpiZmbVr187Ly6sGTm1mZia9euxhHTQaTUpKSur/SktLS/9XfHz8+fPnhRC2traZmZkFBQUPHjwod8+dpKCg4IsvvqhiYFIxyMDAQCrX2tjYqFSq0tpQrVq1hBClW6XOZVtKdym7VQhRtrpUtrM0FWVrLoaGhuUeZyvX/3k4e/asEGLOnDmVvs2tcePGb7755okTJ77//nsKQAAAAACqReEFoGPHjgkhFi9eXLH6U07dunW/++67zp07Hz169CkLQMXFxcuXL09PT69i/z///FMIUVRU9DQnhQLo6+t36NChQ4cOcgfyP1QqlbQY0MM6FBQUmJiYCCEuXrzo5uaWl5eXlZWVnZ2dlpaWk5OTk5OTnZ29cePGEydOWFhYfPTRR/n5+VJjUVFRWlpaUVFRfa+7eAAAIABJREFUdnZ2fn5+Xl5eXl5efn5+bm5uwb+EEJXWkl4cD7tfSV9f/xFrikl3PJVrLCkpkYb8ww8/7Ny5s9KDJyQkCCFu377dtWvXhx1curuqOoOoHktLSwMDhf/uUDA9Pb1u3br17dtX7kAAAABQ0xS+BpCJiUlBQcGNGzeq8t6c/Px8CwsLQ0PDsi/feQKhoaFlX9VURRYWF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WZOWVJaafKN8oysivSs8vSs8ozsSk3Rw2/ZyNLC2NHO2M7G2MHO2N7GxMXJxMXRpIGTiYujkdrq4bcPAAAAADBkNICAu6nIyi2+GFdyNbE06XppYmplgabmHJW5mbGDrZGVlbHa0sjaysjaykhtKYRKCKEyM1WZmQohRKVW6hNpy8u1JaXaktLKwqLKwqJKTVFloaYir7CyqLgyrbgs7VbN7RtZWvw/9u47Loo7/QP4MzvbZgssSxURRUDp3V4wEnuJLWpyxqgpl3LJ5VIv5S7mYi4xxfwul1yaMbbTGMEuBrFXVHoXEAUBqVvYMrN1fn+sR4waBYUdyvN+5Q+Z/c7OhyQiPH6/z8P38RD4egn6eQv6e/H7eQl8vXgSqms/c4QQQgghhBBCvQgWgBBnTBVVutRjikdn8z3cuM7yGzaNjikqYwrLmaJyS91vKjI8uVQ4qL/Q35fv7cH3dOd7uPE93Hhy6f0/1G6gbWqtTauzqTQ2jc7apLI2tlgbW6xNKjvNmC/XmC/X3Lie7+EmGNBPONBXMKCfcICvwN+X4JP3HwMhhBBCCCGEUK+EBSDEDdZma/5yk6Wm3k4zXm/8kes4AAA2lcaQkWs8m8OUVrY1FeJRYlFYkDhksHCQn3CgL6lUdNHTHS2BBH4+t75k1xst9U2WukZrXYOlrtFS12i51mhtVlub1XROsWMNQZKCAT7CgAHCQX7CAD9hgB+PEndRVIQQQgghhBBCPQ4WgBA3dAeOW2rqAcB4oYDOKaZiw7hKYmvVG05cMGTkmC5edtR9CKFAHB4sDh8ijggWBgwgSB5X2Rx4MokoaKAoaOCvl1jW0tBsqaozX71mqa4zV9dZ6hrNV2rNV2qvLyAIQX9vUeBAYZC/KHiQcGB/QoC/2RFCCCGEEEKo78KfCREHbDq9ZvsvAEDFhdPZRaoNO3yjQpxfZzGVX9H9csJwJpu1WAGAEAmp2DDpqFgqPoInFjk5TBvDmWxBPy9hwB2n1BOEwMdT4OMpGRHtuMCazObqOvPlGvPlq+bLNeYrNZaaektNPRw/BwCEgC8MGCAaEiAaGiAeGtB1+5jaWOoaeHIpKZd19YMQQgih7oy1WFu++0kcFiR7YCTXWRBCCPV1WABCHNBs3Wc3GKmooV6vP1338geWmnpd2kmX6YnOeTprsRhOZel+OWG6VA0AwONJEiKlicMlceGcz2Kns4ua1qwjXWT9//2uY2x8OxEioSh4kCh4kOND1mozX6kxV1SZLlWbyq9YahtMZZdNZZdhHwAA38NNFBooHjpYFBYkHNAPCKJzPwtzdd21Nz7me3v4fvomdiZCCCHUl7XuPaw/mmE4lSkODeL7eHAdByGEUJ+GBSDkbJar13SHzwDJc1s2n+CTbo/NaVz9nWbbfum4+K7eMMJabfpDpzU70mwqLQCQcpksaZR88li+l3uXPredWKtNtX4HANha9ZrtB5TL5t3zWxF80nFqTA4AAHaaMZVdNl28bCq/Yiq7bG1WW09mGk5mAgBPKhGHDBaFBonDAoWBAztlH5Z6fQprsVpq6ltTj7nOTrr/N0QIIYR6IpumVbszHQBYi1W1cYfX609znQghhFCfhgUg5Gyq9Slgs7vMfEDo7wsAkmFRVEwonVui2Zbq/uTCrnqq3a4/cUHzc6q1sQUAhIMHuEyfIB0TRwgEXfXEjtMdOG6pa+B7uFlVWt2B4/LJYwS+3p3yzjxKTEWHUtGhAAAsa6mpZ0ovmUouMSWXrE0qY1ahMasQAHhikShksDgsWBweJAwadG/FIOOFfDr/IiESsiazNvkXWeJw0lXeKZ8FQggh1LOoN+2204w4cqi5osp4Pp8puCiOHMp1KIQQQn0XFoCQUxkzcum8Up5Mopg/te2ictn8ulc+1B08JZ881lEV6vSHqn/a5+g5LRjQz23xTMnwqE4/93SfbFqdJvkAALj/8RHj+Txd+mnVD9u9//anzn8SQQgG9BMM6CefNBYArC0aU3EFU1zOFFdYahvo3BI6twQcxaCwICpiiDhiiDDAr53/ulirTb1xJwAoH5tD5xQbswo1W/a6P/to538WCCGEUPdmrqzWnzhP8En3Py42ns1R/3dPyw/bfT97i/PhEgghhPqsPlQA0mq1paWllZWVer3eYDCIxWKZTBYQEBAWFubm5sZ1uj6BtVjVm3cDgNujs3lyadt1gZ+PfPLY1gPHVT8m+7z7Yic+0drY0vL9NsesdL6Ph2LhDNm4hO5W+nHQbN1nN9BUfAQVGyYM9DecyabzSunsIiouvEufy3dX8MclSMclAIBN08oUVTDF5UxRuaWmns4uorOLAIAnk1CRQ8WRQ8VRQwU+nnd4t9a9hy3XmgR+PrJJY6iYMDq/VHfkrGzyWFGgf5d+FgghhFD3wrIt65KBZV1mPyjw8XSZlaQ/ctZSU69PPyWfOv7+396YVSjw8RT075ydwgghhPqI3l8Aslqta9eu3bhxY0ZGBsuyty4gCGLEiBFLly594oknhEKOewD3bq17DlvqmwQD+smSRt/0kmLxDMOpLKagzHg+XzI8qhMeZre37j+m/mkfazLzZBK3R2fLkkYRZDdtSGy+UqM7cpYgSeXjcwGAdJEpFkxTbdihWpfsGxXitD7KpMJFOiZOOiYOAGxqLVNYRheUMYVl1sYWw9kcw9kcAOB7uVORQ8VRIVTU0BureABg0+i0Ow4CgHLZfIIk+T4e8mmJrXsOq9Yl91v1l+5Zd0MIIYS6gv7EeVNpJamQu86dBAAEn3RbMqfxk+/VW/dJx8Tf9AdoR9E5xY0ffsN3V/T/4u+cz69ACCHUg/TyAlBdXd2kSZOKi4tvvEgQhFQqtVgsJpMJAFiWzcjIyMjI+Pzzz/fs2RMSEsJR2F7OpmnV7kqH69WBmzc/86QS14XTVD9sV23YQcWG3mdrHnPl1eZvtporqwFAOjZBuXx+N29Do1qXAna7fHZSW9Mf+fREXfppS12D7pfjLjMnOj8S6eYqHTdMOm4YAFgbW+j8UqbgIpNfZm1s0R0+ozt8BghCNHiAODqUigkRDRlM8En1f3fbaUYyPIqKCXW8ieLhaYYTF0wXKw1nchx1JYQQQqjXY01mzdZ9AOD22BweJXZclIyIpqJD6bwSzfZU5YqH7/3NbTbVjykAYG3RaHceVCye2SmZEUII9QW9+RAyTdNz5swpLi4WCASzZ89eu3ZtSUlJU1OTxWLR6XQMw5hMpoqKig0bNixcuFAikZSXl0+cOLG5uZnr4L2TetMuO81IRsZQ0bcvsblMGSf097U2NLfuO3rvj2FZTfKBujc/MVdW873cvd9+zvOlZd28+mM4k80Ul5Ou8hv7IrXtBtJsS7VpWrlLBwDA93KXPzjG8y8rBqz70PeTv7o9NoeKGkrw+aZL1dodafV//9fVZa/X//3/9MfOAZ/n9thcAGAtFrDbeZRYsXgGAKg37mBNZm4/ix6JZTXJvxhOXuA6B0IIoQ7QpKRZm9XCwf6y8cNvvK5cPh9IXusvJ83Vdff85o6REaSbKxCEds9hx3QLhBBCqD16cwFo1apVFy5cUCqV6enpu3fvfuKJJ0JCQjw8PMj/nQMSCoWBgYFLly7dtm1bRkaGv7//tWvXVq9ezW3sXslcWa0/cYEQ8N2WPPS7i3g85fIFAKBNSbOptffwFJtO3/DPrzU/7QcWXGZN7P/521Rs2D1ndg7WbHH0RVI8MpMnpW58ydEPyE4zmm2pHKW7BUEIA/xcH3rQ++8v+G/42Pud511mJwn9fe2MiSmuAJYFq73hg6+a/r2pesWb9Su/AJaVJ40WBfpbWzTa3Ye4Tt/zGE5lan7a1/zlZkttA9dZEEIItYu1saV17xEgCOWK+Tcdfxb4+cgfHAt2u2MLzz2waXWa7QcAwOO5P0jHJrBmi3rTrk4IjRBCqG/ozQWg5ORkAPjggw8SExPvujgyMvLbb78FgN27d3d5sr6mrQ/irKQ7txAWRw6RDIuyMyb1lr0dfYip7PK111bTOcWkq9z7neeVj8/rEafitbsPWRtbhIP85BNH3fqqo5mO7tBpx3G2boUQCqiYUOXSub5r3nJ/4mEAIAR8npSy1jcbjp9jaYYprqh75cPW/UddZk0EgtDuSrc2qbhO3ZOwJrP6v3vAsdv/h5+5joMQQqhdVBt3shaLbPwwcUjgra+6PTKTJ5cyBReNFwru4c1vHBmhfOwhQiQ0nM1hisrvOzVCCKE+oTf3AKqqqgKACRMmtHN9YmIiSZJXr17twkx9kuFUlqm0EgCM5/McI8bvwK43AID+2DmXaYnCwQPa+YjW/cfUm3ayVps4JNDz5eWkUnGfmZ3DptJc74u0Yj7wblONFfT3lk8b37rvqGpdis/7L3XPPsqs2eLY3eP+9GJZ4nD90Yzmr7c4XjJX16nW7wAAQiRkGXPzvzd6v/1cjyjMdQfanenWZrVwUH9rk4rOv+iEkXAIIYTuE1NYZszIJURCxSOzbruAJ5MoHp6mWpes2pBCxYQSgg58K37zyAilwnXuJM1P+1U/Jvt+/MZtv5FACCGEbtSb/6hwdXUFgEuXLrVzfVVVlc1mc9yFOpH56jXHLyw19ebK6jv/c/0oO8uaa+rb8+asxdr0f+tVPyazNrvL7CTv9/7cU6o/AKDatIs1maWj48Rhwb+3RvHwdNJFxpReMmTkOjNb+2l3HLQ2q4WDB8gmjACC0KWfBgDFgqmiwf4AIBzoS7rKWZMZCGCKK6off73hg/+0ph631mOzrTuxNqu1ew8DQSifXKh4eBoAqNYlsxYr17kQQgj9PrtdtT4FABTzpvA93H5vlXzKeKG/r7W+uTX1WIfe/vrIiOmJbSMjXB96kO/lbr5Sqzt85j5yI4RQp2LZlu9+0vzcbbpYoBv05h1A48ePT05O/uKLLyZOnEhR1J0X22y2Tz75BACGDx9+55WooxSLpktHxgCwHbiHzxcO6HfXVXaaafz4e6bgIk9Cefzpsc6ZH+8sprLLhlNZhFBwp75IADwppVg8s+W7n9Qbdkjiwrvb9plf6xTLFwBB6I9mmCqqSKXCde5kKi782ttrLHWNvmveshto9cadTHEFa7XSOcV0TrFq3XaBrzeVECGJDReFBRKkk0bd9xTqDTtYk1k6bpg4JFAUHKA7fNZy9ZrulxMuszgYCYcQQqg9dIfOmK/U8j3cXGY+cIdlBMlTLp9f/96/tcm/yMYPI93a9VeP10dGuMgUC6b9+lYCgdtjc5o++0GzdZ90dPxNzQQRQogT+qMZuoOnAEAcEXyHv+dGnOjNBaB33nknNTX14MGDiYmJK1eunDhxolgsvnWZxWI5efLkRx99lJ6eThDEn//8Z+dH7d0Ikmz/Ya72s2l1DR98ba6sJt1cvd95Tjiwf6c/oguxrGpdMrAs31Pp+Pp4J3Y7QZLWZnXrvqOu86c4JV97qTftZE1mwYB+Nk2r/vg5x4Ev6choY3YRAIhDBjMll5r/vcll1kTZxFGmssus1SZLGs0yJjq3xFLXYNnT0LrnMI8SUzGhVFw4FRfezUe2OYeptNKQkUsIBW5/mA2OHxWWzW94/0vNz6nScQmkwoXrgAghhG5mpxnNT/sAgLVYr/3t/+5+A8mz04zm51T3Pz5y17U3jIyYdVOVRzoqVhcWzBSXa5IPKB+fd4/pEUKok9hpRr11n+PXqnV4QLXb6c0FoOjo6PXr1y9atOjChQszZsyQSCQJCQmenp7u7u5yuVyv16tUqubm5qysrNbW62O233///aSkJG5jo/awNrY0vP+l5VqTwNfL+53n+V7uXCfqGEt9s6miCgAstQ3a2vR23mU4ndWtCkDWFo3hTA4AWK5ea/rsh7brranHIfV424emsss3vmoqu9z/87dZm910sZLOLqKzi8zVdYazOYazOUAQokB/KiFCEhchDPDrnj2Putz/mqa7zpvcdoKAig6h4sLp7CLNtv3t+VEBIYSQk9l1BpvBCAA2rc6m1bXzLktdY3uW/ToyIul2IyNWzK97/ePW1GPyiaME7dhAjRBCXUeb/ItNrRUNCbCpteYrtbrDZ+WTxnAdCv2qNxeAAODhhx9WKpWvvPJKXl6e0Wg8ceLE762MjIz88MMPZ8yY4cx46N6Yr9Q2fPAfm1orCvT3euvZnrhnRNDP0+OFx2zq1g7dJQ4L6qI894ZUuLhMT7SptADAMiZjXgnYWXHkUFImaVtjrqm3XL1GSMRUVKijniOODgEAguSJw4LEYUFuSx6yNqnorEJjViFTVG6qqDJVVGl+2s93V1BxEVRCBBU5lBAKOPoUOeAY+sb3cHOd9ZtitHLFgrr8Ut2hM/JJY4SD/bmKhxBC6Lb4Xu5+X6606/Qdu6uf113X3HVkhKMwpEs/rVqf4v23P3UoAEIIdaLr3c0IQrligbVJ1fTZD5qte6Wj4/CAavfRywtAAJCUlJSbm5uXl5eWllZYWHj58mW9Xm80GimKksvlAQEB4eHhkydPjo2N5TopahdzZXX9e/+2G2gqaqjna0/xqNsc6+sRZIkjuI5wvwiSp1y+wPHrxtXfgZ2VPTDS4/klN65hLZbaP6+yNrZQUUPlk8fe9n34nkr51PHyqeNZk5kuuEhnFdHZhdYWjS79lC79FCESUpFDqfgISXwEqezlPdrtNKPZlgoAbo/Pu3jZ1wkAACAASURBVKnfk8DHUz41sXXfkZZ1yf3e/0sf3R6FEELdGN9TCZ7KTn/bdo2MeGSW4Uw2nVeKIyMRQhxSrU9hLVZZ0ihR0EBR0EA8oNoN9f4CkEN0dHR0dLRznqXT6Z5++unm5vYOOaqrqwOA0tLSrgzVS1hqGxpW/cduoKWjYj3+vIzgY+fgboEpKDNeyOeJRW6P3jz1tq0/pXrLHumoWJ5ceof3IURCSUKkJCESAMyXa4xZBXRmoelStTGzwJhZ0EIQwoABkoQISUKEMGBAr6yAaLal2jStopDB0pExt76qWDTdcCrT0SFIOgpr1ggh1Pu1c2SEozm0asMO1bpk36gQ/AYJIeR8TMFFY2YBjxK7LZ7puOI4oKpLPS6fNKZtfCHiVl8pADlTdXX1tm3bWLYjQ68AqqqquihPr2FtUjW8/6WtVU/FR3i8tAznRnUXdrvqx2QAcJ0/5bajTKSjYvXRIXReqSblF+Wy+e18V2GAnzDAT7Fgmk3TSmcVGbMK6LxSc2W1ubJa83MqqXSVxEdQCZG96YCYpb5Jl3YCCMJ9xYLblrd4lFixaHrLt910JBxCCKFO9r+REQJfb8OZ7LustVoJkrTUN+l+Oe4yE0dGIoScirXZVT+mAIDrgqltPxHccEB1h/dbz3IaEF3X1wtAjpNfaWlpXl53P4PdTuHh4Tk5OU1NTe1c/913323fvt3FBSf73IlNq2t4/0trs1ocGuj1yhNY/ek+WtNOmqvr+N4ed5h6q1w2v/bVD1sPHJdNHCX09+3Q+5MKF1nSKFnSKNZiYQrLjZmFdFaBtVmtSz+tSz9NiIRU1FAqIVISH9HTx2Op1iWzFuudW/zIHxyjSz9jrqzW7j2iWDDVmfEQQgg5maW2wTEywnylxnylpp136Y+fxwIQQsjJdGknzNV1fB8Pl+kTbrx+/YBqdhGdU0zFhnGUDv2qrxeAcnNzAcBisXTu23bouFlaWhoAEL3xPEtnsRvohve/stQ1CgcP8HrzmV6z46MXsBuM2p8PAIDy8XmE4Hf/uwgG9JMnjdYdPKVen+L99xfu7VmEQEDFhlGxYfDUQvOVGmNmIZ1ZYLpUbbxQYLxQ0EIQokB/ybAoKiFCOLD/PX4+3HE0buBRYsWiO7aiJwjlivn1f/s/7c6Dsgkj2saEIYQQ6n0E/b3dn1xobVZ36C4qIaKL8iCE0G3Z9UbNdsdPBPMJwW8qDKSLTDF/qmrjTtX6FN+oofi3+JzrtQUglmXtdns7F1utVpvN1vYhif9fdiesydz44TfmKzUCX2/vt5/jSbCHfDei2bbfptMLA/yEA32tDXfqeyV7YKT+6Dk6/yKdVUjF3+/3psJBfsJBfooFU21qrTGrkM4qpPMvOiaIqbfu5Xu5SxIiqPhIcXhwz+iDYLer1m0HAEIkaP5iw12XE3w+azJrtu7zeOGxrg+HEEKIIwQhnzqe6xAIIXQX6q377DqDOHKoZFjkra/KZ0zQHTpjqW3QHcADqtzrtQWgNWvWvPrqq+1cPGjQoBs/7Gj7HtSFWLb5q81M6SW+h5v33//UEye+d4idMTWu/lYUHHBrN+VuyKbRtaadBADz5Zqa51e28y7Nz6n3XwBqQ7q5yh8cI39wDGu20PmldGahMbPA2tjSmnq8NfU4jxJTsWFUQqQkLpx3w3D67samM1jqmwDAptHRmovtvMtUfqULMyGEEEIIIXQ3lpp63aFTQPKUy2/f65MgSeXjcxs+/EazLVU6dhip6OU/0HVzvbYA5Oray8dF9xHaPYcNZ7J5Esr7b3/qC6ddtDvSmIIyprBckhAhGhLAdZy74FEiKnKo5Vp7210BABAgjg7pijCEUOCYIObOLjZdqjZeyKezCs1Xag1nsg1nsoHHE4cEShIiqGFRgn6eXRHgfpCu8v6fvmlVt3boLkF/HKaAEEIIIYS4pPoxBWx2l+mJd2j0ScVHULFhdE6x5uf97k8vdmY8dJNeWwB68sknSZJ88cUX9Xq9u7v7+++/f9s2zwsWLACAb7/91t3d3ekZ0V3Q+RfV/90DBOHx4tK+8LOutaG5de9RAACWbVm73Xf1a9180jkhEnq/8zzXKW5BEKKggaKggW6PzLI2qYyZBXRmAVNUzhSXM8XlsHGnoL+3ZFgklRApHhIAPB7Xca8TDOgnGNCP6xQIIYQQQgi1l/FcHp1XwpNKFA9Pv/NK5bL5dfkXdemn5Q+OvsPAE9TVem0BCACWL18+fvz4JUuWZGRkrFy5cu3atbNm3f5YzYwZM/r373ldY3s3a2NL0+frwG5XLJwuSbjNadLeR7VxJ2uxSEfFMhcrzZXV+hPnZYkjuA7Vs/E9lS7TEl2mJdpphs4ppjMLjNlFltoGbW2Ddtch0kVGxUdIEiKomDAcqY4QQgghhFAH2O2qTTsBgLVa6/76yd3XEwAsq968+55nwqD715sLQAAQGBh48uTJVatWrVq1avbs2U899dSaNWtkMhnXudBdsGZL4yff23UGSUKk4uFpXMdxBqagzHgujycWKZcvoAtKm/+9Sb1pl2R4NI8Scx2tN+BRYunoOOnoOLDbmZJLxswC+kKBpb5JfzRDfzSDEAjEkUMkCZGShAhSqeA6LEIIIYQQQt2d3WyxtxoAgDWZ7zwN5kbWZk1XhkJ30csLQADA5/NXrlw5ZcqUJUuWfP/990eOHNm4cePo0aO5zoXupOWbLebLNQJfL48Xl3bzY1Cdw25XrU8BANd5U0ilq2z8cF36aVNppXZneo/oBt2T8Hji8GBxeDA8Ps9SU2/MLDBeKDCVXaazi+jsopbvCWHAAMmwSElCpDDAj+usCCGEEEIIdVM8scjv6/fsemPH7urtU326ud5fAHIYNWpUbm7uCy+8sGHDhvHjx//1r3999913BQIB17nQbbSmHtOfuMCjxF6vP91Hhr7rDp4yV9XyvT1cZj0AAEAQ7isW1L3xSeueQ7KJIwU+3a5pce8g8PNx9fNxnTPJ1qqnswqNmQV0bom5stpcWa3Ztp/v4UbFR0qGRYrDgwlBX/lSiRDqI2JjYwEgLS3tth0SEUIIofbgSSU8afedtItu1Yd+qpHL5evXr58+ffozzzzzwQcfHDhwYPPmzVyHQjczV9epN+0CgvB4fonAz4frOM5gNxg121IBQLl0LvG/oqRwsL9s/DD98fPqzbu9Xn2S04C9H+kikz0wUvbASNZiYQrKjBcKjFmF1ma1Lu2ELu0EjxJTMaFUQgQVF07K8QApQqg3yM3NBQCLxcJ1EIQQQgg5Tx8qADksXLhw9OjRS5cuPXr0aHx8PNdx0G+wNlvzvzeyFqt88ljJyBiu4ziJZtt+m04vjhwiGRF943W3x+YYz+cbM3LpvFKqa0ano5sQAgEVF07FhbuzrPnyVeOFAmNmgflyjeFsjuFsDvB44iEBVEKkZFhkXxhLhxDqiViWtdvt7VxstVptNlvbhyRJdk0ohBBCCHUL3WUEsjP5+fkdOnTo448/vvGbHtQdaLcfMF+u4ft4uC2dy3UWJ7HU1OvSTgGPp1w2/6aXSIWL65xJAKBan8La2vvdPOocBCEc7K9YNMP3k7/6ffO++5MLqZhQguQxpZfUm3fV/vn92hfeU23cyRSX438ahFC3smbNGn47OBYPGjTo1osIIYQQ6q366B/2PB7vtddemzNnTnV1NQB4eHhwnQiBqaJKs/MgEITH84/xxCKu4ziJasMO1maTTxkvHNj/1lddZifpj2ZYrl7THz4jnzzW+fEQAPA93ORTx8unjrczJia3xJhZQGcVWa41WfYcbt1zmCeTULHhkmGRVExoH2lZhRDqzlxdXbmOgBBCCKFuqo8WgByCg4ODg4O5ToEAAFizpfnLTWCzu8xOEocGch3HSYwXCuicYp5Uolg8/bYLCAHfbclDjZ+uVW/ZIx0Vy5NLnZwQ3YgnFklGxkhGxoDdzpRdpjMLjBcKLLUNhpMXDCcvECQpCgtyzJLne2NNGSHEjSeffJIkyRdffFGv17u7u7///vu3bfO8YMECAPj222/d3d0769E2m+2LL75oaGho5/oTJ04AgNVq7awACCFnYk3mhn9+LfDzcX9qEddZEELt1acLQKj7UG/Za6mpF/j5uD0yk+ssTsJabepNOwFAsWj6HVoLS0bGUNEhdF6pJuWXW4+JIW7weOKQQHFIoNuSOZZrTfSFfGNWIVN6iSm4yBRcVP2YLBjQz1EJEg0JAILgOi5CqG9Zvnz5+PHjlyxZkpGRsXLlyrVr186aNeu2K2fMmNG//232n96bnJycl19+uaN36XS6zgqAEHIm7c50pqicKSqXDI+iokO5joMQahcsACHuMcUVrfuPAsnzeGFp2xisXq9131FLXaPAz0c+ZdydVyqXza999cPWA8dlE0cJ/X2dEw+1k6Cfp2B2ksvsJLvBSGcXGzML6Nxiy9Vr2qvXtDsPki4yKj5CEh8hjgntOwcbEUKcCwwMPHny5KpVq1atWjV79uynnnpqzZo1MlnXzjGMj4///vvvW1pa2rk+NTX1xIkTcrm8S1MhhLqCtVmt3XvY8WvVjym+n71JYBd5hHoCLAAhjrEmc/OXm4BlFfOmigL9uY7jJHYDrU35BQCsDS1XV7zZjhtYYFnN1n1ebzzd5eHQPeFJJdJxCdJxCazNZiq+ZMwqNGbmW+ub9Ucz9EczCAFfHB4sSYik4iP4nkquwyKEej8+n79y5copU6YsWbLk+++/P3LkyMaNG0ePHt11TyQI4sknn2z/+ubm5hMnTmDzaYR6IvWGHazJLB0Va75SY6mp16WddJk+getQCKG7wz90Ecc0O9KsjS3CAD/XBVO5zuI8rMUKPB4AsBYLa7G09y6cW9cTECQpjhwijhyiXDbPUlNvzCykMwuYsst0bgmdWwJrfxYO7E/FR0gSIkTBg/CAGEKoS40aNSo3N/eFF17YsGHD+PHj//rXv7777ruCPrPZFiHUFUyllYaMXEIocHt8nvlyTePqbzXbUqXjEu7Q0wAh1E1gAQhxyVrf3LrnCBCE+9OL+9TGUVIhH7D2A9bc3tKPAw6Z6nEEfj6ufj6ucx606fR0djGdWUDnlpiras1VtdodaaSrnIoLl8RHiKNDeJSY67AIod5JLpevX79++vTpzzzzzAcffHDgwIHNmzdzHQoh1GOxbMu6ZGBZ13mT+R5ufA83KjaMzinW/JTq/tRCrsMhhO4CC0CIS6oNO1iLRfbASFHwIK6zOBshEPSdhkeIlMtkicNlicNZq40prqCzCoxZhb8eEOOT4vBgR7cgnCCGEOoKCxcuHD169NKlS48ePRofH891HIRQT6U7dNpcWc33cHOdleS4onx8Xl3+RV36KfnkMcKBndZXHiHUFXhcB0B9F51bYryQz6PEbn+YzXUWhJyE4JNU1FDl8gV+X67s/3/vuC2ZIw4LYu0snVeqWpdc8/zK2pdWqTfvYoorwG7nOixCqFfx8/M7dOjQxx9/bMMDxQihe2KnGc22VABwe3weIRI6Lgr8fORTxoLdrlqfwmk6hNDd4Q4gxA3WZlP9mAwArg9PIxUuXMdBiANtB8TseiOd45ggVmKpqdfW1Gt3HeLJJFRMmCQhgooJ48kkXIdFCPUGPB7vtddemzNnTnV1NQB4eOCWQ4RQB2i2pdo0raKQwdKRMTdeVyyaYTiZxRSUGc/lSUZEcxUPIXRXWABC3NDtP2apbRD4euPIAIR4srYJYnZT6SU6q9CYVWipbTCcyjScygSSJx4SQMVHUHHhQn9frsMihHq84ODg4OBgrlMghHoYS32T7pfjQBDuKxbcNMWCJ5UoFk1vWfuzauNOKi4Muxwg1G1hAQhxwKbRabYfAADl8vkEvw/1fkbozgiSJw4PFocHuy2da61vNmYV0lkFTHEFU3KJKbmk3ryb7+VOxYZLEiLEEcH43RVCCCGEnEb1w3bWapNPGisc7H/rq/LJY3Xpp81Vta17j7rOm+z8eAih9sACEOKA+r+77TQjGRZJxYZxnQWhborv4+EyY4LLjAl2mmHySo1ZhXROkbWxRZd2Qpd2ghAJqcihVHw4FRfBd1dwHRYhhBBCvRmdV0rnFPMosWLR9Nuv4PGUy+bXv/eFdkeabMIIUunq3IAIoXbBAhByNlP5Ff2xc4SAr3x8PtdZEOoBeJRYMjJGMjIGWNZ0qZrOKjRmFZkvXzVmFhgzCwBAOKg/FRchiQsXDRkEPGztjxBCCKHOxNpsqh+2A4Bi4fQ79O4URw6RDI8yns9Xb93r8fwSJwZECLUXFoCQs6m37AGWdZk1ke+DvScR6giCEAUNFAUNVCyaYVNrjdlFdHYRk1dqvlJrvlKr3ZHGk0upmFBJXAQVE8qTS7mOixBCCKHeQJd63FLXIPDxlE8df+eVysfn0Tkl+mPn5FPGiYIGOiceQqj9sACEnIopLmcKynhSieucSVxnQagHI91c5Umj5UmjWYuVKa6gswvp7CLLtSbDyUzDyUzg8UTBgyTx4VRcuHBg/5s6NSKEEEIItZOdMWmSDwCApb6p6pGX2nmXevNun5UvdmUuhNC9wAIQcirNT/sBwGXWRJ6E4joLQr0BIeBT0SFUdAgsX2C51kRnFdLZhUzJJdPFStPFSvWWvaRSIYkLp2LDxNEhPLGI67wIIYQQ6lHsdkIkBAPdoZsIAf6YiVB3hL8zkfMwBReZ4gqeXOoyYwLXWRDqhQT9PAUzH3CZ+YCdMTF5pXROsTG7yKbS6A6d1h06TfBJUWiQJDaMio8Q9PfmOixCCCGEegCehBrw3Qdcp0AIdQ4sACHnUW/bDwCus5N4lJjrLAj1DPrDZ0kPNyo6pEN38cQiyYhoyYhod5Y1V9XS2UXG7CJT2RWm4CJTcBE27rw+Tj4uTBwxhBAJuyg8QgghhBBCqPvAAhByEjq3xFRaSbrI5NMSuc6CUM9AZxc1f/1fQiTs/6+/8T3c7uUtCEI4yE84yM913hS73kjnldDZRXRO8a/j5IUCcXgwFRtOxYUJfDw7+zNACCGEEEIIdRdYAEJOotm2HwBc5kzCLiQItQdrs6nW7wAA1mRW/3e355+X3ecb8mQS6Zh46Zj46+Pks4vo7CLTpWo6p5jOKYZ1IOjnScWGU7Fh4vBgQijohM8BIYQQQggh1G1gAQg5A51dZCq/QirkLlPGcZ0FoZ7BMXKV7+1hU2sNp7Jcpo4XDR3cOW/dNk5+4XSbVkfnltA5xXReieVak+XasdbUY7gtCCGEEEIIod4HC0Co67GsY/uP65xJN3UbsTMm3BCE0K1sWp1j5Kr7kwtNZZc12w+0rP3Z9+M3On2gO+kqlyUOlyUOB7vdVFFFZxfTOUWmyqu/bgvy8aTiwqjYcHFYEHYL6vFYtvmrzaSLzG3pXK6jIIQQQgghZ8MCEOpyxsxC06Vq0s1VPvk32380yQc021I9X14hHRXLVTaEuifN1n12A03FRzgOZOmPnTNfrtEfOyd7YGRXPZLHEw0JEA0JUCye8ZttQfVNltTjranHCYFAHB5ExYRRcWEC3543REx3+AzpIpcMi+Q6CJd0h8/oj50DAHFUCBUTynUchBBCCCHkVFgAQl1Os/0AALjOnXxjVxFrY4s25SCwrHp9iiQuHHcWINTGfKVGd+QsQZLKx+cCACEUuD06u+lf69X/3S0ZEc2TUF0d4OZtQTnFdE6x6VI1nVtC55bA+hS+lzsVE0rFhokjh/aITXx0bknL11sIAd/387f77KE2O81oftrv+LVqfYrvZ28SJMltJITQvWFN5qY160ShQa5zHuQ6C0IIoZ4EC0CoazHFFebKalIhl08afeN11cadrMUCBGFt0Wh3pisWz+AqIULdjWpdCtjt8tlJbRttpGPjdQdPMiWXtDsOui15yHlR2rYFLZph0+mZ3FI6p4jOK7U2tugOntIdPEXwSVFIIBUTSsWECgf27/QTap2CtdnVG3cCAGuxqjfs9Hrjaa4TcUOz/YBN0yoOCbRpWy019bq0ky7TJ3AdCiF0LzQpacasQmNOMRUbKhzYn+s4CCGEegwe1wFQL9e67wgAyCePIwS/bv9hCsuMGbmESOj5l+VAENo9h6yNLdxlRKgbMZzJZorLSReZYv7UX68ShHL5AiCI1n1HLHWNnAQj5TLpuASPFx8fsPafvh+/7vbILHFIIMuyTGGZevPuulc/uvrU281fbjKcyrTp9Jwk/D26tBPm6jq+twePEhsv5NN5pVwn4oClvkl34DgQhNvy+W6PzwMAzbbU7vZfCiHUHtbGlta9RwAA7HbVjylcx0EIIdSTYAEIdSFrfbMxs5AQCOQ3Dv+y21XrUwBAMW+KdHScdGw8a7aoN+/mLCVC3Ubb7wXFI7N40t8c9RIOHiCbMIK12tSbdnGU7n8IQjjY33X+FJ9Vf/Fft9rzlSdkSaP47gqbplV/7FzT/62/uuLNa3/9RPPTPqb0EmuzcxvWrjc6TqEqVyxwnT8FAFQ/JrM2G7epnE/1YwprscqTRosC/SUJkVRsmN1gbDsRhhDqQRx7qCXDo0i5zPE3alwnQggh1GNgAQh1odYDx8Bul45LIF3lbRd16afNV2r5Hm4uMx8AAOVjcwiR0LHrgbukCHUL2t2HrI0twkF+8qRRt77q9oeHeBLKeCGfzi1xfrbb4kkp6ahYj2f/4PftKt81bymXzqWihhJ8vqmiSpP8S/07n19d/kbjJ2t16aetTSpOEqq37rXrDOLIoZL4CJeZEwW+Xpaael3aKU7CcIXOv0hnFfIocdthW+Xj8wiS1KWfNlfVcpsNIdQhjooPTyxyf3KR43f09TP1CCGEUDtgAQh1FbuR1h/JAIJwmTHh14v/+ztn5bL5jsbPpFLhOmcS/K/vCUdhEeKeTaXR7koHAOWK+cC7zRdnUiF3nTcZAFTrU7rhHhahv6/L7CTvv7/gv36191vPukyfIPD1thtp47nclm+31jz799oX31etS6azi1iT2TmRLFev6Q6dBpKnXD4fAAg+6fbYHADQbNvfd04/sTa76sdkAFA8PI1UuDguCvx85FPG4fkRhHqY//2edZ03mVS6yieNEQ7q/+uJMIQQQuhusACEuor+8Fk7zYgjhtzYnlDzc6pNpxdHDJGMjGm76PrQg3wvd8fkIy6SItQtqDbtYk1m6eg4cVjw761xmTlR0M/TUlOvO9h997AQIiEVF65csaD/F3/z+8977n98RDIihielLHUNranHGv75dfWy1+vf+0K7K918pQZYtuuSqNangM3uMnW80N/XcUUyLIqKCbUbjNqfD3Tdc7sV3YHjlqvXBD6e8mmJN15XLJp+/fzIOTw/glDPoDt4ylxVy/dyd5k1EQCAx1Mumw8A2h0HbSotx+EQQgj1BFgAQl3Dbm89cBwAXGc+0HbNUtug++Vk2/crbQihwDHYSLN1n91AOzkpQt2Bqeyy4VRW2++F30PwSbfH5gKA5qeesYeF7+UunzTG67UnB6xb3W/Vy4oF00TBg1ibnSloax39VvMXG/THz9s0rZ37aOO5XDqvlCeTKBZMu/G6ctl8giRb006aq+s694ndkF1v1KT8AgDK5fMJwW/mfvKkEsWi6QCg2oDnRxDqAewGo2ZbKgAoH5/bNlhDHDFEMiLGzpjU/93DaTqEEEI9A46BR13CeD7f2tgi8PWm4sLbLjrOrcgnjxUOunlkqXR0nO6Xk0xxuSblF+XSuc4NixDXWFa1LhlYllS4aHcevOtyQihw7GFRPvGwE9J1CoLkiUIGi0IGKxbPsOuNdH4pnVfC5JVam9X6Exf0Jy4AQQgH+lJRIeLoUHFY4I1zA+8Ba7U52mm7PTKLJ5fe+JLAz0c+eWzrgeOqH5N93n3xvj6rbk+9ZY9dZ6CihlLxEbe+Kp881tEGqHXvEdd5U5wfDyHUfr/uoR4Rc+N15eNz6ewi/YnzskmjxSGBXMVDCCHUI2ABCHUJ7b4jAOAyPREIwnHFmFVI5xTzpJRi8czb3qJcMb/u9Y91+4/JHxwt8PV2XlaEuGbT6kyXqgHA2tiiSz/dzruM2UU9qAB0I55MIh0dJx0dBwCWmno6r5TOK2GKys1Xas1XarV7DhNCgTg0SBwdQkWHCP19276MtF/rnkOWa00CPx/Zg2NufVWxeIbhVBZTUGY8ny8ZHtUJn1K3ZLl6TXf4DJA8t99uuvwVj6dcPr9+5RfaHQdlE0aSSlfnBkQItZelpv62e6gBwHEiTLsjTbUuxXf1a/fwBRMhhFDfgQUg1PlMFVWm0kqeVCJ7YKTjCmu1qTfsBADFw9NJF9lt7xIO8pNPHKU7dFq1Yaf3m884Ly5CXCMVLt7vPGdt7NicLFHwoK6J41QCPx+Bn4/LjAmsxWoqraTzSuj8UvPlGjqvhM4rUQOQChcqaqg4OoSKCiHd2lWhsGl02p3p4Dj3RN7mpDNPKnFdOE31w3bVhh1UbOh97jbqtq63QJr5QFsLpFuJI4ZIRkQbz+Wpt+zx+NNjzoyHEGo/1YYdrM0mnzLu1j3UAOA6b7L+WIa5slp/4oIscbjz4yGEEOopsACEOl/r/qMAIJ80xjHnCwB0qccsdQ2C/t7yaePvcKPikZmGs9l0ViGdU0zFhjkjK0LdAxUdynUEjhECvjhyiDhyiBs8ZGvVMwUX6dwSJr/U2qK5fkYMQOjvK44KoaJDxGFBbV9ebqXetMtOM5IRMXf4t+oyZZw+/bS5uq51/zHHIMJexpjxvxZI86feeaVy6Vw6u1h//Lx8yrjeUVVEqJcxZhbQOcU8qcQx9/1WPLHI7dHZzV9uUm/aJRkexaPETk6IEEKop8ACEOpkNpXWeCaHIEn51Ou1HptWp0n+BQBkiSMsVbV3vl06MlZ3+Ixqww7fqKEESXZ5XIRQ90O6yKRj4qVj4sFxRiy/lMkrZYrKzdV15uq61n1HCD4pGjqYig4RR4WIBg8A3q/bfMyVwoGluwAAIABJREFU1foT5wk+eed22sDjKZcvqH/vC21KmixxeDv3FvUYLKvatAsAWIu17q+ftPMW9YadPqv+0rXBEEIdxFpt6o07AUCxcDopv/0eagCQJQ7XpZ00lV/R7kp3e2SWEwMihBDqSbAAhDqZ/tg51maTjorle7g5rrTuP2Y30gCg3rJHvaVdUyosNfXGM9nSccO6MChCqCe4fkZs+gTWZjNdvMzkl9L5paaKaqaonCkqhy17eVKJOHIIFRUijhoq8PFsWbvd0U67dc+hG9+HtdoIHnFjqQgAeJTYTjPqLXs9nl/i3E+ra7EWK2tkAIA1ma0Nze28y9baA+bKIdTXtKYes9Q1Cvx85FPH3WkdQSiXz7/29prWPYdlD4wU+Hg6KyBCCKGeBAtAqFOxrP7oWQCQTRzVdk0cMpgJGsja2fa/DcEnBb/ftAIh1AcRJCkOCxKHBSkWz7QbaKawjM4vZfJLLdeajBm5xoxcAOB7Kq1NKgCwNqvb306bzivtwtxcIISC/v9Zae9gQYdUuHRRHoTQvbEbaG3yLwBgU2trX3z/7jfwCNZi1fy03/OlZV2dDSGEUE+EBSDUmZjSSsu1Jr67gooOabtIxYXfOAweIYTuE09KSUZES0ZEA4C1sYUuuMjklzL5ZY7qDwAAEHx3hcDPW9DfR9DP03AmhympAADpqDhx1NCb3k00NMCp6Z2CR4mxDwhCPZ2dMbE2GwDYDbTdQLf3LoOxK0MhhBDqwbAAhDqT/shZAJBOGHHTOQuEEOoifC93edJoedJoYFnzlVo6r5QpuMiUXrK2qK0tajqvlCB5rI0FAoAFY36p8umFd+ijgRBC3QffXTHguw/sekOH7iI9lF2UByGEUE/XhwpAWq22tLS0srJSr9cbDAaxWCyTyQICAsLCwtzc3LhO1xvYacZ4NgcIQv7AqLuvRgihzkUQwgA/YYCf65wHWYvVdLGSzr/I5JeaKqoBWGABAFiD8drrH7vMnCiOHCIc0A8IguvQCCF0JzwpxZNSXKdACCHUS/T+ApDVal27du3GjRszMjJY9jZtaAiCGDFixNKlS5944gmh8HfnCqO7Mp7JsTMmcVgw38eD6ywIoT6NEPDFEUPEEUMMg/o3rVnHo8SS0XFM4UVrQ4u1SaX6MRkASIVcHDGUihwqjhzC93LnOjJCCCGEEEJdq5cXgOrq6iZNmlRcXHzjRYIgpFKpxWIxmUwAwLJsRkZGRkbG559/vmfPnpCQkN95M3QXOkf756SRXAdBCCEAANZsUW/eDQBuS+fKJ40BgOav/6s/fJZ0lQPJs6m0hlOZhlOZAMD39nBUgsQRQ0hXOce5EUIIIYQQ6gK9uQBE0/ScOXOKi4sFAsG0adNmz549ZswYDw8PNzc3kiQBwGw2X7169fTp0/v379+3b195efnEiRPz8/M9PHADS4dZ6hpMpZU8SiwdGct1FoQQAgDQ7j5kbWwRDvKTJ10/l6pcOpc+X2DT6rxefVLg58MUltEFF5nCcmtDs66hWXfoNBCEcEA/ceQQccRQcXgQT4InLxBCCCGEUC/RmwtAq1atunDhglKp3LFjR2Ji4q0LhEJhYGBgYGDg0qVLCwoKZs6cWV1dvXr16k8++cT5aXs6/ZEMAJCOiSdEeIwOIcQ9m0qj3ZUOAMoV89va0vOkEsXiGS3fb1OtT+n/r7/Jp46XTx0Pdrvpcg1TUOboHm2urjNX17XuPwY8nmjwAHHkUHHEEHHIYPzihhBCCCGEerTeXABKTk4GgA8++OC21Z+bREZGfvvtt9OmTdu9ezcWgDqKtdn1x88DgGwinv9CCHULqk27WJNZOjpOHBZ843X5pDG69FPmK7Wt+466zp8CAMDjiQL9RYH+rnMeZK02U9llprCMKbhoKr9iqqgyVVRpdx4k+KRoSIA4Yog4cqgoeBDBJ7n5rBDqiOrq6rNnzxYWFvr4+CQmJkZERNx2WVVV1a5duwDgz3/+s3MDIoQQQsipenMBqKqqCgAmTJjQzvWJiYkkSV69erULM/VSdE6RTa0V+PmIhgRwnQUhhMBUdtlwKosQCNyWPHTzazyectn8+pVfaHakSROH8z1+MwWS4JPisCBxWBAsnM6azEzJJabwIlNQZrpcwxRXMMUV8HMqIRKKQwLFEcHiiCHCwf4EyXPeJ4ZQ+1it1vfee+/DDz+02WyOKwRBzJs374cffnB1db1pcWlp6UsvvQRYAEIIIYR6u95cAHJ1dW1sbLx06VI7+zpXVVXZbDZsAHQP9EczAEA2Eae/I4S6AZZVrUsGlnV96MHbjvcSRwyRjIwxZuRqtu71eGHp770NIRJSMaFUTCgA2A00U1zOFJQxhWXmq9fovBI6rwQAeJRYHBYkDg8WRwwRBvjhXHnUTSxevDglJQUACIIYMGBAS0uLwWBISUmpqKg4fPiwuzuOvUMIIYT6ot7895bjx48HgC+++IKm6bsuttlsjpNfw4cP7/JkvYtNq6MzCwmSlI3Hf3UIIe7pj50zVVSRSoXr3Em/t0a5dC4hEOhPXGBKL7XnPXlSSjIsSrlige+atwas/afnyyvkk8cKfL3tNGPMKlRt3Fn3+urqZW80rv6udf8xc1UtsGznfUIIdcyBAwcc1Z8//vGPLS0tVVVVGo3mhx9+kEqleXl5zzzzDNcBEUIIIcSN3rwD6J133klNTT148GBiYuLKlSsnTpwoFotvXWaxWE6ePPnRRx+lp6cTBIH7nzvKcPICa7NJhkWRCpydjBDimJ1m1Fv2AoDysYfu0LaZ7+XuMnuiNiVNtS7Fd/VrHdq5Q7rKpaPjpKPjAMCm0tKFZUxRmWOUmPFCvvFCPgCQcpno+s6gYOGAfrgzCDnT119/DQDz5s375ptvHFf4fP6KFSuCgoImTpyYnJyckpIyf/58TjMihBBCiAO9uQAUHR29fv36RYsWXbhwYcaMGRKJJCEhwdPT093dXS6X6/V6lUrV3NyclZXV2trquOX9999PSkriNnaPYzidDQCyRNz+gxDinnbHQZtay/f24Pt6myur77CSig7R7T9mrqzWnzgvSxxxb48jla6y8cNk44cBgLVZzRSWOf6xNquN53KN53IBgHSRicODHf8I/HywGNT9mUyme75XJBJ1YpJ7U1paCgB/+ctfbro+fvz4V199dfXq1a+//vqsWbOEwk4ebEfT9KuvvtrU1NTO9Xl5eQBgNps7NwZCCCGEfk9vLgABwMMPP6xUKl955ZW8vDyj0XjixInfWxkZGfnhhx/OmDHDmfF6AWuTylRRxROLqLhwrrMghPo6O8207jsCANaG5mtvfNzOuzTbD9xzAehGfA832YQRsgkjHAGYonKmqJwpLLO2aAxncwxncwCAdJVf7xmExaBu7Lb7hduJ7Qan/6qrqwFgyJAht770zjvvbNy4sbKy8quvvrq1QnSfysrK/vOf/3T0LqPR2LkxEEIIIfR7enkBCACSkpJyc3Pz8vLS0tIKCwsvX76s1+uNRiNFUXK5PCAgIDw8fPLkybGxsVwn7ZEMZ3OAZan4CEIo4DoLQqivI4RCSUKkpaGlQ3eJw4M6PQnf20Pm7eFojW+tdxSDyujCcpvq5mKQ46QYHhNDncjNza2+vr62ttbLy+uml2Qy2UcfffT444+/9957Cxcu7N+/fyc+Nzo6+siRI83Nze1cv3nz5j179ri4uHRiBoQQQgjdwT0WgHQ63cmTJ8+fP9/U1OTl5fXuu+/W1tZ6enp2+nbizhIdHR0dHe2cZzEM89prrzU0NLRzfY/eAm08kw0A0tFYPkMIcY8geZ6vPMF1ipvxfTxkPh6ypFEAYKlvYorKTUXlTFH5b3YGYc+g7iQzM5PrCPclKiqqvr7+008/3bx5M3HL/0tLly7dvHlzenr6ihUrUlNTSZLsxEc/8MAD7V98/vx5AODxevNAEoQQQqhbuZcC0Ndff/3WW29pNBrHhzExMe++++5PP/300Ucfvfnmmy+//HKnJux5SktLv/zyy47e1RO3QFsbW0yXqnliERWL578QQujuBD6eAh9PedJoaNsZVFzOFJXf2DOIJ5OIQ4PE4cHisCDhoP6APx47XXx8PNcR7suSJUsOHjy4ZcsWkUj0r3/9Sy6/eUTD999/HxkZefDgwaVLl3733XechEQIIYSQ83W4APSPf/zj3XffBQAejzd48OCKigrHdR6P19zc/Morr9TW1n722WedHLNrXLx4MScnh2GY6Ojo8PDwztq+FBMTc/jw4ZaW9p5B6LlboA0ZuXj+CyGE7s2NO4OsDc1McQVTWM4Ul1ubVG3TxHgSShQaKA4LEocFCQf7EyQWg7qv/Px8oVAYEhLCdRBYsmTJ+vXrjxw58uOPP27dujUqKmrZsmXPPvts24KBAwdu2LBh8eLFW7ZsOXbsWGhoKIdpEUIIIeQ0HSsAZWZmOqo/S5cu/eyzzzw8PNq2Fv/pT3+iafrtt99es2bNo48+2n3+9qykpOTrr7/Oy8tTq9UxMTFvvPFGeHi4zWZ78sknN2zY0NasUaFQfPPNN4sWLeqUh06cOLH9i3vuFmg8/4UQQp3ies+gB0YCgLWxhSmuYIrKmeIKa0MznVVIZxUCAE8sEg0d7GgbJAoaSAh6fxe/HsRut0+YMEEoFNbX13OdBQiC2Ldv33PPPbdp0yaGYc6fPx8XF3fTmrlz56ampi5YsKCurq6uro6TnAghhBByso59+/ivf/0LAGbMmLFhw4abXhIIBG+99VZdXd1XX3312WefbdmypdMy3of169c/99xzNE07PiwoKNi1a9fx48f37Nmzfv16x0WSJG02m0ajWbx4cVlZ2d/+9jfO4vYoeP4LIYS6At/LXeblfn2aWIvGVFzOFFcwxRWW2gY6r4TOKwEAQiAQBQ8ShweJQoPEQwMIUTdtwNebsCx76NCh8+fP6/X6W18tKipSq9X3Mz6sc1EU9eOPP7733nu7du2qrq4OD7/Nn9RJSUlXr17dsGHD1q1bKyoq2t+7ECGEEEI9VMcKQBkZGQDw6quv/t6Cp5566quvvsrPz7/fXJ3hypUrzz77LMMwHh4eU6dOlUqlJ06cKCkpeeaZZ8rLy0Ui0YcffviHP/zBzc2tpKTk3Xff3bVr1wcffPDoo48GBgZynb0HuD7/KyESz38hhFAX4bsr+OOGSccNAwCbppUpuWQqrmCKys1XrzHF5UxxOQAQJCkMHCAODRKFBopDA3lSCdepeyGr1bpkyZJt27bdednYsWOdk6ed/P39X3zxxTsskMlkzz///PPPPw8ABoPBWbkQQgghxI2OFYCuXr0KAHc43z5gwAAAuHz58n3G6hT//Oc/GYaJioo6fPiwh4cHANhsthkzZqSlpQHAP/7xj7/85S+OlVFRUTt37kxKSjpy5Minn3769ddfc5m7hzCezQEA6Sg8/4UQQs5AKlyko2IdX3XteiNTcokpLjcVV5iu1JjKrpjKrsDuQ0AQQn9fcdj1YhDp5sp16l4iJSXFUf0ZNmyYt7d3enq6yWSaO3cuSZIajebUqVMMw7z99tuvvfYa10nvnVQq5ToCQgghhLpWxwpArq6ujY2NV69e9fHxue2C4uJiABg4cGAnRLtv2dnZAPDPf/7TUf0BAJIkP/roI0cBaPny5Tetf+21144cOVJQUODknD3R9fNflJiKDeM6C0II9Tk8mUQyLFIyLBIA7DRjunjZVHqJKSo3VVSbq2rNVbVw4DgACPp5ikICHfUggY8n16l7sB9++AEAnnrqKcfMrM8+++zVV199+eWXHVt+MjIyJkyYUFJS4uqKFTeEEEIIdV8dKwANHz583759mzdvHjZs2G0X7N27FwCio6M7Idp9Ky8vB4DY2N9sUYmIiCAIQiAQ9O/f/6b1YWFhAFBSUuK0hD3X9fNfOP8LIYS4xqPEVEwoFRMKAKzFYqqoNhVXMKWXTKWVlmtNlmtN+qMZAEAqXcWhgaKQQMmwKL6HG9epexjHzNO2QVqOUQ/nzp1zFIBGjhz54osvfvLJJ/v27Zs5cyaHORFCCCGE7qBjk6deeuklgiC+/PLL77///tZX9+7d++mnnwLA3LlzOyfd/XFsZr6pqSGfz5fJZBKJpG1+WRuj0QgAVqvVaQl7Ljz/hRBC3RAhEIhDA13nT/F++zn/DR/7fvy6ctl8yYgY0lVuU2kNp7NVP2xveO8LrmP2PNeuXQMAf39/x4fBwcHwv6qQwxNPPAEA3377LRfpEEIIdW8sq1qX3LrvCNc5EOrgDqCkpKTXX3999erVTz/99DfffPPggw8CgEql+vzzz8+cObNjxw673f7www8vXLiwa9J2TEhIyLVr144fP37TJqC8vLzbrs/JyQGAW3cGoZvg+S+EEOoBeDzhYH/hYH+XmQ8AgKW24f/Zu++Aps62DeD3ySQhgSSAAxC3daC4beugFbfVurWtYh3Vt3Zra5f9rNVW7Xy77VsXal0FFQfWUbVO3KOoFAUFZClkkJCdnO+PWGsBERRyQnL9/oLn3Cdc76dfDXfO/Tyux4JEDfHPXJXVrVs3MzPTaDQGBQURkUwmCwoKSktLu1PQtGlToVDoOisDAADgboYDx4uTDhDDiFs1EzeN4DoO+LSqNYCIaMGCBRKJ5LPPPjtz5oxrk52srKyZM2cSEcMwEyZM+OYbT/loMTo6ev/+/XPmzOnUqVPPnj3vrDdu3LhssVarnTt3LhENGDDAfRFrp5KjZ3D+FwBA7SIMqysMqyuPeZzrILVSy5YtMzMzk5OTXYddEFGzZs1Onz7tcDj4fD4ROZ1Ou93uepQYAADgDqfJrFm7jYiIZdXLfq3/8UwqM4kC4DZVGwEjIoFAMHfu3LS0tA8++GDEiBGRkZHh4eG9e/eeMWPGkSNHVq1apVAoaiLoA5g9e3aTJk1KSkp69+79xBNPTJ06tdyyvXv3Lly4sFWrVleuXJFKpXcm/OFejMnnCPNfAADgM1x7/cyePfv48eOulc6dO+t0uo0bN7q+TUpKYlm2WbNmnEUEAACPpEvY5dDoxM0a8hUBlrRrJYdPc50IfFqVnwByCQ0N/eijj6o3SrWTSCRbt26dOnVqcnLyH3/8ceTIkaVLl5YtGzJkiNlsdtWvWLHCNdgP9+LQFlvSsxixyLXhKAAAgNebNWvWypUr09PTH3300S1btjz99NNDhw79/vvvp06deu7cOT6f//333xNR3759uU4KAAAexF5QWLxjPzGMauoYW1Zu4Q+/qFdvkXZtx4hFXEcDH/WADaDaok2bNkePHk1MTDx58uRff/11rzKVSjVkyJDZs2e7DgKDCpjOXCSWlUS2wPwXAAD4CIlEsmHDhueee+7Oe4l+/fr1799/165dn376qWulQYMG77//PncZAQDA46hXbmJtdtmTj4qbNRQ3jdDvPmy5mqnbskcxdjDX0cBHeXkDiIgYhhk2bNiwYcPuVZCdnR0cHOzOSLWa8cxFIpJ0bMN1EAAAAPfp1KnTxYsX09PTVSqVa2XTpk0LFixISkri8XiPP/74vHnzlEoltyEBAMBzmP9MM568wJP4KZ8dQkTEMKrJo/Le/1KXuFf25KOCOkFcBwRfVLUGUJcuXSpZefLkyaqH4Qa6P5XHOhzmC38REc7/AgAAX8Pn81u0aHHnW6lU+sknn3zyySccRgIAAA/ldKpXxBNR4Mj+fGWga03corF/904lh09pfkkMeWMyp/nAR1WtAXTq1KkaygG1guVSutNoEkWEomMNAAAAHsKhLc6f+7UkqpVq8iiuswAAEBEV7zpkzcoV1A0OGPzk3evKCcOMJy+UHDkj79/TrzU2nwV3q1oDaMmSJWUXWZYtKCg4derUjh07WJadOHHiyy+/XE3xwLMYz6QQ5r8AAADAk2h+2WrLKbDl3vTv3lH8SBOu4wCAr3OWGHUbdxKR6vkRjPBfv3ELghSBT/fRbkxSL08I/XQ28ap8KjfAw6haA2j69OkVXD127NiAAQPi4uI6dOjQuXPnhwsGnsiEDYAAAMD3eOUIvNewZmQbDhwnImLZoqUbQz99mxiG61AA4NO063c49Aa/ti2kXdqVvRo4rK/hwHHr9Rv6fcfkfbq7Px74surcBPqxxx778ssvp06d+s4770yfPt3Pz68aXxw4Z79ZZMsp4Ekl4kcac50FAADAfTAC77lYtmjZr8SyAQOjjSfOW6/dMBw4LnvyUa5jAYDvst3I1+8+TDyealL5Q6mMSKh87ulbXy3Xrtvu/3hHnlTi5oTgy6r5FLChQ4cSkdlsvnr1amRkZPW+OHDLeCqFiCQdWjF8PtdZAAAA3Acj8B7LcOiU5a8MvkKueHaIuEWjW1/HaX5JlHaLwi9UAMAV9coE1uEIGBgtigi9V41/9476XQfNl67q4n9Txg53ZzzwcdXcAFKpVEKh0GazNWzYsHpfGThnOov5LwAA8EUYgfdMrMWqXbuViJTPPc2T+Pn36Kzffdh8OV23abdy/NNcpwMAX2Q8ccF07jLPXxo4ZmDFlapJo3Lf/rQ46YCsz+PC0LruiQdQzQ2gU6dO2Wy2+vXry+Xy6n1l4BZrtZkvXiGGkUThAHgAAIB/YASeK7rNu+2FGlGTBrInuhERMcztX6i275P1fkwYWofrgADgY5xOzarNREQMFcz/4b7lDJ/P2mya1Yl13p5W49kAiKh6G0ApKSlTp04lotat0SPwNqYLqazVJm7eiK9Aaw8AoLYy/5nGSMTiZnhKt5phBN797IUa3bZ9xDCqyaPu7PrsagYZ9idrVm/BL1QA4GZOk8Wu1hKR02C0GrIqeZctt6AmQwH8S9UaQCEhIfe6ZLFY9Hq96+tZs2Y9VCjwPKbTF4lI0gnzXwAAtZX1+o38+d/xRMKwbz7gqxRcx/EqGIF3P03cJtZi9e/Zxa9l07vXlc89bTx+3njyguncZUn7VlzFAwAfxPOXhH0716krrtJdgjrBNZQHoKyqNYAKCwsrLhAKhYsXLx448D4Tj1DrmM5dIiIpNgACAKi11MsTyOl0mi2aNYnBr07kOo5XwQi8m1lSM0qSzzEiofK5oaUu8RXywBH9NGsS1SsTQr94FydXAIA7CYIUFISPWMBzVa0B9Ntvv1VwVS6XR0VF+fv7P1wk8DjWrFz7LTVfESBq3IDrLAAA8CBKjp01X7rCl8ucZovh0Cn5gF7iFo25DuUlMALvbixbtDyeWDZwRD9BsLLs9YCnehv2HXOdxBwwMNr9AQEAADxT1RpA/fv3r6Ec4MlMp1PIdf7X3zP2AABQi7BWm2b1FiJSPDvEXqjWJewqWvpr6OK38F/1SsIIvEfR7z1izcgSBCsDh8SUW8AI+MoJw24u/p92/Q7/Hp34cpmbEwIAAHimaj4FDLyMvVCjXhFvzy8kzH8BANRausS99ptFokbh8pjHWJu95I8T1owswx8nbp+dBPeDEXjP4TSZtRuSiEg5cQQjFt2rTNqlnaR9K9O5y7qNO1VTRrsxIAAAgOeqqAGUnp7+wK/btGnT+xeBx1OviDceP08MMXy+X1RLruMAAECVOdRa3ZY9RKSaPJJ4PEYsUjwzpPDbVZo1W6TdongSnFl+fxiB9xzajUkObbG4ZRP/R9tXXKl6fmTurIXFuw7J+nYXRYS6Jx4AAIAnq6gB1KxZswd+XZZlH/he8BDmP9OMx88TEbHED1LglwQAgNpIvTqRtVj9H+vg17q5a0XWq4thzxFzarpu827ls6X30IWyMALvIRxqrX7nH0RkSc24PvqVSt6lXbcdR8IDAAAQEa96X04ul0dGRkZGRlbvy4L7sQ6nekU8EQnrBhORQ61zqHVchwIAgKqxpF0rOXyKEQqVE4b9s8owqskjiWGKt/5uy7vFXTqAKuLx+IqAqt3CMHxlFW8BAADwUhU9AaTVassuxsXFvfHGGwzDjBo16tlnn23SpIlKpcrIyPjtt9/++9//mkym119/fcqUKTUWGNzEsOewNStXUDeYhAIiYu12zS9bg1+ZwHUuAACoNJZVuw5LerqPoE7Q3VdETSJkvboa/jiuWZNY562pXAX0TBiB91h8RUD4kvlcpwAAAKitKmoABQYGllo5fvz4zJkzxWLx3r17H3/88TvroaGhPXr0mDhxYteuXadPn/7II4/06NGjRvKCWzhLjK4dFhWjBhT+8AsjEhJLhoMnZH0f92uJt7YAALWD4cBxy9VMvkoROLxv2avKCcOMJ84bj58znb8siWrl/ngeCyPwAAAA4JWqNgL26aefOhyO+fPn3939uaN58+affPKJw+FYvHhxNcUDbmg37HDoDX5tWzAiEbGsX+tmAUN6E8uqlycQ3toCANQGTrNFs24bEakmPF3uYUl8hdzVGFKvSGAdTnfn8y4YgQcAAADPV7UG0OHDh4moT58+9yp48skniSg5OfkhYwGHbDfy9bsOE4+nen6kOSWNiPwiWwSO6MdXBVozsgwHT3AdEAAA7k+XsMuh1olbNPLv0fleNQFD+wjrh9hu5Bv2HHZnNg+nLc/XX3/N4/H4fP7YsWMTExP//PPPnJycQ4cOvf/++/7+/q4R+D///JPr7AAAAAD3VLUGkGtXoAoeb7ZarUSk1+sfMhZwSL0ygXU45P16iBqGmVP+IiK/yBY8P7HyuaFEpFm9xWkyc50RAAAqYi8oLN6+nxhGNXk0Mcy9yhgBXzn+aSLSrN/u1Je4MaBHCywjNTXVNQJ/8ODB9evXDx06NDIy0jX/vmDBgrNnz8pksunTp7s+JwP3sxdqsqe+V/j9Gq6DAAAAeLSK9gAqKzQ09Pr16/v372/fvn25Bfv27SOi8PDwaogGXDCe/NN07jLPX6oYO8ih1tnybvGkEnGTBkQk69VV/9shy5Xrui17lM8M4TopAADck3rVZtZmE9QJMqekuZ7lrADPX+o0GLW/7lRNHuWeeLVOZUbgZ8yYsXjxYuyByAnN6s3APpAuAAAgAElEQVQObbFhf7KsV1e/ti24jgO1m+nCX4JghTC0LtdBAACqX9UaQAMHDvzxxx//7//+r2/fvmUH3S9evPjBBx+4yqotILgRa3doVm8mIsXYQXy5zPDHCSIShtZxmi08qYQYRjVpZN77XxZv/V325KPCeiFc5wUAgHI4zRbjiQtEZL9ZpFmTWMm7DAdPoAF0LxiB92Tm1PSSo2ddX6tXxId+/g7x/nnC3bA/WdQwVNQkgqN0UMuYL6cXzP+OrwwI+/oDnsSP6zgAANWsag2gjz76aNu2bTdu3OjatevLL788atSo5s2bE9GVK1cSEhK+/fZbk8kUHh7+4Ycf1khYqGHF2/fbcm8Kw+vJ+/ckIteHxparmYXfrq7z9jQiErdoLOvVxfDHCc2axDpv4thgAABPxPMTh7w20Xo9p0p3+bV+8KOvvB5G4D0Xy2pWJBDLBo7obzx2xpqVq999WD6gl+tiyaGThd+v4QfKwr77EL/Mw/2xrHr5r8SyDrVOt2m3a/cDAABvUrUGUHBw8Pr165966imtVvvZZ5999tlnpQqUSuXGjRuDgoKqLyG4iUOn123aRUSqiSMYPp+IzH/+5bpkPHnBdD5VEtWSiJTPDTUeP29MPndnBQAAPI1/j84V7P0MVYUReI+l//2oJT1LEKRQjOwvbtrg5mdLNeu3+3fvxJP7sxarOm4TETl0Bt2vO5Wxw7kOC55O//tR67Ub/ACZQ19SvH2fvM/jgrrBXIcCAKhOVdsEmoi6d++enp7++uuvy+Xyu9cVCsXbb7+dkZHx2GOPVV88cB/N2q1Oo0naua2kQ2sist8sshdqiIgvl9HtnaGdRMRXKQKH97t7BQAAwLu5Ztv/7//+LyUlpexVDx+B1+l0x48fX7du3c8///zf//53yZIla9asOXLkiEaj4Traw3KazNr1O4hIGTuCEYuk3dpLolq5NrQiIt3mPQ7t7Weyirfvt+Xd4jIreLw7f51UU8fIoruyNrt61WauQwEAVLOqPQHkolKpvvrqq6+++io3NzctLU0kEjVv3jwkBDvC1GLW6zmGfclEZM3KzZ39KRE5NDoiImJ4gXKn2WLLzrsxYy4/UE5EZLcTkS07z3AgWR5TznaYAAAA3qQ2jsDb7falS5euWrUqOTm53OE1hmG6desWGxs7ZcoUkUjk/oQPT/frToe2WPxIE//HO7hWVJNG5sz6pHjXQUmnSF3iHiIikYCsdtbpVK/4te57M7iMC55NuzHJoS0Wt2zi/1gHv9bNjcfPG4+fN52/LIlqxXU0AIBq8yANoDtCQ0NDQ0OrKwpwyF5wi1iWiOw3i+hm0V1XWNuNPNdXjiKNo+hfnxbac2+6LyIAAABHat0IfG5ubt++fS9dunT3IsMw/v7+NpvNYrEQEcuyycnJycnJX3311datW1u2rGVj3fb8wuKdf7hOqCCGcS0Kw+vJ+/TQ7zpY+PVK1mYnouDpz+p3HbakZZjOXDKdveR6zBmgFFv+Lf1vB4lhVJNGEcPwFfLA4f00a7eqVySEfvEew6/yzAQAgGd6qAYQeA1pt/Zh/53DWq2ub41nLroegg1+NVYUXo+I1Ms2mv+6LuveMeDpv89AYXjCCLT/AADAJ7hG4OfPn79s2bK7N3tWKBTTp09/5513FAoFh/HuZjKZhg0bdunSJaFQOHDgwKFDh3bv3j04OFipVPL5fCKyWq3Z2dlHjhzZsWPH9u3br1y50rt37wsXLgQH16btTtQr4lmbXR7zuLhZw7vXlc88VXLopKPYQESiJg1kvbqIGobmvvUpsc6iZRvDvv7AtdEhwN3Uy+NZm13et7u46e0D4wKGxhj2H7PdyDfs+WdbcQCA2q6ifrZQKBQKhY8++miplcqo+eRQzYTh9URNIkRNIoRh9fS7DhMRT+In69nFtRj04niGzzccO0cCgWtF1Dgcn4cAAIDvcI3AFxcX5+Tk7N+//8iRIzdv3tRoNIsWLfKc7g8RLViw4OTJkyqVas+ePYmJiVOmTGnZsmVwcDD/78aHSCRq2rRpbGzshg0bkpOTIyIi8vLyFi9ezG3sKjFd+Mt4OoUn8VOMG1zqEs9fwohvT7S5nuYQNQqXPfkoEdnzC13vcADuZrrwl+nMRZ7ETzH2n79OjICvHP80EWnWb3fqS7hLBwBQnSp6AshutxORw+EotQLeTbtpl2sDIEnHNv96prpfj+Kdf6hXxNeb+yqnAQEAALjk4SPw8fHxRPTxxx9HR0fft7ht27Y//fTTwIEDExMTy462VYlWqx03blxhYWEl62/cuEFEBQUFnTuXPrGOYZh33313xIgRdy/m5+c/99xzOp2OWLLl5LNWK1+l5Pc9UKpev/eI623MLbNx4rhRJUI+EbEOhy0rj1gn7Y8XRoTyBPyKXr+M++RBfa2uZ8mWk/9i43bPvvcmXxHwr/r3ZhWmXmVNJt6BBEGw0kPzox71qK/5+rLrtRh7bwcOHDhw4MDp06fvrByutApeFkp58803iSgsLIzrICzLsrZb6uvPvnFt5EvXRr5UvPtff44OQ0nW829fG/lSyYnzXMUDAAAOqVQqIioqKuI6CFRELBYT0eXLlytZbzQa+Xy+n5/fQ/7cy5cv86tvuuqNN94o9fonTpxg/v5c6l71DqMpc+Jb10a+dH3sa4m9R9+z+kFfH/XeWv9C1GNOq81z8qAe9aj3qHpvev/DsOUdDAHu9NZbb33++edhYWGuj8K4deuLZSXHzjIiAWu1h307V1j/X4e7Fe/8Q73sV0Hd4LD/zmGE2EAKAMC3BAUFqdXqoqIi1zshb+WaZO/UqVNycvLdK5Vhs9lqKlal1a1b9+bNm9u3bx88uPR4VLlSU1NbtWpVt27d/Pz8h/zR169fLyoqun8dERF9/fXXq1evrlOnTlJSUqlLPB6vbdu2AkHpdxrp6enq3Pybn/3PWWJWPT/Cr3WzUvXqFQnFO/YTkfKZpyxXMi//cdje/hHFqIFExDqdtxb/bNfoiKG6Myd37BdT7utrtdqyUSvIg/paXe80mm9++j8ymXt98p68a1S59brNu0uOnRM3axg0bayn5Uc96lHvnvq6det6zfuf6vkdnmXZrKyskJAQqVRaLS8InDCnppcknyOhgLXa+arAUt0fIgro39Ow54g1K7d4x/7AYX05CQkAAFCjavsIfK9eveLj47/55pvevXtLJJKKix0Oh2vyq2vXrg//oxs1atSoUaNKFtetW5eIhEJhp06dKnlL06ZNFftOh4jkflHt64wY8q9rFqslO1f/2x9EJFAGyGIe84tq1fDsRbpaEKoMETWJICLTu5KChUuISHzskmBQ/3Jfv5JJUO8d9UU/rQ8RyyVdOpft/typdz7S6sYr85xFJXVYkbRz2xrNg3rUo7621NdeD9IA2rdv38GDB2fOnBkQEEBEJ0+eHD16dGZmplAo7N+//6pVq5RK5X1fBDwOy6qXJxDLStq2NJ1JkbR9pJwaHk81aVT+vG90Cbtk0V35ykC3pwQAAKhZBw4cICK5XH5n5fDh2rRz8Jw5c5KSknbv3h0dHf3hhx/27t3bz8+vbJnNZjt06NCiRYv27NnDMMxrr73m/qhVZb+l1u85TETmy+lZE2ffs0xTnD31/TvfatZtr/v+DCKSdIqUtHvEdOEvy+V044kL0q7t3JAZPJYtO0+/7yjxeapJoyoo48mkilED1Svi1SsTJFGt8Ag8ANRqVf5P2LRp037++WcievHFFwMCAoxGo6v7Q0Q2m2379u29evU6e/Zs2aeqwMPp9x6xZmQJgpU8sYCIxH8/Vl2KX9sW0i7tjCcvaNZuC35pvHszAgAA1Liyeyd3796dkyQPJioqauXKlWPHjj158uTgwYOlUmnnzp1DQkKCgoLkcrnBYFCr1YWFhadPny4uLnbdMn/+/JiYGG5jVwbjJxKG1XWoy9mnk3U4WLOFiIjP5/mJ774kivhnx27VlDG5b3zMOp1FSzdKOrRi/j3cx1qshUvW+rVuJu/bo0b+B4AnKVq6kRxOYXg94+kUOp1SQSVrdzACvj2/sHjnH4FDa8H/pwAA3EvV2jSbN292dX+aN28uEomIKD4+PjMzs379+uvWrSsuLp48eXJKSsq6desmTJhQI3mhZjhLTNp124lI2KC+6XwqEdlu5Ou27Cm3mK+QE5HhwHH5gF7iphHuzAkAAOA5PHYEfvTo0SqVatasWefPnzcajQcPHrxXZdu2bRcuXFjJ3YI4x5fLwr56v+w663DkvPqR3WwhhmTRXfgB8lIFmjWJd74WNg63pGc51Nri7fsDh/e7u0y3ZU/JoVPGY2f9Ih8pOwgP3sReUGi+eIWIbDfy7/7rUTHDvmNoAAFArVa1BtAPP/xARJMmTVq2bJlro+xt27YR0dtvv+36uOyTTz6ZNm3a6tWr0QCqXYwnLziKDURkOnvJtVK8bd997mFZw+9H0QACAAAfUbtG4GNiYs6dO3f+/Pldu3alpKRcu3bNYDAYjUaJRCKXyxs3btymTZt+/fp16NCB66TVwHYj315QRETEkmFf8n3rXWe9GPYl390AshdqdFt/JyLW7tDEbarzzvSaCQseQVAnSDV5VLlPk1XAL7JFDeUBAHCPqjWA0tLSiGjmzJl3jkk7dOgQEQ0YMMD1bZ8+fYgoIyOjOjNCzZN2bqsYNZC12205BcaTFwR1g/wf63ifexhGFl0NG0YCAAB4vlo6Ah8VFRUVVc7utl6G4fEe5Dax6O7vNKs3sxarpENrS2qG8dSfpnOXJe1bVU8+8EAMEzDoCa5DAAC4W9Xepty8eZOIQkNvj1JnZmYWFBQEBwc/8sjtDYMVCgUR5eTkVGvI6qHT6VJTUzMyMgwGQ0lJiZ+fn0wma9y4cevWrT3qIztO8GRSxbjBRKRemUBEsicfdZ2ZCgAAABiB93CCesHy/j2dxYYq3XX3JtCWvzJKjp5lRMKgaeNKjp7RrN6iXpkQ+sW7DJ9f3WEBAAA4U7UGUIMGDa5cuZKfn69SqYho//79RNSjxz/75F2/fp2I6tSpU50ZH47dbl+6dOmqVauSk5NZli1bwDBMt27dYmNjp0yZ4npX58ssf10jInGLJlwHAQAA8BQYgfdwjFAY9MLYB7+fZdXL44llA4f3E4SoAgY/afj9qO1Gvn7XITwkAgAA3qRqT8y2atWKiFatWkVELMsuX76ciAYNGnSnYO3atUQUEeEp+8Lk5uZGRUW9+OKLx44du9P9YRhGJpOJxbdPiGBZNjk5ecaMGZGRkampqdyF5R5rs1uv3yCGETfzlD9BAAAAzmEE3rvpfz9qSc8SBClc+/syAr5ywnAi0m5Icuir9lQRAACAJ6taA+jVV18lok8//XTUqFH9+vU7dOiQn5/fkCFDiCgtLe299977+uuviWjMmDE1kbWqTCbTsGHDLl26JBQKhw4dunTp0suXL9+6dctms+n1erPZbLFYrl69GhcXN2bMGKlUeuXKld69excWFnIdnDPW9CzWZhdFhPKkEq6zAAAAeIpaPQIPFXOazNr1O4hIGTuC+XtXIGmXtpIOrZ0lRu36JE7TAQAAVKeqNYBiYmKmTZvGsmxCQsLevXuJaNasWfXq1SOi1atXL1y40GaztWjR4oUXXqiRsFW0YMGCkydPqlSqPXv2JCYmTpkypWXLlsHBwfy/x7lFIlHTpk1jY2M3bNiQnJwcERGRl5e3ePFibmNzyPxXBhGJH2nMdRAAAAAP0qBBAyLKz893fVsrRuChkrS/7nRoi8WPNPF//F8HoqkmjmD4fP2ew9ZM9PUAAMBLVPnQhJ9++mnt2rXjx49/+umnlyxZsmDBgjuX6tevP3ny5JMnT/r5+VVryAcUHx9PRB9//LFrPr9ibdu2/emnn4goMTGxxpN5KkvaNUIDCAAA4N9q3Qg8VJI9v1C/8w9iGNXkUfT3fJ+LMLyevH8Pcjpd52MAADwMXeLeksOnuE4BUMVNoF2eeeaZZ555ptTinDlz5s+fXx2Rqo3rcNYnnniikvXR0dF8Pj87O7sGM3k2S9p1wg7QAAAA//bqq69u3br1008/vXr1qk6nKzUCv3LlSo8agYfKU6+IZ212eczj4qblNO8UYweXHDpt/jPNePy8tFuU++MBgHcwHj+nWb2F4fNFjcKF4fW4jgM+rcpPAN3LnT2VPUdgYCARpaenV7I+MzPT4XC47vJB9ptFDo2OL5cJ6wVznQUAAMCD1K4ReKgk04W/jKdTeBI/xbjB5Rbw/KWKsYOISL1qM2uzuTcdAHgJ1mbXrE4kItbhUC//les44OsesAGk1+uTkpI+/PDDl156ad68eUSUk5NjtVqrNdvD6tWrFxF98803JpPpvsUOh+Ozzz4joq5du9Z4Mo90+wD4RxqXegQaAAAAatEIPFQG63BqViYQUeDogXzlPT/8k/frIWoYZi8oLN62343pAMB7FG/bZ8u/JQyvx/OXmC78ZTqdwnUi8GkP0gD68ccfIyIiBg8ePG/evB9++GHLli1EtH79+rCwsC+//LK6Ez64OXPmSKXS3bt3R0dHJyUlmc3mcstsNtu+ffsGDhy4fPlyhmFee+01N+f0ELc3AGqBDYAAAADK8cwzz6xevXrLli3Tp0+/szhnzpzc3Nxly5YFBARwmA2qSv/bQWtWrqBecMDACneK5PFUz48kIt2mXQ61zk3hAMBbOLR63ebdRKSaNEoxehARqVcksDY717nAd1V5D6CPPvpo7ty5RMTj8Zo0aXL16lXXOo/HKywsnDVrVk5OzhdffFHNMR9IVFTUypUrx44de/LkycGDB0ul0s6dO4eEhAQFBcnlcoPBoFarCwsLT58+XVxc7Lpl/vz5MTEx3Mbmivkv7AANAABQNR44Ag/35TSatBuTiIg1W/Per8SHl3ye02zRbtgR9OKzNR4OALyIZvUWp8ks7dZeEtXSL7KF/vejtuw8/c4/Aob66K+cwLmqNYBOnTrl6v7ExsZ+8cUXwcHBzN/jQi+//LLJZHr//fe//PLLZ599tlOnTtUftupGjx6tUqlmzZp1/vx5o9F48ODBe1W2bdt24cKFgweXPwTu9ViL1Zp5g/g8UXmbIAIAAAAR6fX6Q4cOnThx4tatW3Xq1Jk7d25OTk5ISIhIJOI6GlSB02BkzRYicmiLHdriSt5lL9TUZCgA8DbWjCzDwROMgK8c/zQRMXyeatKogo++1f66079XF74Cz40CB6rWAHIdcjF48OC4uLhSl4RC4XvvvZebm/v9999/8cUXrvNQPUFMTMy5c+fOnz+/a9eulJSUa9euGQwGo9EokUjkcnnjxo3btGnTr1+/Dh06cJ2US5armeRwipo04Pnhk0wAAIBy/Pjjj++9955Wq3V92759+7lz565fv37RokXvvvvuzJkzuY0HlSeoExS+5COHpiojXQxPGF63xhKBp3AajHkffCVuEhH8ygSus0Atx7JFy+OJZQOG9hHWD3GtSdo9IukUaTqdol2/I+g/pY/VBnCDqjWAkpOTiejNN9+8V8ELL7zw/fffX7hw4WFzVbeoqKioKDed32m327/44guNprIfE7meS7JxerqEawdov0dwADwAAEA5atEIPFQGXxlYwd7P4LO0G3bYsvNs2Xn+3TtKOrbhOg7UYoaDJyypGXyFPHB437vXVZNG5l5I1f9+VNa3uxizF+B2VWsAZWdnE1HLli3vVdCgQQMiunbt2kPGqtXOnz//zjvvVPUug8FQE2EqCTtAAwAA3EutG4EHgAdgy84r3n3I9bV65abQqJYMn89tJKilWItVu247ESknDONJ/nVApLBeiHxAr+Jt+9Qr4uvPfwPnL4ObVa0BFBgYePPmzezs7Hr16pVbcOnSJSJq2LBhNUSrPllZWceOHUtJSalXr150dHRkZGS5ZZmZma4TzR7yILCOHTuuWbPmxo0blaxPSko6ePAgl6eHsOztBhB2gAYAACijNo7AA0BVqVduIodT3reH+eIVW26BfucfAU/15joU1ErahF32Qo2oSYSsV9eyVxVjBpUcOmVJzSg5dtb/8Y7ujwe+rGoNoK5du27fvn3NmjVdunQpt2Dbtm1E5LZhq/uy2+3z5s1buHChw+FwrTAMM2LEiGXLlgUGln7uNzU19fXXX6eHbgAxDPPcc89Vvr6wsPDgwYN87j5hsOUXOooNfIVcUCeIqwwAAAAeq/aOwANAJRmPnzedv8zzlyqfHWK5cr3gkx+1G5L8e3TGTr1QVfabRcXb9hHDqCaPLPcBH57ETzF2cNFP6zSrNks7RTJiHCMA7sOrUvXrr7/OMMx33333888/l726bdu2zz//nIiGDx9ePeke2rhx4xYsWOBwOBiGiYiI8Pf3Z1k2ISEhOjq6qKiI63SewpKWQURibAAEAABQHozAA3g31u7QrNlCRMpnnuLJ/SUd20g6tHaazNoNSVxHg9pHvWoza7PJenXxa9n0XjXyPo+LmkTYCzW6bb+7MxtA1RpAMTExs2fPdjqd06ZN69Sp09tvv01EarX6q6++Gj169LBhw5xO5+jRo8eMGVMzaatm586dCQkJRDR9+vSioqLMzEytVrts2TJ/f//z58//5z//4Tqgp3DtAI0NgAAAAMrlemrY1QYql2eOwANAJRVv+92Wd0sYXk/Wt4drRTVlNCPg6/cesWZkcZsNahdzSpox+RwjFimeGVJRHcMETR5FDKPbtNteWNmzgwAeXtVGwIhowYIFEonks88+O3PmzJkzZ4goKyvLdfQpwzATJkz45ptvqj/mA/nxxx+JaMSIEUuWLHGtCASCyZMnN2vWrHfv3vHx8QkJCSNHjuQ0o0ewpF0nbAAEAABwD7VuBB4AKs+h1es27SYi1fMjGf7tT8eF9ULkA6KLt+8rWo6deqHSWFa9MoGIeP5STdym+5bzxCKn2aJduzX41Yk1Hw6A6AEaQAKBYO7cuS+88MKSJUsuXryYlpam1WpbtGjRsmXL8ePHP/bYYzWR8sGkpqYS0RtvvFFqvVevXm+++ebixYtnz549ZMgQkcinpy5Zm82WnUd8nrhxA66zAAAAeKLXX399x44d3333XWRk5AsvvFDqqgeOwANA5Wl+SXSazNKu7STtW929rhg7qOTwKUtqRknyOf/HOnAVD2oRZ4nJmp1HRA61tuTY2UreZU7NqMlQAP9S5QaQS2ho6EcffVS9UapdVlYWEbVo0aLspTlz5qxatSojI+P7778v2yHyKdbMXNbhEEWEYvsxAACAcrlG4BcvXjxt2rQlS5b06dOH/h6BP3r06KZNmzxqBB4AKs+akW04cJwR8JUTSjdweRI/xdhBRT+t18RtknZsg7fKcF88mTR04Vu2gsIq3SVugo/hwX0esAFUMbvdLhDUyCtXiVKpzM/Pz8nJqVOnTqlLMpls0aJFEydOnDdv3pgxY8LCwjhJ6Ams6VlEJMJ/dwAAAO6tFo3AA0Blsax6eTyxbMCQGGH9kLLX5X266/cctWZk6bbtU4wa4P6AUOuImjTAL1bgySq1CbTD4UhLSzt48GBGxn2eTzObzYmJiWUbLpxo164dEX3++ecsy5a9Ghsb27dvX51ON3ny5DuHxPsg67UbhAYQAABAhVwj8GlpaR988MGIESMiIyPDw8N79+49Y8aMI0eOrFq1SqFQcJ0RAKrGcOiUOTWdr5AHjuhXfsXfJ3nrNmOnXgDwBvd5TsdsNr/77rs//vijxWJxrXTo0GHJkiVdu3YlogsXLsTFxZ0+fVqn0+l0upKSksLCQqfTWeOpK2f8+PG7d+9eu3atWCz++uuv5XJ5qYKff/65bdu2u3fvjo2N/d///sdJSM5ZMvAEEAAAQKXUihF4AKgM1mLVrt1KRMrnnuZJ/O5V5teyqf+j7UuOncVOvQDgBSpqALEsGxMTc/To0bsXz549GxMTc+TIkYKCgqFDh5rN5hpO+ODGjx+/cuXKffv2rVixYt26de3atXv++edffPHFOwUNGzaMi4sbN27c2rVrDxw40KpVqwpezSuxdoctO48YRtTQd4fgAAAAqoWHjMADQGXoNu+xF2pEEaHSzm2dJcYKKgNH9DeeuGA4dEo+oJe4BY7NBYBarKK3KRs2bHB1fxo1avTCCy80a9YsPz9/48aNR44cmTp1qkajMZvNUql08ODBDRs2lEgkdrtdoVCEhYU98cQTbopfIYZhtm/fPmPGjNWrV5vN5hMnTnTs2LFUzfDhw5OSkkaNGpWbm5ubm8tJTg7ZsnJZm10YXq+Czz0AAAB8k8PhSE9Pz8/PDw8Pb9KkSQWVZrN5165dkyZNUqvVbosHAA/MqS/Rbd1LRNas3KxJb1fyLs0vW+vNe60mcwEA1KyKGkBxcXFE1Lp16+Tk5DvzU6+88srIkSM3b95MRE2aNDl69GjdunXdEPTBSCSSFStWzJs3b8uWLVlZWW3atClbExMTk52dHRcXt27duqtXrxYUFLg/J1cs17KJSIQD4AEAAO5Sq0fgAeD+eDxBnSCHprhKNwlCVDUUBwDAPSpqAKWnpxPRzJkz7949h2GY2bNnuxpA77//vid3f+6IiIh49dVXKyiQyWQvvfTSSy+9REQlJSXuysU9a0Y24ehBAACAu9T2EXgAuC+evyTsv3O4TgEA4G4VNYAyMzOJqFmzZqXWW7Ro4fqidevWNRSLQ/7+/lxHcB+cAQ8AAFBKbR+BBwAAAChXRQ0gq9VKRIGBgaXWVarbTz9KJJIaigVuwDqc1qxcYhhRo3CuswAAAHgKLxiBBwAAACjr/mdVMAzjhhzgfrYbeazVJqwfwvNHIw8AAOA2rxmBz8rKOnbsWEpKSr169aKjoyMjI8sty8zM3LJlCxG99hp2twUAAPBmOKzUd7k2AML8FwAAwN28YATebrfPmzdv4cKFDofDtcIwzIgRI5YtW1b2ye7U1NTXX3+d0AACAADwdqfwP7gAACAASURBVDyuAwBnrLePAIvgOggAAIAH8YIR+HHjxi1YsMDhcDAMExER4e/vz7JsQkJCdHR0UVER1+k8mjklzX4T/ycCAADvhAaQ77LgCSAAAIB7qL0j8Dt37kxISCCi6dOnFxUVZWZmarXaZcuW+fv7nz9//j//+Q/XAT2X6dzl/A+/yf/oW9Zm4zqLN3CWmHJnL1Yvj+c6CAAA3IYGkK9iWdv1HCISN8YO0AAAAN7jxx9/JKIRI0YsWbJEqVQSkUAgmDx5clJSEp/Pj4+Pd7WHoBTW4VCvTCAie35h8bb9XMfxBtpfk6wZ2cVJB8yXrnKdBQAAiCrTAOrZs6eyjAou3V0AHsuWU+A0WwR1gnhyHzr2HgAAwOulpqYS0RtvvFFqvVevXm+++SYRzZ492zXmBnfT/3bIdiOfHyAjIt2mXQ6NjutEtZvtRr5+50HX1+rl8eR0cpsHAACoMg0gvV6vLaOCS3cXgMfC/BcAAIBXysrKoru2rL7bnDlz6tevn5GR8f3337s9l0dzGoza+J1EFPTSeGm3KKfZovllK9ehajd13CbW4ZA90U0QorJev6Hfd4zrRAAAUOEpYO+8847bcoCbuY4AE6MBBAAA4F2USmV+fn5OTk6dOnVKXZLJZIsWLZo4ceK8efPGjBkTFhbGSUIPpFm71akvkbR7RNopUhRez3TmkuGPE/IBvcTNGnIdrVYynk4xnb3E85coJw43X0i79dVy7brt/o915Pl7+gbqAADeraIG0MKFC92WA9zs9hnwjdEAAgAAKEfPnj35fH5VL2k0mpoMVSnt2rXLz8///PPP16xZU3Yr69jY2DVr1uzZs+fOrkCchPQotuw8/e9Hic9TPj+SiAR1gwOeelK3ebd6+a/1P55FtXY7cK6wdocmbjMRKUYP4stl/t076ncdMl+6ok34TRU7nOt0AAA+DZtA+ySWtV6/QWgAAQAA3EPtHYEfP348Ea1du3bKlCl6vb5swc8//yyXy3fv3h0bG1tSUuL2gB5HvTKBHM6AAb1EEaGulcCR/fmqQEvadcOhU9xmq42Kkw7YcguEYXXlA3u5VlSTRxKPp99xwJZbwG02AAAfV9ETQOCtbHm3nEaTIEjBV8i5zgIAAOBZavsI/Pjx41euXLlv374VK1asW7euXbt2zz///IsvvninoGHDhnFxcePGjVu7du2BAwdatWpVLT83Ly+vZ8+earW6kvVGo5GIOO9AGZPPmc6n8mRSxaiBdxZ5fmLlM0MKv1+jWZMo7dqO5yfmMGHt4tDpdfG/EZHq+ZHM38+XiRqFy598VP/7UU3c5jrv/ofTgAAAPg0NIF9kvebaATqC6yAAAAAep7aPwDMMs3379hkzZqxevdpsNp84caJjx46laoYPH56UlDRq1Kjc3Nzc3Nxq+blWq7WwsFCnq9rhWU5OD4dibXbNmkQiUj4zpNS5qLInuul3HbJczSzeskcx7imOAtY+2rXbnEaTtFOkpEPru9cVzw4pOXbWtTdQqUsAAOA2GAHzRX9vABTOdRAAAACofhKJZMWKFRkZGV9//fWsWbO6du1atiYmJiY7O/u7777r3r173bp1H/6HNmzYsKCgQF1pr7zyChHJ5Vw+jFy87Xdb/i1hg/qyPt1LX2MY1eRRxDC6xN/tN4u4SFf7WK/f0O9PZgR85cTSe/3wA+WBowYQkXplAutwcJEOAADwBJBPun0GfFM8AQQAAOC1IiIiXn311QoKZDLZSy+99NJLL1E1jWKJxWKxuLLTUpWvrCEOrV63eQ/dHlYq5zNRcYvG/j06lxw6qVm9JWTWFLcHrH3UyxPI6ZQ/FSMMLaelGDDoCcPeo7acAv3OgwFPPen+eAAAgCeAfA/LukbAxNgBGgAAAIiIyN/f//5F3kWzeovTZJZ2ay+JanmvGtWEpxmxqOTYWfPFK+7MVhuVHDljvnSFHyhXjBxQbsGdJ4O0vyY59Ab3pgMAACI0gHyQ/ZbaaTDyFXK+KpDrLAAAAOBu77777vz587lOwTFrRpbh4AlGwFeOf7qCMr5KETi8LxGpV8QTp9sVeTjWatP8kkhEimee4vlL7lXm2hvIWWLSrt/hxnRQUyxp12z5t7hOAQBVgBEwn2PNzCUiUSNsAAQAAOCLFi1aFBwc/MEHH3AdhDssW7R0I7GsMKye8fi5+xQ7WeLxrNdz9L8fk/cts1UQEBGRbsse+80iUaNwee/HKq5UTRyRe+Ev/Z4j8r49RI3C3BMPaoI1Iytvzlf8QHnYNx/wJH5cxwGASkEDyOfYsnKJSBQRynUQAAAAAA5Ys/MsadeJyJqZY83MqeRdht+PogFULqe+RJe4l4is129cH1PRtlN306zdWve9F2syF9Qkli1aHk9Op0Oj0yX8phw/jOtAAFApaAD5HGtWLhEJ0QACAAAAnyQKr6ecMMypr9q+1xVsFeTjWJblSfwcFmuV7uKJRTWUB9yg5MhpS2oGz1/qNJqKdxyQ9ekurBfCdSgAuD80gHyOLTuPiEQR9bkOAgAAAMAFHi/w6T5ch/Ae/ABZg6WfcJ0C3Ie12jS/bCUi1fMjzJeuGvYna+I213l7Gte5AOD+sAm0b2EdDltuATGMMKwe11kAAAAAAKCW0W3ebb+lFjUOlz3RTfnc0zypxHjyguncZa5zAcD9oQHkQxzFBstf11i7Q1gvmMFjtwAAAD7p0Ucf7dSpE9cpAKBWshdqdFt/J4ZRTR5NDMNXyANH9CMi9coE1uHgOh0A3AdGwHyF01By4z8fkNNB2AAIAADAhx07dozrCABQW2lWbWYtVv+enf1aNXWtBDzV27DvmO1Gvn734YCB0dzGA4CK4QkgX3HrvytYq421OwlHgAEAAAAAQBVZUjNKjp1lRELls0PvLDICvnLCMCLSrt/h0Bu4SwcA94cGkE+wpGeZzqX+870QT34BAAAAAEClsax6RTyxbODwfoIQ1d1XpF3aSdq3cpYYdRt3cpUOACoDDSCfcOur5UTED5QTj0dE+p2HuE4EAAAAAAC1hn7vUUt6liBYGTg0puxV1fMjGT6/eNcha1au+7MBQCWhAeT99L8ftecXEpFq2lhiWSKya7T63Ye5zgUAAAAAALWA02TWbthBRMrY4eUeJiMMryfv14OcTvWKeLenA4DKQgPIy7F2h2ZlAhGJmzYUBCmJZRmxkCFSr9rE2uxcpwMAAAAAAE+n/XWnQ1ssfqSJ/2Md7lWjGDeYL5eZ/0wznrzgzmwAUHloAHk59fJfnSYLMUzIm1