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class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" alt="Baru Debtera" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/22658377/6554680/22152649/s200_baru.debtera.jpg" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Baru Debtera</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://acadamia.academia.edu/">Addis Ababa Science and Technology University</a>, <a class="u-tcGrayDarker" href="https://acadamia.academia.edu/Departments/Chemical_Engineering/Documents">Chemical Engineering</a>, <span class="u-tcGrayDarker">Head of Department</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" 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title="Book Reviews"><span>2</span> <span class="ds2-5-body-sm-bold">Book Reviews</span></a></li></ul></div><div class="divider ds-divider-16" style="margin: 0px;"></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Baru Debtera</h3></div><div class="js-work-strip profile--work_container" data-work-id="89380634"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/89380634/Extraction_and_Characterisation_of_Avocado_Seed_Starch_and_Its_Blend_with_Enset_Cellulosic"><img alt="Research paper thumbnail of Extraction and Characterisation of Avocado Seed Starch, and Its Blend with Enset Cellulosic" class="work-thumbnail" src="https://attachments.academia-assets.com/93192317/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/89380634/Extraction_and_Characterisation_of_Avocado_Seed_Starch_and_Its_Blend_with_Enset_Cellulosic">Extraction and Characterisation of Avocado Seed Starch, and Its Blend with Enset Cellulosic</a></div><div class="wp-workCard_item"><span>Advances in Materials Science and Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Avocado seed starch was extracted to prepare a biodegradable plastic film with enset cellulosic r...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Avocado seed starch was extracted to prepare a biodegradable plastic film with enset cellulosic reinforcement and glycerol plasticiser. The characterisations were made in detail. Fourier transform infrared (FTIR) spectroscopy, differential scanning calorimeter (DSC), scanning electron microscopy (SEM), X-ray diffraction (XRD), rheometer, and Rapid Visco Analyser (RVA) techniques were employed to characterise the physicochemical properties of avocado seed starch, enset cellulosic, and the blend. Starch and enset cellulosic extraction yields are 18.3 ± 0.02% and 67.5 ± 0.08%, respectively. As a result, avocado seed starch and enset cellulosic blend can be used as alternative raw materials to develop biodegradable plastics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="52a2b0c50fb48d7907ba1f91488cef72" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":93192317,"asset_id":89380634,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/93192317/download_file?st=MTczMjQ5MDcxNiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="89380634"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="89380634"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 89380634; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=89380634]").text(description); $(".js-view-count[data-work-id=89380634]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 89380634; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='89380634']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 89380634, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "52a2b0c50fb48d7907ba1f91488cef72" } } $('.js-work-strip[data-work-id=89380634]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":89380634,"title":"Extraction and Characterisation of Avocado Seed Starch, and Its Blend with Enset Cellulosic","translated_title":"","metadata":{"abstract":"Avocado seed starch was extracted to prepare a biodegradable plastic film with enset cellulosic reinforcement and glycerol plasticiser. The characterisations were made in detail. Fourier transform infrared (FTIR) spectroscopy, differential scanning calorimeter (DSC), scanning electron microscopy (SEM), X-ray diffraction (XRD), rheometer, and Rapid Visco Analyser (RVA) techniques were employed to characterise the physicochemical properties of avocado seed starch, enset cellulosic, and the blend. Starch and enset cellulosic extraction yields are 18.3 ± 0.02% and 67.5 ± 0.08%, respectively. As a result, avocado seed starch and enset cellulosic blend can be used as alternative raw materials to develop biodegradable plastics.","publisher":"Hindawi Limited","publication_name":"Advances in Materials Science and Engineering"},"translated_abstract":"Avocado seed starch was extracted to prepare a biodegradable plastic film with enset cellulosic reinforcement and glycerol plasticiser. The characterisations were made in detail. Fourier transform infrared (FTIR) spectroscopy, differential scanning calorimeter (DSC), scanning electron microscopy (SEM), X-ray diffraction (XRD), rheometer, and Rapid Visco Analyser (RVA) techniques were employed to characterise the physicochemical properties of avocado seed starch, enset cellulosic, and the blend. Starch and enset cellulosic extraction yields are 18.3 ± 0.02% and 67.5 ± 0.08%, respectively. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88479014"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88479014/Multistage_Framework_for_Automatic_Face_Mask_Detection_Using_Deep_Learning"><img alt="Research paper thumbnail of Multistage Framework for Automatic Face Mask Detection Using Deep Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/92444751/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88479014/Multistage_Framework_for_Automatic_Face_Mask_Detection_Using_Deep_Learning">Multistage Framework for Automatic Face Mask Detection Using Deep Learning</a></div><div class="wp-workCard_item"><span>Computational Intelligence and Neuroscience</span><span>, Aug 11, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2322362ff483bcd7510ac35bad9bbfcb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92444751,"asset_id":88479014,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92444751/download_file?st=MTczMjQ5MDcxNiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88479014"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88479014"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88479014; 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COVID-19 cases are upon us in waves, with subsequent waves turning out to be worse than the previous one. Scores of human lives are lost while the post-COVID-19 complications are on a rise. Monitoring the behaviour of people in public places and o ces is necessary to mitigate the transmission of COVID-19 among humans. In this work, a low-cost, lightweight two-stage face mask detection model is proposed. In the rst stage, the model checks if a face mask is worn. In the second stage, it detects if the mask is worn appropriately, by classifying and labelling them. e proposed models are trained to detect faces with and without masks for varied inputs such as images, recorded videos, and live streaming videos where it can e ciently detect multiple faces at once. e e cacy of the proposed approach is tested against conventional datasets as well as our proposed dataset, which includes no masks, surgical masks, and nonsurgical masks. In this work, multiple CNN models like MobileNetV2, ResNet50V2, and InceptionV3 have been considered for training and are evaluated based on transfer learning. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="87278124"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/87278124/Optimal_Facial_Feature_Based_Emotional_Recognition_Using_Deep_Learning_Algorithm"><img alt="Research paper thumbnail of Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/91532306/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/87278124/Optimal_Facial_Feature_Based_Emotional_Recognition_Using_Deep_Learning_Algorithm">Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm</a></div><div class="wp-workCard_item"><span>Computational Intelligence and Neuroscience</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Humans have traditionally found it simple to identify emotions from facial expressions, but it is...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and e...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="39e6bcfcea5b54c2354443b65c70ba4e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":91532306,"asset_id":87278124,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/91532306/download_file?st=MTczMjQ5MDcxNiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="87278124"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87278124"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87278124; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=87278124]").text(description); $(".js-view-count[data-work-id=87278124]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 87278124; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='87278124']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 87278124, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "39e6bcfcea5b54c2354443b65c70ba4e" } } $('.js-work-strip[data-work-id=87278124]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":87278124,"title":"Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm","translated_title":"","metadata":{"abstract":"Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. 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This research focuses on improving facial features and e...","publisher":"Hindawi Limited","publication_name":"Computational Intelligence and Neuroscience"},"translated_abstract":"Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. 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Previous researchers relied on trend identification before making any decision to buy or sell stocks but fail to make accurate decisions due to complex systems. Some studies showed analysis to apply to stop loss on every stock transaction that got wrong levels due to limited features scaling that relied on single indicators without checking the performance metrics such as mean, standard deviation, and value at risk. Some existing models are based on theoretical implementation and they possess inaccurate success in real-time stock market transactions. Earlier risk management techniques were based on fundamental statistics of the company performance based on specific quarters that propose the future expects in the positive direction that is not every true which results in huge financial loss. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="87277173"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/87277173/An_Attribute_Extraction_for_Automated_Malware_Attack_Classification_and_Detection_Using_Soft_Computing_Techniques"><img alt="Research paper thumbnail of An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques" class="work-thumbnail" src="https://attachments.academia-assets.com/91531630/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/87277173/An_Attribute_Extraction_for_Automated_Malware_Attack_Classification_and_Detection_Using_Soft_Computing_Techniques">An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques</a></div><div class="wp-workCard_item"><span>Computational Intelligence and Neuroscience</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Malware has grown in popularity as a method of conducting cyber assaults in former decades as a r...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eeac7fa0f6cfc7bba2eef9280cb569ff" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":91531630,"asset_id":87277173,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/91531630/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="87277173"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87277173"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87277173; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=87277173]").text(description); $(".js-view-count[data-work-id=87277173]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 87277173; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='87277173']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 87277173, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "eeac7fa0f6cfc7bba2eef9280cb569ff" } } $('.js-work-strip[data-work-id=87277173]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":87277173,"title":"An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques","translated_title":"","metadata":{"abstract":"Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.","publisher":"Hindawi Limited","publication_name":"Computational Intelligence and Neuroscience"},"translated_abstract":"Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83832327"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83832327/An_Enhancement_on_Convolutional_Artificial_Intelligent_Based_Diagnosis_for_Skin_Disease_Using_Nanotechnology_Sensors"><img alt="Research paper thumbnail of An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors" class="work-thumbnail" src="https://attachments.academia-assets.com/89051717/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83832327/An_Enhancement_on_Convolutional_Artificial_Intelligent_Based_Diagnosis_for_Skin_Disease_Using_Nanotechnology_Sensors">An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors</a></div><div class="wp-workCard_item"><span>Computational Intelligence and Neuroscience</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Skin disease is the major health problem around the world. The diagnosis of skin disease remains ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Skin disease is the major health problem around the world. The diagnosis of skin disease remains a challenge to dermatologist profession particularly in the detection, evaluation, and management. Health data are very large and complex due to this processing of data using traditional data processing techniques is very difficult. In this paper, to ease the complexity while processing the inputs, we use multilayered perceptron with backpropagation neural networks (MLP-BPNN). The image is collected from the devices that contain nanotechnology sensors, which is the state-of-art in the proposed model. The nanotechnology sensors sense the skin for its chemical, physical, and biological conditions with better detection specificity, sensitivity, and multiplexing ability to acquire the image for optimal classification. The MLP-BPNN technique is used to envisage the future result of disease type effectively. By using the above MLP-BPNN technique, it is easy to predict the skin diseases such as...