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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Sampling</h1><div class="u-tcGrayDark">10,707 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Sampling</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Sampling">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Sampling/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Sampling/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Sampling/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Sampling">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_31443362 coauthored" data-work_id="31443362" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/31443362/LES_PR%C3%89L%C3%88VEMENTS_DES_RESTES_BIOARCH%C3%89OLOGIQUES_MANUEL_DE_TERRAIN">LES PRÉLÈVEMENTS DES RESTES BIOARCHÉOLOGIQUES : MANUEL DE TERRAIN</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Sur un chantier de fouille, l’archéologue est très fréquemment confronté à des vestiges organiques, ou biorestes. Qu’ils soient visibles ou invisibles, rares ou abondants, ils sont souvent source de questionnements : que prélever? Où,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_31443362" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Sur un chantier de fouille, l’archéologue est très fréquemment<br />confronté à des vestiges organiques, ou biorestes. Qu’ils soient<br />visibles ou invisibles, rares ou abondants, ils sont souvent<br />source de questionnements : que prélever? Où, comment et<br />en quelle quantité? Quelles sont les conditions de stockage<br />idéales? Quellle est la marche à suivre pour tamiser les sédiments?<br />C’est à ces interrogations que vise à répondre le présent<br />ouvrage. Il décrit de manière simple et précise les prélèvements<br />à effec tuer pour obtenir, dès la phase de terrain, un assemblage<br />de biorestes cohérent et représentatif. Il propose un aperçu de<br />différents types et méthodes de prélèvements, qui possèdent<br />chacun leurs avantages et inconvénients. De manière concrète,<br />leur mise en oeuvre dans les contextes archéologiques les plus<br />courants est passée en revue. Ce manuel guidera les choix de<br />l’archéologue en fonction de la réalité de terrain et des problématiques<br />de recherche envisagées. Les questions du stockage<br />et du traitement des échantillons sont égaments abordées.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/31443362" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7ad875e77698f036456b6a2b2c01863a" rel="nofollow" data-download="{"attachment_id":51803281,"asset_id":31443362,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/51803281/download_file?st=MTczMzAxNDk4NSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="838195" href="https://naturalsciences-be.academia.edu/AlexandreChevalier">Alexandre Chevalier</a><script data-card-contents-for-user="838195" type="text/json">{"id":838195,"first_name":"Alexandre","last_name":"Chevalier","domain_name":"naturalsciences-be","page_name":"AlexandreChevalier","display_name":"Alexandre Chevalier","profile_url":"https://naturalsciences-be.academia.edu/AlexandreChevalier?f_ri=57531","photo":"https://0.academia-photos.com/838195/515620/647319/s65_alexandre.chevalier.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-31443362">+4</span><div class="hidden js-additional-users-31443362"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://naturalsciences-be.academia.edu/QuentinGoffette">Quentin Goffette</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://bristol.academia.edu/FabiennePigi%C3%A8re">Fabienne Pigière</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://ugent.academia.edu/KoenDeforce">Koen Deforce</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://naturalsciences-be.academia.edu/MonaCourtPicon">Mona Court-Picon</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-31443362'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-31443362').html(); 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Qu’ils soient\nvisibles ou invisibles, rares ou abondants, ils sont souvent\nsource de questionnements : que prélever? Où, comment et\nen quelle quantité? Quelles sont les conditions de stockage\nidéales? Quellle est la marche à suivre pour tamiser les sédiments?\nC’est à ces interrogations que vise à répondre le présent\nouvrage. Il décrit de manière simple et précise les prélèvements\nà effec tuer pour obtenir, dès la phase de terrain, un assemblage\nde biorestes cohérent et représentatif. Il propose un aperçu de\ndifférents types et méthodes de prélèvements, qui possèdent\nchacun leurs avantages et inconvénients. De manière concrète,\nleur mise en oeuvre dans les contextes archéologiques les plus\ncourants est passée en revue. Ce manuel guidera les choix de\nl’archéologue en fonction de la réalité de terrain et des problématiques\nde recherche envisagées. Les questions du stockage\net du traitement des échantillons sont égaments abordées.","downloadable_attachments":[{"id":51803281,"asset_id":31443362,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":838195,"first_name":"Alexandre","last_name":"Chevalier","domain_name":"naturalsciences-be","page_name":"AlexandreChevalier","display_name":"Alexandre Chevalier","profile_url":"https://naturalsciences-be.academia.edu/AlexandreChevalier?f_ri=57531","photo":"https://0.academia-photos.com/838195/515620/647319/s65_alexandre.chevalier.jpg"},{"id":1336001,"first_name":"Quentin","last_name":"Goffette","domain_name":"naturalsciences-be","page_name":"QuentinGoffette","display_name":"Quentin Goffette","profile_url":"https://naturalsciences-be.academia.edu/QuentinGoffette?f_ri=57531","photo":"https://0.academia-photos.com/1336001/2365841/2756739/s65_quentin.goffette.jpg"},{"id":2185472,"first_name":"Fabienne","last_name":"Pigière","domain_name":"bristol","page_name":"FabiennePigière","display_name":"Fabienne Pigière","profile_url":"https://bristol.academia.edu/FabiennePigi%C3%A8re?f_ri=57531","photo":"https://0.academia-photos.com/2185472/909988/36814565/s65_fabienne.pigi_re.jpg"},{"id":2344500,"first_name":"Koen","last_name":"Deforce","domain_name":"ugent","page_name":"KoenDeforce","display_name":"Koen Deforce","profile_url":"https://ugent.academia.edu/KoenDeforce?f_ri=57531","photo":"https://0.academia-photos.com/2344500/756403/51216990/s65_koen.deforce.jpg"},{"id":42665106,"first_name":"Mona","last_name":"Court-Picon","domain_name":"naturalsciences-be","page_name":"MonaCourtPicon","display_name":"Mona Court-Picon","profile_url":"https://naturalsciences-be.academia.edu/MonaCourtPicon?f_ri=57531","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":392,"name":"Archaeology","url":"https://www.academia.edu/Documents/in/Archaeology?f_ri=57531","nofollow":false},{"id":25566,"name":"Archaeological Fieldwork","url":"https://www.academia.edu/Documents/in/Archaeological_Fieldwork?f_ri=57531","nofollow":false},{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531","nofollow":false},{"id":106423,"name":"Geoarchaeology and Paleoenvironmental Reconstructions","url":"https://www.academia.edu/Documents/in/Geoarchaeology_and_Paleoenvironmental_Reconstructions?f_ri=57531","nofollow":false},{"id":111574,"name":"Archaeobotanical analysis","url":"https://www.academia.edu/Documents/in/Archaeobotanical_analysis?f_ri=57531"},{"id":270929,"name":"Archeogeology","url":"https://www.academia.edu/Documents/in/Archeogeology?f_ri=57531"},{"id":1323808,"name":"Archaeology - Archaeozoology","url":"https://www.academia.edu/Documents/in/Archaeology_-_Archaeozoology?f_ri=57531"},{"id":2530447,"name":"ArchaeoScience","url":"https://www.academia.edu/Documents/in/ArchaeoScience?f_ri=57531"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_11044470 coauthored" data-work_id="11044470" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/11044470/Using_power_analysis_to_estimate_appropriate_sample_size">Using power analysis to estimate appropriate sample size</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The main aim of this paper is to provide some practical guidance to researchers on how statistical power analysis can be used to estimate sample size in empirical design. The paper describes the key assumptions underlying statistical... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_11044470" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The main aim of this paper is to provide some practical guidance to researchers on how statistical power analysis can be used to estimate sample size in empirical design. The paper describes the key assumptions underlying statistical power analysis and illustrates through several examples how to determine the appropriate sample size. The examples use hypotheses often tested in sport sciences and verified with popular statistical tests including the independent-samples t-test, one-way and twoway analysis of variance (ANOVA), correlation analysis, and regression analysis. Commonly used  statistical packages allow researchers to determine appropriate sample size for hypothesis testing situations listed above.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/11044470" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b9c461e6a6a772091eb7a25f290f7296" rel="nofollow" data-download="{"attachment_id":36753916,"asset_id":11044470,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/36753916/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="26019681" href="https://independent.academia.edu/TomczakMaciej">Maciej Tomczak</a><script data-card-contents-for-user="26019681" type="text/json">{"id":26019681,"first_name":"Maciej","last_name":"Tomczak","domain_name":"independent","page_name":"TomczakMaciej","display_name":"Maciej Tomczak","profile_url":"https://independent.academia.edu/TomczakMaciej?f_ri=57531","photo":"https://0.academia-photos.com/26019681/167923722/157881580/s65_maciej.tomczak.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-11044470">+2</span><div class="hidden js-additional-users-11044470"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://amu.academia.edu/EwaTomczak">Ewa Tomczak</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://one.academia.edu/RobertLew">Robert Lew</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-11044470'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-11044470').html(); 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The paper describes the key assumptions underlying statistical power analysis and illustrates through several examples how to determine the appropriate sample size. The examples use hypotheses often tested in sport sciences and verified with popular statistical tests including the independent-samples t-test, one-way and twoway analysis of variance (ANOVA), correlation analysis, and regression analysis. 