NsWblEJIlqwzAMa7YWLd3IdToAAAAAAPBotvxb+p1/EMOoJo8ihrlXGc9fGjhmIBGpV+KTZgAPhc2AvZlDo9PvPUpEsuiughCVNSuPiMTNI/hyif73YyX7kxWjBwqClVzHBAAAAAAAD6VZkcDa7MLwetaMbGtGdgWVDMMwYpG9oLB4x/7AYX3dlhAAKgkNIG926/Nl5HTyRKKg6eOIyJadS0TCiNCAITElR047zdZbXy6v/8ksrmMCAAAAAIAncqh1xtMpRGS7kV/007pK3qXfewQNIAAPhAaQ1zJfvmr+K4OIlBOGMkIhEbmeABI1qM8I+MoJw4t+3mBJu2a6kCpp15LjrAAAAAAA4Hn4ygBl7HB73s0q3SXp0KaG8gDAw0ADyGsVfrWSiPjBSvnAJ4jIaTA6NDqexE8QoiIief+eusS99ptFhd+savDzxxVM8wIAAAAAgI9imHLPfQeA2gibQHun4m377WotEYW8Nsm1Ys3MISJhg/p3ej0hMycTMQ5tsS5xL1c5AQAAAAAAAMAN0ADyQqzVplm/jYj8Wjfza9XEtWjNyiUiUUTonTJxs4Z+bZoTkXbDDtZi5SIpAAAAAAAAALgDRsC8UOGStazFSgwjH9DLdOEv16Lp7GUiIj7/zgoRBQzsZb58hbXZC39cG/L681yEBQAAAAAAAIAahwaQ12FZ4+HTri9ufbm81EX9roP6XQfL3lRy5HTIKxOIz3dDQAAAAAAAAABwM4yAeSFG4lfVW3hSP+LhLwMAAABANTOnptsLNVynAAAAwBNA3odhGixfyJotd6/Zi7S5Mz/hy/zDvp9b/k1+YhwEBgAAAFC9zBev5H/4jbBecOiX7zNCvPEGAAAu+dC/QzqdLjU1NSMjw2AwlJSU+Pn5yWSyxo0bt27dWqlUcp2uOjF8PuMvvXvFkXadiISNw3j/XgcAAACAmuJ0qpfHE8va8m4V7zgQOKwP14EAAMCneX8DyG63L126dNWqVcnJySzLli1gGKZbt26xsbFTpkwRiUTuT+gG1szSR4ABAAAAQI3S7z1qzczhyaROg1GX8Jvsia58RQDXoQAAwHd5+bYvubm5UVFRL7744rFjx+50fxiGkclkYrHY9S3LssnJyTNmzIiMjExNTeUubA2yZecRkbBBfa6DAAAAAPgEZ4lRu247EQW/+Ky0S1unyaxZu43rUAAA7mC5mmm7kc91CiiHNzeATCbTsGHDLl26JBQKhw4dunTp0suXL9+6dctms+n1erPZbLFYrl69GhcXN2bMGKlUeuXKld69excWFnIdvPpZs3IITwABAAAAuIt2Y5JDb/CLbCHt1l41cSQjFBj2J1uuZnKdCwCgZtmy8/Lf/zJvzpcOvYHrLFCaNzeAFixYcPLkSZVKtWfPnsTExClTprRs2TI4OJj/92HnIpGoadOmsbGxGzZsSE5OjoiIyMvLW7x4Mbexq5/TacspIIbBE0AAAAAAbmC7ka//7RDxeKrnRxKRoF5wwOAniGVdWwJxnQ4AoAapVyawDofTYNSu38F1FijNmxtA8fHxRPTxxx9HR0fft7ht27Y//fQTESUmJtZ4Mvey5d9irTZBiIpX9ePhAQAAAKCq1HGbWIdD3re7qFGYayVw5AC+MtCSdq3k8GluswEA1BzjyT9N51N5UgnD5+v3HLFm5nCdCP7FmxtAmZmZRPTEE09Usj46OprP52dnZ9dgJi7YsvKISITHfwAAAABqnvF0iunsJZ6/VDFu8J1FnsRP+cxTRKRevYW1WLlLBwBQU1i7Q7N6MxEpxj0l79+TnE71igSuQ8G/eHMDKDAwkIjS09MrWZ+ZmelwOFx3eRMrdoAGAAAAcAvW7tDEbSYixZhBfLns7kuyJx8VN2voUGt1iXs5SgcAUIOKt++z5d4UhteT9++hGDuIL5eZU9KMx89xnQv+4c0NoF69ehHRN998YzKZ7lvscDg+++wzIuratWuNJ3Mv1wbswgb1uA4CAAAA4OWKkw7YcguEYXXlA3qWvsYwqsmjiGF0W/bYbxZxkQ4AoKY4tHrdpt1EpHp+JMPn8/ylirGDiEgdt5m12bhOB7d5cwNozpw5Uql09+7d0dHRSUlJZrO53DKbzbZv376BAwcuX76cYZjXXnvNzTlrmi23gIiEoXW5DgIAAADgzRw6vS7+N/r795+yBeIWjf27d2KtNs0v3rbpJAD4OM0viU6jSdqlnaR9K9eKvF8PUcMw+82i4m37uc0Gdwi4DlCDoqKiVq5cOXbs2JMnTw4ePFgqlXbu3DkkJCQoKEgulxsMBrVaXVhYePr06eLiYtct8+fPj4mJ4TZ2NWNZe94tIhLWr8N1FAAAAABvpl27zWk0STtFSjq0vleNcsIw48kLJUfOyPv39Gvd3J3xAABqiPXaDcOB44yAr5ww7J9VHk81aWT+h9/oNu2SPdGNr/K2vVZqI29uABHR6NGjVSrVrFmzzp8/bzQaDx48eK/Ktm3bLly4cPDgwfcqqKXsap3TbOEHynkyKddZAAAAwCN06NCBiHbt2lWnDj4fqjbW6zf0+5MZAV85cXgFZYIgReDTfbQbk9TLE0I/nU08b34eHwB8Asuql/9KLBvw1JPC0H/9s+IX2ULaLcp4/Lxm7dbglydwFRDu8PIGEBHFxMScO3fu/Pnzu3btSklJuXbtmsFgMBqNEolELpc3bty4TZs2/fr1c70TqhYsyy5fvrywsLCS9SdOnCAih8NRXQHuZr89/4W3dwAAAHDbuXPniMiGTRmqlXp5PDmdwkYR5otXzRevVlDJk0qIz3M1jOQxj7stIQBATSg5fNp8OZ0fKA8c0b/sVVXscNOZS4Y/Tsj79xQ3b+T2dPAv3t8AcomKioqKinLPzzp9+vTUqVOretedMbTqZcvBBkAAwAGz2fzhhx9qNBqug/gugUDwxhtvNGvWjOsg4FYsyzqdzkoW2+32uz9/4pe3Zw1Uku1GvvnSVSKyZmQV/ZRVybsMuw+jAQQAtRprtWnWbiUi5XNDeVJJ2QJB3eCAIU/qNu1Wr4iv//EsYhi3Z4R/+EoDyJ06dOjwxRdf3Lx5s5L1+/fvP3HihFwur4kwttybRCQIQwMIANzq0KFDixcv5jqFr5PL5YsWLeI6BbjVl19++eabb1ayuFGjRnd/y7Js9QfyGYL6dRRjBjk0uirdJe3SrobyAAC4h27LHvsttahxuOzJR+9VEziiv+HAcUvadcOhU7JeXdwZD0pBA6j68fn8mTNnVr7+rbfeOnHihEBQI38WtrybhBEwAHA7u91ORJGRka+88grXWXzRnj174uPjXX8K4FMCA7HFJjcYPk8xZhDXKQAA3MpepNUl7iUi1aRRFTzaw/MTK58ZUvj9Gs2aRGnXdjw/sRszwr+gAeTlMAIGABxq0KDBtGnTuE7hi/R6fXx8PNcpgANTp07l8/mvvvqqwWAICgqaP39+uds8jxo1ioh++umnoKAgt2cEAAAvoVm1mbVYBfWCbTkFrl8874lleVKJQ60t3rJHMe4pdwWE0tAA8maszW4v1DB8vqAu3t4BAAD4hEmTJvXq1Wv8+PHJyckffvjh0qVLhwwZUm7l4MGDw8LC3BwPAAC8g9NkLjl6hojs+YVFP62r5F36PUfQAOIQGkDezJZ3k5xOQWhdBts6AgAA+IymTZseOnRowYIFCxYsGDp06AsvvPDll1/KZDKucwEAgPfgSfxUk0fZsnKrdJdf20dqKA9Uhtc2gIxG4+XLlx/s3k6dOlVvGK7Yc7EBEAAAgC8SCAQffvhh//79x48f//PPP+/bt2/VqlWPP44DpwAAoNoEDIzmOgJUjdc2gC5fvty5c+cHu9drTsGw5bo2AEIDCAAAwBc99thj586de+WVV+Li4nr16vXOO+/MnTtXKBTW0I8zGo0Wi6WSxZWvBAAAgGrhtQ2gxo0bv/zyyxs3bqz8cezex5aDM+ABAAB8mlwuX7ly5aBBg/7zn/98/PHHO3fuXLNmTU38oIyMjMjISJPJVKW7iouLayIMAAAAlOW1DSCVSvXtt99+8sknsbGxW7ZsIaIffvhh0CDfOp7z7yeA0AACAADwaWPGjHn88cdjY2P3799fQ6PuMpmsWbNmN27cqGS963EhgcBr34sCAAB4Gi//R1culy9dunTXrl0mk6lOnToNGzbkOpFb2fKwBxAAAAAQEYWHh+/du/eLL76YM2dOTbx+nTp1Lly4UPn6t9566/PPP5dKpTURBgAAAMricR2gxgUFBfXv35/rFBxw6PROg5HnL+EHyrnOAgAAANzj8XhvvfVWSkrK3r179+7dGxwczHUiAAAAcB8vfwLIpX379q4pMJ9iu30EGOa/AAAA4B/Nmzdv3rw51ykAAADA3XyiATRp0qTOnTt7zeHulYQNgAAAAKCsd999VyqVfvDBB1wHAQAAALfyiQZQREREREQE1ynczZ5TQETCMGwABAAAAP9YtGhRcHAwGkAAAAC+xvv3APJZGAEDAAAAAAAAABc0gLyWawRMgCPAAAAAAAAAAHweGkDeiXU47AVFxDDC+iFcZwEAAAAAAAAAjqEB5J3sBYWswyEIUTFCIddZAAAAAAAAAIBjPrEJtA+y5dwkImEYNgACAACAf3n00UcDAwO5TgEAAADuhgaQtzGduWj+K4Px8yMiITYAAgAAgH87duwY1xEAAACAA2gAeRWHVn/rqxVOk9mveWPCEWAAAAAAAAAAQETYA8jLaNZudZrMRGRJzyQ0gAAAAAAAAACAiNAA8ibWjGzD/mRGwBc3a8g6nYQz4AHAS9lvFmVPeqfwu9VcB3EHtVp9+vRpo9HIdRAAAAAAqN3QAPIe6pUJxLIBg59UTR3jWnFqddxGAgCoCeq4zQ69wXDguDkljessNW7u3LmdO3e+fPky10EAAAAAoHZDA8hLlBw+Zb50lR8oDxzRj7XaXIvqFZuIZbkNBgBQvcwpacbj51xfq1ckkNPJbZ4aZTAYNm7cyHUKAAAAAPAGaAB5A9Zq0/yylYiUzw4t/HZ1wfzviIgRCMyp6SXJ57hOBwBQfZxO9YoEIlKMGSSsF2LNzNHvOcJ1pppSVFQ0YcKEmzdvch0EAAAAALwBGkDeQLdlj/2WWtQ4nCf3N/5/e3ceHmV97n/8ni37JJkkJGEJGIJAkC2QI1RRCggKKKIsWoWIxSOeY8tR4LRCESocFRfcell+IlJFzw8piwiiLBKKIFAgxbAvJpKwBbJvk2Qyk/n98dj80gQwgZDH+T7v19WrF3yf75D7uZ15vP0w88z+Q95qt4gE9uoqIoUfrfFWufQuEACaR+nmna6sc9boyLAHhjom3C8ihcvX15SW611Xc8rOzp43b96DDz4YFxe3du1avcsBAACAIgiAfJ47v6j4869FxDHxgcJln9Wu29rF+N3U1p1XWPLFNv2qA4BmU1PuLFrxpYhEPPaAyWYL6t87sFfXmjJn0aqv9C7t8lwuV3l5k8OpQ4cOzZ0797PPPquoqLgRVQEAAMCYCIB8XuGyNd4qV/CAZFdmdnVOrq1drCU8VETKv01zTBgtIkVrNrnzCvUuEwCuV9Ffv/SUlgV07xzUr7e2EjFpjFjMJRu/cWWf17c2TUZGhslkiouLczqdTz/9tMPhCAkJCQoK6t2799KlS7U9GzduHDJkiMPhsNvtffv2Xbx4cc2/3saob9++q/7pz3/+sx7nAQAAAAVZ9S4A16XqRGb5rgMmP1vofUMuvvCOiEQ89uCl15eIiDuvqPrMhaD+vZ17vitavj7qtyl6FwsA1676bE7pxh1iNkdMGlO7aItrbR9yW+nmnYUfro6Z81sdy6vL4/GMHz9+w4YNImI2mysqKtLT0ydPnpybm1tTUzNr1ixtvaam5h//+MeUKVPOnj07b9682ofHxsaOGfPjOWZlZelyCgAAAFAP7wDyZV5vwdJV4vWGjR5aunF7jbMi6N96+t3U1lvlMgcGikjRX78Me/Buk79f2Tf7Ko9n6F0uAFy7go/WeD0e+9Db/W5qW3fd8cgosz244uAJZ9phvWqr58KFCxs2bEhJScnIyHC5XLt3705ISBCRP/zhD7NmzRo5cuTRo0ddLldaWlr37t1FZMGCBVVVVXpXDQAAAMXxDiAfVpq6uyoj2xoZHtiz64Xn3zRZLY6Jo90X80XE1jbaHBpS8Y8jZVt2ht47qHj1poKlq9u88t9iMuldNQAD83rz31/hzslt6uM8JWWu0+fEYqk+e+HivD/VO2oJCa4pLc9944OAm+PF3MSrnMkUOuKXgX27N7Wkqxs7duxHH32k/bp///4LFy4cPXq0x+Pp16/funXrzGaziPTp02fRokV33HFHdXX1yZMne/To0bw1AIDuKg4ctUZH2trG6F0IAECEAMh31VRUFn26QUQcE0cX/u/n4vWG3jvY1ia67NQPImKNbRX+8MjzB4+Xfr2r9fxnyrfvdWVml32zN2RgP70LB2BcNZVVZX/7u9dVfY2P93gqj3zfcNkrYhLxVlVXHD55DX+qNSaq2QOgadOm1f1t3759a9e19EeTnJys/aKkpKR5CwAA3VUcPHHxpUUWR1jbd543B/jrXQ4AgADIZxWv3ugpLBaTqWj1puozF0xWS9Wp0xfn/Ul7B1DVycyCxZ9awkPdeYUXX/4/5sAAESn833XB/ZNM/n561w7AoMyBAW3emOW+VNCkR5XvPlC2Zac1yhHx1CMm8+U/uVyVeabok7Vmf7/IpyeaQ4KaUJKfza9jXJPqaYxOnTrV/W1gYKD2i8TExLrrAQEBzf6jAeDnwOupKfxwtXi9noKi4jWbHY/cp3dFAAACIJ9VdeIHERGvt/rMBRHxuj2VR07VHnVfKqj9T6ya8oqa8goR8RSWuHMLbO1idSgXAERExBbbyhbbqvH7PcWlua8vEZHIKb8K7J14pW2BPbtUHfu+Iu1w5cHjkU/9qhkKvT7mKwRVfn5E8AAMoWzLTlf2eUt4qKe4tGR9qv2u26zRkXoXBQBGRwDkq1pNf8KVfb5s29/Ld+y1to6OfGK8dn+fgr+sqj5zwfHYg34d2oqIM+1w6YZt5jB71H9OsDrspD8AfEvR8i9qnBUB3Tv7d76pptx5lZ3hY+6uOHC0dOsu+913+MW3a7EKAQD11JQ7C1dsEJHIKQ8796SXbf97wUefRf/3E3rXBQBGRwDkqyzhdps72rn3OzGZov7z0YDEBG29prRcRIJ/kWSNcohIYI/OVccyXJnZrh/OBPW9R8+KAaCJ3BfzSlN3i0jl4ZPZj/2ukY8q+vSL6JlP3ci6AABXU7j8i5rS8oAenYP+raf/zfHOvenOv39XkX48sFdXvUsDAEPja+B9WOGyz7xVruABfWvTH2+Vy1NcarJZrRFhP24ymSJ/PVZMpuLPNrvzCnWrFQCazhQY4BfX2hwc1IT/2YNt//o98QCAllR9Nqdsy7diNkc8PlZELOH2sNFDRaTgw9VeT43e1QGAofEOIF9VeSyjfPcBMZmCf9HHlZmtLVbn5IrXawkPdZ0+W7vT5Gf179Sh6tTpov+7LmrqYzrVCwBNZgkNabNwpt5VAACaoODD1V6PJ3T4QL/2bbSV0FFDyrbtqT5zoezrb+1336FveQBgZARAvqror1+K1ysil15dXO+QO7fg/O9ebfiQsh37w8aNsLVuwu1XYgmMBQAAIABJREFUAQAAgEZy7j1Y8d0xc3BQ2PjhtYsmm9Ux4f5Lry8pXL4++LY+ZnuwjhUCgJERAPmqwF5dte/2qqumuMSdX2QODbFGRTR8iMUebAkNaZHqAAAAYCxet6fw489EJPzhkRb7v8ycQf17B/bqWpF+vGj1xohJY3QqEACMjgDIV4WNHqp9oLqugqUrS77cHv7AsND7ButSFQAAAIypZP3W6gu5tnax9mEDGh6NmDTm3IyXS77aHjL4F7WfDgMAtCQCIKVU5+SJiDUmSu9CAMCIEhISvF5vw/XIyMjLrovIldY1HTp0uPoGAPiZ8BSVFq/ZLCIRk8aYLJaGG2xxre1DbivdvLPww9Uxc37b4gUCAPgWMLW4L+aJiDWWAAgAAAAtp/B/P6+pqAz6t56BvROvtMfxyCizPbji4ImKtMMtWRsAQEMApBCv132pQESs0ZF6lwIAAACjcGWeKfvb301Wi2Pi6KtsM4cEhY+5R0QK/rLaW+1uqeoAAD/iI2DqcBcUe6urLeF2c4C/3rUAAADAGLze/KUrxeu1tYlx7k3/ib1uj8liqc7JLf1qe+ioIS1TIABAQwCkDvfFXBGxRvP5LwAAALSQ6gu5VcczRcSVfd71yeeNfFTZ3/5OAAQALYwASB3ui/kiYuMGQAAAQERE8vPzIyP/5YPhJ0+e3LVrV3p6usPh6N2794ABAyIiIvQqD2qwtW4V8etxnoKiJj0qsNcVbxUEALhBCIDU8eMdoPkKMAAADG/58uWvvfaaxWLZt2+ftuJ0OufMmfPWW295PJ7abZGRkW+99daECRN0KhNKMJlCRwzUuwgAwE/jJtDqqCYAAgAAIs8888wjjzxy4MCBuosTJkxYuHChx+OxWq1dunTp1KmTyWTKz8+fOHHiiy++qFepAACgxRAAqYN3AAEAgM2bN7/99tsiMnbs2EWLFmmLn3766WeffSYiM2bMKCwsPH78+KlTp3Jych599FERmTdv3qFDh3SsGQAAtAACIHW4c/JExBbDd8ADAGBcb7zxhohMnDhx5cqVycnJ2uLy5ctF5LHHHnvttddCQkK0xejo6E8++eSee+5xuVxaZgQAABRGAKSImsoqT0mZyWazOML0rgUAAOhGey/P1KlT6y5+9913IjJ58uSG+6dPn167AQAAKIwASBHa23+sMZFiMuldCwAA0E1JSYmIhIaG1l3Mzc0Vkfbt2zfc36VLFxE5duxYi1QHAAB0QwCkCO0GQDZuAAQAgLElJCSIyM6dO+suxsfHi0hGRkbD/SdPnhSR8PDwFqkOAADohgBIEXwFGAAAEJGHHnpIRF566aXs7Ox6i4sXL264X7v7T//+/VuqQAAAoA+r3gWgebgv5gsBEAAAhvf0008vXrw4IyPjjjvumD179vjx48PCwmbMmLFixYoVK1Z07tz5+eeft9lsIlJSUvK73/1u/fr1IvKb3/zm+n/0kSNHLly40MjNWVlZIuL1eq//5wIAgMYgAFKE+2KuaPcAAgAABhYaGvr555+PGDEiOzv7ySefnDp1alJSUuvWrbt3737s2LH58+cvWrQoKSmpoqIiPT29tLRURObOnTto0KDr/LlHjx7t0aNHUwMd7Y5FAACgBRAAKeLHewDFttK7EAAAoLOePXumpaXNnj37ww8/rKys3L17d92jeXl5W7Zs0X592223Pffcc/fdd9/1/9C4uLhx48YVFBQ0cv/58+ePHj162ftSAwCAG4EASAlerzuvUEwma6sIvUsBAAD6i4mJef/99xcsWLB+/fr9+/efOHGisLDQ6XR6vd7g4ODo6OjExMTx48f369evuX6i3W5fsWJF4/evXr167NixiYmJzVUAAAC4OgIgFbjzCr3VbktEmMnfT+9aAADAz0VkZOSkSZMmTZqkdyEAAEB/fAuYCtw5eSJii+YO0AAAAAAA4DIM9A6g4uLi48ePZ2ZmlpWVlZeXBwQEhISExMfHd+vWzeFw6F3ddam+lCci1lgCIAAA8BNmzpwZFBT0/PPP610IAABoUeoHQG63e8mSJcuWLduzZ89lv5nCZDL169cvJSVl8uTJfn4++REq7Q7Q1mi+AgzAz8v27dsTEhL0rsKIiouL9S4BP18LFiyIiooiAAIAwGgUD4DOnz8/dOjQo0eP1l00mUzBwcHV1dVVVVUi4vV69+zZs2fPnjfffHPdunVdu3bVqdhr92MAxFeAAfjZuOmmm/z8/JxOZ2Zmpt61GNfNN9+sdwkAAAD4uVA5AKqoqBg9evTRo0dtNtvw4cNHjRp1++23R0VFORwOi8UiIi6X68yZM99+++2GDRu++OKLU6dODR48+ODBg1FRPvZZKvfFfBGxxfAOIAA/F4mJiefOnSspKdG7EOOy2WxxcXF6VwEAAICfC5UDoP/5n//Zt29fRETEmjVrBg4c2HCDn59fQkJCQkJCSkrKoUOH7r333uzs7FdeeeW1115r+WqvR3VOrohYY3wstwKgtqioKJ/L0wEAAABVqRwArVq1SkRefPHFy6Y/9fTo0eO9994bPnz4559/fv0B0Lp163Jychq5OT09XURqamqu7WfVOCtqypwmfz9LmP3a/gQAAAAAAKA2lQOgrKwsEfnlL3/ZyP0DBw60WCxnzpy5zp974MCB+++/v6mPuuYPSngKikXE1rqVmEzX9icAAADj6N+/f1hYmN5VAACAlqZyABQWFnbp0qWMjIxG3tc5KyvL4/Fc/wcWunXrNnPmzPz8/Ebu//7771NTU7t163ZtP87WJjp8/Aj/LvHX9nAAAGAou3fv1rsEAACgA5UDoDvvvHPVqlXvvPPO4MGDAwMDr77Z4/Fon/y69dZbr/Pn+vv7v/TSS43fv3r16tTU1Pbt21/jzzObw8ePuMbHAgAAAAAAAzDrXcANNHv27KCgoM2bNw8cOPDLL7+srKy87Lbq6urU1NThw4cvXbrUZDL913/9VwvXCQAAAAAAcEOp/A6gXr16ffjhhw899NC+fftGjhwZFBSUnJzcqlWryMhIu91eVlZWUFCQl5eXlpZWe/+d+fPnDxkyRN+yAQAAAAAAmpfKAZCIjBs3LiIiYvr06enp6U6n85tvvrnSzh49erz88ssjR45syfIAAAAAAABagOIBkIgMGTLku+++S09P37Rp0+HDh3/44YeysjKn0xkYGGi32+Pj42+55ZZhw4YlJSXpXSkAAAAAAMANoX4ApOnVq1evXr30rgIAAAAAAEAHKt8EGgAAAAAAAEIABAAAAAAAoDwCIAAAAAAAAMURAAEAAAAAACjOKDeB/vk7fPjwc889dw0P9Hq9a9eutdlsdru92avyLTk5OUFBQaGhoXoXorOLFy8GBgbSh4sXLwYEBISFheldiM4uXbrk5+cXHh6udyE6y83NtVqtDodD70J0lpubGxMTc8cdd1zbw51OZ/PWAzD/NJ7R5hyjzTNGm1uMNp/k5ubabDZDnW9sbOyAAQP0LqR5qDT/mLxer941GN1XX301YsQIvasAAOCnmc3mkpKS4OBgvQuBz2P+AQD4CmXmHwIg/Xk8nqVLlxYUFFzbw8+dO/enP/0pNDT09ttvb97CfEtpaenOnTvtdrsySfO1KSsr27FjR3Bw8J133ql3LXpyOp3bt28PCgoaOHCg3rXoqbKyctu2bf7+/oMHD9a7Fj25XK6tW7f6+fkNGTJE71r0VF1d/fXXX/v5+c2bN+/a/gSz2dy7d++hQ4c2b2EwJuafJjHanGO0ecZoc4s2nwQEBAwaNEjvWlpCVVVVamqqceYQbe7y9/d/4YUX9K6leag0/xAA+by0tLTk5OS+ffvu379f71r0dOjQoZ49e/bo0ePgwYN616KnEydOdO3atUuXLsePH9e7Fj398MMPHTt2jI+Pz8zM1LsWPZ07d65du3Zt27Y9e/as3rXoKTc3Nzo6ulWrVpcuXdK7Fj0VFxeHh4eHhYUVFRXpXQtwvYw2/xhtzjl+/HhiYmLXrl2PHTumdy0tITMzMyEhoWPHjhkZGXrX0hLOnDnTvn37uLi47OxsvWtpCRcvXoyNjY2JicnJydG7lpZQUFAQGRkZERGRn5+vdy2oj5tAAwAAAAAAKI4ACAAAAAAAQHEEQAAAAAAAAIojAAIAAAAAAFAcARAAAAAAAIDiCIAAAAAAAAAURwAEAAAAAACgOAIgAAAAAAAAxREAAQAAAAAAKI4ACAAAAAAAQHEEQAAAAAAAAIojAAIAAAAAAFAcARAAAAAAAIDiCIB8nt1ur/1/IwsJCTGZTPSBPmiCg4PNZjN9CAoKslgs9CEwMNBqtdIHf39/Pz8/+gA1GG3+Mdq/3412vkabW4x2voGBgYaaxwICAmw2m3HO17dY9S4A16tz584bNmxITEzUuxCdxcfHf/XVV506ddK7EJ21bdt206ZNHTp00LsQnUVHR2/ZsqVNmzZ6F6Izh8Px9ddfR0dH612IzkJCQrZu3epwOPQuRGcBAQGpqakhISF6FwI0A6PNP0abc9q1a7dp06abbrpJ70JaSExMzObNm9u2bat3IS0kIiLi66+/jomJ0buQFhIaGpqamhoREaF3I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width="768" /></p> <p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p> <pre class="r"><code>mkinparplot(fit)</code></pre> -<p><img src="data:image/png;base64,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" 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width="768" /></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <pre class="r"><code>summary(fit)</code></pre> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Thu Nov 19 14:46:12 2020 -## Date of summary: Thu Nov 19 14:46:13 2020 +## Date of fit: Mon Feb 15 14:11:17 2021 +## Date of summary: Mon Feb 15 14:11:17 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -498,7 +472,7 @@ print(FOCUS_2006_D)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted using 401 model solutions performed in 0.148 s +## Fitted using 401 model solutions performed in 0.143 s ## ## Error model: Constant variance ## @@ -541,11 +515,11 @@ print(FOCUS_2006_D)</code></pre> ## ## Parameter correlation: ## parent_0 log_k_parent log_k_m1 f_parent_qlogis sigma -## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.171e-06 -## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.481e-07 -## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.209e-07 +## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.172e-06 +## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.483e-07 +## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.205e-07 ## f_parent_qlogis -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 1.305e-06 -## sigma -1.171e-06 -8.481e-07 8.209e-07 1.305e-06 1.000e+00 +## sigma -1.172e-06 -8.483e-07 8.205e-07 1.305e-06 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. diff --git a/vignettes/FOCUS_D.rmd b/vignettes/FOCUS_D.rmd index b857a3c3..f75f72fd 100644 --- a/vignettes/FOCUS_D.rmd +++ b/vignettes/FOCUS_D.rmd @@ -1,7 +1,7 @@ --- title: Example evaluation of FOCUS Example Dataset D author: Johannes Ranke -date: "`r Sys.Date()`" +date: Last change 31 January 2019 (rebuilt `r Sys.Date()`) output: rmarkdown::html_vignette: mathjax: null diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 1c03f484..0ed46483 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -11,10 +11,22 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-11-19" /> <title>Example evaluation of FOCUS Laboratory Data L1 to L3</title> +<script>// Pandoc 2.9 adds attributes on both header and div. 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max-width: 940px; @@ -1566,7 +1540,7 @@ div.tocify { <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-11-19</h4> +<h4 class="date">Last change 17 November 2016 (rebuilt 2021-02-15)</h4> </div> @@ -1585,10 +1559,10 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> <pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO)</code></pre> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Thu Nov 19 14:46:14 2020 -## Date of summary: Thu Nov 19 14:46:14 2020 +## Date of fit: Mon Feb 15 14:11:19 2021 +## Date of summary: Mon Feb 15 14:11:19 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -1671,32 +1645,32 @@ summary(m.L1.SFO)</code></pre> ## 30 parent 4.0 5.251 -1.2513</code></pre> <p>A plot of the fit is obtained with the plot function for mkinfit objects.</p> <pre class="r"><code>plot(m.L1.SFO, show_errmin = TRUE, main = "FOCUS L1 - SFO")</code></pre> -<p><img 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" 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y8N1WG/iUEdqiD6PPqPofVaTSvzvqrssUaBaIqvF5Re8XibDtvvmFTwh/sjvNMjOu1CRO1/zYgvG6xPoP2hA9biGBdku8REhy0OPzCq58PRRZ7qZ8oLBPgTSRerE2gbatPPZoLD4oPzbGKNl2Ao0Iu6a5qoZ27UYRNfblidQK3GY/wd+pyam9PKeAmGAuGdtdsk93WvN+6yG5v4cmPNIP4HR+0ppEB2BzBOVzd6O2hvvARTgXBfG89ND9LmO3diE19u/CTA8MyBxh9+8CNQ/WUYn0XrqLnIhsZLMBbohqLNA4x/cerFJj4gGPwI9LltxCKvNp5HMva4mOiuhbFAF939nNs2rhPtzSY+IBj8CFQ4z9Pt/YIxCKH+L4yXYCzQ2TbN1p77s/CBkk18QDB4vQ90Nc5kmx7GAj2tftgzE+P4PmbEB/hHrCatjAXCs7uOGouTPH/iNj7AEZYvUNFSJxs3/8Pchge4wvIFwrgg2jeX2+gAZ0hBIIxDFnAbHeAMaQj0n9tlbsMDXCENgfAPbaGFvWUiEYFUvVdwGx/gCIkIhG8pr3GbAMANUhEIr+oCQ0FZIpIRqDiIURtGQGAkIxD+y+0OtykAXCAdgfBnvemDWEqgQ/XXTHc/CgiMhAQqaheD8fZKjZcsqGtzuvzigCBISCB81TUNO4ZTM6pu0DrIUpCSQHh595sKda8NdxTwxqGFICmBijtPr6iZQc+5TQcwF0kJhP92Uaibdm+pxG02gNlISyD8laPrFYx/th/MbTaA2UhMoOLO7pVqOFXqlMdtNoDZSEwg/E/NNdM/gt4TLQepCYTXNYHWiZaE5ATCQ2dxmQdAiPQESvc8yWUiABnSEwjv9X3GYSIAGRIUCL/3FmdpAKRIUaAXDePLLwQIgxQFwr8rb3OWCECGJAXCyzrDSxoWgjQFKu7xGVeJAGRIUyD8rzvcjbYMJCoQ3lfvCTeJAGRIVSD8wVBO8gAIkaxAeS3XcJIIQIZkBcLX3JimDvCIdAXCsU1yOEgEIEPCAuGR75HXARAiZYGeN4ojrwQgQ8oC4ctuf3FQC0CCpAXCPzQ10Q01IBTSFgiPglGgREbiAsFpkNhIXCB8RZnCTUWAeUhdILy5PjwUExP+BMpPzyxjK2cC4YkDoe87EeFJoLtzfRQIVfGbbaorKO4EKghaxlVVAHv4EeiCnff4yA1xURN9nS8ZL8GdQPi+1xHO6gLYwo9AXYO1j6kKR/Q0XoJDgfAxz7vcVQawgx+BqiXo5k47GS/BpUB4eSt4rCoW/AgUOEE3t7Cd8RKcCoSHh3NZG8ACfgRKUATHJl9NPRsXUjHBeAluBcoJWK3+3PXB6FXZXFYMlAdPV2H7uyEaRfdEEwW4FQjf9jhJadS3zeq14d5wZ1FIeLsPlHklKSnlscHKkzWctSg4vvY+Qp1ILxxKd725oSW3NQNlwuud6KxrBp2pqjJ12HP7DYTxitYvmvyunqsLw7IICE8CbeiQgh8MQMhxlYkCHB/CKMYMUT5UzwRBL+QCwo9AkajzA9zb67uDMyqZeFrOvUD5nb1O0J+Fyn+4rhowDT8C+czB+AGiR+qe3cp4Ce4Fwvdr+GViXDwnmPOaAdPwI5DLDowvI/qCOtHReAkeBMLn7VzHTG3R9SH3NQMm4UegN/rl40LHo9TcHEFuJGrZ6rniK+j/Tlj4EeiqsunyE0s8fzwxx2az8RK8CIQXBUAbaYHh6Srs2vga6juJTbaaKMCPQKrwUBiFRVh4uw9UcPOXA8mmB4bjRyCc32UeL/UCppB8k1YDHvlCK3tBsTaB8J/ucBotJFYnED4F72kIiVkC3b1MHJc/gfBmn/u81Q0YYoZAh2oihHtEkcXlUSD8SQC0CRIM9gJtqDRuA8LzFTFEcfkUCL87EHoBFgr2AjWeijOohRlNieLyKlBBH+g5SCjYC2SXqBYo0Z4oLq8C4RcdFvJZPfAS9gK1/lgt0CfNieLyKxBO9/+a1/oBHewFWmuzOBk9iq38FVFcngXCNzx38BsA0GDGVViUC0KoymyyN9L5FgifV57hOQJAY859oOxzW4+nE8blXSB8xB26ARYA67sTXcIuj795jwGwF2iADqK4AgiE19W7x38QucNeoNE0bygrTCSKK4RA+NOmhi+mAVxj7iEsu3cborh8CVT6FvS0jvBQg2fMPgc6iR6RxOVFoLQhTlXb7NNboXqvey4PcYCXmC3QuqpE1/F8CJTqvjIz/1D97/RWFY/sk8d9IOAl7AXaqGahR1eiuHwINFx9b/O6i74yRaEhhdxHAkpgL5CtGrv2V4ni8iGQt2Ys59a/6a/M6/UOdMLJI9Z0H8hT805z27Ol1mZ3fh8M4g9rEmiQus7/nAyuvLJfmwQG8QZLgVz1IIrLh0C/u28pximBnxquf9o2gvtggAaWAkVHR69y8Ir46iN/7/1EcXm5jP+1o6PS6/tXv26etJnGQzSAhv0h7P0u9FVOUV+yRn883UjMNt6zwuOWc3gJB5ghkJem28zdnkRxBXmU8ZKswA8FjScfzBBI0+lYVG2iuAILhLNaB9St3PR7eHOea9gLNLY6/bBgf3WLPISZ5EUj5Tv5ZzuNEzaqDGAv0POuqEbTGqg72WNKoQVaOSwzMEL1otYVYcNaP+bcBzq+NOKLnwjjCi3QkG34ScexxeO+K78owAZrupFYFoN2UZdoPUaMJ3yhFjCEpUBrkvAaHURxhRbok/H439/u93aELoA5hqVADsOxkw6iuEILlOFe1yOwmo/ydWgfxC1yOYT959bKvq5D644juj8TNrC1Y6ZAqQcJWxsLLdBH03DOrSJVixNTAknfSAL0YS/Q3V7T8Y6KyNXEWJYMEVqggXvUH5Mj8ceN/hU2tHXDXqA3PHfjln1u9rL813r0GRqv/ngnBuPP66UJG9uqYS9Qje/xPZSE492I4gotUHQ/+iF9Zs3r1DTG84Kwwa0Z9gI5bcE/2ubifQ5EcYUWKL9DyNl7e5tqHsrvdjssbHQrhr1AvTsebTYIZ/cPJIortEA4b1mgR/fd2oWTSlMdoAMsYS/QJXdU7QJuUHlfWaXLRXCBSnPJC/oP4gYzLuNzzlOX8NsIhwUUWSB8q+FkaNrBBfx185ufnlnGVrEFwpmdQ+GmNAfw1M3v3bk+CoSq+M02NVqG6ALhvDe7ZYmdgxXATze/F+y8x0duiIua6Ots4n6j+ALh4slNb4udg/Thp5vfrsE5mpnCET2Nl7AAgTCOqfWr2ClIHn66+a2WoJs7beKhvUUIhA8pTYyHBzCFn25+Ayfo5hYKOeQley54rxA7BYnDTze/CYrg2OSrqWfjQiomGC9hIQLhf1q+my92DpKGp25+93dTj3ip6J5oooClCISzQzrBMM8E8NbNb+aVpKQUw0ZDhTd02FuKQFj1sS+8qmE+bAV6fnjrHeqP/t+fJ/sz2Gm3QTd4p+v5aqmwnE2W/LJRuVfsFKQLS4H+8qYOTOP/qE0fnpjsdNTUFos5hNFcqDMLnmuYCUuBBrpsSNns5tVhy5FzZb1YuDNMA+oRFma8hEUJhB91GfhU7BwkCkuB3JZRk2WovDu4B+xQQHsK1Kh9e+MlLEsgnPdOC9NDlANlwFIgRLej2VH+GxqpAW3pcQYkcgijWe1+UOwUJAlbgejbOrsZvOJTMMvhO0kJhM94wYmQGfAlEMYnvYPvS0kg/Khbf3g8zxr+BMJZb7pKSiBcMLE+2btKcoStQDa2trY2SN1VdPn7bI4wOeCSJQpEnd25rxI7BanBUqB5ehDFtUyBcGqz8Bdi5yAt5PJuPFOy/9c8VewcJAUIZMj3bnFipyAlQKBX+KtZOIwyxhgQ6FWyRzdOETsHyQACGWODWxQMr8EMEMgotzr1ggF7GQECGadgthd0wMAEEMgUx2tPzhE7BwkAApnkSXij38XOwfIBgcpgk3JpUfml5A0IVBZ3unck7IXE6gGBykQV47oUWgmVBQhUDn936HFL7BwsGRCoPIqWun4DdxVNAgKVT1qXTibbNckeEIgBRV+6roTLMeOAQIy43rUdPF81CgjEDNV691l5YidhiYBATLkX4n9C7BwsEBCIOfvqDH1UfimZAQKx4HmE+1q4oi8NCMSK39t2ZtJHtowAgdihWu8xGTry0AMEYkv6O7XiTRzHHuzbLrs+PkAg9iQHvGb0Feglyt79PMbIbAAFEMgMimNqTnh1zNjvGtR3q+0eOF6EhEQEBDKLzInKyAKDdXXcj2J8ubmtvF4qA4HMJLV/g22l1yjUXWLfqMj0L2odgEBms9+/b6lLegU9HitWVfxJnHREAgQyn4Io97H3Xy7aRNDT+Mo3xMpHFEAgErJmuCx6rlvoWXP43iNTXDzkda8aBCLj1kiPb7Vn0ylur/fuNthjp7gJCQ0IRMqFXvW3ab50rg9TVu+ZLHI6QgMCkZMU0OaI2DmIBgjEBUnNO50UOweRAIE4oWhtnQHnxU5CFEAgjiiI8eopx1fpQSDOyF3lESqvu9A0IBCHvPjSI+Si2EkIDAjEKTkrPQfJ61wIBOKYnNW1+8rpaRgIxDn5sfVfS5TN8wz+BMpPzyxjqxULRF3Ux7dosalQ7CyEgSeB7s71USBUxW+2qTbCVi0QxqrELj6rZdGyjB+BLth5j4/cEBc10dfZxABKVi4QxS8hbvMeiJ0E//AjUNdgbQenhSN6Gi9h/QJhfH2889g/xU6Cb/gRqFqCbu60k/ESchAI4/RPPPocsu7zaX4ECpygm1vYzngJeQiEcd66lo2+s+aTIX4ESlAExyZfTT0bF1IxwXgJuQhEcSLE5UPrbebK01XY/m6IRtE90UQBGQmE8e2ZrgMPWmlnr7zdB8q8kpSUYvj23f0YHVW+ZVmftMmJDfBb8eq7iFYAXwIV3dS84ptb6kr2r3E6bGQ3uu25UU7hZ8ROgnv4EahwflVUdSbdL+U6E/vJ6hCm5fGX/k1XW9vQ9PwItLzShwlTK72NQaDSqI6PcAo/ZVXX9fwI1GAONdmI9oBAr5Cxson/UisazI4fgewP0NMwn1wQyAg/j3EesCNf7Cw4gh+B2kyjpw+VH4BARsle10U52ToanvEjUBSafCQP4wMVR80CgYxzc5Ffk2X/iJ0FOTxdxi+uhtKoj32eCAQyher0OJfuP0q9w0W+7gPl31D36154LNr4dhCIJm9niFPoDkl3igdNWkUmc02PGqMPSbf5IggkPvdWdXB775hEhwMCgSyC218EKt8/KkWHQCBL4cayNm5jD0nu9hAIZEHcWtGpRtiOF2KnwQoQyLK4922vaq/HSmhQIBDI4siKH+bUaelfYqfBEBDIEsk7NKF2valHpXBCBAJZKheXtHcaEmvxz+1BIAvm0fo3a7Sa85NF32UEgSybojMfBTiHxNwWOw+TgECWz8ON4e4NJux6InYeRgGBJIHq4he9HTvMO2F5I4+DQJIh99jcdo69P//Fsk6JQCBJ8WRPRIvq/ZefsxyJQCDJkbFjcvNqfT87Yxl3iUAgSZE2rm3PZbkYP949tY1jl/kHxW/PCAJJiQPK0DdHdG+i6Tvw+eF5XR1bfLDplqgpgUASotDdKyTyU//6U0vWFJz9aoinR8iKMzli5QQCSYjzVX+kpvkd3Euvvr1pUhu7tpM2XBMjJxBIQqy3VX/srPzqppwzXw6rUyN4wd77r27jFRBIQmy3UR+pvq9iYvuDvfP7ungPXnIoXbicQCAJcc0u/DnGlz0allXoxraZPZxqv/HJfmEe5INAUqJdO2W/TjXrm3jX7iWq61tn9XKp2Xf2tr/57hgNBJISt1q2HzncaxJDKe7uXTTY16Hde9+e5vE5LAgkKYp2ffwluzHJnp7+elx7h7oD58SnFPCREQgkB4rTdn4ytFHVJsM+2ZHK8WM0EEg+5F+KnzPIr2qzYQu3/sFZuxAQSG7kXoyfF9qkql//6d+fekheHQgkTwqv7Vn2bkcXp7Zhi7f8/oygIhBI1mQk/zgntIVdzc5vf7r1d7Ou1UAgAON/Tvwwa0hLR9d2I+b9ePIuq1tHIBBQwsOfNywaFeRZpUGf95dtPcfsBAkEAgzJvXrgm+mhga52jfuN/3zTmX/L/EYCgQBTPL+y7+uZwzt6Vq4TFDbnm72XMowVAoGA8si/eSru0/EDmteo2iAwaLPBWA0gEMCY2108O/ZwX1dqHQgEMKbLnEKMr9Y5pr8OBAKYctm3GOdhvC5EfyUIBDBl5+D1Das6jTjSVH8lCAQw5bh34NmirM9cSo2jDAIBTLlT4Qg1VTUI0l8JAgFM2R+gXHh0S5fA1vorQSCAKXv7347oMWz9b630V4JAAFP+c1E/r180Xn8lCAQwZuprV3H+1zVv6a8DgQDGFEV5uNr3Sy21jj+B8tMzy9gKAkmTdMNXO3gS6O5cHwVCVfxm3zRRAASyEvgR6IKd9/jIDXFRE32dLxkvAQJZCfwI1DVY219N4YiexkuAQFYCPwJVS9DNnXbSX//HUB02K9nUB1gs/AgUOEE3t7DUg5PH23TUPs6mPsBi4UegBEVwbPLV1LNxIRUTjJdo/wub+gCLhaersP3dEI2ie6KJAiCQlcDbfaDMK0lJKY9NbgaBrASx7kSDQFYCCAQQAQIBRIBAABEgEEAECAQQIZpAs2NKM2hkGAeEBnNRS1jPEVzUMqwPF7WE9RzORS1v9uailrDeyw3+b14iCfTDOANs6zXiAK9qXNTSqIovF7V4O3BRS6OqdbmopbY9F7U0su9t8H+bzHTEKY4FeoX6nAwVsnUYF7Uw/14uk70DuaiFo6P90R5c1IK7mf0MEwRiDQikDwjEGhBIHxCINSCQPiAQa0AgfUAg1oBA+oBArAGB9AGBWAMC6cO3QI1MvUHGip0juagFt/6Ti1oODuaiFhz0Gxe1nOrDRS2412lz9+RbIKN9yLKmkJuh1bhJpiir/DIMeKziohaV6dahbDA/Gb4FAqwcEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACL4FSihTfVuF4lr2anulGgMWSVJe7jISFMLYUKr2zn4f1FImoyuFuTvJasAAAP7SURBVLJknkf42Adsx+Ynw6tA+xXjt/e1v0tazQplNMVJojqK207nICNtLWQJLUbT9s+uNJ8wmZJayJIJc1yZ+DZKMj8ZXgXq1hfjHO+5pNVMIG409c83r6HpxBmV1EKUUH61ydT0w6pFRMm8rIUomSeKOIxV/qPN/8vwKVAmiqWm7/uQ1tN3HGkNiUFBttOJM9LVQpbQDUQPz5WAbhIlU1ILWTLXuqZR09eGmf+X4VOgKyiZmkYq8gnradCntX2LGMJK/KZzkZG6FrKE8tLyqOnUqrlEyZTUQv7XUSVW3WD+X4ZPgY6iq9R0A0onq6a4skvk7jFoBVkt6n89cUbqWjhIaGOlGRz8eehaiJOJtEURBH8ZPgVKQvQIMHGorNFZGJC/5QY1HVWtmKgW9b+eOCN1LcQJPQpHowuJk9HUQpzMzV0zbFaYnwyfAqWgs9Q0qgonle1EaUT7q//1xBlpDmGECR1Q+uwmT0ZbC2kyNFPrmZ8MnwI9pk/x8aR6hNU8PE+3+N6LHhDVov7XE2ekroUwoQMVJ+Ri4mR0tZAls70fvfca9MLsZHi9jO8egnGh7yzCWpLQJmr6Xm2yWjTfHaQZaQ+EJAkVeoZr50iSKamFLJlE9Cs1fbeW+cnwKlBixUVnRjqTvhlW1E65+MDkCtvJatEIRJqRuhayhI6hmetocomSKamFLJmCjr7rDk2vEG3+X4bfRxnb21bvQf4oIyeioWPHg4SVaM9eCDPS1EKUUDTS8IAomZe1kP11no3xdwikv8PMTQYepgJEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESBQeYRqXyJGS7DDGrGTsTxAoPL4JSEhwbsTNbmKhyeJnYzlAQIxoelwsTOwWEAgJmgFcqIOYbVWdrDz/fbeAKc6dJ8W6wLsmvwobm4iAwIxQV8gmwXHBihqrUxsa/scR9ksSIxQfCtydqICAjFBX6ChGKeiKRjvR5ezXRZTa8cqxU1OXEAgJugLtAzjIhRPW3TpHErOyMjYgIgHc5AwIBAT9AVaQQuUoBZoq/YCP0Xk9MQEBGKCCYGOo0ciJyY+IBATTAj0qAo9wMR84rFgpAwIxAQTAuFZlZckzlBEipydqIBATDAlkGpFU7vG0SInJy4gEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEDE/wFycCHypSvetAAAAABJRU5ErkJggg==" /><!-- --></p> <p>The residual plot can be easily obtained by</p> <pre class="r"><code>mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time")</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> +<p><img 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" /><!-- --></p> <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p> <pre class="r"><code>m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE)</code></pre> <pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge: ## false convergence (8)</code></pre> <pre class="r"><code>plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC")</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre> <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Thu Nov 19 14:46:15 2020 -## Date of summary: Thu Nov 19 14:46:15 2020 +## Date of fit: Mon Feb 15 14:11:19 2021 +## Date of summary: Mon Feb 15 14:11:19 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 380 model solutions performed in 0.085 s +## Fitted using 369 model solutions performed in 0.082 s ## ## Error model: Constant variance ## @@ -1724,22 +1698,22 @@ summary(m.L1.SFO)</code></pre> ## ## Results: ## -## AIC BIC logLik -## 95.88778 99.44927 -43.94389 +## AIC BIC logLik +## 95.88781 99.44929 -43.9439 ## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 92.47 1.2820 89.720 95.220 -## log_alpha 16.92 NaN NaN NaN -## log_beta 19.26 NaN NaN NaN -## sigma 2.78 0.4501 1.814 3.745 +## log_alpha 13.78 NaN NaN NaN +## log_beta 16.13 NaN NaN NaN +## sigma 2.78 0.4598 1.794 3.766 ## ## Parameter correlation: -## parent_0 log_alpha log_beta sigma -## parent_0 1.000000 NaN NaN 0.002218 -## log_alpha NaN 1 NaN NaN -## log_beta NaN NaN 1 NaN -## sigma 0.002218 NaN NaN 1.000000 +## parent_0 log_alpha log_beta sigma +## parent_0 1.0000000 NaN NaN 0.0001671 +## log_alpha NaN 1 NaN NaN +## log_beta NaN NaN 1 NaN +## sigma 0.0001671 NaN NaN 1.0000000 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -1747,9 +1721,9 @@ summary(m.L1.SFO)</code></pre> ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 9.247e+01 NA NA 89.720 95.220 -## alpha 2.223e+07 NA NA NA NA -## beta 2.325e+08 NA NA NA NA -## sigma 2.780e+00 NA NA 1.814 3.745 +## alpha 9.658e+05 NA NA NA NA +## beta 1.010e+07 NA NA NA NA +## sigma 2.780e+00 NA NA 1.794 3.766 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -1778,7 +1752,7 @@ FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)</code></pre> <pre class="r"><code>m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE) plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - SFO")</code></pre> -<p><img 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Ar6r090JQKcsaigrwWPHh/epNOZ76Jfct1AVdCpE3UlApyxqKB3xpYuP+9GLULiVC7wVBV0VU9diQBnLCqonfQvt6hdeK0q6JEaXiQC3DhSKJo7xSep5/fVLR+qgt4JxQPuhOK3k/xx03MjnKC0KS6qB3cRT9DnF7LNBEyNeIK+159tJmBqxBP0oMq5J+CXiCdoOk52gruIJyhtuI9tKmBmBBR04DtsUwEzI6Cgi/AgcJCNgILubcQ2FTAzAgp6KwSjxoMsBBSU1tV93yqwLCIK2v89trmAiRFR0IVD2OYCJkZEQb9uyjYXMDEiCpoacpttMmBeRBSUxmotClgeIQV9YinbZMC8CCno3OFskwHzIqSgOzCoLHAgpKBXwjLYZgOmRUhBaczPbLMB0yKmoL2Xs80GTItOQe+5SxdPW7gdyMu9oK+/rLEqYHV0CpqQkLAgrPLo+WNjova4be5pIC/3gm5ro7EqYHX0H+Kff0A+z5PR6Tl3rT0O5OVe0AsRNo1lAYujX9Dya5TZhnLuWnscyMu9oLTqEY1lAYujX9By9gcrvFnRXWuPA3l5EPSpBI1lAYujX9BnIzZK043F3B7iPQ7k5UHQxX01lgUsjn5Br7UixesUJ21S3bX2OJCXB0GPVtJYFrA43vSDbo8fMWeXh+aeBvLyIKitVO4hloB/4lVHfdoJDRu4Gsjrx8FZBM13v3WPTzXWBayNF4K+GxVAaO+5Gra58UuuK48vLE50UORd95u+MVRjXcDa6Bf0g4DBKwmdX8CtYbb5PWnakEBSZIZKh6aHQzw9gEeIARn9gtYaTi9KK5PruGsdHzCKTgqN3zK5kIqIngS9E35JY2HA0ugXNGSTIuhWlQGQ7FQaQ2nVeGlhSn3XDTwJStt9qbEwYGn0C9pwsiLobLfH4Mh1lBaT+0s3h7lu4FHQqWM1FgYsjX5B3w+avpecSwxTGYTTTse+0p9p0sIk7zrqKd3eQmNhwNJ48St+QXFCSOGxbi/nOBDa6bNVJd7YNb1QousGHgW9EYrn2ALv+kGv7/9sxz8emh/oXkDuqK/4tsr7HgWlTXdrrAxYGf2CDlW5gC43V37cuuu46r1FngUd9Zq2PMDSeHG5Haky6RfDeT0Lur6T4STA/OgX1JY8qgppPP9PY3k9C3ohArd2Ai9vmvtufI0CHQ3l9SworXnIUAZgCbwT9OqqJwsXMJRXg6CD3jSUAVgCLwQ9u6hjoaAO73n6He8eDYIue8xQBmAJvDiTRAp3XZpiNK8GQU+VMZoEmB/dgqY3ev8qg7waBKUVjjNIBMyNbkGPEiZP/dAiaH+M6AX0H+LbPcQirxZBV3ZjkQmYGv2CfhrbeMK8BRKG8moRNCUCAyb5PfoFrZCFobxaBKUtkgzlABZAzKfbOfg3niHm9/C7q9M9mgQ9WNNwHmByuN7V6QZNgtrKnjSWBZgeTnd1ekSToOhoApzu6vSINkHR0eT3cLqr0yPaBEVHk9/D6a5Oj2gTFB1Nfg+nuzo9olFQdDT5O5zu6vSIRkHR0eTv8Lqr0xMaBUVHk7/j5Zmk4xsvGMurUVB0NPk7+gU922E43VyQRH5rKK9WQdHR5OfoF7R72dX0vvtOdunsaQsDA3ndBR1Nfo5+QYsn0iuBK+jKkm6bGxvIy8HNLQnbmm/TWCCwJPoFLbaCrgr4m65WeWydHYMDednZFd32udYlumssEFgS/YJ2aJPcuCW99nAjd62NDuQl80ep/0jTDwN/11ghsCL6Bf1fGRKym9YNWu+utdGBvGTmPK/MSuFp9f6MF91MNw/+Q+mao25bGx3IS2aIvYepRy1N9QFr4k0/6MWv13573X1rowN5yUyYrMyGBd/UWCKwIPoFzRhRmBASOct9c4MDecl8W3F241JN5pa7d63GEoEF0S/o5IAxRy7/ODrA05OTXA3ktYNk40FwCVv1Ii99PKxwvfd6aywRWBD9gsaMVmajtVywvEH1lL2GPehXcclDu7ywr/aGYm6HBQWWxouO+i+U2ZeRWjbarvaOBkFH2Xey0yZ0+URDKmBNvDjV+ZIyG9LFXeu+dki7virjamsQdNhbymzOqOU4H++/6BT08OHDSfc8uWHf+t5l3fYztSSVmkmQms2auW6gQdD3eiqzzh9fi8Coc36LTkHv/sgh97lrfWd0pNyRb+gQfyNm5m2aNqVWGu21RGORwHLoFPTEXc67b/955MjbxgSlZx6JbFisl5Rn9YMaiwSWQ/d3UNvKwffXf3LRbbeNZX5rfO8ZY4JSmnLosjy7VfwvLa2BBdEr6K/NScUO3WMDanke4SB9WHGjgmbR9y09rYGF0CnozVrRX8nz/fWiNXROrh3xq9pb+gRNaqCnNbAQOgWdHOZQ7nzkeEN59Qlqq/GNoWzAtOgUtPWQrPWRrQzl1ScoXfCkoWzAtOgUNCLbqw+LGcqrU9ArkfiZ5J/oFLTqzKz1OVUN5dUpKB00w1A6YFZ0Cto7+8DeqaehvHoFPVwRI3f6JToF3RHo2JMtJpsM5dUrKG2xwVA+YFL09oOOJ60++98vG3qSQcby6hb0Y2OD1wKTovtM0rpK8on44ouNPTtMv6DpZVT7VIGF0X+5XebxDZ/96PlMpwd0C0pfxZMY/RGhh6HJwZl7PNyoB6yIeQSlD+OiOz/ERIJurWvwey8wISYSlN67hm0NwASYSdCNtTPZFgHEx0yCYhfqh5hK0E3YhfodphKUtlzNtgogPOYSdDN2of6GuQSl961iWwYQHZMJil2ov2EyQbEL9TfMJujWWm7GtgHWg6OgTMZJykPneTnXbetGvfgxrra3LLwEZTJOkiuOl8zxzJ2rrZvPXfhg/T+8jAZEh5OgTMZJcs2Ex53Xhg6WryCZgsGUrAonQVmMk6TCzWjn5+mUUEZRuhWOhzBbFE6CshgnSY3Pq90dvvN2kH1e5ZTX4YDQcBKUxThJqnR7/e5yGcXM1PAb3ocDIsNJUBbjJKly5p7fspfHPvrfOa8nDe3nfTQgNLx+xTMYJ0mdaXcfGnGlYlCT5sElThuIBkSGXz+oq3GS/lmc6KDIu3rjOZEWuzJrcWyv/86euX0Y9qBWhZugt84oZ81v5njo14+DswiarzNeDn4ok/WjCN9BLQ4nQW+/WJBU2ystLFHZztAhntKFTdLtefAr3uJwEnRW4enLWxU9zU1Q2yPj7AvoB7U4nASNnS4d3Rv34CYoTam8TZkPGyifSZrcw1g0ICycBA3eKk2+CzzATVC6u+yf8uxqm2Zz32zfAOfirQonQasrfekD6t7iJiid2FF5joNt/agXP8HVTJaFk6CvFZmwh9JLZTuP5CbonfsnGA0BxIeToOnjgmOl2a+1CTdB6cUabxuOAUSHWz9o5jl5avs60fXbDASlJ8vhqcuWx2y3fOTg25J7GUQBImNqQelXZY6xCAPExdyC0kU1LjKJA0TF5ILS8fUxwpelMbugND76JKNIQERMLyh9uzKG/7Aw5heULi9/mFksIBoWEJSuLo3Bui2LFQSlW0olMIwGRMISgtLj9friknprYg1B6a2nG+DHvCWxiKCULiz9FeOIQAQsIyjdU2VACuuYwOdYR1B6Y2w5ladEAPNiIUEpTa7V9RyHsMCHWEpQmjbpnhn4OW8prCUopacHl45P5xMa+AKrCUrpt+2qr8ZQIJbBeoJSuq1JjXe9ONBPaVD1Edy+LBpWFJTSg/2Kv6jz51Jq2eAeA2MKLOdTEPAWawpK6fEXij/2pZ4Ra7qUkr+6ji6EUW7EwqqCUnrlvftKj/hec/PQRcqsMAYKEwvrCipxYnKVGmP2aHvsSMGvlVmJmTwLcsueyS99kOa5mZ9haUEptX03pWHJp1f/47llseny9E7BbbxLUiFjQMy0+d2r/uSj9MJicUFlzrzdtVjdF9d7uP3zuZBjlGa2VxmUhD9vPiDvPZfWt/mqAEHxA0ElMg7M7hxe7ckFybfU29wfWKNJaNED+VdUTpr/5/3encb+Xu1HXxUgKP4hqEzmTx8ObRxcs/e/151w/a10R7+us3zXw1+p8YMrN48r1XirzyrwNT/Mm7ou77+M6QaTNcbtHz4d17VKkRpdR76z6SehztpHPSBPtxf01z2obUhYifASMXkuOzfdYLIsSPtp/ZzBHWsG39Po4SHTFn954IwAqkbVk59i/mbIt74uxEfMLTz0yLmPi0Xnft18g8ky5O9vP39n8rNd4qKKhFRs3OHxIRPmJK7aknzkt5Tb+V9Llf5Ro19rV+c+X/Ui+JqyveXpzwVy30JuvsFkuZB6av+WTxf9e9Sgxzo0q1UpMigosmLVuGbtuz3Wb/DwsePi4xcmJn6yatXmpKQdBw8ePHRS4vcUGXa/uR9cd3j2hNU3y/7GLKK5CNqszCLm5Xo9fweTPdA+i4Jv6IqX39xOOX3iYHLSF6uWJS6Mf33s2BcHD+7z2GMd27dvGxcX1yBaolykTIAynh4pGumgbHQO6sd5oEX2B1I/pGX79u0q3tPeTwms0769JGdI7h1X/g4mm5qURcx/dcUTnGspDv44mYPDBz3wTfYHkjS8WLN2ZVquS/JToituTjpJEwvkfhacrwaTbYZHz+bmwhfL/fUnvMQ3wRVmvN0l5F+5X/fVYLIQFOQkqWKxkiFj89wNkb+Dyd4FgoJc2E4dcnGiz1dnkiAo0AQEBUIDQYHQ+ErQB7K7Du8SSgK4wTE0T0xadmDef1xvKah1HBfGgt5Kycun7U5xI2o3t9ATn+EW+lhBbqFP9Z3OLfT2GBf/ul5yRatRjAV1xRfd+MWucopb6HkjuYW+U5BbaDp0EbfQR2O5hVYHgqoBQXMDQfUDQXMDQXUDQXMDQbUDQdWAoLmBoPqBoLmBoLqBoLmBoNqBoGpA0NxYVdDjHIfjmpLKLfTX67mFto3mFpqu3sct9JV/cwutTj4ICoD3QFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQ8Bd0aVxE6z1cIq9VHmQ2kEPkEfbLjXiUbg/NofT0GbEhtWbLD5FmXnZ2aH6fuCrcBf2YvLyuR3DuJ0AzYVbpBIldzOPaNocqFnEoPSs0h9LHFZm5ZWrhF3mUnR2a1yfuBu6C1utDaUbsIB6hB3XgEZWuL0qIYhH70rNDsy89M3i8NJ0edIt92XdDc/rE3cFb0LNkrTQdV5ZH7LYvpBy6zD7s5SNHomSLOJSeFZpD6edi5aP6cnKGfdnZoXl94u7gLeg+clCaJgRqG/pVH1GxhQjpeYFD5BjZIj6lK6F5lX6zVUwmp09cDs3xE1eFt6CbyHFpuoJoGPdVL2nBHY9e+7zEI+wj2y3iU7oSmlPphxoXTeZUthKa4yeuCv896CFpmhjIbYiiBUTzc6i049iD8ig95u79SIxLv/R0YI/TfMp2hLbD5RNXhf930M+l6cQy3BJsVnYYjImxfwflUbqToGxLP1aujjJkLoeys0Lb4fKJq8L9V3ydgZTa6j/DIfL2Itul6aQIDiPF2i3iUroSmkPpttqdHI+CZ152dmiOn7gq3AVdHvhG8uBgrc/T1UNmXIXZmycUeodDaLugXEpXQnMofQ8ZuUTmJvuys0Nz/MRV4X8m6cOG4a1URvY0yF9Plw1ruopHZMdxmEfp9tDsS0+0D45H/mJf9t3Q/D5xVXAuHggNBAVCA0GB0EBQIDQQFAgNBAVCA0GB0EBQIDQQFAgNBAVCA0GB0EBQIDQQFAgNBAVCA0GB0EBQIDQQFAgNBAVCA0GB0EBQIDQQFAgNBAVCA0GB0EBQIDQQFAgNBAVCw1jQDfEAaGDODd8I2qzfWAA8U1Lrw81YC7qXbTxgUepBUCAyEBQIDQQFQgNBgdBAUCA0EBQIDQQFQgNBgdCYWtBdMyZ/ZWMQB4iLiQW92bPW+Gn3tszXEUtBfpO/gtpSsogzLuiYf92RIr7Si0FdQFjyV9BdkVkELDAcrOwpeXoz/JrhSEBcfHWID1tsNEJGAfvXz2rHDBcDxMW8gtLSyrjPaRFXDUcC4mJiQV/uJw+mO7GH8WKAuJhY0OudGsyc0zruLwbVAGExsaCUbn71lVX5OSQ5yH9MLSiwPhAUCA0EBUIDQYHQQFAgNBAUCI2pBT3wRvwOBmGAwJhY0PS+0S+Prdc+hUE1QFhMLOiE7mmUZg5/gkE1QFhMLGj54/L0eniq4UhAXMwrKC638wvMKygteU6epkdcNhwJiIuJBR0+UN6FvtbVeDFAXEws6NXWzee/06n2OQbVAGHRKWiCE4bysugHta0dMWTZHeNxgMDoFPQeJzxvc+W46tWaOJMENMHpED9wF6UXHiYk4h2VBhAUaMJrQY+6vW+YSF8AepRYsGVSodWuG+QVNOO9Xu1HnddYDfAXvBB0xZjREi2auG2dQFPIl9LC+HtdN8gjaGrzTquTJpTarrEc4CfoF3R6YP1CpZuGh+1y2zqBfkeuSwubwpxf3kmymZVri8l9lQYV8aMHOKNf0Cqj6YedaGqzDW5bJ9BLQb9IC7PKu26QZw/aYL8yq/+txnqAf6Bf0MJf0HORNvpFc7etS3d4oUYnSrdXeNZ1gzyCVlYeZEMf3KaxHkakbr2UvwmBPvQLWnYOpffsp8lh7lqvnTWobVQwpUUa/+26QR5BO6yVp7fLnNZYDxNO1CRBARX25WdKoA/9gg4q/RHt2Of8kOoeN0mT9qBqHaF5BP282glK0194WGM5TEgPq/cHTW0XhGc/iIt+Qa/270G/DyYFPzaUN28307ulOj9R6dF8vfx4YlFlVvGx/EwKdOFlP+iVpFPG8rroqL+06ZOfjAXVS5tWyuyZavmbFujAxBeLGOfBZsqsT6yP6wDq6Be0q4MxhvIKIeiiQvLV+JmRw/I37baZcw/kb0YTo1/QARL9Oxer8YGhvEIISitGfnh5Q4Xw9PzMeemBRq+Oin4yX3OaGG8P8VcfeM9QXjEEvdW7CCnULn97QnuPzKQ07ZFJ+ZrUvHj9HXRnPUN5xRDUB1wLvyHPfq3o60JMgteCbnTbUe8RvxX0V3v/sa0AHmyqCf2CfqywsGIbQ38N17UAAArqSURBVHn9VtCUCOXb5+kyvi7EJOgXtIhCSPOjhvL6raD0oRnSJLP/SF/XYRL8uh/UJ5yr07R799rt8LgJbUDQ/Obyg1ENG5UacNvXdZgE72+a62Ior/8K2md4BqU3u0z1dR0mQf9txwvCKo+ePzYmao+hvH4r6LVw+UYDerSSj+vIB258NHGRwUs2vDnEP/+AfHTK6PScobyuBD17xA9Orzi6mTKt382UXKnn1CGl5hmMol/Q8muU2YZyhvLmFXRHzajaEVMs/9UsJUL5K54p7etCeHO9/NCYwiWfitptLIx+QcstVGZvejwVkvGPmxvg8gi6v+wWSn/vPFRjOealo3y7oO3Zl3xdB2/WV2h7OPPPl0s+ZSyMfkGfjdgoTTcWc3+IPzywTCAJLPOMWvg8gvZUXrga+Y/GekzLmdhuCW82b2X5QcTHh92UZw8avNhWv6DXWpHidYqTNm478r4Ojh2XsCJhYv3gZNcN8gha9YQye8D6N8bffn/w8DU2X1fBneejlNnQKGNhvOkH3R4/Yo7bu+Ipvb9nhjK3DWrtukEeQWv9oMyafa2xHiA4C4Llfp5L5ZsaC8Opoz58nWPhmwjnl3dGZhEQHBlZ4iyl/SMd88JF5PkvhYvleh1zk87DAwMfXzQhpECwwTg6BR3w9oIs3LVu+IpjYU5cjtcvpzgIW5iSclV6IT3FMT9aYdjBP1dUfC8l1+uYm3XevcO0oVOGVThpLE4dnYKSXhWycNf6A/LE2u+OH/p8YOAy1w3ydjNdfLF6mU44wFuHW9Miy4T2PmMwit5D/PVb2povr6s8gamu2s3Jfnsmya/40/iTtrz8Dnp84wVPG5zdt2nfWdV3ISjQhH5Bz3YYTjcXJJHGnvIFQYEm9Avavexqet99J7t0NpQXggJN6Be0eCK9EriCrixpKC8EBZrQL2ixFXRVwN90NW6aA/mAfkE7tElu3JJee7iRobwQFGhCv6D/K0NCdtO6QesN5YWgQBNedDPdPPgPpWuM3dQJQYE2vOoHTTthOC8EBZrwQtB3owII7T3XWF4ICjShX9APAgavJHR+gXcN5YWgQBP6Ba01nF6UVibXMZQXggJN6Bc0ZJMi6NYQQ3khKNCEfkEbTlYEnV3XUF4ICjShX9D3g6bvJecSw94wlBeCAk148St+QXFCSOGxxm77gqBAE970g17f/9kOo3cHQ1CgCTzdDgiNXkFTt314UJrdPJLU1VBeCAo0oVPQnytK3z+H/BAbSEiAobwQFGhCp6DdSnz648qS5Zuv3Lb/uqG8EBRoQqegJV+TJvHE2zGzd0VnETjLY+M/xnZ87L0MLzMBi6BTUPKZNFnj9Q+mzFMnHYR63IPuKj12y6qO9+JZ7v6NXkHlh4NuYPCL3uMhPrNKkjx7aqLxXMDECCvo97WV2SFjZ1SB2RFW0J32gcLOlzeeC5gYvYIGFSlSJIgoY3kZyutR0HMllQfWf/GAoTTA7OgUdKIThvJ67mbqPlQy9FSNdZ7aAUsj7qnOy70qPdGl1Fts0wKzIa6glP70yVcX2WYFpkNkQQGAoEBsICgQGggKhEZkQW99f9L6wwkB93AUVN9QiHm4PalYg+hqW3SnBZaCl6C6h0LMw7CH/qR0Rzljo34Ds8NJUP1DIebmQrEr8mxZN115gdXgJKj+oRBzs7uVMjvjcVBlYGnydyjEu3gUdL99iLqfq+vKC6wGJ0HVhkLMxqOg6aV+lGevWn8AeeAOToJ6MRRibj4pv+zskRGV/tSVF1gNXr/iGQyFuL9bVO2Rl/SlBVaDXz8ohkIEDBD5TBIAEBSITf4KuicuiwLGHi8K/AVOgo6+i/PLt7876KD2N7riAX+Fk6AjQkhUjB3XDZrt1RUP+Cu8DvEbyRG370NQoAlegtpCIShgALcfSesuu30bggJN+KqbCYICTXAVdJz6UAsQFGiCq6DkF9W3ICjQBAQFQgNBgdBwFXT7DdW3ICjQBH7FA6HxmaDjEvMwo2Nf1vRpzzzkk22Zh+zLIWS7PsxDduzNPGSX8Xk1yEl5Hwm6eHBe2oTVZE2VIsxDVi/APGTNwBrMQxaOZh4yLIp5yMg4Fx7k4MWrvhHUFV+wvwf+cH3mIVMimYekRa8xD1nb/almb+i8iXnI5xKYhYKgDiAoQyAoBGUHBIWg7ICg7IGgDIGg7IGgDIGg7IGgDIGg7IGgDIGg7Nncg3nIIyrPLjPAtVLMQ9Li6hcteEt99Qt1vKVbEvOQw95nFiofBM1wf5eIV3AYCcxvQ6ZkMg95LZ1ZqHwQFADvgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKh4S/o0riI1mxHh1+rjHY3kF3AEfYhIZhWao/JrtT0GbEhtWbfpgzLzA7JrsqrL1QObbhCXmJVJXdBPyYvr+sRfJhlyFmlEyR2sQpn2xyqyMSy0qyY7EodV2TmlqmFX2RZZnZIdlU+Vvq9LQPJRoZVche0Xh9KM2IHsQw5qAPLaOuLEqLIxLDS7JjMSs0MHi9NpwfdYlfm3ZDMqrxEPpSmdfoy/DB5C3qWrJWm48qyjNn2hZRD7C6CvnzkSJQsE8tKs2KyK/VcrHy8XE7OsCszOyS7Kn8bcFqatnmK4YfJW9B95KA0TQjMYBgzKrYQIT0vsAsYI8vEuFIlJuNSb7aKyWRcphySaZV/75ledCfDD5O3oJvIcWm6gqg/1l43acEdj177vMQj7CIqMjGuVInJttRDjYsmMy5TCcm0yvjgkk/dYFgl/z3oIWmaGHibdeAF5AqzWI49KNNKY+4OFsmm1EtPB/Y4zbZMR0g7zD5QW59HGFbJ/zvo59J0YhnmgTcr/0fZEGP/Dsq0UidBmZR6rFydA/KcYZlZIe2wqHJTP3m6hGSyq5L7r/g6A6X/U/WfYRhxe5Ht0nRSBLubEe0ysa1UicmuVFvtTrfsS8zKzA7JrsokZcf5fDmGVXIXdHngG8mDg7U+T1cLmXEVZm+eUOgddhHtgrKtVInJrtQ9ZOQSmZvsyswOya7K2/VjPto6psDbDD9M/meSPmwY3iqZacS/ni4b1nQVw4COwzHTSu0xmZWaSOz8xa7MuyHZfaDnnigX2ugjeYlVlTgXD4QGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaCs6eV4ogx5zdeVWAIIypq9a9asiWopTY5eJwN8XYz5gaA8qPO4PL3V621fF2J+ICgP7ILSCgsorfzRsIrRi37vck8lefigpXGhdZb6tDSzAUF54Cxo5U2Z0wNqHsjoF3yLLgyauPGFgHd9XJ2pgKA8cBb0aUp/JzMo3UGOXS8+XXp1cJRvizMXEJQHzoLOojRDHjXoCPllP0m+ePHiyoA0H5dnJiAoD5wFnSsLukER9DNHBxS74R+sDwTlgYqgO8h5HxdmPiAoD1QE/adQovTqgt6+Lc5cQFAeqAhKxxSdu21qwTk+rs5UQFAeuBS03u/UNrd2SE2G4+f4ARAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCM3/AWXpqbelV6EuAAAAAElFTkSuQmCC" 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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the residual plot.</p> <p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that point.</p> <p>We may add that it is difficult to judge the random nature of the residuals just from the three samplings at days 0, 1 and 3. Also, it is not clear <em>a priori</em> why a consistent underestimation after the approximate DT90 should be irrelevant. However, this can be rationalised by the fact that the FOCUS fate models generally only implement SFO kinetics.</p> @@ -1789,12 +1763,12 @@ plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, <pre class="r"><code>m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.FOMC, show_residuals = TRUE, main = "FOCUS L2 - FOMC")</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Thu Nov 19 14:46:15 2020 -## Date of summary: Thu Nov 19 14:46:15 2020 +## Date of fit: Mon Feb 15 14:11:19 2021 +## Date of summary: Mon Feb 15 14:11:19 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -1836,10 +1810,10 @@ plot(m.L2.FOMC, show_residuals = TRUE, ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09 -## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.617e-07 -## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.386e-07 -## sigma -7.436e-09 -1.617e-07 -1.386e-07 1.000e+00 +## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09 +## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.602e-07 +## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.372e-07 +## sigma -7.828e-09 -1.602e-07 -1.372e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -1867,12 +1841,12 @@ plot(m.L2.FOMC, show_residuals = TRUE, <pre class="r"><code>m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - DFOP")</code></pre> -<p><img 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" 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" /><!-- --></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Thu Nov 19 14:46:15 2020 -## Date of summary: Thu Nov 19 14:46:15 2020 +## Date of fit: Mon Feb 15 14:11:20 2021 +## Date of summary: Mon Feb 15 14:11:20 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1912,18 +1886,18 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 93.950 9.998e-01 91.5900 96.3100 -## log_k1 3.117 1.929e+03 -4558.0000 4564.0000 +## log_k1 3.112 1.842e+03 -4353.0000 4359.0000 ## log_k2 -1.088 6.285e-02 -1.2370 -0.9394 ## g_qlogis -0.399 9.946e-02 -0.6342 -0.1638 ## sigma 1.414 2.886e-01 0.7314 2.0960 ## ## Parameter correlation: -## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 6.459e-07 9.147e-11 2.665e-01 8.413e-11 -## log_k1 6.459e-07 1.000e+00 1.061e-04 -2.087e-04 -9.802e-06 -## log_k2 9.147e-11 1.061e-04 1.000e+00 -7.903e-01 -2.429e-09 -## g_qlogis 2.665e-01 -2.087e-04 -7.903e-01 1.000e+00 4.049e-09 -## sigma 8.413e-11 -9.802e-06 -2.429e-09 4.049e-09 1.000e+00 +## parent_0 log_k1 log_k2 g_qlogis sigma +## parent_0 1.000e+00 6.783e-07 -3.390e-10 2.665e-01 -2.967e-10 +## log_k1 6.783e-07 1.000e+00 1.116e-04 -2.196e-04 -1.031e-05 +## log_k2 -3.390e-10 1.116e-04 1.000e+00 -7.903e-01 2.917e-09 +## g_qlogis 2.665e-01 -2.196e-04 -7.903e-01 1.000e+00 -4.408e-09 +## sigma -2.967e-10 -1.031e-05 2.917e-09 -4.408e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -1931,7 +1905,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 93.9500 9.397e+01 2.036e-12 91.5900 96.3100 -## k1 22.5800 5.303e-04 4.998e-01 0.0000 Inf +## k1 22.4800 5.553e-04 4.998e-01 0.0000 Inf ## k2 0.3369 1.591e+01 4.697e-07 0.2904 0.3909 ## g 0.4016 1.680e+01 3.238e-07 0.3466 0.4591 ## sigma 1.4140 4.899e+00 8.776e-04 0.7314 2.0960 @@ -1943,7 +1917,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Estimated disappearance times: ## DT50 DT90 DT50back DT50_k1 DT50_k2 -## parent 0.5335 5.311 1.599 0.0307 2.058</code></pre> +## parent 0.5335 5.311 1.599 0.03084 2.058</code></pre> <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p> </div> </div> @@ -1961,7 +1935,7 @@ FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3)</code></pre> mm.L3 <- mmkin(c("SFO", "FOMC", "DFOP"), cores = 1, list("FOCUS L3" = FOCUS_2006_L3_mkin), quiet = TRUE) plot(mm.L3)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes of 7%. Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level considerably.