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7646a767ca794ae2f71a14e984a00a91" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89051717,"asset_id":83832327,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89051717/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83832327"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83832327"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83832327; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83832327]").text(description); $(".js-view-count[data-work-id=83832327]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83832327; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83832327']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83832327, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7646a767ca794ae2f71a14e984a00a91" } } $('.js-work-strip[data-work-id=83832327]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83832327,"title":"An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors","translated_title":"","metadata":{"abstract":"Skin disease is the major health problem around the world. 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By using the above MLP-BPNN technique, it is easy to predict the skin diseases such as...","publisher":"Hindawi Limited","publication_name":"Computational Intelligence and Neuroscience"},"translated_abstract":"Skin disease is the major health problem around the world. The diagnosis of skin disease remains a challenge to dermatologist profession particularly in the detection, evaluation, and management. Health data are very large and complex due to this processing of data using traditional data processing techniques is very difficult. In this paper, to ease the complexity while processing the inputs, we use multilayered perceptron with backpropagation neural networks (MLP-BPNN). The image is collected from the devices that contain nanotechnology sensors, which is the state-of-art in the proposed model. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83832324"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83832324/Optimization_of_Hello_Message_Broadcasting_Prediction_Model_for_Stability_Analysis"><img alt="Research paper thumbnail of Optimization of Hello Message Broadcasting Prediction Model for Stability Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/89051708/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83832324/Optimization_of_Hello_Message_Broadcasting_Prediction_Model_for_Stability_Analysis">Optimization of Hello Message Broadcasting Prediction Model for Stability Analysis</a></div><div class="wp-workCard_item"><span>Wireless Communications and Mobile Computing</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In mobile ad hoc networks, nodes are connected and disconnected regularly; since every mobile nod...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In mobile ad hoc networks, nodes are connected and disconnected regularly; since every mobile node travels autonomously, those nodes are dispersed not uniformly. Connection damage has straight consequences on the network characteristics. For that motivation, various techniques are quick and efficient damage of connection identification using alert packet is difficult for that time to identify next connection in a mobile network. It increases end to end delay and reduces the network lifetime. The present effective connection alteration rate- (ECAR-) based communication scheme depends on the details of neighbouring nodes to survey the connection between the connection alteration rate and the hello time gap in terms of entire transmission rate. Nevertheless, the hello time gap tracking algorithm is constructed to increase transmission rate displaying a stable choice of connection alteration rate, still although node velocity alters. It reduces end to end delay and increases the network...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4d467d59e34910cd8d492921f6f93168" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89051708,"asset_id":83832324,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89051708/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83832324"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83832324"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83832324; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83832324]").text(description); $(".js-view-count[data-work-id=83832324]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83832324; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83832324']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83832324, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4d467d59e34910cd8d492921f6f93168" } } $('.js-work-strip[data-work-id=83832324]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83832324,"title":"Optimization of Hello Message Broadcasting Prediction Model for Stability Analysis","translated_title":"","metadata":{"abstract":"In mobile ad hoc networks, nodes are connected and disconnected regularly; since every mobile node travels autonomously, those nodes are dispersed not uniformly. 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It reduces end to end delay and increases the network...","publisher":"Hindawi Limited","publication_name":"Wireless Communications and Mobile Computing"},"translated_abstract":"In mobile ad hoc networks, nodes are connected and disconnected regularly; since every mobile node travels autonomously, those nodes are dispersed not uniformly. Connection damage has straight consequences on the network characteristics. For that motivation, various techniques are quick and efficient damage of connection identification using alert packet is difficult for that time to identify next connection in a mobile network. It increases end to end delay and reduces the network lifetime. The present effective connection alteration rate- (ECAR-) based communication scheme depends on the details of neighbouring nodes to survey the connection between the connection alteration rate and the hello time gap in terms of entire transmission rate. Nevertheless, the hello time gap tracking algorithm is constructed to increase transmission rate displaying a stable choice of connection alteration rate, still although node velocity alters. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="82002885"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/82002885/Scrutiny_of_Solar_Water_Heating_System_Employing_Supercritical_Fluid"><img alt="Research paper thumbnail of Scrutiny of Solar Water Heating System Employing Supercritical Fluid" class="work-thumbnail" src="https://attachments.academia-assets.com/87849124/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/82002885/Scrutiny_of_Solar_Water_Heating_System_Employing_Supercritical_Fluid">Scrutiny of Solar Water Heating System Employing Supercritical Fluid</a></div><div class="wp-workCard_item"><span>Mathematical Problems in Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to det...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to detect implicit water heating and boost the collector’s heating rate efficiency. Solar water heating system efficiency, cost, and environmental friendliness all depend on the working fluid used. CO2 is a possible natural refrigerant replacement. Even a little increase in temperature or pressure may have a big impact on the density of CO2 at the critical point. Because of this, solar heating can readily generate a spontaneous convection flow of supercritical carbon dioxide. The most basic collector characteristics, such as CO2 pressure and temperature, were determined by building and testing an experimental setup using a CO2-based solar collector. Due to solar radiation, liquid, gas, or supercritical CO2 pressures and temperatures change throughout the test. There was a 50% time average collector efficiency (ηcol) and a 30% heat recovery efficiency (ηRE). Solar thermal collectors based on su...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6ee5d0dcc5371b8fcc424864d19ccbc8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":87849124,"asset_id":82002885,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/87849124/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="82002885"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="82002885"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 82002885; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=82002885]").text(description); $(".js-view-count[data-work-id=82002885]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 82002885; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='82002885']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 82002885, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6ee5d0dcc5371b8fcc424864d19ccbc8" } } $('.js-work-strip[data-work-id=82002885]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":82002885,"title":"Scrutiny of Solar Water Heating System Employing Supercritical Fluid","translated_title":"","metadata":{"abstract":"This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to detect implicit water heating and boost the collector’s heating rate efficiency. Solar water heating system efficiency, cost, and environmental friendliness all depend on the working fluid used. CO2 is a possible natural refrigerant replacement. Even a little increase in temperature or pressure may have a big impact on the density of CO2 at the critical point. Because of this, solar heating can readily generate a spontaneous convection flow of supercritical carbon dioxide. The most basic collector characteristics, such as CO2 pressure and temperature, were determined by building and testing an experimental setup using a CO2-based solar collector. Due to solar radiation, liquid, gas, or supercritical CO2 pressures and temperatures change throughout the test. There was a 50% time average collector efficiency (ηcol) and a 30% heat recovery efficiency (ηRE). 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Solar water heating system efficiency, cost, and environmental friendliness all depend on the working fluid used. CO2 is a possible natural refrigerant replacement. Even a little increase in temperature or pressure may have a big impact on the density of CO2 at the critical point. Because of this, solar heating can readily generate a spontaneous convection flow of supercritical carbon dioxide. The most basic collector characteristics, such as CO2 pressure and temperature, were determined by building and testing an experimental setup using a CO2-based solar collector. Due to solar radiation, liquid, gas, or supercritical CO2 pressures and temperatures change throughout the test. There was a 50% time average collector efficiency (ηcol) and a 30% heat recovery efficiency (ηRE). Solar thermal collectors based on su...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="75226bca060b1d31816371c4cca921f5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":87849110,"asset_id":82002879,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/87849110/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="82002879"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="82002879"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 82002879; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=82002879]").text(description); $(".js-view-count[data-work-id=82002879]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 82002879; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='82002879']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 82002879, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "75226bca060b1d31816371c4cca921f5" } } $('.js-work-strip[data-work-id=82002879]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":82002879,"title":"Scrutiny of Solar Water Heating System Employing Supercritical Fluid","translated_title":"","metadata":{"abstract":"This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to detect implicit water heating and boost the collector’s heating rate efficiency. 