Applied Sciences | November-2 2024

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return false;">Ok</a> </div> </div> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> </div> <div> <div style="clear: both"></div> </div> </div> </div> <div class="jscroll"> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525574" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 13 pages, 2731 KiB   </span> <a href="/2076-3417/14/22/10766/pdf?version=1732174535" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="EPR Spectroscopy Coupled with Spin Trapping as an Alternative Tool to Assess and Compare the Oxidative Stability of Vegetable Oils for Cosmetics" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10766">EPR Spectroscopy Coupled with Spin Trapping as an Alternative Tool to Assess and Compare the Oxidative Stability of Vegetable Oils for Cosmetics</a> <div class="authors"> by <span class="inlineblock "><strong>Giulia Di Prima</strong>, </span><span class="inlineblock "><strong>Viviana De Caro</strong>, </span><span class="inlineblock "><strong>Cinzia Cardamone</strong>, </span><span class="inlineblock "><strong>Giuseppa Oliveri</strong> and </span><span class="inlineblock "><strong>Maria Cristina D’Oca</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10766; <a href="https://doi.org/10.3390/app142210766">https://doi.org/10.3390/app142210766</a> - 20 Nov 2024 </div> Viewed by 264 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Antioxidants are the most popular active ingredients in anti-aging cosmetics as they can restore the physiological radical balance and counteract the photoaging process. Instead of adding pure compounds into the formulations, some “precious” vegetable oils could be used due to their content of <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10766/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Antioxidants are the most popular active ingredients in anti-aging cosmetics as they can restore the physiological radical balance and counteract the photoaging process. Instead of adding pure compounds into the formulations, some “precious” vegetable oils could be used due to their content of tocopherols, phenols, vitamins, etc., constituting a powerful antioxidant unsaponifiable fraction. Here, electron paramagnetic resonance (EPR) spectroscopy coupled with spin trapping was proven to provide a valid method for evaluating the antioxidant properties and the oxidative resistance of vegetable oils which, following UV irradiation, produce highly reactive radical species although hardly detectable. Extra virgin olive oil, sweet almond oil, apricot kernel oil, and jojoba oil were then evaluated by using N-t-butyl-α-phenylnitrone as a spin trapper and testing different UV irradiation times followed by incubation for 5 to 180 min at 70 °C. The EPR spectra were manipulated to obtain quantitative information useful for comparing the different tested samples. As a result, the knowledge acquired via the EPR analyses demonstrated jojoba oil as the best of the four considered oils in terms of both starting antioxidant ability and oxidative stability overtime. The obtained results confirmed the usefulness of the EPR spin trapping technique for the main proposed purpose. <a href="/2076-3417/14/22/10766">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/6FH9CE5347 ">New Insights in Functional Cosmetic Materials and Industrial Manufacture</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10766/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525574"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525574"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525574" data-cycle-prev="#prev1525574" data-cycle-progressive="#images1525574" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525574-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g001-550.jpg?1732174706" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525574" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525574-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g002-550.jpg?1732174707'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525574-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g003-550.jpg?1732174709'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525574-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g004-550.jpg?1732174710'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525574-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g005-550.jpg?1732174711'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525574-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g006-550.jpg?1732174712'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525574-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g007-550.jpg?1732174714'><p>Figure 7</p></div></script></div></div><div id="article-1525574-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g001-550.jpg?1732174706" title=" <strong>Figure 1</strong><br/> <p>EPR spectra of the PBN spin adducts in EVOO after 30 min of incubation at 70 °C with the not-irradiated (black line) sample and UV-irradiated ones (30 min: green line; 60 min: blue line; 120 min: red line). Results are reported as means (n = 6).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10766'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g002-550.jpg?1732174707" title=" <strong>Figure 2</strong><br/> <p>Determination of the induction time (IT) for EVOO (sample not irradiated). Results are reported as means (n = 6).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10766'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g003-550.jpg?1732174709" title=" <strong>Figure 3</strong><br/> <p>H<sub>p/p</sub> intensity against incubation time at 70 °C for EVOO samples. Means (n = 6) ± SD.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10766'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g004-550.jpg?1732174710" title=" <strong>Figure 4</strong><br/> <p>H<sub>p/p</sub> intensity against incubation time at 70 °C for SAO samples. Means (n = 6) ± SD.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10766'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g005-550.jpg?1732174711" title=" <strong>Figure 5</strong><br/> <p>H<sub>p/p</sub> intensity against incubation time at 70 °C for AKO samples. Means (n = 6) ± SD.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10766'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g006-550.jpg?1732174712" title=" <strong>Figure 6</strong><br/> <p>H<sub>p/p</sub> intensity against incubation time at 70 °C for JO samples. Means (n = 6) ± SD.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10766'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10766/article_deploy/html/images/applsci-14-10766-g007-550.jpg?1732174714" title=" <strong>Figure 7</strong><br/> <p>Slope of the linear portion of the H<sub>p/p</sub> intensity against incubation time at 70 °C graphs as a function of UV irradiation time for EVOO, SAO, AKO, and JO samples. Means (n = 6).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10766'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525569" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 21 pages, 2088 KiB   </span> <a href="/2076-3417/14/22/10765/pdf?version=1732120212" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Study of the Applicability of Thermochemical Processes for Solid Recovered Fuel" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10765">Study of the Applicability of Thermochemical Processes for Solid Recovered Fuel</a> <div class="authors"> by <span class="inlineblock "><strong>Juan Jesús de la Torre-Bayo</strong>, </span><span class="inlineblock "><strong>Montserrat Zamorano</strong>, </span><span class="inlineblock "><strong>Juan Carlos Torres-Rojo</strong>, </span><span class="inlineblock "><strong>Noemí Gil-Lalaguna</strong>, </span><span class="inlineblock "><strong>Gloria Gea</strong>, </span><span class="inlineblock "><strong>Isabel Fonts</strong> and </span><span class="inlineblock "><strong>Jaime Martín-Pascual</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10765; <a href="https://doi.org/10.3390/app142210765">https://doi.org/10.3390/app142210765</a> - 20 Nov 2024 </div> Viewed by 306 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Within the context of the new circular model for wastewater treatment aimed at achieving zero waste, this research seeks an alternative to landfill disposal of waste screenings. It examines the feasibility of thermochemical processes—combustion and gasification—for the valorisation of solid recovered fuel (SRF) <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10765/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Within the context of the new circular model for wastewater treatment aimed at achieving zero waste, this research seeks an alternative to landfill disposal of waste screenings. It examines the feasibility of thermochemical processes—combustion and gasification—for the valorisation of solid recovered fuel (SRF) derived from screening wastes, which are the only waste in wastewater treatment plants (WWTPs) that typically have an absence of existing recycling or valorisation processes. Laboratory-scale experiments assessed the technical viability of gasification, and energy balances were calculated for both combustion and the syngas obtained from gasification experiments. Results indicate that both processes are feasible for SRF valorisation. Combustion demonstrated the highest energy efficiency, yielding up to 1.6 MJ per kg of raw SRF, compared to gasification’s maximum of 1.4 MJ. The moisture content in SRF feedstock influences both processes, underscoring the need to optimise moisture levels. Additionally, combustion showed a higher conversion efficiency due to the complete oxidation of the feedstock, whereas gasification produced valuable syngas that can be further utilised for energy production or as a chemical feedstock. The study concludes that, from a purely energetic perspective, combustion is the most efficient process for SRF valorisation. However, gasification offers significant environmental and sustainability advantages, including lower greenhouse gas emissions and the potential for integrating with renewable energy systems, making it a more attractive option for long-term sustainability goals. <a href="/2076-3417/14/22/10765">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/95DR88UIHS ">Application of Municipal/Industrial Solid and Liquid Waste in Energy Area, 2nd Edition</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10765/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525569"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525569"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525569" data-cycle-prev="#prev1525569" data-cycle-progressive="#images1525569" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525569-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g001-550.jpg?1732120351" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525569" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525569-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g002-550.jpg?1732120353'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525569-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g003-550.jpg?1732120355'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525569-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g004-550.jpg?1732120357'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525569-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g005-550.jpg?1732120359'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525569-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g006-550.jpg?1732120360'><p>Figure 6</p></div></script></div></div><div id="article-1525569-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g001-550.jpg?1732120351" title=" <strong>Figure 1</strong><br/> <p>Scheme of the experimental gasification setup.