</p> </div> <div id="accessing-mmkin-objects" class="section level2"> @@ -1969,10 +1943,10 @@ plot(mm.L3)</code></pre> <p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> <pre class="r"><code>summary(mm.L3[["DFOP", 1]])</code></pre> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Thu Nov 19 14:46:16 2020 -## Date of summary: Thu Nov 19 14:46:16 2020 +## Date of fit: Mon Feb 15 14:11:20 2021 +## Date of summary: Mon Feb 15 14:11:20 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -2019,11 +1993,11 @@ plot(mm.L3)</code></pre> ## ## Parameter correlation: ## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.671e-08 -## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.148e-07 +## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.664e-08 +## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.147e-07 ## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 1.022e-06 -## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.929e-07 -## sigma -9.671e-08 7.148e-07 1.022e-06 -7.929e-07 1.000e+00 +## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.926e-07 +## sigma -9.664e-08 7.147e-07 1.022e-06 -7.926e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -2056,7 +2030,7 @@ plot(mm.L3)</code></pre> ## 91 parent 15.0 15.18 -0.18181 ## 120 parent 12.0 10.19 1.81395</code></pre> <pre class="r"><code>plot(mm.L3[["DFOP", 1]], show_errmin = TRUE)</code></pre> -<p><img 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" 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wO1Yhdo9u30cr5qJNC8ujfkzgdVEqNAU2d5WAmCtVfoFYkFjARKBm2QNR9UVWwCPWXrHhQeHRUx0dPpjOEljAX6yaty5oMqi02gvgEZuiu5I3oZXsJYoBcay5kPqiw2gdrHFF077Gh4CWOB7nV4Ey8YAVaBdgguujavs+Elyg/0Xq92bfu17X1PzpxQJbEJNMYqYE3iheTjUYOrxRheovxAX309P3pI/rjxcuaEKonRs/hYP0HDqmecxALlBpprf4fcdMpLc8AL6y0es+2g6ecSEpLulLrxoFBscTnfe1tzaJFWPxJnHMDB4jHdk3T3kuQpj8r/DWp3l5BJC9PtcyswKVQpjAKN7pJEbvYXBLuPJRYo/2/QsRMLyLf+wdgWChUKNPUXIwuHCz1ukmfcVu6ZUV3i3MXlB5r+VOdF86r1wJnjoQKB7m0gCMS/3HcNeYQRclP4QbwW+qThJYxsBy34JiS0JTaEQgUCja4+Plogc6xWlbOw8zZCfhEeiNfi7AwvYWxDvWhuiImrBlWY/EC9p5A08ZMZrctZeFDfbPGZzn7xWliFNtRr/dDBxFWDKkx+oLZx2kDjapez8AWX1h8eXOD65cGwGhsNL2FCoDkO2MoE8gNtN1cb6HvlvqT4UlBd7dbOVpslFjAhUPIi3h0P8gNdW2N+onBrTc1l5S+fc+XY7kSpV4OaFuiPHtiTZPEq8Cw+wln81WgdWqBoXlMCJV0lduSD5ajIdtAHJzYfuK1wXpMC3dZR4Sygepy+aU4n3wsHq7d08gPtX0TRvCYFSj4ZqmgSUD/5gY7VGOTyP2XH9zIt0P9cUhTNAqpX0Yf4B88o+/vQtEBJ6FuKZgHVq/DfoIeEW0rmNTHQP52wsd6yVTjQdbUUbWcyMVAy+gMls4DqyQ90vda8hr6K5jU10CS3bEXzgMrJD9RGy9bngqJ5TQ2U9FqpaB5QOa63g2qcr59Md2ZQFe4DJZ+1x4O8BZMZaD09iuY1PVDyXJh4kbtq1AvL/lM0JaiRzEAjIyM/ruM2ednbLdxjFc0rI9Dbbt+RW236R20KbPKbojlBheQ/xE94Oku8zHv2dUXzygiU7HNPe2WG5sqn/ormBBWSH6ib7jVwO10VzSsnUDJlsOPfmo+5juUdExeqogoEqnure4Sy4yPKCjTrif/p9gp44THe0sgPdJzDLvEy1qHyHuIJ+a36bM2Huw4ZiiYF9ZEf6H1foW7rukLPB4rmlRcomVlLfCqfOVLZ/xSgQhXZDnrgg8kf/aBwXpmB5o2r3uq1Ji9hO5PF4X9DfaGTnn5JdFcB1EBmoF8kkC+KKJpXdqDkbpex+AVqeWQGWudF4lhE0bzyAyUPRrU8rWhOUCHVPMRrbHWZK3nAUaiaKhho8p7SB0+WqUKBkt+f7nld2bygMvIDTe09nWyrJtSTOEOXiSoWKMl73yUch122JPIDHeS6k7Ttc6V3Zbzt2ICL/ZrjVN0WRH6gdVeTv4QEsqG+onkrHCghCd69flU0N6iI/EAdN5EvbTLJrjqK5lUQKMleXC8oVdHsoBryA32m6/7HnyMP+ik7vKySQAlJm1tvPJ4tWQT5gZ55RLA/RZrX3KVoXmWBEnJ7pvOb+C1qASqwmSnj5B1CtlxSNq/SQAn5e4bzaGNnGwHVY3MaGuOUB0rIvx836vat8mGAZ2xOQ2McjUAJyfq8RYf1eNNnVcbmNDTG0QmUkIKE/g1C/qAzFnCIzWlojKMVqOjX8U4j3xj8wqf4TVoVsTkNjXEUAyXkmKNds6CBrf6mOCRwgtFpaIyiGmiPz/NjA1w6D6M4JHCC2WlojKAZ6G0nzelqUoKtntmGF5JUNexOQ5N9u7w3sdMM9Dcv7YeC6lE9XOdcozcucIDRaWhSZ3lYiRV7hUqdyotmoBn22v8VzjYl5NxbzgHbc+gNDeYmN9D78Zt/F39Z/fnroX7lLHzK1j0oPDoqYqKnk8TLRqn+DTphxH+EpD21VHM9M/qphmE49UKVITPQ8+7iw3vQ2cbipVU5C/sGFB5hIXdEL8NLUA00Y7T7qBceCSl6O8iFaY/4rc+kOD6Yj8xABzhHJ22s79Zl074T5R24wb74HIaHJd5bRzVQscmoTb/rfZodE+D8xkmqM4B5yAy0/mLxYrFg7JlIh+Cia/Mqfr54Za6/59FmudITNoLZyQxU0Jxee5vRd3jGWAWsSbyQfDxqcDWJ88EyD1T8Q/nAKMfBu7DhSd3kBqrpbafxtyDH+mlPF2/VM05igUoIVHRvddcG03BAEjVjFCgh6ecSEpJKvzf5t5AiNRW+GspkF992bx+Bh3rVYhao1s5SZ6NL/aCI9aemjqFYfsIox4HbsiptPqBJbqA1bGxsagjaUyWZ8k37pb5SOQ/xRe6v61kv6KiiU+OBecgMdLaechbeHqgj+AcGGl6icgMVpS70bjoXB2hWHTbHZtptK7T3EQktfXwML1HpgYp+ntzAJwKvyVMXRgcPS27f6SLh5yG+SF5coGNA9H1zTA0Vw+rodjkhdVbyF6jovw39HUZ8i1ffqwW7wy8ecg+4wWGgorSVTzmPO4gDOaoCw+OD3n2hHp+BilI/etJ10jE8recf0wPYbpx8UepLZg5UlPxuy0dDcMhm3qnqCMu0nQ1r2nw2jk7CNYsOVPTTjCbe87RHc8xbM/zZkD/NvT5QiqUHSkjBsamNW80996Bb7017Ql0OmHt1oCQEKio4Pq1JvdaaN6ccaJJn7pWBEhCoTkHzQA+vkJ/IEz+Ze02gBARayOMK+TmseZPGy7B9lCsItFDvbZrLM3beDV6Px34mfiDQQjubXyEk+60B5PKHXeu+tBX76zmBQIuscOk38tHnte8BuBHZx37AF3jdEw8QaLHbu9Y/3Gj/z8YXnLp/hNePmh0ClZS9d4Jby5BEPGkyKwRanoKf5jzh8so2/EFqPgjUmGsRve2eXfG78QWBBQRqgntbR9d7IuwodjKZAQI1TV7irCfqjdpU3iFPgQUEarrUlQPseywy9R8MqECgsmTufbNpo3Hb/zX3elgOBCrbxY/72Pstxi/SyoFAK+K/3RO9XF/eXPrQU0AfAq2olBUDHHzeOYrDO7KFQBXIPhDazmlIpNSJIoACBKrQ3+vHNPQK3nHX3OtRVSFQCpKW9rHzmX0ILyNlAIHSkXUgrKN9wJLTOBYEZQiUnvRtwc3rv7AKr9GjCYHSlbp2lFvjsVE4gz0tCJS+5JXD6jcbv/GGudejSkCgTBQkhQ9y8g7ejLeNKIVAmck/tWygJlL8JlUCgTKV9/OygXUfG/81/iatKATKXP7Z8CEunmO+vGTuFVElBFo5zq8KdG8wLPw0XpUvEwKtPFejxrV0CFjwfYa5V0RNEGjlurVjmo9tl2k78PTeRAi08v13aH5fx2ZjVp/DW+6NQ6Dmkf9L5OhmjgHv7ce7R8qHQM3o750zu9dpM+EryVNNAAI1t5wT4SOaOPd9b98/5l4TPiFQHtzYEfK0XXO/0RvwBpLS2AWafbu8oxwg0NKW1G3XrGZ1n2mbrpp7TbjCKNDUWR5WgmDtFSr1fh0EWspW7+uE5E7quOh5N5d+83bfMvf68IJNoKds3YPCo6MiJno6nTG8BAItxX+n5jLXVfw/+o8ds3o7egz/8OA9c68UB9gE6htQuLckd0Qvw0sg0FIevar98Ey87tOCi+snd7drOerjIw/Mt048YBOofUzRtcOOhpdAoKW0O6b98PhJvdvykr58o3Pt1mM+Pmy5RyhlE2iH4KJr8zobXgKBlvLecM377eI9y7yaJPf0F0GdbFuOXHrQIjdEsQk0xipgTeKF5ONRg6vFGF4CgZaS4ftU1LdTHvnB8Fdzz3w5sVudpsMW7blZuatldoyexcf6CRpWPeMkFkCgpeVHjxo4p9zXkOSd/3q6v5Nrv7e3pljOu5uZbQdNP5eQkFT66Fr56UVqI9AKurrjnYFN7LtP/PynTHOvSmVgFWjeFd0/X2aJh6RDzk6FrBbLGw9KSP9u2Zi2tVq9tHhvVT+BOJtAc+fUEmrN1Py9v07i+/AQr1z26XVTe9Wv7z9l7c9V95cpm0A/rD4tZkr1lwkCZe+vvYsD29TyHr7gm8tV8S9TNoE2DxMv1gvfINBKknP265C+je06jwvfX8V2krIJtPZuzWWgRyYCrUx3D0cGP13Xpeebq45UmcNBsgm041TN5d8ubyDQyvfnvmWvdrZr9MzUz49VgU37bAKNEN7al0XI7mqjQxCoeVyL+/DljnaNek9edVjVh9JntJlpvr2QIn7Y5SogUHO6tmfJq10cXfyCPtmv0oObsNoOmn05S/Mh97tIw19HoJXoz/2fBPk1cOg8dtH25Bxzr4xMeMuHxUhPXDNzYLNaLQaFrElMM/fKmAyBWpjsX7ctHNPZoV7XVz7Y/mtW6a/mJ8We52trKgK1TDcPrZ4+oLmNZ583P42/UvwSvzPtH+vn2SPZnCtWGgK1ZLmXYpdO8G9s4z1o+qoDqQW3G35NSMFnHjy9iB+BAsk4G/PBq083tGnYZMaq767lD/rK3CukB4FCkQeDX1/06tONbOo3nfxJ3EVOzvqEQKHYFO1rIDNeGbkkqI+XdZOery3afLK8gxtUBgQKxfa10rxs716TU5pPci8nRM4c8qSDU7uhMyPjL5nrFyoChYfGPrn17NePTS9xW9qPmxaO8/e0bvz02Hlf/XC9sg8ZiUBBT8xzbYbsNfiV3KsH1swO7OZq7dXrtQXrj/xRWaEiUJAl62L8qrARXRqKob7y7lffX6N2uLOCreOGLSj7uhYEChWSlRy/evao7o2t3buPDFsZe07pgXgz+/is3hLcILH07QgUFMm99kPUgvEB3nUcH+8ftDD6h6sVfDXKwuc1u1hjvUr/6YBAgY47Z779NOSl7o1ruvoMnbxsy9E/5D34tz+q/dD6VKnbESjQlffH0S3LJg/t4lbTrcuwyUs3Hblm0haqUgdPK4ZAgZHc60c3L58yvFvjmg3aDQiavy7+1/LegeK3S3OZ1yil1O0IFFgr+OvkNyveHtPb2962xVMjpy3fdDil7LnMNra+QUj+22WO1olAofI8SD4U/dGk4d09bZxa9R4dGr71yOXiVBe5vPRG655ljo2GQMEc7pyL/2rhW0O6edZyaOk7csqS6P3nzn796ZGyCyJQMK+75w+sXzo1sKe3s/V0A19GoMCLrDLvQCEIFDiHQIFrCBS4hkCBawgUuIZAgWsIFLiGQIFrCBS4hkCBawgUuIZAgWsIFLiGQIFrCBS4hkCBawgUuIZAgWsIFLiGQIFrCBS4hkCBawgUuMYu0Ozb5Z0gAoGCSRgFmjrLw0oQrL1Cr0gsgEDBJGwCPWXrHhQeHRUx0dPpjOElECiYhE2gvgGFBy3LHVHmeHo6CBRMwiZQ+5iia4cd9W8/O6xIjeVyxgOLxSbQDsFF1+Z11r/9zpYijQ/IGQ8sFptAY6wC1iReSD4eNbhajOElfI7JGQ8sFqNn8bF+goZVzziJBRAomITZdtD0cwkJSWVPHFYEgYJJzLUnCYGCSRAocA2BAtcQKHANgQLXEChwzWyBhq4q4/0+gQz4sRg0YAiDQYdb+s8/smwTq9zMFOjn48vyrdOSgf+1YDCogyuDQd0t/eevZyCKt+6ZJ1BDvh3AYtRaZU9cqtzorxgMip9fyXcjUH0c3kFSLOXnR6D6OLyDpFjKz49A9XF4B0mxlJ8fgerj8A6SYik/PwLVx+EdJMVSfn4Eqo/DO0iKpfz8CFQfh3eQFEv5+Ssh0D3PsxjVIYvBoK9sYDAofn4l310JgebdZTFqGotB7+UwGBQ/v5LvroRAASoOgQLXEChwDYEC1xAocA2BAtcQKHANgQLXEChwDYEC1xAocA2BAtfYBxrT0cHvNM0BP+lcp8VHufRH3r2dUB70wvPOnhG0B839oFntdlupjprwDSkxHp2BC0dVeHcxDzTWKmjrs7VT6Q04X5gaG1p9DvWRk20DCd1BL7kExIQK6ymv6WzrRbEThN0UR83vNF3zoXg8OgMXjqr07mIeqN+zhGS4z6I2Xrb9W+LltFp5lEfOaS9oAqU5aFDrbEJ6dKX8b+A6WbzoOJTaqNdXPCVoUyoej8bARaMqvrtYB5ourBEvJ3hQG/CysE+8jBGuUB45pFP7QLqrm1P3Q/HyZhLlf4P674oXfftTGzWue3cbTUrF41EZuGhUxXcX60DPCYniZbhVNq0Bs1I0LyWfUiuT7siH6lz0CaS7uleEg7nn/ia0/w0muJ1IW1VjLc1RvTQpFY9Ha2DtqIrvLtaB7hcuiJfRwm2qo66vPoPuyHcbrySaQGkOekx4304Q+tyk/G+Q00EQhGCqq6pNqXg8WgNrR9VScnexDjRBSBYvo4TyzkAr161RwthcuiO/EEC0gdIcdLfQcN/97936U/43GOe69shCu9U0R9WmVDwerYGLAlV2d7EONEk4Ll5GWFMccreLx07KI290vqELlOagicKX4uVK4S7Vf4Nk7R91853zKY6qTal4PFoDFwaq8O5iHegdqyjx8s2m9EbcXS04k/bIkwWdnTQHFf8GFS/3CJeo/htECWnaUX+jOKo2peLxaA2sC1Tp3cV8M1PPwYTkeoZQGy/XdRT9kS/uF7X033+L6up6h4kXM+3yqQ56QtBs/55tk0NxVF1KxeNRGlg7quK7i3mgcdXePfKSk9T55eX7Tpi5TiOT+siah3iqg26qMWvPrOrLKf8b9HWO2Pt2jfk0R9UFWjwepYG1oyq+u9jv6tzaycGf4g7JyMJH45vUR9YGSnXQjZ3qtF1He9D/wprXbvt5Ac1RC/9aLB6PzsDaURXfXXixCHANgQLXEChwDYEC1xAocA2BAtcQKHANgQLXEChwDYEC1xAocA2BAtcQKHANgQLXEChwDYEC1xAocA2BAtcQKHANgQLXEChwDYEC1xAocA2BAtcQKHANgQLXEChtQwsP9iIsIHW+MPfKqB8Cpe1YTEyMezfx4gJ5McHcK6N+CJSF1i+aew2qDATKQmGgjuJDfKPlXWw9P/urv2OTr8Vb1rW3bfWleddNZad8Y78AAAEFSURBVBAoC/qB1njnu/5WjZbHdbK5TyJqvBM32eozM6+dqiBQFvQDHaY5rvwkQmKFXx44zxdvHedi3pVTFwTKgn6giwnJEzZoKj1zQkhMS0uLFiieGLLKQ6As6Ae6RBNojDbQzYUboJLMvHpqgkBZkAj0gHDLzCumPgiUBYlAb1lrzlc5x9+8K6cuCJQFiUBJSM0FcTOsws28dqqCQFmQCrRgSWtb70gzr5y6IFDgGgIFriFQ4BoCBa4hUOAaAgWuIVDgGgIFriFQ4BoCBa4hUOAaAgWuIVDgGgIFriFQ4BoCBa4hUOAaAgWuIVDgGgIFriFQ4BoCBa4hUOAaAgWuIVDg2v8BfwzyZuLtxxgAAAAASUVORK5CYII=" /><!-- --></p> <p>Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit model based on the <span class="math inline"><em>χ</em><sup>2</sup></span> error level criterion for laboratory data L3.</p> <p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter <code>g</code> is quite narrow.</p> </div> @@ -2074,13 +2048,13 @@ mm.L4 <- mmkin(c("SFO", "FOMC"), cores = 1, list("FOCUS L4" = FOCUS_2006_L4_mkin), quiet = TRUE) plot(mm.L4)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <pre class="r"><code>summary(mm.L4[["SFO", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Thu Nov 19 14:46:16 2020 -## Date of summary: Thu Nov 19 14:46:16 2020 +## Date of fit: Mon Feb 15 14:11:20 2021 +## Date of summary: Mon Feb 15 14:11:20 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -2141,10 +2115,10 @@ plot(mm.L4)</code></pre> ## DT50 DT90 ## parent 106 352</code></pre> <pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3 ## R version used for fitting: 4.0.3 -## Date of fit: Thu Nov 19 14:46:16 2020 -## Date of summary: Thu Nov 19 14:46:16 2020 +## Date of fit: Mon Feb 15 14:11:20 2021 +## Date of summary: Mon Feb 15 14:11:20 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -2186,10 +2160,10 @@ plot(mm.L4)</code></pre> ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.456e-07 -## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.169e-08 -## log_beta -5.543e-01 9.889e-01 1.000e+00 4.910e-08 -## sigma -2.456e-07 2.169e-08 4.910e-08 1.000e+00 +## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07 +## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.478e-08 +## log_beta -5.543e-01 9.889e-01 1.000e+00 5.211e-08 +## sigma -2.468e-07 2.478e-08 5.211e-08 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -2211,8 +2185,8 @@ plot(mm.L4)</code></pre> ## parent 108.9 1644 494.9</code></pre> </div> <div id="references" class="section level1 unnumbered"> -<h1>References</h1> -<div id="refs" class="references"> +<h1 class="unnumbered">References</h1> +<div id="refs" class="references hanging-indent"> <div id="ref-ranke2014"> <p>Ranke, Johannes. 2014. “Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0.” Umweltbundesamt Projektnummer 27452.</p> </div> diff --git a/vignettes/FOCUS_L.rmd b/vignettes/FOCUS_L.rmd index e017b674..96239051 100644 --- a/vignettes/FOCUS_L.rmd +++ b/vignettes/FOCUS_L.rmd @@ -1,7 +1,7 @@ --- title: "Example evaluation of FOCUS Laboratory Data L1 to L3" author: "Johannes Ranke" -date: "`r Sys.Date()`" +date: Last change 17 November 2016 (rebuilt `r Sys.Date()`) output: html_document: toc: true diff --git a/vignettes/mkin.html b/vignettes/mkin.html index 1f696c37..3a49375a 100644 --- a/vignettes/mkin.html +++ b/vignettes/mkin.html @@ -11,10 +11,22 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-11-27" /> <title>Introduction to mkin</title> +<script>// Pandoc 2.9 adds attributes on both header and div. 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max-width: 940px; @@ -1631,7 +1605,7 @@ div.tocify { <h1 class="title toc-ignore">Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-11-27</h4> +<h4 class="date">Last change 15 February 2021 (rebuilt 2021-02-15)</h4> </div> @@ -1668,42 +1642,47 @@ f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE) # Plot the results separately for parent and metabolites plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright"))</code></pre> -<p><img 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" 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" /><!-- --></p> </div> <div id="background" class="section level1"> <h1>Background</h1> -<p>Many approaches are possible regarding the evaluation of chemical degradation data.</p> -<p>The <code>mkin</code> package <span class="citation">(Ranke 2019)</span> implements the approach recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe <span class="citation">(FOCUS Work Group on Degradation Kinetics 2006, 2014)</span> for simple decline data series, data series with transformation products, commonly termed metabolites, and for data series for more than one compartment. It is also possible to include back reactions, so equilibrium reactions and equilibrium partitioning can be specified, although this oftentimes leads to an overparameterisation of the model.</p> +<p>The <code>mkin</code> package <span class="citation">(Ranke 2021)</span> implements the approach to degradation kinetics recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe <span class="citation">(FOCUS Work Group on Degradation Kinetics 2006, 2014)</span>. It covers data series describing the decline of one compound, data series with transformation products (commonly termed metabolites) and data series for more than one compartment. It is possible to include back reactions. Therefore, equilibrium reactions and equilibrium partitioning can be specified, although this often leads to an overparameterisation of the model.</p> <p>When the first <code>mkin</code> code was published in 2010, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were KinGUI <span class="citation">(Schäfer et al. 2007)</span>, a MATLAB based tool with a graphical user interface that was specifically tailored to the task and included some output as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data.</p> -<p>The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>), where the code is still occasionally updated.</p> -<p>At that time, the R package <code>FME</code> (Flexible Modelling Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the relative standard deviation that has to be assumed for the residuals, such that the <span class="math inline">\(\chi^2\)</span> goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass using an significance level <span class="math inline">\(\alpha\)</span> of 0.05. This relative error, expressed as a percentage, is often termed <span class="math inline">\(\chi^2\)</span> error level or similar.</p> -<p>Also, <code>mkin</code> introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to <code>mkin</code> for the case of linear differential equations (<em>i.e.</em> where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases. This, however, has become somehow obsolete, as the use of compiled code described below gives even smaller execution times.</p> +<p>The ‘mkin’ code was first uploaded to the BerliOS development platform. When this was taken down, the version control history was imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>), where the code is still being updated.</p> +<p>At that time, the R package <code>FME</code> (Flexible Modelling Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the <span class="math inline">\(\chi^2\)</span> error level as defined in this guidance.</p> +<p>Also, <code>mkin</code> introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to <code>mkin</code> for the case of linear differential equations (<em>i.e.</em> where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases. This, has become somehow obsolete, as the use of compiled code described below gives even faster execution times.</p> <p>The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in <code>mkin</code> from the very beginning.</p> <div id="derived-software-tools" class="section level2"> <h2>Derived software tools</h2> -<p>Soon after the publication of <code>mkin</code>, two derived tools were published, namely KinGUII (available from Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.</p> -<p>CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.3 of CAKE release in March 2016 uses a basic scheme for up to six metabolites in a flexible arrangement, but does not support back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites.</p> +<p>Soon after the publication of <code>mkin</code>, two derived tools were published, namely KinGUII (developed at Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.</p> +<p>CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.4 of CAKE released in May 2020 uses a scheme for up to six metabolites in a flexible arrangement and supports biphasic modelling of metabolites, but does not support back-reactions (non-instantaneous equilibria).</p> <p>KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.</p> <p>A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named <code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin/">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html">manual</a> for further information.</p> <p>A comparison of scope, usability and numerical results obtained with these tools has been recently been published by <span class="citation">Ranke, Wöltjen, and Meinecke (2018)</span>.</p> </div> -<div id="recent-developments" class="section level2"> -<h2>Recent developments</h2> -<p>Currently (July 2019), the main features available in <code>mkin</code> which are not present in KinGUII or CAKE, are the speed increase by using compiled code when a compiler is present, parallel model fitting on multicore machines using the <code>mmkin</code> function, and the estimation of parameter confidence intervals based on transformed parameters.</p> -<p>In addition, the possibility to use two alternative error models to constant variance have been integrated. The variance by variable error model introduced by <span class="citation">Gao et al. (2011)</span> has been available via an iteratively reweighted least squares (IRLS) procedure since mkin <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version 0.9-22</a>. With <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release 0.9.49.5</a>, the IRLS algorithm has been replaced by direct or step-wise maximisation of the likelihood function, which makes it possible not only to fit the variance by variable error model but also a <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component error model</a> inspired by error models developed in analytical chemistry.</p> </div> +<div id="unique-features" class="section level1"> +<h1>Unique features</h1> +<p>Currently, the main unique features available in <code>mkin</code> are</p> +<ul> +<li>the <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">speed increase</a> by using compiled code when a compiler is present,</li> +<li>parallel model fitting on multicore machines using the <a href="https://pkgdown.jrwb.de/mkin/reference/mmkin.html"><code>mmkin</code> function</a>,</li> +<li>the estimation of parameter confidence intervals based on transformed parameters (see below) and</li> +<li>the possibility to use the <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component error model</a></li> +</ul> +<p>The iteratively reweighted least squares fitting of different variances for each variable as introduced by <span class="citation">Gao et al. (2011)</span> has been available in mkin since <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version 0.9-22</a>. With <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release 0.9.49.5</a>, the IRLS algorithm has been complemented by direct or step-wise maximisation of the likelihood function, which makes it possible not only to fit the variance by variable error model but also a <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component error model</a> inspired by error models developed in analytical chemistry <span class="citation">(Ranke and Meinecke 2019)</span>.</p> </div> <div id="internal-parameter-transformations" class="section level1"> <h1>Internal parameter transformations</h1> <p>For rate constants, the log transformation is used, as proposed by Bates and Watts <span class="citation">(1988, 77, 149)</span>. Approximate intervals are constructed for the transformed rate constants <span class="citation">(compare Bates and Watts 1988, 135)</span>, <em>i.e.</em> for their logarithms. Confidence intervals for the rate constants are then obtained using the appropriate backtransformation using the exponential function.</p> <p>In the first version of <code>mkin</code> allowing for specifying models using formation fractions, a home-made reparameterisation was used in order to ensure that the sum of formation fractions would not exceed unity.</p> <p>This method is still used in the current version of KinGUII (v2.1 from April 2014), with a modification that allows for fixing the pathway to sink to zero. CAKE uses penalties in the objective function in order to enforce this constraint.</p> -<p>In 2012, an alternative reparameterisation of the formation fractions was proposed together with René Lehmann <span class="citation">(Ranke and Lehmann 2012)</span>, based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter confidence intervals.</p> +<p>In 2012, an alternative reparameterisation of the formation fractions was proposed together with René Lehmann <span class="citation">(Ranke and Lehmann 2012)</span>, based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter confidence intervals. In the current version of mkin, a logit transformation is used for parameters that are bound between 0 and 1, such as the g parameter of the DFOP model.</p> <div id="confidence-intervals-based-on-transformed-parameters" class="section level2"> <h2>Confidence intervals based on transformed parameters</h2> <p>In the first attempt at providing improved parameter confidence intervals introduced to <code>mkin</code> in 2013, confidence intervals obtained from FME on the transformed parameters were simply all backtransformed one by one to yield asymmetric confidence intervals for the backtransformed parameters.</p> <p>However, while there is a 1:1 relation between the rate constants in the model and the transformed parameters fitted in the model, the parameters obtained by the isometric logratio transformation are calculated from the set of formation fractions that quantify the paths to each of the compounds formed from a specific parent compound, and no such 1:1 relation exists.</p> -<p>Therefore, parameter confidence intervals for formation fractions obtained with this method only appear valid for the case of a single transformation product, where only one formation fraction is to be estimated, directly corresponding to one component of the ilr transformed parameter.</p> +<p>Therefore, parameter confidence intervals for formation fractions obtained with this method only appear valid for the case of a single transformation product, where currently the logit transformation is used for the formation fraction.</p> <p>The confidence intervals obtained by backtransformation for the cases where a 1:1 relation between transformed and original parameter exist are considered by the author of this vignette to be more accurate than those obtained using a re-estimation of the Hessian matrix after backtransformation, as implemented in the FME package.</p> </div> <div id="parameter-t-test-based-on-untransformed-parameters" class="section level2"> @@ -1716,36 +1695,39 @@ plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", <div id="references" class="section level1"> <h1>References</h1> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references"> +<div id="refs" class="references hanging-indent"> <div id="ref-bates1988"> <p>Bates, D., and D. Watts. 1988. <em>Nonlinear Regression and Its Applications</em>. Wiley-Interscience.</p> </div> <div id="ref-FOCUS2006"> -<p>FOCUS Work Group on Degradation Kinetics. 2006. <em>Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration. Report of the Focus Work Group on Degradation Kinetics</em>. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<p>FOCUS Work Group on Degradation Kinetics. 2006. <em>Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration. Report of the Focus Work Group on Degradation Kinetics</em>. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> <div id="ref-FOCUSkinetics2014"> -<p>———. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<p>———. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> <div id="ref-gao11"> -<p>Gao, Z., J.W. Green, J. Vanderborght, and W. Schmitt. 2011. “Improving Uncertainty Analysis in Kinetic Evaluations Using Iteratively Reweighted Least Squares.” Journal. <em>Environmental Science and Technology</em> 45: 4429–37.</p> +<p>Gao, Z., J. W. Green, J. Vanderborght, and W. Schmitt. 2011. “Improving Uncertainty Analysis in Kinetic Evaluations Using Iteratively Reweighted Least Squares.” Journal. <em>Environmental Science and Technology</em> 45: 4429–37.</p> </div> <div id="ref-pkg:mkin"> -<p>Ranke, J. 2019. <em>‘mkin‘: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin" class="uri">https://CRAN.R-project.org/package=mkin</a>.</p> +<p>Ranke, J. 2021. <em>‘mkin‘: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin">https://CRAN.R-project.org/package=mkin</a>.</p> </div> <div id="ref-ranke2012"> <p>Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In <em>SETAC World 20-24 May</em>. Berlin.</p> </div> <div id="ref-ranke2015"> -<p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf" class="uri">http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf</a>.</p> +<p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf">http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf</a>.</p> +</div> +<div id="ref-ranke2019"> +<p>Ranke, Johannes, and Stefan Meinecke. 2019. “Error Models for the Kinetic Evaluation of Chemical Degradation Data.” <em>Environments</em> 6 (12). <a href="https://doi.org/10.3390/environments6120124">https://doi.org/10.3390/environments6120124</a>.</p> </div> <div id="ref-ranke2018"> -<p>Ranke, Johannes, Janina Wöltjen, and Stefan Meinecke. 2018. “Comparison of Software Tools for Kinetic Evaluation of Chemical Degradation Data.” <em>Environmental Sciences Europe</em> 30 (1): 17. <a href="https://doi.org/10.1186/s12302-018-0145-1" class="uri">https://doi.org/10.1186/s12302-018-0145-1</a>.</p> +<p>Ranke, Johannes, Janina Wöltjen, and Stefan Meinecke. 2018. “Comparison of Software Tools for Kinetic Evaluation of Chemical Degradation Data.” <em>Environmental Sciences Europe</em> 30 (1): 17. <a href="https://doi.org/10.1186/s12302-018-0145-1">https://doi.org/10.1186/s12302-018-0145-1</a>.</p> </div> <div id="ref-schaefer2007"> <p>Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. “KinGUI: A New Kinetic Software Tool for Evaluations According to FOCUS Degradation Kinetics.” In <em>Proceedings of the Xiii Symposium Pesticide Chemistry</em>, edited by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. Piacenza.</p> </div> <div id="ref-soetaert2010"> -<p>Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” <em>Journal of Statistical Software</em> 33 (3): 1–28. <a href="https://www.jstatsoft.org/v33/i03/" class="uri">https://www.jstatsoft.org/v33/i03/</a>.</p> +<p>Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” <em>Journal of Statistical Software</em> 33 (3): 1–28. <a href="https://www.jstatsoft.org/v33/i03/">https://www.jstatsoft.org/v33/i03/</a>.</p> </div> </div> </div> diff --git a/vignettes/mkin.rmd b/vignettes/mkin.rmd index a672f2a6..8fcb5f9e 100644 --- a/vignettes/mkin.rmd +++ b/vignettes/mkin.rmd @@ -1,7 +1,7 @@ --- title: Introduction to mkin author: Johannes Ranke -date: "`r Sys.Date()`" +date: Last change 15 February 2021 (rebuilt `r Sys.Date()`) output: html_document: toc: true @@ -66,17 +66,14 @@ plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright")) # Background -Many approaches are possible regarding the evaluation of chemical degradation -data. - -The `mkin` package [@pkg:mkin] implements the approach recommended in the -kinetics report provided by the FOrum for Co-ordination of pesticide fate -models and their USe [@FOCUS2006; -@FOCUSkinetics2014] -for simple decline data series, data series with transformation products, -commonly termed metabolites, and for data series for more than one compartment. -It is also possible to include back reactions, so equilibrium reactions and -equilibrium partitioning can be specified, although this oftentimes leads to an -overparameterisation of the model. +The `mkin` package [@pkg:mkin] implements the approach to degradation kinetics +recommended in the kinetics report provided by the FOrum for Co-ordination of +pesticide fate models and their USe [@FOCUS2006; -@FOCUSkinetics2014]. +It covers data series describing the decline of one compound, data series with +transformation products (commonly termed metabolites) and data series for +more than one compartment. It is possible to include back reactions. Therefore, +equilibrium reactions and equilibrium partitioning can be specified, although +this often leads to an overparameterisation of the model. When the first `mkin` code was published in 2010, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were @@ -86,29 +83,27 @@ as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data. -The code was first uploaded to the BerliOS platform. When this was taken down, -the version control history was imported into the R-Forge site (see *e.g.* -[the initial commit on 11 May 2010](https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770)), -where the code is still occasionally updated. +The 'mkin' code was first uploaded to the BerliOS development platform. When +this was taken down, the version control history was imported into the R-Forge +site (see *e.g.* [the initial commit on 11 May +2010](https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770)), +where the code is still being updated. At that time, the R package `FME` (Flexible Modelling Environment) [@soetaert2010] was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the -output requested by the FOCUS guidance, such as the relative standard deviation -that has to be assumed for the residuals, such that the $\chi^2$ -goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass -using an significance level $\alpha$ of 0.05. This relative error, expressed -as a percentage, is often termed $\chi^2$ error level or similar. +output requested by the FOCUS guidance, such as the $\chi^2$ error +level as defined in this guidance. Also, `mkin` introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to `mkin` for the case of linear differential equations (*i.e.* where the FOMC or DFOP models were not used for the parent compound), greatly -improving the optimization speed for these cases. This, however, has become +improving the optimization speed for these cases. This, has become somehow obsolete, as the use of compiled code described below gives even -smaller execution times. +faster execution times. The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in `mkin` from the very beginning. @@ -116,16 +111,17 @@ metabolites were present in `mkin` from the very beginning. ## Derived software tools Soon after the publication of `mkin`, two derived tools were published, namely -KinGUII (available from Bayer Crop Science) and CAKE (commissioned to Tessella +KinGUII (developed at Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling. CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. -The current version 3.3 of CAKE release in March 2016 uses a basic scheme for -up to six metabolites in a flexible arrangement, but does not support -back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites. +The current version 3.4 of CAKE released in May 2020 uses a scheme for up to +six metabolites in a flexible arrangement and supports biphasic modelling of +metabolites, but does not support back-reactions (non-instantaneous +equilibria). KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) were supported early on, @@ -142,34 +138,33 @@ for further information. A comparison of scope, usability and numerical results obtained with these tools has been recently been published by @ranke2018. -## Recent developments +# Unique features + +Currently, the main unique features available in `mkin` are -Currently (July 2019), the main features available in `mkin` which are not -present in KinGUII or CAKE, are the speed increase by using compiled code when -a compiler is present, parallel model fitting on multicore machines using the -`mmkin` function, and the estimation of parameter confidence intervals based on -transformed parameters. +- the [speed increase](https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html) by using compiled code when a compiler is present, +- parallel model fitting on multicore machines using the [`mmkin` function](https://pkgdown.jrwb.de/mkin/reference/mmkin.html), +- the estimation of parameter confidence intervals based on transformed + parameters (see below) and +- the possibility to use the [two-component error model](https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html) -In addition, the possibility to use two alternative error models to constant -variance have been integrated. The variance by variable error model introduced -by @gao11 has been available via an iteratively reweighted least squares (IRLS) -procedure since mkin +The iteratively reweighted least squares fitting of different variances for +each variable as introduced by @gao11 has been available in mkin since [version 0.9-22](https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26). With [release 0.9.49.5](https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04), -the IRLS algorithm has been replaced by direct or step-wise maximisation of +the IRLS algorithm has been complemented by direct or step-wise maximisation of the likelihood function, which makes it possible not only to fit the variance by variable error model but also a [two-component error model](https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html) -inspired by error models developed in analytical chemistry. +inspired by error models developed in analytical chemistry [@ranke2019]. # Internal parameter transformations -For rate constants, the log -transformation is used, as proposed by Bates and Watts [-@bates1988, p. 77, -149]. Approximate intervals are constructed for the transformed rate -constants [compare @bates1988, p. 135], *i.e.* for their logarithms. -Confidence intervals for the rate constants are then obtained using the -appropriate backtransformation using the exponential function. +For rate constants, the log transformation is used, as proposed by Bates and +Watts [-@bates1988, p. 77, 149]. Approximate intervals are constructed for the +transformed rate constants [compare @bates1988, p. 135], *i.e.* for their +logarithms. Confidence intervals for the rate constants are then obtained using +the appropriate backtransformation using the exponential function. In the first version of `mkin` allowing for specifying models using formation fractions, a home-made reparameterisation was used in order to ensure @@ -185,7 +180,9 @@ proposed together with René Lehmann [@ranke2012], based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter -confidence intervals. +confidence intervals. In the current version of mkin, a logit transformation +is used for parameters that are bound between 0 and 1, such as the g parameter +of the DFOP model. ## Confidence intervals based on transformed parameters @@ -201,9 +198,8 @@ fractions that quantify the paths to each of the compounds formed from a specific parent compound, and no such 1:1 relation exists. Therefore, parameter confidence intervals for formation fractions obtained with -this method only appear valid for the case of a single transformation product, where -only one formation fraction is to be estimated, directly corresponding to one -component of the ilr transformed parameter. +this method only appear valid for the case of a single transformation product, +where currently the logit transformation is used for the formation fraction. The confidence intervals obtained by backtransformation for the cases where a 1:1 relation between transformed and original parameter exist are considered by diff --git a/vignettes/references.bib b/vignettes/references.bib index 69ef74a7..18b93fd3 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -53,7 +53,7 @@ @MANUAL{pkg:mkin, title = {`{mkin}`: {K}inetic evaluation of chemical degradation data}, author = {J. Ranke}, - year = {2019}, + year = {2021}, url = {https://CRAN.R-project.org/package=mkin} } @@ -121,6 +121,22 @@ url="https://doi.org/10.1186/s12302-018-0145-1" } +@Article{ranke2019, + author = {Ranke, Johannes and Meinecke, Stefan}, + title = {Error Models for the Kinetic Evaluation of Chemical Degradation Data}, + journal = {Environments}, + year = {2019}, + volume = {6}, + number = {12}, + issn = {2076-3298}, + abstract = {In the kinetic evaluation of chemical degradation data, degradation models are fitted to the data by varying degradation model parameters to obtain the best possible fit. Today, constant variance of the deviations of the observed data from the model is frequently assumed (error model “constant variance”). Allowing for a different variance for each observed variable (“variance by variable”) has been shown to be a useful refinement. On the other hand, experience gained in analytical chemistry shows that the absolute magnitude of the analytical error often increases with the magnitude of the observed value, which can be explained by an error component which is proportional to the true value. Therefore, kinetic evaluations of chemical degradation data using a two-component error model with a constant component (absolute error) and a component increasing with the observed values (relative error) are newly proposed here as a third possibility. In order to check which of the three error models is most adequate, they have been used in the evaluation of datasets obtained from pesticide evaluation dossiers published by the European Food Safety Authority (EFSA). For quantitative comparisons of the fits, the Akaike information criterion (AIC) was used, as the commonly used error level defined by the FOrum for the Coordination of pesticide fate models and their USe(FOCUS) is based on the assumption of constant variance. A set of fitting routines was developed within the mkin software package that allow for robust fitting of all three error models. Comparisons using parent only degradation datasets, as well as datasets with the formation and decline of transformation products showed that in many cases, the two-component error model proposed here provides the most adequate description of the error structure. While it was confirmed that the variance by variable error model often provides an improved representation of the error structure in kinetic fits with metabolites, it could be shown that in many cases, the two-component error model leads to a further improvement. In addition, it can be applied to parent only fits, potentially improving the accuracy of the fit towards the end of the decline curve, where concentration levels are lower.}, + article-number = {124}, + doi = {10.3390/environments6120124}, + url = {https://www.mdpi.com/2076-3298/6/12/124}, +} + + + @Article{gao11, Title = {Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares}, Author = {Gao, Z. and Green, J.W. and Vanderborght, J. and Schmitt, W.}, diff --git a/vignettes/twa.html b/vignettes/twa.html index 663625bf..0bdc358c 100644 --- a/vignettes/twa.html +++ b/vignettes/twa.html @@ -12,10 +12,48 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-10-08" /> <title>Calculation of time weighted average concentrations with mkin</title> +<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); +</script> +<script>// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); +</script> + +<style type="text/css"> + code{white-space: pre-wrap;} + span.smallcaps{font-variant: small-caps;} + span.underline{text-decoration: underline;} + div.column{display: inline-block; vertical-align: top; width: 50%;} + div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} + ul.task-list{list-style: none;} + </style> + @@ -215,7 +253,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; } <h1 class="title toc-ignore">Calculation of time weighted average concentrations with mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-10-08</h4> +<h4 class="date">Last change 18 September 2019 (rebuilt 2021-02-15)</h4> @@ -251,9 +289,9 @@ code > span.er { color: #a61717; background-color: #e3d2d2; } \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) \]</span></p> <p>Note that a method for calculating maximum moving window time weighted average concentrations for a model fitted by ‘mkinfit’ or from parent decline model parameters is included in the <code>max_twa_parent()</code> function. If the same is needed for metabolites, the function <code>pfm::max_twa()</code> from the ‘pfm’ package can be used.</p> -<div id="refs" class="references"> +<div id="refs" class="references hanging-indent"> <div id="ref-FOCUSkinetics2014"> -<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> </div> diff --git a/vignettes/twa.rmd b/vignettes/twa.rmd index 6f283eb2..52baf8aa 100644 --- a/vignettes/twa.rmd +++ b/vignettes/twa.rmd @@ -1,7 +1,7 @@ --- title: Calculation of time weighted average concentrations with mkin author: Johannes Ranke -date: "`r Sys.Date()`" +date: Last change 18 September 2019 (rebuilt `r Sys.Date()`) bibliography: references.bib output: rmarkdown::html_vignette vignette: > diff --git a/vignettes/web_only/FOCUS_Z.html b/vignettes/web_only/FOCUS_Z.html index e8e6e2b4..035b8f84 100644 --- a/vignettes/web_only/FOCUS_Z.html +++ b/vignettes/web_only/FOCUS_Z.html @@ -11,10 +11,22 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-04-02" /> <title>Example evaluation of FOCUS dataset Z</title> +<script>// Pandoc 2.9 adds attributes on both header and div. 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type="text/css">code{white-space: pre;}</style> <style type="text/css"> pre:not([class]) { @@ -1392,6 +1413,7 @@ h6 { + <style type="text/css"> .main-container { max-width: 940px; @@ -1583,7 +1605,7 @@ div.tocify { <h1 class="title toc-ignore">Example evaluation of FOCUS dataset Z</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-04-02</h4> +<h4 class="date">Last change 16 January 2018 (rebuilt 2021-02-15)</h4> </div> @@ -1613,35 +1635,35 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <p>The next step is to set up the models used for the kinetic analysis. As the simultaneous fit of parent and the first metabolite is usually straightforward, Step 1 (SFO for parent only) is skipped here. We start with the model 2a, with formation and decline of metabolite Z1 and the pathway from parent directly to sink included (default in mkin).</p> <pre class="r"><code>Z.2a <- mkinmod(Z0 = mkinsub("SFO", "Z1"), Z1 = mkinsub("SFO"))</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE)</code></pre> <pre><code>## Warning in mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.2a)</code></pre> -<p><img 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" 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" /><!-- --></p> <pre class="r"><code>summary(m.Z.2a, data = FALSE)$bpar</code></pre> -<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 9.7015e+01 3.394528 2.8580e+01 6.4978e-21 91.66556 102.3642 -## k_Z0_sink 1.6067e-10 0.225471 7.1261e-10 5.0000e-01 0.00000 Inf -## k_Z0_Z1 2.2360e+00 0.159156 1.4049e+01 1.1405e-13 1.95303 2.5600 -## k_Z1_sink 4.8212e-01 0.065492 7.3616e+00 5.1697e-08 0.40341 0.5762 -## sigma 4.8041e+00 0.637651 7.5341e+00 3.4463e-08 3.52677 6.0815</code></pre> +<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper +## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642 +## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600 +## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762 +## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000 +## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</code></pre> <p>As obvious from the parameter summary (the component of the summary), the kinetic rate constant from parent compound Z to sink is very small and the t-test for this parameter suggests that it is not significantly different from zero. This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.