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href="https://www.academia.edu/36421219/Les_formes_de_l_appropriation_musicale_Entre_musique_graphosph%C3%A9rique_et_audiovid%C3%A9osph%C3%A9rique">Les formes de l’appropriation musicale : Entre musique graphosphérique et audiovidéosphérique</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Ce mémoire vise à analyser les différentes formes de l’appropriation musicale à travers, notamment, un travail de définitions. Nous tenterons plus précisément de déterminer l’influence de l’environnement technoculturel sur... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36421219" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Ce mémoire vise à analyser les différentes formes de l’appropriation musicale à travers, notamment, un travail de définitions. Nous tenterons plus précisément de déterminer l’influence de l’environnement technoculturel sur l’appropriation, et plus particulièrement comment ces formes ont évolué en passant de la graphosphère à l’audiovidéosphère.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36421219" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="3ec9335666350b8e040fa3ea45ba4442" rel="nofollow" data-download="{"attachment_id":56333807,"asset_id":36421219,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56333807/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="35799906" href="https://cecille.academia.edu/CharlineAugier">Charline Augier</a><script data-card-contents-for-user="35799906" type="text/json">{"id":35799906,"first_name":"Charline","last_name":"Augier","domain_name":"cecille","page_name":"CharlineAugier","display_name":"Charline Augier","profile_url":"https://cecille.academia.edu/CharlineAugier?f_ri=57531","photo":"https://0.academia-photos.com/35799906/12268689/13661076/s65_charline.augier.jpg"}</script></span></span></li><li class="js-paper-rank-work_36421219 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36421219"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36421219, container: ".js-paper-rank-work_36421219", }); 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$(".js-view-count[data-work-id=36421219]").text(description); $(".js-view-count-work_36421219").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36421219").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36421219"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="57531" href="https://www.academia.edu/Documents/in/Sampling">Sampling</a>, <script data-card-contents-for-ri="57531" type="text/json">{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="77267" href="https://www.academia.edu/Documents/in/Remix">Remix</a>, <script data-card-contents-for-ri="77267" type="text/json">{"id":77267,"name":"Remix","url":"https://www.academia.edu/Documents/in/Remix?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="78855" href="https://www.academia.edu/Documents/in/Appropriation">Appropriation</a>, <script data-card-contents-for-ri="78855" type="text/json">{"id":78855,"name":"Appropriation","url":"https://www.academia.edu/Documents/in/Appropriation?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="183587" href="https://www.academia.edu/Documents/in/Musique_populaire">Musique populaire</a><script data-card-contents-for-ri="183587" type="text/json">{"id":183587,"name":"Musique populaire","url":"https://www.academia.edu/Documents/in/Musique_populaire?f_ri=57531","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36421219]'), work: {"id":36421219,"title":"Les formes de l’appropriation musicale : Entre musique graphosphérique et audiovidéosphérique","created_at":"2018-04-16T10:42:55.529-07:00","url":"https://www.academia.edu/36421219/Les_formes_de_l_appropriation_musicale_Entre_musique_graphosph%C3%A9rique_et_audiovid%C3%A9osph%C3%A9rique?f_ri=57531","dom_id":"work_36421219","summary":"Ce mémoire vise à analyser les différentes formes de l’appropriation musicale à travers, notamment, un travail de définitions. Nous tenterons plus précisément de déterminer l’influence de l’environnement technoculturel sur l’appropriation, et plus particulièrement comment ces formes ont évolué en passant de la graphosphère à l’audiovidéosphère.\n","downloadable_attachments":[{"id":56333807,"asset_id":36421219,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":35799906,"first_name":"Charline","last_name":"Augier","domain_name":"cecille","page_name":"CharlineAugier","display_name":"Charline Augier","profile_url":"https://cecille.academia.edu/CharlineAugier?f_ri=57531","photo":"https://0.academia-photos.com/35799906/12268689/13661076/s65_charline.augier.jpg"}],"research_interests":[{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531","nofollow":false},{"id":77267,"name":"Remix","url":"https://www.academia.edu/Documents/in/Remix?f_ri=57531","nofollow":false},{"id":78855,"name":"Appropriation","url":"https://www.academia.edu/Documents/in/Appropriation?f_ri=57531","nofollow":false},{"id":183587,"name":"Musique populaire","url":"https://www.academia.edu/Documents/in/Musique_populaire?f_ri=57531","nofollow":false},{"id":381764,"name":"Musique Et Musicologie","url":"https://www.academia.edu/Documents/in/Musique_Et_Musicologie?f_ri=57531"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_31721567" data-work_id="31721567" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/31721567/Releasing_capacity_of_pre_sterile_cotton_swabs_for_discharging_sampled_microorganisms">Releasing capacity of pre-sterile cotton swabs for discharging sampled microorganisms</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Surface monitoring by using swabs forms a regular part of environmental monitoring of cleanrooms. There are different factors that affect swab recovery, from tip type to enumeration method. One factor, for swabs where the microorganisms... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_31721567" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Surface monitoring by using swabs forms a regular part of environmental monitoring of cleanrooms. There are different factors that affect swab recovery, from tip type to enumeration method. One factor, for swabs where the microorganisms are detached from the swab tip and which are then membrane filtered, is the period of vortex mixing. This paper discusses microbial surface sampling, and the factors that affect swab recovery. The paper presents some experimental data where vortex times are considered for a range of microorganisms. The study outcome indicates that 15 seconds vortex mixing is sufficient to obtain microbial recoveries from the swab tip above 50%.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/31721567" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="683734026ef16cefe934969ae83e465a" rel="nofollow" data-download="{"attachment_id":52033897,"asset_id":31721567,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/52033897/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="725124" href="https://manchester.academia.edu/TimSandle">Tim Sandle</a><script data-card-contents-for-user="725124" type="text/json">{"id":725124,"first_name":"Tim","last_name":"Sandle","domain_name":"manchester","page_name":"TimSandle","display_name":"Tim Sandle","profile_url":"https://manchester.academia.edu/TimSandle?f_ri=57531","photo":"https://0.academia-photos.com/725124/247895/293007/s65_tim.sandle.jpg"}</script></span></span></li><li class="js-paper-rank-work_31721567 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="31721567"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 31721567, container: ".js-paper-rank-work_31721567", }); 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There are different factors that affect swab recovery, from tip type to enumeration method. One factor, for swabs where the microorganisms are detached from the swab tip and which are then membrane filtered, is the period of vortex mixing. This paper discusses microbial surface sampling, and the factors that affect swab recovery. The paper presents some experimental data where vortex times are considered for a range of microorganisms. The study outcome indicates that 15 seconds vortex mixing is sufficient to obtain microbial recoveries from the swab tip above 50%.","downloadable_attachments":[{"id":52033897,"asset_id":31721567,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":725124,"first_name":"Tim","last_name":"Sandle","domain_name":"manchester","page_name":"TimSandle","display_name":"Tim Sandle","profile_url":"https://manchester.academia.edu/TimSandle?f_ri=57531","photo":"https://0.academia-photos.com/725124/247895/293007/s65_tim.sandle.jpg"}],"research_interests":[{"id":159,"name":"Microbiology","url":"https://www.academia.edu/Documents/in/Microbiology?f_ri=57531","nofollow":false},{"id":11801,"name":"Environmental Monitoring","url":"https://www.academia.edu/Documents/in/Environmental_Monitoring?f_ri=57531","nofollow":false},{"id":43659,"name":"Pharmaceutical Microbiology","url":"https://www.academia.edu/Documents/in/Pharmaceutical_Microbiology?f_ri=57531","nofollow":false},{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531","nofollow":false},{"id":113903,"name":"Bacteria","url":"https://www.academia.edu/Documents/in/Bacteria?f_ri=57531"},{"id":438328,"name":"Cleanrooms","url":"https://www.academia.edu/Documents/in/Cleanrooms?f_ri=57531"},{"id":1118324,"name":"Aseptic Processing","url":"https://www.academia.edu/Documents/in/Aseptic_Processing?f_ri=57531"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_30575340" data-work_id="30575340" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/30575340/Preliminary_observations_on_the_calcaneal_trabecular_microarchitecture_of_extant_large_bodied_hominoids">Preliminary observations on the calcaneal trabecular microarchitecture of extant large-bodied hominoids</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/30575340" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="084fd89fd1b98187c89e8cb1cb5abc54" rel="nofollow" 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type="text/json">{"id":392,"name":"Archaeology","url":"https://www.academia.edu/Documents/in/Archaeology?