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10765'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g002-550.jpg?1732120353" title=" <strong>Figure 2</strong><br/> <p>Combustion energy balance diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10765'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g003-550.jpg?1732120355" title=" <strong>Figure 3</strong><br/> <p>Heat exchanger energy balance diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10765'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g004-550.jpg?1732120357" title=" <strong>Figure 4</strong><br/> <p>Gasification energy balance diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10765'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g005-550.jpg?1732120359" title=" <strong>Figure 5</strong><br/> <p>Energy balance diagram for the combustion of gasification gas.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10765'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10765/article_deploy/html/images/applsci-14-10765-g006-550.jpg?1732120360" title=" <strong>Figure 6</strong><br/> <p>Heat required for drying (MJ/kg of wet SRF vs. moisture remaining in the SRF after thermal drying).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10765'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525506" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 13 pages, 46604 KiB   </span> <a href="/2076-3417/14/22/10764/pdf?version=1732178684" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Human Activity Recognition Based on Point Clouds from Millimeter-Wave Radar" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10764">Human Activity Recognition Based on Point Clouds from Millimeter-Wave Radar</a> <div class="authors"> by <span class="inlineblock "><strong>Seungchan Lim</strong>, </span><span class="inlineblock "><strong>Chaewoon Park</strong>, </span><span class="inlineblock "><strong>Seongjoo Lee</strong> and </span><span class="inlineblock "><strong>Yunho Jung</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10764; <a href="https://doi.org/10.3390/app142210764">https://doi.org/10.3390/app142210764</a> - 20 Nov 2024 </div> Viewed by 263 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Human activity recognition (HAR) technology is related to human safety and convenience, making it crucial for it to infer human activity accurately. Furthermore, it must consume low power at all times when detecting human activity and be inexpensive to operate. For this purpose, <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10764/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Human activity recognition (HAR) technology is related to human safety and convenience, making it crucial for it to infer human activity accurately. Furthermore, it must consume low power at all times when detecting human activity and be inexpensive to operate. For this purpose, a low-power and lightweight design of the HAR system is essential. In this paper, we propose a low-power and lightweight HAR system using point-cloud data collected by radar. The proposed HAR system uses a pillar feature encoder that converts 3D point-cloud data into a 2D image and a classification network based on depth-wise separable convolution for lightweighting. The proposed classification network achieved an accuracy of 95.54%, with 25.77 M multiply–accumulate operations and 22.28 K network parameters implemented in a 32 bit floating-point format. This network achieved 94.79% accuracy with 4 bit quantization, which reduced memory usage to 12.5% compared to existing 32 bit format networks. In addition, we implemented a lightweight HAR system optimized for low-power design on a heterogeneous computing platform, a Zynq UltraScale+ ZCU104 device, through hardware–software implementation. It took 2.43 ms of execution time to perform one frame of HAR on the device and the system consumed 3.479 W of power when running. <a href="/2076-3417/14/22/10764">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10764/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525506"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525506"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525506" data-cycle-prev="#prev1525506" data-cycle-progressive="#images1525506" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525506-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g001-550.jpg?1732178762" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525506" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525506-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g002-550.jpg?1732178766'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525506-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g003-550.jpg?1732178767'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525506-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g004-550.jpg?1732178768'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525506-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g005-550.jpg?1732178769'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525506-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g006-550.jpg?1732178772'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525506-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g007-550.jpg?1732178776'><p>Figure 7</p></div></script></div></div><div id="article-1525506-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g001-550.jpg?1732178762" title=" <strong>Figure 1</strong><br/> <p>Data collection setup.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10764'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g002-550.jpg?1732178766" title=" <strong>Figure 2</strong><br/> <p>Configuration of dataset classes and their corresponding point clouds: (<b>a</b>) Stretching; (<b>b</b>) Standing; (<b>c</b>) Taking medicine; (<b>d</b>) Squatting; (<b>e</b>) Sitting chair; (<b>f</b>) Reading news; (<b>g</b>) Sitting floor; (<b>h</b>) Picking; (<b>i</b>) Crawl; (<b>j</b>) Lying wave hands; (<b>k</b>) Lying.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10764'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g003-550.jpg?1732178767" title=" <strong>Figure 3</strong><br/> <p>Overview of the proposed HAR system.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10764'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g004-550.jpg?1732178768" title=" <strong>Figure 4</strong><br/> <p>Proposed classification network.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10764'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g005-550.jpg?1732178769" title=" <strong>Figure 5</strong><br/> <p>Training and test loss curve and accuracy curve: (<b>a</b>) Training and test loss curve; (<b>b</b>) Training and test accuracy curve.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10764'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g006-550.jpg?1732178772" title=" <strong>Figure 6</strong><br/> <p>Confusion matrix.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10764'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10764/article_deploy/html/images/applsci-14-10764-g007-550.jpg?1732178776" title=" <strong>Figure 7</strong><br/> <p>Environment used for FPGA implementation and verification.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10764'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525478" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 13 pages, 1236 KiB   </span> <a href="/2076-3417/14/22/10763/pdf?version=1732179054" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Comparative Analysis of Chemical Composition and Radical-Scavenging Activities in Two Wheat Cultivars" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10763">Comparative Analysis of Chemical Composition and Radical-Scavenging Activities in Two Wheat Cultivars</a> <div class="authors"> by <span class="inlineblock "><strong>Nari Yoon</strong>, </span><span class="inlineblock "><strong>Sung-Hwan Jeong</strong>, </span><span class="inlineblock "><strong>Jong-Suk Park</strong>, </span><span class="inlineblock "><strong>Woo Jung Kim</strong> and </span><span class="inlineblock "><strong>Sanghyun Lee</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10763; <a href="https://doi.org/10.3390/app142210763">https://doi.org/10.3390/app142210763</a> - 20 Nov 2024 </div> Viewed by 285 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> <i>Triticum aestivum</i> (wheat) is one of the most significant crops worldwide. This study compares the chemical composition and radical-scavenging activities of two cultivars of <i>T. aestivum</i>, Saekeumkang wheat (SW) and Baekkang wheat (BW). Sprouted wheatgrass extracts of SW and BW were analyzed <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10763/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> <i>Triticum aestivum</i> (wheat) is one of the most significant crops worldwide. This study compares the chemical composition and radical-scavenging activities of two cultivars of <i>T. aestivum</i>, Saekeumkang wheat (SW) and Baekkang wheat (BW). Sprouted wheatgrass extracts of SW and BW were analyzed using assessments of total polyphenol and flavonoid contents, liquid chromatography–electrospray ionization/mass spectrometry (LC-ESI/MS), and high-performance liquid chromatography with a photodiode array (HPLC-PDA). Radical-scavenging activities were evaluated using 2,2-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS·<sup>+</sup>) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays. The results indicated that SW had a higher total polyphenol content than BW, while no significant differences were observed regarding total flavonoid content. HPLC-PDA analysis, guided by LC-ESI/MS, identified four compounds—saponarin, schaftoside, isoorientin, and isovitexin—with isoorientin (3.02 mg/g extract) and schaftoside (4.23 mg/g extract) present in higher concentrations in SW compared to BW. In the ABTS·<sup>+</sup> assay, the two samples did not show noticeable differences, with SW displaying a scavenging ability with an IC<sub>50</sub> of 3.36 mg/mL, and BW with an IC<sub>50</sub> of 3.19 mg/mL. Contrarily, the DPPH assay results showed an inverse trend, suggesting that the radical-scavenging behavior may be influenced by the synergistic and antagonistic interactions of the compounds in SW and BW extracts. <a href="/2076-3417/14/22/10763">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/7P1CXUS416 ">Advances in Bioactive Compounds from Plants and Their Applications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10763/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525478"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525478"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525478" data-cycle-prev="#prev1525478" data-cycle-progressive="#images1525478" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525478-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10763/article_deploy/html/images/applsci-14-10763-g001a-550.jpg?1732179130" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525478" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525478-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10763/article_deploy/html/images/applsci-14-10763-g001b-550.jpg?1732179131'><p>Figure 1 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525478-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10763/article_deploy/html/images/applsci-14-10763-g002-550.jpg?1732179135'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525478-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10763/article_deploy/html/images/applsci-14-10763-g003-550.jpg?1732179136'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525478-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10763/article_deploy/html/images/applsci-14-10763-g004-550.jpg?1732179139'><p>Figure 4</p></div></script></div></div><div id="article-1525478-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10763/article_deploy/html/images/applsci-14-10763-g001a-550.jpg?1732179130" title=" <strong>Figure 1</strong><br/> <p>Chemical structures of syringic acid (<b>1</b>), saponarin (<b>2</b>), 3-feruloylquinic acid (<b>3</b>), schaftoside (<b>4</b>), isoorientin (<b>5</b>), isoschaftoside (<b>6</b>), vitexin-2-<span class="html-italic">O</span>-rhamnoside (<b>7</b>), isovitexin (<b>8</b>), scoparin (<b>9</b>) and 2-hydroxycinnamic acid (<b>10</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10763'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10763/article_deploy/html/images/applsci-14-10763-g001b-550.jpg?1732179131" title=" <strong>Figure 1 Cont.