</p> <p>A similar result can be obtained when formation fractions are used in the model formulation:</p> <pre class="r"><code>Z.2a.ff <- mkinmod(Z0 = mkinsub("SFO", "Z1"), Z1 = mkinsub("SFO"), use_of_ff = "max")</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE)</code></pre> <pre><code>## Warning in mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.2a.ff)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.Z.2a.ff, data = FALSE)$bpar</code></pre> <pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper ## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642 ## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600 -## k_Z1 0.48212 0.063265 7.6207 2.8155e-08 0.40341 0.5762 +## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762 ## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000 ## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</code></pre> <p>Here, the ilr transformed formation fraction fitted in the model takes a very large value, and the backtransformed formation fraction from parent Z to Z1 is practically unity. Here, the covariance matrix used for the calculation of confidence intervals is not returned as the model is overparameterised.</p> @@ -1649,12 +1671,12 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <p>In the following, we use the parameterisation with formation fractions in order to be able to compare with the results in the FOCUS guidance, and as it makes it easier to use parameters obtained in a previous fit when adding a further metabolite.</p> <pre class="r"><code>Z.3 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), Z1 = mkinsub("SFO"), use_of_ff = "max")</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE)</code></pre> <pre><code>## Warning in mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.3)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.Z.3, data = FALSE)$bpar</code></pre> <pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper ## Z0_0 97.01488 2.597342 37.352 2.0106e-24 91.67597 102.3538 @@ -1669,46 +1691,48 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <pre class="r"><code>Z.5 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO"), use_of_ff = "max")</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>m.Z.5 <- mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE)</code></pre> <pre><code>## Warning in mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.5)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>Finally, metabolite Z3 is added to the model. We use the optimised differential equation parameter values from the previous fit in order to accelerate the optimization.</p> <pre class="r"><code>Z.FOCUS <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO", "Z3"), Z3 = mkinsub("SFO"), use_of_ff = "max")</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>m.Z.FOCUS <- mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, quiet = TRUE)</code></pre> <pre><code>## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : ## Observations with value of zero were removed from the data</code></pre> +<pre><code>## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : Optimisation did not converge: +## false convergence (8)</code></pre> <pre class="r"><code>plot_sep(m.Z.FOCUS)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.Z.FOCUS, data = FALSE)$bpar</code></pre> <pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 96.838599 1.994271 48.5584 4.0281e-42 92.826628 100.850570 -## k_Z0 2.215405 0.118459 18.7019 1.0415e-23 1.989466 2.467003 -## k_Z1 0.478298 0.028257 16.9268 6.2399e-22 0.424701 0.538660 -## k_Z2 0.451618 0.042137 10.7177 1.6306e-14 0.374328 0.544866 -## k_Z3 0.058693 0.015246 3.8499 1.7803e-04 0.034805 0.098977 -## f_Z2_to_Z3 0.471509 0.058352 8.0804 9.6629e-11 0.357739 0.588318 +## Z0_0 96.838822 1.994274 48.5584 4.0280e-42 92.826981 100.850664 +## k_Z0 2.215393 0.118458 18.7019 1.0413e-23 1.989456 2.466989 +## k_Z1 0.478305 0.028258 16.9266 6.2418e-22 0.424708 0.538666 +## k_Z2 0.451627 0.042139 10.7176 1.6314e-14 0.374339 0.544872 +## k_Z3 0.058692 0.015245 3.8499 1.7803e-04 0.034808 0.098965 +## f_Z2_to_Z3 0.471502 0.058351 8.0805 9.6608e-11 0.357769 0.588274 ## sigma 3.984431 0.383402 10.3923 4.5575e-14 3.213126 4.755736</code></pre> <pre class="r"><code>endpoints(m.Z.FOCUS)</code></pre> <pre><code>## $ff ## Z2_Z3 Z2_sink -## 0.47151 0.52849 +## 0.4715 0.5285 ## ## $distimes ## DT50 DT90 ## Z0 0.31288 1.0394 -## Z1 1.44919 4.8141 -## Z2 1.53481 5.0985 -## Z3 11.80963 39.2308</code></pre> +## Z1 1.44917 4.8141 +## Z2 1.53478 5.0984 +## Z3 11.80986 39.2315</code></pre> <p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p> </div> <div id="using-the-sforb-model" class="section level1"> @@ -1719,51 +1743,51 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO", "Z3"), Z3 = mkinsub("SFORB"))</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>m.Z.mkin.1 <- mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE)</code></pre> <pre><code>## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.mkin.1)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.Z.mkin.1, data = FALSE)$cov.unscaled</code></pre> <pre><code>## NULL</code></pre> <p>Therefore, a further stepwise model building is performed starting from the stage of parent and two metabolites, starting from the assumption that the model fit for the parent compound can be improved by using the SFORB model.</p> <pre class="r"><code>Z.mkin.3 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO"))</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE)</code></pre> <pre><code>## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.mkin.3)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> <p>This results in a much better representation of the behaviour of the parent compound Z0.</p> <p>Finally, Z3 is added as well. These models appear overparameterised (no covariance matrix returned) if the sink for Z1 is left in the models.</p> <pre class="r"><code>Z.mkin.4 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO", "Z3"), Z3 = mkinsub("SFO"))</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin.3$bparms.ode, quiet = TRUE)</code></pre> <pre><code>## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin. ## 3$bparms.ode, : Observations with value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.mkin.4)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>The error level of the fit, but especially of metabolite Z3, can be improved if the SFORB model is chosen for this metabolite, as this model is capable of representing the tailing of the metabolite decline phase.</p> <pre class="r"><code>Z.mkin.5 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO", "Z3"), Z3 = mkinsub("SFORB"))</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin.4$bparms.ode[1:4], quiet = TRUE)</code></pre> <pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin. ## 4$bparms.ode[1:4], : Observations with value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.mkin.5)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>The summary view of the backtransformed parameters shows that we get no confidence intervals due to overparameterisation. As the optimized is excessively small, it seems reasonable to fix it to zero.</p> <pre class="r"><code>m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = c(m.Z.mkin.5$bparms.ode[1:7], @@ -1773,35 +1797,35 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = c(m.Z.mkin. ## 5$bparms.ode[1:7], : Observations with value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.mkin.5a)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>As expected, the residual plots for Z0 and Z3 are more random than in the case of the all SFO model for which they were shown above. In conclusion, the model is proposed as the best-fit model for the dataset from Appendix 7 of the FOCUS report.</p> <p>A graphical representation of the confidence intervals can finally be obtained.</p> <pre class="r"><code>mkinparplot(m.Z.mkin.5a)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> +<p><img 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/><!-- --></p> <p>The endpoints obtained with this model are</p> <pre class="r"><code>endpoints(m.Z.mkin.5a)</code></pre> <pre><code>## $ff -## Z0_free_Z1 Z1_Z2 Z2_sink Z2_Z3_free Z3_free_sink -## 1.00000 1.00000 0.46344 0.53656 1.00000 +## Z0_free Z2_Z3 Z2_sink Z3_free +## 1.00000 0.53656 0.46344 1.00000 ## ## $SFORB ## Z0_b1 Z0_b2 Z3_b1 Z3_b2 -## 2.4471363 0.0075126 0.0800072 0.0000000 +## 2.4471322 0.0075125 0.0800069 0.0000000 ## ## $distimes -## DT50 DT90 DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2 -## Z0 0.3043 1.1848 0.28325 92.265 NA NA -## Z1 1.5148 5.0320 NA NA NA NA -## Z2 1.6414 5.4526 NA NA NA NA -## Z3 NA NA NA NA 8.6636 Inf</code></pre> +## DT50 DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2 +## Z0 0.3043 1.1848 0.35666 0.28325 92.266 NA NA +## Z1 1.5148 5.0320 NA NA NA NA NA +## Z2 1.6414 5.4526 NA NA NA NA NA +## Z3 NA NA NA NA NA 8.6636 Inf</code></pre> <p>It is clear the degradation rate of Z3 towards the end of the experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the system of two differential equations representing the SFORB system for Z3, corresponding to the slower rate constant of the DFOP model) is reported to be infinity. However, this appears to be a feature of the data.</p> </div> <div id="references" class="section level1"> <h1>References</h1> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references"> +<div id="refs" class="references hanging-indent"> <div id="ref-FOCUSkinetics2014"> -<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> </div> </div> @@ -1817,7 +1841,7 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> // add bootstrap table styles to pandoc tables function bootstrapStylePandocTables() { - $('tr.header').parent('thead').parent('table').addClass('table table-condensed'); + $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed'); } $(document).ready(function () { bootstrapStylePandocTables(); @@ -1861,7 +1885,7 @@ $(document).ready(function () { theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); }, ignoreSelector: ".toc-ignore", scrollTo: 0 diff --git a/vignettes/web_only/FOCUS_Z.rmd b/vignettes/web_only/FOCUS_Z.rmd index 2da7fde7..6a994bfe 100644 --- a/vignettes/web_only/FOCUS_Z.rmd +++ b/vignettes/web_only/FOCUS_Z.rmd @@ -1,7 +1,7 @@ --- title: Example evaluation of FOCUS dataset Z author: Johannes Ranke -date: "`r Sys.Date()`" +date: Last change 16 January 2018 (rebuilt `r Sys.Date()`) output: html_document: toc: true diff --git a/vignettes/web_only/NAFTA_examples.html b/vignettes/web_only/NAFTA_examples.html index dda93242..eaddfe04 100644 --- a/vignettes/web_only/NAFTA_examples.html +++ b/vignettes/web_only/NAFTA_examples.html @@ -11,10 +11,22 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-10-08" /> <title>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</title> +<script>// Pandoc 2.9 adds attributes on both header and div. 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type="text/css">code{white-space: pre;}</style> <style type="text/css"> pre:not([class]) { @@ -1332,6 +1353,7 @@ h6 { + <style type="text/css"> .main-container { max-width: 940px; @@ -1518,7 +1540,7 @@ div.tocify { <h1 class="title toc-ignore">Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-10-08</h4> +<h4 class="date">26 February 2019 (rebuilt 2021-02-15)</h4> </div> @@ -1537,14 +1559,14 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p5a)</code></pre> -<p><img 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" 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" /><!-- --></p> <pre class="r"><code>print(p5a)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 465.21753 56.27506 32.06401 +## SFO IORE DFOP +## 465.218 56.275 32.064 ## ## Critical sum of squares for checking the SFO model: -## [1] 64.4304 +## [1] 64.43 ## ## Parameters: ## $SFO @@ -1564,7 +1586,7 @@ div.tocify { ## Estimate Pr(>t) Lower Upper ## parent_0 9.99e+01 1.41e-26 98.8116 101.0810 ## k1 2.67e-02 5.05e-06 0.0243 0.0295 -## k2 2.17e-12 5.00e-01 0.0000 Inf +## k2 2.26e-12 5.00e-01 0.0000 Inf ## g 6.47e-01 3.67e-06 0.6248 0.6677 ## sigma 1.27e+00 8.91e-06 0.8395 1.6929 ## @@ -1573,7 +1595,7 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 67.7 2.25e+02 6.77e+01 ## IORE 58.2 1.07e+03 3.22e+02 -## DFOP 55.5 5.83e+11 3.20e+11 +## DFOP 55.5 5.59e+11 3.07e+11 ## ## Representative half-life: ## [1] 321.51</code></pre> @@ -1584,14 +1606,14 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p5b)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAKgCAMAAABz4j/3AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dB3gURRuAKRFCDaQXSKMGQk1QOkGaQIQgSE0IqAgIooBAEJQSxAgICIICkWYhIkWKGkQhYAPp4i8oXTEqhEAaKZfc/Lu3lxA2tzdzN3O3s5fvfZ5c7na/zM5+eZPZ+m0FBAAcU0HtDgCAOUBQgGtAUIBrQFCAa0BQgGtAUIBrQFCAa0BQgGtAUIBrQFCAa0BQgGtAUIBrQFCAa0BQgGtAUIBrQFCAa0BQgGtAUIBrQFCAa0BQgGtAUIBrQFCAa0BQgGtAUIBrQFCAa0BQgGtAUIBrQFCAa0BQgGtAUIBrQFCAa0BQgGt4FPRyzwGr1e4DwAk8Cjr7KOqidh8ATuBR0Cz9X1Fq9wHgBPsL+kN73+DZRaiCm7u7+9tIv6SlR5cDspC9kX9jm0kx4bCpaYC2sbugOd5fFWU8sRZVyDZ8nNDretGRgJ0PhRyYqce3QyToiiCf2JwHbxbW9483zkk4aGnHAVWwu6AXvYoQOv2ZUdBfvbKE10MNikqHPNt/SPTDP6RPk74efCIS9KxbWka/+SVvdrf4N7XLt5c7tFyCbg9nsjLagGDMKk1iXOlPao9KdhdU37LLpr/EBRsEXTPOMNH3ukL0O0ENpxcde6pNnPiFFgUHvWL4hAyJM8zssxuh8CPSe3ky984V0h1d8mbWawitfnn6t6jj/ZfP22wFuYNgzDKyI1R8LeeCooIPhtTpftHw9xyMZkhDbsdD0rwXIgrQiPcfxB5u+V/28GXHalxE4ldy64y8nlvEd0hMnDRzUwy6EqyX3huSOX2gyGJjC7fDdpW82dIuPa33sMsd2771x0Q7rSwPmBuzHgxLIhmXxNfyLqjAvx16G9O1YpJhQvAFacbJvz8/3KHUBuhrgRERocOOdUdI/Jq5AqFPx4jvkJg4aeY9r4LFC4yBZZO5OWjzgzdFk7xDYgz/smNSbbdy3KE8ZhkGI8PYkxXlE/g5Ohape7Fem+fikoUNoLhENN3Hc1h+SpQ0Ty3sLuhn4jbhSVdjuo775wmvp3x0xbM/bfVLqeCEhQgV6Y5FCrkUvmasFH48RnyHREGlmWjggdZXjYFyQYuiB6SWepN/X1A+Qfj4/Tw0u1X3P225mjyhOGaJg5E09rw3RP/DGCHJ63vkpocYBb0QkatrdyQlSpqnFirsJP2I7sx/sniLaPjgf9GJJltKZr/0YungUyHpusFrigXdH5aV32djiaDSTLQtontxoHyI39VTfNVf1ElvjjTO+i/4hvAmKutmL7RzoR3WlhdMj1niYCSNPefqzz4iJnmEsG36ZvF/0LQDb7gnp0RJ89TC/kP8vrYe3tH/FQuqmx/i1WFPycyfW298KHht43rjS/6DogUNgqcWlggqzUTZ1TcXB8r/g051cnZ2jsmrcF16g171aSHuHGxfh9CsthH/2HAleUJ5zBJTaRyH7m0Y1En4PFLYYl9qEHRy4vFmy45FCYJK89SCrzNJujY/njtyWu1eOBjKY5YoqDT2xE9H6TXvHovc0Csvs2VcSnNdelDi4ikoNXBfSpQ0T63e8yVowgSEJjypdi8cDcUxyzAYGcaeGx18A1eJO0mTA9oufjs/tnG/BZ9da9v8yaXhKVHSPLXgS1AAkIEV9MTcZ2LnnLRHVwCgLDhBlzaZv3HjwtC37dIZAJCDEzQoU3zNDbVDVwCgLDhBGxkOZ99qZYeuAEBZcIImecfMmRPr96ldOgMAcrA7Sbc2xS/aeMseXQGAsli5F//PunLM+kzmvwbIpxJW7sVvaPp8+aWeuct9rQPyqYSle/G6lQkifbuz+LVolD7JzJtcP455k9rBbD4t3YvPmztLpHl96WPO6qGB3n2uUfZQY4CgbKESVGEvPqap4dv94LoVKrt61s4qoOmg1gBB2UIlqMJevFHQBOcOwR9X2VSlqlPTM9Z3UGuAoGyhE9Q0RkF7dwx/EjXtXNH/tXq1c6xrSYOAoGyxoaDR9V6s/KqLa8VdKKtK+TmWD4KyhUrQFoESsslGQc9Wa+hcoUJFT+FtneUUXdQWIChbqARNa5l4U0Q22Sgo+tSzimt3j4rTUhc7XRL26fd++i9FR7UCCMoWuiF+uckaMcWCCjvyCN3zdKpYZTxC17y8fWuxP4jNHSAoW2y4DWok75OpXX1X3u/o+uHuFrWta1BLgKBssb2gImcG+TyyHqE7FR1/kAdB2WIfQRH6pXKtNzJuVEwrO8fBAEEtQ5fy5R1z8+0lKOrn3KpmTS/rGtQSIKhF/Bfavq+XuWqXdhP0dgs3j+ou0/HFZzUOCGoRz81B6Hs/MyVf7SYoKjp3PPfmy67jL1vXqFYAQS2i1cxmAeP9/lIOsJ+gErdfcx9+1rpmtQEIahEtgk9fj3HKVw6wt6AIZS7ze+KwdQ1rAWpB+5WZ4siCtqq7bGMb5wzlAPsLilB+YtOwQR0iv7Gudc6hETRapEa0rMS5Qwv62IeTx37mYea2NjUEFbZHW9WoGuSuYt0+20Ej6FKXhKQkj6Sk4s+6dxz+DoX4/hlFCR3MBKgj6LVqI95tU7GhI94PSjXEn2iXhEpdeiO7Q8ERKXihRo3Hr5kJUEfQL6sUCmE16o6/aN0SOIZuGzQrdqxPmYn4fGoZ3X2zs+kENX3bMT6hxx75GuW0C/lvnlfkIVysxqDdSfqk7HMJHFtQDFSCKtx2jE9onpt3U1f/txDKXd+szeY8bC81hA0OM4GgSlhZPIwgod/4NfUYbriZTv9VH58F/2F/QDOAoGxhetuxEZKE5p19cJnzb70qVQ4+hf8ZTQCCssWGtx0T851TRJ9HKm3U4SM1AAjKFqa3Hee+aKhW0sjCwyJ9Wgv/UasF1nvDEUZ6EJQtVILqd+5GW4fMLzlOULjJUO+pvfwuOgzh4pMRAiedfa5uzDHLfpJDQFC2UAk6JbzzwL67Rz8jm2xpQudUO6PfUfkQQunLGoRvNH9YjHtAULZQCeqTXeDyO8r0k022NKG65lUqVR1meFv0ZaTH9D8s+3G+AEHZQiVovbQs55PolnyT0+KE6r9e++Bo/7U4r547tFvOCQRlC92Beh//V8ITusXJJtMmNO+Trj5zb9C1oRogKFvo9uLPnEdHZ22VX7DPIKH/m+LWf08hdTMqAIKyRZ2LRbC8EdJsbEe/udeoG7I7IChb+BR0aJURUZVn/e9lj97bzdwNwCUgKFv4FLTSNwi9U13cGn3ca9qvtK3ZFRCULVwKmlVBh9CFSob3V+b4PbbuHmWDdgQEZQuXgqJqsxEa5mn8UPjF4DrR3xbRtmknQFC28CnoxkrudZ1K3VR3e2XrwNev0LZqF0BQtvApKPpzfoKsYs+Zlzy7fmDm9lReAEHZwqmgpij4fFCdkcm8HxylETRnXvQu4VuMbPJD+cxd0m/kj9YvQnNoSFCBtHcf85nG9yNDaASN6ZcYtgUhZ/nkknzqT34dNfDL9T7fW78MraEtQQUuvhYUmsDxeVAaQb0z0e2g1AeCFu3eLtI12Pg5u1uzbpU+EzbRR1J2UkNgBY0zIr8zzgw23mbSfz/Bves6s0UlVYRG0CBhpTYNfCDo/ZinRQKKLxd7baz+XCOPXPRND8pOagisoImJic81S1jS4k3yNm2/UZ+/Z6jLgG1cPnyJRtDl7nMRGtnLSTa5JJ+9vkb5Po1P6UYstH4ZWoNkiG9zF6GMtuRt2mWvM2PLE3VG7eXvRCjVXvzZvcK4vvNl2dSSfI557YURz1Z85JFWuRTL0BgkgvoJ+ciz4DYOex0WubWmi9uzX3O2W48TNKoY8iZL8plUKfJFpzr7d4Z8ZnX3NAeJoJN67NzVa4qpAGsrizDjr+WPeU48zJOjOEFTiiFvsvR/0Eg3j3/RTtHu/zKt7aKmIBFUt37EyA2mbgq2urIIS669FeY9KYWbM6EEQ3z+keTkfeHkTZbeBr0Q0vYU+rIf+j3Ms/YIjd++RQSJoEXLhunWmPovZX1lEbZcfrOtzwuH+Pg/SiDos+3cowKWkTdZkk9hL74oqNb9/zq/j8LWotwRs6zroqYgEXTa8NCiwRNMzKeoLMKaS2+GeTx/gINbmQgEbah74/jfA8ibLMlndrdmfVybVK89R3/bXfh4prVVPdQWJII2yI5AOlPFGBhVFmHE1aUd3GL3qL2DSyCod27y26g5eZOlzyQl30H3hc2ZnNr5CB3uYlUPtQWJoAG3ItCdJqYCZJVFcvr3FPEpW+LSTtx8xbWS92JVrx4lEHRqRFrIlDDyJk39wY8cevZw6CbyNjQLiaArWwUuCHnPxHzdxuzMOVEJJQcj9UcOivRsyLaP5PzpUtPD+ZFaT6z7R60eEB0HvYCOL75O3mSxoJlLx60q3i/KntX8sU2W9k2LEJ2LP/RawmlT80f3zH168EeR42WT1bs87NWal9CF6nM+G+XaPuE3dbpAIGiiiAWHMo35zA55Zv3T7Y2b2Zc7uNaIdajCqgqQCJqi9CAw7zzkmY2yqAs3MCOynfDSejBCBQcn+zd65agKO/YEgsbFxU0OsuBspTGfHzwtvHTbK017dKU+Z8irVvRPa5AIGh684JrJ+W1/QWHX0a0Q2WT1BJ1b41d0ttoi6cPp+W3doj+19wYp4anOO/3JmzTm83XR6RdXGd6nGfbi21jYNy1CNMRfXty6+xYT848HDB3QbHLjzbLJ6gn6d51adV1cH1Rx/Ou9frV6rLBrrSdCQXWNyZs05nPPYwUou4l0sXJ2bWGoP9LJ4t5pD7LrQQsORFQ3FZC3e9nra6/Jp6p4i8LZAQ0HPXybcvbn43wbT/vGbleVEAg6XKBZLHmTxnzqYxqP8p9qnDZs1G8/tNpgefc0B4mgu2O8Bm7LJm+Tt3to9KcWPuby1Iab+EgGEAiaLJBiwV9McT6zp/d+vXi/KHNa0/B1lvdOe5AI2meTZRtyvAkqcuvDEe6t4lJsX2YcJ6j1F4DnhMasjursGIXSySEQNE1evQ4Hj4IKFP70WnidQessOABpDThBrb8AfONTwmDQZR9F37QIgaBFbSx8Hhyngorc+ijGq+lLX9rwWnyCId7KC8BL78WXH0iG+EE1u0dGRpK3ybGgAvrTb0bU6pFw0kYX6BEIauUF4Ls66tD9kHJ0Q6cBEkGPGSBvk29BRbL3TwlxH5Zoi9GeQFArLwDXD2se22Aybf+0BtX1oArwL6jIXxtHeDaauIP13aEEglp9AfgPm02ecXZoqK4HVUAbggroz77dr3bYjGQLDqFhwQoaeinSgIlZZS4A/166+MZPeEkV/u8fLIffI2iuB1VAM4KKFHy/oFvNLvNSWF14gRV0R4biJpP8AvCcvtLli9WEl40I/dizHH4Ppboe1DSaElQk5+u4x2o9vvA7FqebCE913jU1ka8LwPmA6npQBTSZ0MwvZrSr1WPBEdr/pASC7h6DOtVcbWqO7AJwI5rMJyuorgdVQLMJzdg/87Ga3V7/luYwKYGgAT+kDLoTjA0rQbP5ZAFp8TCTQ5ICmk5o1oE5nWt2mLkv3cqfJxDURx+3Ackfz2cGTeeTFqKLRRSHJNNoPqH3Uxb2rh068SNriugRCDpgiH/GNAsuldN8PmkgumlOcUhSvbKI7dCdWPGUV73hq0+THQAu2XQlEDTzvXMo/k/yrjhEPq2FRFDFIYmLyiK25I/N45rV7vn6V7iruRKqV3LdIb0lENQxT3zYCBJBFYckXiqL2JT0/XMiaoWO2/ib0p1ZCO2pPPv7YU5XDe8JBHXYEx+2gERQxSGJo8oitkV3cvWo4Lp9FySb3lfs0VF48Z5peE8gqEOf+GANiaD3ZrbvGm+qUFX5OrD8757ZEbWbxq49VabATmex4nHAi4b3JIeZWJ/4+KJ7swlljp46CCSCDhl54uSoMaYCZAeWs0OCRWp5sewhVxSe2/Bsi5qdXv7kUump7zntSHur8inDewJBWZ/4SKm/99eXOnBT348tRAVshQ36QsXrF/uVev/XFZEoC+5Y1CJZKUuGBLj2mbvn7+IpI50qV4mX3pKc6mR84mNsIkL6ptp6pCkxJIJ2S0XoZgcT86NFakRHyyY76BD/MP/ue72fp++AhV8YbnJOP1d8RRRW0Gs/5SKUcdGCK+Nx+RwiHkBo/xN5g1qC5CkfI7zHj/c0VcViqUtCUpJHUpJscrkQ1MD1nbN7u9YfuGB/askknKArKwV4funvUr8b+VJw+dzQ9S76op6DVrPFCvq+EbmFBk60S0Jlx/7yI6iBq9vjermVVELECep7Bh2p8JHyISsT4PKpn1qrXsMfLGlRQ5DcNHft0AWlm12zYseWrbVYzgQ1UHJ9CU7QuuKXZTs0+Hzet889/2qAF3RHsG/HIJ+tSjGflH1cRXkUtAScoGJRJW/LmrQon4Ubn5+nXvFJ9mAF/SrwsPB6ssle8jZBUDO4pqWleQpfaeRNWpTPp3uum+rrQP9QsYJ2+s7w9ueO5G2CoGZwLsbEPJKnHZvk5tniXaT/BRcIO7azSXqqDbCCekjbS3oLhiUQ1EqwTzs2jW6UVyufr6T3X/UVXj5xoIfNYgUNlS6K/Meqov/lEJZPOzaCy+fK/nnoJy/pQOzfro8+4h+23Po+CBQuadNqAS8PmcQKmtA7S3jNe8ryisDlE5ZPOzaCy+cgcbugk1RyJMe1ztNNq1mww2CCBd1OnH5iGlUT7MAKqh/vNS5+kv9wC6qqgaBWUuZpx38SnToe9y5CRcG/G94f7Xol6bt146zvg0DwJYRuuVl0qNZ2EBwHPb9mxorjlrQJglqL7GnHhBffnPBOPBzdRxLqu87Cy/vPU/QBIa//ELpfi48H9xHfNGcJICgFl8tOwubzp6ER87Kkt/cbvJ9zssFBqi7ETioojLPgacw2BQRlDK2gJh4ya1E+f+tZvbGp5wlYQPqA2nX6/EvXBjNAUMaoLSgTMlR9WN9DgKCMoRV0fdlJds/n9y9M+MbOi1QEBGUMraAmsHc+P/V/590GvDxBBARljAMI2vIHhH71t+8yFaET1IELN1iLAwha9y5COmdOHidCJajDF26wAgcQtJcwvG9/1L7LVIRK0HJRuMFCHEDQC/49+vidsu8yFaEStNwUbrAABxAUZSd/mWnnRSpCJWj5KtxABneCZh1IxQfxC91OElQELgNvgs6qVKViZ2ZdsT9UgsqvAM+uWcGAB5OuaRPOBD1XKQn9Uc3Sh1lyBJWgVl4B7tBwJugsX+FlaAtmfbE7VIJaeQW4Q8OZoMtdhJduGh7j6Q4zWXcFuEPDmaAZzh0+fKbSV+w6Y2+oBC1zBbgECMoWqnyeaVbdV7GogQag24uXXQFuBARlC+RTCbhYxHKoBe1XZgrkUwkQ1HJoBC3P5SyVAEEZQyNouS9naQIQlDFUQzyUsywDCMoYum1QWTnLrHSRYZBPBUBQy6HdSSpdzjLbp65IVQue6+lwgKCMscF98evpCoVoGxCUMTa47RgEVQIEtRwQlC0gKGNscF88CKoECGo5NjjVCYIqAYJaDgjKFhCUMSAoW0BQxoCgbAFBGQOCsgUEZQwIyhYQlDEgKFugeBhjQFC2QPEwxoCgbGFbPIzssSkODQjKFqbFw4yPTakTxKJnGgUEZYstiodBQtkC+VTCyuJhkFC2QD6VsPIwEySULZBPJawVdHS6kUtXibio/bArxav8uA0EtSSfv19msk4kq82mmSt/pZvDbD6tFHS/W12JOhUqEuEAYRWNq1zX47R1SWOZTwbrRLIkRs1UrmsOs/m0UtAScqoThWXVJArLrEUUds+FKCy9LlFYmhtR2C27lEQlyefEtdiQSw3xzQzYgw1J6YZvJvwENuTjkfhmlABBQVBlQFAZICgIKgMEBUGVAUFlgKAgqAwQFARVxgEELYpnGVa4iGWY7g2isILFLMMoIUnUXvxhriz55Wcm2HoVG5Jq4g5pOe+mYUMuyM+UWwCtoABgU0BQgGtAUIBrQFCAa0BQgGtAUIBrQFCAa0BQgGtAUIBrKAXd3iR4GS5mRZBPbA5J5JJoggaPh3nHFuLDltbznaLHhaWJz7CWYsxFGsKI14IKbPtE3cAnkiCL+AwSpI8+dXSC3vK6kdGkTNmRhznrlpbRbz5B5EmXaHyDeQHn8npuwoad9UnLDt+JCVvRMrB4FcxFGsKI14IK/HqRdAOfSIIs4jNIkD4GqaMTdFMMQvPnmo/ZK8xPjMZHZrVLiMY3uHsoQrk52LBf/LMKOu/ChB1MDCxeBXORhjDStaAD2z5JNwgSSZBFfAYJ0scgdXSCLpqD0MZnsGG3w3bhI2OTkqLxDS4bGO41NBPf2vRqLgOxrd0MLF4Fs5E3A8nXgg6S9rHdIEgkSRbxGSRIH33q6ASNF5cai4vaHLQZH/nJWCTkFRs2L+Ra9uC52LCj4ScuPvEhLkxMnxRjNlLKMtFaUELQPrYbJIkkyCJBBgnSR586OkHFJS581XxMUfSAVILIKJ9Ajxr9sGFrJyP0URQ2bFa8MI4NwYWJ6ZNizEaKYYRrQQm2fYJukCSSIIsEGSRIH33q6AT9z+9Wbihmy3dXT9JI4Q8fG3atydWsyARs2NY2tzNHz8eFiemTYsxGimHka0EDtn2ybmATSZBFggwSpI8+dZSHmZJahuKOHUx1cnZ2jiGJFPKKD9sU5Pd8PjZMP8vPd+x9XJhhAJJizEWKYeRrQQWufbJu4BOJzyJBBgnSR586OFAPcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA2Hgl7uOWC12n0AeIFDQWcfRV3U7gPACxwKmqX/K0rtPgC8YFdB611E+iUtPbocEN47u7rX9V0pdMDN3d193ENheyP/xjaVYsJhU9MAjWNvQSf0ul50JGCnIGgaQmcqXUMVsuVRB2bq8U0RCVpc1Hehk5NT5baoQWUnp53GVwMJBy1eBcDO2FnQX72yhO+HGhQZBEUBP5kQ9Nn+Q6IfnqJPk74efCIS9KGivnO25fuI36XXyx1aLkG3h1u/KtqBcNgqTWJc6U/qDkx2FnSNlBXf6wZBv/O4b0LQUrwT1HB60bGn2sSJX2hRcNArhk/IkDXDzD67EQo/Ir2XZ7J0Ud/fR6Nfg0a1W6KXXqd/izref/m8TdaSM8iGLSM7QsXXcizoDOlp0x0PIWdP77oVvjD+MRsfHv1CRAEa8f6D+MMt/8sevuxYjYtI/EpunZHXc4v4DolZk2YKEl4J1kvvDZmcPlDE8Ojs0kV9+19F3/S4cr3NZun1cse2b/0x0X5rriKYYevByCSScUl8LceCrphkeBN8QcxV0Yf+6KE/5pN/f364Q6kN0NcCIyJChx3rjpD4NXMFQp+OEd8hMWvSzHteBYsXGAPlmSxV1PfACGnSh0MfvMak2mIducPssGUYjwzDT1aUT+Dn6Fik7sV6bZ6LSxa2fuIS0XQfz2H5KVHSPHWws6DH/fOE76d8dIZc6V0yZKPNp61+KfUpYSFCRbpjkUIiha8ZwsbTZzHiOyQKKs1EAw+0vmoMlAtaqqjvmG0I/SyM6DtHSq/CpO/nodmtuv9pu7XlBLPDljgeScPPe0P0P4wR8ry+R256iFHQCxG5unZHUqKkeepg77344YP/RSeabJE2h1D9izJBX3qx9KdTIem6wWuKBd0flpXfZ2OJoNJMtC2ie3GgfIiXivrqL+pQvsddYfuq9d38vlulV2FuVNbNXmjnQvusuYqYHbbE8Ugafs7Vn31EzPMIYVP1zeL/oGkH3nBPTomS5qmDvQXVzQ/x6rAHGQWNWIUqeHp7e7c3BvzceuNDP7C2cb3xJf9B0YIGwVMLSwSVZqLs6puLA8tsLBmK+uZVuI72GbYLpns3fFVvfEXb1yE0q23EP7ZcXy4wO2yJ2TQORfc2DOokfB65C6GlBkEnJx5vtuxYlCCoNE8duDqTpGvz47kjp9XuhYNhdtgSBZWGn/jpKL3m3WORG3rlZbaMS2muSw9KXDwFpQbuS4mS5qnTfa4ETZiA0IQn1e6Fg2F22DKMR4bh50YH38BV4k7S5IC2i9/Oj23cb8Fn19o2f3JpeEqUNE8duBIUAOSAoADXYAU9MfeZ2Dk2fDQQAJgDJ+jSJvM3blwY+rZdOgMAcnCCBmWKr7mhdugKAJQFJ2gjw6mWW63s0BUAKAtO0CTvmDlzYv0+tUtnAEAOdifp1qb4RRtvyademFWeYX/66YLaq6Qq5vJp5V78+kcTyi+Nk6lkNAXkUwkr9+LXK1+O7fj0oRA0Z170LuFbjGwy5FMJS/fi9SkHRSYNZNEzjUIjaEy/xDDxpLhsMgiqhKV78TmRPUV8/Iyfz2wcP+jtfMoeagwaQb0z0e2g1FKCnjspMmsog35pFSpBFfbiY5oavumH1qlYoVaVpkU0/dMcNIIG3UFo08AHgmZ3ChNx82HRMY1CJajCXrxR0E+qtqxcr8o2pzatp92zvoNag0bQ5e5zERrZy0k22ZjP8gmdoKYxJjSyVdiAmU5dK1YJrRZSaF1LGoRGUHR2L0JFO1+WTQVBlaATdEDzYfUL3StVXInOO6VY15IGoRLUNCCoEjhBWwRKyCYbE/p51SGPVK1UsVKXP5HrWzR91BQgKFuoBE1rmXhTRDa5OKGTank5efgGLvJa7PSD8LGowPpuagcQlC10Q/xyk/WLShJ67cBN9Gu1mn4V695B+eOdq/YuBzebg6BsseE2qJHzIzrPmeB/cHL1HoOrh1nXoJYAQdlie0ENfO1fRdg3/a5SlnUtaggQlC12EhTdq+x1COVXlG+uOh4gKFvsJShqVN8nZlAVguKeGgcEZYvdBE2pUadKpVjrGtQSIChb7CYo+i1u6opGQx2+mgwIyhb7CSqSO9dzrYNfOwKCssW+giL0vy6PnrKuVRngG/YAACAASURBVI0AgrLF3oIi/SbvSekI3frqhGPuMIGgbLG7oAjdmeiduN2jfYOIHOua5xsQlC0qCIrQqQ6VXTr5159rXfN8A4KyRRVB0XeVvUZe7drYuub5xk6C3p0Q0GyFY24kPYw6gn71yJ157mF+uDAtYidBo567frrdGuaL4g86QU3fF48X9K/qPT6Je8R5nQ4XqD3sI2iWi5C67zsyXxR/UAmqcF88wTbT4FbdItw+6hmyGxupNewj6B034eVUW+aL4g+m98UbIRA0b2n/2FMIJbfqYLgZ5Mzuy9gf0Qh2GuI7LS7KeHI+80XxB9P74o1YsNdZ9HGDPieKujpXrTqZ+Gf4xk6CXn+8eo2J5eEWBRveF09GwXv1mlf56tbqyj9Z8EMcY7fDTPcdcAPeBEzvi892qWDAw6Iu3A+o+vR5VH+KRT/ELXAclC1Ugup37kZbh8y/L5tsaUIfb7fU+2mXeZb9EK+AoGyhEnRKeOeBfXePfkY22dKEfuk0ekNQxScc42EMIChbqAT1yS5w+R1lyg+4W5zQVTUr++xbVb+Pas98ZAgIyhYqQeulZTmfRLfqyyZbkdBc4Ss/sVHHPZo/eweCsoXuQL2P/yvhCd3iZJOtTmjRZ+HNPsiz8oc5AQRlC91e/Jnz6OisrfL/ejQJPdTPd1Ga9T+uPiAoW9S5WMQcvz7rOvFE92o1BmnzOB+NoPJaV0Vb14m0Dy4dlLo7pfzUCuRRUIT+nedUZea6Wv3oWlEJGkHlta5yJz0v0qheqZhtnlHtwqDeqoRKgqKiikvDGj1bhbIVdaAa4jG1rgRy3X5DaNJMhM69teY/iiVpBi4FzamYhX7oU2H8Wcp21MDG26Bn2ggvh7qjjfVmPuP1P+aL4g8uBUUeEbqc5g0W+XfYLD9LxT02FjTNLQuh1WOKXC8jtO4p5oviDz4FPVmnYkXvP1Hhnn5uU87TNmZfbL0XP6nDB697/vq3eHLktxDmi+IPPgVF6I8/pe83Xq/XIVFLNfFsLaj+42dmXUZFbn8g9N5g5oviD14FfUDh/kF1xxzVzBkmOx0H3eI7bYz3BeaL4g/+BRX4d1mzhguvM27URtjrQP2vy97X9AkNUrCCxhmR33hkBluc+fh5knvExgz27TIHziSxBStoYmLic80SlrR4k7xN2yQ0f/egOsP3cP9gRRCULSRDfJu7CGVYcAehzRKavq6L2/OH+S6PB4KyhURQv1yE8uQPQzKDLRN6Y0kb35d/5HiXCQRlC4mgk3rs3NXLgluGbJzQiwuaB0w/xqujOEGjiiFv8uF85p3702x0+hGHuYVbhERQ3foRIzeYvLbI2soitPz6ekj9l7/jcqzHCZpSDHmTD+XzcL2W3k+ZuWb2E8+ufiMc6HInEkGLlg3TrTG1ztZXFqHn1wWtfCYc4O/OcIIhPv9IcvK+cPImS+ezwPcwyh+ySDH2ludFlN/nffLG1abw2EGzB2dIBJ02PLRo8AQT862vLMKES0s6uI7cnmmfhZFCIOiz7dyjApaRN1k6n7+0FF4O9lSMPdBHePkomrxxlbndtnUP78NmAkgEbZAdgXTyG49E6CuL0PLPur4uT6y5Ybfl4SEQtKHujeN/DyBvsnQ+U72FQWPLcMXYsyHChk/8K+SNq8z4mcJWi7+ZABJBA25FoDtNTMxnUVmEmqwdo91bz/mJlw1SAkG9c5PfRs3Jm3won08NObTZ96hibFHPEV8s9fqDvHGVaSVeUlnvL+UAEkFXtgpcEPKeqQBZZZH70U+LBNi78Gfh97NbekRvu4PQlbGdnrtm56U/DIGgUyPSQqZY8NjShwTNiY942tzt2dlv9B+voXP0/XYgdLeWmZ0+onPxh15LOG1qvm5jduacqISSsztFn28X6RpsKtjG3Hh/oEuHuDreTt5e6SosvgSS46AX0PHFFlxa4MjHQb/1Wf3JYzPNBJAImqJ0zHF0z9ynB38UOV42Wa2E5n3TuZLLsBlVLdj/YA+BoIkin5E3ic3nmTH9EjR3ZbeR4+OGf2TumDaJoOHBC66ZnO+dhzyzURaDwg2MeDL85gfDnGq/tE+9K0gJBI2Li5sctJC8SVw+z3i9+8XgSF7PXGA4NGLAe+bu3yUa4i8vbt19i4n5bX9BYdfRLfmF3eoJOs0lFf1Va+zix2t2ev2IOteVEJ7qvNOfvElcPse/I2xtBV4yfvol8QsNHaf/sv6mPd1eNBNAdj1owYGI6ibmHw8YOqDZ5MabZZPVE/RSrVotatcWNu/uH4x7tFavN3+y/731hILqLHjECS6fUWIh9c7fSR8W+z/b+VHtPIGqzx6EMmvlmgkgEHR3jNfAbdmmAvJ2L3t97TX5VBU36r8OrdLqkPH9vb0vt3Lpm/CjfSUlEHS4QLNY8iYf5PPfJbNMNL8sqgCd9pROWPzplSb8wFvkjatM6zPCi99N5QASQftssqxQAEd7nXd2v9ymdq+FR+y3C0EgaLJAigUbICX5vOozKSH01TLzC56q18lrj/T+y77CyycjyBtXmckv6tGXDcwEEAiaJi8OhoMjQUXu7ZvZoUbHmXvtc4cETlCaOxTGJyCUXtfEUbRL3xef8P3dXxgup71G3rjK3O3cuH39H80EEAha1OaiZQvlTFCRnEMLetcOeXbjBZvv6+IEpblDoec3wkv4CbPBYzssf85fQzVH9OePmR3eSIb4QTW7R0ZGki+TQ0FFCs++OyrIrf+ib216eQnBEG/1HQrTZiB0w9X8ITT9jmlv3yVvm3tIBD1mgLxNTgU1kLrrlc41W4774LytDsUQCGr1HQq3Gz8xzkdDl9KxgOp6UAV4FlSk4MS7MU1qR8zcYYuroAgEVb5DAXcBeN7O9b9T9U57UF0PqgDvghq4eyB+gI9X/3n7Utm2SyCo4h0Kal4AzitU14MqoJ2E3tw99wkPvyfn7TVzIM5CsIKGXoo0YGKW/ALwvLmzRJq7CC8HEbo4qxx+D6e5HlQB7Qhq4PqOV/t4ej4xe/slFrv4WEF3ZChu08svANetTBBp6ya8CPE3Esrh9/ZU14OaRmOCGvhr7/yogFqdJ60/QXlMn/BUp8kdbewF4KefHfgOf7dh2RSq60EV0KKgBtIPrRjTplrIsMX7rd99IhB09xjUqeZqU3NkF4AbKcnnCa9Vu/uOtLprmoS0eJglh9Y0K6iBgnMfzujtW6fLhPeOWnPlM4GgAT+kDLpjwUXdJfkcvU7YLvX6x4peaReii0UU/+JNo21BJdIOrXq+Q+16vacnHrfsuD6BoD76uA3IgttiSvLZ+2vhpa0lg5n2Ibppztq/eM1z/Yu3YsNrBDwxfcOPpEMIgaADhvhnTOtE3ouSfM6PLkI/e9n+SWe3t27i5t80iaDKf/FqVRaxK/qr+5eMfbSWb48X3j1o5vZDIwSCZr53DsWbr1/zECX5vN+7YRfvL8h/0EpOeI2M8bSg8olNIRFU8S++XB1Y/vPrVRMf96kVNnJh0mn5xbE5C54uPtVGICjFmbnzR+1Qp6LLdoQOtLT9coggEVTxL17lyiJqcO/4llcHt3Cu32PC8v1/FB/xyapbvXGVZtJRVAJBeT8zV+ceQoVVOXnQH4mg92a27xpv6vCg+pVFVKLo2oHVk/sEOzfqO2V18mXd8+65KPWRrYZZBILyfmYu/CBCPze07zIVIRF0yMgTJ0eNMTGfi8oiKpJ/Ye/bE3sFVq3q8cJW1HisYRrJYSbOz8wle736uq8Ft0XbFKICtsL2UqHJy8NkB5aLdqtXuEFF8iM93nnnfjXpQByBoNyfmfttwbxzdl6kIiSCdktF6GYHpaBSz3y9H6NO6Ru1SXX261vbU9oiJTnVWV7OzLGA5CkfI7zHj/cse68WQtEiNaLlxf7KX0JvDmk93rh7jRX02k+5CGVcXEXeOjaf9z9abuY+kNvkS+IQrKDvG0kyMX+pS0JSkkeSfFb5E7QUOEFXVgrw/NLfpX438iZx+UxrFDU1SKmm7btVK1TSzk2eZSG5ae7aoQsKxxxOtEtCZTdOQVAz+J5BRyqYrUZUBlw+46YJ/ybdTd8o90elePTNI6pWq6IDL+iOYN+OQT5bTUdkxY71KTMRBDVDXfHLsmKmuHw+uVd46Wb61M/rXsLLoDYWLY8rsIJ+FXhYeD3ZZK9CzCdlH1cBgprBXfjytqxJXD5fiUPorofps+eL3YSXvo9ZtkCewAraSar583NH8jZBUDO4pqWleQpfFpSRwOXz34BR85qa2okV5zkNO7+48ofkC+MNrKAe0nCkt+CvHgQ1g3Mx5E1i85nx3nzFazu+8KpUYz75srgDK2iodHH5P9bWVC9vwJPm2IIVNKG3WMki7ymGBVcdGhCULVhB9eO9xsVP8h9uwcUtkFC2QD6VkI6Dnl8zY8VxS9qEhLIF8qlEBTPzzAAJZQvkUwkQ1HIcQdBdTw/+hJdnMoCgjHEAQdeFbNveaol9l6kICMoYBxC02WmErvjad5mKgKCMcQBBXe8gVFBNQ/ckWQoIyhZ753OAMLyv62bfZSoCgjKGRtAWgRKyyfbO543QFm0bW/hcApsBgjKGRtC0lok3RWST7Z5P3amT3NTQoxO0XFQWsQyqIX75wYc+5kT2FPHhZYdFDagELVeVRQhhuQ2qTzko0pOXe9TVgErQclhZBAutoJfLTirOp/6rVYfpGtcgVIKW28oiZqAVNLzsJGM+C/s+9mIL+R20Dg+VoOW9sogpbCfo9gg9Kmh5mK55zUG3kyQvWZ2dLjIMBLWe9WUnGQV9Tbwkd4oF99M7BFSC5syL3iV8iyn+nO1TV6SKB5OuaRPbHajfGolQkdmHXjgiVILG9EsM24KQ/PYaGOLZYsxn/qNPLu7xhGV3LGsfKkG9M9HtoFQQtDQ2PNWZv+nVbbZ6xCi30B1muoPQpoEgaGkc4Fw8V1AJutx9LkIjeznJJkNC2QL5VAK7F392r7DdvvNl2VRIKFsgn0rAxSKWA4KyBQRlDAjKFhCUMSAoW0BQxoCgbAFBGQOCsgUEZQwIyhYQlDE2FZSTOy3tCQjKGBsKuiP4kWZfM2+dc0BQxthO0F98T6BvvW4wb55vQFDG2E7QhDjh5dkPmDfPNyAoY2wn6NLpwsvozcyb5xsQlDG2E/R37+TMz3xSmTfPNyAoY2y4k3SwXe0ux5i3zjkgKGPgOChbQFDGgKBsAUEZA4KyBQRlDAjKFhCUMTSCym/jNgL5VAKq21kOjaBwG3dZoLodY2gEhdu4y8K0ul1OP6hnSSMo3MZdFqbV7fRHDPUsJ5V9hHz5gUZQ+W3c2U2DRWpZ+Hx5h8IW1e3Wj6PslJah2ouX38Z984rIojH03dIsbKvbSYCgFJgoYAv5VMLKw0yQUApM1AeFfCphraCtZknMbNCeiCCiqMd4DgsxrvKsIBsIakk+m7XChjxK0EzjNtiQsEb4ZhqGY0PadJllDrP5tFLQGwlG4iv3JaEXYZgTUVhPwrBHiMJ6EIZVLV7nt9KtS1oxJgrYWpRP/+bYkK7V8c14tsWGPOqKb8alIzakVaMEc5jNp5WClpBTnSgsqyZRWGYtorB7LkRh6XWJwtLciMJu2aVmL0k+J67FhlwieGrIgD3YkJRu+GbCT2BDPh6Jb0YJEBQEVQYElQGCgqAyQFAQVBkQVAYICoLKoBU0rw5R2H0yV3JcicKy3YnCMj2Jwu6RnWZM9yEKo4Qkny9uwIZcIzi3/9SX2JDve+CbaX8GG7J9NL4ZJWgFRXcgjCkES8nKZ9LMXfzTGvQEB9QIlqTLwMcoQS0oANgSEBTgGhAU4BoQFOAaEBTgGhAU4BoQFOAaEBTgGkpBtzcJXoaLWRHkE5tDErkkmqDB42HesYX4sKX1fKfocWFpLVDxEs1FGsKI14IKbPtE3cAnkiCL+AwSpI8+dXSC3vK6kdGkTFWHhznrlpbRbz5B5EmXaHyDeQHn8npuwoad9UnLDt+JCVvRMrB4FcxFGsKI14IK/HqRdAOfSIIs4jNIkD4GqaMTdFMMQvPnmo/ZK8xPjMZHZrVLiMY3uHsoQrk52LBf/LMKOu/ChB1MDCxeBXORhjDStaAD2z5JNwgSSZBFfAYJ0scgdXSCLpqD0MZnsGG3w3bhI2OTkqLxDS4bGO41NBPf2vRqLgOxrd0MLF4Fs5FiGOla0EHSPrYbBIkkySI+gwTpo08dnaDx4lJjcVGbgzbjIz8Zi4S8YsPmhVzLHjwXG3Y0/MTFJz7EhYnpk2LMRkpZJloLSgjax3aDJJEEWSTIIEH66FNHJ6i4xIWvmo8pih6QShAZ5RPoUaMfNmztZIQ+isKGzYoXxrEhuDAxfVKM2UgxjHAtKMG2T9ANkkQSZJEggwTpo08dnaD/+d3KDcVs+e7qSRop/OFjw641uZoVmYAN29rmdubo+bgwMX1SjNlIMYx8LWjAtk/WDWwiCbJIkEGC9NGnjvIwU1LLUNyxg6lOzs7OMSSRQl7xYZuC/J7Px4bpZ/n5jr2PCzMMQFKMuUgxjHwtqMC1T9YNfCLxWSTIIEH66FMHB+oBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa7hUtDLPQesVrsPAB9wKejso6iL2n0A+MB+ghZWcHPzGp0pLNLN3d39baRf0tKjywFhhpObe+0ul0qHZun/isK2l2IixNQ0QNPYU9A8lDmhr7DIbMPnCb2uFx0J2CkImo3uRT/5UOzeyL+x7REJWlLWVyopLFUNlkoQG0g4aOFaAHbGvoKifK/fjIL+6pUlvB5qUCQKipKDS4cemKmX/bA+Tfp68IlI0JKyvlJJYalqsFSC+HKHlkvQ7eEMVoxnLBi2HiIxrvQnNQcmOwuKopKMgq4ZZ5jqe10UNGt0v9Khz/YfUvwv7p2ghtOLjj3VJk78QouCg14xfEKGrBlm9tmNUPgR6b08kyVlfaWSwlLVYKkE8fRvUcf7L5+38UqrjSXDlsSOUPG1/Ao6bqXhjzkYzYg3TO14CDl5etdo97sU9EJEARrx/oMfOtzyv+zhy47VuIjEr+TWGXk9t4jvkJg1aaYg4ZVgvfTekMnpA0UWizElZX2lksJS1WCpBPHljm3f+mOi3dZeJUiGrQcjk0iG4d9q+RU0apsxVysmGaYGXzDkqpiTf39+uEOp8f21wIiI0GHHuiMkfs1cgdCnY8R3SMyaNPOeV8HiBcZAeSZLyvpKJYWlqsFSCWKRmFRbrSwv4Ictw3hkGH6yonwCP0fHInUv1mvzXFyysPUTl4im+3gOy0+JkuapgZ0FLfD+nzFXx/3F1J3y0T0kqCBgq19KfUpYiFCR7likkEjha8ZKhD6LEd8hUVBpJhp4oPVVY6Bc0JKyvlJJYalqsFSCWJj4/Tw0u1X3P223yuqDH7bE8Ugaft4bov9hjJDn9T1y00OMgl6IyNW1O5ISJc1TA/sKmjO5T8nm0PDB/6ITTbYgmaAvvVj606mQdN3gNcWC7g/Lyu+zsURQaSbaFtG9OFA+xEtlffUXdVJJYalqMELG/6BRWTd7oZ0Lbb/q6oEftsTxSBp+ztWffUTM8whhE/XN4v+gaQfecE9OiZLmqYE9BfXy8h6VUSKobn6IV4c9SCboz603PvRTaxvXG1/yHxQtaBA8tbBEUGkmyq6+uTiwzMaSoaxvXoXrUklhY9Vgo6Db1yE0q23EP7ZZXT7AD1tiNo1D0b0NgzoJn0fuQmipQdDJicebLTsWJQgqzVMDzs4k6dr8eO7IabV74Tjghy1RUGn4iZ+O0mvePRa5oVdeZsu4lOa69KDExVNQauC+lChpnhprwJmgCRMQmmDq6AdgFfhhyzAeGYafGx18A1eJO0mTA9oufjs/tnG/BZ9da9v8yaXhKVHSPDXgTFAAeBgQFOAarKAn5j4TO8eGjwYCAHPgBF3aZP7GjQtD37ZLZwBADk7QoEzxNTfUDl0BgLLgBG1kONFyq5UdugIAZcEJmuQdM2dOrN+ndukMAMjB7iTd2hS/aOMte3QFAMpi5V789grlmIo/Mf81QD6VsHIvfv04yt+IlumTzLxJyKcSFu/FZ6SLrBjLoGNaBQRlC5Wg8r34bL+6IlW9jZ9vfL1kymfyO4gcHBCULVSCKuzFxzSVvr9Ys1IF50faU/ROg4CgbKESVGEv3ijonqrNKraourVy+8dmZ1nfQa0BgrKFTlDTGAUdEBrevZ1z/4pVmjqHFlnXkgYBQdliQ0H7tx7UUFenUsW30Fmno9a1pEFoBN2J9JuGjtwq32oHQZXACdoiUEI22SjotirjnOpUqlSxx3Xk9hZNHzUFjaBOKKHthzv7zpFNBkGVwAma1jLxpohscvFO0nAXj8o13AIXecyqLP4HTb1SHgZ6OkHDbyCU10g2GQRVAjvELzdZvahYUHTus4vovHPt4ErVz6OMiJp1G16wpovagkbQymjkFYTuessmg6BK0G2DGjn3dPu5Kz3mDa728mt15RsDDgiNoA19gnqg861fkU0GQZVgIqiBvwdXmoDQ7xVVuffPrlDtxef9+g36eYd8KgiqBDtBEariPvpWZkWHLyfD4DCT7rpsAgiqBEtB23oNrtughnUNagl6QS+WpD27SbBIbX/aJjWM3QS9WMfLvXLDE9a1qCHoBdXnlbxNvSIS1YS2SQ1jN0FR+pb3r27xHXfbujY1gw3OJJnOZznBfoIauDfVc5XOulY1AgjKFjsLitD/ejX/2rpmtQGNoObPzJVP7C4oQp83fPL37HndhnxnXeucQyMo5sxcuUQFQVH+UveA5mGPuf9gXfN8QzXEY87MlUfUEBSh005Vn3/fpa91zfMNbIOyRR1Bv6jyv6jA3vWsa55v7CXotcSkTOZL4hA6QU3fdowX9MdHjqBvazqz/12qj50E/czrmah6V5kvij+oBFW47RgvaK6rZ0vPeqNCurO/iVxt7COo3vMXhFY4+pPGRNjedixBsM10wKe515CCwg/8B5wzfM7DxGsH+wj6t5/w8lsI80XxB9Pbjo2QbNTfP2G4JCJvpffwC2hVrcreB/A/ownsI2hhHSH1WwYwXxR/2PK2YyKyEzx7VB7zYdcqDnKhk522QVcFvzHN4xTzRfEH09uO7496WiTAz6IuZAY/Mvp3ves8i36IW+y1F/993OIbzJfEIVSC6nfuRluHzL9f8nnPdpGuweZ+qCyP9Yr3GOU9xbIf4hU4DsoWKkGnhHce2Hf36Gdkky1N6JtVvrz+ZIXHHeMJSCAoW6gE9ckucPkdZcpHdEsTWtStapWqk1b49XWEu+eZCgrXg9IJWi8ty/kkulVfNtnyv/izn18T9ug3NOy0R/OlxlgKmt3UcEV9Lc/SE69u3V/AbhHcQ3eg3sf/lfCEbnGyyVYPSUXbw5t9oPFDorYe4jd5x0Q0u8N8GdxCtxd/5jw6OqtMqRaabaZD/XwXpVn/4+pjY0FzXK8gNH0q82VwizoXi5jj12ddJxzvWMW5bz5NK6phY0FPtxFeDkcwXwa38CcoQv/Oc6q64GOXnnStqISNBU13vYfQcvlxEweGR0GRruKy1k0mVqFsRR1svQ06o/XK6V6/M18Gt3ApaE7FDHSkR4XJv1G2owY2Pw76+UsL5feEODJcCoq8OuRnNG78um9EkuY2ROFAPVv4FPSce8WK/qmoYPvj3nGXaRuzL/YSdO/46eeZL4lD+BQUoVTjob6L0z16faqlf6N2EnRhi/cXeTrmfbEPw6ugD8jb1sNz6q8sW7Qp9hFUV/s/hD51yLsOZWAFjTNiwUPhmW8zXXmtXrs16YwbtRE2qFFvIp+pvsLL/5pZvyjNgBU0MTHxuWYJS1q8Sd6mDTbqC5OH1xm6XwtFc2xQo95UPv1+Qig+1vpFaQaSIb7NXYQy2pK3aZu9zrvvd/R6qcwdpNxhgxr1pvKZ7BnVtfHf1i9KM5AI6pcrZM2C6t02OyxyeX6jkHjO9+ptUKP+4XwWXjFs7Nze+bWW9h2thkTQST127uplwQXvtjxu99OLXo+t4PlANU7QqGJMzFOoUf9QPn9qEFwnVgvbOowgEVS3fsTIDSZzYm3hBhoKD4x17bbmP1suggacoCnFmJppukZ96Xzq/PejnMgldH3UEiSCFi0bpltTaGK+1YUbKMnbG1338bX/2Hgp1kEwxOcfSU7eF644+0GNev0+6R6vBg9mnheLEHzdi6qLmoJE0GnDQ4sGTzAx3/rCDdTk7omu22Wl/HEDHEAg6LPt3KMClinOflCj/v5I6S5Z3wcz//IT/lN8PJSujzgKjv3AzWXjJII2yI5AOvl9HSIUhRsYkLf/WY+wRbwdwicQtKHujeN/Kxdd0MvleCiffWNP7PK3bQngq03CHgv+n00XQQ6JoAG3ItAdUzdusSjcQEXh4ZcCG04/zNM+A4Gg3rnJb6PmpuYQbNNnzAzv86X13SOh/0qENna27TKIIRF0ZavABSHvmQqQFW7QHzko0rMhyx7iOLMgzG3UNuN5pqK9S/ere98dgaBTI9JCpoSZmMFim/7GiKCO+yyIN4GrkMyCapz81ROdiz/0WoLJm9Z1G7Mz50QllByPy+nXU8THh2UPCbj5fmStbm+dF/x83K+B7xOqPrOW5DjoBXR8sanNZwbb9PnNF139sh5d7ermJxC6ZFl1GNtBImiK0v+k0T1znx78UeR42WQ1rl+8/8ULwfXHveQ8c+9U5zLHaewJgaCJIp+ZmIHdpt/eoelE88/xOfao8LJqErYP5tjYaPNHzVdSNcEOEkHDgxdcMznfOw95ZqMs+vvi2XBxpVelLouONxqhzuIlCASNi4ubHLTQxAzcNv3+4G9+m9zNbNuHHhdeNo0l6KgZvhg5fBddC+wgGuIvL27dfYuJ+W1/NWQsYAAADhZJREFUQWHX0S15mUr1rgAf5Z88PbRS4HuX1OoA8anOO/1NTZVt0xspyefQbcJ2foDZusqZfl+g1FY7ifqgCciuBy04EFHdxPzjAUMHNJvceLNssnqCHqredV6naq+N9gsY+5FKV1IQCqprTN5kST77fyG8tDB/If33TVxd3iBvm3tIBN0d4zVwW7apgLzdy15fe00+VcV7aGa7BNWZJ3y/sGawW5MJ21Q42UQg6HCBZrHkTZbkc/Xj99C2YFPn9EpzBxegKUgE7bPpnkVtqnmT15/fFl9JUnR25UC3JuM+tHMVTQJBkwVSLLgUqSSfRVNqerU4Y12/uOX3k2bPWhEImiavvYSDn7sQi35ZPdS7/qi1v9jvyBNOULo7FPK4vUjGSjIiAtsGHDcTQCBoUZuLli2UH0EN/P7BmCYufRd+Y5/HCuEE5eMOBW6Y8kIR2mPuxA7JED+oZvfIyEjyhXKY0Fufz+xcs9WETb/Z/DQTwRDP/g6FW78+2GLIPq2lav9txC0WPzM7tCSCHjNAvlAOBRUpOL5qVAOX3nP32PQXSCAo6zsUCse6NfcrfsrnLq82Hs+pei7NInrvE/6kat9XDqC6HlQBTgU1cPuLeX3d6w1alFzmaCMjCASluEPh7/Mmdq5W97mPjvpIh1nSvM6hnO6J5I2rzD7/j77q8YKZAKrrQRXgWVADV7fPfNzFPyp+vw2OlRIIqnyHgmlK8lkwzCu0ftlL8Z8ST/t0lM6/f91bePkwhrxxtfl6SJ/V5ipGU10PqgD3goroL306q5eHV5+4pAtMjxtiBQ29FGmAoK3812eJNK8jvBxC6I9ujV+Ztcy34I9Zxs/G70/3njVrpt8Fw+cxQXohrt1D8/n+nnPb7Px2NNeDKqAJQSX+2hs/pEGN8Ofe+ZZVWWesoDsyiLfpC5YniLR1FV5+ROha42eE763PXkswfjZ+311r6MTwcL3h8+JWzx6Orzntofk8f1/0RE33Fq+YiXuM6npQ02hIUANZP77/Qpc6Pr2mfXA8i7oxwlOddy1osiSfIzcL2we+f5UJOBrVcXaG8f29VyNGaehY/twh99G75o5oUF0PqoDWBJX486u3RofVCOz7SuIxS+yRQyDo7jGoU83V5E2W5POI38dHhzvWAzrbnRBevP5VDiAtHmbVX7z2KLqy763YdrV9Hp+4MvmqVQdrCAQN+CFl0B0Lnsb3IJ+HorrMN3lRhGaJ+FbYkqlj5hwK0cUi1v7Fa5a/Dr77Qs/6zi0Hz974nYUnFwkE9dHHbUAWXLFu73zmzw0OnG7m0CRLNocePT9ymJkAopvmrP6L1zbZp5PiY9q71Q4b9urGIzcJz0ERCDpgiH/GtE7k/bB3PuOe+OPKUxPttLAPHg2dYW7Ln0RQ5b94NSqL2J304x8vjO3k7dwscsrKPedxIyyBoJnvnUPxf5J3wN75DLyG0B1XTp75RyKo4l+8WpVFVCHnl8+XvxjZrJpHu6dnvLvv/EObTatcKnoZ7wUmEJT3M3M+/4inHzm5qpREUMW/eBUri6jHv8eSEib2a16jbqsnJ7/1yXc3ChD6qvKLyQOcpAwRCKp4Zo68gK1NGT8mJ2/ycPsuUxESQe/NbN813tRGs7qVRVQm7fSed14Z3ql+Fd/2nt5LvkKesw2TCQRVPDNnQQFbW5I5qnqNIbw8DpRE0CEjT5wcNcbEfNUri/BA4c0fmzZ5ZR4KlO71JTnMpHRmzoICtralgJ/So0QFbIXNkUKTl4fJ7kLMDpEeH+3Fsoca4J1Hvs5dW1m6LpxAUMUzc0QFbMsZJIJ2S0XoZgeloH6l3v95RSTKgjsWHYMop0qPSCM80alOpTNzJAVsyxskT/kY4T1+vOerJuZHi9SIjpZNLocJTf2x+FQbVtBrP+UilHFxlal5+AK25Q6soO8bSTIxf6lLQlKSR5J8FiTUDCsrBXh+6e9Sv5tixIMCtkbo8pn1hQWHXPmD5Ka5a4cuKFxee6JdEiq7cQqCmsH3DDpS4SNzR8EvVpBNoMrnjEpVKnak+Hm1wQu6I9i3Y5DPVtMRWbFjy5ayA0HNUFf8MnsZivkCthZyptJn6HK1WdY3oDZYQb8KPCy8nmyyVyHmk7KPqwBBzeAufMl300uQnTpmUM5yllg+fGgL6xtQG6ygnaQnlv5swTABgprBNS0tzVP4MnUFv/zUMYOCwCtqCy9deSmXbAVYQT2k4Uiv+FdfFhDUDM7FmJhng1PHGc7tN8dWOmB9A2qDFTRUqm30j8ma6qYBQa3EFqeOz4XWqPcxxc+rDVbQhN7i1Xp5T5kquKoACGolcOq4LFhB9eO9xsVP8h9uzX3c5RGq58VjCtiWRwiOg55fM2OFufpjZYCEUmDiWbmQTyXkR4wJgYRSYOIRidh8nn0mckku3WK5BQRljAqCnvV6Z99TAzi5RYM1IChjaAVdX3YSLp8TViKkC1TxwRG2BARlDK2gJsDlM+pz4aXzd8yXywUgKGNUEHTZUzp0xtM+BaTtDgjKGBUELYjy7+K1m/li+QAEZYwKgiJ08bsMXIhWoRO0XBRusAwbCnpjcp/pjvaUDyxUgparwg2E2E7Q2/UXJL/SxLFqh+GhErRcFm7AYDtB33tGeIk09ZRkR4ZK0HJduEEB2wk6f57wMmkN8+b5hkpQuPqmLCwFzfatK1LFw/Dp22Z30T/+59g1rwnodpJkV9/oVko11S14BpDDwfQ/aGa6yDBjFZI5Hp3clzNsXRNQCZozL1p8AkrJQ0/y5kpPpbDgiSAOhy0PM/3zPatHPWgHKkFj+iWGbUFIfvcCDPFsgXwqgRPUOxPdDkoFQUsDgrKF7jDTHYQ2DQRBSwOCsoVK0OXucxEa2ctJNhkSyhbIpxLYvfizexEq2vmybCoklC2QTyXgYhHLAUHZAoIyhkZQTmrUcwUIyhgaQTmpUc8VIChj6ATlpEY9R4CgjKERFGrUlwUEZQyNoFCjviwgKGOo9uLN16j/5tE6XY5RtK5JQFDG0B9mUqpR/7v3l3c/9UmlbV5jgKCMoRdUqUb9sunCy+gttM1rDBCUMfSClqpR/9BzpxLihJdnE2mb1xggKGOYXlHfzPDkPpcgw6dffE+gQ9432DWvCUBQxtjgVOf6cdL3HcFVmn3NvHXOAUEZQyNoi0AJ2eRiQRHi5xmvdgMEZQyNoGktE2+KyCY/ELQcAoIyhmqIX37Q1FQQVAkQ1HJsuA1aLoHaTIyxQY16EFQJqM1kOTYoAQ6CKmFxbaYsqdAACGo9IOjDMK3NlO1jKNVS1Y9FzzSKDWrUg6BKWFmbCRLKFsinEpbWZjICCWUL5FMJKw8zQULZAvlUwlpBR6cb+ftqeeHf4lV+3AaCOnY+76Sbw2w+rRR0d91iKlQkwgHCKpas88/WJY1lPhmsE8mSGDVTua5ZzOXTSkFLyKlOFJZVkygssxZR2D0XorD0ukRhaW5EYbc8iMIoIcnnxLXYkEsN8c0M2IMNSemGbyb8BDbk45H4ZpQAQUFQZUBQGSAoCCoDBAVBlQFBZYCgIKgMEBQEVcYBBC2aQRRWOJNlmG4WUVhBHFFY/myWYZSQ5HM7vrRD5nx8M+//gQ35awW+mbfwj278ZTO+GSVoBQUAmwKCAlwDggJcA4ICXAOCAlwDggJcA4ICXAOCAlwDggJcQyno9ibBy3AxK4J8YnNIIpdEEzR4PMw7thAftrSe7xQ9LiytBSpeorlIQxjxWlCBbZ+oG/hEEmQRn0GC9NGnjk7QW143MpqUKTvyMGfd0jL6zSeIPOkSjW8wL+BcXs9N2LCzPmnZ4TsxYStaBhavgrlIQxjxWlCBXy+SbuATSZBFfAYJ0scgdXSCbopBaP5c8zF7hfmJ0fjIrHYJ0fgGdw9FKDcHG/aLf1ZB512YsIOJgcWrYC7SEEa6FnRg2yfpBkEiCbKIzyBB+hikjk7QRXMQ2vgMNux22C58ZGxSUjS+wWUDw72GZuJbm17NZSC2tZuBxatgNvJmIPla0EHSPrYbBIkkySI+gwTpo08dnaDx4lJjcVGbgzbjIz8Zi4S8YsPmhVzLHjwXG3Y0/MTFJz7EhYnpk2LMRkpZJloLSgjax3aDJJEEWSTIIEH66FNHJ6i4xIWvmo8pih6QShAZ5RPoUaMfNmztZIQ+isKGzYoXxrEhuDAxfVKM2UgxjHAtKMG2T9ANkkQSZJEggwTpo08dnaD/+d3KDcVs+e7qSRop/OFjw641uZoVmYAN29rmdubo+bgwMX1SjNlIMYx8LWjAtk/WDWwiCbJIkEGC9NGnjvIwU1LLUNyxg6lOzs7OMSSRQl7xYZuC/J7Px4bpZ/n5jr2PCzMMQFKMuUgxjHwtqMC1T9YNfCLxWSTIIEH66FMHB+oBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGu0Y6g/d1dK7i7T1C7Gw6DRvKpHUERShMfFbIjVO1uOAyayKfmBM24pHY3HAZN5FNzgh6LPBb5TJcJ8QNbpKJ3ghpOL1K7V9pFE/nUoqC1swrdE9GMVYdb/pc93KZ3ATs2msinFgXthVDrG+jd+a8FRkSEDlO7V9pFE/nUoqBPCAm9KSQ0YSFCRTq1e6VdNJFPLQt6KiRdN3iN2r3SLprIp5YFRWsb1xvP21+8htBEPrUk6MPoeUulxuE0n9oVFCgXgKAA14CgANeAoADXgKAA14CgANeAoADXgKAA14CgANeAoADXgKAA14CgANeAoADXgKAA14CgANeAoADX/B9wFMNUwmnFhwAAAABJRU5ErkJggg==" 