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="767" href="https://www.academia.edu/Documents/in/Anthropology">Anthropology</a>, <script data-card-contents-for-ri="767" type="text/json">{"id":767,"name":"Anthropology","url":"https://www.academia.edu/Documents/in/Anthropology?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1440" href="https://www.academia.edu/Documents/in/Visualization">Visualization</a><script data-card-contents-for-ri="1440" type="text/json">{"id":1440,"name":"Visualization","url":"https://www.academia.edu/Documents/in/Visualization?f_ri=57531","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=30575340]'), work: {"id":30575340,"title":"Preliminary observations on the calcaneal trabecular 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href="https://www.academia.edu/Documents/in/Experimental_Research">Experimental Research</a>, <script data-card-contents-for-ri="30561" type="text/json">{"id":30561,"name":"Experimental Research","url":"https://www.academia.edu/Documents/in/Experimental_Research?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="54284" href="https://www.academia.edu/Documents/in/Generalization">Generalization</a><script data-card-contents-for-ri="54284" type="text/json">{"id":54284,"name":"Generalization","url":"https://www.academia.edu/Documents/in/Generalization?f_ri=57531","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=28318454]'), work: {"id":28318454,"title":"Describing Baseline Conditions: Suggestions for Study 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itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/7139956/Determination_of_inorganic_ions_and_trace_elements_in_total_suspended_particles_at_three_urban_zones_in_the_Mexico_City_Metropolitan_Area_and_one_rural_site">Determination of inorganic ions and trace elements in total suspended particles at three urban zones in the Mexico City Metropolitan Area and one rural site</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/7139956" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" 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href="https://www.academia.edu/Documents/in/Principal_Component_Analysis">Principal Component Analysis</a>, <script data-card-contents-for-ri="5069" type="text/json">{"id":5069,"name":"Principal Component Analysis","url":"https://www.academia.edu/Documents/in/Principal_Component_Analysis?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="9095" href="https://www.academia.edu/Documents/in/Trace_Metals">Trace Metals</a>, <script data-card-contents-for-ri="9095" type="text/json">{"id":9095,"name":"Trace Metals","url":"https://www.academia.edu/Documents/in/Trace_Metals?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="15372" href="https://www.academia.edu/Documents/in/Ion_Chromatography">Ion Chromatography</a><script data-card-contents-for-ri="15372" type="text/json">{"id":15372,"name":"Ion Chromatography","url":"https://www.academia.edu/Documents/in/Ion_Chromatography?f_ri=57531","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=7139956]'), work: {"id":7139956,"title":"Determination of inorganic ions and trace elements in total suspended particles at three urban zones in the Mexico City Metropolitan Area and one rural site","created_at":"2014-05-23T16:08:20.326-07:00","url":"https://www.academia.edu/7139956/Determination_of_inorganic_ions_and_trace_elements_in_total_suspended_particles_at_three_urban_zones_in_the_Mexico_City_Metropolitan_Area_and_one_rural_site?f_ri=57531","dom_id":"work_7139956","summary":null,"downloadable_attachments":[{"id":48585094,"asset_id":7139956,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":12314779,"first_name":"Hugo","last_name":"Padilla","domain_name":"esia","page_name":"HugoPadilla","display_name":"Hugo Padilla","profile_url":"https://esia.academia.edu/HugoPadilla?f_ri=57531","photo":"https://0.academia-photos.com/12314779/3530708/4145174/s65_hugo.padilla.jpg"}],"research_interests":[{"id":2442,"name":"Atmospheric Science","url":"https://www.academia.edu/Documents/in/Atmospheric_Science?f_ri=57531","nofollow":false},{"id":5069,"name":"Principal Component Analysis","url":"https://www.academia.edu/Documents/in/Principal_Component_Analysis?f_ri=57531","nofollow":false},{"id":9095,"name":"Trace Metals","url":"https://www.academia.edu/Documents/in/Trace_Metals?f_ri=57531","nofollow":false},{"id":15372,"name":"Ion Chromatography","url":"https://www.academia.edu/Documents/in/Ion_Chromatography?f_ri=57531","nofollow":false},{"id":57433,"name":"Seasonality","url":"https://www.academia.edu/Documents/in/Seasonality?f_ri=57531"},{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531"},{"id":60285,"name":"Atmospheric sciences","url":"https://www.academia.edu/Documents/in/Atmospheric_sciences?f_ri=57531"},{"id":120698,"name":"Particles","url":"https://www.academia.edu/Documents/in/Particles?f_ri=57531"},{"id":125564,"name":"Statistical Significance","url":"https://www.academia.edu/Documents/in/Statistical_Significance?f_ri=57531"},{"id":174778,"name":"Principal Components Analysis","url":"https://www.academia.edu/Documents/in/Principal_Components_Analysis?f_ri=57531"},{"id":224577,"name":"Trace Elements","url":"https://www.academia.edu/Documents/in/Trace_Elements?f_ri=57531"},{"id":591142,"name":"Atomic Absorption","url":"https://www.academia.edu/Documents/in/Atomic_Absorption?f_ri=57531"},{"id":709300,"name":"Trace element","url":"https://www.academia.edu/Documents/in/Trace_element?f_ri=57531"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_10990165" data-work_id="10990165" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/10990165/Monte_Carlo_Minimization_and_Counting_One_Two_Too_Many">Monte Carlo Minimization and Counting: One, Two, ..., Too Many</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/10990165" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="32b8a57e0d5d86bcec2b91c36485442d" rel="nofollow" data-download="{"attachment_id":46981376,"asset_id":10990165,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46981376/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="26620040" href="https://independent.academia.edu/FrancisSullivan1">Francis Sullivan</a><script data-card-contents-for-user="26620040" type="text/json">{"id":26620040,"first_name":"Francis","last_name":"Sullivan","domain_name":"independent","page_name":"FrancisSullivan1","display_name":"Francis Sullivan","profile_url":"https://independent.academia.edu/FrancisSullivan1?f_ri=57531","photo":"https://0.academia-photos.com/26620040/26558851/25095960/s65_francis.sullivan.jpg"}</script></span></span></li><li class="js-paper-rank-work_10990165 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="10990165"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 10990165, container: ".js-paper-rank-work_10990165", }); });</script></li><li class="js-percentile-work_10990165 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x 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}); });</script></span><script>$(function() { $(".js-view-count-work_10990165").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="10990165"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="440" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="6974" href="https://www.academia.edu/Documents/in/Monte_Carlo">Monte Carlo</a>, <script data-card-contents-for-ri="6974" type="text/json">{"id":6974,"name":"Monte Carlo","url":"https://www.academia.edu/Documents/in/Monte_Carlo?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34019" href="https://www.academia.edu/Documents/in/Monte_Carlo_Methods">Monte Carlo Methods</a><script data-card-contents-for-ri="34019" type="text/json">{"id":34019,"name":"Monte Carlo Methods","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Methods?f_ri=57531","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=10990165]'), work: {"id":10990165,"title":"Monte Carlo Minimization and Counting: One, Two, ..., Too Many","created_at":"2015-02-22T09:21:10.119-08:00","url":"https://www.academia.edu/10990165/Monte_Carlo_Minimization_and_Counting_One_Two_Too_Many?f_ri=57531","dom_id":"work_10990165","summary":null,"downloadable_attachments":[{"id":46981376,"asset_id":10990165,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":26620040,"first_name":"Francis","last_name":"Sullivan","domain_name":"independent","page_name":"FrancisSullivan1","display_name":"Francis Sullivan","profile_url":"https://independent.academia.edu/FrancisSullivan1?f_ri=57531","photo":"https://0.academia-photos.com/26620040/26558851/25095960/s65_francis.sullivan.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=57531","nofollow":false},{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=57531","nofollow":false},{"id":6974,"name":"Monte Carlo","url":"https://www.academia.edu/Documents/in/Monte_Carlo?f_ri=57531","nofollow":false},{"id":34019,"name":"Monte Carlo Methods","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Methods?f_ri=57531","nofollow":false},{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531"},{"id":73645,"name":"Sampling methods","url":"https://www.academia.edu/Documents/in/Sampling_methods?f_ri=57531"},{"id":421210,"name":"Discrete Optimization","url":"https://www.academia.edu/Documents/in/Discrete_Optimization?f_ri=57531"},{"id":556845,"name":"Numerical Analysis and Computational Mathematics","url":"https://www.academia.edu/Documents/in/Numerical_Analysis_and_Computational_Mathematics?f_ri=57531"},{"id":1333436,"name":"Monte Carlo Method","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Method?f_ri=57531"},{"id":1561665,"name":"Minimization","url":"https://www.academia.edu/Documents/in/Minimization?f_ri=57531"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_73368006" data-work_id="73368006" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/73368006/Lag_one_autocorrelation_in_short_series_Estimation_and_hypotheses_testing">Lag-one autocorrelation in short series: Estimation and hypotheses testing</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In the first part of the study, nine estimators of the first-order autoregressive parameter are reviewed and a new estimator is proposed. The relationships and discrepancies between the estimators are discussed in order to achieve a clear... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_73368006" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In the first part of the study, nine estimators of the first-order autoregressive parameter are reviewed and a new estimator is proposed. The relationships and discrepancies between the estimators are discussed in order to achieve a clear differentiation. In the second part of the study, the precision in the estimation of autocorrelation is studied. The performance of the ten lag-one autocorrelation estimators is compared in terms of Mean Square Error (combining bias and variance) using data series generated by Monte Carlo simulation. The results show that there is not a single optimal estimator for all conditions, suggesting that the estimator ought to be chosen according to sample size and to the information available on the possible direction of the serial dependence. Additionally, the probability of labelling an actually existing autocorrelation as statistically significant is explored using Monte Carlo sampling. The power estimates obtained are quite similar among the tests ass...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/73368006" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5adf579204ae94fd1ceed1f9b86f079d" rel="nofollow" data-download="{"attachment_id":81915411,"asset_id":73368006,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/81915411/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3856180" href="https://ub.academia.edu/RumenManolov">Rumen Manolov</a><script data-card-contents-for-user="3856180" type="text/json">{"id":3856180,"first_name":"Rumen","last_name":"Manolov","domain_name":"ub","page_name":"RumenManolov","display_name":"Rumen Manolov","profile_url":"https://ub.academia.edu/RumenManolov?f_ri=57531","photo":"https://0.academia-photos.com/3856180/1416314/13898952/s65_rumen.manolov.png"}</script></span></span></li><li class="js-paper-rank-work_73368006 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="73368006"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 73368006, container: ".js-paper-rank-work_73368006", }); 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The relationships and discrepancies between the estimators are discussed in order to achieve a clear differentiation. In the second part of the study, the precision in the estimation of autocorrelation is studied. The performance of the ten lag-one autocorrelation estimators is compared in terms of Mean Square Error (combining bias and variance) using data series generated by Monte Carlo simulation. The results show that there is not a single optimal estimator for all conditions, suggesting that the estimator ought to be chosen according to sample size and to the information available on the possible direction of the serial dependence. Additionally, the probability of labelling an actually existing autocorrelation as statistically significant is explored using Monte Carlo sampling. The power estimates obtained are quite similar among the tests ass...","downloadable_attachments":[{"id":81915411,"asset_id":73368006,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3856180,"first_name":"Rumen","last_name":"Manolov","domain_name":"ub","page_name":"RumenManolov","display_name":"Rumen Manolov","profile_url":"https://ub.academia.edu/RumenManolov?f_ri=57531","photo":"https://0.academia-photos.com/3856180/1416314/13898952/s65_rumen.manolov.png"}],"research_interests":[{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=57531","nofollow":false},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=57531","nofollow":false},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=57531","nofollow":false},{"id":34019,"name":"Monte Carlo 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Richness","url":"https://www.academia.edu/Documents/in/Species_Richness?f_ri=57531"},{"id":223715,"name":"Sample","url":"https://www.academia.edu/Documents/in/Sample?f_ri=57531"},{"id":952977,"name":"Extrapolation","url":"https://www.academia.edu/Documents/in/Extrapolation?f_ri=57531"},{"id":1587858,"name":"Confidence Interval","url":"https://www.academia.edu/Documents/in/Confidence_Interval?f_ri=57531"},{"id":1681572,"name":"Unit","url":"https://www.academia.edu/Documents/in/Unit?f_ri=57531"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_66586074" data-work_id="66586074" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/66586074/Coralville_Reservoir_Water_Quality_Project">Coralville Reservoir Water Quality Project</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_66586074" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div 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href="https://www.academia.edu/36998832/Spatio_temporal_variations_in_water_quality_of_Nullah_Aik_tributary_of_the_river_Chenab_Pakistan">Spatio-temporal variations in water quality of Nullah Aik-tributary of the river Chenab, Pakistan</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36998832" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b921bc4e036c4178b66fe27c899bffe9" rel="nofollow" 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Analysis","url":"https://www.academia.edu/Documents/in/Spatial_Analysis?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4523" href="https://www.academia.edu/Documents/in/Water_quality">Water quality</a>, <script data-card-contents-for-ri="4523" type="text/json">{"id":4523,"name":"Water quality","url":"https://www.academia.edu/Documents/in/Water_quality?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5069" href="https://www.academia.edu/Documents/in/Principal_Component_Analysis">Principal Component Analysis</a><script data-card-contents-for-ri="5069" type="text/json">{"id":5069,"name":"Principal Component Analysis","url":"https://www.academia.edu/Documents/in/Principal_Component_Analysis?f_ri=57531","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36998832]'), work: {"id":36998832,"title":"Spatio-temporal 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Variation","url":"https://www.academia.edu/Documents/in/Total_Variation?f_ri=57531"},{"id":2303517,"name":"Temporal Change","url":"https://www.academia.edu/Documents/in/Temporal_Change?f_ri=57531"},{"id":2516014,"name":"temporal variation","url":"https://www.academia.edu/Documents/in/temporal_variation?f_ri=57531"},{"id":2519467,"name":"Statistical techniques","url":"https://www.academia.edu/Documents/in/Statistical_techniques?f_ri=57531"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_34974820" data-work_id="34974820" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/34974820/Sampling_from_Social_Networks_with_Attributes">Sampling from Social Networks with Attributes</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling , edge sampling, random walk sampling, and snowball... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_34974820" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling , edge sampling, random walk sampling, and snowball sampling) on social networks with attributes. We consider the special case of networks (i) where we have one attribute with two values (e.g., male and female in the case of gender), (ii) where the size of the two groups is unequal (e.g., a male majority and a female minority), and (iii) where nodes with the same or different attribute value attract or repel each other (i.e., homophilic or heterophilic behavior). We evaluate the different sampling techniques with respect to conserving the position of nodes and the visibility of groups in such networks. Experiments are conducted both on synthetic and empirical social networks. Our results provide evidence that different network sampling techniques are highly sensitive with regard to capturing the expected centrality of nodes, and that their accuracy depends on relative group size differences and on the level of homophily that can be observed in the network. We conclude that uninformed sampling from social networks with attributes thus can significantly impair the ability of researchers to draw valid conclusions about the centrality of nodes and the visibility or invisibility of groups in social networks.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34974820" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fa278d680ce5f5b58ec696d4e46fc5ab" rel="nofollow" data-download="{"attachment_id":54838327,"asset_id":34974820,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54838327/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4649847" href="https://gesis.academia.edu/FaribaKarimi">Fariba Karimi</a><script data-card-contents-for-user="4649847" type="text/json">{"id":4649847,"first_name":"Fariba","last_name":"Karimi","domain_name":"gesis","page_name":"FaribaKarimi","display_name":"Fariba Karimi","profile_url":"https://gesis.academia.edu/FaribaKarimi?f_ri=57531","photo":"https://0.academia-photos.com/4649847/1950337/2306413/s65_fariba.karimi.png"}</script></span></span></li><li class="js-paper-rank-work_34974820 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34974820"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34974820, container: ".js-paper-rank-work_34974820", }); 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$(".js-view-count[data-work-id=34974820]").text(description); $(".js-view-count-work_34974820").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_34974820").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="34974820"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1233" href="https://www.academia.edu/Documents/in/Social_Networks">Social Networks</a>, <script data-card-contents-for-ri="1233" type="text/json">{"id":1233,"name":"Social Networks","url":"https://www.academia.edu/Documents/in/Social_Networks?