</strong><br/> <p>Chemical structures of syringic acid (<b>1</b>), saponarin (<b>2</b>), 3-feruloylquinic acid (<b>3</b>), schaftoside (<b>4</b>), isoorientin (<b>5</b>), isoschaftoside (<b>6</b>), vitexin-2-<span class="html-italic">O</span>-rhamnoside (<b>7</b>), isovitexin (<b>8</b>), scoparin (<b>9</b>) and 2-hydroxycinnamic acid (<b>10</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10763'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10763/article_deploy/html/images/applsci-14-10763-g002-550.jpg?1732179135" title=" <strong>Figure 2</strong><br/> <p>Total ion chromatograms of the sprouts of the SW in negative (<b>a</b>) and positive (<b>b</b>) ionization modes.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10763'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10763/article_deploy/html/images/applsci-14-10763-g003-550.jpg?1732179136" title=" <strong>Figure 3</strong><br/> <p>A HPLC/PDA chromatogram of compounds <b>1</b>–<b>10</b>. The labeled peaks represent syringic acid (<b>1</b>), saponarin (<b>2</b>), 3-feruloylquinic acid (<b>3</b>), schaftoside (<b>4</b>), isoorientin (<b>5</b>), isoschaftoside (<b>6</b>), vitexin-2-<span class="html-italic">O</span>-rhamnoside (<b>7</b>), isovitexin (<b>8</b>), scoparin (<b>9</b>), and 2-hydroxycinnamic acid (<b>10</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10763'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10763/article_deploy/html/images/applsci-14-10763-g004-550.jpg?1732179139" title=" <strong>Figure 4</strong><br/> <p>HPLC/PDA chromatograms of the sprouts of SW (<b>a</b>) and BW (<b>b</b>). The labeled peaks represent saponarin (<b>2</b>), schaftoside (<b>4</b>), isoorientin (<b>5</b>), and isovitexin (<b>8</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10763'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525471" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 19 pages, 6034 KiB   </span> <a href="/2076-3417/14/22/10762/pdf?version=1732168130" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="GMN+: A Binary Homologous Vulnerability Detection Method Based on Graph Matching Neural Network with Enhanced Attention" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10762">GMN+: A Binary Homologous Vulnerability Detection Method Based on Graph Matching Neural Network with Enhanced Attention</a> <div class="authors"> by <span class="inlineblock "><strong>Zheng Zhao</strong>, </span><span class="inlineblock "><strong>Tianhao Zhang</strong>, </span><span class="inlineblock "><strong>Xiaoya Fan</strong>, </span><span class="inlineblock "><strong>Qian Mao</strong>, </span><span class="inlineblock "><strong>Dafeng Wang</strong> and </span><span class="inlineblock "><strong>Qi Zhao</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10762; <a href="https://doi.org/10.3390/app142210762">https://doi.org/10.3390/app142210762</a> - 20 Nov 2024 </div> Viewed by 342 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The widespread reuse of code in the open-source community has led to the proliferation of homologous vulnerabilities, which are security flaws propagated across diverse software systems through the reuse of vulnerable code. Such vulnerabilities pose serious cybersecurity risks, as attackers can exploit the <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10762/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The widespread reuse of code in the open-source community has led to the proliferation of homologous vulnerabilities, which are security flaws propagated across diverse software systems through the reuse of vulnerable code. Such vulnerabilities pose serious cybersecurity risks, as attackers can exploit the same weaknesses across multiple platforms. Deep learning has emerged as a promising approach for detecting homologous vulnerabilities in binary code due to their automated feature extraction and high efficiency. However, existing deep learning methods often struggle to capture deep semantic features in binary code, limiting their effectiveness. To address this limitation, this paper presents GMN+, which is a novel graph matching neural network with enhanced attention for detecting homologous vulnerabilities. This method comprehensively considers the information contained in instructions and incorporates types of input instruction. Masked Language Modeling and Instruction Type Prediction are developed as pre-training tasks to enhance the ability of GMN+ in extracting semantic information from basic blocks. GMN+ utilizes an attention mechanism to focus concurrently on the critical semantic information within functions and differences between them, generating robust function embeddings. Experimental results indicate that GMN+ outperforms state-of-the-art methods in various tasks and achieves notable performance in real-world vulnerability detection scenarios. <a href="/2076-3417/14/22/10762">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/AR8N3O7JFV ">Artificial Intelligence for Cybersecurity: Latest Advances and Prospects</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10762/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525471"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525471"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525471" data-cycle-prev="#prev1525471" data-cycle-progressive="#images1525471" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525471-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g001-550.jpg?1732168222" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525471" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525471-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g002-550.jpg?1732168224'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525471-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g003-550.jpg?1732168225'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525471-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g004-550.jpg?1732168226'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525471-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g005-550.jpg?1732168227'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525471-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g006-550.jpg?1732168228'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525471-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g007-550.jpg?1732168228'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525471-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g008-550.jpg?1732168229'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525471-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g009-550.jpg?1732168230'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525471-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g010-550.jpg?1732168230'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525471-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g011-550.jpg?1732168231'><p>Figure 11</p></div></script></div></div><div id="article-1525471-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g001-550.jpg?1732168222" title=" <strong>Figure 1</strong><br/> <p>Architecture of the GMN+ model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10762'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g002-550.jpg?1732168224" title=" <strong>Figure 2</strong><br/> <p>An example of instruction normalization and instruction type extraction. (<b>a</b>) Original assembly instructions; (<b>b</b>) Normalized instructions; (<b>c</b>) Instruction types.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10762'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g003-550.jpg?1732168225" title=" <strong>Figure 3</strong><br/> <p>BERT input embedding.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10762'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g004-550.jpg?1732168226" title=" <strong>Figure 4</strong><br/> <p>Graph Learner of GMN+.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10762'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g005-550.jpg?1732168227" title=" <strong>Figure 5</strong><br/> <p>Comparison of ROC curves for different methods across architectures.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10762'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g006-550.jpg?1732168228" title=" <strong>Figure 6</strong><br/> <p>Comparison of ROC curves for different methods across optimization levels.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10762'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g007-550.jpg?1732168228" title=" <strong>Figure 7</strong><br/> <p>Comparative results of homologous function search using various methods.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10762'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g008-550.jpg?1732168229" title=" <strong>Figure 8</strong><br/> <p>Comparison of time overhead for different methods.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10762'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g009-550.jpg?1732168230" title=" <strong>Figure 9</strong><br/> <p>The performance of GMN+ variants with different blocks in the Semantic Learner.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10762'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g010-550.jpg?1732168230" title=" <strong>Figure 10</strong><br/> <p>The performance of GMN+ variants with different blocks in the Graph Learner.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10762'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10762/article_deploy/html/images/applsci-14-10762-g011-550.jpg?1732168231" title=" <strong>Figure 11</strong><br/> <p>Comparison of detection results of different methods on real-world vulnerability detection tasks.