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HSLexZhya2lTbzejOfmTChBE599PC5uTxA2rJCi26BX6jb+HR2IQH818ajcX+W3bxFBImiN9PYoU//GIxHLexZhy/V3G3uPOcrH7yiBoH2aufb2m05eZGE+hb34/OpOGf+1WYOafIoy+0+1rIqqgkTQwEftUUJNI9MpehZhzbV3m7j/7xAHtzIRCOqeO/PMzR7kRRbmM61dvXCXOhUqz9A8chM+nmtoUQ3VBYmgK0MDFwevNTKdUc8ijLi5vKXrkD1K7+ASCBqQFbsa6Tc7Jih6JikuCWUImzPplbMROtbWohqqC6K9+GOzlho/v6jXs0h6144i3oZdXNqI+5NcSnotVvTqUQJBp3a8U/etruRFGlvhB/Q5f6z+JvIyVAuJoOu1GAs4mJk6K3JZ4cFIzfHvRToa2xywCXedK7mXK+P0ytqHStWA6DjoPXTyXRNPXtGnQNCU5SNWFewXpU0Nab7J3LqpERJBY2JiRnkb22Ya2imrb6/YrmP0Rit3edj0StfQlQozdgx0abHkT2WqQCCo/ApvHF0+0+q+se61FrrN7OstXSoOsauOVWUgPVD/xFiHYF5ZyDMNpehPUk7Qbk2FQcPeCOV8/1ZArUk/KrBjTyCo7Aovgy6fG14TBu32SuOardCkR5lxJEC1kAqaa+y4XfNLqPFNdL2W3mjlBJ1Z8TI6X36h9OHs3Mau0V/beoOU8FSn0RVeBl0+Z4tn4Meu0r5P1O7FNzKzbmqERNB+AiFDjEw/G9inh/dg36/1Risn6D9VnKo6uzzrxfHe6ginDh/atK8nQkGNrvAy6PK5p3kOSqsjXaycVllo6o+3Nrt26oNE0DiBeKOXZWTuWDZntcHmvoK3KJzvUbPX87cpp307wqf2hCM2u6qEQFDZFV4GXT41g2oPDBivG9d34J8nQz8zv3qqAytoTAHkZfJ2D43m9/nNnV/97D4+kgEEgsqv8MYpyGfaxM6zC/aLUiYEhxk7NG13YAUV9jeHB46bVXsGeZm8CSqS8GV/t9CYeOt3M44T1PIVPr3+oI8i29hHR+nkkDTxjZIQSm5MXiaPggrknZoVVqXX2tvWnQtOUMtX+I2vCo1B2+8o6qZGSAT1yxC2NquRl8mpoCIJsYM8g98+YMVr8QmaeAtX+KJ78cUHEkHHtlqzrvXb5GVyLKiA5uy77Z06LDljpQv0CAS1cIXf1SoXZdQtRjd0aiERNO+z/gM3mHHMm29BRdL2javr1ne9NVp7AkHlV3iTF4Br+oYMqfEWbf3UBlbQqFsoSgt5mfwLKnJvY3+PWqO/YX13KIGgsis87gLwk58byGv3YAU9koosvkWBdzTn34+o3GRyXBrDMnGCmlrhDS4APyFdfOMrDB4Iv/vfF8PX9qTn4s34H6lGUJGcE/PaVWo7J57VhRc4QU2t8PoXgKd3kS5fLC8MNiL0c8di+FqfQNC9w/KbOpl/9Y16SD8c09zp5fk/sTjdRHou3thIvi4A5wOSnaQWp+IGP2pDXqYqE5qyf3JTpw7zjtP+khIIKr/C610ArkOV+WQFiaC1Ne9szTXjKnnVJjR535TmldrN/oHmMCmBoMVihWcFiaBDX/NNmmTG/S+qTmjqoRltKrWc8t1jC79PIGixWeFZQCJoyurLaL4Zl1qoPqEZ8fM7V64/OtaSTvQIBC1WKzwtKu9ZxHrknv7wVU+/fh+dJTtDUbjpSiBosVvhaVB/zyLW5O/PR9Sr3HH2Qdxl+UsqlHT5RnpLIKj8Cm8c+8mnBdhFzyJW5fG+Ge2d6o/Y+KdGNmRPqWkn+pa+qX1PIKjsCi+DfeXTTOylZxHrknvmo4FBVbvMizN+vqJDK2HgNUX7nkBQ2RVeBvvLpxnYUc8i1ubfPdPaVw4e8unvBh3stBF7PK4m/SYSCCq7wsuAzef+l+qNSiAvT1Uw7VkkrW6QiJMnu/pxRt6Fz4a9UKn1O19dKzp2delvEpeW+l37nkBQ2RVeBpyg8f57L7/dkpv+/diCF/TWqQyU/9/1j+WCIoq8v3dDJLI2q9rxSWr8sqhqLuEz9/xTMGZA6VKOC6S3JKc6ZVd44+AEHboeIU2wuh5pSgxW0A8dAtz3e1b2b2c4+VG0SMXoaL3xdtrEP8+/382O8PDpMX+/9ibnxxcKrojCCopb4Q3B5TNKPIDQ4hR5gWoCK6jPb+h4ic1G24/cKW6rtm1z37ZNb3yxEFTL7Z3TOrv495y370HhKJygJlZ4OXD5/OzFJ2i/n532ZosV1EX4qyq3fXOs6Xco0GBs8RFUy83tMZ1cC08M4QQ1scLLgcunZryTX82T5pSoIrCCin2seMlGPOk/wvCkcjETVEvh9SU4QU2u8MbB5zPDNvf8KwH+FzQxMdFD+EuUiYk1fFxFcRS0EJygpld4o5iVz7yN/5ujXOeT7MEKWq4A8jJBUBPgVngjmJXP1zquHe9jRz+oan5OEpfgBDW1wpM87dgo988X7CL9EZSDUMw0goqqBBCUMVZ92rFxcgd6hnoflN4f7CIMvrKjh82CoIxh+bRjHbh8ruiahU55Sgdi/3FpViagyQeW10Egb1mj0Hm8PGQSBGUMy6cd68Dls9cuYdBa6nIk3aXKa8Hl91peB4F57U6ffWUCVRHsAEEZw/Rpx3eJTh2P+Bih/KC/tO9/fPHGtp/WjrC8DgJB1xBKcJW/utCmgKCMYfm0Y8KLb057rT8WHS4J9ZN4M96a/1HUASHP/4T9NSc+HtwHgrKGSlCB64ajsPk81af9nFTpbUaNNelnanxPVYUhb+bkxZjxNGarAoIyhlbQMMNRZuXzz44Van9BV4XHPSpXCf+XrgxmgKCMUVpQJiQr+rC+5wBBGUMr6DrDUTbP54kxo47YeJaygKCMoRXUCLbO59cBKz+uwcsTREBQxtiBoA1OInQ5wLbzlIVOUDvuuMFS7EDQqk8Qyi3HyeNEqAS1+44bLMAOBO0kNO/bm9l2nrJQCVosOm4wEzsQ9EpAh3Df3207T1moBC02HTeYgR0IitLiDqTYeJayUAlavDpuIIM7QVMPPcAH8QvdThL0CGwAb4JOLenoYEZnudxBJaj+FeBplUpocWdRM5XCmaAXSm5Df5c348GgvEElqIVXgNs1nAk61UcY9HmBWV1sDpWgFl4BbtdwJugHzsKgnYrbeLq9eMuuALdrOBM0uVzLL98oeZBdZWwNlaAGV4BLgKBsocrnuXoVfDYzq4rtoduL17sCXAcIyhbIpxxwsYj5UAsaYTAG8ikHCGo+FIIW6+4s5QBBGUMhKHRnaQQQlDFUTTx0Z2kACMoYum1Qve4sUx+L9IV8ygCCmg/tTlLR7izTvKuKlDXjkTV2BwjKGCvcF7+OrqMQdQOCMsYKtx2DoHKAoOYDgrIFBGWMFe6LB0HlAEHNxwqnOkFQOUBQ8wFB2QKCMgYEZQsIyhgQlC0gKGNAULaAoIwBQdkCgjIGBGULdB7GGBCULdB5GGNAULaw7TyM7LEpdg0IyhamnYfpHptSpTqLmqkUEJQt1ug8DBLKFsinHBZ2HgYJZQvkUw4LDzNBQtkC+ZTDUkEHP9Zx7SYRV9UfdqNgkV+2gqDm5POv60yWiWSx2RRz495jU5jMp4WC7nOtKlGlhAMRdhDmoFvkqu4Gh4WpMTufDJaJZE6MiilV1RQm82mhoIWkVyAKS9Xvfcw4KU5EYU+dicIeVyUKS3QlCkuwSZeoJPkc/Sk25FpNfDE99mBD4tvhiwk7jQ3ZMgBfjBwgKAgqDwiqBwgKguoBgoKg8oCgeoCgIKgeICgIKo8dCJq/gGVY3kKWYbmLiMJyFrMMo4QkUXvxh7lS38cXs/kmNuSBkTuk9fk4ERty5Wt8MXLQCgoAVgUEBbgGBAW4BgQFuAYEBbgGBAW4BgQFuAYEBbiGJ0FzDe4ssTZPmYZxg60SSZAX6tRRCrq7TtBKXMy6Op7RGSSRU6IJCjzb3HsYQdh7/r7vaHBhyQ1QQVGmIrVhxEtBBbZ8omrgE0mQRXwGCdJHnzo6QZP87iTXuWw65kzgw+SIuQSR8W7R+AIzAy9mdTqIDTvvnZgWthMTtig4sGARTEVqw4iXggps+UTVwCeSIIv4DBKkj0Hq6ATdLKyqc+eYjtm1HKEN0fjIJ01XReML3NEHoaxMbNjFgPScNrsxYWd2BRYsgqlIbRjpUtCBLZ+kGgSJJMgiPoME6WOQOjpBF81AaONwbFhS0934yP57t0XjC1zWs7Fn/zR8aZPKO0diS7sfWLAIJiPvB5IvBR0k5WOrQZBIkiziM0iQPvrU0Qm6QJzrEFxUbM1YfGTsCCTkFRs2x/tGaq/Z2LATwb9daR2LCxPTJ8WYjJSyTLQUlBCUj60GSSIJskiQQYL00aeOTtBNwhznzzQdkz+ox0OCyEjvQPeKEdiw1UPEJ7Vhw2KmI/RFFC5MTJ8UYzJSDCNcCkqw5RNUgySRBFkkyCBB+uhTRydogm9CZsjvpmN2ddAQRgorPjbsnu+N1G7LsGFbGiak9JiPCxPTJ8WYjBTDyJeCBmz5ZNXAJpIgiwQZJEgffeooDzNtDw35EBMy3rFSpUpDSCKFvOLDvq7pNzobG6aZ6e8zIhMXpm2ApBhTkWIY+VJQgSufrBr4ROKzSJBBgvTRp46nA/UAYAAICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA2Hgt7oOXiN0nUAeIFDQSefRW2VrgPACxwKmqG510vpOgC8YGNB/S6j/EX13VvvF96Xc3Fz8V0hVMHVzc1tRJGg7T0fYoqJjyQZZZrHt8z8AqAEthd0aNe7+SeDtgmCJiL0m8MtVCLt+Zh9EzS4YkgELejRd37p0qVLNUa1S5UuvVMa3m5RR5gy5qrliwHYDJsLet4nQ3g9GZCnFRQFnDIQdFjXqOjnRuTo/p59IhD0uR59J3+hcRNfpeHIuLzGyX+NoVkOFUHUaBVwJEocro8pOs7stokpNhf0o5HaNwE3tIL+6J5RRNAJtX5FO4ILXVxVveaE/BMD+s0U/9DCoOqTtZ+QNmfaieG7EQo7rn2rn8aiPfqe7o7uvSC+kYZTPrhVL2PwP1ZdUH4gabQKSb0lDou1oJOl5023OorKeXhVLXFAtzpLj4/eGZ5R7WBBbHxoQlq/hSecryLxL65OclabzeI7cVKkNHHTIHQj6Jj2rZTGLtW0XHy+s+K2f6Af/Tt4Ds2QhskjOx4+Pc22C64cuEYrx/ArxVrQlaO0bwJvi9nK3+yVXzRbmlpDuhd+mBXYvn1I1xPtEBL/pgjCffK6+A6JOZMmPvXMWTxPequfxiI9+h7ojdCF93MSw+dKQ3Fc1JO94ZOyrLacHGGy0dK2SNoWKOWN2i33oviovHH+jYbHxPVDKGY9mlrNo292fKQ0TRlsLugZ30zh9Rcfqb3Jr/zfc6vzUsdrz96/i1Bm8oluQhqFv8lC075mkPgOiYJKE1HPQw1vSm8lQTv7arnwXI++A75CKDdPaPV7SUNh1KFliY3vzHrfVoutJCYbLbFFkhqj1VGaU8MEQT9rmZEUqBP0XsfM3GbH4yOlacpg+7346J4PNL/W2CUJivyvPydoP9dnv2rnghOyIg4UCHqwbmp2202FgkoT0db2L+ni9H9BpR59NVdzUZbLY4RWhGcmd39fGgq/1D0y73TQbJ9ugyVWHJONltgiSS3QZd/xhzSCoP3XIDSu4Bf07pZZrnHxkdI0ZbC9oHnz6rk3jEU6Qdt9jEp4eHl5tdBOPuHdPfZZ8JoavmNRgaBoQc2gifmFgkoTUVqFz3VxBltK2h59s0rcRntfEj7lTg3yH58rDRHath6hOXXb/muDJVYck42WmE9dY5T01eCmgqAD1yE0WyvoqPUn664+3VsQVJqmDFydScpvsulYk1yla2FnmGy0REGlFmj+5NxHTk/ioza0zkquExMfkvvUe/27b6GbfnviI6VpylSfK0E3hGlQtN9NpathX5hstLQtkrYFut/Gq8167U5StcbTY7IGV/frvv5uRP3u04LiI6VpysCVoACgDwgKcA1W0Mtzhg+ZedYWVQEAQ3CCrgyet3Hj/JAVNqkMAOiDEzQoRRxm1LdBVQDAEJygte+Iw/9CbVAVADAEJ+guv+gZMwb77LBJZQBAH+xOUtLmRQs+f6Q/9srU4gzugn/zuaL0IimKqXxauBe/rtmS4kvtOCoZjQH5lMPCvfh1xi7HLi6EUwiaMTf6W+FliN5oyKcc5u7Fa+K/F3mzJ4uaqRQaQaMjNoTFIlRJbzQIKoe5e/Hp3TqKePvqPp/bOLLX+9mUNVQZNIJ6pKDEmg+LCHrhjMjUPgzqpVaoBJXZix8UrH3R9KniUMLJMTifpn6qg0bQ2sIOwZc9NIWCprVuIuLqzaJiKoVKUJm9eJ2gX5VtUMrPcWvpRg0nPLW8gmqDRtC11ZcL7XwHR73RunwWT+gENY4uod1Cm/SYUvpFB8f65evmWVaSCqERFJ3ZhVD+znf0xoKgctAJ2iOkr3+eW0mHFehS6XjLSlIhVIIaBwSVAyPo0waBEnrjdQn9tmxUmbIlHUq2vYtcllJVUk2AoGyh+gVNDt51X0RvdEFC33TyLO3uE7jQc3Hpk8LHfCM3WdsfIChb6Jr4RWeMjS1M6K1D99Hl8pV8HaomoeyR5cp2fmBJFdUFCMoWK26D6rjUv82MUQHfv1WhQ+8KTSwrUE2AoGyxvqBaDgc4CvumP5VMtaxEFQGCssVGgqKnpTyPomwH/c1V+wMEZYutBEW1/L0H9XJUqgsK2wGCssVmgsZXrOJYcohlBaoJEJQtNhMU/Rkz/sNafdhfzssZIChbbCeoSOZMj0/t/NoREJQtthUUoT/aNvvdslJVAgjKFlsLijSbvN58jFDCwdP2ucMEgrLF5oIilDTaa/129xY12qdbVjzfgKBsUUBQhH5vWcq5dYD/TMuK5xsQlC2KCIp+KuU54OaLtS0rnm9sJOiTUdXqfWifG0nPo4ygB8skzXFr4osLU/M0zsUAACAASURBVCM2EjRy+O2zTT9hPiv+oBPU+H3xeEHvVejwVUyZcmvtsMNk2wia6iyk7kQr5rPiDypBZe6LJ9hm6h3arr1rbMe6u7GRasM2gia5CoPfGzOfFX8wvS9eB4GgWcu7DvkdobjQltqbQc7tvo79ikqwURPfenF+cve5zGfFH0zvi9dhxl5n/pYa4afzXyxXtuxbxN/hGxsJevvlChVHF4dbFKx4XzwZOav9QhwPJnxU6pQZX+IYmx1myrDDDXgjML0vPs25hBZ3s6qQUa3sa5eQ/zizvsQtcByULVSCanbuRrFR8zP1Rpub0JebLvd6zXmOeV/iFRCULVSCvh3WplebL7rr921lbkIPlB78WXWHV4zegKc6QFC2UAnqk5bjfBUl6x9wNzuhqyqV8v5ulX/4cTO/xyMgKFuoBPVLSit/GiUE6I22IKHiVkL2+lqt9qj+7B0IyhYqQd/zDpgUPL2h/kUfFic0f0dYvQ0qf047CMoWur348xfRTzFb9MfSJPRohM/CRMu/rjwgKFuUuVjEFJeHuYw+/VL5ir3UeZyPQlCDvq7yN68VaRFUNOrB7vji01cgj4Ii9O+c0o5T1jpF0JWiEDS/oPp9XWW++T+RWn5FYrZ6RDZtAv2tSigkKMp3WN6k1jD9flzVAVUTj+nrSiDT9U+E3pyC0IWln/xHMSfVwKWg6Q6p6GR4iZHnKctRAitvg55rJAyOvoQ2+k15w/MP5rPiDy4FRe7tc9NDaiwMaPl5Bm1RtsbKgia6piL00ev5LtcRWvsq81nxB5+Cnqni4OB1F+XtiXAdd4m2MNti7b34N1tumO1x+R/x5MifdZnPij/4FBShv+9Kr3dm+7Vcr6Y+8awtqGbLG1Ovo3zXvxFa3Zv5rPiDV0GfkbevV9XXf1TNGSYbHQf9wmfC615XmM+KP/gXVODf9+rVnH+bcaFWwlYH6i+/t0bVJzRIUYWgAr+96dZ+YzL7cpkDZ5LYohZBEcre3atKvz3cP1gRBGWLegQVeLy2rev/jvHdPR4IyhZVCSpwZ1kjn3d+5niXCQRli9oEFbg6L6TaxF94dRQnaGQB5EU+n8+sC3dNRj8+bje3cIuQCJq/sGvyAaMBlvYsQsvl2XX93/mJy7YeJ2h8AeRFPpfPY34NvF41cc3sVx4v+va3o8udSAQd3y8kM2KskemW9yxCz+V5od6jDvF3ZzhBE599PC5uTxA2rJCi+czxOYayoxbKxiZ4XEXZ4WvIC1eavF++N3lwhkTQGuntUaaxnr4s71mECdeWtXQZsD3FNjMjhUDQPs1ce/tNJy+yaD4vNhAG33eUjT0ULgxio8kLV5hHjRt28DpmIoBE0MBH7VFCTSPT6XsWoeXh2i7Or3xyx2bzw0MgqHvuzDM3e5AXWTSfD7yERuOLfrKx5+sKGz4LJpEXrjAjpwhbLfo3tRWFRNCVoYGLg9camc6iZxFqUr8Z7NZwxileNkgJBA3Iil2N9JsdEzyXz1ejjn7u86NsbH7H/vuXe/5NXrjChIqXVPrdkw8g2os/NmvpOaMBej2LZES/JlLN1h1/5p2Y1sA9emsSQjeGth5+y8Zzfx4CQad2vFP3ra7kRT4naPqC9q+Zuj07bVHXkSo6Rx/xDUJPnEzs9JEIul6LsYCDmamzIpcVnt3J/3a7yItm7AAw486ans4tY6p4lfbyfKzA7AshOQ56D51818SPhj72fBz0B++Pvmo+xUQAiaAxMTGjvI1tMw3tlNW3V2zXMXqjlUpo1pE2JZ37Ti77njKzlyAQVH6FNw42n+dej1iiuiu7dfw6ol+sqWPapAfqn/gbme6VhTzTUIr+JOXW+O5h9zf0LV357e+Uu4KUQFDZFV4GXD7PeX68v3c3Xs9cYDjav8dqU/fvkgqaa6zZbn4JNb6JrtfSG62coBOcH6B7TkMXv1yp9ezjylxXQniq0+gKLwMunyNXCv+fwGu6TxfX71fRcfoD/pv2tDN2jL0AEkH7CYQMMTL9bGCfHt6Dfb/WG62coNecnF6oXPm2sLv2fUwzp07vnrL9vfWEghpd4WXA5TNS7Ei9zU/Sh8UBw9o0U88TqML3IJTipN8/YtEAAkHjBOKN/h5l7lg2Z7XB5r6CG/WH6zuGHtW9f7r3nVDnLkt+tq2kBILKrvAyPMvnv8umGin+vcgcdNZDOmFx1zNR+MJS8sIVpqF4eMj3vnwAVtCYAshnytFeZ9LudxpV7jT/uO12IQgElV/hjVOYz5veby6pb3gKKudVv9aee6T3B7oIg6/6kxeuMG+N1aADNUwEYAUV9jeHB46bVXsG+Uw5ElTk6XdTWlZsNWWvbe6QwAlKs8KPXILQ46pGjqJdO1FwwvevAKG5nDCLvHCFedKmdgv/n00EkDTxjZIQSjbjkSecCSqSfnRe58p1h228YvV9XZygNCt8xyPCIOy0yeChLT8YHqCiPkc0l34x2byRCOonlJBZjXyeHAoqknf+44HVXbsu/MGql5cQNPEWr/ATJiN0x8X0ITTNNxPef0JeNveQCDq21Zp1rd8mL5NTQbU82DWpTaUGIzZcstahGAJBLV7hH9V+ZYS3ii6lYwGJoHmf9R+4wYx/KM+CiuSc/nhQncrtp3xjjaugCASVX+FxF4Bn7Vz3F1Xt1AdW0KhbKEoLeZm8C6rlyaEFPbw9u8757gHbcgkElV3hlbwAnFewgh5JRVS3KHDN/d0zX3H37T5nr4kDcWaCE9TUCq9/AXjWzKkiIc7C4HuErk4thq9hpOfizfgfqUdQLbe/mR7u4fHKtO3XWOzi4wQ1tcLrXwCeu2KJSGNXYfCLsH+0pBi+tiAQdO+w/KZOLK++4ZB7e+dGVnNq8+a605TH9EnPxRsbib0A/Oywniv5uw3LqpDsJLU4FTf4URvyMtUoqJbHRz98vVH5un0X77N894lAUPkVXu8CcB2F+TztuWp3lwEWV02VkAhaW/PO1lxv8jJVK6iWnAtfTu7sU6XtqNU/WnLlM4GgFq/wg9cK26WeDy2olXohEXToa75Jk9qSl6luQSUSj676X8vKfp0nrv/VvOP6BIJavMJ3PiwMGhschrJrSARNWX0ZzTdjP9ceBJW4vX/pkLCK1V6Z+NnPpHuJBIJavMLPjc5Hv3la/0lnjzZv4uZnWp09i9gUzc19y4Y2c/LpMObj7/F3EhEIavEKn9G5Zluv/eRftJDTngMGeZhxWNGqqLZnEZtz9/Cq0S97OzUZMH/b2TS9aenzXvtEd+SdQFD5Fd44RfJ56Ucb9FPRdjtChxpYfz5EqLlnESV4+usX03u/UM6/w6gP9v1dcMQntWqF2o71pKOoBILKrvAy2DqfVZ4ilFeWkwf9qbxnEYXIv3Xoo7fCg8rV6jLuo7jruf9zy0QPymzWTiIQVHaFl8HW+Qz7HqHfjP2/lUD9PYsoSPaVve+P7hRYtqz7mM2o9lDtOAJBZVd4GWydzzjP6bMN/qlKwbRnkfzdynXcoCDZ3dxXrswo/5H2A4Ggsiu8DDZf4f+cN+eCjWcpC17QW6cyUP5/1z+WCyryzNeMQcp0faM0D8r5dqnsIW2RkpzqlF3hjVNsWiRjYAX90CHAfb9nZf92hpMfRYtUjNbv7K/4JfR+VMORut1rrKC4Fd4QbD4zYj8wcR+IwalTVYEV1Oc3dLzEZqN9x+VOcVu1bZv7tm1644ufoEXACWpihZcDl8/EWpHjq8v1aftx2RIl1XOTpyFYQV2Ev6pyfRsea/odCjQYC4KawMQKLwcunzEThJ9JN+M3yv1dcgE6UkbR3qrowArqJvx5yUY86T/C8KQyCGoCkyu8cXD57L5XGLQzfupntqcw6NXIrPlxBf4XNDEx0UP4k7urPNbwcRUgqAlMr/BGweVzUozwS+Fu/Oz5Yldh0KW5eTPkCayg5QogLxMENQFuhTcCLp//Vhs4J1imz/t/S/e9tLjUl+Qz4w0VPieJb3CCWmOFT149V/bajv2eJSvOJZ8Xd4CgjIEnzbEFBGUMCMoWEJQxIChbQFDGgKBsAUEZA4KyBQRljD0Iuuu13l/x8kwGEJQxdiDo2rpbt4cus+08ZQFBGWMHgtY7i9ANH9vOUxYQlDF2IKhLEkI55VV0T5K5gKBssXU+ewjN+9p2tp2nLCAoYygEfdogUEJvvK3zeaf+C41rX7XtPGUBQRlD8wuaHLzrvojeaJvnM/f3M9z0oUcnaLHoWcQ8qJr4RWee+5jeraOINy87LEpAJWix6lmEEJbboJr470U68nKPuhJQCVoMexbBQivodcNRBfnUHFx1jK5wFUIlaLHtWcQEtIKGGY7S5TOvS/OxL+jfQWv3UAla3HsWMYb1BN3eXoNyGhyjK1510O0k6XdZnfZYpC8IajnrDEfpBJ01XxiMW0VXvOqgEjRjbvS3wsuQgs9p3lVFHN1Z1EylWO9A/eZuCOWbfOiFPUIlaHTEhrBYhCrpjYYmni26fGY36764wyvm3bGsfqgE9UhBiTUfgqBFseKpzuxN07da6xGj3EK3F/8QoS97aEDQItjBuXiuoBJ0bfXlQjvfwVFvNCSULZBPObB78Wd2CdvtO9/RGwsJZQvkUw64WMR8QFC2gKCMAUHZAoIyBgRlCwjKGBCULSAoY0BQtoCgjAFB2QKCMsaqgnJyp6UtAUEZY0VBvwkqU+8w89I5BwRljPUEvehzGv3geYd58XwDgjLGeoIuiREGwzYwL55vQFDGWE/Q5ROFweDPmRfPNyAoY6wn6F9ecSk7vB8wL55vQFDGWHEn6fumldv+wrx0zgFBGQPHQdkCgjIGBGULCMoYEJQtIChjQFC2gKCMoRFU/zZuHZBPOaB3O/OhERRu4zYEerdjDI2gcBu3IUx7t0uPgP4saQSF27gNYdq7nea4tj/LNw0fIV98oBFU/zbutOAgESczny9vV1ijd7t1IygrpWao9uL1b+O+f0Nk4ev01VItbHu3kwBBKTDSgS3kUw4LDzNBQikw0j8o5FMOSwUNnSoxpUYLIqoTRTXnOayubpGnVreCoObks14oNqQZQTG1G2FDmtTCF1MzDBvSqO1UU5jMp4WC3lmiY0GpLiR0IgwrTRTWkTCsDFFYB8KwsgXLvPSxZUkrwEgHtmblMyAEG/JiBXwxHo2xIc1c8MU4t8KGhNZaYgqT+bRQ0ELSKxCFpeofVzFOihNR2FNnorDHVYnCEl2JwhJs0mcvST5Hf4oNuUbw1JAee7Ah8e3wxYSdxoZsGYAvRg4QFASVBwTVAwQFQfUAQUFQeUBQPUBQEFQPWkGzqhCFZZC5ku5CFJbmRhSW4kEU9pTsNONjb6IwSkjyOfYzbMgtgnP7rx7AhpzogC+mxTlsyPbB+GLkoBUUJUEYUwjmkprNpJgn+Kc1aAgOqBHMKTcZHyMHtaAAYE1AUIBrQFCAa0BQgGtAUIBrQFCAa0BQgGt4EjTX4MJ9a/OUaRg32CqRBHmhTh2loLvrBK3Exayr4xmdQRI5JZqgwLPNvYcRhL3n7/uOBheW3AAVFGUqUhtGvBRUYMsnqgY+kQRZxGeQIH30qaMTNMnvTnKdy6ZjzgQ+TI6YSxAZ7xaNLzAz8GJWp4PYsPPeiWlhOzFhi4IDCxbBVKQ2jHgpqMCWT1QNfCIJsojPIEH6GKSOTtDNwqo6d47pmF3LEdoQjY980nRVNL7AHX0QysrEhl0MSM9psxsTdmZXYMEimIrUhpEuBR3Y8kmqQZBIgiziM0iQPgapoxN00QyENg7HhiU13Y2P7L93WzS+wGU9G3v2T8OXNqm8cyS2tPuBBYtgMlIMI10KOkjKx1aDIJEkWcRnkCB99KmjE3SBONchuKjYmrH4yNgRSMgrNmyO943UXrOxYSeCf7vSOhYXJqZPijEZKWWZaCkoISgfWw2SRBJkkSCDBOmjTx2doJuEOc6faTomf1CPhwSRkd6B7hUjsGGrhYDYSGxYzHSEvojChYnpk2JMRophhEtBCbZ8gmqQJJIgiwQZJEgfferoBE3wTcgM+d10zK4OGsJIYcXHht3zvZHabRk2bEvDhJQe83FhYvqkGJORYhj5UtCALZ+sGthEEmSRIIME6aNPHeVhpu2hIR9iQsY7VqpUaQhJpJBXfNjXNf1GZ2PDNDP9fUZk4sK0DZAUYypSDCNfCipw5ZNVA59IfBYJMkiQPvrU8XSgHgAMAEEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBrgFBAa4BQQGuAUEBruFS0Bs9B69Rug4AH3Ap6OSzqK3SdQD4wJaC5pVwdfUcnCLM1NXNze19lL+ovnvr/cKE0q5uldteexaYobnXC1NWfCTJKKMU9j/8+BbZFwAFsa2gWShl1CvCTNO0n4d2vZt/MmibIGgaehrdvUjk9p4PMWWRCFrYp6/Un7DUZbC2/+HbLeoIU8ZctXhRAFtha0FRtucfOkHP+2QIw5MBeaKgKC7oWeC+CZrnv5mj+3v2iUDQwj59pf6EpS6Dpf6HR8blNU7+awybxeIZ4karkCNR4nB9TNFxpG2TNbC5oKjXVp2gH43Ujg24IQqaOjhc/DCh1q9oR/DQrlHR2mmrqteckH9iQL+Z4h9aGFR9svYT0uZMOzF8N0Jhx7Vv9dNY2Kev1J+w1GWw1P/wlA9u1csY/I+tFlw5yButAlJvicPiLOiIFdrVOQhNXqAd2+ooKu3hVbHJFe2nneEZ1Q4WfCE+NCGt38ITzleR+BdXJzmrzWbxnTgpUpq4aRC6EXRM+1ZKY5dqWi4W6a5Y6k9Y6jJY6n84eWTHw6en2XDRlYKo0cox/F5xFrTwF3TlKO3YwNvabOnQ1BrybL2eFdi+fUjXE+0QEv+mCMJ98rr4Dok5kyY+9cxZPE96q5/Gwj59pf6EpS6Dpf6HRaKe7A2flGWlReUFfKOlbZG0LVDKG7Vb7kXxUXnj/BsNj4nrh1DMejS1mkff7PhIaZoS2FzQHO/Lumyd8c0Uhr/4oKKCoqWOz7aMlr4rbEAmn+gmpFH4myw07WsGie+QKKg0EfU81PCm9FYStLOvlgtF+vSV+hOWugyW+h8WRh5altj4zqz3bbToSoFvtMQWSWqMVkdpTg0TBP2sZUZSoE7Qex0zc5sdj4+UpimBrQVNHxteuEEU3fOB5tcau54XtJ/rs1+1c8EJWREHCgQ9WDc1u+2mQkGliWhr+5d0cfq/oFKfvpqruVJ/wlKXwUjq3hVpemTe6aDZPt3Ki6w0+EZLbJGkFuiy7/hDGkHQ/msQGlfwC3p3yyzXuPhIaZoS2FZQT0+vgcmFgubNq+feMBY9J+gJ7+6xz76xpobvWFQgKFpQM2hifqGg0kSUVuFzXZzBlpK2T9+sErel/oR1XQbrBN22Xmj667b915rLywH4RkvMp64xSvpqcFNB0IHrEJqtFXTU+pN1V5/uLQgqTVMCzs4k5TfZdKxJrtK1sB/wjZYoqNQCzZ+c+8jpSXzUhtZZyXVi4kNyn3qvf/ctdNNvT3ykNE2JJeBM0A1hGhTtd1PpatgN+EZL2yJpW6D7bbzarNfuJFVrPD0ma3B1v+7r70bU7z4tKD5SmqYEnAkKAM8DggJcgxX08pzhQ2aetUVVAMAQnKArg+dt3Dg/ZIVNKgMA+uAEDUoRhxn1bVAVADAEJ2jtO+Lwv1AbVAUADMEJussvesaMwT47bFIZANAHu5OUtHnRgs8f4aIAwDpYuBe/vUQxxuEU838D5FMOC/fi142g/I+omfA45kVCPuUwey8++bHIh0MZVEytgKBsoRJUfy8+zbeqSFkv3ec7h5eN26HMhViKAYKyhUpQmb34QcHS69hKJUuUK9OConYqBARlC5WgMnvxOkH3lK3n8ELZzaVaNJ+WankF1QYIyhY6QY2jE7RH/bCXmpbr6uAYXK5+vmUlqRAQlC1WFLRrw141c6uUdFiKzpf+0bKSVAiNoDuFPc65vT7QvyYbBJUDI+jTBoESeuN1gm51HFG6SsmSDh1uI9elVJVUEzSClkZoyCubu03QGw2CyoH7BU0O3nVfRG90wU5SP2f3UhVdAxe6Ty0l/oI+uFEcGnpKQT2SUHKA3mgQVA5sE7/ojLGxBYKiCzuuokvlKgeVrHAJJbevVLXmFUuqqC5oBC2FUIcclFlDbzQIKgfdNqiOC6+1mLnCfU7v8u/Mqqq/MWCH0Ahax+0F//fzwqfqjQZB5WAiqJZ/epcchdBfDorc+2dTqPbiNfd+vJC7AnaSimAjQRFydBuckOLwwLISVQT9Yabc23ojQFA5WAra2LN31RoVLStQTdALerUw7Wl1gkQq6+81FSdsJujVKp5upWqetqxEFUEvqOZZ9z4PbohE1qEtUsXYTFD0+Is1N7/wGWHvlzdb4UyS8XwWE2wnqJan4z1W2XfnNSAoW2wsKEJ/dAo5bFmx6oBCUMyZueKJzQVF6Nua3f9Km9Mu6ifLSuccml9QzJm5YokCgqLs5W7VQpo0dztpWfF8Q9XEY87MFUeUEBShs6XL/m+NcxfLiucb2AZlizKC7nf8IzKws59lxfONrQS9tX5bCvM5cQidoMZvO8YL+nOZ4+iHSuXY/y+Vx0aC7vB8I7JYdJVKJajMbcd4QTNdPBp4+A2s+xL7m8iVxjaCajwuIvRhP+az4g+2tx1LEGwzHfIO8YzKydsQ0OOC9rP9PPHFNoL+4ysM/qzLfFb8wfS2Yx0kG/UZp7WXRGSt8Op3Ba1yKuV1CP8dVWAbQfOq3EXoix7MZ8Uf1rztmIi0JR4dSr3+5YuOdnKhk422QVcFLZrg/jvzWfEH09uOMwa+JlLN16wqpASVGfyXxmWOWV/iFlvtxZ+IWXyH+Zw4hEpQzc7dKDZqfmbh5z3bRV4MMvUlQ5p3WuA+0GuceV/iFTgOyhYqQd8Oa9OrzRfd9a+nNTeh7zoeuN29xMv20dc9CMoWKkF90nKcr6Jk/Rbd3ITmtyvrWPbND3272MPd80wFhetB6QT1S0orfxol6F/wbf4af/7bW8Ie/Wc1W+9RfVdjLAVNC9ZeUe/kUXTkzc37jDwi226hEvQ974BJwdMbztQbbXGTlL89rN4GlR8StXYTv8lrUPt6ScznwS10e/HnL6KfYrboj6XZZjoa4bMw0fKvK4+VBU13uYHQxPHM58EtylwsYorLw1xG/drKsVyXbJpSFMPKgp5tJAyOtWc+D27hT1CE/p1Tuuy8Lc4d6UpRCCsL+tjlKUIfvMF8HtzCo6Ao1+G9hnVGO1KWogzW3gad3HDFRM+/mM+DW7gUNN0hGR3vUOKtPynLUQKrHwf99u35+veE2DNcCoo8W2Yn164926f9NtVtiMKBerbwKegFNweHgAcoZ/vLXjHXaQuzLbYSdO/IiZeYz4lD+BQUoQe6Q31XJ7p3+lpNP6M2EnT+C2sWetjnfbHPw6ugz8ja2sFj/GWWJVoV2wiaW/k/hL62y7sO9eBfUIEbs/yafvKYcaFWwgp91BvJ5wMfYfBHPctnpRpUIShCeXH9qvTZp4ZOc6zQR72xfPqeQmjBEMtnpRpUIqjAkzWtPN822rEBV1ihj3pj+YzziHyx9j+Wz0o1qEdQgetza9VdwPlevRX6qH8+n3k3tBs7j3YeVtO+o8WoSlCBU2M9m3/I84FqnKCRBRiZJtNH/XP5PFUjqMoQNWzrMIJE0PyFXZMPGA2wtOMGGvIODXVp98l/1pwFDThB4wswNtF4H/VF85kbsA+ld1tGWUkVQSLo+H4hmRFjjUy3uOMGSrL2Rld9+dOHVp6LZRA08dnH4+L2yN+39ayPes130j1eRdr8S2InBIc7UVVRVZAIWiO9Pco0dqem5R03UJO5J7pq2xX6jxvgAAJB+zRz7e03XXbysz7qMwZId8n6PJt4zzcPoS196OqII+eXk9xcNk4iaOCj9iihppHpFB03MCBr3zD3Jgt5O4RPIKh77swzN+U7XdDoy/FcPrsMOb0rwLpdAN+s06R50B9WnQU5JIKuDA1cHLzWyHQWHTdQkXfs7cCaE4/xtM9AIGhAVuxqpN/saCHYpk+eEhZufIeAGV2FbbaNbaw7D2KI9uKPzVp6zmiAXscNmuPfi3Q09mtrNc7Na+I6cKvuPFP+3uX7lL3vjkDQqR3v1H2rq5EJLLbp7/Sv3uo7M+KN4CIkM6c8J2s9iaDrtRgLOJiZOityWeHxuPSIjiLe3kyriOf+mm5O7ZZeEvx82beGzyuKPrOW5DjoPXTy3XtGxjPYps8OWXjzgB9d39UhpxG6Zl7vMNaDRNCYmJhR3sa2mYZ2yurbK7brGL3RSly/mLF/TJD/iLfLTdk7vtw3tp/9MwgElV3hsdv021sGjzb9HJ9fmgmDVW9i62CKjbU+jzX4GVcK0gP1T/yNTPfKQp5pKEV/klIX2F5d4Vmy7cJfa/VXZvYSBILKrvC4bfp9QUf+fKudybKPviwMNg0lqKgJ9g/ot4uuBHaQCppr7Lhd80uo8U10vZbeaOWuAB8YEDexfsnA1deUqgDxqU6jK7z+Nr2Ownz22Sps51cz2a9yiu9+9CB0J1EdVAGJoP0EQoYYmX42sE8P78G+X+uNVk7QoxVenNO6/KzBvtWGxip0JQWhoEZXeBkK89l1vzB4wfSF9CfquDgvIi+be0gEjROIN3plQuaOZXNWG2zuK3gPzTTn6lXmCK9XPuntWmfUVgVONhEIKrvCy1CYz49efoq2BuVhwpNwAaoCK2hMAeRlKnmT190fCq4kyT+/oqdrnRFf2rgXTQJB5Vd44xTmM39cJc8XjB/wUy9/nTF51gorqLC/OTxw3KzaM8hnyc9diPkXP+rj5T/w04u2O/KEE5Ruhc/i9iIZC0luH9i42q8mAkia+EZJQkGNyWfKj6Ba/trweh3nJKUzSAAADGxJREFULvOP2OaxQjhBVb3Cs2fcmHy0x9SJHRJB/TKErc1q5DPlMKEJ305pUyl01KY/rX6aiaCJZ7/CJ1x+tsWQdlZNvf03ErdYfE3s0JIIOrbVmnWt3yafKYeCiuT8umpgDefOM/dY9R9IICjrFT5vqGuI7/e6D7s8G7kPV/Rcmll0/k5YpSpnyAeQCJr3Wf+BG8zYNeRUUC2P9s/p4ubXa2FcgpVmQCAoxQr/zyUjO1cfhWegH73TtO8TPS+g9JeMnpfmku8CYg920D8VWRSsoFG3UJQW8pnyLKiWm9unvOwcELlgnxWOlRIIavEKn9PXs76/4aX4r4qnfVpJ598PdxYGXw4iL1xpDkeFf2Sqx2isoEdSkfwtCsbhXlARzbWvp3Zy9wyP2XaF6XFDnKDmrPDZs6eKhFQRBkcR+rtd7UlT3/PJ+Xuq7rPu9bXOU6dO8b2i/fx6dY0Q1/S56Xy/pj8yOb0p6bl4kn+ODlUIKnFv74KoGhXDhq/8gVW3zjhBzVnhcz5YItLYRRj8jNCt2m8Irw3P31qi+6x73e3UZ3RYmEb7eXHosGMLKk14bjrPrwtfqeT2wiQTcc0JBN07LL+pkxmbNSoSVEvqz2vGtK3i3WnChl9TqQsjPRdvRpGF+RzwOUK5PoYX6v0Y2Wpasu790+ntB6roWP7MqAz0sakjGiQ7SS1OxQ1+ZMYV1moTVOLuwaWDm1QM7DJp/S/m2KMPgaAWr/DHfbf82M++HtDZ9LQw8PxXPoBE0Nqad7bmmnERsjoF1ZJ/47ulQ5pW9n559Iq4mxYdrCEQ1PIV/mhk27lpltSKW9r/IGzJVDFxDoVE0KGv+SZNaks+UxULquPe9x+P6ehfrkHvaRt/MvPkIoGgvK/w2TODAieaODTJks/r/3hpQF8TASSCpqy+jMzpdlr9gkqknd22YFAL18pN+k7fePw+4TkoAkF5X+FjXvn7xqujbTSzDc3qTza15a++nkVszuNft8wf0tqrXL1u41bsuYRrYQkE5X2FD7yFUJILJ8/8U2XPIoqQfvHbD8Z2q1fevelrkz/+7tJzm02rnB08daswgaDyK7xxbJ1P74fi6UdOripVa88iyvHvL9uWjI4IqVg1tPtbS7/66U4OQgdLjY3rUfqudjKBoLIrPHkHtlZl5OvpWW/1s+08ZVFxzyIKk3h2z8pJ/Vr7O/q08PBadhB5TNOOJhBUdoU3owNba5IysELFKF4eB6runkV4IO/+z8F1Js1BgdK9vgSCyq7wZnRga11y+Ol6lGnPIml1pcdHe7KrnypYWeZw5qelpOvCCQSVXeGJOrAtZuAFvXUqA+X/d/1juaCIIu/v3hCJrM2qdmohsnTJMlILT3SqU26FJ+nAtriBFfRDhwD3/Z6V/dsZTn4ULVIxOlpvfDFM6IOfC06PYgU1tcLjO7AtdmAF9fkNHS+x2ehZv9wpbqu2bXPftk1vPCTUBCZWeB3POrDVQZfP1P13ab6uNFhBXYS/qnJnpY81/Q4FGowFQU1gYoXXcbWE3giqfE4u6ejQiuL7SoMV1E3485KNeNJ/hOFJZRDUBCZXeC2mO7A1k3Mld6Dr5fW3alUE/hc0MTHRQ/iTu6A31vBxFSCoCUyu8Hqnjhl0ZzlV7D68zwuWF6A0WEHLFUBeJghqAlMrvP6pYwYdAn9YWRi8yEt3yRagtuckcQ9OUFMrvBVOHSeXa/H5kJKHLC9AaUBQxtA8ac4ap44v1K/ot4Xi+0oDgjKGRlA4dWwICMoYqufFYzqwLY6AoIyhElTAyLNyIZ9ygKDmQytomOEobD7Pv9FtWSbdbLkFBGWMAoKe91z53as9OLlFgzUgKGNoBV1nOAqXz1ErEMoNVPDBEdYEBGUMraBGwOUz8lth0OYn5vPlAhCUMQoI+t6rueich206kLY5IChjFBA0JzKgredu5rPlAxCUMQoIitDVn5JxIWqFTtBi0XGDeVhR0DtvhU+0t6d8YKEStFh13ECI9QR95D8vblId++o7DA+VoMWy4wYM1hN09RvCoJv+iXp7h0rQYt1xgwzWE3TuHGHw5ifMi+cbKkHh6htDWAqa5lNVxNFd++mHek/Qw4AL7IpXBXQ7SXpX3+SukPpUN7yPrvjA9Bc05bFI3zrSpxnurd0+YFi6KqASNGNutHgSY0jB56yZ0lMpjD4LvZhgzcNMD0+wetSDeqASNDpiQ1gsQpX0RkMTzxbIpxw4QT1SUGLNhyBoUUBQttDtxT9E6MseGhC0CCAoW6gEXVt9udDOd3DUGw0JZQvkUw7sXvyZXQjl73xHbywklC2QTzngYhHzAUHZAoIyhkZQTvqo5woQlDE0gnLSRz1XgKCMoRSUjz7qOQIEZQyNoNBHvSEgKGNoBIU+6g0BQRlDtRdvuo/6I82qtP2FonRVAoIyhv4wk1wf9X95HXjytfcD2uJVBgjKGHpB5fqof2+iMBj8BW3xKgMEZQy9oEX6qH/uuVNLYoTBsPW0xasMEJQxTK+or6d9cp9zde2niz6n0VGvO+yKVwUgKGOscKpz3Qjp9Zsgx3qHmZfOOSAoYygEfdogUEJvfIGgCPHzjFebAYIyhuYXNDl4130RvdHPBC2GgKCMoWriF50xNhYElQMENR8rboMWS6BvJsZYoY96EFQO6JvJfKzQBTgIKofZfTOlSh0NgKCWA4I+D9O+mdK8tV21lPVlUTOVYoU+6kFQOSzsmwkSyhbIpxzm9s2kAxLKFsinHBYeZoKEsgXyKYelgg5+rOOfm8WFfwsW+WUrCGrf+Ux6bAqT+bRQ0N1VCyjhQIQdhDkULvNvliWNZT4ZLBPJnBgVU6qqSUzl00JBC0mvQBSWqt+5k3FSnIjCnjoThT2uShSW6EoUluBOFEYJST5Hf4oNuVYTX0yPPdiQ+Hb4YsJOY0O2DMAXIwcICoLKA4LqAYKCoHqAoCCoPCCoHiAoCKoHCAqCymMHguZPJgrLm8IyLFe/Yw7j5MQQhWVPYxlGCUk+t+O7dkiZiy9mzd/YkHsf4otZin9048XP8cXIQSsoAFgVEBTgGhAU4BoQFOAaEBTgGhAU4BoQFOAaEBTgGp4EzTW4s8TaPGUaxg22SiRBXqhTRyno7jpBK3Ex6+p4RmeQRE6JJijwbHPvYQRh7/n7vqPBhSU3QAVFmYrUhhEvBRXY8omqgU8kQRbxGSRIH33q6ARN8ruTXOey6ZgzgQ+TI+YSRMa7ReMLzAy8mNXpIDbsvHdiWthOTNii4MCCRTAVqQ0jXgoqsOUTVQOfSIIs4jNIkD4GqaMTdLOwqs6dYzpm13KENkTjI580XRWNL3BHH4SyMrFhFwPSc9rsxoSd2RVYsAimIrVhpEtBB7Z8kmoQJJIgi/gMEqSPQeroBF00A6GNw7FhSU134yP7790WjS9wWc/Gnv3T8KVNKu8ciS3tfmDBIpiMvB9IvhR0kJSPrQZBIkmyiM8gQfroU0cn6AJxrkNwUbE1Y/GRsSOQkFds2BzvG6m9ZmPDTgT/dqV1LC5MTJ8UYzJSyjLRUlBCUD62GiSJJMgiQQYJ0kefOjpBNwlznD/TdEz+oB4PCSIjvQPdK0Zgw1YLAbGR2LCY6Qh9EYULE9MnxZiMFMMIl4ISbPkE1SBJJEEWCTJIkD761NEJmuCbkBnyu+mYXR00hJHCio8Nu+d7I7XbMmzYloYJKT3m48LE9EkxJiPFMPKloAFbPlk1sIkkyCJBBgnSR586ysNM20NDcNe0jnesVKnSEJJIIa/4sK9r+o3OxoZpZvr7jMjEhWkbICnGVKQYRr4UVODKJ6sGPpH4LBJkkCB99Knj6UA9ABgAggJcA4ICXAOCAlwDggJcA4ICXAOCAlwDggJcA4ICXAOCAlwDggJcA4ICXAOCAlwDggJcA4ICXAOCAlwDggJcA4ICXKMeQbu6uZRwcxt1JErpitgJKsmnegRFKFF8VEjqLaWrYTeoIp+qExRghiryqTpB46N+aRvqPWpu7db30KrqNSfkK10r9aKKfKpR0Aq3U5zmoBHL40MT0votVLpW6kUV+VSjoJ0Qavg3+jhmVmD79iFdla6VelFFPtUo6CtCQm8LCV36LkKZyUrXSr2oIp9qFvRccEJWxAGla6VeVJFPNQuK1tTwHat0pVSMKvKpJkGBYggICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnANCApwDQgKcA0ICnDN/wHAENVf1c2oRwAAAABJRU5ErkJggg==" /><!-- --></p> <pre class="r"><code>print(p5b)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 94.81123 10.10936 7.55871 +## SFO IORE DFOP +## 94.8112 10.1094 7.5587 ## ## Critical sum of squares for checking the SFO model: -## [1] 11.77879 +## [1] 11.779 ## ## Parameters: ## $SFO @@ -1611,7 +1633,7 @@ div.tocify { ## Estimate Pr(>t) Lower Upper ## parent_0 9.84e+01 1.24e-27 97.8078 98.9187 ## k1 1.55e-02 4.10e-04 0.0143 0.0167 -## k2 1.04e-11 5.00e-01 0.0000 Inf +## k2 8.63e-12 5.00e-01 0.0000 Inf ## g 6.89e-01 2.92e-03 0.6626 0.7142 ## sigma 6.48e-01 2.38e-05 0.4147 0.8813 ## @@ -1620,7 +1642,7 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 86.6 2.88e+02 8.66e+01 ## IORE 85.5 7.17e+02 2.16e+02 -## DFOP 83.6 1.09e+11 6.67e+10 +## DFOP 83.6 1.32e+11 8.04e+10 ## ## Representative half-life: ## [1] 215.87</code></pre> @@ -1631,14 +1653,14 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p6)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p6)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 188.45361 51.00699 42.46931 +## SFO IORE DFOP +## 188.454 51.007 42.469 ## ## Critical sum of squares for checking the SFO model: -## [1] 58.39888 +## [1] 58.399 ## ## Parameters: ## $SFO @@ -1658,7 +1680,7 @@ div.tocify { ## Estimate Pr(>t) Lower Upper ## parent_0 9.66e+01 1.57e-25 95.3476 97.8979 ## k1 2.55e-02 7.33e-06 0.0233 0.0278 -## k2 3.88e-11 5.00e-01 0.0000 Inf +## k2 3.22e-11 5.00e-01 0.0000 Inf ## g 8.61e-01 7.55e-06 0.8314 0.8867 ## sigma 1.46e+00 6.93e-06 0.9661 1.9483 ## @@ -1667,7 +1689,7 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 38.6 1.28e+02 3.86e+01 ## IORE 34.0 1.77e+02 5.32e+01 -## DFOP 34.1 8.42e+09 1.79e+10 +## DFOP 34.1 1.01e+10 2.15e+10 ## ## Representative half-life: ## [1] 53.17</code></pre> @@ -1678,14 +1700,14 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p7)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAKgCAMAAABz4j/3AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOydB0DU1h/H79h7wx2bA0RRBBFwK6i4UUCxKoJ7rzor1j2LW1tXlbpbca9qtf6r4Kp71tG698LBkn3vn1wOxCO5vFxydzl4n/bOJO+Xl5cf38tLXt77PQFAIHiMQNsFQCCUgQSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DU8Fej9yE4/absMCD7AU4FOOgmaarsMCD7AU4FmS5/FaLsMCD6gFYGeaeDiPakECOwdHBwWA+mCQMemRxVMDkS9oM0mjUTDZNsQOow2BJor/qMks+0qIMiRrQ5p9bgk3XP3VyZHv5PS5wMl0IVuLqPkeS1IkG9LPsaswAjtoQ2B3hWVAHBlp1yg/4iyse/jPiXlTfp3iEv4eidpBvH5sgYl0GvOGTmhhPgvWSeA+w0DF4B33Tk4CR0CosIqT0pS+TVtV0naEKg0sOmGZ/ixZQJdOVC20eUxhfVyie+4knOdg5PwD5jjLRkvWwMy38kS2+wFIDSdWFb05w2P7MIme/Cl7LDkBDDuL9Do8+ib6jozXgJRYcnZFYB/I4GCwl/ibJrflf2kvcGE2bJtjY4TacMiCkGPNV9sTwS+yem+6Jz5XYB/jtTJzI/chC8B3HdE4oZE8MBbSizL/DkuGmeebP9xptbRsoXeqanYFbRR3fn/DdXcmfIBZRXWlzoJJ/Me/o0EivO6YWu5x5YOl23wvkMkXHqx70TDcjegU70iIgK6nWsOAP75bikA2/vgSwD3HZH4SVQ4b6bcUNGfJ0Mv3m27BVv4rS9IJe4aEl+q99T4BnWFJauJZBVPdoyz1z5wLqpopFvwgKQj2D1QUgoY5+zUrSAthkjTFtoQ6M4Z2NclO7nHznvkY9+XnYtKk7cH3ShnnDwLgJKic1GYO7HPhGXY7on4EsAFSiSC6KN1HsoNFQU6Ebs+743DFjAvO5q3xxZOTweTgpo/Ve8p8grKCguviYiKZ3Wc9EwfzMNrW+Z98JcL9E5EXlFYeloMkaYttPOQdBa8n9Gx9Kaoe5fX4GL1TWXJ344sb3zZ/0NRl5WlAv09JLugzfoygRKJYFtE81JDxSp+c/C7rF4zpHdx+RNX0Jjs563A7lkaOVPeQF5h4TURUfFcd5+Ujnu4B3Zv+kPpFTTj6FyHI2kxRJq20EoVf7CuozjhTalAi2b4ixruL0u8UGf9V8ar/NwGl11BwUwf7zHFZQIlEkGO2cZSQ8UrqHSiq0vfz/kCvEaTCXTHz9hltW7EK/WdHd+grrBwP8oroU/rYhtj6/HY8+RCmUBHpJyvuehcDCZQIk1b8O5NUlHw2evpV7RdisoEdYWFC5SoeGaPAx8sPp6LWtcqPyswKa1W0QdJyrxR4KXXwbQYIk1bpeedQJOHADCko7ZLUamgrLBkNZGs4nnS0MXrR/whaYRn3XmLC3r7tZ+581HdWh0XhqbFEGnagncCRSDKgwSK4DVIoAheQyvQi1P69Z58SRNFQSAqQifQhdVnrF8/K2CxRgqDQChCJ1BJFv6dF6CBoiAQFaETaDXZO8G3QRooCgJRETqBpooTJ0/u7bpdYfOrn6swa7M4/zMgf1JB+5D0dsPsOevfKm5dV2NQ1cVNWXdf1UD+pELFp/i1A1n9PXSbNkc4zxL5kwoVn+JLHXrQVt9QFPtI9cLpIkig3MJKoIpP8XnDZVflkDqytUsCfaFAYGE9OG5JPuty6gxIoNzCSqCKT/Elm2X3tQ28ZWt+eu76BgIDgXeTwHYQwzArCUig3MJKoBRP8Yk1ZP9YCbELKIbflNbmVWckGhIot7ASKMVTvFygrgJrQROBnkDfxsxoHQD7Y1qvKKqQQ6UDCZRb2AmUHLlAw4kLqEA0takgBWy1tTR1GqJahroEEii3qFGgo61MBeZ6BgI9kZ5gIfAMe5k1xKDyX0KRQLmFlUBrexEobJYL9IWVoewKaim2EC4CxvsAKBI+Z1NWnQAJlFtYCTQjMOU5jsJmuUDBrTgLgZ5QYBpoJdwJ7Fv37z5MP5dFUXUDJFBuYVfFLyENtFUqUIz3p+vZGRnoCfeDrkIM97KEv6Yue8+gmLoDEii3qPEelKCTzeApwaZWE1xd7S2qGxTLt072atHIuVLGR0ACZUZR2mGlVyq1C/SHb6YPWuF0fqyw2QVQIpRrMsPMd1qidV/Vsuc3rARa9fo2vAlo0E6kLOCl2gWaG9a0n9sCAOwautWZoPeZ2HjMIAOABA/Vsuc3bARK07ehMjIgumlwLxclLxrVLlBQfPiXW9g/47FbUIHRQmKM/18GLwHoqvj0XylgI1CKEQqVWaAe3n9e7mr2jNpA/QKVU89aZOdi3stuKK7Wj2bOIzvYVspIh2wESjFCoTIL1H4KAC+FSu5CNSZQ4z0AFApfvJ7uHHmgBCxyaV7Pu8I70soAG4FS9G2ozAKtYbdofV2L19QGGhOo+Trs2Uj4CYCCLfW8F324snRL5WwTZfWQpNC34XNiV5yalXjQ16S2Q/sOVnZ+GhNonNm6A24SYvl8om3vjf8qyyJj517ux/ZoBDYCzZ2egAckTyxdL9m7A6eZNwfl4il53W1c6txWYqAxgRZGG+rXLZs95lcLI6O61BI8ZevrLVJWbP7CRqCJ7VNCNgFgoriZzJ+Vho/KpxTSmEAxisuWPts6hNqZWEyimhvBVbx4nnVd1Y6uZdgIVJwF3kleVjGB0qBJgX7hrOHZ3PwmkjEOHQ6WkCQXCLGr524D1Y6uZVg1M2FPsxuikUDLw06g5G8+6B162CDMzDLMFXzeUM9zTsVpCwqE/wCwq+oJdInDFADiWymeOBIoFSqO6qR36ENh3UPJ+oH44pVBtl2OKl5G3USL5lqH0OXCS1g9xV87gD0Z7R6tsBUJlAoVYzPRO/SGpcjK3qwZsZK1pq5k7teX0TP4Q9Idulx4iRo6iyCBUqFibCZ6h/5rHhhS37Jl6WrhrDqWMb8XlzN4v2t/Nl0m/AQJlFvUOKpTCY+Nkl6cdmohX8tzN7QwrNHAbeojuv10gKonUOntSwXqy53bUZ3PHuDE+NHtdzmwp2ud8e3la6Mtn4HTBr/fHO3Yalse3a58p8oJ9H0jn2CJ+mZeYSVQ6e69YHPcjM+l6zk1vHEsRXRHzXM+CQpiFsjXQjphX5IhAORvb20/XMcDNlc5gQ4cJwU71BcilpVAR4U2iW63t1c/hc0QDj3mGizuVihfifIHoMCCaAt4MlMSuEyXe5FUOYFW72trFmebQW+oGqwE6pxTaP0vyHJV2Azj0LyrX/oAXjMMGuxqWfriU3o80SZmXyH5bjJS6ngNVJtD2FLlBOoZ9jp3hIGyvxcrWAnULSPb5BJ4666wmbFD0wLtw++XW8/6panTt5S3Nb+5+ksCwhkeQmNUOYF6uf56qKmx2tpc2DXUO3uMD00OT1LYzIVDH0z3rr2QvBtBXfeT/w4y5ut8mlVOoHV/7NJupXWOurJn9xR/9SY4OXGz4ogSbhwqPTnArvVmkl+mw1wASvQvcHEMNVDlBPpDi4cfhndSW/ba6SxCy+v5SzOxG9XtHW0SjhQrpPmGfJSuMrpPup/2qXICLZ7iYttLfY8E/BToFn1bK8M0fOndigaiUee+SpzvaW7p40s1EFDbsXKrnEDVDD8FajYegM6lran/zfDznXb3S2LxMHPbsFvkO26x1bdTfLGlWZBAuYWXAs0WFAFwR+/LhotjXOou+tIuVUA1CvCyQZctHQ20Gi1XDYEbkECp0NoVVA+r3leYld9S8tcA+2Yr6Vrwe/piX16KLw40ihoCNyCBUqE1gcYa9+5mME5hY8GBnjatf/mgbL+OeB/SwC5sD88GNQRuQAKlQntP8VOq+a8g2fx5V5x1h00fKXfbarA9Y6vBbtaHZ4EaAjcggVKhPYFSk70t1rojpUa7GBsZx6vx6PSoIXCDxgX69KdlDzR8SEp0TqAYWb/GWHXYQP6gdHuf0vH2JPzevunsz/RmsHAZuCHHxRbHyJF9sZhwytpbYnVYs8ekhFagSXIYTAqvgV981m+drdqs5aLX00HXqA5hHF50OW1myv6A003DV1CJx47dfk6aPSYltAJNSUkZUDN5Qe0f4PPUTJWUs6ObTfOfWAe9b2I975cQE+rbWqbwrh306i9/kg3tVoL+aQDuC3ky3wVMFR+M/fkyGYRR0Ng9U97+3vb15//HKg88vNpHPYp2fxVgI1Dlk1KoxveSfvXCmQ3JMNgGwHE9HRKoax4A+QyCeXIg0Ec/rlIS8awcRceGugRMuaT6VIuubQrBHkOmN67U0Ak0phSSNJpJKVThjrituF7DVeSJn8l93NRi+LeWgaofk1NgBDq85e49rUbB58leoL8ZGRiYpEMal5ybWN19+DEVu8wOrSaq4W6n2CFFdegEmlYKWSLtpBSM2WS+8tVRm85kScVDLUS1rpIkPPUXOfuwq5e4A0agRWt7xK8jveKr69WcRc+iz+EuDHa4k9zQrkfqJxUOlRFUt7nT7yrsSAFEFV+QfuTIwVD4LNn4c4U59oerF06WtKTNi2e/+ZDdnxZfv8rdL5YlMAItWdStaCVZidX1ai5HgN1UXNOjNyzPq7VRVpHLHzI+WNGJA+8Y76TA26kJi+RNVRAC7R/mEOO5CD53Nv484By+rI8DaSzrVm2tXf0kfI8hCCPQsd0DSrqQTbOprldzJfi7+FWmjPfL2dtPVCvptMZ//e9F7g5udYk6BkKgvkVzz79g0MWXjT/fO08cPcX7L7Kk2nVzwU59qgCDfAFGoD45EaBIceARjtpezbUyGZho1F+VPUvOTa7jkJDKXbMRDJNMdz/6EZ/rEUAJVJx3ZDGoBZ89K3+erGnusJo0Jdjl2K1B5pXhCur5NgK8r06SrrZXc5/72TlNULmd4+nqKMvw+RrsdNcED+LjMUK2DCHQMREZ/qMYhEaj9WfxsV+VvJqkGi4UN7pxzZFivs+eCiPQZUFeM/1Jf4WKMdWHDcKp5sZlCRlxu1tAPN5k9PnwMInnkANqG8r1NbFeheC9+XzZMkw76B1wfh6DqpVUoNLHZcMsskMbxIvJr5LKOCw5eK1vO8a7aRiod/HHpyaTDgIuWp+TNTkmuawZuGTrzzgNtBZT/ZWttZmNvfyZ5/aillatlmiiCjtp4RHjYEFciyAEmoKzEz57BYF+xBvUbgV42sbKp6GY3v/QhjQH5lOf7msRNF6Vhg+NAiPQNKpm8F6ReV27bI0arLBZe93DppvdAJdNv7yVzd432NNz8J5MdR93lbWVyyFiEUKgSUlJIySz4HP/yp/Xg23Mx5SAoMGJA9rLe8y29w7v4+wL22ysY8AINNR75iPSdHE+cMoB2awDN3BGVBj2VefrDsu3l7a1bDbnAsP30QwpKbvPgXzV+b4DfOZf/Lm7SU3r1a9fNf/prVnY5uUO8pqqHnbW9/Wuw2eoS0BV8ffn1Wm+iSS97g0Q8hi89VfYrD2Bfmf5GNw3n664+fPRsQEO3VKeaKIIkAItoo0A+IUyfx508HczMhOZd2yTKXwJwExbYnOk7bgVDZ2r8BUUgMKjEWYk6ec9v+lUc4TfRoXN2hPoEytzbwtr0oAkLzbEO1YfsV/ttT2EQLtj1OwNn2WpP8cJBfr99AwffQoMfGkac/6wRB4U69txM4ZvFiuZ71KXgRHo3kRR9DbSB+L8vYumrXqkuFXdAj3fJ3YlRRvU2WaOzSljjpRcmR9p2WjaSbUFusKBEOgRjDQGHYzk/twuEBjrYf8P/MElGHj0CWsRK28pfuI8YlnYcFUKqwPACLTNBmbPemoW6Cnxqj0tB6i48+djE0Ot2i26qrZbUjqBqt4B3FfPx0gsEAiMzGw7gjRxq5CgN3KD1/O+3ataaXnAm/tK/xYQAs1QDA5Gh5oFGrdi4dSjtizeFb3fPayGQ9xK9UzSQCdQ1TuA21uGCo0wgXYM0WuHifLHLTzpscmK3AgTS1dlz3cQAi0JvqvEhgQ1CzTEbvisao4My6TIi819vZx7ruM+whNEFa9iB/A2AqEQ06fQvVFjN3DOvX61RkqHYOsG3U0Xb/J2UGIAU8XHWjSPioqCP6iaBVqroRTs1+NgrtkHvyS4uif+wu0ARgiBqtgBfI9MnkJB+JBuzsCrgalNiGIDtA5iPR+AD0Il7SswAj0nA/6gahZo/eDqTZw9ORqk8d/anphIU7jrngshUOoO4Er714420xM6GAoE4eHCyNdGk/NeNaSdC4D/WKwCIE9PyYAGVv1BKVCzQAfNvH7qpi2HM5/8l5Lo4dx95U3Vx42UA0KglB3AlfevjQ1yETkbCISBDS2jXwux59alVuyLq20ibX//u6m5Es+z6g9KgZoF+sIreqjzemUWhbuXn2eY6aNN/f3sOy06x7oJilagAfeiZJAkVehfe/oYTqQr9vUSTDIPNgg0MDSNHz21zn7DBvs2OLnLtoOcY7r77y53K2vzeUrsItj0B6VA3e2gOdtWKR3klhMcOcr7e+b5vtoxMsiy+bQ/Wd3e0gp0VyblLZNi/9rcdpE4zqbY13pwzc4eq+CFtqGOJl6R9v7tonz8ZNvB2Ugd/jflzNE/laUHsOkPSoG2YwktiPqm8Uixag/onw5939Si7shtKr+XgXzVSdpKRtO/NtXNzHePrW1kdacp4F2Crcfs0gZE6YWDfA3Zzxp2/UHJ0bZAo63XnR5mu0fl/QvOLoxxcu/+4yVVGhohBLq3D2hs8RNZiuLMfQRf/PkZXA/88OeVYy2/Sv/cvGaU4xbmRdUJWPUHpUDbAm1aH/ujGbEMtPzfxkEBlhHfH2Q6oA5CoJ5n0mLfM+gzW96fr0TYXfKGHgAcGTSutCl4bs9Te085aHaUi8aADR7G5PS1LdAEp56zQzwOsc/o05Hpra2rJa68wuBSCiFQZ2nSOqA4+5kSvvJn1+ijKc6nwThTcyMT+dj6qJrBMU5+J4mVGw0NXNfC5817oDqLUFZJ5GhboFsaLJ2yXMR6LDFByT8p/WtZNpuwG/KuFEKgneI8Msc2hi/CV/7MS27d4ywo0p8LPjfwITbVD5GCO3rXZMu5jlZNRZYc/Dj5AtSgOcoqiZ8x1aUj7QPF+7jMMfPYnI4il9gfjtM/4EMINGv1dTD7KfzRSfz5Dx40YI8JsdLCeubWVk5Ef9ADhv+CglpN4DPnOzACpaySeBtT/d0NNUzr/Sh1bBPLmr1XXiB5R3DvcGknVAiBcvDiI1t4FoBB8hCJI8aPiF3hSozCW4x3Yu7nC585KRn8mc4XRqCUVVJli6n+mrb3RdG1dQPrmIUNXX+j/G2ptIW+nsEwYhlCoFy8+Ag3bhNqJI9O8q+ZsZ1JW2L5vNGavMt2XctZ3lx4FP5QMjJa2zs2I5+GklsK1wz+gc7lMAKlrJIqV0z1xw2cbDvC9Hz9fGZ5or9Fo1GbbspVmmx0Qfqb/inZMoRAuXjxkT3M3a+05e/nBqP7jnMlQu+UNBIZOViV61rQXc9O34dZe1nPbwYNSGDQOUhVSlp1XDPYm0ahMAL99F2DZqRRsvkSU50bmi+QFg6G7iCUlb443s+84Yj114pAwzbYBglxTYRpZuL4xUfcLuyr4d/ESu68qCHluiKe0bsAMm2YdXuyclu5xtdY/QGETgb/2GdGAk3PbRiBxsVfvNSzD5mBYsPyswc4MQwGhPGHPAvsOvOIQS84jMy0JT39zcLcPc7nAYepsk0QAuX6xUe/tdhdRnXy7l3j8CAaPWrCHwwDb0I+xTB0mypsFnfeMNJuoHIjqAC22I+pmPIv1/7LYo6/N46lTnYDK7G++fOG44q3KxBkn/lWaFujmRERDRbmVSfHLz5Oitq2atqYfODEajNse1gLBkcDwLTe6XMtDNTfX3+j0UdQ4tpduRGMQMNfAvC8IUl6Ao55QoLCZh2t4jsaxUQaMh3eQrBFZOh9mlikFeijv/OwS+/dH+Fzp/PnPzbVPL0o7hkLrCVJEXoVWgKV0rpbvdCe9RjtohJbarv1rFV7pHIjmFk+eogHD3Yi6x200Do5NdUxNVVhs44KVDI4pGHfNox3K/5tavnRQXQCXabn6XTYw9o9HP4IdP4cit3GFXpQDAx409o5+Az8sXDuekW0ciMLvcwxt93/3nq6OU0nAlqBrpGjqEIZF8NSQcW6XzcFmmuOfb1gEtZZRnGLwBZBTb50I6UTqMtVkC7YyqhvNJ0/o/GXEk1PMclSObnHjmZzlxs1c1zi/LrSjK+FGTT36PgdqhuS7N59nSts1E2BAtebAOyOYLrXftuIaS1sfytbpxMo3pBuy2zQM50/539TDP5xVHtICjVwb+dlOhN6ge7ydmkkcd5MZfNbxekqdFSgv7pOHefEsDbEHqFdS4DU68udPp1A8RGMYmaHoPNnfjvflk47mOWpM9AK9A+vE9j3peoH4PPUUYGCK7OSmYe4HyguBiXuX6aqoxOoXUZGhhP2yVBuVh56f974H/PgizoCrUAbE/c2FxrB56mrAlWJQ3Yho8PsvkywTCdQk1LgD1Gl/KkIrUAdifslKYNqqUo5VBpTo0XNdl9uKnk3FaLmSB85msHgdEhoBRpADKp/pamg/zqH9NCCg+UeyquuQDdIli503cV1rrQCTW6Ntzjkd1Y1InBVo+oKVHIdgFOcz6BIK1DpYNHA2cM9ujN486UjDlUPVVagJcaYRDKtuc4Woh305soJSxnFQdANh6oJLgVaMHUiTi0GXfO0SD3sSXFDS3o7ZsAOmmMCEihHFC1NxqnryV2WauSic8d2bv9wnSsSKMdU2SoegE/7DnL/hhQJlGOqsECfrFn7kvNMkUA5puoK9H9Og/o6/c11rkigHFN1BVr7LwD2MnjhCAcSKMdUGoF+WjpuO5NegUQzE+chS5FAOYZ3An32+w1Vdnsv6beocTy93RdqnAHgjzBVjqUMdgLlZ2QRrcI3gS6z8rbvosKkO3OGA1Dgy2R+xQPiiWOd/mJ+JOWwEihvI4toEZ4J9KFJw8UJpiuZ79jvlzPb73dmFMPy9g/zuZ2QAoeVQCtbZBEu4JlA51sWAtCkAfMdF4sDvxFZcT9ND1NYCbRyRRbhBp4JdLYN9qATGcp8x2V2jQe6WTxS/cgcwUqglSuyCDfwTKC3jGO3TTIln9buzcLJ1LeM/VIOr70Wq3qYaq5g95CkEFnkc6+uOJ4MgrNWOngmUPCdnY9LQ9I5e+6Lh82pMYNqv5ljASiqwSS+hHpgJdDc6Qn4TyyxdL1kzw6cZgzCW1c6+CZQcGXtUfKH+AELAXhnQzUY9LXb6HVtojmZOooVrASa2D4lZBMAisNrUBXPLWryZwQeQbwOZYCGNzMHpPBgulpWAhVngXeSl0ig5dEdgY78/vieC7a5asmbO9g1M70HYEM0Emh5dEeg/xnbSPRoYstpH1YCXeIwBYD4VgYKm5FAVUWjb+bGD9ux+nf7fLXkzR3snuKvHcCejHaPVtiKBKoimn0zF3EC+wq6ppa8uQN1FuEYNgLV7Ju5XisAyLKlDcuvZZBAOYaNQDX7Zu6m0/R19b9VS9YcggTKMWwEquE3c/cnDmDU41MrIIFyDKuHJLrJZKsgSKAcw0agim/m5CB/UoEEyhw2AlV8M5cTQExKUTFGcNUBCZRj2Ai0wpu5J7JpfWYrzlJRlUAC5RhWzUzkb+bW8v51jxpBAuUYNgKleDOHBEoFEihzWD3Fk7+ZQwKlAgmUOWw7i5CMAkICpQIJlDlsBUoyfAgJlAokUOYggXILEijHsBXoWpJNSKAUIIEyRw0dlpFAqUACZQ4SKLeg2EwcgwTKLSg2E8cggXILt7GZnsreHcf4cVAwXQUJlFs4jc2U4y/rfWMj4aJkOgoSKLeoIzYTcii3IH9SwTQ2kxzkUG5B/qRCxWYm5FBuQf6kQlWB9vog595DKO7qvtmD0lNuoQaBMvHnv/c5OSeY0+YmmwfPPihDqT9VFOjv9rYENgIhFJXATCg/ZVtH7iMWMvYnB+cEcySOstG3VYZSf6oo0DJyzaDMsi2gzLIsocw+wU24+8EWyizDHsrsrSOUGUtg/Dl0Fa3JPV/6bDrtpzVJC6fPJvQircmvjKYS+RokUCRQapBAFUACRQJVAAkUCZQaJFAFkECRQBVAAkUCpaYSCLRkNpdmxXO4NCuaC2VWOI9LM5bAOOoAfTNXtmL3MxI2P6Q1eUnS+V+RFRm0JncU35QzgK1AEQi1ggSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhegwSK4DVIoAhew1KgO6p7L6KzWSpx7p0LY7kgASLD8yHi3sX0ZgvdXEZJ6cwyaoPSIyqzlJlBnwUraPOHKga9IyG8SO9BCPexdx07gb4VPcmsXiHsyNdcs8/IbD8DwvKSdQJ9hvme1/MjN9CaXXPOyAndTWO2NNCr9BSUWcrMoM+CFfTnBVMMekdCeJHegxDu48B17AS6IRGAGVOU2xzA0lMS6C2zw5IT6DPc+w0Aebm0Zjc8sgub7KExO5biVXoKyixlZrBnwQ7a/GGKAeFICC/SexDCfRy4jp1A50wGYH0/WrN3IXvoLXunpibQZ7goOlT0TRZ9buNMraNpc3vuVXoKSi1xM9izYAdM/rTFgHAkjBfpPQjhPvauYyfQ2fhRe9NZbZRspLf8rS/A/EprNt3/UU6XKbRmJ0Mv3m27hc4Mdx9ho9SS8DLUWbAEIn/aYsA4EsKLEB6EcB9717ETKH7EWd8rtylJ6PQSwjLG2cvRvD2t2aoRAGyNoTWbOBurx+LozHD3ETZKLXEzyLNgCW3+EMWAcSSEFyE8CMkrE4UAACAASURBVOE+9q5jJ9A3rm/zAmjufPdEwlpiP3xas0fVH2ZHJdOabQ5+l9VrBp0Z7j7CRqklbgZ/FmygzR+uGLSOhPAihAch3MfedSybmVIDA+jaDsYYmJiYJMJYYn6lN9sgcR1UQGsmnejq0vcznZmsAiJslFniZvBnwQq6/OGKQe9Iei9CeBDCfexdhxrqEbwGCRTBa5BAEbwGCRTBa5BAEbwGCRTBa5BAEbwGCRTBa5BAEbwGCRTBa5BAEbwGCRTBa5BAEbwGCRTBa5BAEbwGCRTBa5BAEbwGCRTBa5BAEbwGCRTBa5BAEbwGCRTBa/gp0PuRnX7SdhkQvICfAp10EjTVdhkQvICfAs2WPovRdhkQvEDTAnW7C6QLAh2bHsWWTewcbF2WYWWwd3BwGPiV2YGoF7RZpZFomGwbQpfRgkCHtHpcku65GxNoBgBX9R4BQY6i1dHvpPRZQQmUiBOMIwv2K//GST7GpNwILaF5gf4jysb+Pe5TIhMo8PybRKD9O8QlfL1FmkF8vqxBCZSIE4wvyYL9Et/3GwYuAO+6sz0VHQGyzipPSlL5Ne3WSpoX6ErCMS6PZQI95fiZRKDlWC7xHVdyrnNwEv4Bc7wl42VrQOY4WWKbvQCEphPLis4k4gTjS7Jgv8T3uL9Ao8+jb6rnBHkHXJ0lZ1cA/l21BTqBmHC60XFg4iS2FRyS/57l80cPiygEPdZ8sT8R+Can+6Jz5ncB/jlSJzM/chO+BHDHEYkbEsEDbymxLHPmuGgcYvZsWZxgGUQsVfz7fqO68/8bqqET1jo0ddaXagkn8x7+XbUFunS4bMH7Du6uki0e4Kvf86UX+040LHcDOtUrIiKg27nmAOCf75YCsL0PvgRwxxGJn0SF82bKDRWdScQJli1+EShO4ks1nSDvUFpnySojWd2THePstQ+ciyoa6RY8IOkIdgOUlALGOTt1K0iLIdK0g+YFet4jH/v3snORzF1S60yFCmd70I1ya8mzACgpOheF+RL7TMDun3Ym4ksAFyiRCKKP1nkoN1QUKBEnWLb4lUBPTweTgpo/VdNJ8gqldRZeGRF1z+o46Zk+mJPXtsz74C8X6J2IvKKw9LQYIk07aOEpvnuX1+Bi9U3EHRFwv6sg0G9Hll+77P+hqMvKUoH+HpJd0GZ9mUCJRLAtonmpoWIVT8QJlt4tUhBoTPbzVmD3LPWfr/ZRWmfhlRFR91x3n5SOO7kHdqv6Q+kVNOPoXIcjaTFEmnbQgkCLZviLGu4HcoFG/AgETmKxuIHc4EKd9V/tsMrPbXDZFRTM9PEeU1wmUCIR5JhtLDVUvIIScYLzBY+/FuiOn7GLa92IV2o8Ud6gtM7CXSmvhz6ti22Mrcdjj5QLZQIdkXK+5qJzMZhAiTTtwLc3SUXBZ6+nX9F2KSoTSussXKBE3TN7HPhg8fFc1LpW+VmBSWm1ij5IUuaNAi+9DqbFEGnaKT7fBJo8BIAhHbVdisqE0jpLVhnJ6p4nDV28fsQfkkZ41p23uKC3X/uZOx/VrdVxYWhaDJGmHfgmUATiK5BAEbwGCRTBa2gFenFKv96T1Th3FQKhDDqBLqw+Y/36WQGLNVIYBEIROoFKsvDvvAANFKVqgGokZtAJtJrsdeDbIA0UpUqAaiSG0Ak0VZw4eXJv1+0Km+9MrMqweANFUSPd0fYpaRVl/qR9SHq7Yfac9W8Vt66tl1x18TvCTJTloaiRkD+pUPEpfi11d+zKTxsWAqWokZA/qVDxKb7Uoa8bO3lFzc9lUTodhI1AqWokJFAKmD7FFy6RXZXbEsPW3wgFGCZ+GxaeYl1M3YGNQHOnJ+AjUBJL13MsBDLEHJRLV2ElUMV7poJpstvaWh6ytVBhPQMXgZ3Qp6VnEkUGlRA2Ak1snxKCdytS3FyDVYl0G1YCpbhnkjvUWt8A+/ULBa7D3S0esymjTsFGoOIs8E7yEgm0PKwESnHPJHeoSGArMMEEaqRf23ALVuOvWXZX9XLqDGwEKnkPwIZoJNDysBMoOXKHNiZuoARWI50EKeCmbTU/m22qZahLsBHoEocpAMS3MlDYjARKBTuBxov0BCZ6egKhgYlgFahnu/q3muaqZahLsHqKv3YAgJLdoxW2IoFSQSfQ2l4ECpvlDr1saiXEEJhYOQp/AKbrsTsC4Ws2ZdUJWAmUHCRQKugEmhGY8hxHYXOpQ3+1EwrMBEIzc6FwKzBP/vuvy3oZFfKobCCBcgu7Kn4JaYytLw79tKSvn4E5VsXH5rfSM7UQOnwxepXPoJQ6BBIot6jxHpSgpcOkpGFmTYJC9awd9GxKt16sITIfXaJa9vwGCZQhxcqvVGoX6LfxAfadfK+tF3b5+/h94TtiY6GLj7mF50rVsuc3SKCMKBppbtZSWSu52gV6Q7w2vX94CTCpFv7wrfANsfGy8Wbwpkal7EeKBMqIOe0zcuY2UmKgdoGCi92bff8JgHAzO30LW/m2Q0ZSAEa7qZY9v0ECZUS9OHOzFnbvqA3UL1A5swRGRgLr/4iVu6aj7hxxDFcte36DBMoIz4aZJXMNPlEbaEyg9iP/PHBQ6DCvEF+RhtT1DXQ+oFr2/AYJlBGSmtefDTaq8Lb8CxoTqOH/sC9hWrvAs/jaywTX4C2q5c5zkEAZ0Wx4dfeBNny4grp1AmCFgRSkug56r1q2ukGVE2jJD77ug5UoTDlrQq88GdhJiYHGBPo/A3tX/enYwqeRorUl0m1DJz+hzqFkfcfOu1U7uLapcgJd3PjW035xqu4tXVbTY8gHJQYaEyh4Mnrw38TS1cZhnWtERjj8S5nD907WtvZrKJP5TJUTaOgFAAqt1DawR3MCLYd0pdB2ynCrHpQGxnGvHzd2Uu3oWqbKCTRwlI/rQOtMdWWvFYGCowZDLRN7ulMlfxZ+BOCcvmpH1zJVTqCR9hceBavvYsJOoOTDjukdmqbv0M5ez76IIrlIfxUAo3Wz82iVE2hIF3Pzdpb8rOIphh3TO/Sl0LZbfWPr6jspJjVsZxPgazuILhdeUuUEGnS9OL/Ehp9VPEWoFnqHXnA10jO0bfFnaCj54d93N7UY/JkuF15S5QQ6tdOHwsmRasue02HHcugd+sJgws+rnVsA6Q7/pukAPO/XdGL21xbFEBPG8pIqJ9CC4RbmENNPq4oahx0r4ZZt45TJ1q2xpeJN1ZrvNbOqY+JRSLeTbsA7gf4xa6Oa+4aXFKgxc06HHUv378Bp5k233+2aW/tPWtVFtly00dLoD/DaaAN9YXUBvgm0f92pnWpn09uVoyg5KGA6XwY8sBKodPdesDluRtnN4ueeXXE8XemOWlJ7Ud7twFT5Wp0Q/wYHfHTzmagCPBPoNZ99MzfEL2G0zzTvaj6+o1Q/JqewEuio0CbR7fb26qewGcKhD9qZeiwvXenp/Hl3oHBg5ajjWQlU1WY7arZL6k6Ldu3LaB/bBlduRJrw5CGAlUCdcwqt/wVZihdMpg59bW4VbCJq4b4Irq1CmuxqEfuM2SE0BxuBqtxsR80uw48ASGJJ04r+t4v04Ub/HABPhVSN1BqGlUDdMrJNLoG3im+EGDv0eb8mSTngcg/7sY8grNd6SsSBIQwPoTFYhb5RtdmOmu2+ARPaeCjWcTI+1mnYxZEs1otpn8LiMQbFqh+US9g11Dt7jA9NDleMXaeyQ59OcOiSRmtV2/vqy3HGfL2EshGoys121NyUHFmwvdtysqRx32JPq/YkD0M9JZbW3h1UPyansHuKv3oTnJy4WfFuhYVDs1fWCFpH89rMYS5WzetfVPkY6kUNEZZZPcUPDxjfOoT0nUfEukFdVwdeq5jwMcbcsv0bFsfkEu10FlGC9Ggn+9F3P67dmEdlUcP/cd4sY74GdOQywnJukxAce2c2BTqxcAf542cHy6U7wo1I+2LmZLE5IqfwTqAYjybZCE1Mjf+mSF7m5Whaqxa7Q6gPTpuZrl/CaV+Nwyy/EGU59ecQEyUDKnkBHwUKgEXrlk4Sqi5cJUlW5i3uk6f94Wko+R/bo7OCZ+2gSmjxa9KgnYHX1ZI3d/BSoNmCAnB/kKDROop2JylV9X/bsElyfcMHLA/PCjYCVR4tkGsm95OCCyK+tz7zUqBAD6ve15oejLVJOMYogFMfT+zLdTDbw7OBjUBpogVyTE5EjRaiQ2rJmkP4KdD2piMGGA4F4N3yELeJN+H361AP+womb5bWEKyqeLpogdwivfqXyqMxNQY/BQpGu3vNJZZuJbkHzlcyAPQrfjY8If3TcBPrw7NAd+5BdQOeCrQ8JWmDHZqsgIrNLG1mrGfcitOjMwUJlFt0QKAYhb8n2rZYDdN2fOrnswzzfv5D0gkVikSFNgQqPbtPfT2GtQytQJPkMJgjWi2/+Lx9PW0jflJ8fmDPLcfobt4/cJefFgSa2ywo2mkj54flB7QCTUlJGVAzeUFtBn9DdVVJeft72TeY/x+3mcY6RcY7mFK+tmKMFgQ6u5cU3Hf4yPlxeQFMFR+MnXtmXfg81XjPVPjnUNeak/7mMHi40wAA/tG/zVl+WhBo9OpR8auanuT8uLwARqCu2PUlX7H1WAlcCPQD5dtg6YXva4v67WE2ioEaxyEA/Kt/h6PctCLQeIt5v7Y2fcT5cXkBjECHt9y9pxWDIQDsBfrM38iggZI2uofLW1m1XkYd24kBnew7DnA05W6IM51AY0qBz5LOnz0tp26KMH8In6EuASPQorU94teRdrDmfogCQXXnHRutw5WaZO8Z6OYz/Pcctoe65ti5u98Mtrl8gU6gaaXAZ0lbxf88odf6JlW4ii9Z1K1oJVkHazUMUZBRKMQuBwcNae1uzG9h2TL5Mrs70odTRx9mlQHOizOl3dYgqviC9CNHDobCZ07nzx96lIA7jvx/KaQSMAId2z2gpMsQknQ1DFGQkY9PV3NKcU5VUnIOjqzh2C3lEcsjsmSMtcRqNbEIIdD+YQ4xnovgcyf1596BY0o7IuVFVm/n9Bt8fjoFjEB9ciJAEVkoOjUMUSBwDrp6zqM2rPXT9fFin4G/vWJ7VJXZa9F4tJ8ZcUsMIVDfornnXyiLKqwAmT9nBv08T1RWq18+rCTKu24DI1DPtxHgfXWSdHUMUZBxxdbM1AX2BTyO9ObyGDv/Ydu1M1ltW3/sqm89TbYMIVBx3pHFgEGPaxJ/FlltGDBqYVv4PHQWGIEuC/Ka6b+azEBhiAK4RfQA92NdrPwLVxiPKiy5sqSTrU/bRZofTtesCfblTMyiDSHQMREZ/qMYDEslEehLyzrr5tszaPrTWaDexR+fmnyFLL1ofU7W5JjkstA8OQ2IMTQiLkvIiOHWjkZmXgmrb2p0HtD5pvP+HGHyl2wZph30Djg/j8GYKhKBFuodAqAbbQSXSgCMQNOogkz0iszr2mVrlGIHYe31vjli3np2C7MtKX2q2bSfc4J1ExQsn2tKfN06EV6CEGgKzk747MmuoPZOnZpI2NdU/AdGoKHeMx+RpovzgVMOyGYduIEzekiw21G3PtjS673jGpnXHb6F4zf3FOSvn7pH/iuGEGhSUtIIySz43L/2Z8FN/B7G/dC+/03tw6SMOgpUFX9/Xp3mZL2A694AIY/BW3+FzdoTaBf8yD495WsFZ5d283BoP/OPDA0WAfJV53sGgRG+8meae4CoawE45tShsb/22i00B1x/0MKjEWYk6ec9v+lUc4SfYk8v7Qk01XTIrwNN95ff9HLf9y2tfXssPsnVy3saIAVaxKB6Lu/PQte/QEGXuQBkHDzO9/FunAAj0L2JouhtpDd0+XsXTVv1SHGr+gVKFbyypJ1LdZfoCrfMJXc2j2xoUTNx2Un1BySAEGh3jJq94bMs788bgf+bvGin+iJu8w4YgbbZwOw1mpoFWjLJ0oxqXLz0j2Wk484wiq6lDK1vXiN+4f/UOhUjhECPYKQxiEpc3p8vLPznDLGOUqFgOgqEQDMUg4PRoWaB/hj+pmhRHdX2Lbq+8dtmVh6dpu1+oKb4l3QCZTVCIWPpRIOYtM2YQF92NrVP4kkAOrUCIdCS4LvM8lSzQCNnta4/2YNFbCbp/Z1TOnpYNR665gz3s6fQCZTNCIXHLj2/MY5o1mVZBGj57c7U5skql1J3gKniYy2aR0UxqFXULNAQ8f5z3U1YjxL7cGL5gDBzSadJ225wOQkARBWv8giFwQHiWnoGkrBOoz5ZiDo28SSvRl6oc04DjQMj0HMy4PNUs0Drel17PdaQm6BXJfd2z+rqb1qz68ydt7l5KIYQqMojFHybFABXgcTDcGOm3mkABjnIt1/p3f6H0i7XJ7xEFlOZFJjnsOoPSoGaBRo+yk+U6H2PwxwLrm/7PtbPtFbXaduush0+ByFQ6hEKyjuAF9j7rH9gKWza1rrNS+MxHx+Eyl8pXxWtPBQXRdxTZ9qLXM2dUxWz0F1Y9QelQM0CndUlD+zy4f4ZJ/966rRvAk29249bm656+FYIgVKOUFDeAbxAJBQI9ASWz0B/tyLLWGvXrvLeTIOXF2cVexG/2APGx8Fbr3CVi887WPUHpUDNAi2It/Xxq3CZKc/7+SN/VbW3SPG9Awv6N7a3qd9rzo5rKkyRSivQgHtRMkiSFDuA50+ZiFPLGvs6BkYI9QQY5tLzItuJTb1//sG+p2w7iKxhZOTkcwrcxVZbmwFwt6a9bDu+rvP/UkyWSUDXH5QCtTfUZ9xXKr83HkN+bEo9GT0U707/MrFzgIl7i8GL9t1mMqkVrUB3ZVLe0yt2AC9aloxT1x77OgdcDdzN3AVCgZ6eXePk5AUDx/yZLNsOok2/T26n9w48wVaHmky5u9aktmw7vp58optvg7HJX9Z17d8GrPqDkqPtWEIzYpp4d/NkEBSPkpJHf64c3cHPxLPFoAW7r0G9LIV81UkaZkF5B3AXI+fWblgd36Sewqx8cfVdG4mqnZctF9YK8q5tf6EsrchB3D9Y/xZUmXgJq/6gFGhboB1sDz2Ya7mLuwyLH/65amx0gKmoYcK0jSefK739hRDo3j6gscVPZCmKHcAJ5P4chonTUCAUTtu97esAk7023j+THXCDWHnZr0ZkuRjTv5hgN7tB2o2nxgrY4GFMAqtoW6DhdbC7AEMGHS5heXlq07SExs7G1dsNW7jzEnkfKQiBep5Ji31PO5/pF+T+fG2M3YEKDQSXwB/tvko/KjnxdEoIeTPLGDfsK6Em/MFkSDXa31spUJ1FKH/x5GhboP196/d18zuqtvzzbv3+09jOwbaWtTuOXLzrwtdP/BACdZYmrQMMesPL/ZnmbWCkr2dkMOtss1XYjyX1YFmL2FYf2yiKsGrH9e+AAgdmUyHmDLCwSOBLqCeoQXOq/eK1xr7aqRvWuah/nPjHa/uXjY4NcTD1bzPw59KNEALtFOeRObYx/HHk/pzq7GQT4VJXoK/nnQsOWro5u8hHX32s2WaIG9Uw5g56rkZiZu+Whsd/+DQwgdEu6gNGoNS/eHVFFmHJD9YOfumaO1zurcM/ry9dgRBo1urrYPZT+Pzl/uzl+cZWXyDol12QMF1qHrRrrXULIn3ySKz+d6B6tZY2fjP8oWS4dLcw72rFk1oeRqCUv3h1RRZhTbEm+9DjvLlY2u0EQqAqvpkba/XD/2KN8cl5/mr5WIhVECvNifSOB7CvcAaxdGiwiMnM6WukQ7MdU/7i1RVZRFsUqtp/Lc5A33AKsQghUBXfzO2sN6DZcF/bfOzRPP6Z8BUACyyI9DFTsb+RE3exfS1aP33Z1UiHrqCfvmvQbDZZ/De1RRbRCm9izMwHqvQqfoXhwU9L5bOHQghUxTdzJR3rDvPv26PDgVXiv4G9+7JpZvIIec9dBybXGcO41JS4D3QWDbTRzBVUutDDsbeyHuQwAo2Lv3ipJ9kIQrVFFtEKncYXZHYdp8qeTfAhGJ7DZcswzUwqvpmT/m/lGVCwJKo39lM4Y+/sEFT6h32ZGLmCWYmVMiHq8fNugzjMUAkLLXxCzRsqMYAKYIvVfMWk3cOUNizrGAWW2LPuPV9Vdo3EPSwiBh5ACJSTN3M5p6+WXuM+VG8/0mM+fH505E9wdR6loaACzt9IwR96SlpcYAQa/hKrRihV3v7LYo6VQIYjw1LygiLLDxev3WDapC3jkP6Y47EGRCwpmFedHL+ZmzQau1Q46Gb4MLMt2JfeDWoDmFk+eogHD3b6niQ9Acc8QbHJTDevoKCjRaCP5USVdl1soed0gFikFeijv7H73My7P8LnTudPrp/iNYlf8PuiKfpK7vxpBbpGDlkf2IXWyampjqmKSToq0OBWbt4RKnSCenLkEXb9LV2jE+gyPU+nwx7W7uHwB6Dz57jJmOKddHOmpMMWJsa2A5QYwAyae3T8DmkAcAAuhqWCijenuinQzxYlADxl8HAtZ4pTfacJX1bpBOpyFaQLtjJ6RKbz50v3vrMCJii34S2nE2N/UdaiRS/QXd4ujSTOFG8jsnv3da6wUTcFKrV7BcCpMKa7nXVwaOzgeLxsnU6gtviHWRsjrT8//jiVKhqAzkMr0D+8TmDfl6ofoLD5reJ0FbopUDCj3p6tPr8y3Wu09Tvw3m5g2TqdQPGBbmJmh9BRf3IDrUAbn5ItXmgEn6eOOlS6MfYb5tMp9MJ70fjHla3TCdQuIyPDCfsweBmro/7kBlqBOhLVkZTBr75KOXSD5Yi9oy1Xlq3TCdSkFPhDVCl/KkIr0ACiee8Vu5jqlZcsSeNGTdw/lK2j6bi5hVagya3xoTj5nVUOuFrZeTWmzbflmniQQLmFVqDSwaKBs4d7dKdoaCIDOZRbkD+pINpBb66csPQ8kzyRQ7kF+ZMKgZI0JSCHcgvyJxVIoMxhJVCeDqHRJkigHMNGoLwdQqNFkEA5ho1AK9sQGlqKn9BGFUIC5Rg2Aq1cQ2jo2evsYU0XJRoJlGPYCLRyDaGh5bn4CnjpTxNgAwmUY1g9JFWmITT0pHb7c/72+TSjwJBAOYZvzUyfprQd9C9nReGUw67B37VypolXjgTKMVwKVLp/B04zH9WzKAwNDq3nwGJKFAZ8OnCI0XR+J41m/rPZZKZyI3YCRe12FWAj0NpeBKXrn+O74niymHb7hPngPxaYj1Y9A3guuXRo7c4kEunmzr1rdRw/UrkRK4GidruKsBFoRmDKcxyFzWz8OdsOLxPjcQKqUG8XAOtbMtjhQo0CAL5ZpdyIlUCrXLsdBKyq+CWkIzfY+HONyQOQ66NKANunE+KXMol9XmJSiNXy1kwOkRgytX1dspA15WAl0KrWbgcDzx6SnlrbNRGJf2O+4zPnKb92bs1k8JTkBgCnAxkd5PDsX+lmp2Il0CrWbgcFW4GSzJLLyp8b7Ws7DFUhztK077CLYq2LDPbYIFm+2I3DwOsE7B6SFNrtcmp441iKuCiZjsJWoKEVN7H7wX88/Ywq6eYp6unJ+/+CfcXsZXKktOGjGMxHCAkrgeZOT9iD/ZNYtuH5A5xYBoGxKh28EygluS2rNRFTlnZt29O7zogYhNlVE6wEmtg+JWQTAIrjv1AVz4K1FTepyZ9T+pSAv8VU94B5IiNnvS5qOTAjWAlUnAXeSV4igZaHZw9JSmiBR5uoTRW3a1XHKweviTXTwq8Mds1M77Fb42gk0PLojkDjsbvMPMqgeP3we9DYPUwy3N61G1V4D9VhJdAlDlOw82xloLAZCZRb1OTPv8Ur97fpRZU6YywAxTUuM8hvSe3ULdXXcVCwr2D3FH8N+8WU7FZ8kYYEyi3q8ueBQEk/yglqXrmOXd+uI5P2Kbd7AFz256BcX4E6i3CM7gj0H6fJK0PGUia/nt5vLV0renlKTDGxf7RlX66vQQLlGN0RaO8fAci05W5GuXCsel/ckbPs5CCBcozuCDTiBPYVdI2z/O74hgXX5PyxHwmUY3RHoOOwZ4c79irNu0NOwfmLDOLPQIIEyjG6I9APgY27O6nQjUSzIIFyjO4IFBT9mcrd9HTqAgmUY3RIoDoBEijHIIFyCxIoxyCBcgsSKMcggXILEijHIIFyCxIoxyCBcgsSKMcggXILEijHoAC23IIEyjEogC23IIFyDApgyy1IoByDAthyCwoexjEogC23oOBhHIMC2HILp8HDcttH4ji7cFEyHYWNQCsEwiBAAqWCafAwafoxnOEVp5CvOrARaIVAGJkfcLqhSC0UqBg8bO1AloXSZdgIVDEQRo6bLY6xOxcF01E4DR4mBwlURSgCYSB/UqFiMxNyqIpQBMJA/qRCVYEGTST4zqcBFBIoq/p8NvOXn/JECZunePJAGIz8WTOI1qQeRDZ+wbQmIdXos/ENpTUJbjpRGUr9qaJAnyTLma3fDoZWkGYGUGaRkGaGUGYtIc2MS895/gfVnFYKSQBbRv70qEVr0syMPhunurQm9ezos7FuRGsSVC1ZGUr9qaJAy8g1gzLLtoAyy7KEMoMMk/4BLgZGhj2U2VtHKDMISOKDlgHjz6E0kxJg3POlz6bTflqTtHD6bELpQzT/Gk+fDRVIoEig1CCBKlA1BEoSwLYMJFAFkEA1L1BlIIEqgASKBEpNJRBovg2U2Wc4reTaQZnlOECZZTlBmX0SQ5l9cIYyYwmMP0fSh5B9BPFuv/NhWpPTELPKNbhKa7KDMoYuPWwFCt4jM06BOEo2ZUhaRtl8LKY1kUI0qEEcqSiT3oYK1gJFINQJEiiC1yCBIngNEiiC1yCBIngNEiiC1yCBIngNEiiC17AU6I7q3ovobJZKnHvnwlguSIDI8HyIuHcxvdlCN5dRUjqzjNqg9IjKLGVm0GfBCtr8oYpB70gIL9J7EMJ97F3HTqBvRU8yq1eI6vA11+wzMtvPgLC8ZJ1An2G+5/X8yA20ZtecM3JCd9OYLQ30Kj0FZZYyM+izYAX9ecEUg96REF6k9yCE+zhwHTuBbkgEYMYU5TYHsPSUBHrL7LDkpLXTMAAAIABJREFUBPoM934DQF4urdkNj+zCJntozI6leJWegjJLmRnsWbCDNn+YYkA4EsKL9B6EcB8HrmMn0DmTAVjfj9bsXcgeesveqakJ9Bkuig4VfZNFn9s4U+to2tyee5WeglJL3Az2LNgBkz9tMSAcCeNFeg9CuI+969gJdDZ+1N50VhslG+ktf+sLML/Smk33f5TTZQqt2cnQi3fbbqEzw91H2Ci1JLwMdRYsgcifthgwjoTwIoQHIdzH3nXsBIofcdb3ym1KEjq9hLCMcfZyNG9Pa7ZqBABbY2jNJs7G6rE4OjPcfYSNUkvcDPIsWEKbP0QxYBwJ4UUID0K4j73r2An0jevbvACaO989kbCW2A+f1uxR9YfZUcm0ZpuD32X1mkFnhruPsFFqiZvBnwUbaPOHKwatIyG8COFBCPexdx3LZqbUwAC6toMxBiYmJokwlphf6c02SFwHFdCaSSe6uvT9TGcmq4AIG2WWuBn8WbCCLn+4YtA7kt6LEB6EcB9716GGegSvQQJF8BokUASvQQJF8BokUASvQQJF8BokUASvQQJF8BokUASvQQJF8BokUASvQQJF8BokUASvQQJF8BokUASvQQJF8BokUASvQQJF8BokUASvQQJF8BokUASvQQJF8BokUASv4atA70d2+knbZUDwAL4KdNJJ0FTbZUDwAI0KtFhgby/qlYUd1d7BwWExkC4IdGx6FEswsHewanqvvGm29FkMbX5pJCZk2xC6i4YFmg+yhrTDjpojWx/S6nFJuuduTKA54FNCx69sD0S9oM0PSqBEpGCMsc4uo6VglrvHbHlK8jHGZ4DQNBoXKCgQ3ZYL9B9RNvZ93KcEFyg44l3e9Oh3UoWdpRnE58salECJSMHYwu+1P2TW+n1v7dcvm/51v2HgAvCuOxfnxG8Y1FlfkZJUfk2btZLmBQpiUuUCXTlQttXlMS7Q7F7ty5v27xCXIF9cLvEdV3Kuc3AS/gFzvCXjZWtA5jhZYpu9AISmE8uKziQiBWML10+DonZ/TJwKwE+jx/0FGn0efVPNp8sDmNRZBLsC8O8qLdCBy2S/Z28wgahrGx0HBk5i87B/CaNhEYWgx5ovO50IfJPTfdE587sA/xypk5kfuQlfArjjiMQNieCBt5RYljlzXDTOPNn+skjBMuYbdQabwj5ktO52v1Hd+f8N1dRpaxGYOutLtYSTKbusVmmBxmyTu2vpcNlW7zsyd5Vy6cW+Ew3L1e9TvSIiArqdaw4A/vluKQDb++BLAHcckfhJVDhvptxQ0ZlEpGBi+XLghpLhYv9E2ZU78aWazpJP0NdZsspIVvdkxzh77QPnoopGugUPSDqC3QAlpYBxzk7dCtJiiDRtoHmBFopvyd113gP33mXnoq8Eigkw6Ea5teRZAJQUnYvCfIl9JiwDYGcivgRwgRKJIPponYdyQ0WBEpGC8VzTsMq9X8FnTPLJ2Orp6WBSUPOnajxbPkBfZ+GVEVH3rI6TnumDOXlty7wP/nKB3onIKwpLT4sh0rSBxgWaO6JN2R1R9y6vwcXqm4CCQL8dWX7tsv+Hoi4rSwX6e0h2QZv1ZQIlEsG2iOalhopVPBEpWHq3aE1E0afYn9L9st94P8G2x2Q/bwV2z9LEaWsR+joLr4yIuue6+6R03Mk9sFvUH0qvoBlH5zocSYsh0rSBhgUqEol7ZpYJtGiGv6jhfqAg0At11n+11yo/t8FlV1Aw08d7THGZQIlEkGO2sdRQ8QpKRArOFzwu6idxHVEEvneujT/T7/gZu7jWjXilrlPlCfR1Fu5KeT30aV1sY2w9HnukXCgT6IiU8zUXnYvBBEqkaQP+vUkqCj57Pf2KtktRSaCvs3CBEnXP7HHgg8XHc1HrWuVnBSal1Sr6IEmZNwq89DqYFkOkaeMM+CfQ5CEADCFrAEEwh77OklVGsrrnSUMXrx/xh6QRnnXnLS7o7dd+5s5HdWt1XBiaFkOkaQP+CRSBKAcSKILXIIEieA2tQC9O6dd7shrnrkIglEEn0IXVZ6xfPytgsUYKg0AoQidQSRb+nReggaIgEBWhE2g12cvAt0EaKAoCURE6gaaKEydP7u26XSOFQSAUoX1Ierth9pz1bxW37hBUYYR/c/5nQP6kQsWn+LUDWf09dJs2RzjPEvmTChWf4ksdWjQpotvsQywKp4sggXILK4EqPsVL04/hDI+VrRUY45do8zbp2x+yLqbugATKLawEqvgUn9s+EsfZhcha0MjAXeAqdG3kWIXCLCCBcgsrgVI8xSfWkP1jq28gEGL/2XdxtKjsXSu/gATKLawESvEULxeovcAak6dAYGISargNgIyUldQjWSsPSKDcwk6g5MgFGorJUw8TqOUAe+EacMvOx8d6p2oZ6hJIoNyiRoHGOQpkVbzQ2FywAtS3W7utloVqGeoSSKDcwkqgtb0IFDbLBXraxFGoJxQKTEwdhfOA6XrsjkD4mk1ZdQIkUG5hJdCMwJTnOAqb5QIFP5kLBaYCobWtULAemM/Ztj5dmFEhj8oGEii3sKvil5BG2CoVKCi89120p4GpganA61xzoZmF0K7M5sPx64zKqTMggXKLGu9BCVraj/m2j8VEZ0cDW5GhaenWvU7Bnq0+q5Y9v0ECZUhhttJktQt0fPdQl2+8/nmvb78o/bVQHlHms7V9M2+Xaaplz2+QQBlRMMjMotkDJQZqF+gD0azUTp2kwHy4f3S68AOx8bTBFVAcUU217PlN1RPogzmTzqq884zoT3mL6ikxULtAwYPR3X4sACBWX18osC4gth02zAdgmJtq2fObKifQc04T5nitVnXvsG/MzVraV3jX8wX1C1ROrJ6Jhb5pDeKp6rlpm92LLKNVy57fVDmBtt0GwEM7xYjCsEjqfyyebZBJbaAxgZr//Ojua2GqT5dH+FrXWg0b2VfKEDZVTqDVTi+ee8XpjYp7S6qff9DX+B21gcYEarwPgCLhi7w5DlNzsHvjZdEDK2c7U5UTaEvLEZNENqru3XRMkPdIm0/UBhoTaKjn009trLCFZ/Hum7EKIadYWRbFd++rWmlomSon0Ajb2H72lqr+tVaHXX82WFmwLY0J9I2bvr5lmmzxXIOw9SEWFt9Tn9R/NUQOobr5WrTKCbTa1W0pT1Wu4qVLqrsP+qDEQGMCBdLblwtLF7camJtYuK2lzKG25YBE87aqHV3LVDmBRmN/xhsitdV3mhNoea4adXfq41uHKrlA7wR28TdS7ehapsoJ9LY4Ybhoh9qyZydQ8lGd9AL93bCH2NHQkureOB9/4/SXPl0uvKTKCRRkpKz4T325q2NUJ71Ab+s1urTSwMZpfi55uk3QkQOe3uRpPKcSCDRvtJP9EOVvyDUHp6M65dAL9K6xm5mdVZO73VyW55Olp1pLJFZaCszPkkog0FHVnBz8EjV7TEo4HdUph16gN2xEtmKzFgBc6+S+Mh9I0zYqNIpen52srAcBj+GbQO918Wz6J7NdLFs/fRlnVMLioByixlGdSnim/zN47dkMX7zYwf3HIGOhEV9+sWzhmUBzfJc8PuB8ldE++uexv5BQaTu15lDjqE4lXAiob27Vs418xU84Nmubvm7W6BVgJVDuQwn92Tjer9WAJNK03G0r/yHbbt7pfWZipbiCSnfvBZvjZpR1PM6xJAI+OdIdNcfhTm5x/9L+oGHN4kRz3YdDlFYHYCNQmlBCqrDDIvnfnVY9yZJeeXca5kLWDWmwr6WlXzfVj8kprAQ6KrRJdLu9vfopbKa/goJtTtF1GpdOdtK6LrjTS9i0ckyPyUagFA+dbAS62+g4eOJAOrf4iKkAPLUhaUfJ6W9hkaCVeY9IYCVQ55xC639BlqvCZgiBglf7z5S9fEgz6LA4yHiw3YA79Pvh7GwWNF5JBy3twkagFA+dbAS6r7GPnW3nkWRJESvi2y8KIr09LeHJDShgKVC3jGyTS+Ctu8JmGIF+xa9iA7/z4P1scdRfENZ7xUH+Ae0YHkJjsBEoxUMnG4G+FZ9+/6zmYbKkKMuUQ62NslTPWyOwa6h39hgfmhyueAvOWKCl5K2rGbyRtGG0PPXEv1+ON1bSCVursHpIUnjolJ6QRQscxqZn9xF3F5sFpCkdbYbMqmauai8PTcHuKf7qTXBy4mbFjgIqCxT7kxxpK55GczPqMBuAIv0LKh9DvXDZzJTbqXy0QFV5UUS+veXuJVPTa99klbf60U5nEWXcGWbX/dT7Jasp37VJGueC3wzV+PqXFWpoB2XnT0pm9CwC6S4U6uUN/BMoAJ+WOwtNzYxOUSTP9LByc3On6t9VwO7YrGEjUOWhhLgmr4N7fVeYm36twkeBAmDepYudlz1FYmGitas/xbuRrbZ69urr+gUDG4HShBLinHvn+B87g5cCzRYUgBcjBaFrKBqTPj6h2PGyQZctHQ20elvFqoqnCSVUFeGlQIFeOgCrTY/E2fROZ9RVu6cv9uWl+OJAo+jOPahuwE+BdjIelGDwLQBvFgf4zHoMv1/HUOwrsAvbw7MBCZRb+ClQkCTxW0IsXRzhGL4e9r3RVoMdH3412M368CxAAuUWngq0PAV7O9t02wf3dN7ZyNA4ntOjMwUJlFtoBZokh8Gc29w79MPPzewH/AXzgvj2vn8Z5v1b48BxHL7Z145AKYbOVAJoBZqSkjKgZvKC2j/A56mWX/zTRaHi4encd1Lc4dGqRX0Oo0RpQ6CzrczCrnF+WH4AU8UHfwQgsy58nuqqku7PCxYPO85xR5v6dmv2RJi85yw/LQh0a+gL6WYJbR8G3QRGoK55AOQrvt5Qghrvme7NC3EccIjLv4X9DABy9G5xlp8WBNotNW3nkzC+dk5gCYxAh7fcvafVKPg8ORDoxeFjqTqHPl7SzKbrr8qipTDCPTwXbDC6z1V2tAKNKQU+Szp/9vAJjXNyrZyx2KAEWrS2R/w6Bp0K2At0qaGtpSF1a9Hb9dGWEYuYPg2RM6a6lbO3iLt7WzqBppUCnyWdPxMsT78aY8DX/ocsgRFoyaJuRStJb/1UjSxCh8kEUBLnoMzi88FBbtVG/8m+ss9qVi3M5QTrbMqAqOIL0o8cORgKnyWdPzuNDRR3q19JBh0qAiPQsd0DSroMIUlXObIIDfi7ePCPHo2V9MqcRlYdV7KtnaXXT+fQWynndkKjEfL5ciEE2j/MIcZzEXzudP4cOQ+APBeq/gk6DoxAfXIiQJHiuA4clSOL0CDV3wPAHHMIy/epfV18h+7W8givpza2Bk6eRFskhEB9i+aef9EJPnsyfxavje1ZepdwX5S0rlkf+Px0ChiBer6NAO+rk6SrHFmEjh6GrZsZkA/mrsiNRW0t608+nsf2oKozwvKa9LDRVtkyhEDFeUcWg1rw2ZP5c0zTPSluh+QrT78fsIUnw9g5B0agy4K8ZvqTDaBWObIIHUWzagSsZNCPKf/45IYWLWaf0lJv5UatsS/JMNkyhEDHRGT4jwqBz57EnwWWa7r1XxgJn4fOAvUu/vjUZPIJDxQGeeU2DcGxd+aufPBkHRofalnHQd+ftE+lWklweg/umayQLcO0g94B5+cx6KNFItBX5g22rXDyhM9DZ4ERaBrVtaxofU7W5JjkL9eta5dw2mtthq4rJsaWQv3BBzQ8Z+11S8sAK1vioBACTcHZCZ892RVUbzuQRlfKiaYUgBFoqPfMR6TpvSLzunbZGjVYYbP2et98a/sePDFr0tqqZv9fbmlwFobfA82bXSYWIQSalJQ0QjILPncFf776jH0c3Br6B1WFXk5QVfz9eXWabyJJF+cDpxyQzTpwA2eUdlguvroywdemzfTDSibgUROQrzrfd4DP8it/XvZ3thhaDCS/n702kd+Rl7kBrj9o4dEIM5L0ujdAyGPw1l9hs/YE+oPpsfw/TJYRK28PTI609um+OF2jwYIhBVrkB59leX8WS3aAzNZLwUmX5oEhmv/5aR4Yge5NFEVvI2vMPu/5TaeaI/w2KmzWnkA/iESGYpdyvTuldzaPbGjhn7A0XVPBnCAE2h2jZm/4LMv781atu2t3HWgDQPZfFypry9JXwAi0zQbK2Q72Lpq26pHiVnUL9Cc/cSLFJErPR7YaUyEwSdG1X4Y1MK/Wdd7hF+otGA6EQI9gpDFoESvnz/wrti4D2zpHqVIy3QRCoBmwLealqFmgm+pcezkunOlexbd+nRDp6NBy7IZLah0MTidQNiMUSkaZOwrb/3PEplJOw0sOhEBLgu8yy1PNAm03u0PTGc6qXQxfHpmfUMfUr8u07f8U0lurAp1A2YxQ+Cmkc30bq5pNZ0WAT0O9ai3T0dkiGQFTxcdaNI+KYlCrqFmgdUQ7T3YxZ9DQrUjRre3TutQw9e8y+bfLrPuJKAJRxas8QqGR3fo9hsaRA0cPALF9D+0NW6laEXUKGIGekwGfp5oFGljzTfECA9Zt8QU3t8/sVsfMI3L4j0cecjeOBEKgqo5QKHYM2icNENSprbcn28IryDO4vopl1CXY9QclR80CbZZgZd7Si6PodiUP/1g2NNLDxD96/Jr/PeJApxACpR6hoLR/bZGnQCAUGQo9PVy/ea+/B5R8UzYXQMGXKJ/PEv3C/1Ch3HyFVX9QCtQs0Bnd8vP3S7htY/l8fXfygObuxn7thi/ec41NkxSEQClHKCjvXzteaCAUCASWBSDW47XhDyWZzZ2IdOl3Fk415DVcQY1g7yCHv1UvP99g1R+UAjULND/OqYb3eWUW//RtO00lkeXf/v3H0Z1qW9iFdBm7/MA1VbqZ0go04F6UDJIkxf61BdMm4tSywb6OA3f9AH09gcAo967EerxhoJl5uIdsO5jn9u3Ezk554D9sNcF4yp2VxjVk2/F1nf83jE1/UArU3lD/8o7SEVL/iZZs69uUTXX99sKOhSOialtb1mo/eM6mE/8x6GtKK9BdmZT39Ir9awuXJOPUtcO+zgJnI9ua8UKhwN7aLji5YeiBzW6Jsu2gfXyfruNqnQOPsNVocwAe1XGSbcfXk8+mTZTlUrauY//WZ9MflAJth2qZ2NLK0dubi2G4n24eXDUpMdzHxDGow6AZKYeu00d0h3zVSXpxVt6/tq/AwACfhSosRG8vKFzeqkvpkXq6R/RxERNBJw8a/wlee0Z82bmDvouRmGE/2czdO7kLFMASdv1BydG2QFu6vwLHjBX/zKx4c/Xgmmn92td2NHJvGD1s5roD559RjNeDEOjePqCxxU9kKUpn7ntqjKnTSE/QI2lG76/TLY/8N8yA+O1kOriILV1Sy9KO698BBQ5f29Nx3aVTrIgvt7GwwcOY3I5pW6AR1T+D20ap9IYqkP/k7L4V0wZ0rOdmbOvfNG7EzFV7Tt4p32kDQqCeZ9Ji3zOYbFzuz8tiA6GlgZH+bnC8+Vfpnb5vVK1/Q/mozhNeYstpX9LG4H1Ge9aEPxhG83WHDqQyaKdVK1CdRSh/8eRoW6Cj64kbOvlRhbjnjPe30lJ/nDoopnF1WwNx2Rg4CIE6S5PWAcXZz5Qg9+fcGoYSOxsT/b/BsIlfpY/Gg6OIn5WuPi9foa8zwW7Xg1rBHwzD0rVNe2cjnsyuADVoTrVfvNa46LJ+x+RamoxVVPiyrPMKhEA7xXlkjm0Mn3vpPahLssFgU1OhqZkX3kSRWabDpy7Dl4aOIN+3xEE8qK4+s8g+lkkALDZmtIv6gBEo9S9eXYEbWHI03DfhqZaODSHQrNXXwWwG5ZP7c5TF9lGmAqOEtStq7AIPwkyNE0t/g+eb1BhGdcHL7uYTzjDylIV43gIPQx26glL+4tUVuEHneB1fb6Q8RCeEQFV8M7choqVbVIBICsDuWOBn2aeDqfyqeddp5pbIHkzLTE3QLxPGb/XkLj9WwAiU8hevrsANWuLanPmPVNrxtYm4uaUzccmBEKiKb+Y+1+75U6dQfPmPdu/0rgMwzpZIH74Ae3hze8i01JTsclu8XLKes+zYASPQT981aDabrBOl2gI3aIXfXL8f7XhWlT3j3aQg14ToWwQhUFXfzOUuH7E2r3oKeNd01T29QgC2mBLbo/djX81OMiy0Ev4eNZw3kZ5gBBoXf/FST7LQKmoL3KAVXG9gF48IeruKhHbEvvz6ypZhmpnYvJm7Vd/cMklabBj339+egcSmuT2l4D9HvkzwzjFQAWyx+6Vi0u5hCg3LBVOJd8ceXJZQU+TisaBeqDRva3/HAvDWiBiaBSFQlm/mcvDb1/nW9k4W8sfTz81rdXTaAp+fTgEj0PCXADxvSGXU/stikfzdsSc3ZdMwXn0DQxJaq7Jnlo15LaMaRP92mFedXLyZS584u6zP9qthHX/R1d716wJ9Riubsx5mlo8e4sGDnb4nSU/AMU9IUNiso1V8O+O4dobjVdo15/tOy+SP5bQCffR3HgCZd3+Ez53On0+cRy4LGwqfH59YbenobaksxhStQNfIIXtzuNA6OTXVMVUxSTcFWmx1c/UvJ2oz3i8txDS4XDgoOoEu0/N0Ouxh7R4OfwQ6f34786s3STqFe/tisE1PySUUZtDco+NU3dsuhqWCijenuinQPAvsJB9KmO72xK66hZ/dvbJ1OoG6XAXpgq2MKmTaCMsHsK9w3jx3M8IMD1qpd4PagF6gu7xdGkmcN5NbZPfuWzGUnW4KFLSaLS3oN5zpXslmB7IPm00pW6cTKN52actsOACdP6cNwZ4RHDQcMI0jvBvngiX6SuYhoxXoH14nsO9L1Q9Q2PxWcboKHRXo03A7qzhl9+ukJOCBf+rElq3TCRSPvC9mdgg6f2bWadbHeQWzPPnCdksTa5uuSgxoBdqY6BV0oRH8QXVUoAC8ZyxPAOaY/VWcbj6pbJ1OoHYZGRlO2IfBBY/Wn0WHNnA0hlDzHGjbeIGyfj20AnUkqiMpg1+9zgpUFR7aSQy8bL+EtqATqEkp8IeoUv5UhFagAcTsEa9YxlSvvBytZeD/+5dVNNsxt9AKNLk1Hr0wv7PqAVcrPV91TEIC5RZagUoHiwbOHu7RXaMzzekwSKDcAtEOenPlhKVKh6ErghzKLcifVAiUpCkBOZRbkD+pQAJlDhIotyCBcgwrgfJ0jJc2QQLlGDYCRWO8KoIEyjFsBFrJxnhxAhIox7ARaOUa48UNSKAcw0aglWuMFwQlz2lnCmAnUHRTXwFWD0lKg4fxnyfPmdkfcHG1XkRjw0qg6Ka+InxrZrrd3in0IGdFUcbTBm7icPr4lF94Ib4InlenmZyalUDRTX1FuBRobrtIHGcW05tnuwaKathfVGHP98kjNjMKAtx+rrRkYncGO2zvduHnowtpRoGxEii6qa8IG4HW9iIo23DqGM6IONWz3Ge64uURq17Md3zrMfSn5kwOXGLxGYAMewZ7HPL0HVTPa6pyI1YCrXI39RCwEWhGYMpzHIXNa1nMajwLnwkkKlSFHUcBUFjtKoM9RFi57/gy2OGs4Zw7vxrPUW7E7iFJ4aZemib7xUcyKWVlg1UVv4T0hoyNQH81OQT+dejCfMd+eHCm2D0M9pjaNP2vsIUMdtgcG1+j3ZiRyo1YCTR3egJ+Boll61HEPZNK4TkqCWp4SGIj0Ax7LzN7p/+RJxYoGXfyU5fX1546M4lIVry8SfjPTIYDnqmN3eMmLlduxEqgie1TQjYBoDh6AVXxLLhfcRMbgYJ0fwuHVaQpxSPN7YOuU+2X72pgp8dhTEcSpDHNF8fXyFZuxEqg4izwTvISCbQ8bAVKcrvISqDYgzzF9mWtPoHN1agueb82OrTxgIhi1nOOKPlt3Eq6yVLZNTO9B2BDNBJoefgnUCraHMW+atyhSB24FshDQmgXVgJd4jAFgPhWBgqbkUBZsJZkk3oE2nkndg1zowpFPmkm9hX6//bOLbbJMozjEE2Yx2UyDtuIbESdJGNqqN6IcSomBBa2BDIMKVvgQpAY4zLnRGdcmBdTMYsn5GJxE5QshB301kQMajI8hElMRElGiEhkzMatmxy27rXfDkSr/Z6nfZ+mz1v+v4tdbP88fd9/fl3Xrn2/rxMZGOrqSeJz2wR2z+L7o/ewSNezMd+FoLKkSNDeOz//eedj8X56cvH7X+58IJGD6o8trlxX4HOITXLgzSLCuCOoOXB3XnX85/HHN6+qT+h6c4FeYz58hM4lBgQVxh1BQ6UPVi08KDYuMi/663Y4W2zeDBBUGHcEras15qf5CVwnl2DZcWOOxv7b2xoIKow7gpYdiX65p19s3v6lb7bk94qNmwGCCuOOoDXvRB+Sc0JyA7+qfe4/7w22BoIK446gPy5s3BeoTcloQSCoMO4IagZ2bT+s/toLEFQYhwR1AggqDASVBYIKA0FlgaDCQFBZIKgwEFQWCCoMBJUFggoDQWWBoMJAUFkgqDAQVBaczSQMBJUFZzMJA0FlkT2bKRzy2ARBRYGg8Uj0bKbRvByPeQUSK3MUCCpLKs5mQqGyoM94JHo20wwoVBb0GY8kX2ZCobKgz3gkK2h1aIbfBq4Vfp/d8qMpEDSz+/wj5Idvn0kK2pMzy5y5LDIgNvfqnr9JrjTJPgX2xLkloTHX5fji12eSgl5l7EZWLHwzKzZyCyv2J++j2aEcVox5ZPDgAlbMEk6fT/3/UXb/5BTj/Nb1n5CRLx6mxwTow8c/3kyPiQcEhaDxgaAxQFAIGgMEhaDxgaAxQFAIGgMEhaDxyQBBI/Ws2MTzkrHxBlbsygus2OVdkjFLOH0e6iMjI030mH2/kJFfW+kxr9FXnjvRQY+Jh62gAKQUCApUA0GBaiAoUA0EBaqBoEA1EBSoBoIC1UBQoBpLQQ8VL9tDZVqL8mrGOMnXg4yBx1YurpmgY28syX9mkooNrTCzt+iXnIqxd2EFOZ+1DLpIRot0g4z67KuzE3Rw0ZnhYuKyD/3zh4bXNjGS32UH6YGXlv5waXU7GevPGxoNdBGx1tLC2S168eRAAAACyElEQVT4Jadi7F1YQe+Lswy6SEaLdIOM+gSqsxO0fYsxTY3+mU+jP28L0snw/S1BemBPlTEXx8jYidvDV1Z1E7HP2gpnt+CXnIpxd2EHOZ+zDEaRjBbpBhn1CVRnJ+irLxnzwTYydmFlN52s6ewM0gP3VAQWVY3Q0+puyK4gp50tnN2Cb9KLcXdhB2c+uQxGkZwW6QYZ9dlXZydos3erNVSqo6iDTh7caqK9krFXlp8e3dBIxo4Gvj255gAV8+qbzvgmp1tm7cISxnxyGZwiGS0yGmTUZ1+dnaDeLe5+0T8TCa4/x0hW5hUuuGktGdv7tDEfVZKxhubo49hGKubVN53xTXox5i4sIeczlsEpktEio0FGffbV2Ql6vmDwYgnxl2/3am4yescnY6eLB8LlLWRs/30XRqqbqJhX33TGN+nF+LuwgZzPWwZZJKNFRoOM+uyrs3yZqbO0hHrtoPb6rKysLZxktFc61l5U8ORlMjbZUJC/9S8qNvUANJ3xS3ox/i6soObzlkEXSbfIaJBRn311eKEeqAaCAtVAUKAaCApUA0GBaiAoUA0EBaqBoEA1EBSoBoIC1UBQoBoIClQDQYFqIChQDQQFqoGgQDUQFKgGggLVuCPoutzb5uTm7kj3MjIGR/p0R1BjhrxLhRwuSfcyMgYn+nRO0OFT6V5GxuBEn84J2lfeV77toR3NFSvOmbeK7qiLpHtV7uJEny4Kemt4IrfN1L99pPT86BMp/RRwZuNEny4K+rgx954x7za9XFhWVrIp3atyFyf6dFHQNdFCz0YLbdltTGQ83atyFyf6dFnQ75eHxje8l+5VuYsTfbosqNl715Lt2u7xDuFEny4J+m8mtVXpOEr7dFdQcE0AQYFqIChQDQQFqoGgQDUQFKgGggLVQFCgGggKVANBgWogKFANBAWqgaBANRAUqAaCAtVAUKCavwEl1rPiteki2wAAAABJRU5ErkJggg==" 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Ki5bcRt6hm32urb7eS6dE5gQflFkIJ+EhUCcFevdMTF0W61F5del8qnewrwskHHrTEGWs2Wq4bEDVhQOrS2B9UjmveVZmXHSH7vb99kFewKfo8A4sOnL9fFc0ENiRuwoHRoTdB4495dDMYqjcxP72HTesM7pvliyD6koR25Lp4LakjcgAWlQ3tn8VOqBK+kGJ3zS4J1u5/e086WarDzbarBHs6L54AaEjdgQenQnqD0fNoebx1D62hHYyPj7mpcOhw1JG7QuKBPflj2QMOLpEXnBCX4uC3Oqt0m6hOlO/sYn7en4NfoxrNyuK9UMXwmbsh2syUxcuS+Wmw4be3na3VIs8ukRRcFJfj489dWbdbx0evpgHv7dhE87nR5vcz06R1JFw3vQX29du0JdNLsMmnRUUEJsnd1sWn2A+ek95HWczfUMaE/rGWL4K6DXt1wjOrRbgb0zwBwXyyQ913orqAEuft729db8DenOsj0au/1aK77qwAHQSEvpVCNSb596zZl90iGwXYATuhhQZl4tOJHhoxnZSg8PsSt+pRLqr9q0b1NAUgzZHvgSg9M0LhiKKZBXkqhCnddvnKp2+BH6ok51DFubDHsW8tQ1ZfJKyiCSma3y2JzzMxd0J+NDAxMTiEWlvw1oarnsOMqdpkdUsU5yNNOuUOK6sAEzSiGaiL0pRSs+cl81YujNl9TTSoaYuEccpViwpNgZ1d/bu0Sf6AIOrprSG70CKoC6ro1Z9GjMKepG4sZ7s5vYNdtxwcVFvU2rHYzp19VmJEGhCY+/9SRI/tZZKzjEs+V5kRTXbcp1aSlbf57+rM/1fFp0fWr/G2xHEER1P9zFMhVfq6DRF235rJFuQBc04MXLMuLde2tWi5/yHphhSfTy71tlC2vkxMXKy5VIQjaua59R49J6LVziWe6a9Nl3zhQ5rJu9ZW1e6Cv0HMIogjq8yYKvA6gmK6uW3MS8l78j6as58ve29c5JOmMxrf+TGdPB4/a8rMKBEEdC6dcesiiiy+XeGa6Thg1xe93qkk1an8Gu/XpEgwKBRRBl4f5zA1aSzFdbbfmWpkM6GnUT5U5JX9NrumQuIO/y0YoTDTd82gF+a5HgCSoV17qasDiQV1O8fyjmrnDasoptdyO3x5oXhH2oOBk8gKqg2n13ZrL6WvnNF7l6xxPVre3bLpAg53uIskkPl7DZcMIgk5o+Th4eDv06qHxLDq+jeHWJN3jQgmjGlUb4SL0t6eiCJoig6qAck71oQNJqnjwuYasuNOlenfyklHOoaG+3oPT1fYo15fE+xSATPMFsmGU66BPwdl5DG9eUYZSUOm/JY9ZfAqv392Fei/JxCHfA9f6tGU9m4ZBETQpKWmwK+Ux0+HcT8lxC0suA0tS15LU11pO9Re21mY29oot5s7iFlatlmqiCfvDwivOwUK+L0IQlH6Dp0ZJ0PfkBbXb1b1t4xWvoZjW7+CmDAf2rz7d1zxsnCoXPjQK6oX6954U0/u0yusSn9puqNJo7XUPm2Z2A1w2nVfy/dO+Qd7eg9Ky1L3cH62t3A7KBxEEpd/gqfkintdr2ZiPloCwQT37Ryt6zEb7Nf3GNQD1srGOgSpoIdVe0SUPOGeDj8ruak/Q9hHER80vOyzf+f4ryyazL7C8H80SSUm/FcRbnZQbPA2l8dwTWc169csXzX54bRaxZbmD4t+kLvGr7+tdR69Ql0ARtCtBSG+K6fVugtoPwf0qSqO1J+h3lv+C++bTlEfnHB1T3aFLymNNrAKioJQbPA0l8TzgEOxhZOZsHtMmS/wcgBm28tEtbceubOBaifegRwgyqPobXPHpHOvay135MUrtCfrYytzPwpoyIcl/m7o7Vh2+X+2tPYKgtBs8DcXxHCsW6ffVM3z0ITT0uWnc+UO+ipsn346dPmyLC4uzLl0CKmhSMVQFcncvnLa6XGTULej5b+JX0VyD+rOJYzPanCOSKwtaWjac+ofaEl2RIAhKu8HToIjnTpHIWI/4/4B5brWA1zcRzeMVV4ofuw5fFjFMlZXVAaCCEueb/X1GJgdORq9TzYKedvkxrUV/FWfOOT4h3Krt4qtqOySFCcq4wVOjiGeAnr+Ri0gkMjKzjQEZLq3qhL1SFHg599u9qq2tAHh1n/HfAqWJr5UJQBaL12qpWdCElYuSj9pyuFeUuWdokEPCKvW8pAEmqOobvL1luNiIEDSmjl5bQsoVWwXSY5MTn6NMLN2Zzu9QBPXIIRpzb/SFqlnQOnbDZlZxvAcvyMR/W/r4uPZYz3+GJ4QmXsUNvo1ILCb8FHs2bOQB/vKsV6Uh4yPYukFX0yU/+TkwFEARdETDNesafYu+UDULGtJACvbr8fCu2QcbEt09e27g9wFGBEFV3ODTZHqKRU0Hd3EFPvVNbeoMUnEdBYT1AgDeiRmur6AIWrS+W48NLLoIqVnQerWqRrp68/SQxt/rehCSpvDXPRdBUPoNnrF/7SgzPbGDoUjUtKm45UujybkvGkDfBSB8LH4kNlY9hgcaoIImPAIJMtAXqmZBB864fvqmLY9vPvk7paeXa9dVN1V/bqQMCILSbvDM/Wvjw9ycXQ1E4tAGlh1eij8A8L0V99XVNi1tfz3X2Jwh8lBBf/sE6B9RoEbNgv7n02GI60amEgV7lp9nWemjn/oF2scu/ovzJSiYoEwbfLn+tWeOk7R0Jz6eg4nmtQxCDQxNu49KrrnfsP6+TU6esvEg+7ju/vcXTytr87kM5aJQ78Wz+DdS93XQ7O0/Mj7kll2r5Ug/Fj3Wi3mxa0SYZbOpxzgd3sIEZdrglfvXfm7bksTVlPjYCK7Z2RMNvNg23NHEp6V9cNv2/oGy8eDPljr835SzR48xTa+OIGh6P0mEpeq9bzTOwvadG41wUe0E/cPBSY0tao/YrvJ9GdR78VQjIf1rd3iYBaTZ2ras6jQFvEm09ZpVfAFReuGAUFP2cwblJKn+uSO93kSi16ltQTtYrz8z1DZN5fnz/1wU5+TZdcUlVS40IghKv8Erv7lPTmk8c8D10HfHrhxv8cX0nGbV2jtuZb+qOgGKoIHSUdsLXdHr1LagjesR/2hGHBMt/715YHXLqEkH2D5QhyAolw3+hTNxlLypGwBHBo4tvhQ8p8fpvacdNPuUi8ZAEbRPJ/fMcY3R69S2oIlOPWbV8TrIvaIPR6a1tq7Sc9UVFrtSBEE5bfCdOhxNcT0DxpqaG5kojmPbV6sV5xT4h/zLjQYG7uvQ6xY8KIJ+XH0LzGTx7Iq2Bd1a//spy505P0ssR3IrpV+IZZPxexCPShEE5bTB585v3e1PUKg/B+TU95ePqldHCu7qXZMNf3a0auxsycPGKRS4ZRYRZk516Qj7UJd9fNaYdXx2jLNb/LwT8BN8BEF52OBvkUkD0kzkX5pbz0ht5STvD5pu+D+QH8Li+EHocMosItic6m9uqOG13o92jIm0rNZ71QWKewT/HCruhIogKA+phD6J/wRgoCJF4vBxw+NXussfcF9CdmLuS5XEgA1vhfM6X06ZRSpaTvWX0N4XhdfWD6hpFjFk442yh6XS5vp6BoonsxAEpU8lRA1VPJsatwk3Wiwf/p+ZsZ3JV/Lh80Zrci/bdSpT8uaio+iLkvG2tb1jE+rXUPJLwZpB82Ah55RZpGLlVP+3vpNtDMpTjjlnl/cMtmg48qebCkvnG12Q/qx/WjaMICjtBk8DVTw/DfUMLH7SeG39UX3GustT70gaOhs5WJXpWtBVz07fn931sh6dB/ZPbM9qFpWQtIpZM8gPYiinzCJCyanOD80WSgsGIXcQ+nhqSfdA8wbDN14rBA3aECN8B8vGIwhKu8HTAItnwi/ER4Nz8i+f57YfXKYr4lm9CyDLhl23JyuPVWsCjNWfQOiPWiu+mZ64hLkQp8wi5S4sP31AEhfIaj0FQq4FsZ95pJw8lpmsjKU9gs0iPL3O5wKHZNkoBEFpN3gaYIL2XUccZVSl7t41lkyi0a0a+sIIyEvIp1mmblOFLS5fbxphN4C5EFzQR+dygOTVfaq3wsiILh3MDvYjsdTJbmAS65trN51QPlxB4NPZb8W2QU2M5NlgUW510m7w1MAE/cP5q1aNG1E/OLHajBgf0ZzF0gAwrXvmr+YG6u+vv9noPZC4d2UuBBX0e7GX40FnK8+m5Se/SSQxT0xUGq+jTXyMUVxLQxaPCpVhq7Oh3xn5IFRQ2AZfHlg8b9lU8fahOWbMt/ZNitKjzItLS+sudcN71GU1i0psreHRI6QG5GwRKqjbBXBKtIVy8yz8zmHFjh2OO3YojddRQX0H1WnQpw3r2Yp+Ti77dBBMUIYNng5YPIcQh3EFXjQPBrxq7VrrLPqySO75RLXyYLOLV5E7nudSzzSDdCKACmpH/NnSPXd3MuIAKH/UppuCfjYnPv5jk9ZZRlHz0OZhkaXdSGGCMmzwdMDi2YG8KdH4NJsqmfl8/Ogn/mqjZ7ZbQmAnSCiggpIPNLnQlnjfbUD5m8q6KShwvwnAnii2c+23jZra3Pbnku8wQRk3eGpg8VzQuQjcclR7Sgo18M/uy7Ai8D3o27dvnYi/tzRlUsu/rkJHBd3mnjzWiWVrSJxCu0uA1Kf0SB8mKPMGTwksnnltA1o47WJXp84AFdSkGPQ6dVRQcGXmfPYp7ge4FAGJZ+mr6qB7UMgGTwE8njd+Y598UUcQ6HuSdIeDdnVGRdiVvmAZJmgl2uB5AQvKEWlcUPNqbUsPKgX3KkTNcWrEqL94rxQLyhXpwYUHyjw2W3kF3eT7/SL3X/iuFQvKM5VXUN/rAJzm/Q2KWFCeqbSCSowLAciy5rtaLCjP8ClofvIEkhAW6cK1SF3iTHFTC3g5dmBBeYZPQQu/n09S25u/KtXIRdeYth63+K4VC8ozlbaJB+DDvgP83yHFgvJMJRb08Zp1z3mvFAvKM5VX0N+cBvZxOsd3rVhQnqm8gtb4HYC9DfmuFQvKMxVG0A/fj93JJmOq/DIT7ylLsaA8IzhBn/56Q5XZMn37Lm7UHV6ulKCzAByOUGVZTHATVJiZRbSK0ARdZuVn31GFl+7MHgZAfgCb9yumu0wY4/Q7+yUxw0lQwWYW0SICE/ShSYMliaar2M/Yd8PZnfe/ZpXD8s68Bfy+kIKEk6AVLbMIHwhM0AWWBQBE1mc/4xKX0M7OVvy/poctnAStWJlF+EFggs6yIU50Woazn3GZXaMBHhaPVF8yT3AStGJlFuEHgQl62zh++0TTeZTTXi2aTH/I2Dfl0Lpr8aqnqeYLbidJSplFcnp1IvFmkXeowiEwQcF3dv5uDSjf2XPfZejsoOl0880YA0BhULkTYI3DSdCc6YnkE6+9i79L0naRNGHxLvQKh9AEBVfWHaU+ie+/CIA3NnQPg770GLW+TQdeXh3FCU6CJkZvCE8FwEJpNG7i+UVN8YwiM4jXpE3Q8GpG/xQBvK6Wk6BOH8HbgBdY0LLojqAjJp1Iu2D7WS118we3s/gXAGyNlWJBy6A7gv5tbOOrB8ktp304CbrWlziMSWxhpDQaC6oqGr0zN27ortW/2ueppW7+4HYWfymNODPaM0ppLBZURTR7Zy7qJPERdk0tdfMH7izCM1wE1eyduV4rAfhoC03Lr2WwoDzDRVDN3pm76TRtfT3K19YLCSwoz3ARVMN35u5P6M+qx6dWwILyDKeTJNjLZCshWFCe4SKo8p05BTiedGBB2cNFUOU7c9nV5S+lYPHi2QoHFpRnuAha7s7cY9lrfWYpv6WiMoEF5RlOZ/HUd+bWCf52jxrBgvIMF0Fp7sxhQenAgrKH01k89Z05LCgdWFD2cO0sQvEUEBaUDiwoe7gKSvH4EBaUDiwoe7Cg/IIF5Rmugq6jGIUFpQELyh41dFjGgtKBBWUPFpRfcG4mnsGC8gvOzcQzWFB+4Tc30xPZveO4QB5WTFfBgvILr7mZsoNlvW9sfPlYMx0FC8ov6sjNhAPKLziedLDNzaQAB5RfcDzpUPEyEw4ov+B40qGqoL3eKfjnIRL3dL/Yg+Kf3FwNgrKJ5//u8/KbUH42P9U8ePqOCcZ4qijor/a2cmxEYiQqQDGx4ifbOvKfsZB1PHn4TShL4qkafVsmGOOpoqAlfDZDKvZJuQs5NR8tkYp9QHvh7jtbpGJv7ZGKvXZEKsYRlHgO+RFa5J8AeDWx+6FFMprCqwm/CC2yjdWrRL4EC4oFpQcLqgQWFAuqBBYUC0oPFlQJLCgWVAksKBaUngogqGQWn8WKZvNZrHAOUrGCuXwW4whKoNLhl7k+LYFXs+UhtMhzis7/yqx8Cy1ydye8Gjq4CorBqBUsKEbQYEExggYLihE0WFCMoMGCYgQNFhQjaLCgGEEjJEELyz1Zom4+8FpMMGgqkAhx4Rw6joLureq3HFZmXVXnxByUkt8lIlR4pZ5rP4Riiz3dR0lhxbJCQXFVTCVlxZB/BSeg9SOtBjyQCFGERxAhfNxDx03QTI/HWVVvMZe55PMiK3o6QskMh0R4hbk+N/JaHYYWu+b6Njt8D6TYnCCf4p/AVFJWDPlXcAJaP9JqwAOJEEV4BBHCx0PouAm6hdhUp09jLpO2CIANifCS7yNWJMIr3N0ZgLxcaLEbXp8LIvdCil1K8yn+CUwlZcVQfwU3oPWjrAZCIBGiCI8gQvh4CB03QedMBmBjf2ixzIi98JLd0nckwitc2KG2c7dseG3jTK3joLU98yn+CYwlyWKov4IbKPVDVwMhkChRhEcQIXzcQ8dN0FnkUnvDSqUGpMJLpg4ARFyhxaa5PvgUPxVa7EzQhbuNUmHFyPDJyzCWlEcZ6VdwBKF+6GqgBBIhiggRRAgf99BxE3QTscSZU5jLSHrGvkAoGefq42geDS22miiQGgctljQJgJ8SYMXI8MnLMJYkiyH+Co5A60dYDZRAIkQRIYII4eMeOm6CvnZ/nRtymblMWgspYkliw4cWe+r+4FP7hdBi22q+/hg7E1aMDJ+8DGNJshj6r+ACtH601YAGEiGKCBFECB/30HG8zLQrLOR7SJHRRhYWFr1RShJxhRfbGeAxJB9aTDrF021ALqyYrAGSl6+NMdgAACAASURBVGEqSRZD/xWcgNWPthrwQMKjiBBBhPBxD52QLtRjMOXAgmIEDRYUI2iwoBhBgwXFCBosKEbQYEExggYLihE0WFCMoMGCYgQNFhQjaLCgGEGDBcUIGiwoRtBgQTGCBguKETRYUIygwYJiBA0WFCNosKAYQYMFxQgaLChG0AhT0Acdeq3R9jpgBIEwBR1/BTTW9jpgBIEwBc2RPo3X9jpgBIHmBfW4BSRzqjs2OkgMm9g52LkvI9bC3sHBYUCZQrs6vIBUkxGHMqqED2U+Cd49Ql9hjDbRiqB92j2RnPXbQQj6FoAL4kdAlP1lmV/HSGHVoAgqTxJMIsv0K/v8t37V5QAMvafi2mM0jDYEveaWQ/z3rFeRTFDgda6coP3aJSR+MaJA8Vf6DUFQeZJgckiW6Vf+OehIUe2s/w3l/jt0BaQWq5jfEsjPlKSy45gaJvWjDUF/GCQb8HogE/QPx5wygo6pch7sDipxcYVvwBjJme5dp5B/YLaf73jZNyALm2xim70AhJ+SDSpHUp4kmBySZfqVf3639FG1nF7/aeCHCgSUFquET4/Iz8ou6Hj5K6cbngAmTi62okOKLVr+Buk9bXK8DxeXzQh7nd119hnre4D8O1I1Ky9yCzlEToqTT9zUEzzwOykblEeyrbeMG0CRJFiGPJEq+Zk1qOWxixM1+Hu1DazFKig/S2UXdPlg2YDPv2TAJFtcJGUDJq3SO6bkS7JPVFRIuzNNASD/vpsMwKpvyCFAhk0+8YNzwdwZ8kHlSMqTBMsGSwUlSXif3mZcnnp+nuBgbLFkzZGs+fnYN7BBOshIKBrpWat/0pGuACSlgAneTl3yM+Lk07SDNgS95J5L/PcvN3mTI7F69cUWvcDon9LheQDkZp1pT0SS+BtPNO1repJDgBRUPhF0OFrzoXxQLmhrdxnXi5MEy0p/IejRhW9rP05eopFfq30YWyyyOZK3RKsTpOf6EYKub5CT6aMQ9GnL3MK6pzLi5NO0g1bO4hM7PJee90+TCwo8738haFf70n3b1aDXedGHigU9HPwpv/GmEkHlE8H2qGaKcsp7UHmSYOm9wi8FlcbmPm4h3TVJzb9TKDC2WGRzJG9+brmPPiolBO22BoCRxXvQJ9uS7Y9kxMmnaQetCFo0o5pjTbLtlQnadCUQObm4uNSXTT7jGpNaWniNv/sIUCwomBXgN1ZSIqh8Isg226wopyyoPElwnujfLwXdkQLAtODGL9X+S4UBY4tFBlPREmX+3CuCELTHOgCmygQdnHI2ePXFjoSg8mnaQWh3kiR1Np2sU6jttahIMLZYpKDy5mfm+MI3lu8zEjY0ysuqmpQRUvjBNWXecPDQY39GnHyadlZfaIJuCJeCRI+H2l6NCgRjiyVrjmTNz7NIl8gU2UmSd+1JSXm9fD1iUp5EV4+Z6JcRJ5+mHYQmKAbzBVhQjKDBgmIEDVTQW9P6955yRROrgsGUBybo8qAZGzfODFmmkZXBYJSBCer3kfzMqa6BVakc4BaJHTBBAx+Tn6/CNLAqlQLcIrEEJmiaR+Lkyb3cdiuNvjuhMgPr7c8ATYt0V9s/SaswxRN6kpS5Zc6szW+Ux66rO7/yEniEnZRloWmRcDzpUPEsfh1Vd+zKQhsOgtK0SDiedKh4Fl8c0JeNnHzaL/jMYe10EC6C0rVIWFAa2J7FFyyV7ZW/kj+2/kosIjAJ3LToNOfV1B24CJozPXEf8Z/exd+zLUQyXLivls7CSVDlY6b8qbLD2hAv2bdwcV0DN5Gd2L+FdxJNBRUQLoImRm8ITwXAQml0zyBOa6TbcBKU5phJEVBrfQNi6xeL3Id5WvzLZR11Ci6COn0EbwNeYEHLwklQmmMmRUCdRbYiE0JQI/0ahluJHe2aZZXheXMugga+AGBrrBQLWgZuglKjCGgj+QGUyGqEkygF3LStEmizXbUKdQkugq71XUS08y2MlEZjQengJmh3Zz2RiZ6eSGxgIvoR1LVd/XM1c9Uq1CU4ncVfSgNAsmeU0lgsKB0QQT+E+shRGq8I6GVTKzGByMTKUTwPmG4E4LW44j/rw0lQarCgdMD2oFlBac9IlEYXB3SbnVhkJhKbmYvFqcB8/rnfL+u95bCqugEWlF+4NfFzLlGNLQ3oh6V9Ag3MiSY+Pq+VnqmF2KG00IsKmhsBC8ovajwGldPCYWLSULPIsHA9awc9m+KxF4OczUdJVKte2GBBWVLEvKdSu6Dfdq9uHxtwbaO447kT98WKS1IFbv7mFt6rVKte2GBBWVE4wtysBdNVcrULesNl3al+TSXApErTh6/Fr+QjLxtvAa+CKmQ/UiwoK2ZHv82e05ChgNoFBRe7Npn0AYCmZnb6FraKcQeNpACM8lCtemGDBWVF3QRzs+Z25e71lKJ+QRXMFBkZiaz/ln+5Zzry7hHHpqpVL2ywoKzwbpAlmWPwgb6AxgS1H3Es/YDYYa4s46S0Tu2AUFdtJe1TK1hQVvhWu/50kNFr+gIaE9TwN+JDnNE29E/y2/NE91pbVatd4GBBWdFkWFXPATZC2IN6xAKw0kAKdrgPzFStWt2g0gkqmRfgOYjBMGbWhF95PCCWoYDGBP3NwN5dfxox8GGE8zqJdPuQyY/pa5BsjPl6j2oL1zaVTtAljW4/6Zug6tzSZdW8Br9jKKAxQcHjUYPOyYeuNor4OqhllMP/aGuY5GRta6+b7zusdIKGXwCgwEptD/ZoTtAySFeJbacMs+pGW8A44eW/jZxUW7qWqXSCho70dx9gnaWu6rUiKDhqMMSyZw9Pusk54vcA/KWv2tK1TKUTtKX9hUe11Lcz4SYo9WPH8IBm6Du0tdezp0uWXKj/IwCjdLPzaKUTtE5Hc/O2lsJs4mkeO4YH9LnYtks9Y+uqu2my77e1qR5gOxBWiyCpdIKGXS/Kk9gIs4mnSdUCD+gFdyM9Q9vmx8LDqRef2dXUYlAOrBZBUukETY59VzC5pdqq5/WxYwXwgP5nMH7tatfmQLoruPEpAJ71bTzh05clirT1ZhOuVDpB84dZmLdX39sj1fjYMQO3bRulTLZuTQwV/VSl2V4zq5omXhRv3dNFBCfo4Zmb1dw3XJKvxsp5fexYun8XSRM/2Hx3qqX2m/hjR9lw4WZLo8PgpdEm+MrqAkITtF/t5Ngan+DlylA4P6z6NKE88MBJUOmevSA1YWZu8fecHp1IvN1hS5XUWJx7J3SH4lvNOsH10/1185yoHAIT9Jr/vhmbui9lNc9Uvyr+ASNVXyavcBL02/DI+MifYpRzWyEE9EFbU6/lxV96uObsCRUPqBhtPCdBVb1sR89O39pTO7j3YTWPbf0rN1qaCOQkgJOgbtkF1vdAlvIOk21AX5pb1TJxbu65GO1ahXS+u0X8U3aL0BxcBFX5sh09vxi+B8A3nnJa4W+/UJ7c6P8FwBOxQN7ox0lQj8xs04vgtZfSaNYBfdY3MikbXO5mP+YRQul13r4uoXVYLkJjcBFU5ct29OwMqD6+jVdfqknvazbo6EiV68X0m4Ki0QZFqi+UTzgJutjVa1zQpJpTlEarHNAn4x06ZkBL1fC7+nyssVB3oZxyM6l62Y6em75HFu7sspxq0thvibNVe4qToR6+ltZ+7VRfJq9wO4u/dgOcTtqmPJZDQD+tCgpbD7lt5jCHaOb1L6q8DPWihgzLnM7ih1Uf17oO5T2PqPUDO60OvVZ+wvs4c8voVxyWySfa6SzCgPRorP2oe+/Xbc6lKxEU/G/uTGOhJnTkM8Py58g6JPauXFbo5KJd1Kef7Sy/39XUiLIvZvZHLkvkFcEJSvBooo3YxNT4HM3kZT6OpiEh3BahPni9zHT9Ekl0FR6rLKW9ZfLaOiYMD1QKAiEKCoBF6xZOvnRduCRJVubN71NPO+xt6Psb16VzQmDXQRlovi1p4O7Q62qpmz8EKegnUT64P1DUcD3NdScpXfN/xzByfj3DBxwXzwkOgkKyBfLN5L5ScMFZ6FefBSko0COa93WmB+JtEo+zSuD0jTfx4T6I6+K5wGUPCskWyDPZUUHNnQ+qpWoeEaag0abD+xsOAeDN8joeE26iz9euLvFRi/qytIbg1MTDsgXyi/Tq7yo/jakxhCkoGOXpM0c+dDvJM3QBwwOgX7DW8KT0mOFPnBfPAd05BtUNBCpoWSQZgxwiVyLlZpY2MdYzbsXr0tmCBeUXHRCUoODXnrbNV6NcOz699k+WdT+bl3RShVWiQxuCSv/cp74ew1pGNwQlyN3XwzbqB+XzB+7cduzQxW8ef/VpQdDPTcI6OG3mfbHCQGcEJcjd38u+/oK/+a003qlldwdT2ttWrNGCoLN6ScF9h/e8L1cQ6JKgBAXHhrhXm3iOx+ThTv0BuKV/h7f6tCBoh9Uju//Y+A/elysIhCroO9q7wdILk2o4901j9xQDPY6DAfif/l2eatOKoN0t5m5rbfqI9+UKAmEK+jTYyKA+wzW6h8tbWbVeRp/biQWx9jH9HU35e8QZJmhcMehVwuLZwzL5pyjzh+gV6hIogkpmt8s6RFmA/0cU5FR13bXZuiljkU9pAzz8h/2azXVR1xy/7ho4nWstpcAEzSgGvUpoE792fK+NkZW4iR/dNSQ3egTFdDU8oiCjQEzsDg4YQsvdWNDcssX8y9yOSB8mj6Le+tjw39nibmsITXz+qSNH9kOffC0FFs953STgrqPwbwqpBIqg/p+jQC7Vk5pqeERBRh75uprTBihFsw+MCHLskvKI4xI5Mtra12q1fBBB0M517Tt6TEKvnTKeeweMLu6IlNuyalunn9Hr0ylQBPV5EwVeB1BMV8MjCnJcw67+5VUDtfSTjd1d/Af8/ILrUlVmr0WjUYFm8kNiBEEdC6dcesiUVVgJqnjOCFs717mkVb98iCHLu26DIujyMJ+5QWsppqvjEQUZV2zNTN1Qb8CTSG8uj7MLHrpTOy+r/SqY2OtbT5UNIwjqlZe6Gig3OwxQxLPQalP/kYu+Qq9DZ0E6iz+ZvOAqZQGlRxTAbXkP8EDOq5V34QrrpwolV5bG2vp/tVjzj9M1iSQ+XOVv0UYQdELLx8HDWTyURiHoc8ua6xfYK3ccrYigCJoig6rA4dxPyXELS1LzZNeXP0PjzOsqsmGYtaORmU/i6psafQ/oAtO5x4ab/C4bRrkO+hScncdiM6IQtEDvIABdoBlcKgAogiYlJQ12pTpm6tMqr0t8aruhSqO11/vmiHnrWc3NtqZ8U8UmevZJzpegUMmp5hvgEStPxYEgKP0GTw3VHtTeKTbSl3tLJXxQL9S/p8rX7ZIHnLPBR+VJ2hO0my9xOOrxDTH0cu/Yhua1h23l+c49DXkbk9MUqWIQBKXd4Gn4Mp75N8mdr+fBfb8lf8NmHXUUVEELqa7b1bsJaj8E95UfOtSeoB3JJfv3UHzL//P7Ll4O0TMOv9XgKiDe6qTc4Gn4Ip4ZntWdO+WD407tGgVr77qF5kARtCtBSG+K6Vd8Ose69nLfqTRae4LuMB28bYDp/rKjnu+b1MI6oNuSP/i6eQ8BUVDKDZ6GsvEscP8d5HecA8DbAyeE/rwbL6AIeoQggzJJae7uhdNWlzvcV7+gdMkrJW3dqrp1KJeXTXJ3y4gGFtV6LvtD/QkJEASl3eBpKBvPG6G/TV68W30ZtwUHVNCkYtDrVLOgkomWZnTPxUsPLztOM1vhtZQh9cyDui/6Ta2vYkQQlH6Dp6ZsPP+zCJ492Lq9Ciumo0AFJc43+/uMTA6cjF6nmgVd0fRV4eKaqs1beH3zt02svGKn7nmgpvyXMEE5bfBvv59gEJexhRD0+dem9kkCSUCnVlCa+FrELierNnqdaha05czW9SZ7ccjNJL2/e0qMl1WjIWvO8v/2FJigXDb4f916dDaOatJxWRRo8e3uHc3mq7yWugOKoB45xNGmN3qdaha0jsv+v7qacH5K7N3J5f0jzH1jJ26/wedLABCaeJU3+EHVXUL0DHwjYkd+sHCOifSmbkb+U+c7DTQOiqAjGq5Z1+hb9DrVLGhtn2svxxjyk/RK8s+emZ2CTat1mrH7Dj8nxQiCqrzBB0TmA3eRr5fh5iy9MwAMdFCMv9I7el5xl+uTPs4WySzWV+igCFq0vluPDSyOd9QsaNORgc49/f7hscb869snxQeahnSauv0q18fnEASl3+CZO4Dn2/tvfGApbvyVdZvnxqPfPwhX3FK+6rzqYEJ7+TF1lr2zu7nrDuUqdBeooAmPQIIM9DrVLOjMjrngF3/+z3Hyru+Y2jnU1C967LpTqqdvRRCUdoNn7gCe7ywWifRElk9BP49Cy3hr906K3kyDlhd9LPKRb7HpxifAa5+mKq++4IAK+tsnwPcjChzJ727rH0iZxKiYzAUjtqnaW6Ton/SF/RrZ29TrNXvXNRVekQoTlGmDV+4AnjdlAkmINfFxHAwX64kIzKXnnW0nNPZbO8++h2w8aBlkZOTkfxrcI762NgPgXjV72Xjyu87/l+ZlmXLK3otn8W+k9gv1b+8z6vfKa/CKxvQvo0fizZkNE76ubuLZfNDifXfYvNQKJijTBq/cAbxw2XyS2vbEx1/A3cDTzFMkFunp2TWaP3/hgNHH5svGgw6mk+a31XsDHhNfh5hMubfOpIZsPPl9/skuAfXHzC/9rmv/rY8gaHo/SYQlt943GmV6XKRfF28WSfFokTw6tmpUu0AT7+YDF+65hnSzFPVePNVI5g7gbkaurT2INj6yrtJb+RLquTd0rnJeNlwQEuZXw/5CybRCB5d+tfRvI62TIEE5Sap/7kivN5HodWpb0Ha2Bx/MsfyFvwqLHh77cUyH6qbODRKnbv7jGePhL4Kg9Bu8cgdwOYp4DiXkNBSJxVP3bP8ywWSvzffPfqp+Q/7led+glmVyTG8wKQQgTLv51DiBImigdNT2QhaJ/LUtaNOaxFGAofJ+iAeen/5pamIjV+OqbYcu2n2Juo8UgqAqbvAvjYkjULGB6BI43PaL6Ud9Tz6ZUof6MstoD+IjsRr6wmRINdrfmxEUQft0cs8c1xi9Tm0L2i+gXh+PwKNqqz/39q8/jPm6lq1ljZgRS3658OUZP4KgKm7wGX4GRvp6RgYz/2zyI7Gx7DhQckUs1d+2PU1atRP6d0G+A7tXIWb3t7BIFEqqJxRBP66+BWayyCunbUH31dixab2b+p8Tf39t/7JR8XUcTIPbDCh5phBBUBU3+GRXJ5sot9oifT2/z+CApYerm6If2ftqbQZ7LKaZuZ2eu5ELu3tLw7q/+zAgkdUs6kOYmUU4Ms/aIfCU5hb3+fahtRuLvyAIquIG38v7la2+SNT3U37iNKl52C/rrJvLp08eQbT/DnS31jLGbUFflAy3rhbmnawE0soLMrMIZ4o02Yee5NXF4m4nCILSb/DUKOI5xmreb/HG5Mt5fm/xr5hoIFaZy6fHpBMfTVlcqIZgEZeV3cdIh952rPHMItqiQNX+awkG+oaKN5YiCEq7wdOgiOfuuv2bDAuwzSNOzbs/Fb8AYKGFor5kYq/sxF9uX4vWT553MtKhPajmM4tohVdxZuYDVLoVv9LwwIfvFW8PRRCUdoOnQRFPSUztocF9urVL/9HlHLD3XDbVTJEh75n7gPk1R7Nea1o8B7g6D7DRzB5UusjLsTdTD3JhZhbRCrHj8rM6jVVlzkjyEQzvYbJhBEFpN3gaiuMp/W3VWZC/tH1vYlM4a+/qEFb8D/u8Z8uV7NaYkfHt/33WZSCPFTKwyMI/3LwBQwF+M4vI0U1B8y2Jc91/WHhTSksyws7yPvIIgtJu8DRQxjP7zNXifdy7qtEjvBag1wcjb7y760gNJRVw7SwFh/UYrrjABX10LgdIXt2n3USjSwezrUQyHFmvpwAotHx38doNtpe0ZRzUH30i3kCeSwrlViftBk8NbIOfOAqA1w66mT7MbCvxoXeDvgBU0O/FXo4Hna08m5af/CaRxDxR+ZKZbu5BQYxFqL/lBJVmXWKh55QuH4QKCtvgywOLJ99n8ZoksFZm4RR9hiN/qKBuF8Ap0RbKU7rC7xxW7NjhuEO5e6yOClqrlYdflAqdoB4feUTEovgbTFCGDZ4OWDzHTgYgy0k335R0yMLE2LY/QwGooHbEny3dJYeTEQdA+RRruilojgXxK5+wSPihYIpTPafxpV9hgjJs8HTA4vncs8/M6uOZywiWMz3jNzBFAyoo+dyLC22J990GlL+prJuCSu1eAHA6gu1sfzo4NHJwPFHyHSYo4wZPDTSe71ck02UD0Hnge9C3b986EX9092ZSy7+uQjcFBdPrpqX6b2M71yjrNyDTbkDJd5igzBs8JToaT36ACmpSDHqdOhpQ6eb4zuxfp9CLTLIUXPoAB3QPCtngKdDRePKDMN+TpENsshy+d5TlqpLvMEErzwbPD1hQjnz0bdQw0vNdyXf8Om5+wYJy5cXoNt+WucSDBeUXLCjPYEH5BQvKM1hQfsGC8gwWlF+woDzDSVCBPkKjTbCgPMNFUME+QqNFsKA8w0XQivYIDZSix9CsQlhQnuEiaMV6hAbOXlcva1iWaCwoz3ARtGI9QgPlmcsV8DwYkmADC8oznE6SKtIjNHB2dDm2YOcCyFNgWFCeEdplpg9Tvhr4P95WhVcOudf6rpUrJF85FpRn+BRUun8XSRN/1asoCK8VXteBwytRWPAh/SCr1/n9YTTj1haTGcyFuAmKr9uVg4OgH0J95BSPyOneicSbw2u3T5oPOrzQfJTqFaBzya1da082mUi3fN07JGYcJIMFJ0HxdbvycNmDZgWlPSNRGs0lnrPIHvxtWD8noAp1fwFgYwsWM1wIygeg84/MhTgJWumu2yHAqYmfQ5l6n0s815g8AJ/9VUlg+2R89+/Z5D6XmBQQjYA1m0X0rJMcXTuHuQwnQSvbdTsUBHaS9MTaLtLZ5Wf2Mz51nbLt69ZsHp7yvQHAmVBWCzk0axvs7VScBK1k1+2Q4CooxVtyOcVzs30NhyEq5Fma+h2xUwy5yGKOTb7Ll3jwmHhdDreTJKXrdtlBfiSWznysmY7CVdDw8qO4bfDvz5R7X3oxN0/Tv5683wbiI24vmyVlDBv5F5vySHASNGd64j7iP71LRjx7QBJflYcV01UEJygtn1tUiXShXdt1X5355azzE7UsmQ2cBE2M3hCeCoCF0mjcxHNgXflRaornlG8k4JwL3TFgrrORq15HtSyYFZwEdfoI3ga8wIKWRWAnSQw0J7NN1KDL2/VjzJUD11w0c4WfCW5n8S8A2BorxYKWQXcE7U4cZebSJsXrSx6DxqexqXBnpy7p3FdLCU6CrvVdRLTzLYyURmNB+UVN8Tznsmp/m150U6ePAaAo6DKL+pbW2LG16noeVuwLuJ3FXyK2MMke5RtpWFB+UVc800N9+9K+oOaF+5iNbWPYXJ/y+AeAy8E8rNcX4M4iPKM7gt5ymryqzhjayS+n9V0Hu4peFokpIft7W+7r9SVYUJ7RHUF7rwAgy5a/N8o1JZr3JTG8VacAC8ozuiNo1EniI+wab/XdDYioVY33034sKM/ojqBjiXOHu/YqvXeHmvzzFwvhpViCBeUZ3RH0XWijrk4qdCPRLFhQntEdQUHhsR38vZ5OXWBBeUaHBNUJsKA8gwXlFywoz2BB+QULyjNYUH7BgvIMFpRfsKA8gwXlFywoz2BB+QULyjM4gS2/YEF5Biew5RcsKM/gBLb8ggXlGZzAll9w8jCewQls+QUnD+MZnMCWX3hNHvY5uiWJqxsfa6ajcBG0XCIMOVhQOtgmD5OeOk4yrPwr5CsPXAQtlwgj6x1JF5yphQYVk4etG8BxpXQZLoIqJ8LI9rAlMfbkY8V0FF6ThynAgqoITSIMHE86VLzMhAOqIjSJMHA86VBV0LAJcr7zr4+EL1KpekIuFqz4yRN8uZzFUyfCYBXPamHQInURqgmsBS1Spwq8moBwaJFajScwwRhPFQV9PF/BLP22KLRCLGaAVKwlYjFDpGItEIsZF//mBe9UC1oxFAlsWcXTKwRapIkZvBqn2tAide3g1Vg3hBYJqzKfCcZ4qihoCZ/NkIp9Uj7oouajJVIxxDTp79ByYLy1Ryr22hGpGAIU+UFLQInnEMhLCQj+CYBXE7sfWiSjKbyacHiK5m3d4dXQgQXFgtKDBVWicghKkcC2BCyoElhQzQvKBBZUCSwoFpSeCiBong1SsRw0Vz7bIRXLdkAq9tEJqdgHF6Ri71yRinEEJZ4j4ClkHyHc2//6ELTIGYS3ytW/Ci2yizaHLhyugoJMXIxXEJbyiTYlLatq3hdBi0gRLqghLKkwC16GDs6CYjDqBAuKETRYUIygwYJiBA0WFCNosKAYQYMFxQgaIQlaWK7jvrr5wGsxwaCpQCLEhXPoOAq6t6rfcliZdVWdE3NQSn6XiFDhlXqu/RCKLfZ0HyWFFcsKBcVVMZWUFUP+FZyA1o+0GvBAIkQRHkGE8HEPHTdBMz0eZ1W9xVzmks+LrOjpCCUzHBLhFeb63MhrdRha7Jrr2+zwPZBic4J8in8CU0lZMeRfwQlo/UirAQ8kQhThEUQIHw+h4yboFmJTnT6NuUzaIgA2JMJLvo9YkQivcHdnAPJyocVueH0uiNwLKXYpzaf4JzCVlBVD/RXcgNaPshoIgUSIIjyCCOHjIXTcBJ0zGYCN/aHFMiP2wkt2S9+RCK9wYYfazt2y4bWNM7WOg9b2zKf4JzCWJIuh/gpuoNQPXQ2EQKJEER5BhPBxDx03QWeRS+0NK5UakAovmToAEHGFFpvm+uBT/FRosTNBF+42SoUVI8MnL8NYUh5lpF/BEYT6oauBEkiEKCJEECF83EPHTdBNxBJnTmEuI+kZ+wKhZJyrj6N5NLTYaqJAahy0WNIkAH5KgBUjwycvw1iSLIb4KzgCrR9hNVACiRBFBE3PRQAAIABJREFUhAgihI976LgJ+tr9dW7IZeYyaS2kiCWJDR9a7Kn7g0/tF0KLbav5+mPsTFgxMnzyMowlyWLov4IL0PrRVgMaSIQoIkQQIXzcQ8fxMtOusJDvIUVGG1lYWPRGKUnEFV5sZ4DHkHxoMekUT7cBubBisgZIXoapJFkM/VdwAlY/2mrAAwmPIkIEEcLHPXRCulCPwZQDC4oRNFhQjKDBgmIEDRYUI2iwoBhBgwXFCBosKEbQYEExggYLihE0WFCMoMGCYgQNFhQjaLCgGEGDBcUIGiwoRtBgQTGCBguKETRYUIygwYJiBA0WFCNosKAYQYMFxQgaoQr6oEOvNdpeB4wAEKqg46+AxtpeB4wA0LCgRSJ7e+deH4nl2js4OCwBkjnVHRsdJCYY2DtYNf6ntGCO9Gk8pK6MOJRRdLx7hFwUo0U0Lmge+Dj4K2K52bLvfdo9kZz120EImg0+JMaUKbmrwwtIXSiCytMEE3zn4ToWgDlePkvBv/WrLgdg6D0OPwOjMbQgKMh3vq0Q9JpbDvF51quIFBQc8Sst+OsY6ZdzFij+Sr8hCCpPE0wMHA3PeV/1SHr15/+FnRl0pKh21v+G8vODBA5yi1XCbwnkZ0pS2XEsGibe0YagIH67QtAfBsnGej0gBf3Uqw35ZUyV82B3UJ92CYmyaSt8A8ZIznTvOoX8A7P9fMfLvgFZ2GQT2+wFIPyUbFA5kvI0wcTA2cMAdEybQMw2d/R3Sx9Vy+n1n8Z+szZBb7GK+fSI/Kzkgg5YJtui/cD4WbKxDU8AAycX8zp3Zd/2tMnxPlw8Q0bY6+yus89Y3wPk35GqWXmRW8ghclKcfOKmnuCB30nZoDySbb1l3ACKNMHyWqPb522NePe2XtesQS2PXZyoyV+tPZBarILy81VyQUv2oMsHy8b6/CsLmAJpld6lm3ayT1RUSLszTQEg/76bDMCqb8ghQIZNPvGDc8HcGfJB5UjK0wTLBp/vDjssGekS3LMf+S3hfXqbcXlq+pECAt5iyZojWfPzsW9gg3SQkVA00rNW/6QjXQFISgETvJ265GfEyadpA20IWuB6SxGwS+65xOdfbqCsoGCBUenB0YJ5AORmnWlPRJL4G0+00Wt6kkOAFFQ+EXQ4WvOhfFAuaGt3GdeL0wQTo9L+BGDJ4KJCAKbOJL4eXfi29uPkJRr72VoD3mKRzZG8JVqdID3XjxB0fYOcTB+FoE9b5hbWPZURJ5+mDbQg6OcRbUqOiRI7PJee90/7UtCu9qX7tqtBr/OiDxULejj4U37jTSWCyieC7VHNFOWU96DyNMHSe4U/NMvKarH8TrXsV34PiH10bO7jFtJdk9T+c7UOvMUimyN583PLffRRKSFotzUAjCzegz7Zlmx/JCNOPk0baFxQZ2eXHlklghbNqOZYk2yDywh6xjUmtXSONf7uI0CxoGBWgN9YSYmg8okg22yzopyyoPI0wXmifyXfensMKwQz3ML2EaN3pAAwLbjxS/X+ViEAb7HIYCpaosyfe0UQgvZYR7QzMkEHp5wNXn2xIyGofJo2EN6dJEmdTSfrFGp7LSoI8BaLFFTe/MwcX/jG8n1GwoZGeVlVkzJCCj+4pswbDh567M+Ik0/Txi8QnqAbwqUg0eOhtlejYgBvsWTNkaz5eRbpEpkiO0nyrj0pKa+Xr0dMypPo6jET/TLi5NO0gfAExWDKgAXFCBosKEbQQAW9Na1/7ylXNLEqGEx5YIIuD5qxcePMkGUaWRkMRhmYoH4fyc+c6hpYFQymPDBBAx+Tn6/CNLAqGEx5YIKmeSROntzLbbdGVgaDUQZ6kpS5Zc6szW+Ux+4SVWLE53j/Z8DxpEPFs/h1Azj9e+g2bY7wXiWOJx0qnsUXB7RwYlSXWQc5rJwuggXlF06CKp/FS08dJxkmf+Qy35jcRZu3ObWzMt07x4LyCydBlc/iP0e3JHF1k1ctamjgKXIXuzd0/IHzeuoMWFB+4SQozVl8zyDZf2z1DURi4n/2HR0tYE8JVxywoPzCSVCas3iFoPYia0JPkcjEJNxwOwBvU1ZRPcla0cCC8gs3QalRCBpO6KlHCGrZ3168Bty28/e3rgRXTLGg/KJGQRMcRbImXmxsLloJ6tmt2x5ioVqFugQWlF+4CPoh1EeO0niFoGdMHMV6YrHIxNRRPBeYbgTgtbjiP+mDBeUXTnvQrKC0ZyRKoxWCgh/MxSJTkdjaVizaCMxnb994SvyWw6rqBlhQfuHWxM+5RDW2WFBQ8M93HbwNTA1MRT5/NRObWYjtSsq8O3Gd1XrqDFhQflHjMaicFvajv/3GYoKro4Gts6Fp8di9TrW8W+WoVr2wwYKypOAT42S1Czqua7hbZ59bmfr2i0+9FD+Xj8yxtm/i5zZVteqFDRaUFfkDzSyaPGAooHZBHzjP3BEbKwXmw4I7nBK/k488Y3AFFEVVUa16YVP5BH0we+KfKs88vcOH3MV1GQqoXVDwYFSXFfkAxOvri0XW+fJxhwzzABjqoVr1wqbSCfqX0/jZPqtVnTuis7lZC/vX9AXUL6iCeD0TC33ToOOyL89M2+xZbNlBteqFTaUT9KvtADy0UzU3k2+990WzDLLoC2hMUPO1j+69FO/w7/iI/NYppEFD+wr5OGilE7TKmSVzrji9UnFu36rnH/QxLne3vBSNCWq8D4BC8X+5sx2Ss4lj42UdBlTM60yVTtAWlsMnOtuoOnfj0WF+I2w+0BfQmKDh3k8+tLEiBp5299xCNAjZRUxVFN27r52EfpypdIJG2cb3tbdU9V9rdcT1p4Ooso0XozFBX3no61tmyAb/qh+xsY6FxST6H/V3kLNDuG7eFq10gla5uj3licpNvHRpVc+B7xgKaExQIL1zuTjfuTTVwNzEwmMdbQ01LPv3NP9KtaVrmUonaAfin/GGs9raO80JWparRl2dvgmoSTc5X+8ksfM3Um3pWqbSCXrHJXGY8y61Vc9NUOqnOuGC/mrYzcXR0JLu2DiPvOP0uz6sFkFS6QQFb1NW/q2+2tXxVCdc0Dt6DS+tMrBxWvCZerpN2JF0bz/qaQKnAgiaO8rJfjDzHXLNwetTnQrggt4z9jCzs4q818VtOeXrXnZY+/panYLVIkgqgKAjqzg5BPbU7DJp4fWpTgVwQW/YONu6mDUH4Fqs56o8IM3YrHRR9Pqs+Uw9CASM0AT9p6N342PsZrFs/eR5gpGEw0J5RI1PdTLwVH8teOndhBy82M5zRZix2EgoWyxXBCZodsDSf9Ndr7KaR/888S8kZrxOrTnU+FQnAxeq1zO36tFG8SVQPObjdn3dbNHLwUlQ/lMJHWvUPbBV/yTKaZ+3r7pFNd48NjOrZ4XYg0r37AWpCTNzi79nW8oTPjnClprtcPdzUb/i/qARTRKc53gOQ1hbHYCLoJBUQqqwy2L+/3Zb9aCa9MIvdqgbVTekQQGWloFdVF8mr3AS9NvwyPjIn2KU4wffg4LtTh1qNip+2Unr2uBuL3Hj59C5dAEugtKcdHIRdI/RCfDYoSvVpOHJADyxobiOkt3PwiJRK+89ooCToG7ZBdb3QJa70mgEQcGL/WdLbj5kGLRbEmY8yK7/Xfh8JLubhI1j6KClXbgISnPSyUXQfY387Wy/HkE1KWpl9+jFYZSHpxKBHIACjoJ6ZGabXgSvvZRGowj6BdtcDALPg8xZLu1/Ryi91yUsuHpblovQGFwEpTnp5CLoa5czmU+rHaKa1N4y5WBro4+q160ROAm62NVrXNCkmlOURrMWtJjc9dVqbYa+B7uuy6+XuxszdMLWKpxOkpROOqUnZdkCh3Lp2X3E081mIeWUGJvBM6uYq9rLQ1NwO4u/dgOcTtqmPFZlQYl/kiNfuUyFHIw6zAKgUP+CystQL3xeZvocWzZboKr8R/Nu0xZ7liafqnGTU93qRzudRZi4O9Su6+nMpatp77X5NvoMfjZU4+1fTqjhOii3eNIyvUchOOUm9DfzCk9QAD4sdxWbmhmdppk8w8vKw8OTrn9XPrdlc4aDoJBUQnyT286znjvKQb9WEaKgAJh37GjnY08zsaCntXswzb2RVFs9e/V1/UKByx4UkkqId/75S/i5MwQp6CdRPvhvhCh8Dc3FpPePaWa8bNBxa4yBVg+rODXxkFRClRFBCgr0TgGw2vRIgk3vU6y6avcIID58+nJdPBd05xhUNxCmoLHGAxMNvgXg1ZLq/jP/RZ8vJpz4CO3IdfFcwILyizAFBUm+gUvlQxeHOzbdiHrfKNVg17ttBns4L54DWFB+EaigZcnf+7VNl31oZ+dfGxkad+d16WzBgvKLDghK8G5tE/v+v6PcIL6z738s6/65UehYHu/sa0dQmkdnKgC6ISjBk8XhLsNO8d9JcZdXq+b1eMwSpQ1BZ1mZRVzjfbHCQGcEJbg/t5bL0BM8d7SpZ7cmLcokk7f6tCBoavh/0i2+0D4MuokuCUrwz9w6jv0P8vlvYT8dgGy927zVpwVBu+zI2P04QqidEzgiUEEvDhtD1zn036VNbDptY8qWwgrPpp/BJqP7fFUHFTSuGPQqYfHs5h+e4OReMXOxIQkqmd0ui7JHIQ3cBf3e0NbSkP5q0euNHSyjFrM9G6JmdFUrVz9n/o5tYYJmFINeJSyeiZZnXow2EGr/Q46gCDq6a0huNGWnbFUzi8AwGQ8kCQ5MJXIODPSoMuoY98b+Y5MqEW4nOVdTAkITn3/qyJH9LNJSwOIZOybUpUu9CvLQoTIogvp/jgK5ys91kKicWQQCeS8e3NKDlJJemd3QKmYV19ZZev1MNrwUM3cSGw5XvC8XQdDOde07ekxCrx0WzxFzAch1o+ufoOOgCOrzJgq8DqCYrnJmEQhS/TQAZpsjlMzc0cctYMgeLT/h9cTG1sDJW34tEkFQx8Iplx7GoldPFc+idfE9io8S7jsnrW/yDXp9OgWKoMvDfOYGraWYrnJmERjdDFs3MaB+mLs8NxZ/ZVlv8olceEl1MdzymvSQUapsGEFQr7zU1UB5q2aAKp6jG6eleBxUfHkyqf9WgTzGzjtIZ/EnkxdQ9r9UObMIjMKZQdVXsejHlHdicgOL5rNOa6m3csPWxIfvUNkwgqATWj4OHt4OvXqKeOZbrunSb1FL9Dp0FhRBU2RQFVB6yOtz4zok9q58riEqHw+OC7es6aAffFzji050ygT/mKyUDaNcB30Kzs57il49haAvzOtvX+nkjV6HzoIiaFJS0mBXymOmw7mfkuMWlu63rl0iidbaG7qumBhbivUHpWv4nbXXLS2rW9nKF4ogKP0GTw3VHlRvJ5B2qJAvmlIC9UL9e0+K6X1a5XWJT203VGm09nrffGubCR6bRba2qtZvw20NvoXh11DzJpflgwiC0m/w1CjF80UO8efg0SA4rDL0ckIVtJDqup1LHnDOBh+V3dWeoMUdlouurkoMsGkz7RDDC3jUBOKtTsoNnoYv4nk52NViSBHw/fXPaxOEnXmZH1AE7UoQ0ptier2boPZDcF+5RdeeoPNMj+cdNlFcl32dPrmltX/XJac0miwYUVDKDZ6GsvEs8t0Fslp/D/5waxZaR/Obn+ZBEfQIQQbVCfIVn86xrr3cdyqN1p6g75ydDV3cyvTulN7dMqKBRXDi96c0lcwJQVDaDZ6GsvG8HXJv3S/pbQD49PuFinpl6QuggiYVQ1Ugd/fCaavLnY+qW9AfAl160rxE6dmIVqPLJSYpvLZhaH3zKp3mHvpPvStGgiAo7QZPQ5l45l2xdRvwlWt7VdZMN4EKSpxv9vcZmRw4Gb1ONQv6U81rz8c2ZTtX0e1t41s6OrQYs+mSWh8GhwnKuMFTUxJPyUhzR3H0rSM2FfI1vNSgNPG1MgHIqo1ep5oFbTurXePprqrtDJ8fWZBY0zSw49SdtwrgpVUBJiiXDf6HOl/Xs7Gq1nhmFPgwxCdkmY6+LZIVKIJ6ELucXG/0OtUsaE3n3X90NGfxMLIyhbd3Tu0YZBrccfLPlzn3E1EGoYlXeYNvaLcxzdC45YBR/UF8n4N7I1aptoo6BYqgIxquWdfoW/Q61SxoaLVXRQsNOF+Lz7+5c0aXmmZeLYetOPKQv+dIEARVdYMvcgzbJ60uqllDL+2ThU+Yd616qq2iToEiaNH6bj02sPgnVLOgTRKtzFv48JTdTvLw8LIhLb1MgjuMW/PbIx48RRCUfoNn7F9b6C0SiZ0Nxd5e7p0z9dOApHPJuwDyS7N8Pu0Z2PSwCustVKCCJjwCCTLQ61SzoNO75OXt9+X3GkvO9T3z+zfzNA5sO2xJ2jUul6QQBKXd4Jn7144TG4hFIpFlPoj3emk4T5LVzEk+XfqdhVPQX/Lh/KBafmEO51Rff6EBFfS3T4DvRxQ4kpfgFOR3nqnErT5fTVVJsrw7v64YFVvDwq5OxzHL06+p0s0UJijTBq/cvzZ/6gSSEBvi4wTw1K+urycSGX2+52s9zjDUzLypl2w8mOvx7YSvnXLB38TXROMpd1cZB8nGk991/r8RqPfiWfwbqf1C/fO7jGlX/3Zeur1PYy7N9esLuxYNb1/D2jIketDsn07+zaKvKUxQpg1euX9twdL5JLXtiI8/gauRbbXuYrHI3tqu1vwG4elbPHrKxoPo7t90GhvyF3hEfO1gDsCjmk6y8eT3+X9mTJDVUvJdx/5bD0HQ9H6SCEtuvW80yoQWVo5+fnw8hvvh5oEfJ/Zs6m/iGNZu4PSUg9fhGd1R78VTjWTuX9tHZGBAvoUqoo7eXlCwvFXH4iX18Iz6xs1FnnTygPEx8NI7qnTmdvpuRi4s+8lm7dnNX6IAjqCcJNU/d6TXm0j0OrUtaAvPF+C4sfINWE68unpgzdS+0TUcjTwbdBg6Y336+ac0z+shCEq/wTO+ue+JMWGnkZ6oW9L03l9Otzzy91AD+baT5eDmYum2o2TaCf27IN/hy/IwrrvFxjsL5TAWRdBA6ajthSw6IWtb0KiqOeCO0Q54QRXIe/znvpVT+8fU9TC2DW6cMHzGj2l/3C2rFIKgKm7wl10MxJYGRvp7wIlmX0yPndSwSr8Giqc6T/q4WE4tnTaa7DPaoxr6wgiarT+YvoPFdVq1giJon07umeMao9epbUFH1XVp4BRIl+KeNzJvZ+xYkTwwrlFVWwOXku6dCIKquMHPCTL0tbMx0T8Hhk74YvooMjmKS0mXiGdlG/T1JsThelgr9IURWLq3iXY1EsjbFVAE/bj6FpipnDidAW0LetFt467JIZrMVVTwvKTzCoKgKm7wfdzmGwwyNRWbmvmQlyiySjx84jbs+/Dh1PNKHFwG1tZnl9nHMgmAJcasZlEf3DKLqCtxA0eONg1IfKKlZSMIquIGP9Ji50hTkVHiupVBv4AHEabGPYu3wfORQUPpdnifuvg3ZZl5ysJl7kIvQx3ag9JmFlFX4gad42X3uiMUKToRBFUxldCmqBYe7as7SwHYEw8CLb9pZ6rYa95zmrG1ZTeWq8xA2Ibx41K9+auPE5wyi6grcYOWuDZ7wSOVZnxp4tLM0lW+y0EQlD6VEDWKeObU6PFDbDg5fLjtG73rAIy1lU8ftpA4efN4yHatafnFY8ly3428VccNTplF1Ja4QSv87D5plOOfqszZ3UMKPpvI+xYhCEq7wdNQHM/Py4evy62aAt40/vEfvQIAtprKx3fYT3w0+YPlSjNwbuQwwWR64pRZRG2JG7SC+w1i5xEFL1ee8BjiI7CPbBhBUNoNnoYv4nm7nrllkrTIMOHvc96h8lFzekjB345CecE7z3DKLKJ8YTk/WX7vWPn93DrBZzIX1H8qvbe1n2M+eG20WTaMICjtBk+D0gafTd7EXWBt72SheO1XTrOQGKet6PXpFHBBH53LAZJX91fSFYouHSxU3Dv25mvtNIpPn9A6ia1VmfOjjXmIUZC8fzvKrU7aDZ4ayhbp1IRZJX22XwyN2aCrvevXh/qPYnpnPVTQ78VejgedrTyblp/8JpHEPDFRabyONvFtjRPaGo5TadbsSbHLFJ1ToILCNvjywOL52HXEsogh6PUJidWWjn6WTDmmoIK6XQCnRFsoe18WfuewYscOxx3KNxV1U9Aiq5urN5yswXq+jDqmtcqkg4IJyrDB0wGL57czvriTpFN4RheB7XoMu1CooHbEny1d7+CTEQeA8sujdVXQXItCAB76sp3tsV1Vi0C7f0q+wwRl2ODpgGZYTic+mgrmvJsVZmTSSr0b9AWggpKJuF1oS7zvNqD8TWXdFBS0miXN7zuM7VzzzdI/HTKbUvIdJijjBk8NLJ5TBwPwzEHDCdN4wq/RZ7BUn+E9ZPA96Nu3b52IP7rfn1r+dRU6KuiTpnZWCUzH65QkBhMfNeNLvsMEZd7gKYHFM6tmk29cWRzTComdlibWNp0YCkAFNSkGfaE6KigAmaz1BGC22e9Fp8wnlnyH7kEhGzwF0HgWHtzE0zOEmif9q0YLmfr1CPQ9SbrDQztfAx/beyXfYYJWpg2eD7CgXDkaYhD8a+lX/LZjfsGCcueLx/OwoPyCBeUZLCi/YEF5BgvKL1hQnsGC8gsWlGewoPyCBeUZToIK9BkvbYIF5RkuguJnvMqDBeUZLoJWsGe8eAELyjNcBK1Yz3jxAxaUZ7gIWrGe8UJA8gz6pgBuguKD+nJwOkliTB4mfB6zyEZBku7mbr0YUoaToPigvjxCu8x0J9op/ABvq8LEk/oeLk3h+SlL+c/lInhWFfJyak6C4oP68vAp6Oe2LUlcObze/JN7qHOQ/UUV5sycP3wLqyTA0XOkkgldWcyws8uFtUcXQZ4C4yQoPqgvDwdBP4T6yCkZc/o4yXAW7wdQZp/pyudHrHqxn/G115AfmrFZsMQiB4C39izmOOgdMLCuTzJzIU6CVrqDegS47EGzgtKekSiNXsfhrcYzyTeBtA9XYcaRABRUYfN0tDOx3ndZ5KMAfxrOvrvNeDZzIW4nSUoH9dIM2Rbfks1aVjQ4NfFzLlGN5SLoNpOD4H8OHdnP2JdMzhSfxmKO5Manfo9YxGKGLfHdg9qOhiSp4iRozvTEfcR/ehd//9xefsykUnqOCoIaTpK4CPrW3sfM3uk36on5DM+d/NDx5bUnrmwykhUtj2y6ls3jgGdrEMe4PZczF+IkaGL0hvBUACyURuMmngP3y4/iIig4FWzh8CPllKIR5vZh1+nmy3M3sNPjMacjBdK4Zku6B31iLsRJUKeP4G3ACyxoWbgKSnG4yElQ4kSeZvyyVh/Alip0u7xtDQ9uTnemees5T0h+HrsK9rJUbmfxLwDYGivFgpZBeILS0eYo8RF0l2bqgHVAkRJCu3ASdK0vcUyc2MJIaTQWlAPrKEapR9CvdxP7MA+6VOkTZxAf4WfZVPhuz14VntuGwO0s/hJxlifZM0ppLBaUX9Qk6L4qJ/43tAXd1Hsuq08PrcsmUf15l7h27gxJbFQDdxbhGd0RFGwNcu1Ffx5/tXvkeFbvmwvfB8BPzeDl2IEF5RndEfRdaKPOTj/zVp3EmNjdZlnzVp8CLCjP6I6gY0cDcNeexXtyIfhdBeAP5dvenMGC8ozuCBp1kvgIu8ZbfVu8l8x328dbdQqwoDyjO4L2/oFokm3f8VfhmdHjKG/VcgILyjO6I+gtpylrwkerpWoewYLyjO4ICh5OHPSL4N+9gAXlGR0SVCfAgvIMFpRfsKA8gwXlFywoz2BB+QULyjNY0P+3d7ahNYZhHCdFmDRNOg46WLZamUQ+jIiURjO1vHXYB1Z8UIiZ1y0vyUspKVJeO8pLtvjAFx/2wQfJW0sp8taITja2szkzttt5dhzp8DzX5dz3nOs+/r/P/67nuv/9TqunnfuYBYIaBoKaBYIaBoKaBYIaBoKaBYIaBoKaBXczGQaCmgV3MxkGgprF7N1MkWaHJRDUKBDUjb+9m6nNl+0wwG9iM0uBoGbpjbuZUKhZ0Kcbf3s30w9QqFnQpxspvmZCoWZBn26kKujK5h+8ffG/8D5x5Nm9IGhm99nU7IVnnykKWpedoE9fFhkQ6/vzzHdTK81knwbOxHmSoTH9sj3x6jNFQX/SPogViyRf7vRnWoewYp94X81uzmbFmFcGh4ezYppw+lz756vsfuUZ4/7WkmtkpH4mPWYKffn4heX0GDcgKAR1B4ImAUEhaBIQFIK6A0GTgKAQNAkICkHdyQBBuzazYt8qTca+bmHFOqtYsS9bTcY04fR5+Q4Zaa2hx5x4SkYaj9BjDtC/PNdwlh7jhq6gAPQqEBSIBoIC0UBQIBoICkQDQYFoICgQDQQFopEk6NffvlnS23wyGhPDvyqS0Yt2dZqC1uWNI35rWamTeSOCnznJyiBj4INpvlWM2OHR/vXdVKxlokqM8kr2xNin0IKcz1qDLpLRIt0goz796vQEbRr1uiXvsXfmXuBdS3ENI1mfE6QHRgMNHXNvkrFHvg9tU64SsX35gcQRvJI9MfYptCDns9agi2S0SDfIqM9AdXqCno99VGuqvTO1h5Q6FaSTH6ceDdIDryxWqiNKxhrGtHdOryNi92oDiSN4JXti3FPoQc7nrMEoktEi3SCjPgPV6Qm6b7tSp1eTsaapdXRy2fWLQXrgwYWTRyxro6dtGji0lJz2JpA4gmfSiXFPoQdnPrkGo0hOi3SDjPr0q9MTdI/z1HIqFcoN0clQhYr1Ssaqfc8ji3aRsdv5d58UhaiYU18845mMt8w6hSaM+eQanCIZLTIaZNSnX52eoGdiT9y9wzvTtaLkHSNZ6gsMH1xMxo7HAqFSMla1TalzZVTMqS+e8Uw6MeYpNCHnM9bgFMlokdEgoz796vQEDfvD0YL73pnaOd3MZOyDT8Ya/c8jCw6SsQuTwq0lu6mYU18845l0YvxT6EDO561BFslokdEgoz796jRfM10uLKD+p3VD/6ysrHJOMta+ESO9AAABWklEQVQrHbuUO2rtFzLWvWP0yIooFev5AxTPeCWdGP8UWlDzeWvQRdItMhpk1KdfnaQX9QD8BgQFooGgQDQQFIgGggLRQFAgGggKRANBgWggKBANBAWigaBANBAUiAaCAtFAUCAaCApEA0GBaCAoEA0EBaKxR9D5OcP65OSsuVWW7kUyBEv6tEdQpT44PxUSeZnuNTIGK/q0TlBgDCv6tE7Q+rI7Mwp9a2omFDWqo2NzN3aleyt7saJPGwUd9Kp1SLWqOFRfGG5bujfdW9mLFX3aKOhcpSY9VceqdgZmzSqYn+6t7MWKPm0UdF6s0FexQg/sVyraku6t7MWKPm0W9GF+uKP4Rrq3shcr+rRZUHVivH9dupeyGCv6tElQ8B8CQYFoICgQDQQFooGgQDQQFIgGggLRQFAgGggKRANBgWggKBANBAWigaBANBAUiAaCAtFAUCAaCApE8x01lcWKfBrHSAAAAABJRU5ErkJggg==" /><!