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href="https://www.academia.edu/78571922/Formation_of_Low_Mass_Stars_Disks_and_Bipolar_Outflows"><img alt="Research paper thumbnail of Formation of Low Mass Stars Disks and Bipolar Outflows" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/78571922/Formation_of_Low_Mass_Stars_Disks_and_Bipolar_Outflows">Formation of Low Mass Stars Disks and Bipolar Outflows</a></div><div class="wp-workCard_item"><span>JENAM-2007, "Our Non-Stable Universe"</span><span>, Aug 1, 2007</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span 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Study of Production Bio plastic from Banana Peels" class="work-thumbnail" src="https://attachments.academia-assets.com/85514722/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78483665/Synthesis_and_Experimental_Study_of_Production_Bio_plastic_from_Banana_Peels">Synthesis and Experimental Study of Production Bio plastic from Banana Peels</a></div><div class="wp-workCard_item"><span>Research gate</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The synthesis of bio plastic material by using waste banana peels, which can be collected from ev...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The synthesis of bio plastic material by using waste banana peels, which can be collected from everywhere nowadays. The aim of the study was to identify where the most load resistant plastic with respect to the residence time, P H value, and amount of plasticizer used for preparing bio plastic. The paste was hydrolyzed by 0.1 M, and 0.5 M of HCl for breaking the chain of the amylopectin in the starch. In addition to adjust the P H value according to 0.1 M, and 0.5 M of NaOH and different amount of plasticizer (3 ml, 4 ml, and 5 ml) used for making the prepared sample flexible. The prototype of this project represents the process of manufacturing bio-plastic from banana peels were carried out with at P H (neutral, base and acid), residence time (5, 10, and, 15 min), and amount of plasticizes (3 ml, 4 ml, and 5ml) to test the tensile strength and water absorption. The best plastic film we obtained at P H neutral, residence time 15 min and amount of plasticizes 3 ml, which gave as a tensile strength 0.1445 N/mm 2 and per cent of water absorption 18.18. Bio plastic prepared using the banana peels serve as potential alternative to the conventional plastic material. Making bio plastic from banana peels instead of traditional petroleum based plastic is believed to be successful solution for environmental pollution regulation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="68af5488dcca5bdc38225262c247117a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85514722,"asset_id":78483665,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85514722/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78483665"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78483665"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78483665; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78483665]").text(description); $(".js-view-count[data-work-id=78483665]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78483665; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78483665']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78483665, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "68af5488dcca5bdc38225262c247117a" } } $('.js-work-strip[data-work-id=78483665]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78483665,"title":"Synthesis and Experimental Study of Production Bio plastic from Banana Peels","translated_title":"","metadata":{"doi":"10.13140/RG.2.2.25906.15044","abstract":"The synthesis of bio plastic material by using waste banana peels, which can be collected from everywhere nowadays. The aim of the study was to identify where the most load resistant plastic with respect to the residence time, P H value, and amount of plasticizer used for preparing bio plastic. The paste was hydrolyzed by 0.1 M, and 0.5 M of HCl for breaking the chain of the amylopectin in the starch. In addition to adjust the P H value according to 0.1 M, and 0.5 M of NaOH and different amount of plasticizer (3 ml, 4 ml, and 5 ml) used for making the prepared sample flexible. The prototype of this project represents the process of manufacturing bio-plastic from banana peels were carried out with at P H (neutral, base and acid), residence time (5, 10, and, 15 min), and amount of plasticizes (3 ml, 4 ml, and 5ml) to test the tensile strength and water absorption. The best plastic film we obtained at P H neutral, residence time 15 min and amount of plasticizes 3 ml, which gave as a tensile strength 0.1445 N/mm 2 and per cent of water absorption 18.18. 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This study highlighted the importance of recently discovered Agave Angustifolia marginated (AAM) fibers extracted by a manual process from AAM plants. This paper observes the various properties of the mechanical and moisture intake characteristics of AAM fiber and compares these to other naturally occurring fibers. Using isophthalic polyester resin, the mixed chopped AAM fiber-reinforced composite is prepared and detailed preparation techniques are presented. During physical composite testing, fiber pull-outs on the broken samples were investigated. Additionally, the experimental evidence demonstrates that increasing the volume fraction helps to increase the tensile, bending, and modulus of the AAM fiber composite. 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Polyester Composites" class="work-thumbnail" src="https://attachments.academia-assets.com/81505166/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/72667639/Effect_of_Moisture_Content_on_Mechanical_Properties_of_AAM_Natural_Fiber_Reinforced_Isophthalic_Polyester_Composites">Effect of Moisture Content on Mechanical Properties of AAM Natural Fiber-Reinforced Isophthalic Polyester Composites</a></div><div class="wp-workCard_item"><span>Advances in Materials Science and Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Composite fiber reinforcement is one such proficient material that substitutes for useful applica...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Composite fiber reinforcement is one such proficient material that substitutes for useful applications for conventional and polyester blends where little weight and thus less energy conservation is required. This study highlighted the importance of recently discovered Agave Angustifolia marginated (AAM) fibers extracted by a manual process from AAM plants. This paper observes the various properties of the mechanical and moisture intake characteristics of AAM fiber and compares these to other naturally occurring fibers. Using isophthalic polyester resin, the mixed chopped AAM fiber-reinforced composite is prepared and detailed preparation techniques are presented. During physical composite testing, fiber pull-outs on the broken samples were investigated. Additionally, the experimental evidence demonstrates that increasing the volume fraction helps to increase the tensile, bending, and modulus of the AAM fiber composite. The chopped fiber isophthalic polyester reinforcement reaches it...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b4c494e361f5f838f2e23379f542fc39" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81505166,"asset_id":72667639,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81505166/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="72667639"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="72667639"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 72667639; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=72667639]").text(description); $(".js-view-count[data-work-id=72667639]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 72667639; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='72667639']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 72667639, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b4c494e361f5f838f2e23379f542fc39" } } $('.js-work-strip[data-work-id=72667639]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":72667639,"title":"Effect of Moisture Content on Mechanical Properties of AAM Natural Fiber-Reinforced Isophthalic Polyester Composites","translated_title":"","metadata":{"abstract":"Composite fiber reinforcement is one such proficient material that substitutes for useful applications for conventional and polyester blends where little weight and thus less energy conservation is required. 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Additionally, the experimental evidence demonstrates that increasing the volume fraction helps to increase the tensile, bending, and modulus of the AAM fiber composite. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="69465123"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/69465123/Investigation_on_Mechanical_and_Wear_Behaviors_of_LM6_Aluminium_Alloy_Based_Hybrid_Metal_Matrix_Composites_Using_Stir_Casting_Process"><img alt="Research paper thumbnail of Investigation on Mechanical and Wear Behaviors of LM6 Aluminium Alloy-Based Hybrid Metal Matrix Composites Using Stir Casting Process" class="work-thumbnail" src="https://attachments.academia-assets.com/79553577/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/69465123/Investigation_on_Mechanical_and_Wear_Behaviors_of_LM6_Aluminium_Alloy_Based_Hybrid_Metal_Matrix_Composites_Using_Stir_Casting_Process">Investigation on Mechanical and Wear Behaviors of LM6 Aluminium Alloy-Based Hybrid Metal Matrix Composites Using Stir Casting Process</a></div><div class="wp-workCard_item"><span>Advances in Materials Science and Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this investigation, aluminium-silicon-based alloy (LM6) with the addition of (0, 2.5, 5, and 1...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this investigation, aluminium-silicon-based alloy (LM6) with the addition of (0, 2.5, 5, and 10%) copper-coated short steel fiber and 5% boron carbide (B4C) element-strengthened composites was fabricated by the stir casting method. Mechanical properties and tribological behaviors of LM6-based hybrid composites were investigated, and microstructures of different castings were examined by an image analyzer. The test was conducted at different loads (10, 20, 30, and 40 N) and different sliding spaces (500, 1000, 1500, and 2000 m), respectively. The results revealed that the sample loaded with 10% of reinforcement recorded the highest tensile strength of 231 MPa. On the other hand, the hardness value increased from 71 to 144 BHN, when 15% of reinforcement was added to the sample. It was also noted that 10% copper-coated steel fiber improved wear resistance up to 50% when compared to LM6. A field emission scanning electron microscope was employed to observe the morphology of the worn ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fca244edcb536468277b32e3040f97e4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":79553577,"asset_id":69465123,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/79553577/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="69465123"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="69465123"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 69465123; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=69465123]").text(description); $(".js-view-count[data-work-id=69465123]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 69465123; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='69465123']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 69465123, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fca244edcb536468277b32e3040f97e4" } } $('.js-work-strip[data-work-id=69465123]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":69465123,"title":"Investigation on Mechanical and Wear Behaviors of LM6 Aluminium Alloy-Based Hybrid Metal Matrix Composites Using Stir Casting Process","translated_title":"","metadata":{"abstract":"In this investigation, aluminium-silicon-based alloy (LM6) with the addition of (0, 2.5, 5, and 10%) copper-coated short steel fiber and 5% boron carbide (B4C) element-strengthened composites was fabricated by the stir casting method. Mechanical properties and tribological behaviors of LM6-based hybrid composites were investigated, and microstructures of different castings were examined by an image analyzer. The test was conducted at different loads (10, 20, 30, and 40 N) and different sliding spaces (500, 1000, 1500, and 2000 m), respectively. The results revealed that the sample loaded with 10% of reinforcement recorded the highest tensile strength of 231 MPa. On the other hand, the hardness value increased from 71 to 144 BHN, when 15% of reinforcement was added to the sample. It was also noted that 10% copper-coated steel fiber improved wear resistance up to 50% when compared to LM6. A field emission scanning electron microscope was employed to observe the morphology of the worn ...","publisher":"Hindawi Limited","publication_name":"Advances in Materials Science and Engineering"},"translated_abstract":"In this investigation, aluminium-silicon-based alloy (LM6) with the addition of (0, 2.5, 5, and 10%) copper-coated short steel fiber and 5% boron carbide (B4C) element-strengthened composites was fabricated by the stir casting method. Mechanical properties and tribological behaviors of LM6-based hybrid composites were investigated, and microstructures of different castings were examined by an image analyzer. The test was conducted at different loads (10, 20, 30, and 40 N) and different sliding spaces (500, 1000, 1500, and 2000 m), respectively. The results revealed that the sample loaded with 10% of reinforcement recorded the highest tensile strength of 231 MPa. On the other hand, the hardness value increased from 71 to 144 BHN, when 15% of reinforcement was added to the sample. It was also noted that 10% copper-coated steel fiber improved wear resistance up to 50% when compared to LM6. A field emission scanning electron microscope was employed to observe the morphology of the worn ...","internal_url":"https://www.academia.edu/69465123/Investigation_on_Mechanical_and_Wear_Behaviors_of_LM6_Aluminium_Alloy_Based_Hybrid_Metal_Matrix_Composites_Using_Stir_Casting_Process","translated_internal_url":"","created_at":"2022-01-25T23:53:48.641-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":22658377,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":79553577,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/79553577/thumbnails/1.jpg","file_name":"4116843.pdf","download_url":"https://www.academia.edu/attachments/79553577/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Investigation_on_Mechanical_and_Wear_Beh.