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10762'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525439" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 22 pages, 1159 KiB   </span> <a href="/2076-3417/14/22/10761/pdf?version=1732176454" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Energetic Analysis of Passive Solar Strategies for Residential Buildings with Extreme Summer Conditions" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10761">Energetic Analysis of Passive Solar Strategies for Residential Buildings with Extreme Summer Conditions</a> <div class="authors"> by <span class="inlineblock "><strong>Stephanny Nogueira</strong>, </span><span class="inlineblock "><strong>Ana I. Palmero-Marrero</strong>, </span><span class="inlineblock "><strong>David Borge-Diez</strong>, </span><span class="inlineblock "><strong>Emin Açikkalp</strong> and </span><span class="inlineblock "><strong>Armando C. Oliveira</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10761; <a href="https://doi.org/10.3390/app142210761">https://doi.org/10.3390/app142210761</a> - 20 Nov 2024 </div> Viewed by 316 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This study investigates the implementation of passive design strategies to improve the thermal environment in the extremely hot climates of Brazil, Portugal, and Turkey. Given the rising cooling demands due to climate change, optimizing energy efficiency in buildings is essential. Using the Trace <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10761/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This study investigates the implementation of passive design strategies to improve the thermal environment in the extremely hot climates of Brazil, Portugal, and Turkey. Given the rising cooling demands due to climate change, optimizing energy efficiency in buildings is essential. Using the Trace 3D Plus v6.00.106 software, typical residential buildings for each country were simulated to assess various passive solutions, such as building orientation, wall and roof modifications, glazing optimization options, window-to-wall ratio (WTWR) reduction, shading, and natural ventilation. The findings highlight that Brazil experienced the higher discomfort temperatures compared to Mediterranean climates, with indoor air temperatures exceeding 28 °C all year round and remaining between 34 °C and 37 °C for nearly 40% of the time. Building orientation had a minimal impact near the equator, while Mediterranean climates benefited from an up to 10% variation in energy demand. Thermal insulation combined with white exterior paint resulted in Şanlıurfa experiencing annual energy savings of up to 26%. Optimal roof solutions yielded a 19% demand reduction in Évora, while WTWR reduction and double-colored glazing achieved up to a 35% reduction in Évora and 19% in other regions. Combined strategies achieved energy demand reductions of 44% for Évora, 40% for Şanlıurfa, and 32% for Teresina. The study emphasizes the need for integrated, climate-specific passive solutions, showing their potential to enhance both energy efficiency and the thermal environment in residential buildings across diverse hot climates. <a href="/2076-3417/14/22/10761">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/RU4XJ8C683 ">Energy Efficiency and Thermal Comfort in Buildings</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10761/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525439"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525439"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525439" data-cycle-prev="#prev1525439" data-cycle-progressive="#images1525439" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525439-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g001-550.jpg?1732176580" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525439" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g002-550.jpg?1732176580'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g003-550.jpg?1732176581'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g004-550.jpg?1732176582'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g005-550.jpg?1732176583'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g006-550.jpg?1732176584'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g007-550.jpg?1732176586'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g008-550.jpg?1732176587'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g009-550.jpg?1732176588'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g010-550.jpg?1732176589'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g011-550.jpg?1732176591'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g012-550.jpg?1732176592'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g013-550.jpg?1732176593'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g014-550.jpg?1732176594'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1525439-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g015-550.jpg?1732176595'><p>Figure 15</p></div></script></div></div><div id="article-1525439-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g001-550.jpg?1732176580" title=" <strong>Figure 1</strong><br/> <p>House plan for Évora, Portugal.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g002-550.jpg?1732176580" title=" <strong>Figure 2</strong><br/> <p>House plan for Teresina, Brazil.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g003-550.jpg?1732176581" title=" <strong>Figure 3</strong><br/> <p>House plan for Şanlıurfa, Turkey.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g004-550.jpg?1732176582" title=" <strong>Figure 4</strong><br/> <p>A 3D view of the house for Şanlıurfa, Turkey.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g005-550.jpg?1732176583" title=" <strong>Figure 5</strong><br/> <p>True north rotation from plan north.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g006-550.jpg?1732176584" title=" <strong>Figure 6</strong><br/> <p>Results of the base simulations for each location. (<b>a</b>) Thermal load and annual energy demand. (<b>b</b>) Annual indoor air temperature distribution with no AS.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g007-550.jpg?1732176586" title=" <strong>Figure 7</strong><br/> <p>Effect of combined passive solutions on energy needs and indoor air temperature for house with horizontal roof of Teresina, Brazil. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g008-550.jpg?1732176587" title=" <strong>Figure 8</strong><br/> <p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and concrete slab of Teresina, Brazil. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g009-550.jpg?1732176588" title=" <strong>Figure 9</strong><br/> <p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and gypsum board of Teresina, Brazil. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g010-550.jpg?1732176589" title=" <strong>Figure 10</strong><br/> <p>Effect of combined passive solutions on energy needs and indoor air temperature for house with horizontal roof of Évora, Portugal. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g011-550.jpg?1732176591" title=" <strong>Figure 11</strong><br/> <p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and concrete slab of Évora, Portugal. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g012-550.jpg?1732176592" title=" <strong>Figure 12</strong><br/> <p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and gypsum board of Évora, Portugal. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g013-550.jpg?1732176593" title=" <strong>Figure 13</strong><br/> <p>Effect of combined passive solutions on energy needs and indoor air temperature for house with horizontal roof of Şanlıurfa, Turkey. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g014-550.jpg?1732176594" title=" <strong>Figure 14</strong><br/> <p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and concrete slab of Şanlıurfa, Turkey. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10761/article_deploy/html/images/applsci-14-10761-g015-550.jpg?1732176595" title=" <strong>Figure 15</strong><br/> <p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and gypsum board of Şanlıurfa, Turkey. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10761'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525423" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 23 pages, 6823 KiB   </span> <a href="/2076-3417/14/22/10760/pdf?version=1732152164" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Construction of Green Space Ecological Network in Xiongan New Area Based on the MSPA–InVEST–MCR Model" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10760">Construction of Green Space Ecological Network in Xiongan New Area Based on the MSPA–InVEST–MCR Model</a> <div class="authors"> by <span class="inlineblock "><strong>Xiaoqi Feng</strong>, </span><span class="inlineblock "><strong>Zhiyu Du</strong>, </span><span class="inlineblock "><strong>Peiyuan Tao</strong>, </span><span class="inlineblock "><strong>Huaqiu Liang</strong>, </span><span class="inlineblock "><strong>Yangzi Wang</strong> and </span><span class="inlineblock "><strong>Xin Wang</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10760; <a href="https://doi.org/10.3390/app142210760">https://doi.org/10.3390/app142210760</a> - 20 Nov 2024 </div> Viewed by 436 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> With the rapid pace of urbanization, the integrity and connectivity of ecosystems are under serious threat, making biodiversity conservation a top priority. We use the Xiongan New Area in China as a case study to explore the significance and application of constructing urban <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10760/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> With the rapid pace of urbanization, the integrity and connectivity of ecosystems are under serious threat, making biodiversity conservation a top priority. We use the Xiongan New Area in China as a case study to explore the significance and application of constructing urban ecological networks in the development of new cities. This study systematically applied the categorization of green space systems using remote sensing technology; MSPA was used to identify key landscape patches; InVEST was employed to assess habitat quality; and potential ecological corridors were established using the minimum cumulative resistance model (MCR). Moreover, targeted recommendations for optimizing ecological green spaces were put forward. The findings demonstrate that the Xiongan New Area has significant potential and needs for ecological network construction, and it faces the issue of ecological network fragmentation. This research highlights the significance of developing ecological networks within urban planning and proposes optimization strategies tailored to these networks. The objective is to offer scientific guidance for the design and development of emerging cities, such as the Xiongan New Area, to facilitate the alignment and integration of ecological preservation efforts with urban expansion, ultimately achieving the sustainable development goal of harmonious coexistence between the environment and urban areas. <a href="/2076-3417/14/22/10760">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10760/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525423"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525423"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525423" data-cycle-prev="#prev1525423" data-cycle-progressive="#images1525423" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525423-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g001-550.jpg?1732152261" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525423" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g002-550.jpg?1732152263'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g003-550.jpg?1732152266'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g004-550.jpg?1732152268'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g005-550.jpg?1732152270'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g006-550.jpg?1732152272'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g007-550.jpg?1732152274'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g008-550.jpg?1732152275'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g009-550.jpg?1732152277'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g010-550.jpg?1732152278'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g011-550.jpg?1732152280'><p>Figure 11</p></div></script></div></div><div id="article-1525423-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g001-550.jpg?1732152261" title=" <strong>Figure 1</strong><br/> <p>Geographical location of the study area.