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="57531" href="https://www.academia.edu/Documents/in/Sampling">Sampling</a>, <script data-card-contents-for-ri="57531" type="text/json">{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="207049" href="https://www.academia.edu/Documents/in/Homophily">Homophily</a><script data-card-contents-for-ri="207049" type="text/json">{"id":207049,"name":"Homophily","url":"https://www.academia.edu/Documents/in/Homophily?f_ri=57531","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=34974820]'), work: {"id":34974820,"title":"Sampling from Social Networks with Attributes","created_at":"2017-10-27T05:57:03.991-07:00","url":"https://www.academia.edu/34974820/Sampling_from_Social_Networks_with_Attributes?f_ri=57531","dom_id":"work_34974820","summary":"Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling , edge sampling, random walk sampling, and snowball sampling) on social networks with attributes. We consider the special case of networks (i) where we have one attribute with two values (e.g., male and female in the case of gender), (ii) where the size of the two groups is unequal (e.g., a male majority and a female minority), and (iii) where nodes with the same or different attribute value attract or repel each other (i.e., homophilic or heterophilic behavior). We evaluate the different sampling techniques with respect to conserving the position of nodes and the visibility of groups in such networks. Experiments are conducted both on synthetic and empirical social networks. Our results provide evidence that different network sampling techniques are highly sensitive with regard to capturing the expected centrality of nodes, and that their accuracy depends on relative group size differences and on the level of homophily that can be observed in the network. We conclude that uninformed sampling from social networks with attributes thus can significantly impair the ability of researchers to draw valid conclusions about the centrality of nodes and the visibility or invisibility of groups in social networks.","downloadable_attachments":[{"id":54838327,"asset_id":34974820,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4649847,"first_name":"Fariba","last_name":"Karimi","domain_name":"gesis","page_name":"FaribaKarimi","display_name":"Fariba Karimi","profile_url":"https://gesis.academia.edu/FaribaKarimi?f_ri=57531","photo":"https://0.academia-photos.com/4649847/1950337/2306413/s65_fariba.karimi.png"}],"research_interests":[{"id":1233,"name":"Social Networks","url":"https://www.academia.edu/Documents/in/Social_Networks?f_ri=57531","nofollow":false},{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531","nofollow":false},{"id":207049,"name":"Homophily","url":"https://www.academia.edu/Documents/in/Homophily?f_ri=57531","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_3442137" data-work_id="3442137" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/3442137/Engagement_in_after_school_program_activities_quality_of_experience_from_the_perspective_of_participants">Engagement in after-school program activities: quality of experience from the perspective of participants</a></div></div><div class="u-pb4x 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data-has-card-for-ri="221" href="https://www.academia.edu/Documents/in/Psychology">Psychology</a>, <script data-card-contents-for-ri="221" type="text/json">{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="7110" href="https://www.academia.edu/Documents/in/Student_Engagement">Student Engagement</a>, <script data-card-contents-for-ri="7110" type="text/json">{"id":7110,"name":"Student Engagement","url":"https://www.academia.edu/Documents/in/Student_Engagement?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="23986" href="https://www.academia.edu/Documents/in/Intrinsic_motivation">Intrinsic motivation</a>, <script data-card-contents-for-ri="23986" type="text/json">{"id":23986,"name":"Intrinsic motivation","url":"https://www.academia.edu/Documents/in/Intrinsic_motivation?f_ri=57531","nofollow":false}</script><a 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href="https://www.academia.edu/12956656/Ultra_Rapid_Survey_Methodology_and_Toolkit_for_Socio_Cultural_Decisions">Ultra-Rapid Survey Methodology and Toolkit for Socio-Cultural Decisions</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">ISDM (Integrated Social Network Decision Models) is a methodology and tool kit which supports full planning cycles for SSTR, disaster relief and humanitarian missions in cross-cultural environments. ISDM guides users in developing a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12956656" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">ISDM (Integrated Social Network Decision Models) is a methodology and tool kit which supports full planning cycles for SSTR, disaster relief and humanitarian missions in cross-cultural environments.  ISDM guides users in developing a decision model that enhances the efficient collection and analysis of model-related data, especially from non-cooperative populations. ISDM also guides users in useful analysis while seamlessly accounting for missing or incomplete data; finally reintegrating the findings into the decision model for understanding.<br />As part of our continued testing of ISDM, we conducted a one-day “flash” study at UCLA to verify that the entire system could function both rapidly and effectively as a single integrated unit.  The study involved one graduate student and several undergraduate students, none of whom had any prior exposure to the ISDM methodology or toolkit.  We presented them with a scenario in which an academic institution was greatly concerned about the pervasiveness of cheating on tests.  The scenario required the student team to address the problem of potential strategies, resources and inherent uncertainties by gathering data from students on campus.<br />The “flash” study dramatically demonstrated how rapidly ISDM could be used by untrained users as a complete process that begins with an uncertainty to be understood and ends with a confident decision based on robust analysis of empirical data.  The entire process for our “flash” study was completed in a fraction of a day!  The process steps comprised: (1) problem understanding; (2) survey generation; (3) data collection; (4) analysis; and (5) selecting an optimal strategy based on the new intelligence and enhanced understanding of the situation.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12956656" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5e23cebb3944542519a16c5ac258559a" rel="nofollow" data-download="{"attachment_id":37896675,"asset_id":12956656,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/37896675/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="16062607" href="https://independent.academia.edu/RGrannis">Rick Grannis</a><script data-card-contents-for-user="16062607" type="text/json">{"id":16062607,"first_name":"Rick","last_name":"Grannis","domain_name":"independent","page_name":"RGrannis","display_name":"Rick Grannis","profile_url":"https://independent.academia.edu/RGrannis?f_ri=57531","photo":"https://0.academia-photos.com/16062607/6081730/6896575/s65_rick.grannis.jpg"}</script></span></span></li><li class="js-paper-rank-work_12956656 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12956656"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12956656, container: ".js-paper-rank-work_12956656", }); 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ISDM guides users in developing a decision model that enhances the efficient collection and analysis of model-related data, especially from non-cooperative populations. ISDM also guides users in useful analysis while seamlessly accounting for missing or incomplete data; finally reintegrating the findings into the decision model for understanding.\nAs part of our continued testing of ISDM, we conducted a one-day “flash” study at UCLA to verify that the entire system could function both rapidly and effectively as a single integrated unit. The study involved one graduate student and several undergraduate students, none of whom had any prior exposure to the ISDM methodology or toolkit. We presented them with a scenario in which an academic institution was greatly concerned about the pervasiveness of cheating on tests. The scenario required the student team to address the problem of potential strategies, resources and inherent uncertainties by gathering data from students on campus.\nThe “flash” study dramatically demonstrated how rapidly ISDM could be used by untrained users as a complete process that begins with an uncertainty to be understood and ends with a confident decision based on robust analysis of empirical data. The entire process for our “flash” study was completed in a fraction of a day! The process steps comprised: (1) problem understanding; (2) survey generation; (3) data collection; (4) analysis; and (5) selecting an optimal strategy based on the new intelligence and enhanced understanding of the situation. \n","downloadable_attachments":[{"id":37896675,"asset_id":12956656,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":16062607,"first_name":"Rick","last_name":"Grannis","domain_name":"independent","page_name":"RGrannis","display_name":"Rick Grannis","profile_url":"https://independent.academia.edu/RGrannis?f_ri=57531","photo":"https://0.academia-photos.com/16062607/6081730/6896575/s65_rick.grannis.jpg"}],"research_interests":[{"id":3158,"name":"Survey Sampling","url":"https://www.academia.edu/Documents/in/Survey_Sampling?f_ri=57531","nofollow":false},{"id":47878,"name":"Respondent Driven 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href="https://www.academia.edu/50626070/A_stochastic_programming_approach_for_supply_chain_network_design_under_uncertainty">A stochastic programming approach for supply chain network design under uncertainty</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper proposes a stochastic programming model and solution algorithm for solving supply chain network design problems of a realistic scale. Existing approaches for these problems are either restricted to deterministic environments or... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_50626070" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper proposes a stochastic programming model and solution algorithm for solving supply chain network design problems of a realistic scale. Existing approaches for these problems are either restricted to deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution methodology integrates a recently proposed sampling strategy, the sample average approximation (SAA) scheme, with an accelerated Benders decomposition algorithm to quickly compute high quality solutions to large-scale stochastic supply chain design problems with a huge (potentially infinite) number of scenarios. A computational study involving two real supply chain networks are presented to highlight the significance of the stochastic model as well as the efficiency of the proposed solution strategy.