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/79553577/4116843-libre.pdf?1643184660=\u0026response-content-disposition=attachment%3B+filename%3DInvestigation_on_Mechanical_and_Wear_Beh.pdf\u0026Expires=1732494317\u0026Signature=MkCmXpdMsyku8MAQIZwvHqve45R3uhzNQFS6MimB8cWtILlw6gD1TC6fBHUj99VRGtQnZmwymeKeLC-fz7cjz6epl~VG6bwlUG7fPJt-4WARFRLnBpH1K9vvPJe2iZ0TS9DplmSUeAA3rUazZ8tRV6n4UU6FkEeTe7FQvtcd9z3JrgeEGCykZRvuEQuGG48vewnJObKF0EslrvElX233tpQbR9enjDGlNY0MXc~O6740wjAuJIoN0BNOW9wVOR1uIVY0fteDSwDHUKEam31Cr~AnDQqs1ttcUBBOxr0mmeL4PAP6W0pDZmYozLdwo1RJjg1NI62mlM59DyLA4Ge0OA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Investigation_on_Mechanical_and_Wear_Behaviors_of_LM6_Aluminium_Alloy_Based_Hybrid_Metal_Matrix_Composites_Using_Stir_Casting_Process","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":22658377,"first_name":"Baru","middle_initials":null,"last_name":"Debtera","page_name":"BaruDebtera","domain_name":"acadamia","created_at":"2014-12-01T00:23:19.969-08:00","display_name":"Baru Debtera","url":"https://acadamia.academia.edu/BaruDebtera"},"attachments":[{"id":79553577,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/79553577/thumbnails/1.jpg","file_name":"4116843.pdf","download_url":"https://www.academia.edu/attachments/79553577/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Investigation_on_Mechanical_and_Wear_Beh.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/79553577/4116843-libre.pdf?1643184660=\u0026response-content-disposition=attachment%3B+filename%3DInvestigation_on_Mechanical_and_Wear_Beh.pdf\u0026Expires=1732494317\u0026Signature=MkCmXpdMsyku8MAQIZwvHqve45R3uhzNQFS6MimB8cWtILlw6gD1TC6fBHUj99VRGtQnZmwymeKeLC-fz7cjz6epl~VG6bwlUG7fPJt-4WARFRLnBpH1K9vvPJe2iZ0TS9DplmSUeAA3rUazZ8tRV6n4UU6FkEeTe7FQvtcd9z3JrgeEGCykZRvuEQuGG48vewnJObKF0EslrvElX233tpQbR9enjDGlNY0MXc~O6740wjAuJIoN0BNOW9wVOR1uIVY0fteDSwDHUKEam31Cr~AnDQqs1ttcUBBOxr0mmeL4PAP6W0pDZmYozLdwo1RJjg1NI62mlM59DyLA4Ge0OA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":79553576,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/79553576/thumbnails/1.jpg","file_name":"4116843.pdf","download_url":"https://www.academia.edu/attachments/79553576/download_file","bulk_download_file_name":"Investigation_on_Mechanical_and_Wear_Beh.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/79553576/4116843-libre.pdf?1643184660=\u0026response-content-disposition=attachment%3B+filename%3DInvestigation_on_Mechanical_and_Wear_Beh.pdf\u0026Expires=1732494317\u0026Signature=IIB4~5XTHqbZtCu1swiaKv-h7soZ4fRZuWnLRT99xU9~bLwaLKtQXmmBXr2xHR50tn30dkUGHH8TYtFd2voevY0B-8xx7gJF-cTG~hYCb9LK1-JVKbseLppVP7h6qTea~HdE3aGCnsou-C5Gu8Y5geGRdmrkNlY7pdM9d2U9DP0ClEIipKhZ-ZIJ3452sOWp3iLW7F25nwAzStbQ6DR6fb2~6jDjtuxzL681CEffATbxjwY~rUEMyFME-y8xAo6Q~gwIXaQQVbLXMPVMNPyb2PfHoSn39LOmJU8NxjJBX9FAWDP4VNODpJnKmj5TwfSJ9L~NYQTyUTFbpUSxGWHrNA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":24002,"name":"Materials Science and Engineering","url":"https://www.academia.edu/Documents/in/Materials_Science_and_Engineering"}],"urls":[{"id":16900509,"url":"http://downloads.hindawi.com/journals/amse/2022/4116843.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="2227601" id="papers"><div class="js-work-strip profile--work_container" data-work-id="89380634"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/89380634/Extraction_and_Characterisation_of_Avocado_Seed_Starch_and_Its_Blend_with_Enset_Cellulosic"><img alt="Research paper thumbnail of Extraction and Characterisation of Avocado Seed Starch, and Its Blend with Enset Cellulosic" class="work-thumbnail" src="https://attachments.academia-assets.com/93192317/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/89380634/Extraction_and_Characterisation_of_Avocado_Seed_Starch_and_Its_Blend_with_Enset_Cellulosic">Extraction and Characterisation of Avocado Seed Starch, and Its Blend with Enset Cellulosic</a></div><div class="wp-workCard_item"><span>Advances in Materials Science and Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Avocado seed starch was extracted to prepare a biodegradable plastic film with enset cellulosic r...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Avocado seed starch was extracted to prepare a biodegradable plastic film with enset cellulosic reinforcement and glycerol plasticiser. The characterisations were made in detail. Fourier transform infrared (FTIR) spectroscopy, differential scanning calorimeter (DSC), scanning electron microscopy (SEM), X-ray diffraction (XRD), rheometer, and Rapid Visco Analyser (RVA) techniques were employed to characterise the physicochemical properties of avocado seed starch, enset cellulosic, and the blend. Starch and enset cellulosic extraction yields are 18.3 ± 0.02% and 67.5 ± 0.08%, respectively. As a result, avocado seed starch and enset cellulosic blend can be used as alternative raw materials to develop biodegradable plastics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="52a2b0c50fb48d7907ba1f91488cef72" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":93192317,"asset_id":89380634,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/93192317/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="89380634"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="89380634"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 89380634; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=89380634]").text(description); $(".js-view-count[data-work-id=89380634]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 89380634; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='89380634']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 89380634, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "52a2b0c50fb48d7907ba1f91488cef72" } } $('.js-work-strip[data-work-id=89380634]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":89380634,"title":"Extraction and Characterisation of Avocado Seed Starch, and Its Blend with Enset Cellulosic","translated_title":"","metadata":{"abstract":"Avocado seed starch was extracted to prepare a biodegradable plastic film with enset cellulosic reinforcement and glycerol plasticiser. The characterisations were made in detail. Fourier transform infrared (FTIR) spectroscopy, differential scanning calorimeter (DSC), scanning electron microscopy (SEM), X-ray diffraction (XRD), rheometer, and Rapid Visco Analyser (RVA) techniques were employed to characterise the physicochemical properties of avocado seed starch, enset cellulosic, and the blend. Starch and enset cellulosic extraction yields are 18.3 ± 0.02% and 67.5 ± 0.08%, respectively. As a result, avocado seed starch and enset cellulosic blend can be used as alternative raw materials to develop biodegradable plastics.","publisher":"Hindawi Limited","publication_name":"Advances in Materials Science and Engineering"},"translated_abstract":"Avocado seed starch was extracted to prepare a biodegradable plastic film with enset cellulosic reinforcement and glycerol plasticiser. The characterisations were made in detail. Fourier transform infrared (FTIR) spectroscopy, differential scanning calorimeter (DSC), scanning electron microscopy (SEM), X-ray diffraction (XRD), rheometer, and Rapid Visco Analyser (RVA) techniques were employed to characterise the physicochemical properties of avocado seed starch, enset cellulosic, and the blend. Starch and enset cellulosic extraction yields are 18.3 ± 0.02% and 67.5 ± 0.08%, respectively. As a result, avocado seed starch and enset cellulosic blend can be used as alternative raw materials to develop biodegradable plastics.","internal_url":"https://www.academia.edu/89380634/Extraction_and_Characterisation_of_Avocado_Seed_Starch_and_Its_Blend_with_Enset_Cellulosic","translated_internal_url":"","created_at":"2022-10-28T01:55:52.263-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":22658377,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":93192317,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/93192317/thumbnails/1.jpg","file_name":"9908295.pdf","download_url":"https://www.academia.edu/attachments/93192317/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Extraction_and_Characterisation_of_Avoca.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/93192317/9908295-libre.pdf?1666950129=\u0026response-content-disposition=attachment%3B+filename%3DExtraction_and_Characterisation_of_Avoca.pdf\u0026Expires=1732494316\u0026Signature=HLRrw4KEx5zNB4e7y8xeHqWChFnioTaEl9z6FEimpyzfhqBk0KYem~k6LydpvR53~xjKt5SgyPD7M9v2txsl~3IsL6cMXJ5SS4dZ7TAML-2S8UEiOD8LccxAPBDcp~yJPCDZD3Ug8jH7DqdkekwYeFuByJcb~Brw71UZwbJFRP~5b7DtWskhx0ca85nhzBDQhR1sFPtoSUy0nCbBjx0K5P7u3dvXVVly8WFdC~nLe83KF2jqm2ebZedW2FYbmfW8eEu~DS-C7Mm~65IGrGetp173KSNIIb9BeAHdpLvG3KoR-txbpAIHO8fq7T7ierANlPIIzFosk3SaWQJEvcT0ig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Extraction_and_Characterisation_of_Avocado_Seed_Starch_and_Its_Blend_with_Enset_Cellulosic","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":22658377,"first_name":"Baru","middle_initials":null,"last_name":"Debtera","page_name":"BaruDebtera","domain_name":"acadamia","created_at":"2014-12-01T00:23:19.969-08:00","display_name":"Baru Debtera","url":"https://acadamia.academia.edu/BaruDebtera"},"attachments":[{"id":93192317,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/93192317/thumbnails/1.jpg","file_name":"9908295.pdf","download_url":"https://www.academia.edu/attachments/93192317/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Extraction_and_Characterisation_of_Avoca.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/93192317/9908295-libre.pdf?1666950129=\u0026response-content-disposition=attachment%3B+filename%3DExtraction_and_Characterisation_of_Avoca.pdf\u0026Expires=1732494316\u0026Signature=HLRrw4KEx5zNB4e7y8xeHqWChFnioTaEl9z6FEimpyzfhqBk0KYem~k6LydpvR53~xjKt5SgyPD7M9v2txsl~3IsL6cMXJ5SS4dZ7TAML-2S8UEiOD8LccxAPBDcp~yJPCDZD3Ug8jH7DqdkekwYeFuByJcb~Brw71UZwbJFRP~5b7DtWskhx0ca85nhzBDQhR1sFPtoSUy0nCbBjx0K5P7u3dvXVVly8WFdC~nLe83KF2jqm2ebZedW2FYbmfW8eEu~DS-C7Mm~65IGrGetp173KSNIIb9BeAHdpLvG3KoR-txbpAIHO8fq7T7ierANlPIIzFosk3SaWQJEvcT0ig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":24002,"name":"Materials Science and Engineering","url":"https://www.academia.edu/Documents/in/Materials_Science_and_Engineering"}],"urls":[{"id":25233045,"url":"http://downloads.hindawi.com/journals/amse/2022/9908295.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88479014"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88479014/Multistage_Framework_for_Automatic_Face_Mask_Detection_Using_Deep_Learning"><img alt="Research paper thumbnail of Multistage Framework for Automatic Face Mask Detection Using Deep Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/92444751/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88479014/Multistage_Framework_for_Automatic_Face_Mask_Detection_Using_Deep_Learning">Multistage Framework for Automatic Face Mask Detection Using Deep Learning</a></div><div class="wp-workCard_item"><span>Computational Intelligence and Neuroscience</span><span>, Aug 11, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2322362ff483bcd7510ac35bad9bbfcb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92444751,"asset_id":88479014,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92444751/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88479014"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88479014"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88479014; 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COVID-19 cases are upon us in waves, with subsequent waves turning out to be worse than the previous one. Scores of human lives are lost while the post-COVID-19 complications are on a rise. Monitoring the behaviour of people in public places and o ces is necessary to mitigate the transmission of COVID-19 among humans. In this work, a low-cost, lightweight two-stage face mask detection model is proposed. In the rst stage, the model checks if a face mask is worn. In the second stage, it detects if the mask is worn appropriately, by classifying and labelling them. e proposed models are trained to detect faces with and without masks for varied inputs such as images, recorded videos, and live streaming videos where it can e ciently detect multiple faces at once. e e cacy of the proposed approach is tested against conventional datasets as well as our proposed dataset, which includes no masks, surgical masks, and nonsurgical masks. In this work, multiple CNN models like MobileNetV2, ResNet50V2, and InceptionV3 have been considered for training and are evaluated based on transfer learning. 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The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and e...