-- --></p> <pre class="r"><code>print(p7)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 3661.661 3195.030 3174.145 +## SFO IORE DFOP +## 3661.7 3195.0 3174.1 ## ## Critical sum of squares for checking the SFO model: -## [1] 3334.194 +## [1] 3334.2 ## ## Parameters: ## $SFO @@ -1705,7 +1727,7 @@ div.tocify { ## Estimate Pr(>t) Lower Upper ## parent_0 9.89e+01 9.44e-49 95.4640 102.2573 ## k1 1.81e-02 1.75e-01 0.0116 0.0281 -## k2 2.30e-10 5.00e-01 0.0000 Inf +## k2 3.63e-10 5.00e-01 0.0000 Inf ## g 6.06e-01 2.19e-01 0.4826 0.7178 ## sigma 7.40e+00 2.97e-15 6.0201 8.7754 ## @@ -1714,7 +1736,7 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 94.3 3.13e+02 9.43e+01 ## IORE 96.7 1.51e+03 4.55e+02 -## DFOP 96.4 5.95e+09 3.01e+09 +## DFOP 96.4 3.77e+09 1.91e+09 ## ## Representative half-life: ## [1] 454.55</code></pre> @@ -1729,14 +1751,14 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p8)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p8)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 1996.9408 444.9237 547.5616 +## SFO IORE DFOP +## 1996.94 444.92 547.56 ## ## Critical sum of squares for checking the SFO model: -## [1] 477.4924 +## [1] 477.49 ## ## Parameters: ## $SFO @@ -1779,14 +1801,14 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p9a)</code></pre> -<p><img src="data:image/png;base64,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" 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gSlC9FW/MHpC38Vmy9vZBFrfFh26enu5S0uWby31L/dl/oajYRA0LguV+q/3Z08JQgqhVnQdUbEAgQji2R5O/OUd5PfjkkVn/EWu46uMLmT79xb8vNpBsl+0Gvo6Hwrd14RAoJKYRY0Pj5+pJfod6a9OQ+mRy7KK3l9/w7PgCCaLUQnhzvH/kQ1oz0hEFR6hRcHBJXisR31d8XGWxr6Qu6APond3xJMpl7QjEW1W27Syfl4BIJKr/DigKBSPCZogdh+O89c5JGF7gvdtUNBi3a/5Dbxb+pp7QDhoU7RFV4CEFQKs6ADOUJiRea3+g2FXUTn6wkm26egF+M8XviK/dsuEQoqusJLAIJKYRY0hSM1T2T+L/79e3nF+HwpmGyvguZ+/pzXlEv2yU0NAkElV3gJQFApeEHjixELyNm2KGGVxfaoHQv6x1i3F7eJrSvMQCCo5AovAQgqBS8ot7053H/M9MCp5DntWtCcLZ09JvxuxwUoBCeo1RVeHBBUCnMX3zQDocww8px2KOgTm/B/v+vdZm0m9WXQAScoays865AI6pvNGVKLPCf1gn7nU9Z54+MTCr95ufqQ/xVRXgwVCLp4BlZ4/UAi6Oi2q9e2e4c8J+2CXn82dHbn8kefnHh7eZNa7zG444lAUM1XeD1BImjhJ4MGr5dxeibtgk6twS28fk+L6SfHebZbzdrYTgSCSq/w8s+vvTo2crZD38UPK2jUJRRlhDwnbUFf5/cZPt9BZE7B7gHVX96RS3dxyiAQVHKFl39+7Q3vhB2vtmR/77DtYAU98ABJXqIgAW1B95bdeHvPsxLXYd3b0NlleCo7X0dxglpb4S3Orz1iupGXD/fwL0JZ+y1+j4i8cD6/1SLJ+fr/3Yn0WLyM/xH170yRThWrNJU+6+7aoia+E0Tvj6EBOEGtrfDC82sfvmS6FWJF7mEDQse6WPz2c61Yx6NpiOR8/f9uSCDormFFLapoevZN6vLd1j8jf59Wt950JvaOkh6LF5so//zaqRWvogOlDxG3Tjk/9gp9Tc37hZJsJLU+nhJzuz15Tm22Ok9MqNlo9jktlvwEBIJKr/CC82vNWKnnxFa+XdybfS6ngco467H55IxgFcfLJBE00DD2iwIv8pxa7RYxHBnj3WSuxo4SCEpxhZ/97vn9N16Uc024Qmbwt3QJ36feAkkEHdrPJ2Oi2Ea0BBrutys6PMa70UwtB34gEJTiCv+3x8ZTMwNV/EAbt4x76C88PciOkAh6f9UZNEvGBZba7lgu+n6sX/AUzbaZCASlucKf6N1Y1a+Eexpe/OtHz3/UW6CykUXoDdxAg5K71xl+ig/0G3NQk6EfCATV1wov4PlnypWerOLyFI0sQm/gBuUUjKtcvvujkR/QmTnNXWOT1R/7gUBQVYYSshO7G1278LOHiuMVKBpZhN7ADcqZXPG5lys9eQbGtQ9fqNp9jYrdEQ+BoNJDCYnDkqBMfgeVHFmE3sANynHh/uPHSgsHwL2X9EqNZglpKt66lkBQyRVeApYEncGfJvg8Y1vxkiOL0Bu4QTkVubW6sLTI6U0FBycGeQ1PVmt4cQJBJVd4CVgS9C/Pjb9Or6/i7QQUjSwi3LFc9PVWnudkXBBGjeD6FzJeKyuxXfT38herdFn6B0LpB8/auR0Egkqu8BKwJCg60afpCDWHzMILeul4Niq6ef5DqaCIR0+zo/vx1JLRfVHj+8pVK1WbIT0/a+fIWv7PV+vg18++Q+mQHOqUXOHFYUpQtcEKuuwZP7dvParW7Gg5+3Y0T+XoaMF0bQr6Z9yY7zAhRyq7l67u+y7m1Kf7C99RcHAbKyhuhbcEBJWCF9T7J3So1GbR/2nBZNeVSUluSUmC6cwWdHeV906+96yT26D1VvZtX65U0aPMMHmJkzzL1jtueooT1MoKLwWz9VQDrKA1uB9nqc+cgy2+Qf4WU5kt6JYK3Bt5qem19QPdgkYlS5yL36ZBPvqmtKz7hB8p+9KS0GdN9yfBCWplhZeC2XqqAVZQV+7HUzLi7qARlgeVmS3ogRr9d86v+jL3rOiXxd2qtojfJ3IU22U+91B5s5y8EY25jM4Jxuc4Qa2t8I5xnyS64D9B09PT3bmfdImYRMvbVTBb0Eyv4b1fa7jV/CrvUEIHpw4JqYJRyYJ7IfRPaVnnlrbpxj3UfsP4HCeotRXeIe6TRBmsoBWKIc/JbkFTA7yqJjw+4eG++DZOnWY8LumuMq0GVg6RlXbWsz8WbS1zwPgc+wlqZYUX3ifJDLv1VAHm7pNkZ/61vMDswV5O0g7T9hVfHLm/Rd235O2JLgx7tnT5QabnOEGtrfDC+ySZYbmedudpE1SCrP3vdXRqPna7rG2jx0jdULxjU8md5hzhPkm0AUFLyP1+fvfqgUPX/6noyL2iux0L7pOU5VTKiA1DqjsMIOgTFJ3+OLq2a895qTafp6f0dtznLSepW0/G7lkBglryz1fj21QOG/WZTQPrKBW0ueUkFetZOLlqpZfUPEMfCwgqTu6x9/v7uXaflSJ3aB19C7o4/GbeTHo32qQACGqFf3bEd64aOHjFMRkb9UoFXWs5ScV6thvfyH+k203VlocHBMVQdGbjWy0qNRm+Oo1sTGSlgoogt56Fu9fKOVnqcYLq/XRpePkMG//aHoCgJOT99PGw0EphI1b9hL0ZjvaCZgdUKF3hTdsWFVj/j9tjyskZ58jegKDEZB//8LUmFUNfXXHI2ujO2gs60unXvI/K2DY2QJtRAa6xHjfwgaoBgsoj98TaN1tXrtN39q4r4gHaCxrE3znadbbYrPtv+9WZZe1q7DndMw1LRbbTtAMEtYHCP7+I6+bl3GnMuhMWm0/aC9qqI7ceVdgomumVPbs7zbHytwVvVq7ynMiuWO0AQW3m1v4lMU0rBkbN3P7XYx9K2gu6o2yvBXWcxI40FFSuG1q3sfU7qeardXUhIcoEZWtkES0oOJM0pVdApWYlg21oLyha7Vqm4WmxGbllNiDDyCo02qQaigRlaWQRTXnw0/8VP2VAUEkelp2Uf797VUrZ1EGRoCyNLMIKLAtqqNatcuVeMm6AwwCKBGVpZBFWYFlQNLXtzi/qybqaRXMUCcrSyCKswLSgRWtf6vM1rWTqoGwjSTCyyMMI06D/MgZndTiYFlSHKBI0e0Y0vz7Glkw4bLptioxxhxwOEJQuigSNjljfPBEhJ8FkKChdoJ5S4AR1v4/S694AQR8HBKWLsq34Gwh91ssAgj4GCEoXRYKuqb2Y6+fDywsmQ0EpUXynOdjolAC7FZ+WjFDR9rGCqSAoLY7ARiecLEIZ6OLpAoJSBgSlCwhKGRCULiAoZUBQuoCglAFB6QKCUgYEpQsIShkQlC4gKGVAULqAoJQBQekCglIGBKULCEoZEJQuIChlQFC6gKCUUSIo3CfJEhCUMkoEhfskWQKCUkahoHCfJAEgKGWUCAr3SbIExmaiDNwniS4wNhNlaN4nyQzUUwrZYzNdvcATGUihYXoFdjPRherYTFn1A3iq16bRMp1CU9C8hDieEOFW09OEPcZmWjtCYaP0jAJB7zX2N1E8IX/pAp6u7am0TJ9QHZvJDAhqI5nBydd5BJOhnlLYuJsJCmorc9PEpkI9pbBV0Jg7Zv6+iOfvvwiCzhLEXDhHKRFR0Nk74jxPfyNJRj2xLT93HhOAryJ2GfhGXMAEnP+HrJ42CrrbxdlE9VLPUIIoEUkQvUSVnEVxs9gtLA+RW2zIqCeFgitfBoVWliWrp42ClvCwEkHQnKkEQS7p+JhNsQSJQk/iYw6EEyTq8Q1BkA1Yu0kRvp5hP2MCRq/EBCQNwARc88U1wv8SJmBwIibgY8Ib5YGg0oCgkoCg4jiGoCJ3Oy4BBBUAgkpjL0GtAYIKAEGlAUElAUHFAUFBUJkUid5yV8D3/4ePQfPy8TFnviJItPIOPub6OoJEn14iCKIMvp4fZGACvj2BCTiXhAnIWYhrxOKHmICtf2ACftmFW4YJpYICgF0BQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmYUXQe5RinnqwRdJZFRUKuiMoYAU2KLBM2bLbrYdkNsYnM8Zgcq0N8ojOxiQyxWASTfb1mkD49miCXyCm3dhC4qqIryC2ftjamQIItEBKBc3wvZIZdAYTZHDF5pkb7I9NZozB5Erzv5EZMcN6IlMMJtG+5tl3g1KI3h5N8AvEtBtbSFwV8RXE1g9bO1MAgRY8ygTdHI3QjARM0LVG2Dxpyf7YZMYYTK7kxQitj7aeyBSDSXR0L0J9k4neHk3wC8S0G1tIXBXxFcTWD1s7UwCBFjzKBJ07FaENwzFBh2uGewzNth5z3R+fjI/B58posQPbKi4Gm2h7RI9cordHE/wCce3GFhJfRXwFcfXD1o4PINECKRV0Nt+KWEzQqaX56V1nWI/hy4ZLxsdgcyXWTcQm4mOwif7dFrqX6O3RBL9AXLuxhcRWEV9BbP2wteMDSLRASgXdyLVg1jRMUEEh1+v0sR7Dlw2XjI/B5Coa0usGLpEpBpMo+RhCS0cSvT2a4BeIKya2kJgq4iuIrR+2dqYAEi2QUkFv+dzKCcGdQLu8a05mz6XWY/iy4ZLxMZhcyeEGbKtMMZhEH3TOzAxfQfT2aIJfIK6Y2EJiqoivILZ+2NqZAki0QIp3M20NDVmGiymIC6g5TjjeoAC+bLhkxnXfeq5x5Z2cnGKtJzLFYBIVvVPLd1QB0dujCnaBuGJiC4mpIr6C2Ppha2cKINECsbOjHgBEAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmYVbQC71jVmvdBkB7mBV00i+og9ZtALSHWUGzDdfw46IADo8mgvqeQUVzG7q1+5Z7XqGGaw2f5VxDXFxdXUc8FrS19w1MmtRIkkmW3LlE3lRAY7QSdGj3q0VHA5I4QdMR+umZS6hU1pMxu8cbcGlIBDUN5svxSyuvYehy66AVCL111uaWA2qjkaAnvfmRIY/6FRoFRX7HLQQd1j0q+okJ+eafR68IBDUN5ss9yfE/nfvC3jdSCsMyz71F4z3oBqLuqpgDUfzjuvjHpxH1SnZDI0E/eMP4xO+CUdDDbtmPCTq+3o9oW3CJiytr1x1fdOSVgdP4HzQnoPYk4ytkrJxxZtcdCDU/ZHwqLKZpMF/uybb+COXmTH7/UoPsmH9UeZOsQNJdlfDgEv8Igp6ZZLrrdNv/oQruns6l9phXatNofNu7ZtfaWxybGnora+CcI9XOIv4nJSgzt/1m/hk/K9I0c+MQdCHgoPGpqZgv1TJy2pgsokcu92tR7zCPQVmZb3T57sS7Kr9bjcF1VyJ3QQdBz6wYaXzif5mvWdFmz6LHa2aoF9uz5MV0/06dQrof6YgQ/zN5KkIfvco/Q3zlTDPveeTPm2l6allMfjBf7leC14UHfd7jJ0Td3dV1Yq793hxrWO2ujH2Rse+5/1pgm10oNapwTM2mw+NTBiIUvw7F1XIfkJcaaZqnDRoJmuaTw/3+wdvU6xRVvfnESr2w/N+Pns/nvkBmHunBFZP7mcR17auH8M8QL6hpJuq9r8lF01OToC/6GDlVPJgvN2lVLEKJ/Mx9i9LDrkzHD5zqMFjtrvi+yNQNrYoyHB/GCfpJm+wMf7Og17rkFLQ8lBppmqcNWm3FR/f+1/BjnWSToKjm+ScEHejy6BPu1+BbuRF7igXdW/9BXoeNJYKaZqIvOnU2xwk/QU2D+RrOFlzzufCgxyLu07lXzpVww9YpKrxLRrDaXfF9kanvOeMzbp+BE3TQaoTGFH+CXt0y3SUlNdI0Txu0ErRwZgO3JonILGjHD1Epd09Pz9bG2Ue8eiY+Cl5dx2c0KhYUza4bMKGoRFDTTJRVaZM5TiioaTDf3FKX0Zd1fd/MQyhpHdfd1+/wnyrvkwmsdld8Jc3dUMbnMS04QQevReg9o6Aj1x2tv+pEX05Q0zxtYPBIUlGzjQeb4ceGBsiw2l3xgpr6nlmTCm5XuZsatb5dbmZQfGpIwT2vdfPfRhd9d6ZGmuZp03wGBV3f3ICifS9q3QxHwWp3ZeyLjH3P9fae7dcZN5JqhU2Jz42p7dtz3dWIhj3fDUiNNM3TBgYFBYBHgKAA04CgANNgBT2TMDx22i9qNAUALMEJuiJ45oYNs0KWq9IYABCCEzTgPv+Y3VCFpgCAJThBA6/wjzdDVWgKAFiCEzTZN3rq1BjvbYLJf8Y9zeBO9ZfPn1q/JU2xVk/sRlLG5rmzN90WTl3bcsHTS2CKIhnFgHpKYeNW/Fqx07GfFrraQVCopwQ2bsUXF9SwZ8X/KWiaPgFB6aJIUOFWfFbbZjye9YyvCru1HtN4sOIW6gwQlC6KBLXYij+VxhNhEvTLRsFlGgf8T2kLdQYIShdFgkpsxQ8JNv4aWeVIzY8qzVLSOh0CgtJFkaASW/FmQQfUQcv6uY23vW26BASlizJBxTELOt+rZ0J5jw225dAtIChd7Cjonx5xce0rPl3XmYOgtFEi6L3G/iYE082Cor2hFZtXu6ykdToEBKWLok/QzODk6zyCycWC8kwbamPD9AoIShdlXfzcNLGpjwt63+uM7DbpGhCULnb8DmpiqaZjo6gPCCqTQusDudhd0By/o7Zl0SkgqCwKRleuFG5tO8XugqIN7bUad0ITQFBZzIm4Wzi3rZUA+wta1HKLbWn0iWJBIyymOLKgrX7kHtwsjvU8wv6CohPembbl0SUKBL0dzVM5Olow3ZEF7XDo+rlc53vSASoIil6Nsy2PLlEgaMFk15VJSW5JSSVTrl7gmS001oFYXt21drWOVgLUEPQ/t6doV5OiLv5gi2/QY4c9HoYE8FTxUtwqZpnYMrDm84FWAtQQFK1tWWhbJh2i7Dvo3UEjLG20qKcDEfYz9+D1r3SAKoIaXnh6xoRVupGUaLnf2JEF7bIXoeyqD6UDVBEUnXc7b1sq/WGH3UyOLOhXdb4+3OM1KwHqCIqWt3taxvRUW9CH58bW164AACAASURBVEVudKAjdnR7br61Y0kqCWqImGlbLt2hsqBzqtXx+Ir6EhlCmaDilx2LFfSmzzFZ7dIt6gqa0uAmOuVxlfoi2cEelx2LFnRnwB15LdMp6go6cQn3MOhz6otkB6qXHZsRL+iE7kUy2qVb1BV0Nn/jsRf2UF8kO9C97NiEeEELOs6W0S7doq6gf3tuODkjyMpeGt1jx8uOhfzr862clukUlTeS0vqEDrtGfYkMQfWy49x3XuepV1M8Os2Dv0Fm4Y7l38tspJ6A/aB0USSoYfsOlBg1K6f4deGGNTythVfRFbO19k2U28Iv2P0N2e3UDSoLavhu7QnqC2QJRYK+07x9n/af9hSeDSZd0ISWWYsrTkrsW+Fn4gbqDXUFzQ9vOaKOQ4+NoUhQ76z8amdRpo9gsnRBDcPC2zzH/XJx3Nthqivo+u4GlFX7JPVFsoMiQX0zsiqeQLf8BJOtFLSgp0dzbr2v5rjHldQV9J0V3EPsp9QXyQ6KBF3i5TcxeEqTaYLJ1r4zZQeV7TO7ecVzhM3TH+oKuiKG++bf+Aj1RbKDsq34k6fR9/EWlxxZ3ep84FYhsPoikqbpE3UFvR80ZFnnXo58VaJKJ4s8RmajCEceDkflrfgHK8dtceizwdUXFGU9P9iBz72D/aB00UBQ9PCF/tZHk9AzIChdtBAU5Q3sZOVCU30DgtJFE0FR0ZhQRz2HEQSlizaCIrTU50fbkrOOEkGzZ0R/zf2KFUwGQaWwo6DoG3fHHBFHiaDREeubJyLkJJgMgkphT0HRb/XGOuLGvBJB3e+j9Lo3HglatGMrz3MBNBqmU7QTFN3t0UE4NrMDoETQwBsIfdbLUCJo9pB+PLWEJzs8TWgoKDIs93K8U5iVCLqm9mKunw8vL5gMXbwUdhYUoUN+43LwUbpC0VZ8WjLXr28fK5gKgkphd0FRRlSjU7YthFVgNxNdNBYUoU3usx1qWwkEpYvmgqLrL4U50vn1OEEjiyFPCYJKYRa0aE73TPFLr8lHFrHGp54THlzt22aKQ5yVgxM0tRjylCCoFGZBxw0MyYkYLTJfzsgi1rgd61UmoEslh/g/EHTxeYdSUnbK2LcJgkphFrTOw04oR2xfnKyRRazStEL46fQy9L++qQ+BoP1buvT1lXFVFggqhVlQ/9ud0K26IvNljSxildpvfOjxput7sv+OPQgEdSuYlnaxF3lKEFQKs6ArQv3nBa8RmS9vZBFrvFTHcGdYqRFZsv+QOQgE9ctNXIWE3Y4VQFApirfiD05f+KtogGBkkaJkW48d/1fJuVHZloO9Vuj+TGYCQeO6XKn/dnfylCCoFGZB1xkRC9ib82B65KK84pfZsTYfO86K6/cZQqd71/xQ54qS7Ae9ho7OlzGgkt4Epbpfm0TQ+Pj4kV5i35mGvpA7oE9i97cEk5UU9EQP3+W6HqyNQFDpFV4cfQl6rGl534300pHuqL8rNiCYZy7yyEL3hbOUFfSXKM/ZGUoSaAuBoJIrvAS6EjTDe6fhN/9D1PKRClog9r2y1W8o7CI6X08wWWlB/xxWY/TfylJoB+GhTtEVXgJdCbqr19EVXy+eQC0fiaADOUJiReb/4t+/l1eMz5eCycoLemOKe6/9+hyOgFBQ0RVeAl0J+q1/0JiOfuoKmsKRmicWkLNtUcIqi6/7NAqavbZx8Mq7yvOoDoGgkiu8BLoS9HjZ1XdTK9K7OytW0PhiyHNSKuihQdWHHqWSSU0IBJVe4cXRlaBfdOtSvcmbI6nlwwrKbW8O9x8zPXAqeU5qBb21OLjB4huUkqkETlDtVnh1SAviVr3Y96nlI+nim3Ib1Zlh5DlpFvT715x7JGU/OKWbe9jgBNVyhVeFV9rM6dfgAbV0JIL6ZnPfNmuR56Rb0KzPulYq61RxEs2cdoSgi9dyhbc/hh1TP8mml45E0NFtV69t9w55TtoF/aNc47oVS8/Ux+miBIJqu8LrDBJBCz8ZNHi9DD1oF3S8J0KXfKu6jdgjY8tCKwgElV7h6ZwAnvv5ksPy/oJhsIJGXUJRRshz0hb0bX6ndrvOl5a2dx74Beu7nggElVzh6ZwAfr/hi7FB9HZEagxW0AMPkNaXKPxQbuKxJeX4g9f/fdKzSuclZ+mmpwtOUGsrvPAE8NxpcTwh1biH/QidjSP6Pb9uBddKFT8ljmf7d3PSY/Ey/kfUvzPFVapROcp8YOnhrjf8ar+1k95mImVwglpb4YUngBcsX8AT5sI9/IDQlQVEv9vXnrlgdNkE4ni2f7cmEHTXsKIWVTQ9++bSrj8ff/nbkheqPDf7GJOXK5MeixebiDkBfE+jCi0IbtLXvCO3GlcUO8Fcj5BsJLU+nhJzuz15TjW2Oh/uHR9areeyk8zdQZlAUOkVXnACuBlzPf/wPJi9yxM/nNUYt77vNXI/jo3TBySCBhrGflHgRZ5Trd0it7aODK4RuSyNqf1PBILauMIv4ncFv7oRG37GY+iIPi2Z7F5sgETQof18MiZ2IM+p5n67G1+81bDqi7P+j5nvpASC2rjCL5zMPQzdgI//Kar1uHTy9GxDIuj9VWfQLBkjJaq9Yzl91+R2Tk1HffaXuosVh0BQG1f4M15HCvd6OvS9t0VgYWQRGuQdXzbAzzVixre31F/2ExAIausKvyu4bJODtrVKvzAxsggVVjUMGJk09YXq/lHz91lsZ6gHgaDSK7w4cKhTCvVGFlHOcifPek7dEDL8/cWEztX8eiXsuKRBK4gElVzhJQBBpVBvZBHleL1chBJLmy8INZzfNiXCp3rH0Z/8qPZYEASCSq7wEoCgUqg3sohyKnzFPZR+4i7Ktw8sfTWsUp3IqUmn1TvLhEBQyRVeAhBUCrkji6CrF3giA6k1j5xaz2Ub5pWxFLHw7LYZ/RpUDH556ue/PKQ8qIAYBIJKrvAS0BT0rt5GF8ILeul4Niq6ef5DqaCIR0+zGgTwVPGg1jxykpwqVKkWIzU378zWmQNCK3pVK1Nt9BW7Xi5KcqhTcoUXh56gVzs6O/VjZpcxEVhBlz3j5/atR9WaHS1n347mqRwdLZiuTZf0ba9OH2A+H+/79OoQWrFGxdCod9cf+lcy7MYZBd8IsILiVnhL6NXzhdmG3FffppVNFbCCev+EDpXaLHrIu2Cy68qkJLekJMF0Zr8zHfJot+KNGoMf/Jw0J7ate+XQlyet/u6CUOr8VmXLVvpKXuKbo8Knm7fPcIJaWeGloFbPHCfuzV6oQymbOmAFrcH9OEudknGwxTfI32Iqs4J+/mwu9ylSfDVQ5s9b54943v/ZgPDhc7YcLflAjXE6cmNwOVlDmKU7OTV41t9kOk5QKyu8FMX1/Cu23euKDiQVVr2P0K+NlaRQHaygrtyPp2TE3UEjLA8qMyvo15U/vHfc7fknJ+b//d2a+IGtPSsEvThi9ubDlz35sY+f/VpO3tfcstGN8uuNz3GCWl3hxTHX84b30iMz6mTK+lMBr0f+9kMrhZcEn9x5WVkCeeA/QdPT0925H6mzDxItb1fBrKDXXMKrh7RbJD4z+4+9q98d3N7vGad2AyeWmXFCxvX4LXpwD/WGGZ/jBLW+wotirueHr3MPfYTjDMniv7Cy5bvlK8lQNKBOL4/FSjLIBCtohWLIczIrKFroO7RNG8w1sRPKTXivZrmeYR7la7cfNGH51iOXsZtMMZ55KP1Z044j7CcoZoUXwVzPWfwI6W9+TP53lgyO/edc+CwlGT7rXIBu+pzDB9JC+/skqcrvG/bhulfDkLJlfE9wT3IvHN6y+J2otn7lPUK7D0tYtfPH6xIfPv9Vqt6yoo/JY5ygtq/wh+vdRJd9z5D/nQX5VbiV80wDBRnQ6A+4h1dUvJP6UyYoEQbhZ+x/J3d/MuONni18ynk06hoTt2zLwd+f/AC8NqTlOPOXQzveaW6+S2NXRVdy5Fbh1rC/FB1GWTiKq08zFa9qBkHlYPjvdMqnC8a+0qmBS3mfsIjYuKWbU07+88TnqhJBs2dE8xtnsYLJJfW8e0rhTvZe43PvvjxZSYZbNSdt6ddJxWsYQFAbybv+87ebFowd3KWRZ1nnoHYlJycpETQ6Yn3zRIScBJPp1fNW38pV3lR2f+kbUwYvU/MO1SAoBW7/cfjH4udKBHW/j9Lr3rCjoNzXUOauMsQAglJGiaCBN7jN5F4GewqqO0BQyigRdE3txVw/H16++PXDiC48XjIusHM4QFDKKNqKT0tGqGj72JLXh/fzdJFx7qjDAYJSRulupvOWk6CeUoCg8lEqaHPLSVBPKUBQ+YCgdAFBKaNU0LWWk9iqZ95VVfdUgaCUseOhTiZYUK2m9y4VlweCUsbBBU1pcAP95CF9uQx1lAnKztA3zODggo7nT3fut1W9BSoSlKmhbxhB54Lmf5d809r8BP6k1C7036MkigRlaegbVtC3oP8Fd4h0320l4E/Pz/+YE/hQtQYpE5SloW9YQd+CjpiO0AlPa5vpxyLqx6h5UZIiQVka+oYV9C1oU36Dwv+SasvDo2wjSTD0TZa3M095Nxot0yn6FjTyM4TSq8q65trOKBLU4gzw+3d4BgRRaJhe0begP7jPWRP6nmqLI0CRoHY/A1yH6FtQ9PuEEbKGBLA7igRV4Qxw3aFzQVeFBrzF1N0mlW3FwxngFuhb0A2hP1wY3ku1xRGgSFDhGeBmQFC6qFjP5w8gVFiDpY9QZVvxwjPATYCgdFGxnm34G9R5yRj1x+7AySKU0begc3rcNywTOSVVO0BQyuhb0PyRlas8J3LViXaAoJRRLGiExRRV65l/X8WFEQCCUkaBoGwNqc4IIChlFAiquyHV1QAEpYyiLl5nQ6qrAQhKGWXfQZ8cUt188s2zMkdkdihAUMoo3Uh6Ykh108k3y4YqS6lrQFDK2GFkkbUjlKXUNSAoZewwcAMIKgUIKh8QlC4gKGXsMLIICCoFCCofOxzqBEGlAEHlA4LSBQSlDAhKFxCUMiAoXUBQyoCgdIHBwygDgtIFBg+jDAhKF6qDhxXfNsWbRst0CghKF6qDhxkOGW+bMsryFvJPDyAoXewxeBgUlC5QTynkDh5mBgpKF6inFDbuZoKC0gXqKYWtgobGmZhcpzWeRiEEQbVb4WOaBBMkqtMCH9OsHkGiwFfjRKltB0HJ61mnOSagQWNMQNMgTEDLAFwjAlpiAoKbYgJCnyerp42CXllgZnaZl/DUq0MQVC4cH9PIhyBRlXb4mBYuBIncBy0QZeEd24pGp55V22ICajXABDTxwgR0roBrRMWOmADvxpiAkIZk9bRR0BIeViIImjOVIMglHR+zKZYgUehJfMyBcIJEPb4hCKIMvp5hP2MCRq/EBCQNwARc88U1AjtC8+BETMDHb+KWYQIElQYElQQEFQcEBUFlAoLSBQQVAIJKA4JKoh9Bc6sTBC1MIAjyvIeP2TKMIFGzM/iYQ10JEkWqeLe1YvD1bHkKEzBuFSZg+2BMwA3LoU8E1L2GCYj9EhPwyWjcMkwoFRRlEMRkk9y2jCRRfialRAaSPUV3Vb0ptRls67EBD3C3mCnAfhQob0RmASYg7wEuhQnFggKAPQFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpmFFUIK99EQxTz3YIumsigoF3REUsAIbFFimbNnt1kMyG+OTGWMwudYGeURnYxKZYjCJJvt6TSB8ezTBLxDTbmwhcVXEVxBbP2ztTAEEWiClgmb4XskMwh1ZNLhi88wN9scmM8ZgcqX538iMmGE9kSkGk2hf8+y7QSlEb48m+AVi2o0tJK6K+Api64etnSmAQAseZYJujkZoRgIm6FojbJ60ZH9sMmMMJlfyYoTWR1tPZIrBJDq6F6G+yURvjyb4BWLajS0kror4CmLrh62dKYBACx5lgs6ditCG4ZigwzXDPYZmW4+57o9Pxsfgc2W02IFtFReDTbQ9okcu0dujCX6BuHZjC4mvIr6CuPpha8cHkGiBlAo6m29FLCbo1NL89K4zrMfwZcMl42OwuRLrJmIT8THYRP9uC91L9PZogl8grt3YQmKriK8gtn7Y2vEBJFogpYJu5FowaxomqKCQ63X6WI/hy4ZLxsdgchUN6XUDl8gUg0mUfAyhpSOJ3h5N8AvEFRNbSEwV8RXE1g9bO1MAiRZIqaC3fG7lhOBOoF3eNSez51LrMXzZcMn4GEyu5HADtlWmGEyiDzpnZoavIHp7NMEvEFdMbCExVcRXEFs/bO1MASRaIMW7mbaGhizDxRTEBdQchzk9kC8bLplx3beea1x5JyenWOuJTDGYREXv1PIdVUD09qiCXSCumNhCYqqIryC2ftjamQJItEDs7KgHAFFAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpGBb0Qu+Y1Vq3AdAahgWd9AvqoHUbAK1RX9DCUi4uHjH3uUW7uLq6LkVFcxu6tfuWm1HWxbVqh78fBWYbruGG7kmNJJlkOabwnUvy2gxohhaC5qL7I7txi84yvh7a/WrR0YAkTtAsdC+652ORW3vfwOQiEtQ4pjAyjwx8uXXQCoTeOmtr6wGV0UZQlOfxu1nQk978GJFH/Qp5QVFKwKPA3eMNT/5lvvnn0SsSQY1jCqPikYHfSCkMyzz3FoX3oQuIu6sSDkTxj+viH58mUmbV0EhQ1OcLs6AfvGGc6neBF/RBjPEuxOPr/Yi2BQ/tHhVtnLeydt3xRUdeGTiN/0FzAmpPMr5CxsoZZ3bdgVDzQ8anFsU0jimMikcGnvz+pQbZMf+o81a1h7y7KubBJf4RBB2x3LhSB6BJs41T2/4PlXX3rNzsT+Or7V2za+0t/oPU0FtZA+ccqXYW8T8pQZm57Tfzz/hZkaaZG4egCwEHjU9NxXyplpHT/PPrJTeXzmixI/ONLt+deFe1N6s1RN1VvuXfgaAln6ArRhqn+l821syMoV7so7V7un+nTiHdj3REiP+ZPBWhj17lnyG+cqaZ9zzy5800PbUsZomg/MjAPFF3d3WdiLtntWOA766MfZGx77n/WmCbXSg1qnBMzabD41MGIhS/DsXVch+QlxppmqcFGgma73XGXLM0nxzu8Qdv9LigaGH5R9+PFs5HKCfzSA+umNzPJK5rXz2Ef4Z4QU0zUe99TS6anpoEfdHHyCn+uVlQ08jAHPsWpYddmY4fOtURwHdXfF9k6oZWRRmOD+ME/aRNdoa/WdBrXXIKWh5KjTTN0wJtBH04umvJ16Lo3v8afqyT/KSgA10efcL9GnwrN2JPsaB76z/I67CxRFDTTPRFp87mOPFPUMPZAtPIwNzTXjlXwg1bp9jzPTIDvrvi+yJT33PGZ9w+AyfooNUIjSn+BL26ZbpLSmqkaZ4WaCGoh4fn4MwSQQtnNnBrwve9jwl6xKtn4qO/WF3HZzQqFhTNrhswoahEUNNMlFVpkzlOXNDcUpfNYwejpHUIJdTv8J893yMz4LsrvpLmbijj85gWnKCD1yL0nlHQkeuO1l91oi8nqGmeFjB5JKmo2caDzfCjQwNY8N0VL6ip75k1qeB2lbupUevb5WYGxaeGFNzzWjf/bXTRd2dqpGmeFu+ASUHXNzegaN+LWjfDAcB3V8a+yNj3XG/v2X6dcSOpVtiU+NyY2r49112NaNjz3YDUSNM8LWBSUAAoBgQFmAYEBZgGK+iZhOGx035RoykAYAlO0BXBMzdsmBWyXJXGAIAQnKAB9/nH7IYqNAUALMEJGniFf7wZqkJTAMASnKDJvtFTp8Z4b1OlMQAgBLuRlLF57uxNt4VTt5Z6innmOPV/A9RTChu34teOUPT/0DddU6inhHpKYeNWfHFBC76cvVObs1w0BASliyJBhVvxD9s34/GsZ3yV3yF8XOveT5uhIChdFAlqsRV/Ko0nwiTolhAXD8+A75S2UGeAoHRRJKjEVvyQYOOv4dX+bvd/laYraZ0OAUHpokhQia14s6ADAlBcL9extrdNl4CgdFEmqDhmQZf6do6r6PKZbTl0ixJBt3Nf6Gf0eV94NjYIKoUyQS97vjMhqsw523LoFiWClkUottvmHuMFk0FQKTCC3mvsb0Iw3Swo+umlgD7DcCMoORoKBXXPQJl+gskgqBS4T9DM4OTrPILJxYLy5DffaFvD9IoSQcsgFJ6PcuoIJoOgUmC7+LlpYlMfFxT97io2yI/jokTQINdGNZcWdo0TTAZBpVD2HdTMkvaFtqXRJ4q24g3XDp8qWA4bSY9hf0GLXkiwLY0+Ub6bqeBy8bPCTWt4ol5QmlLH2F9QdNP7f7bl0SXKBT1bUvact1/nqeerNKWOUUFQtMcv3bZEekS5oAbh0GXCej5VqCEomtT96TllxA5HkkBQKWgJWtBhvm2ZdAgIShdVBEXXvQ7alkp/KBAUd+DjqUQdQdF+76u25dIdSj5BCQ58CLkz7/VVebYvkX1UEhQtapptWzK9oaiLJzjw8SR3ar+5pmeXIgWLZB21BDUMGGpbMr2h7nfQ94cjVBR2iPoi2UEtQVFWk/dty6Yz1BX0Hf56sJhPqS+SHZQJKn5Vp3hB/6n5jYx26RZ1Bd3cpRDd9TtDfZHsYI+rOiUKeszdkQtZjLqCFkaGvOKTQH2JKnLro4Unrc2nelWnGamCbvH/F5PQAVB7P+jxz3V958Y/PV+b7GNteGa6V3WakCzo7LAHmIz6B3bUy6L/xwhdcLZyvpsdr+oUYViEyI3LHAsQVBaN+Pv9+V2RDqB6VWfheuPpYa2Fh0IeBbz8SpHh4KbTuLw6BgSVxYAPEfq7hr0+QQ3bd6DEqFk5xa9zx5pOD6sp+RfZ7caFe9WrITxn3IEAQWVxzjN2gvdGKwGKBH2nefs+7T/tKTzh2+qhOe9yo9dHVHDcz1AQVB7pq5dY3bmjSFDvrPxqZ1Gmj2Cy1YK2rLgYFdWYhsmsX2gKWvDBAp4wya9MTwGKBPXNyKp4At0SXiZrVdAOzfxXFzq/R9Y6HUJT0NypcTwh0l+ZHB9Fgi7x8psYPKWJ8OPQqqCrKvZ3Dar4O1nrdAh08XRRthV/8jT6Pn6LcKrVghZ29/Av3YukafoEBKWLaieLPOLHpAP+K23LrANAUHmkDo5cbe26dA0E5bhSd4FtqdkHBJXFXt/1O54bYyVAG0HRPw3iHPQ6OhBUFt2+Ruh+lRzpAI0ERXfaxjjmPd9BUFk0+ZV78BFe5PIYWgmKsl7s9dC29GwDgsri7TEGtCfASoBmgqKC11vctC0/06gu6H05qQpOnWPrq9XddkGtax6zEqCdoMgwvc4fti2AZVQWdLVLlbr7iTP9HtSwdptbFJpED8Pp41Yvp9RQUISS3HfbtgSGUVfQQwEX0EHPG6SZWm1AhrgYKo1SC00FRUe8HO5KOjuMUW+lnnELuYf+WwnzP3QqQuhKLRtbpw3aCoouNx1iZReDHrHDGPVW6pmQwD302EWYv6j6bYSONrexddqgsaAoe3DTC7YthVHsMEa9lXqe9tr978e1MkkXEN8+5augTbY2TxO0FhShDz122rYYNrHDGPXW6rm/rVdP8k3NwlUv9tpuW9u0QntB0Q/+Yx1odCGcoJHFiMyTGKPekfeDYiERtGhO98w9ogFyBm6Q5s7Lzc6h87uJOyqmwQmaWozYTPEx6kFQKcyCjhsYkhMxWmS+vIEbrLDKteYz5co4xFnMBF183qGUlJ3SR08ejVGf/77pjPpaVFqmT0gErfOwE8oRXtfBI3PgBisMLBP+77rS52X/HXsQCNq/pUtf3ymSsx+NUZ/3numMeuFW09MEiaD+tzuhW3VF5ssduEGaekNneHzmMk/237EHgaBuBdPSLkqfsw1j1D8OiaArQv3nBa8RmS974AZJwiLQL6Fllsj+O/YgENQvN3EVEnY7Ruh8p3coiLbiD05f+KtogPB23Jl3eAYEyW7G1tKvr29c3nW5/u/5RSBoXJcr9d/uLjKD2nd6B4JE0HVGxAL25jyYHrmoZCdRlo8zT3l3+e1Y7Vkp9Oy58KY/yP9TtiDZD3oNHZ1/TWQ6ve/0jgOJoPHx8SO9xL4zDX0hd0CfxO5vCSYrKOgWn6H/2fzHTEAgqOQKT+87vQQ5CU1bfmzn8+3uz+o1+hK9dKQ76u+KXZvtmYs8stB94SwlBb0/yXWhro/OEwgqucLT+04vwZs9fz7cfAWtbKIUtHl150wvsf7BNkgFLRDbb9fqNxR2EZ2vJ5isrKDnIv0/Z+ukWlkQHuoUXeEtvtOboCZoYdVMhH4WfkDT5XAL7mHCbGr5SAQdyBESKzL/F//+vbxifL4UTFZa0NSWYfuUZdAQQkFFV3gJqAma7cRthF6UsWQb+LI/9/CR8Fuf7ZAImsKRKnq4PGfbooRVFh/nigtq2BbcQa93riAQVHKFl4BeF//8AkP+cHryiHHJ6yrK6fA5tXxYQeOLIc9JoaCFn9bpfFBxFi0gEFR6hReHnqCX27pV7yPrIib5rHJ/3mcYve9oWEG57c3h/mOmB04lz0mloAWb6rXbrcPvojhBtVnhS7h1j14uCW7+j+YZviRdfNMMhDLDyHNSKmhhUpNGm0wfNFf3nKOSUgVwgipY4Qu3zfuG/VXWsPO9dRTvKkgiqC+3vJxa5DmprfGGfS96z7rJfepUdK7Un1JOe0PQxdu4whd07Pxuqz7MGxrdaubLIfRulkEi6Oi2q9e2e4c8J80u6bcRzjEbyk46uqSctXuVMASBoDau8J+/yH2INiO/xlgbfg7kOr0YetdCkgha+MmgwetlHCWne2guY0n18h/fQ+0600xqPwgEtXGFnz6Lexhj393syvliIPeweiS1fFhBoy6hKCPkOWkfOx5Xo79zTFAXukntBYGgNq7w+vgEPV37IUL9PqSWKjCTvAAADLNJREFUDyvogQdI+hIFcWgL+ke5fus7POM2RRfDMuMElbPCF5+wXJ17+B8qaFmrjXfg/yH0V5zxNaO/R9cf6uc9gVq+FqTH4kn+OWaon32zqnK1inGnJ9dsPI/9c+5xgspZ4Ysv+ajBPRxD6PzgF1+dz/2+tMD0mtHf308bMo9evlYEgu4aVtSiioxtFPqnh2Wd5nffFR1+y6PpHMY/R0mPxctICafbSWEWtPXxlJjb7clz2rGghalj/AInpjI8tCiBoNqv8DqCRNBAw9gvCrzIc9q3oIa0hGY1Bmxi9cbJBIIytMKzD4mgQ/v5ZEzsQJ7T/gX9d31/l8bjdrN472QCQdla4RmHRND7q86gWVZGaRaiSkELf5zT2alN/LesDfdAIKi9V/gT/dqOT5f3J+zCwsgiNpOTOuP5KqGjEo3b9g+//oKFbp9AUDuv8Gc81h19uyXDX9NlwcbIIgrI/+H9KF/3HrM+dfPwciIdh9COEAgqvcKLI7Oek+ZyD2G6v/rQDCMjiyjkevK7zs+4tPKqcEHzcykIBJVc4eUPYCvGsPW3TmVHfCvrb9iFkZFFlFNp7YWvJpbyqdrm9ZUHtOzqCQSVXOHlD2ArxmeeNRq7VLe4sokShcnLDtsptSiMjCyinCqLuQ/SZ27cPfTxW53cnFu9Ou+rUxTPSiSHQFDJFV7+ALZibPZ2buRa3U63Ssht03ls/VH2yS0K1ZFFHkZ04fGSsQuFGi9XfO99bw/zi/TvP5kU2aCCz3NDZyce/edRt5+zuO/bf9u3HQSCSq7wNgxgK8Kw9emnc7rbqYtf1Zf7GhJw0j7JxcALeul4Niq6eV7y/JSIR08Nh/bzdBH7dLA3d1tUd/f6+YlJRVf+t+7dQa29nq33/KsJn3z7W4ahW49pb3vY93g+yaFOqRWezgC2dt1IGsV7EP2ZfZKLgRV02TN+bt96VK3Z0XL27WieytHRguna7Fg2nPs5X3xOzrn962cM6xbi/GyZcp7VXLvtTrtmbWyIea6VWssceMCwf9WP5qdYQa2t8FQGsP3dc+3Rt1rZaTfTCu6fnRf8k32Si4EV1PsndKjU5iKx2QWTXVcmJbklJQmmM3vk49Ny275c5ukaEeZTvkrdNj1fnTj/k+0HT13LeiJoWZk3V9Zyk7U3oKBR+dLl+5qe4wS1ssIXZ7ssmCC3nmkD2k7KkPcnxGQ17Du79St2Si4GVtAa3I+zqJ8cB1t8g/wtpjIraFL586igKT/0Bbp37ujO9YsmD+vzXCOfSuXdA1u+0G/EhFnL12/d79Xnwp17ZY7KyZtQ4aTh6zLfGZ/jBLWywps5W0owgal6Zq+bvlPNfXlYQV25H0/JiLuDRlhuETFV0Mc57en+Qu36Ey2m5/539od9W9csnjZmaFSXcu61ncuVquwX1Kzji/1iXp8Yv2Dpmk1bd+0/nPbbhSt37oip1bob9xDwhvE5TlCrK7wRGMD2cfCfoOnp6e7cj9TB3UTL21UwW1BD95emjvK8aD2ou8d/aFaZq5f/TDuYsvXTNYvmxY17PaZfzy7tmzUM8HN2Ll2qgnONgIDgZs06dunSp1+/oa+//k4dt7iNhdVnGf8cJ6jVFV5w6Dh3qumMetFxnJ4SsIJWKIY8J7OCotzlA8ZdwsQ8qFm6TDlrVyVm30m/cOGPtLTU/fu3b926Yc2a5aNK1+kdUsG0CmM/Qa2s8MJDxwUfmM6ot/wW9fTAwH2SWMNwbLvcvdw7/CqEmHdx4QS1tsIzc+iYIUBQyii50xxLh45ZAQSljBJBGTh0fGXO5IPqLY0AEJQyiu4Xb+cBbPGcdJ+woN5i1RZHAAhKGUWCcogciFWxnn3XIfRvVYkjcpoAglJGqaAid3NXsZ5N+XMEamN2xKkKCEoZfQsaOx+hM65WjyOoDAhKGaWCrrWcpGI9r/n3HOq+VbXFEQCCUkapoCKoWc+H2zYIz1XRFhCUMjoXlDlAUMqAoHQBQSkDgtJFmaBw+2gLQFC6KBKUhYEbWAMEpYsiQeHsG0tAULooEhTOvrHEjoJ+27BCM1UHTWABRYIycPYNc9AUNOft13nq+Rpf/eGVmvONp4xBxxwCZRtJgrNvCjet4Wldm0bLdApNQZ+s56JJ3MOrG+ml1wWKBM2eEf019yu2+PWTa/zTif26+IWTuYehG6inZxtFgkZHrG+eiJCTYDJ08XQx1/N3r+/z90AX/zg4Qd3vo/S6N0DQx7HjRtI3DcqFHaKe/UnOxY9ma+BGZVvxNxD6rJcBBH0Mfe9m+sk9YUXDmaotjgBFgq6pvZjr58PLCyaDoHRRsZ59PkXoVlXhyBFaomwrPi0ZoaLtYwVTQVC6qHlGPX/c2v+SasvDAyeLUEbfgg6bgdAvHnBGvQOjRFA6Y9Qr4Ua95we471RtcQSAoJRRIiidMeoVkbsn6YZ6SyMABKWMQkEpjFHvWICglFEiKJ0x6h0LEJQySgSlM0a9YwGCUkbRVjyVMerty84mNbqcUnF5IChllO9mUjxGvT1J8zmYsaHmPfUWCIJSRrmgTI9RP4W/x00PFe+JCoJSRrmgTI9RP3kR99AnWb0FgqCUoXkkKXtIP55aDJ1fe8T/jGGXt4p3owdBKUNT0KIdW3liX6KXUjGbfJ9tIusePQoBQSmjQNB7jf1NCKavHaGsSZTJU3VpIChllHyCZgYnX+cRTGZMUHUBQSmjqIufmyY2FQSVAgSVjx1OtwNBpYCxmeRjhzHqQVApYGwm+dhhCHAQVArZYzM9uMMzAAS1HRD0SaiOzZTl5czzrA+NlukUO4xRD4JKYePYTFBQukA9pZA7NpMZKChdoJ5S2LibCQpKF6inFLYKGnPHzD8XHZZrd8R53g6CPg31fBLCetoo6A7nYko9QwmiRCRB9BJVchbnJ9uKRqeeFAqufBkUWlmWrJ42ClrCw0oEQXOmEgS5EJzgtSmWIFHoSXzMgXCCRD2+IQiiDL6eYT9jAkavxAQkDcAEXMOe/IcdmWRwIibg4zdxyzABgkoDgkoCgooDgoKgMgFB6QKCCgBBpQFBJQFBxQFBQVCZFE0iCDqwhyDoXYLrDH79jCDRPALTr6wgSLTqb4IgyuDrueAWJuCrY5iAM7i7NDycjmvEe1mYgE2nMQE/EN6yXqmgAGBXQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaVgRlGS8VBXHVNUv2CLprIoKBd0RFIA/JhNYpmzZ7dZDMhvjkxljMLnWBnlEZ2MSmWIwiSb7ek0gfHs0wS8Q025sIXFVxFcQWz9s7UwBBFogpYJm+F7JDDqDCTK4YvPMDfbHJjPGYHKl+d/IjJhhPZEpBpNoX/Psu0EpRG+PJvgFYtqNLSSuivgKYuuHrZ0pgEALHmWCbo5GaEYCJuhaI2yetGR/bDJjDCZX8mKE1kdbT2SKwSQ6uhehvslEb48m+AVi2o0tJK6K+Api64etnSmAQAseZYLOnYrQhuGYoMM1wz2GZluPue6PT8bH4HNltNiBbRUXg020PaJHLtHbowl+gbh2YwuJryK+grj6YWvHB5BogZQKOptvRSwm6NTS/PSuM6zH8GXDJeNjsLkS6yZiE/Ex2ET/bgvdS/T2aIJfIK7d2EJiq4ivILZ+2NrxASRaIKWCbuRaMGsaJqigkOt1+liP4cuGS8bHYHIVDel1A5fIFINJlHwMoaUjid4eTfALxBUTW0hMFfEVxNYPWztTAIkWSKmgt3xu5YTgTqBd3jUns+dS6zF82XDJ+BhMruRwA7ZVphhMog86Z2aGryB6ezTBLxBXTGwhMVXEVxBbP2ztTAEkWiDFu5m2hoYsw8UUxAXUHCe8tZoAvmy4ZMZ133quceWdnJxirScyxWASFb1Ty3dUAdHbowp2gbhiYguJqSK+gtj6YWtnCiDRArGzox4ARAFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGm0Y+g3V1rlHJ1HXkgSuuGOAg6qad+BEUo3Yl7eHBJ62Y4DLqop+4EBaihi3rqTtDUqB86hHqNnBHY7hpaWbvu+CKtW6VfdFFPPQpa6fL9KgloxOLU0FtZA+do3Sr9oot66lHQFxBq8hf6MH66f6dOId21bpV+0UU99ShoN66gl7mCLpyPUE6m1q3SL7qop54F/TX4Vm4EyV0WAVF0UU89C4pW1/EZrXWjdIwu6qknQYGnEBAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYJr/B15y47K3jwAQAAAAAElFTkSuQmCC" /><!-- --></p> <pre class="r"><code>print(p9a)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 839.35238 88.57064 9.93363 +## SFO IORE DFOP +## 839.3524 88.5706 9.9336 ## ## Critical sum of squares for checking the SFO model: -## [1] 105.5678 +## [1] 105.57 ## ## Parameters: ## $SFO @@ -1806,7 +1828,7 @@ div.tocify { ## Estimate Pr(>t) Lower Upper ## parent_0 9.85e+01 2.54e-20 97.390 99.672 ## k1 1.38e-01 3.52e-05 0.131 0.146 -## k2 6.69e-13 5.00e-01 0.000 Inf +## k2 9.02e-13 5.00e-01 0.000 Inf ## g 6.52e-01 8.13e-06 0.642 0.661 ## sigma 7.88e-01 6.13e-02 0.481 1.095 ## @@ -1815,7 +1837,7 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 16.9 5.63e+01 1.69e+01 ## IORE 11.6 3.37e+02 1.01e+02 -## DFOP 10.5 1.86e+12 1.04e+12 +## DFOP 10.5 1.38e+12 7.69e+11 ## ## Representative half-life: ## [1] 101.43</code></pre> @@ -1824,17 +1846,22 @@ div.tocify { <div id="example-on-page-9-lower-panel" class="section level2"> <h2>Example on page 9, lower panel</h2> <pre class="r"><code>p9b <- nafta(NAFTA_SOP_Attachment[["p9b"]])</code></pre> +<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> +<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is +## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p9b)</code></pre> -<p><img src="data:image/png;base64,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" 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t8eMZ3HCGbL9KP++d5QhzHnDW/+qJaL0JBlslRsFgJBZ7S/VXNsZ/KQ5vL5yPEhQuMt+L2AIS3hCrVYVCARdLMOI8tdc5FLFsrwEsyWb6/zzkL/OquS+Vd7OvWr3m7qMLkqNg3JcdA76MyHFgxIZTafy7zHvlOT/cPDVkMiaFRU1Cg3Y7+ZmvyJGtxA16oLZst5YFl7fGCVHgeeowvlFv4b98Y0+So2BYGgJhu8Cczn8/e1e1jdYaQB6YH6x8KvSZ4Lvn26uUV67BLMlvnMR/rmli4TN3oGTgrxnCJrxUYhENRkgzdBYT4z/mJhN1BeSAXNM3rcLmfPsnnri3RW8p+auzrXpfLY+XvXMHAEj/BUp9EGb4KCfC6zr+XwuUUrc+PDhb9a9AH2IBE0nCNoEHlMJc4dJ9t3tm/t8Y38FQshFNR4gzeOIZ+nqv2HbnhYcprnJ+cpsz2/tOADDEIiaDxHggW/cxS5uOGoj0vFmrb9v1P6GAmBoFY2+IVzucnozyxYl5DdCP3tYsEHGAQraFQB5DEVuvrmvzz0aF0LpzGnFL1fmUBQKxv82pHcpKfwB785fPhT0bZPLPgEe2AF5fY3h/mOnxMwizymopeHJS1503uagj+8cIJa3+Dvu686u8jfknMRnbcidM7Hgg8wCEkXXz+V21VuQB5T6esXL82qGjDnkkKV4wQV0eCvRDQZdsuSdbnkOmCE87eWfII9SAT1zOb2133IYyotKMf5qd5BCy4rUTNBFy9fg0/dtoGtR8NaDomg45pv2NTCgrEBGBAUIe1PE73enC//9yiBoCps8MpBImj+5/0GbLFgBDVWEso56l1ztsyX4BMIarrBy3kLjUrACto7CfXWQR6ToYRqz07zrzrtZxn36wkENdng5b6FRg1gBT2Widi9RYGI3+YEuY85KtfxUZyg5hp8kVtoTh/lae/BTe4jlHW0BP7bhvRcvAX/R4wJyvHP0mb2A3ZLOEDLC3CCmmvwwltonnZqz+P2BjfZitBP7dsa/m1fcv6tTSDowaGaRjbUrr5RiPsbOlXuVPS6AeqQnos3NhNzC02MayWfg2LWTY2Q7CQ1/Tk+8lFL8phMCsqRsSfCocG8xFd+kGrPHab77BsCQU03eOHTo/UY8nnB8y901vWG2BVUGSSCBmgnfp1H/ARjdgXlyE+YGuAx4kDhgKuZLYPecd5HswYCQa1s8Ev46+aHW3Q5U8Lg8F3qHKi6EBJBB7/rkTq1lbECqjws8u/KEJuOa67pXs8apkW/O9G80Z5AUCsb/Ep+7PoBOyxYlzjvjTvrR1vwAQYhETRj/SW00NhAsqo9LJLxzVC3gIlHclD7o9y7+jRHICUQ1HSDN44hn9dd997/wv2hBevS/Bj329tO3V+hKhtZhB7aC0taVQ5tPJ9bf8dkinEJBDXZ4E1QkM9Trd06WnTawS+J287KDNzqKgLVjSxCk8d7evzPtmGtSJoxCQQ12eBNYHU++3Hde9ybVn6YEdQ4sghFsptXqlT2jRbzz1B7oAiBoCYbvAmszue92g3e8jqPL8cyVEcWQX8n8oSq59F98wZq0C/Oh6bXr9Jtzd9UIhIIarLBm8D6Bp939kQ2vhTTiBtZhCf0xcusJg15HNRzl0EHfiepHvfDLvnrob7uA7+4LToigaCmG7xxVNQj0UfMyCKPIngqRkQI5qsooQM3IZTr/J/+zbWNfZ2qj4z9T1REAkHNN/iiqCif9MELmvRzNtI8vPZp0cV50x3XxMY6xcYK5qsooedc1u5/56UGpr24qptd0Pt7LDmY8yokpzpNDSVkAhXlkz5YQT8p7e30vUtlr9bGCpxo9C3yLTJXTQn9dUj3NYILnfJ/XdGlStCYXdY9fQgrqOkGbwo15ZM6WEHdz6GTpbabenzB437Di54SKQYJzf91ZXf7GsO3W37DBE5Qsw3eOMUgn9aDFdSe+7Mz83iNmKKDrReThGouru3r5tlv3R8WPVwEJ6j5Bm+UYpJP68AK6sj9uVoWszgl9Oq2ITVsO0WfIH6eL05QXIM3QnHKp8Xgv0FTUlKcuT8LLkorbglN3j+1ecVG42OJjkHhBC3hDd5isIK+XgB5zOKY0JxTy8JcPXqvOIO78gn7DQoN3iLU8qQ5JrgRMy64QuNxMeYeMoITFBq8ZYCgFpJ9ekUfb4dO874zcagUnjRHFxDUGh4cmP22nc+7y44XvZQNBKULCGo1/+6c1NImcMCKhFcsBUHpAoKKIv/PbWObVazRq1nDObn6OSAoXUBQ8eSdeu015zd89GOFgKB0AUEpMMg1Hz15bZPuNQhqGWvr+I0xN8QuCEqBYP5JbwGDda9lEPTWoavU61CKTQ3OXR9i7tHkICgF+G/Qx6/p71iXXtAFrp3cGXicCR1an+B+IlUxc18fCEqBtEpVGsv2G/RXvzT0tP731GtRhsYndqz/19nMbbUgKA3SxrSdK9de/Gf8o1GjZ1OvRRkmvt5jpI25219AUMqIETR7fsR+7p9Bgtmv5nN/B37WButrYYrw5hVtGpu7ukucoKoc+kZaxAgaEbolOAahSoLZr+Yzt+7wbyb7FZfHx9a5+DwTeZt5NIQoQVU79I2EiBHUOQOlVHuAERQ9WdhrFs2xUBSl52aEblcxMyqBKEHVPPSNVIgRNOABQju6aTGCFisuOk9Y4LvWTAFRgqp96BspECPoRr/lXD8fUr7gffaYETzVPWmsmFTcv0B8t4Ex7i6d+7O55aIEVf/QN/QRtRefGIeQZu/EgreamI08777zSqGs5ZHRaSIqoYp2lFN9590SViBuJ0kw9E12xLs8PhYM61LsEHuY6VrRWZuGv/zuWfDAL0dXzxBXCzW2t3qK/nS2fhgBLKIEFR4W0ezfzfOWBY+aLnaIFTS46KxXBT3Ylpv02ySuFmoM3cJNukj4uEVRghIdFilhSC7oOv5A/SJWDtTPWMhN6p+VrgJRgpIdFilZiBXUyFfjq4Keq5aBchux8rSPKy7rToxoRm3wyqKI24sveYdFsEhwqvNVQdEHnj39hlKvxFr+iGjzgZRnDUQJKjwsYgAEpYtAUPRP3J/U62AWcXvxgsMiBkBQuggFLVHAxSKUAUHpAoJSBgSlCwhKGRCULiAoZUBQuoCglAFB6QKCUgYEpQsIShkQlC4gKGVAULqAoJQBQekCglIGBKULCEoZEJQuIChlQFC6gKCUAUHpAoJSBgSlCwhKGRCULiAoZUBQuoCglAFB6QKDh1EGBKULDB5GGRCULnQHD7t9nScsgMKKqRUQlC5UBw/LqunPU8WPxpqpFBCULlIMHgYJpQvk0xSWDh5mABJKF8inKaw8zAQJpQvk0xTWChqZZuDqDSKuqL/Y9YJNbieBoJbk859rVLaJZLPphLl+J80cZvNppaDfOdjpqVKqNBHFoFhpwybbORU5LCwai/NJYZtIaqIUpoydOczm00pBC3lagahYpnD0MeNk2BAVe2JLVCzNjqhYigNRsWQnomIiIcnn6M+wRa6aey6RgW4HsEUSWuPDBJ/HFtnZHx/GFCAoCGoaEFQACAqCCgBBQVDTgKACQFAQVAAICoKaphgIqommWSx/Ec1ieYuJij1fQrOYSEgSdRB/mCtzBT7M9hvYIvcJnjTyaQq2yOVd+DCmECsoAEgKCAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDUuCPqFarARDkiG1ZFGkoPtq+K8mKBZQpmzZvbhC6XWIAuqKYQNuquESkY2Ppi+GjTbd020K8caKgaQK7NqSJBKfRZIMEqRPbOrECZrqeSu9xiVsMa0jQazFgb4kAXXFsAETfR+kh87HRtMXw0Y7Epz9uEY84caKgaQK7NqSJBKfRZIMEqRPdOrECbo9AqH587DF7rxJECsxzpckoK4YNmDccoS2RGCj6Ytho505jFCvOMKNFQNJFdi1JUkkPoskGSRIn+jUiRN08SyEtg7DFvvRK8RlcDau1F1fooB8MZKAqY32kaweV4wg2t7QLrmEGysGkirwa0uSSJIskmQQnz6RqRMnaDRf8SBssT9WPE/pOB9Xis8ZQUC+GEHAmGoxJNH4YgTR7u+pe5hwY8VAUgV+bUkSSZBFkgwSpE9k6sQJuo2rdOFsbLG8fK7P6YErxeeMICBfDBtQM7DbA4Jo+mLYaHE/IbRiFOHGioGkCnwySRKJzSJJBgnSJzp14gRN9kjOCfoVW2xVx5z0rtgrFPmcEQTki2EDxoVoSVZPXwwbbW3b9PSQ1YQbKwaSKvDJJEkkNoskGSRIn+jUiTzMtLtu0Cf4Unkz/L0m5eFK8TkjCKhr+7iAk8pXqlRpEDaavhg2mmaCj+f7eYQbKwqCKvDJJEkkNoskGSRIn+jUsXSgHgCKAIICTAOCAkwDggJMA4ICTAOCAkwDggJMA4ICTAOCAkwDggJMA4ICTAOCAkwDggJMA4ICTAOCAkwDggJMA4ICTAOCAkwDggJMA4ICTAOCAkwDggJMw6ag17tHblB6HQAmYFPQaRdQK6XXAWACNgXN1t7BjpQDlAjkF9TzEtIsru3U4nvu9ev2jvYeq7i1cHB0dBz+UqHd3R9gwiSEkcwSjCOc98jwIi3JgjUGFEQRQQd3vq054x/LCZqC0LnSSahU1qtlvpusxYUhElQ3jjAqGAkYoekR6GbTGqsRGnPFupUH5EYJQX9352U5452vExR5/1xE0KGde0e8MuO54e/FOxJBdeMIo4KRgLkCjhFoZHx+g/R/xojeDNVA1GMVcKw3P90c9fI8I5mWESUEXTtS98L7uk7QH52yXxJ0cvVf0J7AQhfX+FWbrDndP3w2/4cW+ftN071DurTpFnbch1DwSd3LIpnUjSOMCkYCRo8brYlA01cm1cqOvCf9drICSY9VSGYSPy3pgk7TP3K6+XH0urOrXalDhhatH8Fvb8dsn8MFZRPqJmeFLzptewXxf/E10nNbbudf8YvC9Au3DUTX/U/oXuoz2clHx0X+9V3fgkipjfahfgdjI1D6yPY/nP9Avs1VHFyP9bzoR0q6oKtH6V743uQTptnuqnk5Ydrqg7oWvpnj26ZNUOfTrRHi/6bPQmjde/wrxKdNv/CJy/MlC/Qvi2ayUFB+JOCY4ShW/8Oh9+ODHafmSrJ17GG2x9J1R7ruJ2NIQLODKKF3/niv+sOi4sMRitqMZvg4932WEKZfpgxKCJrokcP9e9Zd3+VoKj98pUUvLX/1xesPEcpJP92FyyT3N43r2jcM5F8hXlD9QtT9SL0b+pd6Qd/20PEH/9ogqH4k4DA3X6eKodzbI8tSGtyagx1Qt5hgtsfiuyN9T7S+t/bnoZygnzfLTvU1CHqnfU5e45MJYfplyqDIXnxE9/vaX6rG6QVFXtdeETTc4cV322+BybmhhwoEPVwz81mrbYWC6heir9u0NZQz/g2qvZKnHwmYQ/cNqu2WcytEu3umlBvJEGZ7LL470nc/lzwmHdFygvbbgND4gm/Q2zvnOMQnhOmXKYMiguYvqOVULwYZBG39KSrl7Orq2lS3+LRb15gXhTdU9RiHCgRF0dX8p2gKBdUvRFkVvjCUMy5obqmbhtGCDYLGbkZoXs1W/0m9oYxgtsfik2noiVK/imzECTpgE0JzdYKO2nym5vrzvThB9cuUgbUzSZqG2040xI4WDpBjtsfiBdV3Pwun5T2yeZzQe0uL3PQaUQlBeU/cNn84Ft3wPJAQpl+mzOqzJuiWYC2K8Lyh9GoUI8z2WLruSNf93G3p2nKzbifJp8HMqNxIP8+um2+H1u76gX9CmH6ZMrAmKAC8AggKMA0ICjANCAowDQgKMA0ICjANCAowjZWCXp5RksFd7Q/5tAxz+bRS0E2NPyq5BMRblzTIp3HM5tNaQY1djl1S6CiBoJBPE4gU9Ncugb0uWxdCvYCgdJFQ0Fv2gS61XFOsi6FaQFC6SCjonArf3N32xnrrYqgWEJQuEgoa2pibVB9iXQzVAoLSRUJBx9he/zihwmLrYqgWEJQuEgp6wc7WpvTrf1sXQ7WAoHSRci9+h4uNRwuvLzXWRVEpIChdpBRUN/rRT83rH7MujDoBQekiraA82j3Ob1TolGRdKPUBgtJFekHRrop25ct55lsXS3WAoHSRQdC6zTTpw0tFKnTjn9yAoHSRQVD3GdykQhvn5TnWhVMXIChdZBC0nceNvDVlr/3dw3urvp9P+e64kUGpigkgKF1kEPRipUrlHPhhj35uXWufFqEjzi3qBRXboTtAULrIICi6NKrHWv1X5qF6TY9rnVwa+XsPtC40+4CgdJFD0JfQfFXtrTLfI20/B+tCsw8ISheZBUXo+ZJSXf9CSytbF5p9QFC6yC4oynnNvXKDyo2tC80+YgTNnh+xn/tnkGA2CGoKrKCX5g0bNPuCcC4moe/Xblv1f51u49dNlYgRNCJ0S3AMQpUEs2UV9C5jBwNFCbo6cMHWrQuDVglmYxKa92mX8ANR9uPp3/7IAmIEdc5AKdUevCTon4k8UX0prBcZp/w9Kk1Tajhao4gS1D+Dn2bXFswmavEPJzpMTSYopzbECBrAtdkd3bSFgmY1a8jj4EZjxUh46hGPUptvk6s6EkQJGnCLnz6sK5hN2CXdG+sQVfzuWBIj6Ea/5Vw/H1JeMHtgoKg1soAzLbhJbLhc1ZEgStA4z4hZsyLd9whmE/9muj3KYXYqYVm1IGovPjEOIc3eiYK58gn6RxA3Wc/UPpm4naTU7Yujv3hU+DZ7wLs8NYV9vmlujnQsZopKcJhJPkHzm0z5e7/nabmqI0GUoNq9+1BM74WF+33a/bt53vK3YAWShjvMLE4dvaoFRQ9H1gyhvwFiECXohOCWPVp+2VXYJViY0JsjHaIe4YupBHULyh6iBHXPem57BaV7CGZbnNBbo+2nFperR0BQuogS1DM1643zKNlbMNuKhN4dbz/+jsWfYhEQlC6iBP3YzXtq4Mx6swWzrUrof1MdRhSHB8yUNEHTV037Rsoj++L24n+/iE5F7RTOtTKhKXMcI9V/F30JEzTFb/CyZpESViDFxSJWJzR9iWuvRIRyTD2wXA2UMEGjxxOOBFwAACAASURBVCKU63dJugqYEhShp6u82rWpWKmTeneZ5Bb0fqR/k2+oV0nMoC+4Sdh+6SpgTFCEnr1VvmyF2qEiIiiLzILmB8+8ftT3KPU6SVnRH6HH7telq4A5QZHNO1vrVynzVEwIJZFQ0DNtPTv9Llh2qRY32TqIep2kZDd+a5TvXAkrYE/QcscQ2lXKc2WmmCDKIZ2g11323NniLrgC7Bx/5feed6nXSUz+t+t/kzI+e4LaNfnjcpfy5/s6zVTlD1HpBF3JX0TSf8ery3J9vkHJTb6kXiczsCfo1FoBVetwv2yuv28/4h8xgZRBOkGXzOQmwzcJFp6vbW8jPBBdnGBP0NwJDlWGpvOvHs137vajmFBKIJ2gF7z+Rudci57MSMujXiNDsCfoy2Svr954l7qGHZNwJ2m7s533AerRGYdtQRHS7G/luzKdXjzJkfQwUxr12MzDuqAc58MdJidRjSglJexMkuSoQFCEbk9z6M3UZd5mAEHpogpBEcpcW73RTlUMiQeC0kUlgnI/Rr9t7x79UILAlBEh6JM6vnoKZmh2bORpasktNMUN1QjKcXG43Xu/ShOaHmK+QdMD4+7yFLzPGTOCp7onjRVTKVIMfSNdl5Sy1KdFLNs9vagufnGisbnQxZvCyqFvpExo/t627gvuSxdfNPAblC5SDH0jcUIvjbIPPyVpDWLACRpWAHlIENQUBkE1izqnHzK23MTQN5In9MmawLobGb3aCSdoQgHkIUFQUxgEnRQelBM6zshyE0PfyJBQ7bGeDmP/krwaKyDo4p+djI8/YMGeeWE+k8+WvFNJJIJWfdoG5QhvftchGPomb+1HPA18jZWlzd257q2/fvbifdbvTIz+QCBon8YOvTxnkocsEHSuYxOH1cKFKePqvf0DeSzVQSKo76M2KLmasQKHczLnhC0rtCR31gyeIC+qq2iS53tCXGZcM7yJdalrLxx1SwkIBHXKm514oxt5SIOgx2umoXteF19dpmk59rdvPNj9SS4aEkFX1/VdErjRyPLBHXL79ojpPEYwW8bfTP9Mde7wDX/c6b7LXyi9yS7ZKjYJgaDeuTHrEfkAawX5XDCPm4xZ9+qyywFahDYOIQ+mNoj24k/MWWr0sn7XXOSShTKEX5iy/qjP3dna7YPraF/Y8WVfrR0lY8UmIBB0RvtbNcd2Jg9pyOdnQ7lJV8EP/rNNucneXuTB1AaJoJt1GFne5E/U4Aa6Vl0wW+69zitTnNvPd35zeieXGfJWbAyS46B30JkPLRjox5DPZM9Fx2cECA5eZHscReltjXVvxQQSQaOioka5GfvNdMG3Tze3SA9hxyr/YZHc2Cal6+5Y94YFex5SQSCoyQZvgoJ8Jo1uO6nIwP4nff1sx2rIg6kN0gP1j43u+OTsWTZvfZHvAiWO2+1p36S8Y7dB8lcshEBQkw3eBObzqbmRQR5KfZAKmmfNcTsZ+dMvO/877zcGnVT6ERWEpzqNN3jjwIF6UxgEDecIGkQeU5GEjqg7u9ubN1YEVVuk7DCOhIKy3uCZgUTQeI6EZ2bKCVAmoUeit+dy//wyyuHtr7KVWAE9BIKqosGzAlbQqALIYyqc0Jyv37EfcUap2gkEVUeDZwSsoNz+5jDf8XMCZpHHVD6hdz+qWX1hkiJV4wRVX4NXFpIuvn4qQukNyGMykdBzY53e2vxE/npxgqqywUvIvY8XnDW3nERQT+4nXY4PeZ2MJPTZgV5V+hy0oCulAkEXr8oGLxF/Oo+b67POTAESQcc137CpxQTyStlJaNrGVo6jT8l65IlAUJU2eEno9TlCt6qYGTyGRND8z/sN2GLBADRMJfTm4iDfmX/KVx+BoKYbvNz3eClPHf7qLO9bpgtgBe2dhHrrIK+UtYT+Ps2rzkc3ZaqMQFCTDV6Be7yE/PTxdlmP0fXjNvayo5lTtVhBj2Ui9d+ioDk5yqn5WlnGG8UJaq7BC+/xyp2tv77WlpscRejKDMn/bWHXO6z6L9LXU/jvyMqenhW7mCkXTHou3oL/I/YE5Xj+/UC79p9L/+BanKDmGrzwHq+8Vfo7FBy4Cbere+sjqf+dWWneWTRhnOT1vPg30nX0uEaNzZRrSiDowaGaRjZWXH3DGjl7+9iGfmFJU7MC0nPxxmYqd4+XgePtuMm+HnJVx9HhO4SyKpv5VUGyk9T05/jIRy3JK2VVUI6sr3vYdvnypcOjWTtWC59LIA4CQU03eME9Xgbky+ddFy41k+W8aLE+v0vofs90ARJBA7QTv85zI6+UYUE5MnaG2Xb5wnB75AO/nuM9i9yIJgYCQRlu8B/UnN3XX86bDyeOyEd7a5gpQCLo4Hc9Uqe2MlZApYdFMnb2qPzOZv7/YdxshO7Z0bygkkBQlht8QvRmWZ/zl9HBq46/0QF/DJAImrH+Elp418hyBg6LWEvmrndtQz67H/J/3OuG5hJkKQSCmm7wxlFDPq3n+h9mB9sSNbKIMkPf0CI7LsLe9Z3r6JEdzVP2BIKabPAmUEk+pUHUyCJKDX1DjWfbK75ub/c+zZAEgpps8CZQTz4lQNTIIoofFhHN01bObq+7+U48Tu1RLgSCmmzwJlBRPukjbmQR4WGRi4k8ocL7kNll7iANSnT+dVEjh4F76IxGRiCo6aGEjAOCmgI3soiOlx5MnNWsIY+DK6WVk54O/HOC+WNxd9e/U/mddWYuWiCFQFDTDd44IKgpMCOLPIrgqRgRIZivooRGbkAox0l/kj7zm0jHurPOirzHnEBQ8w2+KCrKJ33wgib9nI00D699WnRx3nTHNbGxTrGxgvkqSmiiyyd7O0QWvs0/PSPIZfA3Yh4cRnKq09RQQiawPp8HeoZuVNeD+oqAFfST0t5O37tU9mptrMCJRt+iomMtqkhQ9PuIXp+9uoN0Y21Hm5CPL1sbECuo6QZvCqvzGVM99tvmFtz8xCJYQd3PoZOltpvq9x73G170lIiaBDVK1oGR3n5jvntqzWdxgppt8MaxOp9NTnK/w2zVPS4OVlB77s/OzDbGFB1sXfWC8vy5rK3N2yv/tvhzOEHNN3ijWJ1P/xsIaSure2QcrKCO3J+Fe+XFQlCOjH0jfX1G7LXsPBNOUFyDN4LV+XxvhhZtD7byw4yA/wZNSUlx5v5SyGMWF0F5Lq/qVLnFgp/J9zRwgsrZ4B81rVa3uoz3Y0kBVtDXCyCPWZwE5cg5Oq2eXc/1V8lKY79B5Wzw2ssXqJ0iUwg1PQpRQf7bEenuN3wXwZWSOEGhwVsGCErMX6u62tafeghzRhSeNEcXENQS8s4sbFOp5dwTOaaL0HzasYHim08CQFBLyT76QdNK7aJPm7jMlubTjg0U73xiAEGtIeO7qcE2HRadKhz4KTmx4PQozacdP+3cnsfNXURItQOCWsuTb6cE24QsSOBvme1Ttmy5efrZNH+Dak8e5WlvwZVPxQ4QVAzp389oVqnlB2PK7Xv0cRn9t59YQa8VnVVy8mkEEFQsT/9vfpUydRcgn7G6t2IFNXLip2TlUwAISoH2TX86gAxPEZNB0HsnKFxYrRZAUAp8V2bayV5lk3SvxQq6qegsQT4/cm7jMkVcJSoCBKXB8or/c9ynfyn5gfrffB+hzDr0a2EUEJQOuQUvJBd0/WhusnAO9VoYBQSljOSC7nuHm7z3GdEnEwaH71L60XsiESeoSsdmkhLJBc0OGnfwAx+igU7jvDfurB9NfX1kRZSgKh6bSTKkv1gkdVbXKfeJPtj8GEL37dT9FSpKUHWPzSQNLF3N5JfE3/Ih5h5V5RElqOrHZpIAlgTtx3XvcW9SXRfZESWo+sdmog9Lgt6r3eAtr/NU10V2xO0kCcZmynKz4ynvRGPNVApLgqK8sycUfPAzFUQJmj0/Yj/3z6DCGVlpPH3hG5Qq0COZAidoROiW4BiEKglmQ0LpIsznpa9/pV4Hs4gS1DkDpVR7AIK+jPSCTvEOrzZQ3ceOLEDcXvwDhHZ004KgLyG5oGcDstDzJvup18IoogTd6Lec6+dDygtmg6B0eTWf6/gRyxdZ8Lh5dSNuLz4xDiHN3omCuSColRTZ6dTzaj6/a81N+n5ufS3qAi4WoYwYQYl2Op837bNlaCCd8cpVAAhKGTGCku10Zq8eskzdpy8tAQSljBhBYaezKCAoZcQIKtzpzKrhz2OjnodS0AcEpYyovXjhTuf96zyL3hO/WqoFBKWMBPfFbxouLqSqAUEpI8Ftx4wLKu0IpCAoZUqaoOvdXmsk5RV9IChlJLgvnmVBjwRc0ezylPA5DSAoZSQ41cmyoGPWIcMDJSUCBKVMCRN0/Gpu0ua4dBWAoJQpYYL+6PPTk/X+Vj3yjAwQlDIlTFAUG1TlHcsfd0YOCEqZkiao1ICglAFB6QKCUgYEpQsIShkQlC4gKGVAULrA6HaUAUHpAqPbUQYEpQvV0e2ehsKDp0BQulAd3c7w4Kn3w2ismUoBQekixeh2kFC6QD5NYenodgYgoXSBfJrCysNMkFC6QD5NYa2gdWfomV61KRF+RKWasFyspmGTZ/hJIKgl+axVF1ukMUGYgPrYIg2r48NUC8YWqd9qhjnM5tNKQW99ZCC6TCcSOhAWK0tUrD1hsXJExUIIi71WsM1L06xLGq18egdhi7xVAR/GuQG2SGN7fBjb5tgidat/ZA6z+bRS0EKeViAqlikcqcA4GTZExZ7YEhVLsyMqluJAVCxZlkGlSfI5Gv8EpasEj/fudgBbJKE1Pkww/nalnf3xYUwBgoKgpgFBBYCgIKgAEBQENQ0IKgAEBUEFiBU0twpRsWwyV57aExXLciQqluFMVOwJ2cBdaW5ExURCks9x+KFtkwgu5ul5CFvkdAg+TNPfsEV2R+LDmEKsoIjokaclqZhICGrJfEYlzON8bBEtwQE1gpryRIx1KlpQAJASEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAalgR9QrVYCYYkQ2rJokhB99XwX01QLKBM2bJ7cYXS6xAF1BXDBtxUwyUiGx9NXwwbbbqn2xTijRUDSRXYtSVJJD6LJBkkSJ/Y1IkTNNXzVnqNS9hiWpJTk4sDfUkC6ophAyb6PkgPnY+Npi+GjXYkOPtxjXjCjRUDSRXYtSVJJD6LJBkkSJ/o1IkTdHsEQvPnYYvdeZMgVmKcL0lAXTFswLjlCG2JwEbTF8NGO3MYoV5xhBsrBpIqsGtLkkh8FkkySJA+0akTJ+jiWQhtHYYt9qNXiMvgbFypu75EAfliJAFTG+0jWT2uGEG0vaFdcgk3VgwkVeDXliSRJFkkySA+fSJTJ07QaL7iQdhif6x4ntJxPq4UnzOCgHwxgoAx1WJIovHFCKLd31P3MOHGioGkCvzakiSSIIskGSRIn8jUiRN0G1fpwtnYYnn5XJ/TA1eKzxlBQL4YNqBmYLcHBNH0xbDR4n5CaMUowo0VA0kV+GSSJBKbRZIMEqRPdOrECZrskZwT9Cu22KqOOeldV+BK8TkjCMgXwwaMC9GSrJ6+GDba2rbp6SGrCTdWDCRV4JNJkkhsFkkySJA+0akTeZhpd92gT/Cl8mb4e03CPk6PzxlBQF3bxwWcVL5SpUqDsNH0xbDRNBN8PN/PI9xYURBUgU8mSSKxWSTJIEH6RKeOpQP1AFAEEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGlYFvd49coPS6wAwAKuCTruAWim9DgADyCxofikHB5fIDK5eB0dHxxVIs7i2U4vvuQVlHRwrt7r6omC29g5uMKeEMJJZT0y8TUuyYLUBxZBd0FyUMeodrt4s3fvBnW9rzvjHcoJmoScRXV8qubv7A0wsIkF1IwlzLPb2XWl4e7NpjdUIjbli5SYAsqKAoOiZy18GQX9358eVPOOdzwuK4v1fFPxusvbVTz43/L14RyKobiRhjoO179+re1r/dmR8foP0f8ZQ2Br2Ie6xCjnWm59ujnp5npFMy4YSgqIeXxsEXTtSN9f7Oi9oZmRH/s3k6r+gPYGDO/eO0C1b41dtsuZ0//DZ/B9a5O83TfcO6dKmW9hxH0LBJ3Uvi2RSN5IwxwzuA0sm6d9OX5lUKzvynjwbrDDkPVYBmUn8tIQLOnyVrkX7o2nRurnNj6Oyzq4VG17WvdvbMdvncMEHEuomZ4UvOm17BfF/8TXSc1tu51/xi8L0C7cNRNf9T+he6jPZyUfHRf71Xb2gOxqlpTQJ179NH9n+h/MfyLnVykHUYz0v+rkSLmjhN+jqUbq5vjd1CTOgrT7oRdOe49umTVDn060R4v+mz0Jo3Xv8K8SnTb/wicvzJQv0L4tm0iCoZrxrzYFDC9+i3o8PdpyaK8EGMga+x9J1R7ruJ2NIQLODKKF3/niv+sOi4rnmHLUZzfBx7vssIUy/TAmUEPS52yVDwhI9crjpWXf0sqBoafkXP46WfohQTvrpLlwmub9pXE+9YSD/CvGC6hei7kfq3dC/1Av6toeOP/jXBiPz8xCau7Dw7ZFlKQ1uzcEOqat+8D0W3x3pe6L1vbU/D+UE/bxZdqqvQdA77XPyGp9MCNMvUwIFBH06rmPhb6KI7ve1v1SNe1XQcIcX322/BSbnhh4qEPRwzcxnrbYVCqpfiL5u09ZQzvg3qPZK3t+1sh76Xy8QVNst51aIdvdMSTeVCfA9Ft8d6bufSx6Tjmg5QfttQGh8wTfo7Z1zHOITwvTLlEB2QV1cXAekFwqav6CWU70Y9Iqgp926xrz4xIaqHuNQgaAoupr/FE2hoPqFKKvCF4ZyxgXNLXUTLXCvux8VCBq7GaF5NVv9J912sgK+x+KTaeiJUr+KbMQJOmAT19voBB21+UzN9ed7cYLqlykBe2eSNA23nWiIHS8cIALfY/GC6rufhdPyHtk8Tui9pUVueo2ohKC8J26bPxyLbngeSAjTL1NiC9gTdEuwFkV43lB6NYoH+B5L1x3pup+7LV1bbtbtJPk0mBmVG+nn2XXz7dDaXT/wTwjTL1MC9gQFgJcAQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmsVLQ3aVKMKV/pvt/APk0kxkrBd003LrPFQs6xlMPCfk0hUhB83ZFH1DmKhcFAUHpIqGgz1sENvbvWtIMBUHpIqGgX9iEfxJqQ///i21AULpIKGi3muj/8txGWhdDtYCgdJFQ0J7VtO0b2L9vXQzVAoLSRUJBd9u2melfuociV7IqBwhKFwkF1fTwCfZ4b7Tb9hK1nwSC0kXKw0zo5Oe/IHQuuNVF6+KoEhCULpIKqkczoqxNeAm4B00PCEoXGQT9vqK9fWm3fOtiqQ4QlC4yCNqw4XO0rfSb56wLpjbULugTxr5JZBDUjR/qqOIw98G4IROLBeoW9FxtW5v5stVGggyCtqqagmLLXsmY7rhUPyhI0pZdWcY+VyxQtaDZ3nHoQfBOuaojQQZBf6n4mq0tP2T31e78qABot1Obpp43rQvNPqoW9Kfm3OSrfnJVR4IMgqKzfVsveqp7daxO2980tvbvtnXsaV1o9lG1oL/xQ05/PkSu6kiQQ9CXyN/o2qdMIkJj7awLzT6qFjSv/txbR32Oy1UdCTILilD66FKTs1B0ZetCs4+qBUV3B/g0jZOtNhLECXpp3rBBsy8I55pP6PPXKlbuULk1ftXUiboFZQ9Rgq4OXLB168KgVYLZmIQu8mtiV+ZjkpVTIyAoXUQJ6p/BT7NrC2bjEvr9+1Eba79F/+YyJgBB6SJK0IBb/PRhXcFskoTmb/XqeRlfTH2AoHQRJWicZ8SsWZHuewSzyRKavdx52G2SguoCBKWLuJ2k1O2Lo7949OJ9ZhrPKsKEPp7pMOURvpi6AEHpIkpQ7d59KKb3wpyC91nudjyvuZLW/mCs47wn+GJqAgSliyhBJwS37NHyy67C/A0MJK//5ntOHxarE/MgKF1ECeqe9dz2Ckr3EMy2RFCELvd1XZltyQfYBgSliyhBPVOz3jiPkr0Fsy0TFKGLPT3WFJsHu4GgdBEl6Mdu3lMDZ9abLZhtqaAIXejm+WkxURQEpYu4vfjfL6JTUUUuH7RcUIQSu3iuzcEXYx8QlC5SXCxijaCcot08Vj21rkKWECNo9vwI/ol3gwSzQVBTyCooQr/1cl2WiTTrWzZZbuQh0CpBjKARoVuCYxCqJJgNgppCZkERutTPeWG0f6NGNaZaG0FxxAjqnIFSqj14IWhW84Y8LlVprJhKYUtQhP4d8j/77UcavaHa0UjECBrwAKEd3bQvvkEvJvKEVqewXmqFNUERKtvdfvSp0qrt48UIutFvOdfPh5QXzDafz+tfHdNYXyXzsCfoG8Pvz6pQ+ryYEEoiai8+MQ4hzd6Jgrlm87nBtX/jxpki6mQc9gTt5efk7u3r8c7/iQmiHBIcZjKXzzTH2wgNVfBW9h0BFdr9KWF89gT9r5mXX51rz7YGNfxajQ+Gl1nQU/z93Ae7Uq+TlBN+559u9pXwagr2BEXaa//ww69ov23t80mGqEhKILOgt92yEYoeT71OUsat5SbtJOztGBT0Bef6OkxT2zXNMguKhrVYP9U1iXqdpLz/KTcJOSZdBUwLitDNyQ7h6hp1TG5BtV+NnnePepXEHKt6MW+Ht4Q7aYwLilD6J/7Nd6nox6jcgirNVp9yLX+XMD7zgiKkiWvtuUQ1t4aUNEGlRgWCcvw2xG7Ib7SDSgMIShd1CIrQoyVeLWPVcHoJBKWLFEPfSJPQ/L3t3ObelSIyVUBQukgx9I1kCf17rEPPo4xfRoITNKwA8pAyCpo2uUnXk7LVRgKJoJpFndMPGVtuYugbCROauaFu9Y9TJAtPAZygCQWQh5RPUE2r0Wdj3H6RqzoSSASdFB6UEzrOyHITQ99Im9CfIqsM+FHKCsRB0MU/Oxkff8CfPKTZfOZtiph8jTyWeS4Fcv3TWqYer0oiaNWnbVCO8N5iHhND30jd4tM+qVVzJavHnQgE7dPYoZfnTPKQZvPZr8OO+c7/kAczi+7M/q4+lKJRgURQ30dtUHI1YwUEQ99kNdNdAe5APLKI1ZyKrBJ+9JXLIHM+6R/Fwi4UgaBOebMTb3QjD2lO0CTPXISW0npib6bbjyiz7SZK0ahAIujqur5LAjcaK3A4J3NO2LJnhe9lvAL88boGfgtfnKjXtO+9M8pdwXN+BRAI6p0bsx4Jf7ibwezVTC25yQELbDfPMc9AuzFMXf9MtBd/Ys5So4fJB3fI7dsjpvMYwWy5ftRfGOvYMdZwr/LZOppcNHmBPBWbg0DQGe1v1RzbmTykuXw+cfwXafsvIQ+GIe9KGrVYVCARdLMOI8tdc5FLFsrwEsyWb68z56sODmN0F5PsbVP3NZcIBm5/JDkOeged+fAOeUiz+dzh3DWoXbEYUcA4JIJGRUWNcjPWizT5EzW4ga4Je3RZDyzfiq5Wa+k99HuZL9ClCgzcDEogqMkGbwLz+Xxw8Bzjh4ZFQXqg/rHwa5Lngm+fbm6RHrsEs2U+86E9Ncy+49RAl65V6xk7FiYzBIKabPAmKMznLzukvG6ITUgFzTN63C5nz7J564t0VvKfmsuObVzu3ejflk+Ru+KiEJ7qNNrgTWDIp7Z/YISvZdfOf/du981M7fJYDomg4RxBg8hjKnHuONO7T30Hm+3yVyyEUFDjDd44hnzub5qHsgPPWLAuX1X9al8zCw64sgiJoPEcCc/MlBOgyMUNf3dyrNXHP3DhVQXqfhkCQa1s8HMXcpNxayxYl6YJCCXbqvsrFCtoVAHkMZW7+kb783jXxisVPV5PIKiVDX5rb27S+lsL1sX/BpeSyuq78fBlsIJy+5vDfMfPCZhFHlPRy8Pyfxji0Pqzh4rVjxPU+gb/tNagDb2aW3L3SyTXve9sYMEHGISki6+filC6Bdup9PWLzw4OqNJ+k0In63GCimjwmStGrrNomN+HjQIbVv3Dkk+wB4mgntnc/roPeUylBeXIiQuv0mFjsgI1E3Tx8jV4zcXzFvyUYBISQcc137CpxQTymAwIypH9Tbhtu3X35a6WQFAVNnjlIBE0//N+A7bkk8dkJqHZ+yLsWq5MkrVOAkFNN3g5b6FRCVhBeyeh3jrIY7KU0GeHhjk1WHRJvgoJBDXZ4OW+hUYNYAU9lonYvUWBiPyEib7Vpp2W6XAgTlBzDV54C82zuTN4gqpwk+MI/TujBP7biPRcvAX/R4wJynNhXj3nofvleEYDTlBzDV54C83zlR/xNLDnJj8hlDSmz3jdvx8Z3peEf5sQCHpwqKaRDbWrb5QiaXVI5c4bJD+IT3ou3thM87fQaMNrDfQbK2rlVAjJTlLTn+MjH7Ukj8mmoByPY/s7NJh77uXO/tr0YTupXq1GIKjpBi98erQeQz73NePPxZ+2aG20KhrVyjgkggZoJ36d50Yek1lBOfISptVyGbyn4BHLl5znft6M1h09OggEtbLBW34uPneMTYVeyp1UowKJoIPf9Uid2spYAXUeFrnxaSebNksv8i8Hc//fmQ40TzoRCGplg9/ai5u8Zcm5+A/C0rInd7HgAwxCImjG+ktoobEfb+o9LJJ9aGxVz6F7HuuGXm3wK8XIBIKabvDGKTgXHxS5vmcLS/rsmn9zO1o26n6UtMpGFqHJv2s6V/ZofDb/bwea/4UEgpps8CYoPBe/cvRnFp2Lf5PrJXJt1H2yU30ji9Ak96Bjuf+VG3aDYkgCQU02eBNYnc9F7a7+Nzjcyg8zghpHFqHIn86DB/sHuFYd+U0qpYgEgpps8CawOp/5c32cRz7Bl2MZqiOL5M7Wn/mw4H4bhRm8FqEsh+SLK0MrN5x+hMaBfAJBTTZ4E6iowdNH3MgiPKEvXuat0p/58KW0ctLTvnAn6fmpyAQfYwAACSdJREFU+a0qvTX/pEU/8oxAIKjpBm8cENQUmJFFHkXwVIyIEMxXUUI/GIXQX44FO0lZ8TMa27RfdFrMSM4Egppv8EVRUT7pgxc06edspHl47dOii/OmO66JjXWKjRXMV1FCMxo37Oa8++U56d9OaWjTIfpHa79JSU51mhpKyAQqyid9sIJ+Utrb6XuXyl6tjRU40ehbVLQ/V1NCNT8e+K/IzCffTm1SqfXco9Y84Q8rqOkGbwo15ZM6WEHdz6GTpbabulbtcb/hRU+JFIuEZh6Z3apS40n7LD1TiBPUbIM3TrHIp7VgBbXn/uzMXEsZU3Sw9WKT0JwfF4fa1hi89bIFn8EJar7BG6XY5NMasII6cn8WDkhbrBKq+WPdAF/Hbh/9SHi6CScorsEboVjl01Lw36ApKSnO3J8FTy4ofgm9t3tikwqNxu9MwhfFCVrSG7ylYAV9vQDymMUzoTmnP+7p7tJ9yXHzI3Vgv0GhwVuEWp40xwi390xpWan2kA0XTB4qxQkKDd4yQFCLybuwfkjtSs3G77hs7KckPGmOLiCodWSe/Di8auU2U7/+V3DDCAhKFxBUBGk/fNjLx7bN5J2XM68W9PkgKF1AULGkHPnw3cqlSr+2Qv8WBKULCEqBrWU/PTmrjH4YORDUMh5vW3fF3HIQlAJvteUmXvrRlkBQi7jmHj7GNcZMARCUAm35e4jd9U9wkF7Qy71qdE6kXolC9FuF0BUHM2fWQFAK7Cmz5LdhZfSj40suaKrXuitfut6kXosy1LmI8pD3LdMFQFAazCpfttKX+peSC/o1/zDi8Sup16IMPTvavhFS2czYniAoFbSF99yJETR7fsR+7p9Bgtmv5vNz/nmPulFGigOTyg+dZWvu9hdxgqpzZBFJESNoROiWYG6HoZJg9qv5vOZ2EV33/cX6Wpii6bcfLzzraObCBFGCqndkEekQI6hzBkqp9gAjKNrt4uFgwR1NbNPyFEIaezODe4oSVN0ji0iDGEEDHiC0o5sWIyjS3LNgPHbGWdPsyqPxncwUECWo6kcWkQAxgm70W8718yHlC2ecOsrT3oJblNWGdomvwyBzg7eJElT9I4vQR9RefGIc9/24d2LB26ed2vP4BLxa6tYhpZ/3+BL/1z5wUJHHCVNE3E6SYGQR7Yli3+KxiD3MdK3orE3DX3m7wLWTOzMDLf/qtvfy3NpiR7swgyhBhYdFnnbVtXg3dxprplLEChpcdNargv7ql4ae1v9eXC3UmLKMmzQ7JV0FogQlOixSwpBc0M/GcJPoOeJqocYI/nhCJ0vG6rMQUYKSHRYpWYgVdJORWa8Iur8DN4lcL64WasTVe4gS3NKkq0DcXjzZYZEShQSnOl8VNKfOiL1T/Cx5LJCkLKjs4XNMwviiBC1yWEQPCEoXwU7SkwU9ZzL0ZIRn0j7ZR9xevOCwiAEQlC4CQUsWcLEIZUBQuoCglAFB6QKCUgYEpQsIShkQlC4gKGVAULqAoJQBQekCglIGBKULCEoZEJQuIChlQFC6gKCUAUHpAoJSBgSlCwhKGRCULiAoZUBQuoCglAFB6QKCUgYEpQsIShkQlC4gKGVAULrA4GGUAUHpAoOHUQYEpQvdwcMy03j6gqBUAUFNYengYVludjyvedBYM5UCgtJFisHDIKF0gXyawtLBwwxAQukC+TSFlYeZIKF0gXyawlpBI9MM3LtRUvivYJPbSSBo8c5napo5zObTSkH32RVQqjQRxaBY6cJtPmdd0mjmk8I2kdREKUwZO7OYy6eVghbytAJRsUzh6GPGybAhKvbElqhYmh1RsRQHomLJTkTFREKSz9GfYYtcJRhguNsBbJGE1vgwweexRXb2x4cxBQgKgpoGBBUAgoKgAkBQENQ0IKgAEBQEFQCCgqCmKQaCaqYRFcufTrNY3gyiYs+jiIo9+4BmMZGQ5HP3WWyRjPn4MBv+xRa58wk+zFL8UNAXv8CHMYVYQQFAUkBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlYEvQJ1WIlGJIMqSWLIgXdV8N/NUGxgDJly+7FFUqvQxRQVwwbcFMNl4hsfDR9MWy06Z5uU4g3VgwkVWDXliSR+CySZJAgfWJTJ07QVM9b6TUuYYtpHQliLQ70JQmoK4YNmOj7ID10Pjaavhg22pHg7Mc14gk3VgwkVWDXliSR+CySZJAgfaJTJ07Q7REIzZ+HLXbnTYJYiXG+JAF1xbAB45YjtCUCG01fDBvtzGGEesURbqwYSKrAri1JIvFZJMkgQfpEp06coItnIbR1GLbYj14hLoOzcaXu+hIF5IuRBExttI9k9bhiBNH2hnbJJdxYMZBUgV9bkkSSZJEkg/j0iUydOEGj+YoHYYv9seJ5Skfs9TV8zggC8sUIAsZUiyGJxhcjiHZ/T93DhBsrBpIq8GtLkkiCLJJkkCB9IlMnTtBtXKULZ2OL5eVzfU4PXCk+ZwQB+WLYgJqB3R4QRNMXw0aL+wmhFaMIN1YMJFXgk0mSSGwWSTJIkD7RqRMnaLJHck7Qr9hiqzrmpHddgSvF54wgIF8MGzAuREuyevpi2Ghr26anh6wm3FgxkFSBTyZJIrFZJMkgQfpEp07kYabddYMIrmnNm+HvNSkPV4rPGUFAXdvHBZxUvlKlSoOw0fTFsNE0E3w8388j3FhREFSBTyZJIrFZJMkgQfpEp46lA/UAUAQQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGvUI2tnRvpSj46hjvZVekWKCSvKpHkERSuEfFZKZpPRqFBtUkU/VCQpQQxX5VJ2gCb3PtqrrNmp+QIs7aI1ftckapddKvagin2oUtMLNDJt5aPjyhLrJWeGLlF4r9aKKfKpR0A4I1fsXfRo1x7dNm6DOSq+VelFFPtUo6DtcQm9yCV36IUI56UqvlXpRRT7VLOhvgcm5oYeUXiv1oop8qllQtKGqxzilV0rFqCKfahIUKIGAoADTgKAA04CgANOAoADTgKAA04CgANOAoADTgKAA04CgANOAoADTgKAA04CgANOAoADTgKAA04CgANOAoADT/D92DpdmBh4bRQAAAABJRU5ErkJggg==" /><!