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/50626070" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4fbd9da248582ba8910137481ab086dc" rel="nofollow" data-download="{"attachment_id":68537913,"asset_id":50626070,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/68537913/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="183856235" href="https://independent.academia.edu/ShabbirAhmed154">Shabbir Ahmed</a><script data-card-contents-for-user="183856235" type="text/json">{"id":183856235,"first_name":"Shabbir","last_name":"Ahmed","domain_name":"independent","page_name":"ShabbirAhmed154","display_name":"Shabbir Ahmed","profile_url":"https://independent.academia.edu/ShabbirAhmed154?f_ri=57531","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_50626070 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="50626070"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 50626070, container: ".js-paper-rank-work_50626070", }); 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Existing approaches for these problems are either restricted to deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution methodology integrates a recently proposed sampling strategy, the sample average approximation (SAA) scheme, with an accelerated Benders decomposition algorithm to quickly compute high quality solutions to large-scale stochastic supply chain design problems with a huge (potentially infinite) number of scenarios. A computational study involving two real supply chain networks are presented to highlight the significance of the stochastic model as well as the efficiency of the proposed solution strategy.","downloadable_attachments":[{"id":68537913,"asset_id":50626070,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":183856235,"first_name":"Shabbir","last_name":"Ahmed","domain_name":"independent","page_name":"ShabbirAhmed154","display_name":"Shabbir Ahmed","profile_url":"https://independent.academia.edu/ShabbirAhmed154?f_ri=57531","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1335,"name":"Supply Chain Management","url":"https://www.academia.edu/Documents/in/Supply_Chain_Management?f_ri=57531","nofollow":false},{"id":4974,"name":"Logistics","url":"https://www.academia.edu/Documents/in/Logistics?f_ri=57531","nofollow":false},{"id":6432,"name":"Stochastic Programming","url":"https://www.academia.edu/Documents/in/Stochastic_Programming?f_ri=57531","nofollow":false},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=57531","nofollow":false},{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531"},{"id":89305,"name":"Network Design","url":"https://www.academia.edu/Documents/in/Network_Design?f_ri=57531"},{"id":169020,"name":"Supply Chain Network Design \u0026 logistics","url":"https://www.academia.edu/Documents/in/Supply_Chain_Network_Design_and_logistics?f_ri=57531"},{"id":191772,"name":"Facilities Planning and Design Management","url":"https://www.academia.edu/Documents/in/Facilities_Planning_and_Design_Management?f_ri=57531"},{"id":279027,"name":"European","url":"https://www.academia.edu/Documents/in/European?f_ri=57531"},{"id":407880,"name":"Sampling strategy","url":"https://www.academia.edu/Documents/in/Sampling_strategy?f_ri=57531"},{"id":502866,"name":"Supply Chain Design","url":"https://www.academia.edu/Documents/in/Supply_Chain_Design?f_ri=57531"},{"id":758278,"name":"Large Scale","url":"https://www.academia.edu/Documents/in/Large_Scale?f_ri=57531"},{"id":871199,"name":"Stochastic Model","url":"https://www.academia.edu/Documents/in/Stochastic_Model?f_ri=57531"},{"id":2681394,"name":"Supply Chain Network","url":"https://www.academia.edu/Documents/in/Supply_Chain_Network?f_ri=57531"},{"id":3111568,"name":"Decomposition method","url":"https://www.academia.edu/Documents/in/Decomposition_method?f_ri=57531"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_50282194" data-work_id="50282194" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/50282194/Adolescents_Experience_Doing_Homework_Associations_among_Context_Quality_of_Experience_and_Outcomes">Adolescents' Experience Doing Homework: Associations among Context, Quality of Experience, and Outcomes</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/50282194" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d6e640c1bac579af9f5bdd23caad2be0" rel="nofollow" data-download="{"attachment_id":68326188,"asset_id":50282194,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/68326188/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="45504193" href="https://independent.academia.edu/LeeShumow">Lee Shumow</a><script data-card-contents-for-user="45504193" 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$(".js-view-count-work_50282194").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="50282194"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2971" href="https://www.academia.edu/Documents/in/Cognitive_development">Cognitive development</a>, <script data-card-contents-for-ri="2971" type="text/json">{"id":2971,"name":"Cognitive development","url":"https://www.academia.edu/Documents/in/Cognitive_development?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5187" href="https://www.academia.edu/Documents/in/Statistical_Analysis">Statistical Analysis</a>, <script data-card-contents-for-ri="5187" type="text/json">{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="57531" href="https://www.academia.edu/Documents/in/Sampling">Sampling</a>, <script data-card-contents-for-ri="57531" type="text/json">{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="104446" href="https://www.academia.edu/Documents/in/Academic_achievement">Academic achievement</a><script data-card-contents-for-ri="104446" type="text/json">{"id":104446,"name":"Academic achievement","url":"https://www.academia.edu/Documents/in/Academic_achievement?f_ri=57531","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=50282194]'), work: {"id":50282194,"title":"Adolescents' Experience Doing Homework: Associations among Context, Quality of Experience, and Outcomes","created_at":"2021-07-26T13:58:27.341-07:00","url":"https://www.academia.edu/50282194/Adolescents_Experience_Doing_Homework_Associations_among_Context_Quality_of_Experience_and_Outcomes?f_ri=57531","dom_id":"work_50282194","summary":null,"downloadable_attachments":[{"id":68326188,"asset_id":50282194,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":45504193,"first_name":"Lee","last_name":"Shumow","domain_name":"independent","page_name":"LeeShumow","display_name":"Lee Shumow","profile_url":"https://independent.academia.edu/LeeShumow?f_ri=57531","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2971,"name":"Cognitive development","url":"https://www.academia.edu/Documents/in/Cognitive_development?f_ri=57531","nofollow":false},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=57531","nofollow":false},{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531","nofollow":false},{"id":104446,"name":"Academic achievement","url":"https://www.academia.edu/Documents/in/Academic_achievement?f_ri=57531","nofollow":false},{"id":131567,"name":"Newspaper","url":"https://www.academia.edu/Documents/in/Newspaper?f_ri=57531"},{"id":166024,"name":"Context Effect","url":"https://www.academia.edu/Documents/in/Context_Effect?f_ri=57531"},{"id":569638,"name":"Full Text Vol 5","url":"https://www.academia.edu/Documents/in/Full_Text_Vol_5?f_ri=57531"},{"id":594083,"name":"School Community","url":"https://www.academia.edu/Documents/in/School_Community?f_ri=57531"},{"id":594207,"name":"Expectation","url":"https://www.academia.edu/Documents/in/Expectation?f_ri=57531"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_3280253" data-work_id="3280253" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/3280253/Lipids_as_biomarkers_for_carbon_cycling_on_the_Northwest_Shelf_of_Australia_results_from_a_sediment_trap_study">Lipids as biomarkers for carbon cycling on the Northwest Shelf of Australia: results from a sediment trap study</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Sediment traps were deployed on the Northwest Shelf (NWS) of Australia in November 1996, to determine fluxes of organic matter and inorganic elements from the photic zone to deeper waters in a transect extending from Exmouth Shelf to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_3280253" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Sediment traps were deployed on the Northwest Shelf (NWS) of Australia in November 1996, to determine fluxes of organic matter and inorganic elements from the photic zone to deeper waters in a transect extending from Exmouth Shelf to Exmouth Plateau. Infiltrex II water samplers collected particulate and dissolved organics from the water column near the trap sites. Surface sediments and sediment cores were also collected over the study region. Lipid biomarkers were used to determine the sources of organic carbon and its cycling processes on the NWS.Dry weight fluxes from the traps ranged from 124 to 616 mg m−2 day−1and particulate organic carbon (POC) fluxes ranged from 22 to 42 mg m−2 day−1. The biogenic lipids consisted of biomarkers indicative of marine zooplankton, phytoplankton and bacteria, plus traces of land plant markers. A large contribution of unresolved complex material (UCM), which is indicative of petroleum, was detected at four times the biogenic hydrocarbon flux at shallow stations, and up to seven times the biogenic hydrocarbon flux at the most offshore station.There is essentially no river input, and only trace aeolian-derived material to contribute to primary production on the NWS of Australia. Most of the organic matter produced are rapidly recycled in the water column and the small fraction of lipids that settle to the sediments is already partially degraded and undergoes further rapid degradation in the surface sediments. Natural oil seeps also provide utilisable organic carbon to the system. The production and vertical flux rates of organics determined in this study are comparable to those reported in studies of shallow traps in oceanic areas from long-term studies in the Arabian Sea, and other coastal margins such as the Bay of Biscay (France) and California (USA).In offshore areas, most “living” lipid materials passed through the GFF filters thus invalidating POC estimates based on high volume sampling. To adequately assess living (particulate) carbon, gentle filtration of low volume seawater samples is more accurate, as shown by this solid phase absorption study of individual lipid biomarkers.