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10760'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g002-550.jpg?1732152263" title=" <strong>Figure 2</strong><br/> <p>Technical approach of this study. Step 1. Collect basic site information. Step 2. Analyze terrestrial data. Step 3. Recognition results and analysis of landscape elements. Step 4. Forming habitat networks.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10760'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g003-550.jpg?1732152266" title=" <strong>Figure 3</strong><br/> <p>Sensitivity assessment of terrain conditions. (<b>a</b>) Evaluating sensitivity to altitude. (<b>b</b>) Evaluation of slope sensitivity. (<b>c</b>) Evaluation of slope sensitivity. (<b>d</b>) Evaluation of sensitivity to water buffers. (<b>e</b>) Evaluation of sensitivity to vegetation coverage. (<b>f</b>) Land use sensitivity evaluation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10760'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g004-550.jpg?1732152268" title=" <strong>Figure 4</strong><br/> <p>Overlay analysis of site ecological sensitivity.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10760'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g005-550.jpg?1732152270" title=" <strong>Figure 5</strong><br/> <p>Composite resistance surface.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10760'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g006-550.jpg?1732152272" title=" <strong>Figure 6</strong><br/> <p>MSPA landscape type analysis results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10760'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g007-550.jpg?1732152274" title=" <strong>Figure 7</strong><br/> <p>Results of habitat quality index analysis.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10760'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g008-550.jpg?1732152275" title=" <strong>Figure 8</strong><br/> <p>Schematic diagram of ecosystem services of the Baiyang Lake wetland.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10760'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g009-550.jpg?1732152277" title=" <strong>Figure 9</strong><br/> <p>Identification of habitat sources. (<b>a</b>) Identification results of general and important habitat sources. (<b>b</b>) Nuclear density analysis results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10760'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g010-550.jpg?1732152278" title=" <strong>Figure 10</strong><br/> <p>Habitat corridor identification results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10760'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10760/article_deploy/html/images/applsci-14-10760-g011-550.jpg?1732152280" title=" <strong>Figure 11</strong><br/> <p>Habitat node identification results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10760'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525402" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 12 pages, 276 KiB   </span> <a href="/2076-3417/14/22/10759/pdf?version=1732248952" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Quantum Private Comparison Based on Four-Particle Cluster State" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10759">Quantum Private Comparison Based on Four-Particle Cluster State</a> <div class="authors"> by <span class="inlineblock "><strong>Min Hou</strong> and </span><span class="inlineblock "><strong>Yue Wu</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10759; <a href="https://doi.org/10.3390/app142210759">https://doi.org/10.3390/app142210759</a> - 20 Nov 2024 </div> Viewed by 307 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> A quantum private comparison (QPC) protocol enables two parties to securely compare their private data without disclosing the actual values to one another, utilizing quantum mechanics to maintain confidentiality. Many current QPC protocols mainly concentrate on comparing the equality of private information between <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10759/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> A quantum private comparison (QPC) protocol enables two parties to securely compare their private data without disclosing the actual values to one another, utilizing quantum mechanics to maintain confidentiality. Many current QPC protocols mainly concentrate on comparing the equality of private information between two users during a single execution, which restricts their scalability. To overcome this limitation, we present an efficient QPC protocol aimed at evaluating the equality of private information between two groups of users in one execution. This is achieved by leveraging the entanglement correlations present in each particle of a four-particle cluster state. In our approach, users encode their private data using bit flip or phase shift operators on the quantum sequence they receive, which is then sent back to a semi-trusted party which then determines whether the secrets of the two groups are equal and communicates the results to the users. By employing this method and facilitating the distributed transmission of the quantum sequence, our protocol achieves a qubit efficiency of 50%. Security analyses reveal that neither external attacks nor insider threats can successfully compromise the confidentiality of private data. <a href="/2076-3417/14/22/10759">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/PQU216X6HQ ">Quantum Communication and Applications</a>)<br/> </div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525404" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 16 pages, 5966 KiB   </span> <a href="/2076-3417/14/22/10758/pdf?version=1732114880" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Assessment of Hoisting Conveyance Guiding Forces Based on Field Acceleration Measurements and Numerical Simulation" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10758">Assessment of Hoisting Conveyance Guiding Forces Based on Field Acceleration Measurements and Numerical Simulation</a> <div class="authors"> by <span class="inlineblock "><strong>Przemysław Fiołek</strong> and </span><span class="inlineblock "><strong>Jacek Jakubowski</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10758; <a href="https://doi.org/10.3390/app142210758">https://doi.org/10.3390/app142210758</a> - 20 Nov 2024 </div> Viewed by 290 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Shafts play a key role in the operation of mining plants. They connect underground excavations with the surface and provide the ability to transport people, equipment, and raw materials. The nature of the dynamic interaction of a conveyance moving at a significant speed <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10758/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Shafts play a key role in the operation of mining plants. They connect underground excavations with the surface and provide the ability to transport people, equipment, and raw materials. The nature of the dynamic interaction of a conveyance moving at a significant speed along deformed guide rails is complex, and the method of assessing the interaction of hoisting conveyances with shaft steelwork, despite ongoing research, still requires further understanding and improvement. Misalignments of the guide rails and conveyance movements transverse to the shaft axis induce impact (guiding) forces, which are the key design parameters of shaft steelwork. The reliable assessment of guiding forces allows the design of safe and economical steelworks and the assessment of their structural safety during operation under deformations and corrosive deterioration. Determining the value of guiding forces requires their field measurements or the use of approximate empirical formulas. Both methods have their limitations—measurement is expensive and interferes with normal shaft operation, while empirical formulas are subject to high error due to the lack of consideration of many structural details specific to each shaft that significantly affect the behavior of the system. This study presents a new method for using a relatively simple-to-implement measurement of hoisting conveyance acceleration to assess guiding forces. A finite element model of the skip and steelwork was built, and simulations of the conveyance interaction with the structure were carried out. A strong relationship between the sliding plate’s impact point location and the guiding force was found. Extreme values of the guiding force were observed in the vicinity of the bunton connection. The study showed that reducing the skip load mass does not affect the force value. Simplified methods of calculating the moments of inertia of the hoisting conveyance significantly overestimate the code-based values of the guiding forces. The presented method considers the actual stiffness and mass distribution of hoisting conveyance and, therefore, allows for a more accurate estimation of the guiding forces and the transport of larger loads. This data-driven approach allows for the continuous monitoring of the guiding forces, the adjustments of the hoisting parameters, the rational planning of repairs, and a reduction in the replacement of corroded shaft steelwork. <a href="/2076-3417/14/22/10758">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/7138CJDUI5 ">Recent Advances in Mining Technology and Geotechnical Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10758/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525404"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525404"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525404" data-cycle-prev="#prev1525404" data-cycle-progressive="#images1525404" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525404-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g001-550.jpg?1732114983" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525404" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g002-550.jpg?1732114986'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g003-550.jpg?1732114987'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g004-550.jpg?1732114990'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g005-550.jpg?1732114992'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g006-550.jpg?1732114994'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g007-550.jpg?1732114995'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g008-550.jpg?1732114997'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g009-550.jpg?1732115000'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g010-550.jpg?1732115003'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g011-550.jpg?1732115004'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g012-550.jpg?1732115006'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g013-550.jpg?1732115007'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1525404-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g014-550.jpg?1732115008'><p>Figure 14</p></div></script></div></div><div id="article-1525404-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g001-550.jpg?1732114983" title=" <strong>Figure 1</strong><br/> <p>Shaft plan (<b>a</b>) and side (<b>b</b>) views.