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="39e6bcfcea5b54c2354443b65c70ba4e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":91532306,"asset_id":87278124,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/91532306/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="87278124"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87278124"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87278124; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=87278124]").text(description); $(".js-view-count[data-work-id=87278124]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 87278124; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='87278124']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 87278124, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "39e6bcfcea5b54c2354443b65c70ba4e" } } $('.js-work-strip[data-work-id=87278124]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":87278124,"title":"Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm","translated_title":"","metadata":{"abstract":"Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. 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This research focuses on improving facial features and e...","publisher":"Hindawi Limited","publication_name":"Computational Intelligence and Neuroscience"},"translated_abstract":"Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. 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To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. 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To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.","publisher":"Hindawi Limited","publication_name":"Computational Intelligence and Neuroscience"},"translated_abstract":"Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83832327"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83832327/An_Enhancement_on_Convolutional_Artificial_Intelligent_Based_Diagnosis_for_Skin_Disease_Using_Nanotechnology_Sensors"><img alt="Research paper thumbnail of An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors" class="work-thumbnail" src="https://attachments.academia-assets.com/89051717/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83832327/An_Enhancement_on_Convolutional_Artificial_Intelligent_Based_Diagnosis_for_Skin_Disease_Using_Nanotechnology_Sensors">An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors</a></div><div class="wp-workCard_item"><span>Computational Intelligence and Neuroscience</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Skin disease is the major health problem around the world. The diagnosis of skin disease remains ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Skin disease is the major health problem around the world. The diagnosis of skin disease remains a challenge to dermatologist profession particularly in the detection, evaluation, and management. Health data are very large and complex due to this processing of data using traditional data processing techniques is very difficult. In this paper, to ease the complexity while processing the inputs, we use multilayered perceptron with backpropagation neural networks (MLP-BPNN). The image is collected from the devices that contain nanotechnology sensors, which is the state-of-art in the proposed model. The nanotechnology sensors sense the skin for its chemical, physical, and biological conditions with better detection specificity, sensitivity, and multiplexing ability to acquire the image for optimal classification. The MLP-BPNN technique is used to envisage the future result of disease type effectively. By using the above MLP-BPNN technique, it is easy to predict the skin diseases such as...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7646a767ca794ae2f71a14e984a00a91" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89051717,"asset_id":83832327,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89051717/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83832327"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83832327"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83832327; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83832327]").text(description); $(".js-view-count[data-work-id=83832327]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83832327; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83832327']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83832327, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7646a767ca794ae2f71a14e984a00a91" } } $('.js-work-strip[data-work-id=83832327]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83832327,"title":"An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors","translated_title":"","metadata":{"abstract":"Skin disease is the major health problem around the world. 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By using the above MLP-BPNN technique, it is easy to predict the skin diseases such as...","publisher":"Hindawi Limited","publication_name":"Computational Intelligence and Neuroscience"},"translated_abstract":"Skin disease is the major health problem around the world. The diagnosis of skin disease remains a challenge to dermatologist profession particularly in the detection, evaluation, and management. Health data are very large and complex due to this processing of data using traditional data processing techniques is very difficult. In this paper, to ease the complexity while processing the inputs, we use multilayered perceptron with backpropagation neural networks (MLP-BPNN). The image is collected from the devices that contain nanotechnology sensors, which is the state-of-art in the proposed model. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83832324"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83832324/Optimization_of_Hello_Message_Broadcasting_Prediction_Model_for_Stability_Analysis"><img alt="Research paper thumbnail of Optimization of Hello Message Broadcasting Prediction Model for Stability Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/89051708/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83832324/Optimization_of_Hello_Message_Broadcasting_Prediction_Model_for_Stability_Analysis">Optimization of Hello Message Broadcasting Prediction Model for Stability Analysis</a></div><div class="wp-workCard_item"><span>Wireless Communications and Mobile Computing</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In mobile ad hoc networks, nodes are connected and disconnected regularly; since every mobile nod...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In mobile ad hoc networks, nodes are connected and disconnected regularly; since every mobile node travels autonomously, those nodes are dispersed not uniformly. Connection damage has straight consequences on the network characteristics. For that motivation, various techniques are quick and efficient damage of connection identification using alert packet is difficult for that time to identify next connection in a mobile network. It increases end to end delay and reduces the network lifetime. The present effective connection alteration rate- (ECAR-) based communication scheme depends on the details of neighbouring nodes to survey the connection between the connection alteration rate and the hello time gap in terms of entire transmission rate. Nevertheless, the hello time gap tracking algorithm is constructed to increase transmission rate displaying a stable choice of connection alteration rate, still although node velocity alters. It reduces end to end delay and increases the network...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4d467d59e34910cd8d492921f6f93168" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89051708,"asset_id":83832324,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89051708/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83832324"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83832324"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83832324; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83832324]").text(description); $(".js-view-count[data-work-id=83832324]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83832324; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83832324']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83832324, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4d467d59e34910cd8d492921f6f93168" } } $('.js-work-strip[data-work-id=83832324]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83832324,"title":"Optimization of Hello Message Broadcasting Prediction Model for Stability Analysis","translated_title":"","metadata":{"abstract":"In mobile ad hoc networks, nodes are connected and disconnected regularly; since every mobile node travels autonomously, those nodes are dispersed not uniformly. Connection damage has straight consequences on the network characteristics. For that motivation, various techniques are quick and efficient damage of connection identification using alert packet is difficult for that time to identify next connection in a mobile network. It increases end to end delay and reduces the network lifetime. The present effective connection alteration rate- (ECAR-) based communication scheme depends on the details of neighbouring nodes to survey the connection between the connection alteration rate and the hello time gap in terms of entire transmission rate. Nevertheless, the hello time gap tracking algorithm is constructed to increase transmission rate displaying a stable choice of connection alteration rate, still although node velocity alters. 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Nevertheless, the hello time gap tracking algorithm is constructed to increase transmission rate displaying a stable choice of connection alteration rate, still although node velocity alters. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="82002885"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/82002885/Scrutiny_of_Solar_Water_Heating_System_Employing_Supercritical_Fluid"><img alt="Research paper thumbnail of Scrutiny of Solar Water Heating System Employing Supercritical Fluid" class="work-thumbnail" src="https://attachments.academia-assets.com/87849124/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/82002885/Scrutiny_of_Solar_Water_Heating_System_Employing_Supercritical_Fluid">Scrutiny of Solar Water Heating System Employing Supercritical Fluid</a></div><div class="wp-workCard_item"><span>Mathematical Problems in Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to det...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to detect implicit water heating and boost the collector’s heating rate efficiency. Solar water heating system efficiency, cost, and environmental friendliness all depend on the working fluid used. CO2 is a possible natural refrigerant replacement. Even a little increase in temperature or pressure may have a big impact on the density of CO2 at the critical point. Because of this, solar heating can readily generate a spontaneous convection flow of supercritical carbon dioxide. The most basic collector characteristics, such as CO2 pressure and temperature, were determined by building and testing an experimental setup using a CO2-based solar collector. Due to solar radiation, liquid, gas, or supercritical CO2 pressures and temperatures change throughout the test. There was a 50% time average collector efficiency (ηcol) and a 30% heat recovery efficiency (ηRE). Solar thermal collectors based on su...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6ee5d0dcc5371b8fcc424864d19ccbc8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":87849124,"asset_id":82002885,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/87849124/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="82002885"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="82002885"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 82002885; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=82002885]").text(description); $(".js-view-count[data-work-id=82002885]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 82002885; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='82002885']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 82002885, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6ee5d0dcc5371b8fcc424864d19ccbc8" } } $('.js-work-strip[data-work-id=82002885]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":82002885,"title":"Scrutiny of Solar Water Heating System Employing Supercritical Fluid","translated_title":"","metadata":{"abstract":"This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to detect implicit water heating and boost the collector’s heating rate efficiency. 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Solar thermal collectors based on su...","publisher":"Hindawi Limited","publication_name":"Mathematical Problems in Engineering"},"translated_abstract":"This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to detect implicit water heating and boost the collector’s heating rate efficiency. Solar water heating system efficiency, cost, and environmental friendliness all depend on the working fluid used. CO2 is a possible natural refrigerant replacement. Even a little increase in temperature or pressure may have a big impact on the density of CO2 at the critical point. Because of this, solar heating can readily generate a spontaneous convection flow of supercritical carbon dioxide. The most basic collector characteristics, such as CO2 pressure and temperature, were determined by building and testing an experimental setup using a CO2-based solar collector. 