-- --></p> <pre class="r"><code>print(p9b)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 35.64867 23.22334 35.64867 +## SFO IORE DFOP +## 35.649 23.223 35.649 ## ## Critical sum of squares for checking the SFO model: -## [1] 28.54188 +## [1] 28.542 ## ## Parameters: ## $SFO @@ -1853,9 +1880,9 @@ div.tocify { ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 94.7123 1.61e-16 93.1355 96.2891 -## k1 0.0389 1.43e-06 0.0312 0.0485 -## k2 0.0389 6.67e-03 0.0186 0.0812 -## g 0.7742 5.00e-01 0.0000 1.0000 +## k1 0.0389 1.08e-04 0.0266 0.0569 +## k2 0.0389 2.23e-04 0.0255 0.0592 +## g 0.5256 NaN NA NA ## sigma 1.5957 2.50e-04 0.9135 2.2779 ## ## @@ -1872,17 +1899,21 @@ div.tocify { <div id="example-on-page-10" class="section level2"> <h2>Example on page 10</h2> <pre class="r"><code>p10 <- nafta(NAFTA_SOP_Attachment[["p10"]])</code></pre> +<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> +<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is +## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p10)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAKgCAMAAABz4j/3AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dCXwM5/vA/VtVdZM7IRdCiJAIdVRFRRUpcdQdlCotUkcR1TauaupuVQ9SRLWOOvqjB6UaqupsHS1aFK1bhFxybLLvf2ZnEzE7s/vszjs77yTP9/PJ2p15886bxzd553jf5y1HEIRhymndAASxBgqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA2Lgp6P6r5U6zYgjMCioNP2kXZatwFhBBYFzTT+F6N1GxBGcL6gv7TyDpxWSMq5uLq6LiTGeaFu7XaKimyLvmqzmhQJh6W2IfrG6YJme35fmP7cR6Rclunj6E6XCvf6bX6oyM4pRtv1wARNbcK9bGwQuID/MKt8+fKPhgs7EnfZ2W5EG5wu6FmPQkJ++8os6B8emdzrnrqFJYuM6NZn8MPfZEwVvh58ggm6ONSfkFsel9MbHDVvmb7ufOvQeeR2f6U/h44A9FklSYov+UnrXsnpghpD2636jz+wSdBlI00bvS/JlH4/oN6kwoO9wuL5LzInMOB10ydiCpxpZ+ethETsFd5bBHNXEifoqlhCZrwpbPhrCJn0I2lzf/wpVX44JgH0WWY2hfCvZVxQkv9Znxodzpp+nwPJ5NmmbW32CPtejcwnAz55UPan0JtZ/RccrHyW8F87mqXnRiXz7wgfOGEnp9+FQKPw3hTMST145goVXOEEnTOdkJXDhc/d/iHn24S/9/crzvppGcBan/WgW+JJP8e/lnVBOW60ftYcrsVjTBsCzwg7jl79+qfWJU5A3/KPjAzpd7ADIfzXlMWEbBjGvyN84ISd9zzy5840F7QMJi/obF7QoaaPOwcIm2OvqfWjMYh8n2XqjEx9T2aMl//X5GC0YVztsJfid3AnQPFJZJKXe7+8lBhhn1Y4XdCvZnAvR2uZw3XIN5d7PeZlKNq9oenJEoUTZxFSaDgYzcWS+5q8hPv2WP4d4QUVdpIeO5v9Yy4oLSgv56w3TB+HrTP9sz+BTGva4V+VfkLmkO2z+M5I6Hs+7mP8ZRgX5OUdc9KCzYKeicwxtNibEiPs0woNLpIOkDszni86I+rf+wY50iC5ePdr40oWPhacZui9rEjQb5pn5nVeWSyosJOsi+xQVFC6i7/pcysn5KjxrIHkud01bY3JvNKJbJ7lhJ+WFaT7LL4zEvqeE3Wm7eWDPIA7N3236C9o6s53XHekxAj7tML5Xfz2cDfPwTeLBDXMCPZo/b/inYebrXyo8EdBtUcV/wUlM+sGTigoFlTYSbIqrS4qKP0XlKwPDVlAcstdItuFs4ONnxIyNTzyujo/H3PI91l8KM390L0VPdtynwduIWS+SdCxSYcaLTgYwwkq7NMKtp4kGcIOnNj7m9atKGXI91m8oELfM3sSSaty92D0ik65GaHxKY0NaQFJc+PINf/tKTHCPq1az5agiaMJGf281q0obcj2WabOyNT3XG7t7f8Bf5E01i987sK8oUFdZ351Mbzx8/MjUmKEfVrBlqAIIgIFRZgGBUWYBgVFmAYFRZgGBUWYxqagR94cPnT6UVulEEQdbAk6v8GMlStnhSwUbb7+aRlmeQb1/waMpxy2BA0wfXNOiGjzioYvl11qWxvu6xgYTzlsCVrfNObnVlPR5uUjFf1/6JvOO6hXifGUw5ag6z1jp08f6rNBtBkDSheMpxw2L5JurZo9Z+Ut8VYMKF0wnnI4eBX/IKD7ADMwSxkoKF0UCSpzFV8cUGPr51PF31PaQUHpokhQi6v4k0d54vsWfc5/3Xe/oubpDxSULooEFV/FZ7VuzuPi9aDIt55zCi2/sRSDgtJFjav42IYlPlzt0OGKY03TJ0oE3UyMq/oOXCM+cUdB5XDwKv4hQUnBHE/t5qU6HyWClieJ4Z9v7jJdtBkFlcPBwSJFgh4Od23zJ/fvgcBXsh2rSYcoEzTiMiG59UWbUVA5lAl6oULwy/5P3ObepccGH3OsKv2hRNBHycALhNz1FG1GQeWwJWgTfwHRZrOgQ3y5/r3mNNP7de7vFjjcRl2hRNB6XgEdyalmr4s2o6By2BI0NTTpCo9os1nQDu25l5B+wrbLHdpddKiBekPRVXzuH7vJ4U0PPp8w3bab2lf+O0o9yrr4RZKJNM2Czn7iAjn+WFFekMIFbiulCpc2aN5mympjcduuzKHiOagh6LGqj7V8cM/kVLPuNxyrUE+ocB/04bsiZQw1r+ILNkzdXvKeXt50z02S31CaUCKo9XP6somaglpysGH/0v5wXomg1s/pyybOFZTcn+S9xbFK9YKiLt7qOX3ZxMmCErI/qP9tx6rVB3gOShenC8r9EfUSP7wvTaCgdHG+oNyZaOMY20sd6RUUlC5aCEpy33ZbwV/e3xnu5j+rlI3GQ0HpoomghJx8MvIvQnoMX7vyqfmOHYRVUFC6aCQoKXjfdU5aJY/odr6hjh2EVVBQumglKCH/dg9+JIWQsTUdOwiroKB0UWNWJzSgq/6v6bXbT7vACusFFJQuaszqhAa0oIrf/z3xfEdYYb1QKgXdMeKlHzQ6NN1ZnQLggL4TMs33sc+AhXVCaRQ0qd7yTwJXaXNsqrM6zcAD+r+RcW+6T0iHFtcDpVHQeicIOdZAm2OrPqvTJrdH+HxeihKQlEJBjZVyCMmqrM3Bqc7qLEg2pXRsFWhfGw61eOp3+76DYagKKoyo7yqeReds2q8iZIVG1wqKBDVu3krW9Jlxv+hzzhhTSsf6te1sROEKz9GlZQQJTUGzn2JjRP2ffpFPB5zR5tiKBI2LeKpHl61Dhos2O9Al3X3N/f18u7+LRUphF8/175+v1WriuCJBvbLyq/9FMnxEmx0K6OlnG33vwLcxh04F/XbWmjzZnf9GBPi31Cg/jCJBa6dmVjxKbtURbXYwoN806PKnQ9/IFPoUdEjEW9HNZP9IRs8lxlk9VG+EJMpu1Hv5vh6R2D5etNnRgOYvcR998+FNP3d/cry+JonoUtAjDbg/n/2Xyuw1VskiJL2a2o2QRtlV/O+nyL6pFsmuHA/onfGuc++X+HzUa+3BMa11lfJBl4KuHcS9LBsjt7vOBUL+ClC7EdJoN1hEmvN9fFc/GCE6Zgn3+xuiq3tQuhT0t3o5hLzwkdzuuS13fh8xT+1GSMOaoIT8+lTT74reD/yCe3lmj5LqnI0uBSUjQ1+Napkjt7dweVTUZxo9TGFPUEK+Dm5/UHj3WcSLz7/ioatHofoU9FvXapXYHDnOoqCkIKlOr9P8m9NPPFH7iQhdPQjVpaDXPQ+S/xpIznnWGiYFJSRngfuLlwgZ/27a2Zz6fyiuzonoUtCv+nAvCyeqfRhHYFRQQtITXMdc7b+eexfF5G+2HLoU9PtnuZeEt9U+jCPYFDTejHhUshUoBfT26y7PdMonp93vUKnOSbAq6I892r2dJbczs978vzd4MvmYxKagSUlJLzVKnNfkXXid1H7jr42pUK21+3pKtTkHRgVN8fliX//usrsv9K//7M/Kj6ICkC4+7C7X44bD66TYJf03oNpEfJJEIZ4DkgkxeOtwvRWIoD45hOSKcwJageo50+XRLvEW64gwjC1BY4qQ2CezDA2NeHbmV7VuekJ5Rc4GIuiYjpu3dIqD10n5pP7yK7Vel818e2WQb8RGqodTiC1BU4qQ2CezDA2NeCb2zCU/1NbhiEaIoIblAwauMMDrpH7VeSWuVty/knsKwt8+tyfA2pr3zgbQxeft3bFje4TEDotlaH7ZxRNVT3mz8vu7N/bdp7wepwMRtHBBP8MyO0ZsqHBb5MYUlxF/SWw/Wa/VE7VeGEL9eI4DEHREC9cYvwUSO8TL0GQ/F8XjRWVE/bU/dPj3EyboxP4hhb1HSxVQlrjBLu7McOv7m8XWnx9PLrwc8KQKx3MUgKD1DO8cuip1RS2zDI32I+o1BCJo3axIYhCPSuZRmLjBTjIX1Xn2R9G2XypsJDfq6UxQz5wdC0ljqT3iZWgEUFA5zIL63Yokd6SmRStN3GAveasaNd/w0LnG8aDQ6tViBqt0PEcACDohMjU4rjm8ShRUDrOgS5r6zwz+WGK/4sQNdmPc1i7g/cwHnw1N5p4/UP9b1Y5nP5D7oGfIobmX4FWioHIUPYvf81ai5ekfoZS4wV4O9nGNf5Cg+VIvt8bJVko7HYCgSTxfwatEQeUwC5oiO95NlLgh9zVhXrzU+SpFLsTVGmxxZcYKAEHj4+PHBsyCV0lF0NXN6gzV4zpqEEEjAmdelNxvWJmVMT0msXi+asFKIbOIHU+dHOPefL92m+y4M+tEgI8673SDVwkTNHNa2NNfyu7d0uCXf19vo6uRtQKgLv783GYdpPrRIVE5L/ReGz1KtNkZXVLBpqd8E0FP6ZPbNJ2aabsYLYCCGoLgVcLi2bf/bzsarJPb25s7pTAGXIAflBVg40Hzd0ZWktjvmUvcs0gmpXnx9nJsWM0RtufTfd5oz/H+/ZzQHDMAQftzNBoKrxIUz/Qa+YTsiJLb3Y2/kGzE5IA660AE3Rrr0WOd1FjC8JOk+SVyK1i02Wkn9bfm+rb9Uj4hholnkia/sqVGhnMaRECC7uBIsdHskoDieZXP73K4hdzuj9vdJkkNdLiiCkTQzqvuSe8/5Ne3e6OxQatFm5141VmwNcrzDat3bIJrzVrWpOp1ZzXIpqCqDQAP+ezClb5vyu01xlet3EqHf0AhgqaKM4c8IHfrgrc/uije6tzbIn9NcOm2XX6kQNCT98n8R+z4e6UQW4KqNgB88+Pl/6/OXfn9BbLj6ZkGIGhh2Fn76nT2fbv7q1rVmfGfzM7QrtU9w70uO60xgC5enQHgwV9euTVoGrxWnQDp4ntW6RAdHQ2vU4MbyyfG1Ir+n+R9p+EzM24c8XTe/RWAoKoMAL/CJ2U9IjWIT99ABD1oAl6nJk8+sle39Z72t+X2a0GRfdy/cV47AILKDwBXMDoso0YuId91ApTUFzoZDwri9CSPdqssbnnm7dh4U6q0SgAElR0Armh02KBev26vZ8cDVJ2gbDyoNNo9O87f+nzN4fs0fV5iU9CQc9EmJHZZjA7bL4yo9+FerhGStcvav9kJLZonAMrp7N9IJeNBZdB0cMP1BSGBMzR8YmJT0E3psqdM4tFh2V2EEfVPcC8rCTkQVQb/DVEyHlQGrUffHI1zb7fcyg0XVQE+6pRsniajwxhH0XhQGbQPaP62vjV6b83V4tAAQbcOI22rSKYzFo0OM6N9PDVE0XhQGZgI6L3POtQascf5z/YAgvr9ktLzjh1rSTERT62AJg+zp8dkJaD/LQj3Hm/H7TEqAAT1MsavIOKlUazASjw1ATRYRLZLkoahgJ6d0TAg3qkpxAGCdu/jmz6xLbxKhuLpfECT5nTdJR2fVjdo+nGnHQ4gaMbHJ8hs6UQUkrAVTycDEVT3XdLRqQH14500SQQgqF4efDABRFD5LsmJiRsUcjS+vv+kX5xwzQQQVD8PPhgAIqhsl+TcxA1KOZHQxGv0TrXzvwAE1dWDD62BCHpvSqunZ9+X2O/sxA2KOTevda2BG1UdXg+5zaSzBx+aAhG0z8AjRwcNk9jv/MQNyrn+addqnZepNz4UIKj+HnxoCCiBLXdCXyA1flGnj+YyNw1xbfbmIXVOSCGPOvX44EMrIIK2v0bIldZSBUSP5qimC1SVgp+nNnYftlFmrpUSbAp68dccQtLPfiCxS8UMy7oFssrHAM9Ro9zfkCvUtcR7eglXncDFZV2rRibSzoptS9Alj/i5f+dbvU57iX0qZljWLTYF/cSM1Fobg3kqDxYnl9NPQO9/N7aez4iv0ihWaUtQ79/J3nJrpYesWmRYFtBPPFUAMmnu4p4z0mlm5ldPXL/ebb3YXX0F9NzSbtXazDhAK5GOLUFr8l8yp7/iDMtm9BVPytgWdFOgd5sArzWSBY60WE8sr550F9Dc3VOa1ei5TGJGk/3YEtSV+xIbWIRFhuV9ejplUgebgn7v/xP3erTBNskSmUNftLwi0p2gPDe+GFbbb/gXijM82BK0Vmpqqjv3JZlXSpRhOburXi461cOmoG2FBcgOt5Ep86Xlgj+6FJTnzIc9azYet1XRKaktQSsWIbP/vOUm3caTBjYFdRPOl4xy3ZIEeg5owdF5XaqFTdzm8IwRpSvNScxsL4pn5qJXP9JkloCW2BQ0RHjocl0y6b80ehaUx/DLO89WCx+/1aE1GNUTNLvRU12fbMtmVlT1sClo4rP8TPPcXs7OCKwx+QcSu1YPefVLuYw6sigVdLnlJnM8l7t0nfl0LelrAR2TYT3vs01BjaM8Rs4e49tfy5XmNKLg2OJebn6DPz5lz0NR9RaT7VmfkEKPV6lXryk5g6q5h1vL/QW4D3pq2eTFh+w5aGkR1MSZpGFBNbrM+hGapFk9QfvWMZD7NcdTr15TpvfNIR+HWSkAnTRnD6VKUJ6bX09+qkqzV5KlFmMUo56g6zyCh/i7iZcy0zkRR7gXDyu9PAoKJO/gkv5+3Fng93esl1NP0IJnQ59rOIB67drSYTcX2upWRuiioPZw/etpHaoFDXr/gNTwbQH1BCWF2xb9QL1yjUlunHKyf38rBVBQeyn8c9UrEZWavfTpUcnZIyoKWipZ1SrU6iIsKKhD5B3+aHjTSs1f/vSI+M45CkoXFNSS/G2fnYaUu39g6YtNn2j64vv70h9sREHt5B/r69ijoBakN+0w3GcRtHTukU9Ht6pSd1zRZxTULjKerd0o8JiVAiioBQmjuKshV4scc9YoOHO46C0KahcTRhaQTdZmuCoTVD+JG+yg51bupcNPDn43CmoX4fzsQe+r8gUUCaqvxA1QJr1JSJanHbmTHgIFPdCyYqP/QQt3+oaQ7GrZ8gUUCaq7xA0grvi8srD5GEe/m6agxr06HFF/w2trzj5v6FzErwM2/Nj5ZSsFFAmqx8QNAJa6V2zpcJZ7moLqckT9Ov62+1vgwW/f9nhmobV1ABUJqtPEDTb4tv6RO/Mb2ZfD6bfNRROaynwX/yX/NPbtmbSqU3aRJErckOVVk6eCG42WacZQfnHcMGu3PsQU9q/fy3uG8L7MC3rDa1v+L97UErIqEjQ7YfAW7p/Y4g2ZaTz9dBVQCwZ9wb20tGeA4fqnDeSu30nT+zIvKNkf8VjDrdRqUyRobNek5smEiOd/6SygYr4K/adgZV17Jv9MmjNv9CfDPjO9R0HpokhQzwxyO+BaaROULHB9ou0pe74hsdrIj7tX22V6j4LSRdltpjuErOpR6gQlxM7l5ROrjEvuU3W36T0KShdFgi5yfZOQgZ3KizaXuYBOmjVzyAdDsYtXA2VX8ce3cZewm8XTZMpcQL/oUEAyAoQLVxSULjhYhAKFPRsPrmPOT4mC0gUFpcKBz/80v1MiKCawtQQFpYwSQTGBrSUoKGWUCYoJbMWgoJRRImgpSWCbf15+0qvdoKCUUSKoRQJbAZ3F80u3ujXmU6sNBaWMoqt4UQJbM/qK5wWv0+Ra/Z9oVYeCUqbM32ZazS/59o74Qs9hUFDKUB1Rn6LDEfVbn+deJsyjVR0KShklgjbxFyj6nB1tGlHv34hGw5xFRt25J5Z7XqJVHQpKGSWCpoYmXeERbV4+UlGLnM3FYU37UBuvjILSRlEXv2iX1FadCUoXFJQyKlwkoaByoKD2o1RQiWVoUFA5UFD7UWGVDxRUjrKZ+kYZKChdMPUNZVRYhgYFlcPu1Df/XuCJCaLQML2CF0l0oZr6Jis4kKdGAI2W6RQUlC5qpL7BgNIF4ymHvalvzGBA6YLxlMPB20wYULpgPOVwVNAhaWbO/QPlLLTgub+pV2lHyX/SbPGMCoLaGU/Yj8NqqYt2xNNBQb9xqSlQo9z/QYGXVKFKOw5e0xZuvzkWNIrx1Hep/7Mjng4KWkx2JWjJ3R2hJeeAh8JGb4eWdIGuDP+XtjfQYPG8Vx1S6rYrpNQ1UO7cy76QUudAw1pPB0NKmUFBRaCgkqCgJUBBbYGCgkFB6YKCikBBRaCgkqCgJUBBbYGCgimcDS15JQla8ucfoSWTL0JLzoUu6ZEBXsJTFWDxLJgDKZU/F1IqNxFS6j5oDmemeNSbJOmLIaXMKBUUQVQFBUWYBgVFmAYFRZgGBUWYBgVFmAYFRZgGBUWYBgVFmEahoBsbBC6Alaz7aPnymwHlUptAqzWVhFS7OMBraDakTqEgtKVqAIsnpIWwOIJiCIsfLHgTvbzHG+3QRqGgtzwupzewSDsiRR7oiS/3Y4b6A6s1lYRUe9wlNb3rDECdQkFoS9UAFk9IC2FxBMUQFj9Y8L5pkpbe+Bu4NkoFXRVLyIw3ISX/CBjUYp54+SoJdiX5A6s1lYRUu42rKWkwoE6hILSlagCLJ6SFsDiCYgiLHyx4J/YTQ5fv4dooFZQfd7RyOKTk7o4XLoWtBhS84g+tli8JrPZ28y2wOrmC4JaqAKyRoBbC4giMISx+oOC9V6GXHdooFXQ2f6Sh0NKf9wUU4kMGq/aKP7Ta1QGrYXXyBWFVqgM8njZbCIsjLIaw+AGDdyx0lT3aKBOUP8qsNyAlD58iZPNAQEE+ZLBq+ZKQagsHd78GqlMoCG6pCsB+cFALYXGExBAWP1jwNqQQsnQ4XBulgt70uZUTAjrb3dTsbl6XNYCCfMhg1fIlIdVuiQI2VSgIbqkKwH5wUAthcYTEEBY/WPA+iTTc67kUro3i20zrQ0OA9wsmedZ7A3LpYep0QNWaSgKqnVC+YsWKsYA6zQWhLVUDWDwhLYTFERJDWPxgwTMMD/AZa7BDG7xRj7ANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANCoowDQqKMA0KijANm4Kej+q+VOs2IEzApqDT9pF2WrcBYQI2Bc00/hejdRsQJnC2oLXPEuO8ULd2O7n3FWu51vRewrXBxdXVdeRDxbZFX7VZVYqEw1LbED2jgaCjO10q3Ou3mRM0lZDfH7lIymWJS+2cAsg9AxL0UHPPoQX8m1l1fGcXJfg1kbjL7sYjzsf5gv7hkcn9u6duoUlQ4verhKAjuvUZ/PAWY6rw9eATSNBcvxO5Uau4N1ub3LjW7kchwe/51qHzyO3+Sn8UnQDss0qSFF/yk7a9kvMFXSYExvuSSdCf3e5LCFqC9wPqTSo82Cssnv8icwIDXjd9IqbAmXZ23kpIxF7hvTiYW/sSkpPNvZn6FiFLxwsJfif9SNrcH39KrR+RMWB9lplNIfxr2RZ0srDgdJs9pKK7Z81y35p/n80LOb8amU8GfPKg/E+hN7P6LzhY+Szhv3Y0S8+NSubfET5wws5VseRCoFF4bwrmpB48prWoF/SI8Oibwb1JbpGW+mw/IcHv+Tbh7/39ilN/bA2x0Wc96JZ40s/xr2Vb0MVjTG8Cz/DhKvzclzz0+3z06tc/tS5xAvqWf2RkSL+DHQjhv6YsJmTDMP4d4QMn7LznkT93prmgOJgJwRezevPZ0AvHeAbH8n+7j4Wu4nfEXlPxh2QKq32WqTMy9T2ZMV7+X5OD0YZxtcNeit/BnQDFJ5FJXu798lJihH3a4HxBD/nmcv8e8zKYwmWsni7qcDY0PVniU+IsTi7DwWgultzXZO786atY/h3hBRV2kh47m/1jLigW9KOxhKzlt+Xd52RPFBL8ch/3J5BpTTv8q+qPyghW+yy+MxL6no/7GH8ZxgV5ecectGCzoGcicwwt9qbECPu0QYOr+P69b5AjDZKFMyJS56xI0NfGlfx0LDjN0HtZkaDfNM/M67yyWFBhJ1kX2aGooLiLv9jgn8zoRONZw96gzJuBl4UEv9z2mMwrncjmWc75mbXFap/Fd0ZC33OizrS9fJAHcKeq7xb9BU3d+Y7rjpQYYZ82aCCoYUawR+v/EbOgkR+Qcu6enp6tzAUON1v50Dd8FFR7VPFfUDKzbuCEgmJBhZ0kq9LqooIW50urAnxezsstd4m84dVkc1GCX7LxU+6yKTzyuro/KxtY7bP4UJr7oXsrerblPg/cQsh8k6Bjkw41WnAwhhNU2KcNrD1JMoQdOLH3N61bUZqw2mfxggp9z+xJJK3K3YPRKzrlZoTGpzQ2pAUkzY0j1/y3p8QI+7RpPmuCJo4mZPTzWreiNGG1zzJ1Rqa+53Jrb/8P+IuksX7hcxfmDQ3qOvOri+GNn58fkRIj7NMGm4IeeXP40OnAFRkQhDa2BJ3fYMbKlbNCFjqlMQgixpagAfxdbpIT4oSmIIgltgStb7pXeKupE5qCIJbYEnS9Z+z06UN9NjilMQgixuZF0q1Vs+esvOWMppQJNhPjqr4D12i1kp3+wKt451KeJIZ/vrnLdK3boRscvIo/M7Uso+AJVHkScZmQ3PoYzxJYi6e9V/G5r73M07x2YtklaIeDdnI8SgZeIOSup2jz8pZa/1AaYjWe9l7FF6z8lKeVv4P/QaWBzgoErecV0JGcava6aPNy+eHtpR+r8XTwKj62ocJG6RklgpLcP3aTw5uKPxqWmv6IdOmgvFm6RZGgMlfxKCglcqebzsIa16FXpe5QJqg0RYKei67d+4pjVegXmoKawV94OZQJev0Jd/dHKpaZ2RNmlAjaxF9AtBkFlcOWoNYD+rKHgRx57Im4MjHytxglgqaGJl3hEW1GQeWwJaj1gEZGci9NusW5xpWJ6T1mFHXxiyTzRaCgctjs4q0GNL7KLXL58WXkRoJb7BnHWqdD8ByULiqeg2a7P167Ql0D9y490S36sGN16Q4UlC5qXsVnvtN3Ua7wNmtJ7bbbHAAOvaoAACAASURBVKtNZ6CgdFFT0IfIS27QdlsZGKeDgtLFaYISUrgtIjTZ4FiV+gEFpYsTBeX4uWPAkhzHKtULKChdnCsop2i0Z8I9x6rVBygoXZwtKCEnYj0S7jhWsR5AQenifEEJuRDnGvefY1WzDwpKFy0EJeT61JqxfxFyd1Tt+omFjh2EVVBQumgjKCG3E1yjj/YavndXm0WOHYRVUFC6aCUoIenveZb3DfJqFe7YQVhFkaDSkxBRUDlUFZSQm4+4tf/uBVfHDsIqSgSVmYSIgsqhsqB3Hns7ObhqtdJ1EqpEUJlUQiioHA7Oi4cGNK9KVKUqT1YNLFX37pUIKpNKCAWVw8F58eCAvtL5h61BX/wc7ZmYDfwO9lEiKE5CtESRoEq7pLx57Z/bzP17vBTdu1d0kSSehPjfBZ6YIMWt0i+KBKXXJZ2Pc40rHfPraN5mygoO5KnqQa9K3cHMvPjLcTVi/3bg+1gD74PSheq8+KzWzXlcxLlbYNxKcIk+5tB3sgTO6qSLIkGNm7eSNX1m3C/ecOooT1dx9isoGUt8og84+L2sgLM66aJI0LiIp3p02TpkuGizgoDmJtfX+dQQnNVJF0WCemXlV/+LZPiINisKaOHGRs2SCxRUoDF4DkoXRYLWTs2seJTcEqcOUhhQ47ZWdT/NV1SFhqCgdFF2o97L9/WIxPbxos3KA/pztN8S0737fyYO/FBfrqKgdFF2Ff/7KbJvqkVOdRoB/S3WMyGNnPeYubabpiuS2w0KShctB4tY54/YmnEvzyLEEHiaRnXOAgWlC7uCEnIx7rGnzxHS6Qc61TmH0iho4adRUSs0ymnAsqCEzAqu1XmLm67WuSmNgs59cuf3Ee9pc2y2Bc32Kl+pnN9BWtU5g9IoaJ0LhPyl0cIDbAv6YbdD3+yq6qOne/elUFBj1SxC0qtpc3C2Be2/jnuJ+j45OCxZL+PuS6GgJPodYpyp0c0UtgV9LZE7Qw86RQq/CmuyVh9pnUqjoP+1CPBvdVWbY7Mt6GmPRd/262y6fvy+feDHepgaUhoFJYXnL2iVmJBtQcmpEV3fLZoO8ku017wMajWrRakUVEMYF/RhTg50feu2SnXTojQKakzu2u1zvA8K4fzLtcazndapNAo6L3zb1800ygBjU9B4M+Kpm1ZQNaA3H16S4c74iC67VTyc3SgRVGa9eM0F9fubkDOB2hzbpqBJSUkvNUqc1+RdeJ0qB7TkkgyF7V49ssFnv6rHsw8lgsqsF6+1oMYq3GVABsP3QcPuclbYkUJJ9YA+WJLhdN2JLbtNGKHy8exBmaAPrxef3ZiNWZ1d5hPy7vPq1H1vzUdW50pCBPXJ4aIm+aRLs2RXeckNTUsy7K82+tC6Wu1VPx4cJYJarBf/Lxvz4i82bRgUrs5qbBd8+o72+NJKAYigYzpu3tIpTmK/lsmuCre1aJJs+L38bpLRoK0TjgfFlqAxRUjsk1kvXusunpCCP0+r9CRvwGLu9NbFSuUQQQ3LBwxcIfUcR+NkVz93DBjf2Ldujed6Oud4IGwJmlKE1E7RevFmtBdUPUJPci++l+ULQAQtXNDPsExqHpvmya5+7vLIkN+udvzYWccDAOji8/bu2LE9Al6lU+J5W5vBDr1XEHK5hpWn2BBBJ/YPKew9WmI/A8mukio/+vgwlgaSAAQd0cI1xm8BvEonxHOHr2uN91U/igSn3Me97bfMSgGIoHWzIolBPHXThDjZVVYaTz9ndkmG3aOYWpIBIGg9wzuHrnaHV6m+oDc895NL9TW5n3x1/sxD1vZDBPW7FUnuNJDYb1iZlTE9JjGv6HOWV02eCm6OtNRxbiS4xp517iHlAQjqmbNjIWkMr1J9QTf14V4WTlL7MI4AEXRJU/+ZwVLneUOicl7ovTZ6lGiz80/q+SUZjjj7oNIABJ0QmRoc1xxepfrx/K4z9zLjLbUP4wigZ/F73kr8TWq/Zy5xzyKZlBM3OETmEp+oX6R2FM7wqtb3hvMaArkPeoYcmnsJXqX68cwMXHxxs+cfah/GESCCpsgNZAk/SZpfIreCRZu1uS2SlxwktZzy4nb/pL/e0XnNAAiaxPMVvEonxPNcn4BnJG98qc6pCSO3WNsPETQicOZFyf2H/Pp2bzQ2aLVos1b37Qq3NQ+1SOv01FezpuxxcV7+ZoCg8fHxYwNmwat0RjyPJe3WZDzdfo+5nzZNsFIA1MWfn9usQ7JUgdytC97+6KJ4q3Y3lo3bWouXZGhac/J79SunOq0JwEedd7rBq3RCPKcEjojoqEWOoeh13BVE1Tz5ArDxoPk7IyvBD6rpkw9+SYasEp+bNLxyf1x558UeKKjBjufr6sfzlH8mMXZbofZhJGh6gnupY+U5P0TQrbEePdZlWSknQuNHc0d6ecy5W/yp6UjXSr1qW3mWRhmAoP05Gg2FV0knnvl35fetHcS9fDiGxmHsZMTbhPzqY+XsAiJo51X2rfCu+bPj00Ndplw3vx/yHiEn3Z33rAkg6A6OFCu9mhga8SycWLlG2Am5vb+GcKfuL2oxZv5G8FPdPayFDCBoqji9oi00F5SQy+Nqjb5gevevX/RQd4vxF+phS1AVZyjcvi+/78MOaSS5nlxiYGOPZ5YMCUqHN4ke+bv/Z/UCASBoYZidj2kYENS8nDL/Jvur1c7r4G0LqtoMhbMRrpVflP273PVb7qWx7J3OgrXj38+Et8iJQLr4nlU6REdHw+tkQlD+3n1tDZZkAHTx6sxQaPoJyeqZILe3zwbu76TfP/CDsgJE0IMm4HUyIih/776+1L17VQEIKj9DQRpQPK95cy+HWsrt/l/9lAuvPQ0/JjMoGg8qAzOC8ksyNG7q3CUZAILKzlBQMoXmXk0DIT88I7v/ixaBL+sqj6UZReNBZWBIUGFJhiW5zjseQFDZGQqKptD0GvLH3sZrICV1hbLxoNIwJSgpsSSDM7ApaMi5aBMSu8RTaHLfnMrTuDr3sou7DJpq7d/0V71cuwDK6ezfCCXjQWVgTVBCfo/1SEhzzqFsCropXfacXjyFxrAkkSfchXvhyl9OtPbvDx6RPt5v2S6ns39bKRkPKgN7ghJy7uWacU5JIAh81Cn5WEfJFBqj9y+ETLHjREwnKBoPKgOLghJyKa5G7Dn1DwMQdOsw0rbKUqk94ik0AqB4XvLjXn4PA5TUF9DkYVYe5FrApqD8cspusX+qfRCAoH6/pPS8Y0eqo+J4Fl6Xv2eWU437L1qnUvoPDQENFpH9jZeGVUGF5ZR/VfcQAEG9jPEriHiBUysUxXOFi6eH/DjnyS2TF3r8DK9VJ4Amzcn+xmuW+sZhcj/1VXdJBoCg3fv4pk+0IxuKOZ5HvF94up+77FmK8Yshr/4Or1QvQASV/Y3XMvWNw+TzSzKod+8eIGjGxyfIbDtSHZnj+UbV9/ZOr+bo7HXDji/smAbFDhBBZX/jNU594yiF255snKzWkgwAQR18MteHn6nsI/kEyjZpoU8NdJecFcE4EEFlf+M1T33jMD9H+y+xMjhNAQBBHXwyN6HK5+c/qjLPsWZNHk/IuVpOe1xBD4ig96a0enq21H8nA6lvHObYCx4J9tyagAIQ1MEncyl+UfWe8zzlWLM68mlDwo859s1aAhG0z8AjRwcNkyogum9n3LuLJ6oezRaqxqnYWnHXqNcKuc3k4JO5WS4Rrh862KwRiwhJr+W8ya3UACWw5c6XCmSHh3V98Da7axSPlxelxqnNxbgaL9POywoQ1OEnc3eO2Df3pgRn3eOXNp/g6HdrCETQ9tzfmSutJfYP5qk8eLBosy66eAF+SQa6y9FDHnVq8WTu37fGbqVQjdOBrPIxwHPUKPc3JPbPr564fr3b+vWizToSlPurNNP9BZqnZjYFvfhrDtfbnv0AXqWu4kkbm4J+YkZsoYkjLdYTy75fZwHNWlS7M720L7YEXfKIn/t3vtXrtIdXqbN40gUyae7injNydw0zh75oecKpu4DmJdVvs53S1BBbgnr/TvaWW2vXwXQXT5rYFnRToHebAC/ZodpfWq4GoMOAFm5rEULn3r0tQWvyX/ZN09dhPOlhU9Dv/X/iXo82sOMBtj4DSunevS1BXbkvT5l9rK40pyU2BW0rDJA53AZep14Duj/aI8Hh+zhF2BK0Vmpqqjv3JZWtgNGV5jTFpqBuQndklPutl0C/AT3ezy1BYSI8W4JWLEJin3ilOTP6jScFbAoaIqTluM5UTnX1OPdSrYlXlFRAdaU5AT3HUzE2BU18lk+JktuLsYSr6nFlQq2RCqaGKBHUYqW5faZHx5308ehYHWwKahzlMXL2GN/+dlzi6ltQQlITXIuXU7YbRevFi1aay+6ir0fHagC4D3pq2eTFVpeyEaN3QU1pndo6uGqQIkE5zltu0n88FQCdNGcPpSGgeckNHEvrpFRQiSUSS0M8HQYFlaNwW0SoA/fuUVC6oKBW2NVGvCSDbZQKutxyU6mJpyOgoFb5Odozwb7Ew0oFlaAUxdN+UFAbHI/1SLBnIDoKShcU1BLjz1tKjrM/H+caB793j4LSBQW14H67sJ7uSSW3XJ9aM/Yv4HejoHRBQS14J9ZILro+3K0XL8lgExQ0e+GQmfRm5ykTVH+pbwD05Bc3jfxJtBW6JEOZF9TQekDy2EBqM7oVCarL1Dc2eW0OIbm1LRfEgC3JUOYF3fEU9zLU0enRFigSVKepb2xwweONlR1ipfYY1jSK2GxjNHyZF3TFS9zLvCm0qlMkqH5T31jlXI8nZ8ikTirc2rLhKqsr05Z5QX/3v0fy2lBb20+RoHpOfSNPWoPnx/vPkd39Y5TvB1ZyHJV5QUmCT6/AIdRWp1J2kSRKfZO/QEj670ehYdoxPY6QVLcb8gUsl2S4PC76LfNlAQpK/t4iu2qt/SgSNDthMH/FW3y+lj9LWDbFl0rTtCKGz8DRYY+1IucfXpLhhs/M7aPChMWYUFC6KBI0tmtS82RCxNNrdB7QKdwJfrqHjWdH/JIMfxd9WPgq99JeiCQKShdFgnpmkNsB10qboDf9Ymc2sn0VeivB5QXzkgyTJw9pF9dvlek9CkoXZbeZ7hCyqkdpE5Skf5TwI6Rc8ZIMSY/N2/dK+eOmjSgoXRQJusj1TUIGdiov2lx2ApqbXI9fkmFeU69I9wbfmjahoHRRdhV/nPvfKdw8XrS1LAU0P7lRs+TJ711KuTlUyACPgtIFB4sopXDbk961b5ELXsJ0N5qCZnnV5KngRq9K3YGCUmCn3yN+biuF91T/gmam8fQra/EsCQpKhR3jMszvsIunCwpKGRSULigoZVBQuqCglEFB6YKCUgYFJYTmMpMoKGVQ0D0hj/mvo1YbCkqZMi/oNc8fyLHasBmGAFBQypR5Qb8YxL0kzKBVHQpKmTIv6Pp+3Mv02bSqQ0EpU+YFveW9LmOX1x+0qkNBKVPmBSVH2ldr/j212lBQyqCgdEFBKYOC0gUFpYwSQXGlOUtQUMooERRXmrMEBaWMMkFxpTkxmN2OMrjSHF0wux1lqK40J4DxlMPe7HbZXYWV0bxptEyn0FxpzgwKKoe92e2Me01rS46JodEynYK3meiiRna75SMVNkrP0BT0/qAXePx86FWpO6hmtzODgjpIE3+Bos/GrzfyDO1Ko2E6RY3bTCiog6SGJl3hEW3GeMrhqKBNpwpMqdsKSPP60JJNGkNLBoVDSwY8CSzYMmSqLQKUdPGLdlGI55MBFEsFQkq1hJUCtb5FqB3xdFDQy4lmZj/aBUgLF2jJ+nWhJd2bQ0s+1hFY8OmqibZ4L812gNSNZ1R5SKmOFSClnnkcUiryCUippytBSrWrbkc8HRS0mOxK0JK7O0JLzhE/CJQleju0pEsqsOBfQdAqHUZivfhiYPG8Vx1S6rYrpNQ1L0ipy6CMxefqQUqdDoaUMoOCinCCoBLLcReDgopAQUWgoJKgoCUo7YJKrBdfDAoqAgUV4QRBrYGCilAqaG4NaMm9naEl30uAlowB3/DxvAcseKERtEpVgMUzC6SexZgpSW7VgZS6GggpdRH0xPbvJpBSZpQKSsCL3hrBN2fuW1lE62Hu2li08AHwtXnpreLrELDDl4VSAooFRRA1QUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERplEo6MYGgQtgJes+Wr78ZkC51CbQak0lIdUuDvAamg2pUygIbakawOIJaSEsjqAYwuIHC95EL+/xRju0USjoLY/L6Q1AqaDzQE98uR8z1B9YrakkpNrjLqnpXWcA6hQKQluqBrB4QloIiyMohrD4wYL3TZO09MbfwLVRKuiqWEJmvAkp+UfAoBbzxCmzJNiV5A+s1lQSUu02rqakwYA6hYLQlqoBLJ6QFsLiCIohLH6w4J3YTwxdvodro1RQftzRyuGQkrs7XrgUthpQ8Io/tFq+JLDa2823wOrkCoJbqgKwRoJaCIsjMIaw+IGC916FXnZoo1TQ2fyRhkJLf94XUIgPGazaK/7QalcHrIbVyReEVakO8HjabCEsjrAYwuIHDN6x0FX2aKNMUP4os96AlDx8ipDNAwEF+ZDBquVLQqotHNz9GqhOoSC4pSoA+8FBLYTFERJDWPxgwduQQsjS4XBtlAp60+dWTgjobHdTs7t5XdYACvIhg1XLl4RUuyUK2FShILilKgD7wUEthMUREkNY/GDB+yTScK/nUrg2im8zrQ8NAd4vmORZ7w3IpYep0wFVayoJqHZC+YoVK8YC6jQXhLZUDWDxhLQQFkdIDGHxgwXPMDzAZ6zBDm3wRj3CNigowjQoKMI0KCjCNCgowjQoKMI0KCjCNCgowjQoKMI0KCjCNCgowjQoKMI0KCjCNCgowjQoKMI0KCjCNCgowjQoKMI0KCjCNCgowjQoKMI0KCjCNCgowjSsCno+qvtSrduAMACrgk7bR9pp3QaEAZwqaEE5FxePIRncUV1cXV0XEuO8ULd2O7kd5V1cq7U7V7JopvG/GJv1pUgUkdqG6BcnC5pLMkZ34Y6aZfo8utOlwr1+mzlBs8i9wc8/VHZb9FWb9cEENWURFvL/csyv7R1nzs2SuMveHwBxOk4XlOR5nDYL+odHJve6p24hLyjZ8dBypTuniDP8GFOFrwefYIKasggL+X+5T8e9UrMiNp9vHTqP3O5P4UdiHDv6rIdIii/5ScteyfmCkpj1ZkGXjTRt9b7EC5o5pGvJoiO69Rlsfvt+QL1JhQd7hcXzX2ROYMDrpk/EFDjTzs5bCYnYK7y3CKYpi7CQ/5f7dNI3M/+pLZN+JG3ujz+l7k/LAvb0WQKbQvjXMi3oyCWm3+dAMnm2aWubPaS8u2flFn8JhV6NzCcDPnnwTT+F3szqv+Bg5bOE/9rRLD03Kpl/R/jACTtXxZILgUbhvSmYk3rwzBUqMCdpvd18C//PpCeq9yDn24S/9/crzvmhNQXSZz3olnjSTX9Wy7SgMevM4Vo8xrQ18IwpXEUcvfr1T61L9O9v+UdGhvQ72IEQ/mvKYkI2DOPfET5wws57HvlzZ5oLWgZTENSc/3dfxJGzz33Ov4u9psrPyBa2+yxTZ2TqezJjvPy/JgejDeNqh70Uv4M7AYpPIpO83PvlpcQI+7TA+YLme/5pDtchXz56x7wMDwnKCdj0ZIlPibMIKTQcjOZiyX1NXkLIV7H8O8ILKuwkPXY2+8dcUFpQIf8vx1Tur/bWPtyb/QlkWtMO/6r0k7KC7T6L74yEvufjPsZfhnFBXt4xJy3YLOiZyBxDi70pMcI+LXC6oNljOxefEfXvfYMcaZBMRIK+Nq7kp2PBaYbey4oE/aZ5Zl7nlcWCCjvJusgORQWlu3gh/6/xrGFN2O2MIfzVUkzmlU5k8yy1f2SNsd1n8Z2R0PecqDNtLx/kAdwp6rtFf0FTd77juiMlRtinBU4W1MPDc1B6saCGGcEerf9HRIIebrbyoe/6KKj2qOK/oGRm3cAJBcWCCjtJVqXVRQWl/4IK+X9zy10yTvXxfvE+IRs/5f6ahkdeV+knZQXbfRYfSnM/dG9Fz7bc54Hcufp8k6Bjkw41WnAwhhNU2KcF7D1JMoQdOLH3N61bUUqw3Wfxggp9z+xJJK3K3YPRKzrlZoTGpzQ2pAUkzY0j1/y3p8QI+7T4CdgTNHE0IaOlboAg9mO7zzJ1Rqa+53Jrb/8P+IuksX7hcxfmDQ3qOvOri+GNn58fkRIj7NMC9gRFkBKgoAjToKAI06CgCNOgoAjToKAI06CgCNPYFPTIm8OHTgcu/IkgtLEl6PwGM1aunBWyULR5Y7kyzP/9Sv2/AeMphy1BAzL415wQ0eblIxX9f+ibzjuoV4nxlMOWoPVNA9JuNRVtxoDSBeMphy1B13vGTp8+1GeDaDMGlC4YTzlsXiTdWjV7zspb4q0YULpgPOVw8Cq+OKDGemsdbZduQUHpokhQmav4BwE90fDFLFK2QEHpokhQ8VV8VsNAnmq+xSWyhjcoY8OLUVC60L2K/+8CT0xQiTLr3BeJsyyUalBQuqhxFR/bsOSnf9p0Lu2Te0qiRNDNxLiq78A14l9oFFQOB6/iiwTNmvPCwlxCDAle/3O0efpDiaDlSWL455u7TBdtRkHlcHCwiFnQbPfH/SsEGrh3+wNGlZlrJWWCRlwmJLe+aDMKKocyQadUTSNXHjdlmk0fGnTQsbp0hxJBHyUDLxBy11O0GQWVw5agTfwFRJvNgvL5EkiTF4RtmzzfznewifpCiaD1vAI6klPNXhdtRkHlsCVoamjSFR7RZrOgL3sYyP0qs80br3dr/qeDbdQViq7ic//YTQ5vKv6Y5VWT53Ef5c3SLcq6+EWSWV7Ngt6s5N65Ro3M4s3L3RYU2tk6HUL1NlNmGk+/hrZLllpUPAclF3sGDyp5h+mfyLbyWVFLCyrcB41FQWVQKKgFhe+7vV/a/4gqEdT6OX3ZxKmCEnK+Xbu/HatULygR1Po5fdnEyYLyf0QXFjhWrT5Q1MVbPacvmzhbUEIuPPNkac7/juegdHG+oMS43D0h17GadQAKShcNBCXkakyj/fy/uYntnhWPNNE7KChdNBGUkE0+r9wjZFSXNcsbfO7YQVgFBaWLRoKSe6/4bMirXLdRnbBWjh2EVVBQumglKCG/hEQ9+iUpHFDTsYOwCgpKFzUmzQEDmv92ud55hr61QIV1AwpKFzUmzUEDmlepymO1nmwDK6wXSqWgv78x/YRGh6Y6ac4MOKBjO453qfKJ7XJ6ojQKutl7pmazItRIfQMOaP7ijt1ecJ1fqgaKlkZBgw8Qsq+JNsdWf9KcLf7uGvyDPeUZpxQKaqyYS8j9StocnOqkOeP2jTxP17WvDdvq9bxg33cwDFVB/7Wcxq0FLb8mZJNG1wqKBDVu3krW9Jlxv+jz/YEv8Ph529mI3Lmub2TaLqYLaAqaFWxKhFHVg16VDvGrR/9+Hoe1ObYiQeMinurRZeuQ4aLNDnRJV2N9VpeOkaKlsIvnesoPP0y1XUoVFAnqlZVf/S+SIZ4y41BAD7aK2OfAtzGHTgXdNW+j/LXq1bbenu00yr6hSNDaqZkVj5JbdUSbHQuo8QvfPucd+Ua20KegLzWd3KlFjtzemARj4fTeqjdCEmU36r18X49IbB8v2uxoQO+/4zrhzsOb9se0nnhHujSj6FLQ3+pzcr6wTGavsRp3gXCvutqNkEbZVfzvp8i+qRa5hBwP6M1X3OeXHCp61GvNgVfa6moEvi4FXTuYe1k2Rm537YuEnPdTuxHSaDdYRJozMX5rHlwtjV3M/f6G/O54dc5Hl4IebZBHSP+lcrtntt37U6t31G6ENKwJSsjPrZt+V/R+4BfcyzM/KqnO2ehSUDI0/LWuYffl9hZ88FS7ZRrdZGFPUEK2Nmp/QHiX1Hxg5xEe95RV51z0KejXtao/MUf1ozgCi4KSgpV+3U/yb05WquRbqZmubpDqUtCrnsfI9Ub0W04BJgUlJHeJ54C/CImbd+fP3IYnFVfnRHQp6EY+xduiCWofxhFsChpvRjzo0wpUApr5jtvwiwPWce+idlOozmnoUtAdUdzLWwlqH8YRbAqalJT0UqPEeU3ehddJKaB333Jp1z6XnHBPo1Kdk2BV0C1Ph4yTDWR20Dt/rvU8S+Ew1IF08WF3CUkPh9dJ7Tc+Nf7xSmEeWyjV5hwYFfS7gO9OvdxRdvelIY26sZliGCKoTw53UijOaGUFil3S7dHVXrpMrTZnwKigfbmTpcI6l5RX5Gwggo7puHlLpzh4nVTPmW5Pcxn5D8X61MaWoDFFSOyTWeWDRjy7fM+9hOgw5xBEUMPyAQNXGOB1Uj6pv/OW65AzcjtPR1bwWkL1cAqxJWhKERL7ZFb5oBHPJc9mkK8CdPXQWAAiaOGCfoZldvxs1K86783x6HNMck9e/Y/PHAn5ivLxlADo4vP27tixPUJih3iVD+PXwgyFQOXNKnilqndD6RiyDUTQif1DCnuPhtepwm2RrMW1n5P6m3MsyLdejaj+1I/nOABBR7RwjfFbILFDvMrH/UHCDAUqOeqzr9KoxelABK2bFUkM4kGfJhQlbrCPvKQGrbdaPFM68NhPJLNxSxWO5ygAQesZ3jl0tbvUDulVPrQfUa8hEEH9bkWSOw0k9itM3GAnhZtaNvxMlLdx3xOLMo/7tFbleI4BENQzZ8dC0lhqj2iVDzMoqBxmQZc09Z8Z/LHEfqWJG+zmp67ecx8av3w6oEuVeoNHqHU8BwAIOiEyNTiuObxKFFSOomfxe95KlFxUW2niBgc4NazWuBITQwrbjzq83vuAesezG8h90DPk0Fw7bkmioHKYBU2RW0+bSuIGe7k2za3n3uJPaRNadNuj5uHsBSBoEo8ddx7oxDNPX1NnioAIGhE486J0AVHihuyuUTxeXtSaJ0n2xw3Dk1lNIg4QND4+fmzALHiV4GRscn9IR6lN1wAADpRJREFUOArHV67V9Dj8mMwA6uLPz23WIVliv2FlVsb0mMS84g37dvFE1aPZQimM33f2SrgGLFwgO1tRDYCPOu90g1cJE/TSs5WqTpK9W730mbvk87ql9EY9Ifk7I6VS8wyJynmh99roUaLNTjlnOvNKrQG/AMoZxlau9MxFtVvzAKCgBjvS2cDi2Wqu4Van9+T2duWn0TT+A35QVoAIujXWo8c6qQXfPXOJexbJpDMv3m7uLa4flpRtq9ScrvcK33VimnGAoP05Gg2FVwmK500+P84B2QRKfbgLBaPfRfhBWQEiaOdVMrOCwk+S5pfIrWDRZqdddRp3dHed8Jf1Mq0S2jaLd7tlvRBFAILu4EjJs1msGFA877gUcpezT8vt3lZvz7lx7eHHZAaAoKnixAzFHPLr273R2KDVos3OvC1yaZpHx6+sJRgN9t/9e98Kd53WIFuCqjZD4dkXP1gcIXW3WmDdk/VH34YfkxkAghaGyQ61zt264O2PLoq3Ove+Xd66SM9p8ukbGwb9/u/IxzKc1hxbgqo2QyHpiYqVav0Lr1UnQLr4nlU6REdHw+t0+o3lMxPdOm2U6TNbTAqu87KH8zJfAbp4VWYoFLpyV0Bzh8Jr1QkQQQ+agNepwZOP3HUd3Sf+KbVnet8c8mkz57UEIKgqMxQu8hUeFz/U0z+6GA8K4vz02q0+tbyayxlY3TNMdrwzfQCCys9QUDA6LK8a10t81gtQUl/oZDwoiIJve9cYvNtiSF66UzNbAgSVnaGgaHTYe0HzJrvr8VmRdZSNB5VGw8ENt98P951u48aTutgUNORctAmJXeLRYXlvT+VpXIN72UPI31Ot/vvjiHavQMrp698WSsaDyqDt6JuTk7xaLdNuYIRNQTely57Ti0eH5S9K5Amvxb0c4E4zE8vgv08qGQ8qg9bDwwq+H1ijx0bZXG3qAnzUKXljVpPRYYyjaDyoDAwENDO5U81hP2gxNgIg6NZhpG0VyWScotFhZhiIp3ZAk4fZ8yiGjYBeW9zSY9wvVkagqQNAUL9fUnresWOmJhvx1AjQYBHZ33hpmAnoudmN/SZb3LVRF4CgXsb4FcSOmZrMxFMLQJPmdPwbf+rN+nWnOXM+OEDQ7n180ye2hVfJUjydDkRQnf/G/zatbt2pR5x1NICgGR+fILPteGrOWDydC0RQ+d94J86LV8RvbwT5T9zvlEzNAEH18WSOESCCyv7GO3devDJOzmjiNfoH9Vf+BgiqlydzTAAR9N6UVk/Plrqt6PR58co4N691rYEbVR55BxBUR0/mtAciaJ+BR44OGiaxX4N58Qq5/mnXas99/J+KR4DcZtLVkzmNASWw5c6XCqSGh+nyyUfmpiGuYW8dUuuEFCCo3p7MaQpE0PbXCLkimf9I/OTjz6M8Xe2YsagJBfumNPZ4cVO6GnVDHnXq78mcdkBW+RjgOWqU+xtyhbo+eJvVqjmPiwe99qnGP0ufq/bMfPrzcG0KevHXHELSz34gsUvFDMu6xaagn5hZL7F/ME/lwYNFm/US0OxvXg3wfXkL3YXsbAm65BE/9+98q9dpL7FPxQzLugUyae7injPSCcDnV09cv95tvdhdPQX07OLnqrabfYjesBJbgnr/TvaWWys9RECcYdmMnuJJHduCbgr0bhPgtUaywJEW64nl1ZPOAprzw6Qmtfp8Kj8x1C5sCVqT/5K5QhNnWDajs3jSxaag3/v/RPj1mrdJlsgc+qJlpjAdBvRacqxn3Zc3UJg4bktQV+5LbGAR4gzL2c8Lydi8lTdLt9gUtO3PpreH5ZKqfGm5nooOBeU5teT56s0mbFd4bW9L0Fqpqanu3Feq1E5RhmVjipOSsTGMTUHdhO7IKPdbL4FOBeUwHHgnqmrLqd9nOl6FLUErFiGz/7zlpuJ4Fl53+vBWzbEpaIiw0Nt1yZzq0uhXUJ7cvTMiqzw55RsHL+6VrjQnsTxNUTxXuHh4sLTijlOwKWjis/yfk9xeKiRcZZecvTOjqoW9tsmBCcvqCXrE1adCQM1zyqrXHTYFNY7yGDl7jG9/7Vaa04j8X+dF1wx6caWdawArFXS55SZzPMdVPUZ+emKusuqZ4/jY4RutnbgA7oOeWjZ58SF7jlk6BOUpPLlsoK9bj3n74Uma1VtM9nl+YZDAkdSr15R9HvM/C3vTSgHopDl7KD2CmvhvfVyLSq3Gb4CNgVdP0OmVPz//UaVl1KvXlG4bCEmtaiVZKgoKIntfYg9P757vpdi8vldP0L9dIgJbuDk1j4/6ND3BvdSx8ruPgsK5uG58m8qhLy3/3dr5uIrrxe9pVTPyMPXateWl6YTsr23lJBQFtY/8ox8Na1ylddyaP2UeV6ooaGnkZkirrh67rBRAQR0gc+/CAfWrPvXamj8sB5mgoPZhSPnWaiItFFQKyHD7uz/O61e/cqtXVxx9aE0xFJQuKKgliz0rPC2ZrdmS9L2Lh4Q+ERq7sPg+HApqHxs7tpppLc8bCmrBppBzhhX17FhqMfdo0rjiWcQoqF2sD/r2QMwQKwWUCaqXxA12MXgt99LC0ctlFNQuOnLhyqkmtUqcGUWC6ilxA5wh/LKk4Y7mHENBcz586V1wMsTmfN4sLys3dxUJqrPEDUC+CfotfWEjR3OQlHlBC9r1XjGyHnRU7dTYfJLcxEoBRYLqL3EDiIW1KoT97eg30xQ0q0EgT1V3elWqzw+tjZwC0Cey2b1cAhqfslJAkaC6TNxgk0uery9rO9zObyp+FkL1L+i1CzwxdmQh0Z4V/HCWeZPB5W9dsDoKW9lFkihxQ87Yl3nq1wa3jkUmzCTkvvdFe75lfd3yYcLUGOzifwvMIIanNtKqTpGg2QmDt3D/xBZ9Llj9KU+rACpN04qe/M8U+ZMd33Gk9tGC7Z43Te/LvKAkvs6AoP7U5qYoEjS2a1Jz7pJXPL1GZwEVM3sYIf+6Sk5qk+HtWX9sv9RPSA+AgpLTX1JMu65IUM8McjvgWmkTNKtVy4Gen9jzHQlN/aM9QoTpQigoXZTdZrpDyKoepU1QUvjjF//Y9Q3zK+zOXvHoftN7FJQuigRd5PomIQM7lRdtLnMBfX14aOWnn19peo+C0kXZVfzxbdzfm83jRVvLXEAX83dW2vxgeo+C0gUHi1Ag1ff1Lwe1FsbZo6B0QUFpcGP6gHnZwlslgmJ+UEtQUMooERTzg1qCglJGmaCYH1QMCkoZJYJiflBLUFDKKBFUnB/UjM7ieTSyZgS9K0UUlDKKruJF+UHN6Cuet72/TNvpRW11ChSUMmX+NtOGvtzL9Dm0qkNBKUNV0PQ0nn66Gg/6xSDuZUYCrepQUMooEbSJv0DR56zaNXket2NdT+255vkD+a02tfFMKChllAiaGpp0hUe0ebm+Ui7uCangL7WqlmOgoJRR1MUvksxSpDNBCbGSTdFuUFDKqHCRpDtBaYKCUkapoBKrfKCgcqCg9qPCIgooqBxlM/WNMlBQumDqG8qosMoHCiqH3alvMoUbyygoVVBQOexNfZPlJdxY9qHRMp2CgtJFjdQ3GFC6YDzlsDf1jRkMKF0wnnI4eJsJA0oXjKccjgo6JM3M1X9KGTfTbPGMCoKW3nhKcNOOeDoo6NaaRZT7PyjwkipUacfBa9qE/lJb9sZT36XEIbYWTwcFLSa7ErTk7o7QknPEcx5lid4OLekCzRT2VxC0SlWAxfNedUip266QUte8IKUu+0JKnasHKXU6GFLKDAoqAgWVBAUtAQpqCxQUDApKFxRUBAoqAgWVBAUtAQpqCxQUTCF4rYfL70NL7v4OWvLjc9CS06BzFNJnQqtUBVg8DVMhpfKmQUrlgP4cZL8NKZUxA1Lq7mxIKTNKBUUQVUFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmUSjoxgaBC2Al6z5avvxmQLnUJtBqTSUh1S4O8BqaDalTKAhtqRrA4glpISyOoBjC4gcL3kQv7/FGO7RRKOgtj8vpDUB5IvNAT3y5HzPUH1itqSSk2uMuqeldZwDqFApCW6oGsHhCWgiLIyiGsPjBgvdNk7T0xt/AtVEq6KpYQma8CSn5R8CgFvMAK4zvSvIHVmsqCal2G1dT0mBAnUJBaEvVABZPSAthcQTFEBY/WPBO7CeGLt/DtVEqKD/uaOVwSMndHS9cClsNKHjFH1otXxJY7e3mW2B1cgXBLVUBWCNBLYTFERhDWPxAwXuvQi87tFEq6Gz+SEOhpT/vCyjEhwxW7RV/aLWrA1bD6uQLwqpUB3g8bbYQFkdYDGHxAwbvWOgqe7RRJih/lFlvQEoePkXI5oGAgnzIYNXyJSHVFg7ufg1Up1AQ3FIVgP3goBbC4giJISx+sOBtSCFk6XC4NkoFvelzKycEdLa7qdndvC5rAAX5kMGq5UtCqt0SBWyqUBDcUhWA/eCgFsLiCIkhLH6w4H0SabjXcylcG8W3mdaHhgDvF0zyrPcG5NLD1OmAqjWVBFQ7oXzFihVjAXWaC0JbqgaweEJaCIsjJIaw+MGCZxge4DPWYIc2eKMeYRsUFGEaFBRhGhQUYRoUFGEaFBRhGhQUYRoUFGEaFBRhGhQUYRoUFGEaFBRhGhQUYRoUFGEaFBRhGhQUYRoUFGEaFBRhGv0I2s21VjlX19FaN6PUoJN46kdQQlKrcC+bQrRuRqlBF/HUnaDp4IU9EBvoIp66E/Rg9MHo4e1Gz+7R5Bp5P6DepEKtW6VfdBFPPQpaLbPANYlM/uCn0JtZ/aHTVxELdBFPPQraiZBml8mHM97yj4wM6ad1q/SLLuKpR0Gf4wJ6hQto4ixCCg1at0q/6CKeehb0WHCaofcyrVulX3QRTz0LSj4Kqj2Ktd94HaGLeOpJ0IcxshZKncNoPPUrKFImQEERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGm+X+K97PHHoloVAAAAABJRU5ErkJggg==" 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" /><!-- --></p> <pre class="r"><code>print(p10)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 899.4089 336.4348 899.4089 +## SFO IORE DFOP +## 899.41 336.43 899.41 ## ## Critical sum of squares for checking the SFO model: -## [1] 413.4841 +## [1] 413.48 ## ## Parameters: ## $SFO @@ -1899,12 +1930,12 @@ div.tocify { ## sigma 4.90 1.77e-04 2.837 6.968 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 101.7315 1.41e-09 91.6534 111.810 -## k1 0.0495 6.48e-04 0.0303 0.081 -## k2 0.0495 1.67e-02 0.0201 0.122 -## g 0.6634 5.00e-01 0.0000 1.000 -## sigma 8.0152 2.50e-04 4.5886 11.442 +## Estimate Pr(>t) Lower Upper +## parent_0 101.7315 1.41e-09 91.6534 111.8097 +## k1 0.0495 6.58e-03 0.0303 0.0809 +## k2 0.0495 2.60e-03 0.0410 0.0598 +## g 0.4487 5.00e-01 NA NA +## sigma 8.0152 2.50e-04 4.5886 11.4418 ## ## ## DTx values: @@ -1926,14 +1957,14 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p11)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p11)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 579.6805 204.7932 144.7783 +## SFO IORE DFOP +## 579.68 204.79 144.78 ## ## Critical sum of squares for checking the SFO model: -## [1] 251.6944 +## [1] 251.69 ## ## Parameters: ## $SFO @@ -1953,7 +1984,7 @@ div.tocify { ## Estimate Pr(>t) Lower Upper ## parent_0 1.05e+02 9.47e-13 99.9990 109.1224 ## k1 4.41e-02 5.95e-03 0.0296 0.0658 -## k2 7.25e-13 5.00e-01 0.0000 Inf +## k2 9.94e-13 5.00e-01 0.0000 Inf ## g 3.22e-01 1.45e-03 0.2814 0.3650 ## sigma 3.22e+00 3.52e-04 1.8410 4.5906 ## @@ -1962,10 +1993,10 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 2.16e+02 7.18e+02 2.16e+02 ## IORE 9.73e+02 1.37e+08 4.11e+07 -## DFOP 4.21e+11 2.64e+12 9.56e+11 +## DFOP 3.07e+11 1.93e+12 6.98e+11 ## ## Representative half-life: -## [1] 41148171</code></pre> +## [1] 41148170</code></pre> <p>In this case, the DFOP fit reported for PestDF resulted in a negative value for the slower rate constant, which is not possible in mkin. The other results are in agreement.</p> </div> </div> @@ -1977,17 +2008,22 @@ div.tocify { <pre class="r"><code>p12a <- nafta(NAFTA_SOP_Attachment[["p12a"]])</code></pre> <pre><code>## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance ## matrix</code></pre> +<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> +<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is +## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p12a)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p12a)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 695.4440 220.0685 695.4440 +## SFO IORE DFOP +## 695.44 220.07 695.44 ## ## Critical sum of squares for checking the SFO model: -## [1] 270.4679 +## [1] 270.47 ## ## Parameters: ## $SFO @@ -2006,9 +2042,9 @@ div.tocify { ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 100.521 2.74e-10 92.2366 108.805 -## k1 0.124 5.75e-06 0.0958 0.161 -## k2 0.124 6.72e-02 0.0319 0.484 -## g 0.877 5.00e-01 0.0000 1.000 +## k1 0.124 2.53e-05 0.0908 0.170 +## k2 0.124 2.52e-02 0.0456 0.339 +## g 0.793 NaN NA NA ## sigma 7.048 2.50e-04 4.0349 10.061 ## ## @@ -2024,25 +2060,21 @@ div.tocify { <div id="example-on-page-12-lower-panel" class="section level2"> <h2>Example on page 12, lower panel</h2> <pre class="r"><code>p12b <- nafta(NAFTA_SOP_Attachment[["p12b"]])</code></pre> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> <pre><code>## Warning in qt(alpha/2, rdf): NaNs produced</code></pre> <pre><code>## Warning in qt(1 - alpha/2, rdf): NaNs produced</code></pre> <pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> <pre><code>## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p12b)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p12b)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 58.90242 19.06353 58.90242 +## SFO IORE DFOP +## 58.902 19.064 58.902 ## ## Critical sum of squares for checking the SFO model: -## [1] 51.51756 +## [1] 51.518 ## ## Parameters: ## $SFO @@ -2063,7 +2095,7 @@ div.tocify { ## parent_0 97.6840 NaN NaN NaN ## k1 0.0589 NaN NA NA ## k2 0.0589 NaN NA NA -## g 0.6902 NaN NA NA +## g 0.6473 NaN NA NA ## sigma 3.4323 NaN NaN NaN ## ## @@ -2082,14 +2114,14 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p13)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p13)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 174.5971 142.3951 174.5971 +## SFO IORE DFOP +## 174.6 142.4 174.6 ## ## Critical sum of squares for checking the SFO model: -## [1] 172.131 +## [1] 172.13 ## ## Parameters: ## $SFO @@ -2106,12 +2138,12 @@ div.tocify { ## sigma 3.0811 9.64e-05 1.84296 4.319 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 92.73500 9.25e-15 8.95e+01 9.59e+01 -## k1 0.00258 4.28e-01 1.45e-08 4.61e+02 -## k2 0.00258 3.69e-08 2.20e-03 3.03e-03 -## g 0.00442 5.00e-01 0.00e+00 1.00e+00 -## sigma 3.41172 1.35e-04 2.02e+00 4.80e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 92.73500 NA 8.95e+01 95.92118 +## k1 0.00258 NA 4.14e-04 0.01611 +## k2 0.00258 NA 1.74e-03 0.00383 +## g 0.16452 NA 0.00e+00 1.00000 +## sigma 3.41172 NA 2.02e+00 4.79960 ## ## ## DTx values: @@ -2134,14 +2166,14 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p14)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p14)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 48.43249 28.67746 27.26248 +## SFO IORE DFOP +## 48.432 28.677 27.262 ## ## Critical sum of squares for checking the SFO model: -## [1] 32.83337 +## [1] 32.833 ## ## Parameters: ## $SFO @@ -2161,7 +2193,7 @@ div.tocify { ## Estimate Pr(>t) Lower Upper ## parent_0 1.00e+02 2.96e-28 99.40280 101.2768 ## k1 9.53e-03 1.20e-01 0.00638 0.0143 -## k2 7.70e-12 5.00e-01 0.00000 Inf +## k2 6.08e-12 5.00e-01 0.00000 Inf ## g 3.98e-01 2.19e-01 0.30481 0.4998 ## sigma 1.17e+00 7.68e-06 0.77406 1.5610 ## @@ -2170,30 +2202,26 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 2.48e+02 8.25e+02 2.48e+02 ## IORE 4.34e+02 2.22e+04 6.70e+03 -## DFOP 2.41e+10 2.33e+11 9.00e+10 +## DFOP 3.05e+10 2.95e+11 1.14e+11 ## ## Representative half-life: -## [1] 6697.44</code></pre> +## [1] 6697.4</code></pre> <p>The slower rate constant reported by PestDF is negative, which is not physically realistic, and not possible in mkin. The other fits give the same results in mkin and PestDF.</p> </div> <div id="n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero" class="section level1"> <h1>N is less than 1 and DFOP fraction parameter is below zero</h1> <pre class="r"><code>p15a <- nafta(NAFTA_SOP_Attachment[["p15a"]])</code></pre> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p15a)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p15a)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 245.5248 135.0132 245.5248 +## SFO IORE DFOP +## 245.52 135.01 245.52 ## ## Critical sum of squares for checking the SFO model: -## [1] 165.9335 +## [1] 165.93 ## ## Parameters: ## $SFO @@ -2210,12 +2238,12 @@ div.tocify { ## sigma 3.105 1.78e-04 1.795 4.416 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 97.96752 NA 94.21914 101.7159 -## k1 0.00952 NA 0.00241 0.0377 -## k2 0.00952 NA 0.00747 0.0121 -## g 0.17247 NA NA NA -## sigma 4.18778 NA 2.39747 5.9781 +## Estimate Pr(>t) Lower Upper +## parent_0 97.96751 2.85e-13 94.21913 101.7159 +## k1 0.00952 6.28e-02 0.00250 0.0363 +## k2 0.00952 1.27e-04 0.00646 0.0140 +## g 0.21241 5.00e-01 0.00000 1.0000 +## sigma 4.18778 2.50e-04 2.39747 5.9781 ## ## ## DTx values: @@ -2234,14 +2262,14 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p15b)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p15b)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 106.91629 68.55574 106.91629 +## SFO IORE DFOP +## 106.916 68.556 106.916 ## ## Critical sum of squares for checking the SFO model: -## [1] 84.25618 +## [1] 84.256 ## ## Parameters: ## $SFO @@ -2252,18 +2280,18 @@ div.tocify { ## ## $IORE ## Estimate Pr(>t) Lower Upper -## parent_0 99.83 1.81e-16 97.51348 102.14 +## parent_0 99.83 1.81e-16 97.51349 102.14 ## k__iore_parent 0.38 3.22e-01 0.00352 41.05 ## N_parent 0.00 5.00e-01 -1.07696 1.08 ## sigma 2.21 2.57e-04 1.23245 3.19 ## ## $DFOP ## Estimate Pr(>t) Lower Upper -## parent_0 1.01e+02 NA 98.24464 1.04e+02 -## k1 4.86e-03 NA 0.00068 3.47e-02 -## k2 4.86e-03 NA 0.00338 6.99e-03 -## g 1.50e-01 NA NA NA -## sigma 2.76e+00 NA 1.58208 3.94e+00 +## parent_0 1.01e+02 NA 9.82e+01 1.04e+02 +## k1 4.86e-03 NA 8.63e-04 2.73e-02 +## k2 4.86e-03 NA 3.21e-03 7.35e-03 +## g 1.88e-01 NA NA NA +## sigma 2.76e+00 NA 1.58e+00 3.94e+00 ## ## ## DTx values: @@ -2284,14 +2312,14 @@ div.tocify { <pre><code>## to the terminal degradation rate found with the DFOP model.</code></pre> <pre><code>## The representative half-life obtained from the DFOP model may be used</code></pre> <pre class="r"><code>plot(p16)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p16)</code></pre> <pre><code>## Sums of squares: -## SFO IORE DFOP -## 3831.804 2062.008 1550.980 +## SFO IORE DFOP +## 3831.8 2062.0 1551.0 ## ## Critical sum of squares for checking the SFO model: -## [1] 2247.348 +## [1] 2247.3 ## ## Parameters: ## $SFO @@ -2310,7 +2338,7 @@ div.tocify { ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 88.5333 7.40e-18 79.9836 97.083 -## k1 18.5560 5.00e-01 0.0000 Inf +## k1 18.8461 5.00e-01 0.0000 Inf ## k2 0.0776 1.41e-05 0.0518 0.116 ## g 0.4733 1.41e-09 0.3674 0.582 ## sigma 7.1902 2.11e-08 5.2785 9.102 @@ -2332,8 +2360,8 @@ div.tocify { <p>Therefore, mkin appears to suitable for kinetic evaluations according to the NAFTA guidance.</p> </div> <div id="references" class="section level1 unnumbered"> -<h1>References</h1> -<div id="refs" class="references"> +<h1 class="unnumbered">References</h1> +<div id="refs" class="references hanging-indent"> <div id="ref-usepa2015"> <p>US EPA. 2015. “Standard Operating Procedure for Using the NAFTA Guidance to Calculate Representative Half-Life Values and Characterizing Pesticide Degradation.”</p> </div> @@ -2351,7 +2379,7 @@ div.tocify { // add bootstrap table styles to pandoc tables function bootstrapStylePandocTables() { - $('tr.header').parent('thead').parent('table').addClass('table table-condensed'); + $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed'); } $(document).ready(function () { bootstrapStylePandocTables(); @@ -2390,7 +2418,7 @@ $(document).ready(function () { theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); }, ignoreSelector: ".toc-ignore", scrollTo: 0 diff --git a/vignettes/web_only/NAFTA_examples.rmd b/vignettes/web_only/NAFTA_examples.rmd index d18a3e84..6ac79242 100644 --- a/vignettes/web_only/NAFTA_examples.rmd +++ b/vignettes/web_only/NAFTA_examples.rmd @@ -1,7 +1,7 @@ --- title: "Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance" author: "Johannes Ranke" -date: "`r Sys.Date()`" +date: 26 February 2019 (rebuilt `r Sys.Date()`) output: html_document: toc: true diff --git a/vignettes/web_only/benchmarks.html b/vignettes/web_only/benchmarks.html index 043b777f..a591dc06 100644 --- a/vignettes/web_only/benchmarks.html +++ b/vignettes/web_only/benchmarks.html @@ -11,10 +11,22 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-05-13" /> <title>Benchmark timings for mkin</title> +<script>// Pandoc 2.9 adds attributes on both header and div. 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type="text/css">code{white-space: pre;}</style> <style type="text/css"> pre:not([class]) { @@ -1392,6 +1413,7 @@ h6 { + <style type="text/css"> .main-container { max-width: 940px; @@ -1583,7 +1605,7 @@ div.tocify { <h1 class="title toc-ignore">Benchmark timings for mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-05-13</h4> +<h4 class="date">Last change 13 May 2020 (rebuilt 2021-02-15)</h4> </div> @@ -1688,8 +1710,28 @@ save(mkin_benchmarks, file = "~/git/mkin/vignettes/web_only/mkin_benchmarks </tr> <tr class="even"> <td align="left">0.9.50.2</td> -<td align="right">1.547</td> -<td align="right">3.928</td> +<td align="right">1.714</td> +<td align="right">3.971</td> +</tr> +<tr class="odd"> +<td align="left">0.9.50.3</td> +<td align="right">1.752</td> +<td align="right">4.156</td> +</tr> +<tr class="even"> +<td align="left">0.9.50.4</td> +<td align="right">1.786</td> +<td align="right">3.729</td> +</tr> +<tr class="odd"> +<td align="left">1.0.0</td> +<td align="right">1.770</td> +<td align="right">3.703</td> +</tr> +<tr class="even"> +<td align="left">1.0.3</td> +<td align="right">1.582</td> +<td align="right">3.476</td> </tr> </tbody> </table> @@ -1739,16 +1781,40 @@ save(mkin_benchmarks, file = "~/git/mkin/vignettes/web_only/mkin_benchmarks </tr> <tr class="even"> <td align="left">0.9.50.2</td> -<td align="right">1.371</td> -<td align="right">6.154</td> -<td align="right">2.720</td> +<td align="right">1.402</td> +<td align="right">6.174</td> +<td align="right">2.764</td> +</tr> +<tr class="odd"> +<td align="left">0.9.50.3</td> +<td align="right">1.430</td> +<td align="right">6.615</td> +<td align="right">2.878</td> +</tr> +<tr class="even"> +<td align="left">0.9.50.4</td> +<td align="right">1.397</td> +<td align="right">7.251</td> +<td align="right">2.810</td> +</tr> +<tr class="odd"> +<td align="left">1.0.0</td> +<td align="right">1.373</td> +<td align="right">7.127</td> +<td align="right">2.762</td> +</tr> +<tr class="even"> +<td align="left">1.0.3</td> +<td align="right">1.417</td> +<td align="right">6.418</td> +<td align="right">2.823</td> </tr> </tbody> </table> </div> <div id="two-metabolites" class="section level3"> <h3>Two metabolites</h3> -<p>Constant variance (t6 and t7), two-component error model (t8 and t9), and variance by variable (t10 and t11) for one model fitted to one dataset, i.e. one fit for each test.</p> +<p>Constant variance (t6 and t7), two-component error model (t8 and t9), and variance by variable (t10 and t11) for one model fitted to one dataset, i.e. one fit for each test.</p> <table> <thead> <tr class="header"> @@ -1809,13 +1875,49 @@ save(mkin_benchmarks, file = "~/git/mkin/vignettes/web_only/mkin_benchmarks </tr> <tr class="even"> <td align="left">0.9.50.2</td> -<td align="right">0.742</td> -<td align="right">1.203</td> -<td align="right">1.282</td> -<td align="right">2.845</td> -<td align="right">2.037</td> +<td align="right">0.777</td> +<td align="right">1.236</td> +<td align="right">1.332</td> +<td align="right">2.872</td> +<td align="right">2.069</td> <td align="right">2.987</td> </tr> +<tr class="odd"> +<td align="left">0.9.50.3</td> +<td align="right">0.858</td> +<td align="right">1.264</td> +<td align="right">1.333</td> +<td align="right">2.984</td> +<td align="right">2.113</td> +<td align="right">3.073</td> +</tr> +<tr class="even"> +<td align="left">0.9.50.4</td> +<td align="right">0.783</td> +<td align="right">1.282</td> +<td align="right">1.486</td> +<td align="right">3.815</td> +<td align="right">1.958</td> +<td align="right">3.105</td> +</tr> +<tr class="odd"> +<td align="left">1.0.0</td> +<td align="right">0.775</td> +<td align="right">1.269</td> +<td align="right">1.467</td> +<td align="right">3.767</td> +<td align="right">1.919</td> +<td align="right">2.942</td> +</tr> +<tr class="even"> +<td align="left">1.0.3</td> +<td align="right">0.783</td> +<td align="right">1.258</td> +<td align="right">1.611</td> +<td align="right">3.095</td> +<td align="right">1.951</td> +<td align="right">2.859</td> +</tr> </tbody> </table> </div> @@ -1832,7 +1934,7 @@ save(mkin_benchmarks, file = "~/git/mkin/vignettes/web_only/mkin_benchmarks // add bootstrap table styles to pandoc tables function bootstrapStylePandocTables() { - $('tr.header').parent('thead').parent('table').addClass('table table-condensed'); + $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed'); } $(document).ready(function () { bootstrapStylePandocTables(); @@ -1876,7 +1978,7 @@ $(document).ready(function () { theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); }, ignoreSelector: ".toc-ignore", scrollTo: 0 diff --git a/vignettes/web_only/benchmarks.rmd b/vignettes/web_only/benchmarks.rmd index 7ae12451..c743234d 100644 --- a/vignettes/web_only/benchmarks.rmd +++ b/vignettes/web_only/benchmarks.rmd @@ -1,13 +1,13 @@ --- title: "Benchmark timings for mkin" author: "Johannes Ranke" +date: Last change 13 May 2020 (rebuilt `r Sys.Date()`) output: html_document: toc: true toc_float: true code_folding: show fig_retina: null -date: "`r Sys.Date()`" vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} diff --git a/vignettes/web_only/mkin_benchmarks.rda b/vignettes/web_only/mkin_benchmarks.rda Binary files differindex b5f76c8e..d2b82805 100644 --- a/vignettes/web_only/mkin_benchmarks.rda +++ b/vignettes/web_only/mkin_benchmarks.rda |