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g002-550.jpg?1732114986" title=" <strong>Figure 2</strong><br/> <p>Guide for bunton connections. Drawing (<b>a</b>) and photo (<b>b</b>) of the construction.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g003-550.jpg?1732114987" title=" <strong>Figure 3</strong><br/> <p>Simplified diagram of the skip construction.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g004-550.jpg?1732114990" title=" <strong>Figure 4</strong><br/> <p>Shaft steelwork. Top view (<b>a</b>) and connection detail (<b>b</b>). Skip model (<b>c</b>) and the entire model of the skip–steelwork system (<b>d</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g005-550.jpg?1732114992" title=" <strong>Figure 5</strong><br/> <p>Field test (<b>a</b>) and static simulation (<b>b</b>) displacement in force direction was shown.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g006-550.jpg?1732114994" title=" <strong>Figure 6</strong><br/> <p>Simplified diagram of the skip.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g007-550.jpg?1732114995" title=" <strong>Figure 7</strong><br/> <p>Interpretation of the misalignment coefficient <span class="html-italic">e</span>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g008-550.jpg?1732114997" title=" <strong>Figure 8</strong><br/> <p>Acceleration measurement in the face direction with a winding velocity of 4 m/s.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g009-550.jpg?1732115000" title=" <strong>Figure 9</strong><br/> <p>Acceleration measurement in the face direction with a winding velocity of 8 m/s.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g010-550.jpg?1732115003" title=" <strong>Figure 10</strong><br/> <p>Acceleration measurement in the face direction with a winding velocity of 16 m/s.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g011-550.jpg?1732115004" title=" <strong>Figure 11</strong><br/> <p>Histogram of the absolute face acceleration for a velocity of 8 m/s.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g012-550.jpg?1732115006" title=" <strong>Figure 12</strong><br/> <p>Extreme pulses of the conveyance acceleration.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g013-550.jpg?1732115007" title=" <strong>Figure 13</strong><br/> <p>Position of the conveyance relative to the guide. Proximity to the bunton (<b>a</b>) and guide mid-span (<b>b</b>). Horizontal displacement (m) for the entire model and the details of the von Mises stress (Pa).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10758/article_deploy/html/images/applsci-14-10758-g014-550.jpg?1732115008" title=" <strong>Figure 14</strong><br/> <p>Shear stress distribution in the guide (Pa).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10758'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525453" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 29 pages, 4068 KiB   </span> <a href="/2076-3417/14/22/10757/pdf?version=1732116312" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Multidimensional User Experience Analysis of Chinese Battery Electric Vehicles’ Competition: An Integrated Association Mining Framework" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10757">Multidimensional User Experience Analysis of Chinese Battery Electric Vehicles’ Competition: An Integrated Association Mining Framework</a> <div class="authors"> by <span class="inlineblock "><strong>Quan Gu</strong>, </span><span class="inlineblock "><strong>Jie Zhang</strong>, </span><span class="inlineblock "><strong>Shengqing Huang</strong>, </span><span class="inlineblock "><strong>Yuchao Cai</strong>, </span><span class="inlineblock "><strong>Chenlu Wang</strong> and </span><span class="inlineblock "><strong>Jiaoman Liu</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10757; <a href="https://doi.org/10.3390/app142210757">https://doi.org/10.3390/app142210757</a> - 20 Nov 2024 </div> Viewed by 305 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This study introduces an integrative framework for association mining within the Chinese battery electric vehicle market, aiming to reveal key user experience (UX) factors and their interrelationships through multidimensional analysis. Utilizing latent Dirichlet allocation (LDA), the study discerned primary themes from user-generated content <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10757/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This study introduces an integrative framework for association mining within the Chinese battery electric vehicle market, aiming to reveal key user experience (UX) factors and their interrelationships through multidimensional analysis. Utilizing latent Dirichlet allocation (LDA), the study discerned primary themes from user-generated content (UGC). The entropy weight method categorized level 2 factors, while domain-adaptive sentiment analysis quantified emotional responses to BEV user experience dimensions, highlighting significant sentiment disparities among competitors. Co-occurrence network analysis deepened insights into the emotional fabric of UX by exploring tertiary factor associations. Theoretically, this study advances a novel framework informed by Norman’s UX theory, integrating analytical techniques to capture the complexity of UX. Practically, it delivers strategic guidance for BEV manufacturers by analyzing emotional polarities and attribute associations, guiding product innovation and responding to market dynamics. The empirical evidence corroborates the framework’s efficacy in revealing the emotional associations within BEVUX factors, offering valuable implications for both theoretical development and practical application. <a href="/2076-3417/14/22/10757">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/P1T69SE7Z0 ">Advanced Technologies for User-Centered Design and User Experience</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10757/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525453"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525453"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525453" data-cycle-prev="#prev1525453" data-cycle-progressive="#images1525453" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525453-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g001-550.jpg?1732116390" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525453" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525453-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g002-550.jpg?1732116393'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525453-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g003-550.jpg?1732116394'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525453-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g004-550.jpg?1732116396'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525453-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g005a-550.jpg?1732116397'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525453-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g005b-550.jpg?1732116397'><p>Figure 5 Cont.</p></div></script></div></div><div id="article-1525453-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g001-550.jpg?1732116390" title=" <strong>Figure 1</strong><br/> <p>Research technical framework.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10757'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g002-550.jpg?1732116393" title=" <strong>Figure 2</strong><br/> <p>Sentiment score histogram for the “subjective perception” theme in target competitors.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10757'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g003-550.jpg?1732116394" title=" <strong>Figure 3</strong><br/> <p>Sentiment score comparison for the “interior” keyword in subjective perception.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10757'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g004-550.jpg?1732116396" title=" <strong>Figure 4</strong><br/> <p>Co-occurrence network of keyword eigenvector centrality for the extremely negative “interior” label.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10757'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g005a-550.jpg?1732116397" title=" <strong>Figure 5</strong><br/> <p>Co-occurrence network of keyword eigenvector centrality for the strongly positive “interior” label.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10757'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10757/article_deploy/html/images/applsci-14-10757-g005b-550.jpg?1732116397" title=" <strong>Figure 5 Cont.</strong><br/> <p>Co-occurrence network of keyword eigenvector centrality for the strongly positive “interior” label.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10757'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525359" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 21 pages, 6228 KiB   </span> <a href="/2076-3417/14/22/10756/pdf?version=1732186027" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="DC-DC Buck Converters with Quasi-Online Estimation of Filter Capacitor Equivalent Parameters" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10756">DC-DC Buck Converters with Quasi-Online Estimation of Filter Capacitor Equivalent Parameters</a> <div class="authors"> by <span class="inlineblock "><strong>Dadiana-Valeria Căiman</strong>, </span><span class="inlineblock "><strong>Corneliu Bărbulescu</strong>, </span><span class="inlineblock "><strong>Sorin Nanu</strong> and </span><span class="inlineblock "><strong>Toma-Leonida Dragomir</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10756; <a href="https://doi.org/10.3390/app142210756">https://doi.org/10.3390/app142210756</a> - 20 Nov 2024 </div> Viewed by 309 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The article focuses on devising solutions for monitoring the condition of the filter capacitors of DC-DC converters. The article introduces two novel DC-DC buck converter designs that monitor the equivalent series resistance (ESR) and the capacitance of capacitors using a parameter observer (PO) <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10756/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The article focuses on devising solutions for monitoring the condition of the filter capacitors of DC-DC converters. The article introduces two novel DC-DC buck converter designs that monitor the equivalent series resistance (ESR) and the capacitance of capacitors using a parameter observer (PO) and simple variable electrical networks (VEN). For the first scheme, the PO processes in real time the voltage at the capacitor terminals during a discharge-charge cycle. For the second scheme, the filtering is performed with two or more capacitors in parallel, and the PO processes the voltage at the terminals of each capacitor during two discharge processes without interrupting the filtering operation of the converter. The paper presents the principles and theoretical support on which the two schemes of DC-DC buck converters are based, design details regarding PO and VEN, as well as experiments performed with each of the schemes. In the experimental schemes, the PO is implemented with a microcontroller, and the parameters of some aluminum electrolytic filter capacitors are calculated in a real-time manner of about <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>40</mn><mo> </mo><mi mathvariant="normal">m</mi><mi