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Solar water heating system efficiency, cost, and environmental friendliness all depend on the working fluid used. CO2 is a possible natural refrigerant replacement. Even a little increase in temperature or pressure may have a big impact on the density of CO2 at the critical point. Because of this, solar heating can readily generate a spontaneous convection flow of supercritical carbon dioxide. The most basic collector characteristics, such as CO2 pressure and temperature, were determined by building and testing an experimental setup using a CO2-based solar collector. Due to solar radiation, liquid, gas, or supercritical CO2 pressures and temperatures change throughout the test. There was a 50% time average collector efficiency (ηcol) and a 30% heat recovery efficiency (ηRE). Solar thermal collectors based on su...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="75226bca060b1d31816371c4cca921f5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":87849110,"asset_id":82002879,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/87849110/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="82002879"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="82002879"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 82002879; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=82002879]").text(description); $(".js-view-count[data-work-id=82002879]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 82002879; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='82002879']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 82002879, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "75226bca060b1d31816371c4cca921f5" } } $('.js-work-strip[data-work-id=82002879]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":82002879,"title":"Scrutiny of Solar Water Heating System Employing Supercritical Fluid","translated_title":"","metadata":{"abstract":"This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to detect implicit water heating and boost the collector’s heating rate efficiency. Solar water heating system efficiency, cost, and environmental friendliness all depend on the working fluid used. CO2 is a possible natural refrigerant replacement. Even a little increase in temperature or pressure may have a big impact on the density of CO2 at the critical point. Because of this, solar heating can readily generate a spontaneous convection flow of supercritical carbon dioxide. The most basic collector characteristics, such as CO2 pressure and temperature, were determined by building and testing an experimental setup using a CO2-based solar collector. Due to solar radiation, liquid, gas, or supercritical CO2 pressures and temperatures change throughout the test. There was a 50% time average collector efficiency (ηcol) and a 30% heat recovery efficiency (ηRE). Solar thermal collectors based on su...","publisher":"Hindawi Limited","publication_name":"Mathematical Problems in Engineering"},"translated_abstract":"This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to detect implicit water heating and boost the collector’s heating rate efficiency. Solar water heating system efficiency, cost, and environmental friendliness all depend on the working fluid used. CO2 is a possible natural refrigerant replacement. Even a little increase in temperature or pressure may have a big impact on the density of CO2 at the critical point. Because of this, solar heating can readily generate a spontaneous convection flow of supercritical carbon dioxide. The most basic collector characteristics, such as CO2 pressure and temperature, were determined by building and testing an experimental setup using a CO2-based solar collector. 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href="https://www.academia.edu/78571922/Formation_of_Low_Mass_Stars_Disks_and_Bipolar_Outflows"><img alt="Research paper thumbnail of Formation of Low Mass Stars Disks and Bipolar Outflows" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/78571922/Formation_of_Low_Mass_Stars_Disks_and_Bipolar_Outflows">Formation of Low Mass Stars Disks and Bipolar Outflows</a></div><div class="wp-workCard_item"><span>JENAM-2007, "Our Non-Stable Universe"</span><span>, Aug 1, 2007</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span 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Study of Production Bio plastic from Banana Peels" class="work-thumbnail" src="https://attachments.academia-assets.com/85514722/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78483665/Synthesis_and_Experimental_Study_of_Production_Bio_plastic_from_Banana_Peels">Synthesis and Experimental Study of Production Bio plastic from Banana Peels</a></div><div class="wp-workCard_item"><span>Research gate</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The synthesis of bio plastic material by using waste banana peels, which can be collected from ev...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The synthesis of bio plastic material by using waste banana peels, which can be collected from everywhere nowadays. The aim of the study was to identify where the most load resistant plastic with respect to the residence time, P H value, and amount of plasticizer used for preparing bio plastic. The paste was hydrolyzed by 0.1 M, and 0.5 M of HCl for breaking the chain of the amylopectin in the starch. In addition to adjust the P H value according to 0.1 M, and 0.5 M of NaOH and different amount of plasticizer (3 ml, 4 ml, and 5 ml) used for making the prepared sample flexible. The prototype of this project represents the process of manufacturing bio-plastic from banana peels were carried out with at P H (neutral, base and acid), residence time (5, 10, and, 15 min), and amount of plasticizes (3 ml, 4 ml, and 5ml) to test the tensile strength and water absorption. The best plastic film we obtained at P H neutral, residence time 15 min and amount of plasticizes 3 ml, which gave as a tensile strength 0.1445 N/mm 2 and per cent of water absorption 18.18. Bio plastic prepared using the banana peels serve as potential alternative to the conventional plastic material. Making bio plastic from banana peels instead of traditional petroleum based plastic is believed to be successful solution for environmental pollution regulation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="68af5488dcca5bdc38225262c247117a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85514722,"asset_id":78483665,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85514722/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78483665"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78483665"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78483665; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78483665]").text(description); $(".js-view-count[data-work-id=78483665]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78483665; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78483665']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78483665, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "68af5488dcca5bdc38225262c247117a" } } $('.js-work-strip[data-work-id=78483665]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78483665,"title":"Synthesis and Experimental Study of Production Bio plastic from Banana Peels","translated_title":"","metadata":{"doi":"10.13140/RG.2.2.25906.15044","abstract":"The synthesis of bio plastic material by using waste banana peels, which can be collected from everywhere nowadays. The aim of the study was to identify where the most load resistant plastic with respect to the residence time, P H value, and amount of plasticizer used for preparing bio plastic. The paste was hydrolyzed by 0.1 M, and 0.5 M of HCl for breaking the chain of the amylopectin in the starch. In addition to adjust the P H value according to 0.1 M, and 0.5 M of NaOH and different amount of plasticizer (3 ml, 4 ml, and 5 ml) used for making the prepared sample flexible. The prototype of this project represents the process of manufacturing bio-plastic from banana peels were carried out with at P H (neutral, base and acid), residence time (5, 10, and, 15 min), and amount of plasticizes (3 ml, 4 ml, and 5ml) to test the tensile strength and water absorption. The best plastic film we obtained at P H neutral, residence time 15 min and amount of plasticizes 3 ml, which gave as a tensile strength 0.1445 N/mm 2 and per cent of water absorption 18.18. 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The prototype of this project represents the process of manufacturing bio-plastic from banana peels were carried out with at P H (neutral, base and acid), residence time (5, 10, and, 15 min), and amount of plasticizes (3 ml, 4 ml, and 5ml) to test the tensile strength and water absorption. The best plastic film we obtained at P H neutral, residence time 15 min and amount of plasticizes 3 ml, which gave as a tensile strength 0.1445 N/mm 2 and per cent of water absorption 18.18. Bio plastic prepared using the banana peels serve as potential alternative to the conventional plastic material. 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This study highlighted the importance of recently discovered Agave Angustifolia marginated (AAM) fibers extracted by a manual process from AAM plants. This paper observes the various properties of the mechanical and moisture intake characteristics of AAM fiber and compares these to other naturally occurring fibers. Using isophthalic polyester resin, the mixed chopped AAM fiber-reinforced composite is prepared and detailed preparation techniques are presented. During physical composite testing, fiber pull-outs on the broken samples were investigated. Additionally, the experimental evidence demonstrates that increasing the volume fraction helps to increase the tensile, bending, and modulus of the AAM fiber composite. The chopped fiber isophthalic polyester reinforcement reaches it...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b35dfdfb4f1c0f65b15fe90abe93ff21" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81872805,"asset_id":73298926,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81872805/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73298926"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73298926"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73298926; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73298926]").text(description); $(".js-view-count[data-work-id=73298926]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73298926; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73298926']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73298926, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b35dfdfb4f1c0f65b15fe90abe93ff21" } } $('.js-work-strip[data-work-id=73298926]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73298926,"title":"Effect of Moisture Content on Mechanical Properties of AAM Natural Fiber-Reinforced Isophthalic Polyester Composites","translated_title":"","metadata":{"abstract":"Composite fiber reinforcement is one such proficient material that substitutes for useful applications for conventional and polyester blends where little weight and thus less energy conservation is required. 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Polyester Composites" class="work-thumbnail" src="https://attachments.academia-assets.com/81505166/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/72667639/Effect_of_Moisture_Content_on_Mechanical_Properties_of_AAM_Natural_Fiber_Reinforced_Isophthalic_Polyester_Composites">Effect of Moisture Content on Mechanical Properties of AAM Natural Fiber-Reinforced Isophthalic Polyester Composites</a></div><div class="wp-workCard_item"><span>Advances in Materials Science and Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Composite fiber reinforcement is one such proficient material that substitutes for useful applica...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Composite fiber reinforcement is one such proficient material that substitutes for useful applications for conventional and polyester blends where little weight and thus less energy conservation is required. This study highlighted the importance of recently discovered Agave Angustifolia marginated (AAM) fibers extracted by a manual process from AAM plants. This paper observes the various properties of the mechanical and moisture intake characteristics of AAM fiber and compares these to other naturally occurring fibers. Using isophthalic polyester resin, the mixed chopped AAM fiber-reinforced composite is prepared and detailed preparation techniques are presented. During physical composite testing, fiber pull-outs on the broken samples were investigated. Additionally, the experimental evidence demonstrates that increasing the volume fraction helps to increase the tensile, bending, and modulus of the AAM fiber composite. The chopped fiber isophthalic polyester reinforcement reaches it...