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/3280253" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c9aab6afeaec0117050215c5e2ca5894" rel="nofollow" data-download="{"attachment_id":50367900,"asset_id":3280253,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50367900/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3779334" href="https://independent.academia.edu/JoGentleness">Jo Gentleness</a><script data-card-contents-for-user="3779334" type="text/json">{"id":3779334,"first_name":"Jo","last_name":"Gentleness","domain_name":"independent","page_name":"JoGentleness","display_name":"Jo Gentleness","profile_url":"https://independent.academia.edu/JoGentleness?f_ri=57531","photo":"https://0.academia-photos.com/3779334/1370141/1689207/s65_jo.gentleness.jpg"}</script></span></span></li><li class="js-paper-rank-work_3280253 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="3280253"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 3280253, container: ".js-paper-rank-work_3280253", }); 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Infiltrex II water samplers collected particulate and dissolved organics from the water column near the trap sites. Surface sediments and sediment cores were also collected over the study region. Lipid biomarkers were used to determine the sources of organic carbon and its cycling processes on the NWS.Dry weight fluxes from the traps ranged from 124 to 616 mg m−2 day−1and particulate organic carbon (POC) fluxes ranged from 22 to 42 mg m−2 day−1. The biogenic lipids consisted of biomarkers indicative of marine zooplankton, phytoplankton and bacteria, plus traces of land plant markers. A large contribution of unresolved complex material (UCM), which is indicative of petroleum, was detected at four times the biogenic hydrocarbon flux at shallow stations, and up to seven times the biogenic hydrocarbon flux at the most offshore station.There is essentially no river input, and only trace aeolian-derived material to contribute to primary production on the NWS of Australia. Most of the organic matter produced are rapidly recycled in the water column and the small fraction of lipids that settle to the sediments is already partially degraded and undergoes further rapid degradation in the surface sediments. Natural oil seeps also provide utilisable organic carbon to the system. The production and vertical flux rates of organics determined in this study are comparable to those reported in studies of shallow traps in oceanic areas from long-term studies in the Arabian Sea, and other coastal margins such as the Bay of Biscay (France) and California (USA).In offshore areas, most “living” lipid materials passed through the GFF filters thus invalidating POC estimates based on high volume sampling. To adequately assess living (particulate) carbon, gentle filtration of low volume seawater samples is more accurate, as shown by this solid phase absorption study of individual lipid biomarkers.","downloadable_attachments":[{"id":50367900,"asset_id":3280253,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3779334,"first_name":"Jo","last_name":"Gentleness","domain_name":"independent","page_name":"JoGentleness","display_name":"Jo Gentleness","profile_url":"https://independent.academia.edu/JoGentleness?f_ri=57531","photo":"https://0.academia-photos.com/3779334/1370141/1689207/s65_jo.gentleness.jpg"}],"research_interests":[{"id":407,"name":"Geochemistry","url":"https://www.academia.edu/Documents/in/Geochemistry?f_ri=57531","nofollow":false},{"id":415,"name":"Oceanography","url":"https://www.academia.edu/Documents/in/Oceanography?f_ri=57531","nofollow":false},{"id":6970,"name":"Biomarkers","url":"https://www.academia.edu/Documents/in/Biomarkers?f_ri=57531","nofollow":false},{"id":12124,"name":"Filtration","url":"https://www.academia.edu/Documents/in/Filtration?f_ri=57531","nofollow":false},{"id":14719,"name":"Carbon Cycle","url":"https://www.academia.edu/Documents/in/Carbon_Cycle?f_ri=57531"},{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531"},{"id":87502,"name":"Marine chemistry","url":"https://www.academia.edu/Documents/in/Marine_chemistry?f_ri=57531"},{"id":98731,"name":"Carbon Cycling","url":"https://www.academia.edu/Documents/in/Carbon_Cycling?f_ri=57531"},{"id":163878,"name":"Degradation","url":"https://www.academia.edu/Documents/in/Degradation?f_ri=57531"},{"id":189276,"name":"Offshore","url":"https://www.academia.edu/Documents/in/Offshore?f_ri=57531"},{"id":223604,"name":"Sediment Traps","url":"https://www.academia.edu/Documents/in/Sediment_Traps?f_ri=57531"},{"id":289852,"name":"Primary Production","url":"https://www.academia.edu/Documents/in/Primary_Production?f_ri=57531"},{"id":355044,"name":"Particulate organic carbon","url":"https://www.academia.edu/Documents/in/Particulate_organic_carbon?f_ri=57531"},{"id":365220,"name":"Bioindicator","url":"https://www.academia.edu/Documents/in/Bioindicator?f_ri=57531"},{"id":394429,"name":"Sea Water","url":"https://www.academia.edu/Documents/in/Sea_Water?f_ri=57531"},{"id":522525,"name":"Arabian Sea","url":"https://www.academia.edu/Documents/in/Arabian_Sea?f_ri=57531"},{"id":585192,"name":"Organic carbon","url":"https://www.academia.edu/Documents/in/Organic_carbon?f_ri=57531"},{"id":970387,"name":"Organic Matter","url":"https://www.academia.edu/Documents/in/Organic_Matter?f_ri=57531"},{"id":1271415,"name":"Long Term Study","url":"https://www.academia.edu/Documents/in/Long_Term_Study?f_ri=57531"},{"id":2471742,"name":"Dry Weight","url":"https://www.academia.edu/Documents/in/Dry_Weight?f_ri=57531"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_10026903" data-work_id="10026903" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/10026903/International_Pellet_Watch_Global_monitoring_of_persistent_organic_pollutants_POPs_in_coastal_waters_1_Initial_phase_data_on_PCBs_DDTs_and_HCHs">International Pellet Watch: Global monitoring of persistent organic pollutants (POPs) in coastal waters. 1. 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itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/71245103/Baseline_Study_of_Small_and_Medium_Scale_Poultry_Production_in_Enugu_and_Lagos_States_of_Nigeria">Baseline Study of Small and Medium Scale Poultry Production in Enugu and Lagos States of Nigeria</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A baseline survey of small and medium scale poultry enterprises were carried out in Enugu and Lagos States of Nigeria. The primary purpose of the study is to determine the prospects for the commercialization of the poultry industry in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_71245103" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A baseline survey of small and medium scale poultry enterprises were carried out in Enugu and Lagos States of Nigeria. The primary purpose of the study is to determine the prospects for the commercialization of the poultry industry in Nigeria. Multi-stage sampling method was employed in the selection of representative small and medium scale poultry farms and farmers from the two target states. Characteristics of both small and medium-scale poultry farms, baseline performance indicators as well as needs assessment of poultry farmers, among other information, were elicited and documented. It was established that the identified constraints, notwithstanding, small and medium scale poultry production holds great potential for commercialistion. It was recommended that there is the need for provision of gender-sensitive technologies (eg hatcheries, feed mills, processing equipment etc), formation of cooperatives by poultry farmers, public-private partnership, capacity building of poultry f...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/71245103" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2545b78247ce7d35285b56c9fb949775" rel="nofollow" data-download="{"attachment_id":80673693,"asset_id":71245103,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/80673693/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="20187753" href="https://independent.academia.edu/DanielUgwu">Daniel S Ugwu</a><script data-card-contents-for-user="20187753" type="text/json">{"id":20187753,"first_name":"Daniel","last_name":"Ugwu","domain_name":"independent","page_name":"DanielUgwu","display_name":"Daniel S Ugwu","profile_url":"https://independent.academia.edu/DanielUgwu?f_ri=57531","photo":"https://0.academia-photos.com/20187753/38462576/32172212/s65_daniel.ugwu.jpg"}</script></span></span></li><li class="js-paper-rank-work_71245103 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="71245103"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 71245103, container: ".js-paper-rank-work_71245103", }); 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The primary purpose of the study is to determine the prospects for the commercialization of the poultry industry in Nigeria. Multi-stage sampling method was employed in the selection of representative small and medium scale poultry farms and farmers from the two target states. Characteristics of both small and medium-scale poultry farms, baseline performance indicators as well as needs assessment of poultry farmers, among other information, were elicited and documented. It was established that the identified constraints, notwithstanding, small and medium scale poultry production holds great potential for commercialistion. 