mathvariant="normal">s</mi></mrow></semantics></math></inline-formula>. The calculation accuracy of the equivalent capacity is very good. Regarding the calculation accuracy of the ESR, it is shown that it depends on the fulfillment of certain ratios between the VEN resistances, on the one hand, as well as between them and the ESR, on the other hand. <a href="/2076-3417/14/22/10756">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10756/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525359"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525359"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525359" data-cycle-prev="#prev1525359" data-cycle-progressive="#images1525359" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' 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data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g004-550.jpg?1732186162'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g005-550.jpg?1732186163'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g006-550.jpg?1732186164'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g007-550.jpg?1732186165'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g008-550.jpg?1732186166'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g009-550.jpg?1732186167'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g010-550.jpg?1732186168'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g011-550.jpg?1732186169'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g012-550.jpg?1732186171'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g013-550.jpg?1732186172'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g014-550.jpg?1732186173'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1525359-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g015-550.jpg?1732186174'><p>Figure 15</p></div></script></div></div><div id="article-1525359-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g001-550.jpg?1732186160" title=" <strong>Figure 1</strong><br/> <p>V curve of Equation (4).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g002-550.jpg?1732186160" title=" <strong>Figure 2</strong><br/> <p>The connection of PO to the observed system OS in order to estimate the parameter <math display="inline"><semantics> <mrow> <mi>T</mi> <mfenced separators="|"> <mrow> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math>. For<math display="inline"><semantics> <mrow> <mo> </mo> <mi>t</mi> <mo>∈</mo> <mfenced open="[" separators="|"> <mrow> <msub> <mrow> <mi>t</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>t</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math> the switch SW is in position 2, and for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mfenced open="[" close="]" separators="|"> <mrow> <msub> <mrow> <mi>t</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>t</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math> in position 1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g003-550.jpg?1732186161" title=" <strong>Figure 3</strong><br/> <p>The behavior of the system in <a href="#applsci-14-10756-f002" class="html-fig">Figure 2</a> when <math display="inline"><semantics> <mrow> <mi>v</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> has the Equation (7): (<b>a</b>) input signal; (<b>b</b>) the estimated time constant <math display="inline"><semantics> <mrow> <mo> </mo> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> <mfenced separators="|"> <mrow> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g004-550.jpg?1732186162" title=" <strong>Figure 4</strong><br/> <p>Parameter observer on two edges: (<b>a</b>) the structure of PO2; (<b>b</b>) the block diagram of the connection OS-PO2 for discrete time processing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g005-550.jpg?1732186163" title=" <strong>Figure 5</strong><br/> <p>The influence of the <math display="inline"><semantics> <mrow> <mi>K</mi> </mrow> </semantics></math> parameter of PO2 on<math display="inline"><semantics> <mrow> <mo> </mo> <mi>T</mi> </mrow> </semantics></math> value.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g006-550.jpg?1732186164" title=" <strong>Figure 6</strong><br/> <p>A group of characteristics <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> of parameter <math display="inline"><semantics> <mrow> <mi>K</mi> </mrow> </semantics></math> for the case when the system observed in <a href="#applsci-14-10756-f004" class="html-fig">Figure 4</a>b generates the signal (7); <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>∈</mo> <mfenced open="{" close="}" separators="|"> <mrow> <mn>2.9</mn> <mo>,</mo> <mn>2.95</mn> <mo>,</mo> <mn>2.98</mn> <mo>,</mo> <mn>2.99</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>3.01</mn> <mo>,</mo> <mn>3.02</mn> <mo>,</mo> <mn>3.05</mn> <mo>,</mo> <mn>3.1</mn> </mrow> </mfenced> </mrow> </semantics></math>: (<b>a</b>) Highlighting the segments <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> <mrow> <mn>1</mn> <mo>,</mo> <mi>K</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math> și<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> <mrow> <mn>2</mn> <mo>,</mo> <mi>K</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math>; (<b>b</b>) Obtaining <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> estimates from segments <math display="inline"><semantics> <mrow> <msub> <mrow> <mo> </mo> <mover accent="true"> <mrow> <mo> </mo> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> <mrow> <mn>2</mn> <mo>,</mo> <mi>K</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g007-550.jpg?1732186165" title=" <strong>Figure 7</strong><br/> <p>Principle schemes used for the realization of DC-DC buck converters with the estimation of the equivalent parameters of the filter capacitors: (<b>a</b>,<b>b</b>) Schemes with the monitoring of the discharging and charging of the capacitor using PO2 (<b>c</b>,<b>d</b>) Schemes with monitoring of two distinct discharges using PO.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g008-550.jpg?1732186166" title=" <strong>Figure 8</strong><br/> <p>The time sequence for filtering capacitor parameter estimation by a discharge/charge process.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g009-550.jpg?1732186167" title=" <strong>Figure 9</strong><br/> <p>Buck converter scheme made based on the principle scheme in <a href="#applsci-14-10756-f007" class="html-fig">Figure 7</a>a.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g010-550.jpg?1732186168" title=" <strong>Figure 10</strong><br/> <p>Frequency characteristics of the filter capacitor <math display="inline"><semantics> <mrow> <mi>C</mi> <mn>2</mn> </mrow> </semantics></math> of the converter in <a href="#applsci-14-10756-f009" class="html-fig">Figure 9</a>: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> </semantics></math> characteristic; (<b>b</b>) Characteristic <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g011-550.jpg?1732186169" title=" <strong>Figure 11</strong><br/> <p><math display="inline"><semantics> <mrow> <mi>C</mi> <mn>2</mn> </mrow> </semantics></math> capacitor charging/discharging signal in <a href="#applsci-14-10756-f009" class="html-fig">Figure 9</a> and the result of its processing with PO2: (<b>a</b>) The signal <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> on capacitor terminal; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> <mrow> <mn>1</mn> <mo>,</mo> <mi>K</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> <mrow> <mn>2</mn> <mo>,</mo> <mi>K</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3.291</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math> characteristics generated by PO2.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g012-550.jpg?1732186171" title=" <strong>Figure 12</strong><br/> <p>Buck converter scheme made based on the principle scheme in <a href="#applsci-14-10756-f007" class="html-fig">Figure 7</a>d.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g013-550.jpg?1732186172" title=" <strong>Figure 13</strong><br/> <p>Frequency characteristics of the filter capacitors in <a href="#applsci-14-10756-f012" class="html-fig">Figure 12</a>: (<b>a</b>,<b>b</b>) <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> </semantics></math> characteristics of the electrolytic capacitor <math display="inline"><semantics> <mrow> <mi>C</mi> <mn>21</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mn>470</mn> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">F</mi> <mo>/</mo> <mn>25</mn> <mo> </mo> <mi mathvariant="normal">V</mi> <mo>)</mo> </mrow> </semantics></math>; (<b>c</b>,<b>d</b>) <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> </semantics></math> characteristics of electrolytic capacitor <math display="inline"><semantics> <mrow> <mi>C</mi> <mn>22</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mn>220</mn> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">F</mi> <mo>/</mo> <mn>25</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g014-550.jpg?1732186173" title=" <strong>Figure 14</strong><br/> <p>Experiment performed with the converter from <a href="#applsci-14-10756-f012" class="html-fig">Figure 12</a> in stage 1: (<b>a</b>) Characteristics <math display="inline"><semantics> <mrow> <msub> <mrow> <mo> </mo> <mover accent="true"> <mrow> <mo> </mo> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> <mrow> <mn>1</mn> <mo>_</mo> <mn>1</mn> </mrow> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> <mrow> <mn>2</mn> <mo>_</mo> <mn>1</mn> </mrow> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> <mrow> <mn>1</mn> <mo>_</mo> <mn>2</mn> </mrow> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> și <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> <mrow> <mn>2</mn> <mo>_</mo> <mn>2</mn> </mrow> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>; (<b>b</b>) Voltage variation <math display="inline"><semantics> <mrow> <msub> <mrow> <mo> </mo> <mi>V</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> from the terminals of capacitors <math display="inline"><semantics> <mrow> <mi>C</mi> <mn>21</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <mn>22</mn> </mrow> </semantics></math>; (<b>c</b>) Voltage variation <math display="inline"><semantics> <mrow> <msub> <mrow> <mo> </mo> <mi>V</mi> </mrow> <mrow> <mi>L</mi> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> at the load terminals.