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b4c494e361f5f838f2e23379f542fc39" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81505166,"asset_id":72667639,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81505166/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="72667639"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="72667639"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 72667639; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=72667639]").text(description); $(".js-view-count[data-work-id=72667639]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 72667639; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='72667639']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 72667639, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b4c494e361f5f838f2e23379f542fc39" } } $('.js-work-strip[data-work-id=72667639]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":72667639,"title":"Effect of Moisture Content on Mechanical Properties of AAM Natural Fiber-Reinforced Isophthalic Polyester Composites","translated_title":"","metadata":{"abstract":"Composite fiber reinforcement is one such proficient material that substitutes for useful applications for conventional and polyester blends where little weight and thus less energy conservation is required. This study highlighted the importance of recently discovered Agave Angustifolia marginated (AAM) fibers extracted by a manual process from AAM plants. This paper observes the various properties of the mechanical and moisture intake characteristics of AAM fiber and compares these to other naturally occurring fibers. Using isophthalic polyester resin, the mixed chopped AAM fiber-reinforced composite is prepared and detailed preparation techniques are presented. During physical composite testing, fiber pull-outs on the broken samples were investigated. Additionally, the experimental evidence demonstrates that increasing the volume fraction helps to increase the tensile, bending, and modulus of the AAM fiber composite. 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Additionally, the experimental evidence demonstrates that increasing the volume fraction helps to increase the tensile, bending, and modulus of the AAM fiber composite. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="69465123"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/69465123/Investigation_on_Mechanical_and_Wear_Behaviors_of_LM6_Aluminium_Alloy_Based_Hybrid_Metal_Matrix_Composites_Using_Stir_Casting_Process"><img alt="Research paper thumbnail of Investigation on Mechanical and Wear Behaviors of LM6 Aluminium Alloy-Based Hybrid Metal Matrix Composites Using Stir Casting Process" class="work-thumbnail" src="https://attachments.academia-assets.com/79553577/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/69465123/Investigation_on_Mechanical_and_Wear_Behaviors_of_LM6_Aluminium_Alloy_Based_Hybrid_Metal_Matrix_Composites_Using_Stir_Casting_Process">Investigation on Mechanical and Wear Behaviors of LM6 Aluminium Alloy-Based Hybrid Metal Matrix Composites Using Stir Casting Process</a></div><div class="wp-workCard_item"><span>Advances in Materials Science and Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this investigation, aluminium-silicon-based alloy (LM6) with the addition of (0, 2.5, 5, and 1...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this investigation, aluminium-silicon-based alloy (LM6) with the addition of (0, 2.5, 5, and 10%) copper-coated short steel fiber and 5% boron carbide (B4C) element-strengthened composites was fabricated by the stir casting method. Mechanical properties and tribological behaviors of LM6-based hybrid composites were investigated, and microstructures of different castings were examined by an image analyzer. The test was conducted at different loads (10, 20, 30, and 40 N) and different sliding spaces (500, 1000, 1500, and 2000 m), respectively. The results revealed that the sample loaded with 10% of reinforcement recorded the highest tensile strength of 231 MPa. On the other hand, the hardness value increased from 71 to 144 BHN, when 15% of reinforcement was added to the sample. It was also noted that 10% copper-coated steel fiber improved wear resistance up to 50% when compared to LM6. A field emission scanning electron microscope was employed to observe the morphology of the worn ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fca244edcb536468277b32e3040f97e4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":79553577,"asset_id":69465123,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/79553577/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="69465123"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="69465123"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 69465123; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=69465123]").text(description); $(".js-view-count[data-work-id=69465123]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 69465123; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='69465123']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 69465123, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fca244edcb536468277b32e3040f97e4" } } $('.js-work-strip[data-work-id=69465123]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":69465123,"title":"Investigation on Mechanical and Wear Behaviors of LM6 Aluminium Alloy-Based Hybrid Metal Matrix Composites Using Stir Casting Process","translated_title":"","metadata":{"abstract":"In this investigation, aluminium-silicon-based alloy (LM6) with the addition of (0, 2.5, 5, and 10%) copper-coated short steel fiber and 5% boron carbide (B4C) element-strengthened composites was fabricated by the stir casting method. Mechanical properties and tribological behaviors of LM6-based hybrid composites were investigated, and microstructures of different castings were examined by an image analyzer. The test was conducted at different loads (10, 20, 30, and 40 N) and different sliding spaces (500, 1000, 1500, and 2000 m), respectively. The results revealed that the sample loaded with 10% of reinforcement recorded the highest tensile strength of 231 MPa. On the other hand, the hardness value increased from 71 to 144 BHN, when 15% of reinforcement was added to the sample. It was also noted that 10% copper-coated steel fiber improved wear resistance up to 50% when compared to LM6. A field emission scanning electron microscope was employed to observe the morphology of the worn ...","publisher":"Hindawi Limited","publication_name":"Advances in Materials Science and Engineering"},"translated_abstract":"In this investigation, aluminium-silicon-based alloy (LM6) with the addition of (0, 2.5, 5, and 10%) copper-coated short steel fiber and 5% boron carbide (B4C) element-strengthened composites was fabricated by the stir casting method. Mechanical properties and tribological behaviors of LM6-based hybrid composites were investigated, and microstructures of different castings were examined by an image analyzer. The test was conducted at different loads (10, 20, 30, and 40 N) and different sliding spaces (500, 1000, 1500, and 2000 m), respectively. The results revealed that the sample loaded with 10% of reinforcement recorded the highest tensile strength of 231 MPa. On the other hand, the hardness value increased from 71 to 144 BHN, when 15% of reinforcement was added to the sample. It was also noted that 10% copper-coated steel fiber improved wear resistance up to 50% when compared to LM6. A field emission scanning electron microscope was employed to observe the morphology of the worn ...","internal_url":"https://www.academia.edu/69465123/Investigation_on_Mechanical_and_Wear_Behaviors_of_LM6_Aluminium_Alloy_Based_Hybrid_Metal_Matrix_Composites_Using_Stir_Casting_Process","translated_internal_url":"","created_at":"2022-01-25T23:53:48.641-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":22658377,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":79553577,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/79553577/thumbnails/1.jpg","file_name":"4116843.pdf","download_url":"https://www.academia.edu/attachments/79553577/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Investigation_on_Mechanical_and_Wear_Beh.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/79553577/4116843-libre.pdf?1643184660=\u0026response-content-disposition=attachment%3B+filename%3DInvestigation_on_Mechanical_and_Wear_Beh.pdf\u0026Expires=1732494317\u0026Signature=MkCmXpdMsyku8MAQIZwvHqve45R3uhzNQFS6MimB8cWtILlw6gD1TC6fBHUj99VRGtQnZmwymeKeLC-fz7cjz6epl~VG6bwlUG7fPJt-4WARFRLnBpH1K9vvPJe2iZ0TS9DplmSUeAA3rUazZ8tRV6n4UU6FkEeTe7FQvtcd9z3JrgeEGCykZRvuEQuGG48vewnJObKF0EslrvElX233tpQbR9enjDGlNY0MXc~O6740wjAuJIoN0BNOW9wVOR1uIVY0fteDSwDHUKEam31Cr~AnDQqs1ttcUBBOxr0mmeL4PAP6W0pDZmYozLdwo1RJjg1NI62mlM59DyLA4Ge0OA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Investigation_on_Mechanical_and_Wear_Behaviors_of_LM6_Aluminium_Alloy_Based_Hybrid_Metal_Matrix_Composites_Using_Stir_Casting_Process","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":22658377,"first_name":"Baru","middle_initials":null,"last_name":"Debtera","page_name":"BaruDebtera","domain_name":"acadamia","created_at":"2014-12-01T00:23:19.969-08:00","display_name":"Baru Debtera","url":"https://acadamia.academia.edu/BaruDebtera"},"attachments":[{"id":79553577,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/79553577/thumbnails/1.jpg","file_name":"4116843.pdf","download_url":"https://www.academia.edu/attachments/79553577/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Investigation_on_Mechanical_and_Wear_Beh.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/79553577/4116843-libre.pdf?1643184660=\u0026response-content-disposition=attachment%3B+filename%3DInvestigation_on_Mechanical_and_Wear_Beh.pdf\u0026Expires=1732494317\u0026Signature=MkCmXpdMsyku8MAQIZwvHqve45R3uhzNQFS6MimB8cWtILlw6gD1TC6fBHUj99VRGtQnZmwymeKeLC-fz7cjz6epl~VG6bwlUG7fPJt-4WARFRLnBpH1K9vvPJe2iZ0TS9DplmSUeAA3rUazZ8tRV6n4UU6FkEeTe7FQvtcd9z3JrgeEGCykZRvuEQuGG48vewnJObKF0EslrvElX233tpQbR9enjDGlNY0MXc~O6740wjAuJIoN0BNOW9wVOR1uIVY0fteDSwDHUKEam31Cr~AnDQqs1ttcUBBOxr0mmeL4PAP6W0pDZmYozLdwo1RJjg1NI62mlM59DyLA4Ge0OA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":79553576,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/79553576/thumbnails/1.jpg","file_name":"4116843.pdf","download_url":"https://www.academia.edu/attachments/79553576/download_file","bulk_download_file_name":"Investigation_on_Mechanical_and_Wear_Beh.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/79553576/4116843-libre.pdf?1643184660=\u0026response-content-disposition=attachment%3B+filename%3DInvestigation_on_Mechanical_and_Wear_Beh.pdf\u0026Expires=1732494317\u0026Signature=IIB4~5XTHqbZtCu1swiaKv-h7soZ4fRZuWnLRT99xU9~bLwaLKtQXmmBXr2xHR50tn30dkUGHH8TYtFd2voevY0B-8xx7gJF-cTG~hYCb9LK1-JVKbseLppVP7h6qTea~HdE3aGCnsou-C5Gu8Y5geGRdmrkNlY7pdM9d2U9DP0ClEIipKhZ-ZIJ3452sOWp3iLW7F25nwAzStbQ6DR6fb2~6jDjtuxzL681CEffATbxjwY~rUEMyFME-y8xAo6Q~gwIXaQQVbLXMPVMNPyb2PfHoSn39LOmJU8NxjJBX9FAWDP4VNODpJnKmj5TwfSJ9L~NYQTyUTFbpUSxGWHrNA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":24002,"name":"Materials Science and Engineering","url":"https://www.academia.edu/Documents/in/Materials_Science_and_Engineering"}],"urls":[{"id":16900509,"url":"http://downloads.hindawi.com/journals/amse/2022/4116843.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="8949094" id="bookreviews"><div class="js-work-strip profile--work_container" data-work-id="38440777"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/38440777/Barus_Publication_41_ijsrr_d1165_f_final_pdf"><img alt="Research paper thumbnail of Baru`s Publication 41-ijsrr-d1165.f.final.pdf" class="work-thumbnail" src="https://attachments.academia-assets.com/58502312/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/38440777/Barus_Publication_41_ijsrr_d1165_f_final_pdf">Baru`s Publication 41-ijsrr-d1165.f.final.pdf</a></div><div class="wp-workCard_item"><span>CFD Simulation of a Double Pipe Heat Exchanger: Analysis Conduction and Convection Heat Transfer</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract —Simulation of heat transfer mechanisms in the heat exchangers is very important in diff...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract —Simulation of heat transfer mechanisms in the heat exchangers is very important in different industrial process<br />to recover and minimize the wastage of thermal energy. Ansys Fluent is well-known software for computational fluid<br />dynamics (CFD) which is used to simulate and analyze heat transfer mechanisms in the heat exchanging system. In the<br />present work, a model of simulation for heat transfer rate observed from the double pipe heat exchanger (DPHE) has been<br />developed using Ansys Fluent 16.2 software. The DPHE setup was made with the tube length of 1m. The inner and outer<br />diameters of the tube were 0.05m and 0.055m, respectively. The annulus of the heat exchanger made with the inner and<br />outer diameters 0.08m and 0.085m, respectively. The objectives of this work are to study the hydrodynamic and thermal<br />behaviour of cold and hot fluids used in double pipe heat exchanger, and compare the actual heat transfer observed by<br />number of transfer unit method. Yet another objective is to simulate the obtained results using the CFD. In this approach,<br />a model developed by computational fluid dynamics has been validated by actual heat transfer. The results from the<br />numerical simulation of actual heat transfer well agreed with the effectiveness of heat transfer obtained from NTU method.<br />The actual heat transfer (Q) acquired by CFD simulation and the mathematical NTU method were respectively, 29.7 KW<br />and 30.5 KW. It clears that the percent error between the approaches, CFD simulation and effectiveness of heat transfer-<br />NTU method is 2.6%. It is less than the standard error (15%) and well acceptable.