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href="https://www.academia.edu/Documents/in/Sampling">Sampling</a>, <script data-card-contents-for-ri="57531" type="text/json">{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="98355" href="https://www.academia.edu/Documents/in/Iodine">Iodine</a><script data-card-contents-for-ri="98355" type="text/json">{"id":98355,"name":"Iodine","url":"https://www.academia.edu/Documents/in/Iodine?f_ri=57531","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=53920305]'), work: {"id":53920305,"title":"Chlorine, Bromine AND Iodine in arctic 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class="u-tcGrayDark u-fw700" data-has-card-for-user="30783161" href="https://canterbury-nz.academia.edu/RichardLight">Richard Light</a><script data-card-contents-for-user="30783161" type="text/json">{"id":30783161,"first_name":"Richard","last_name":"Light","domain_name":"canterbury-nz","page_name":"RichardLight","display_name":"Richard Light","profile_url":"https://canterbury-nz.academia.edu/RichardLight?f_ri=57531","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_33044506 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="33044506"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 33044506, container: ".js-paper-rank-work_33044506", }); });</script></li><li class="js-percentile-work_33044506 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span 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href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="57531" href="https://www.academia.edu/Documents/in/Sampling">Sampling</a>, <script data-card-contents-for-ri="57531" type="text/json">{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="81504" href="https://www.academia.edu/Documents/in/Correlation">Correlation</a><script data-card-contents-for-ri="81504" type="text/json">{"id":81504,"name":"Correlation","url":"https://www.academia.edu/Documents/in/Correlation?f_ri=57531","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=33044506]'), work: {"id":33044506,"title":"Measuring Agreement When Two Observers Classify People Into Categories Not Defined in Advance","created_at":"2017-05-16T15:02:49.452-07:00","url":"https://www.academia.edu/33044506/Measuring_Agreement_When_Two_Observers_Classify_People_Into_Categories_Not_Defined_in_Advance?f_ri=57531","dom_id":"work_33044506","summary":null,"downloadable_attachments":[{"id":53152982,"asset_id":33044506,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":30783161,"first_name":"Richard","last_name":"Light","domain_name":"canterbury-nz","page_name":"RichardLight","display_name":"Richard 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Scales","url":"https://www.academia.edu/Documents/in/Rating_Scales?f_ri=57531"},{"id":413194,"name":"Analysis of Variance","url":"https://www.academia.edu/Documents/in/Analysis_of_Variance?f_ri=57531"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1900019" data-work_id="1900019" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1900019/Which_function_describes_the_species_area_relationship_best_A_review_and_empirical_evaluation">Which function describes the species–area relationship best? A review and empirical evaluation</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1900019" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a7d3d305ab3b302303701a17c7e4395e" rel="nofollow" data-download="{"attachment_id":27074516,"asset_id":1900019,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/27074516/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2411331" href="https://uni-hamburg.academia.edu/J%C3%BCrgenDengler">Jürgen Dengler</a><script data-card-contents-for-user="2411331" type="text/json">{"id":2411331,"first_name":"Jürgen","last_name":"Dengler","domain_name":"uni-hamburg","page_name":"JürgenDengler","display_name":"Jürgen Dengler","profile_url":"https://uni-hamburg.academia.edu/J%C3%BCrgenDengler?f_ri=57531","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_1900019 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1900019"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1900019, container: ".js-paper-rank-work_1900019", }); 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$(".js-view-count[data-work-id=1900019]").text(description); $(".js-view-count-work_1900019").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_1900019").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="1900019"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">16</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="400" href="https://www.academia.edu/Documents/in/Earth_Sciences">Earth Sciences</a>, <script data-card-contents-for-ri="400" type="text/json">{"id":400,"name":"Earth Sciences","url":"https://www.academia.edu/Documents/in/Earth_Sciences?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2254" href="https://www.academia.edu/Documents/in/Macroecology">Macroecology</a>, <script data-card-contents-for-ri="2254" type="text/json">{"id":2254,"name":"Macroecology","url":"https://www.academia.edu/Documents/in/Macroecology?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="17823" href="https://www.academia.edu/Documents/in/Biogeography">Biogeography</a>, <script data-card-contents-for-ri="17823" type="text/json">{"id":17823,"name":"Biogeography","url":"https://www.academia.edu/Documents/in/Biogeography?f_ri=57531","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="47884" href="https://www.academia.edu/Documents/in/Biological_Sciences">Biological Sciences</a><script data-card-contents-for-ri="47884" type="text/json">{"id":47884,"name":"Biological Sciences","url":"https://www.academia.edu/Documents/in/Biological_Sciences?f_ri=57531","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1900019]'), work: {"id":1900019,"title":"Which function describes the species–area relationship best? 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This paper presents the first analysis and time trend... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29545210" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Since Flint returned to its pre-crisis source of drinking water close to 25,000 water samples have been collected and tested for lead and copper in more than 10,000 residences. This paper presents the first analysis and time trend modeling of lead data, providing new insights about the impact of this intervention. The analysis started with geocoding all water lead levels (WLL) measured during an 11-month period following the return to the Detroit water supply. Each data was allocated to the corresponding tax parcel unit and linked to secondary datasets, such as the composition of service lines, year built, or census tract poverty level. Only data collected on residential parcels within the City limits were used in the analysis. One key feature of Flint data is their collection through two different sampling initiatives: (i) voluntary or homeowner-driven sampling whereby concerned citizens decided to acquire a testing kit and conduct sampling on their own (non-sentinel sites), and (ii) State-controlled sampling where data were collected bi-weekly at selected sites after training of residents by technical teams (sentinel sites). Temporal trends modeled from these two datasets were found to be statistically different with fewer sentinel data exceeding WLL thresholds ranging from 10 to 50 g/L. Even after adjusting for housing characteristics the odds ratio (OR) of measuring WLL above 15 g/L at non-sentinel sites is significantly greater than 1 (OR=1.480) and it increases with the threshold (OR= 2.055 for 50 g/L). Joinpoint regression showed that the city-wide percentage of WLL data above 15 g/L displayed four successive trends since the return to Detroit Water System. Despite the recent improvement in water quality, the culprit for differences between sampling programs needs to be identified as it impacts exposure assessment and might influence whether there is compliance or not with the Lead and Copper Rule.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29545210" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5a1ece2eb91f4ddda2daff5e86fffe36" rel="nofollow" data-download="{"attachment_id":49989125,"asset_id":29545210,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49989125/download_file?st=MTczMzAxNDk4Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31452952" href="https://independent.academia.edu/PierreGoovaerts">Pierre Goovaerts</a><script data-card-contents-for-user="31452952" type="text/json">{"id":31452952,"first_name":"Pierre","last_name":"Goovaerts","domain_name":"independent","page_name":"PierreGoovaerts","display_name":"Pierre Goovaerts","profile_url":"https://independent.academia.edu/PierreGoovaerts?f_ri=57531","photo":"https://0.academia-photos.com/31452952/13681689/14815440/s65_pierre.goovaerts.jpg"}</script></span></span></li><li class="js-paper-rank-work_29545210 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29545210"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29545210, container: ".js-paper-rank-work_29545210", }); 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This paper presents the first analysis and time trend modeling of lead data, providing new insights about the impact of this intervention. The analysis started with geocoding all water lead levels (WLL) measured during an 11-month period following the return to the Detroit water supply. Each data was allocated to the corresponding tax parcel unit and linked to secondary datasets, such as the composition of service lines, year built, or census tract poverty level. Only data collected on residential parcels within the City limits were used in the analysis. One key feature of Flint data is their collection through two different sampling initiatives: (i) voluntary or homeowner-driven sampling whereby concerned citizens decided to acquire a testing kit and conduct sampling on their own (non-sentinel sites), and (ii) State-controlled sampling where data were collected bi-weekly at selected sites after training of residents by technical teams (sentinel sites). Temporal trends modeled from these two datasets were found to be statistically different with fewer sentinel data exceeding WLL thresholds ranging from 10 to 50 g/L. Even after adjusting for housing characteristics the odds ratio (OR) of measuring WLL above 15 g/L at non-sentinel sites is significantly greater than 1 (OR=1.480) and it increases with the threshold (OR= 2.055 for 50 g/L). Joinpoint regression showed that the city-wide percentage of WLL data above 15 g/L displayed four successive trends since the return to Detroit Water System. Despite the recent improvement in water quality, the culprit for differences between sampling programs needs to be identified as it impacts exposure assessment and might influence whether there is compliance or not with the Lead and Copper Rule.","downloadable_attachments":[{"id":49989125,"asset_id":29545210,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":31452952,"first_name":"Pierre","last_name":"Goovaerts","domain_name":"independent","page_name":"PierreGoovaerts","display_name":"Pierre Goovaerts","profile_url":"https://independent.academia.edu/PierreGoovaerts?f_ri=57531","photo":"https://0.academia-photos.com/31452952/13681689/14815440/s65_pierre.goovaerts.jpg"}],"research_interests":[{"id":57531,"name":"Sampling","url":"https://www.academia.edu/Documents/in/Sampling?f_ri=57531","nofollow":false},{"id":116579,"name":"Lead","url":"https://www.academia.edu/Documents/in/Lead?f_ri=57531","nofollow":false},{"id":120641,"name":"Drinking 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