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2e3ce2706bbc2e9a45c76df27f804c8b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":58502312,"asset_id":38440777,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/58502312/download_file?st=MTczMjQ5MDcxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="38440777"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="38440777"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 38440777; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=38440777]").text(description); $(".js-view-count[data-work-id=38440777]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 38440777; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='38440777']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 38440777, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2e3ce2706bbc2e9a45c76df27f804c8b" } } $('.js-work-strip[data-work-id=38440777]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":38440777,"title":"Baru`s Publication 41-ijsrr-d1165.f.final.pdf","translated_title":"","metadata":{"abstract":"Abstract —Simulation of heat transfer mechanisms in the heat exchangers is very important in different industrial process\nto recover and minimize the wastage of thermal energy. Ansys Fluent is well-known software for computational fluid\ndynamics (CFD) which is used to simulate and analyze heat transfer mechanisms in the heat exchanging system. In the\npresent work, a model of simulation for heat transfer rate observed from the double pipe heat exchanger (DPHE) has been\ndeveloped using Ansys Fluent 16.2 software. The DPHE setup was made with the tube length of 1m. The inner and outer\ndiameters of the tube were 0.05m and 0.055m, respectively. The annulus of the heat exchanger made with the inner and\nouter diameters 0.08m and 0.085m, respectively. The objectives of this work are to study the hydrodynamic and thermal\nbehaviour of cold and hot fluids used in double pipe heat exchanger, and compare the actual heat transfer observed by\nnumber of transfer unit method. Yet another objective is to simulate the obtained results using the CFD. In this approach,\na model developed by computational fluid dynamics has been validated by actual heat transfer. The results from the\nnumerical simulation of actual heat transfer well agreed with the effectiveness of heat transfer obtained from NTU method.\nThe actual heat transfer (Q) acquired by CFD simulation and the mathematical NTU method were respectively, 29.7 KW\nand 30.5 KW. It clears that the percent error between the approaches, CFD simulation and effectiveness of heat transfer-\nNTU method is 2.6%. It is less than the standard error (15%) and well acceptable.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"CFD Simulation of a Double Pipe Heat Exchanger: Analysis Conduction and Convection Heat Transfer"},"translated_abstract":"Abstract —Simulation of heat transfer mechanisms in the heat exchangers is very important in different industrial process\nto recover and minimize the wastage of thermal energy. Ansys Fluent is well-known software for computational fluid\ndynamics (CFD) which is used to simulate and analyze heat transfer mechanisms in the heat exchanging system. In the\npresent work, a model of simulation for heat transfer rate observed from the double pipe heat exchanger (DPHE) has been\ndeveloped using Ansys Fluent 16.2 software. The DPHE setup was made with the tube length of 1m. The inner and outer\ndiameters of the tube were 0.05m and 0.055m, respectively. The annulus of the heat exchanger made with the inner and\nouter diameters 0.08m and 0.085m, respectively. The objectives of this work are to study the hydrodynamic and thermal\nbehaviour of cold and hot fluids used in double pipe heat exchanger, and compare the actual heat transfer observed by\nnumber of transfer unit method. Yet another objective is to simulate the obtained results using the CFD. In this approach,\na model developed by computational fluid dynamics has been validated by actual heat transfer. The results from the\nnumerical simulation of actual heat transfer well agreed with the effectiveness of heat transfer obtained from NTU method.\nThe actual heat transfer (Q) acquired by CFD simulation and the mathematical NTU method were respectively, 29.7 KW\nand 30.5 KW. It clears that the percent error between the approaches, CFD simulation and effectiveness of heat transfer-\nNTU method is 2.6%. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="38440769"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/38440769/CFD_Simulation_of_a_Double_Pipe_Heat_Exchanger_Analysis_Conduction_and_Convection_Heat_Transfer"><img alt="Research paper thumbnail of CFD Simulation of a Double Pipe Heat Exchanger: Analysis Conduction and Convection Heat Transfer" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/38440769/CFD_Simulation_of_a_Double_Pipe_Heat_Exchanger_Analysis_Conduction_and_Convection_Heat_Transfer">CFD Simulation of a Double Pipe Heat Exchanger: Analysis Conduction and Convection Heat Transfer</a></div><div class="wp-workCard_item"><span>International Journal of Scientific Research and Review</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract —Simulation of heat transfer mechanisms in the heat exchangers is very important in diff...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract —Simulation of heat transfer mechanisms in the heat exchangers is very important in different industrial process<br />to recover and minimize the wastage of thermal energy. Ansys Fluent is well-known software for computational fluid<br />dynamics (CFD) which is used to simulate and analyze heat transfer mechanisms in the heat exchanging system. In the<br />present work, a model of simulation for heat transfer rate observed from the double pipe heat exchanger (DPHE) has been<br />developed using Ansys Fluent 16.2 software. The DPHE setup was made with the tube length of 1m. The inner and outer<br />diameters of the tube were 0.05m and 0.055m, respectively. The annulus of the heat exchanger made with the inner and<br />outer diameters 0.08m and 0.085m, respectively. The objectives of this work are to study the hydrodynamic and thermal<br />behaviour of cold and hot fluids used in double pipe heat exchanger, and compare the actual heat transfer observed by<br />number of transfer unit method. Yet another objective is to simulate the obtained results using the CFD. In this approach,<br />a model developed by computational fluid dynamics has been validated by actual heat transfer. The results from the<br />numerical simulation of actual heat transfer well agreed with the effectiveness of heat transfer obtained from NTU method.<br />The actual heat transfer (Q) acquired by CFD simulation and the mathematical NTU method were respectively, 29.7 KW<br />and 30.5 KW. It clears that the percent error between the approaches, CFD simulation and effectiveness of heat transfer-<br />NTU method is 2.6%. It is less than the standard error (15%) and well acceptable.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="38440769"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="38440769"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 38440769; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=38440769]").text(description); $(".js-view-count[data-work-id=38440769]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 38440769; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='38440769']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 38440769, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=38440769]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":38440769,"title":"CFD Simulation of a Double Pipe Heat Exchanger: Analysis Conduction and Convection Heat Transfer","translated_title":"","metadata":{"abstract":"Abstract —Simulation of heat transfer mechanisms in the heat exchangers is very important in different industrial process\nto recover and minimize the wastage of thermal energy. Ansys Fluent is well-known software for computational fluid\ndynamics (CFD) which is used to simulate and analyze heat transfer mechanisms in the heat exchanging system. In the\npresent work, a model of simulation for heat transfer rate observed from the double pipe heat exchanger (DPHE) has been\ndeveloped using Ansys Fluent 16.2 software. The DPHE setup was made with the tube length of 1m. The inner and outer\ndiameters of the tube were 0.05m and 0.055m, respectively. The annulus of the heat exchanger made with the inner and\nouter diameters 0.08m and 0.085m, respectively. The objectives of this work are to study the hydrodynamic and thermal\nbehaviour of cold and hot fluids used in double pipe heat exchanger, and compare the actual heat transfer observed by\nnumber of transfer unit method. Yet another objective is to simulate the obtained results using the CFD. In this approach,\na model developed by computational fluid dynamics has been validated by actual heat transfer. The results from the\nnumerical simulation of actual heat transfer well agreed with the effectiveness of heat transfer obtained from NTU method.\nThe actual heat transfer (Q) acquired by CFD simulation and the mathematical NTU method were respectively, 29.7 KW\nand 30.5 KW. It clears that the percent error between the approaches, CFD simulation and effectiveness of heat transfer-\nNTU method is 2.6%. It is less than the standard error (15%) and well acceptable.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"International Journal of Scientific Research and Review"},"translated_abstract":"Abstract —Simulation of heat transfer mechanisms in the heat exchangers is very important in different industrial process\nto recover and minimize the wastage of thermal energy. Ansys Fluent is well-known software for computational fluid\ndynamics (CFD) which is used to simulate and analyze heat transfer mechanisms in the heat exchanging system. In the\npresent work, a model of simulation for heat transfer rate observed from the double pipe heat exchanger (DPHE) has been\ndeveloped using Ansys Fluent 16.2 software. The DPHE setup was made with the tube length of 1m. The inner and outer\ndiameters of the tube were 0.05m and 0.055m, respectively. The annulus of the heat exchanger made with the inner and\nouter diameters 0.08m and 0.085m, respectively. The objectives of this work are to study the hydrodynamic and thermal\nbehaviour of cold and hot fluids used in double pipe heat exchanger, and compare the actual heat transfer observed by\nnumber of transfer unit method. Yet another objective is to simulate the obtained results using the CFD. In this approach,\na model developed by computational fluid dynamics has been validated by actual heat transfer. The results from the\nnumerical simulation of actual heat transfer well agreed with the effectiveness of heat transfer obtained from NTU method.\nThe actual heat transfer (Q) acquired by CFD simulation and the mathematical NTU method were respectively, 29.7 KW\nand 30.5 KW. It clears that the percent error between the approaches, CFD simulation and effectiveness of heat transfer-\nNTU method is 2.6%. It is less than the standard error (15%) and well acceptable.","internal_url":"https://www.academia.edu/38440769/CFD_Simulation_of_a_Double_Pipe_Heat_Exchanger_Analysis_Conduction_and_Convection_Heat_Transfer","translated_internal_url":"","created_at":"2019-02-26T02:57:05.736-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":22658377,"coauthors_can_edit":true,"document_type":"book_review","co_author_tags":[{"id":32292319,"work_id":38440769,"tagging_user_id":22658377,"tagged_user_id":39259016,"co_author_invite_id":null,"email":"i***3@gmail.com","display_order":0,"name":"Ibsa Neme","title":"CFD Simulation of a Double Pipe Heat Exchanger: Analysis Conduction and Convection Heat Transfer"},{"id":32292320,"work_id":38440769,"tagging_user_id":22658377,"tagged_user_id":26195608,"co_author_invite_id":null,"email":"g***u@gmail.com","display_order":4194304,"name":"venkatesa prabhu","title":"CFD Simulation of a Double Pipe Heat Exchanger: Analysis Conduction and Convection Heat Transfer"}],"downloadable_attachments":[],"slug":"CFD_Simulation_of_a_Double_Pipe_Heat_Exchanger_Analysis_Conduction_and_Convection_Heat_Transfer","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":22658377,"first_name":"Baru","middle_initials":null,"last_name":"Debtera","page_name":"BaruDebtera","domain_name":"acadamia","created_at":"2014-12-01T00:23:19.969-08:00","display_name":"Baru Debtera","url":"https://acadamia.academia.edu/BaruDebtera"},"attachments":[],"research_interests":[{"id":2298,"name":"Computational Fluid Dynamics","url":"https://www.academia.edu/Documents/in/Computational_Fluid_Dynamics"},{"id":5413,"name":"Bioenergy","url":"https://www.academia.edu/Documents/in/Bioenergy"},{"id":6540,"name":"Process Control","url":"https://www.academia.edu/Documents/in/Process_Control"},{"id":61636,"name":"Matlab Programming","url":"https://www.academia.edu/Documents/in/Matlab_Programming"},{"id":249729,"name":"Chemical Engineering Process Design","url":"https://www.academia.edu/Documents/in/Chemical_Engineering_Process_Design"},{"id":2513714,"name":"Aspen Plus simulation","url":"https://www.academia.edu/Documents/in/Aspen_Plus_simulation"}],"urls":[{"id":8707385,"url":"http://www.dynamicpublisher.org/gallery/41-ijsrr-d1165.f.final.pdf%20or%20http://www.dynamicpublisher.org/VOL-7-ISSUE-12-2018/"}]}, dispatcherData: dispatcherData }); 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