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10756/article_deploy/html/images/applsci-14-10756-g015-550.jpg?1732186174" title=" <strong>Figure 15</strong><br/> <p>Integrated circuit diagram associated with the DC-DC buck converter in <a href="#applsci-14-10756-f012" class="html-fig">Figure 12</a>. The integrated chip includes 6 switches (T-sw<sub>ij,</sub> i,j = {1,2}; T-sw<sub>i,</sub> i = {3,4}), the LM2596 circuit, the LDO circuit, and the microcontroller. The unembedded components are the filtering capacitors <math display="inline"><semantics> <mrow> <mi>C</mi> <mn>21</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>C</mi> <mn>22</mn> </mrow> </semantics></math>, the VEN resistors <math display="inline"><semantics> <mrow> <msub> <mrow> <mo> </mo> <mi>R</mi> </mrow> <mrow> <mi>e</mi> <mi>x</mi> <mi>t</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mo> </mo> <mi>R</mi> </mrow> <mrow> <mi>e</mi> <mi>x</mi> <mi>t</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>, the temperature sensors, the inductance L<sub>1</sub>, and the input capacitors <math display="inline"><semantics> <mrow> <mi>C</mi> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>C</mi> <mn>2</mn> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10756'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525311" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 39 pages, 1531 KiB   </span> <a href="/2076-3417/14/22/10755/pdf?version=1732172193" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Omics-Integrated Approach (Metabolomics, Proteomics and Lipidomics) to Assess the Quality Control of Aquatic and Seafood Products" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/14/22/10755">Omics-Integrated Approach (Metabolomics, Proteomics and Lipidomics) to Assess the Quality Control of Aquatic and Seafood Products</a> <div class="authors"> by <span class="inlineblock "><strong>Marianthi Sidira</strong>, </span><span class="inlineblock "><strong>Sofia Agriopoulou</strong>, </span><span class="inlineblock "><strong>Slim Smaoui</strong> and </span><span class="inlineblock "><strong>Theodoros Varzakas</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10755; <a href="https://doi.org/10.3390/app142210755">https://doi.org/10.3390/app142210755</a> - 20 Nov 2024 </div> Viewed by 285 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Since the demand for seafood products is growing and aquaculture provides more than fifty percent of the aquatic food as reported by FAO, the development of more accurate and sensitive analytical techniques in order to screen and evaluate the safety and quality of <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10755/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Since the demand for seafood products is growing and aquaculture provides more than fifty percent of the aquatic food as reported by FAO, the development of more accurate and sensitive analytical techniques in order to screen and evaluate the safety and quality of seafood products is needed. At this point, several omic techniques like proteomics, lipidomics, and metabolomics, or combinations of them, are used for integration into seafood processing and quality control. Moreover, according to the literature, using the respective techniques can prevent, control, and treat diseases in fish as well as address several issues in aquaculture. Proteomic techniques are used for the expression of proteins and their modifications. Metabolomic techniques are used for accurate identification of species, while lipidomics techniques are used for the identification of different or specific lipid molecules in fish species, as well as fatty acid composition and location distribution. This review is to cover the recent proteomics, metabolomics, and lipidomics studies on aquatic and seafood products in the areas of quality, safety, processing, and breeding of fish. <a href="/2076-3417/14/22/10755">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/49DX54G5G9 ">Advances in Food Metabolomics</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10755/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525311"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525311"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525311" data-cycle-prev="#prev1525311" data-cycle-progressive="#images1525311" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525311-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10755/article_deploy/html/images/applsci-14-10755-g001-550.jpg?1732172262" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525311" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525311-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10755/article_deploy/html/images/applsci-14-10755-g002-550.jpg?1732172264'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525311-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10755/article_deploy/html/images/applsci-14-10755-g003-550.jpg?1732172265'><p>Figure 3</p></div></script></div></div><div id="article-1525311-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10755/article_deploy/html/images/applsci-14-10755-g001-550.jpg?1732172262" title=" <strong>Figure 1</strong><br/> <p>Number of publications on omic technology and aquatic food products between 2014 and 2024 (source: <a href="http://www.lens.org" target="_blank">www.lens.org</a>; accessed on 8 November 2024).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10755'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10755/article_deploy/html/images/applsci-14-10755-g002-550.jpg?1732172264" title=" <strong>Figure 2</strong><br/> <p>Publication subject linked to omic technology or aquatic food products (data obtained from the Lens website [<a href="http://www.lens.org" target="_blank">www.lens.org</a>]; accessed on 8 November 2024).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10755'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10755/article_deploy/html/images/applsci-14-10755-g003-550.jpg?1732172265" title=" <strong>Figure 3</strong><br/> <p>Most frequently used omic tools for the examination of quality and control of aquatic food products.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10755'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525302" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 4362 KiB   </span> <a href="/2076-3417/14/22/10754/pdf?version=1732110841" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Detection of Defects in Warp Knitted Fabrics Based on Local Feature Scale Adaptive Comparison" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10754">Detection of Defects in Warp Knitted Fabrics Based on Local Feature Scale Adaptive Comparison</a> <div class="authors"> by <span class="inlineblock "><strong>Yongchao Zhang</strong>, </span><span class="inlineblock "><strong>Weimin Shi</strong> and </span><span class="inlineblock "><strong>Jindou Zhang</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10754; <a href="https://doi.org/10.3390/app142210754">https://doi.org/10.3390/app142210754</a> - 20 Nov 2024 </div> Viewed by 266 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In order to improve the accuracy and detection effect of fabric defect detection, a fabric defect detection method based on local similarity comparison is proposed in this paper. This method first takes each pixel in the image as the central pixel, selects a <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10754/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In order to improve the accuracy and detection effect of fabric defect detection, a fabric defect detection method based on local similarity comparison is proposed in this paper. This method first takes each pixel in the image as the central pixel, selects a specific window as the region size, and then uses the similarity between the central region and the surrounding neighborhood to find the neighborhood most similar to the central region to complete the estimation of the central pixel. Finally, the target image is obtained by the principle of background difference, so as to detect fabric defects. The results show that this method is superior to the traditional detection method, which can not only detect the defect image under the complex background, but also have good detection results for the fabric defect image under the influence of different organization and lighting factors. The detection accuracy rate under factory conditions can reach 98.45%, which has a high applicability and detection rate, and also demonstrates certain anti-interference performance. <a href="/2076-3417/14/22/10754">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10754/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525302"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525302"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525302" data-cycle-prev="#prev1525302" data-cycle-progressive="#images1525302" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525302-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g001-550.jpg?1732110997" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525302" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g002-550.jpg?1732110997'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g003-550.jpg?1732110998'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g004-550.jpg?1732110999'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g005-550.jpg?1732111000'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g006-550.jpg?1732111000'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g007-550.jpg?1732111001'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g008-550.jpg?1732111001'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g009-550.jpg?1732111004'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g010-550.jpg?1732111010'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g011-550.jpg?1732111011'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g012-550.jpg?1732111012'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g013-550.jpg?1732111013'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g014-550.jpg?1732111013'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1525302-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g015-550.jpg?1732111014'><p>Figure 15</p></div></script></div></div><div id="article-1525302-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g001-550.jpg?1732110997" title=" <strong>Figure 1</strong><br/> <p>Pixels within the radius of plane <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>r</mi> </msub> <mo stretchy="false">(</mo> <mi>c</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> scale. (<b>a</b>) r = 1, (<b>b</b>) r = 2.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g002-550.jpg?1732110997" title=" <strong>Figure 2</strong><br/> <p>Schematic diagram of algorithm.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g003-550.jpg?1732110998" title=" <strong>Figure 3</strong><br/> <p>Partial defect diagram of standard fabric defects. (<b>a</b>) Knots, (<b>b</b>) holes, (<b>c</b>) oil stains.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g004-550.jpg?1732110999" title=" <strong>Figure 4</strong><br/> <p>Some fabric defects collected in the laboratory. (<b>a</b>) Knots, (<b>b</b>) holes, (<b>c</b>) crease.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g005-550.jpg?1732111000" title=" <strong>Figure 5</strong><br/> <p>Partial fabric defect images collected in the factory environment. (<b>a</b>) Warp breakage; (<b>b</b>) warp breakage; (<b>c</b>) warp breakage caused by interference from light sources and other factors.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g006-550.jpg?1732111000" title=" <strong>Figure 6</strong><br/> <p>Homomorphic filtering algorithm flow.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g007-550.jpg?1732111001" title=" <strong>Figure 7</strong><br/> <p>Comparison of experimental collected images before and after filtering. (<b>a</b>) Original image, (<b>b</b>) image after homomorphic filtering.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g008-550.jpg?1732111001" title=" <strong>Figure 8</strong><br/> <p>Image comparison before and after homomorphic filtering of standard image. (<b>a</b>) Original image, (<b>b</b>) image after homomorphic filtering.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g009-550.jpg?1732111004" title=" <strong>Figure 9</strong><br/> <p>Different types of fabric defect detection. (<b>a</b>) Holes, (<b>b</b>) scaled image, (<b>c</b>) test results, (<b>d</b>) greasy dirt, (<b>e</b>) scaled image, (<b>f</b>) test results, (<b>g</b>) warp breakage, (<b>h</b>) scaled image, (<b>i</b>) test results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g010-550.jpg?1732111010" title=" <strong>Figure 10</strong><br/> <p>Detection results of different algorithms. (<b>a</b>) Holes, (<b>b</b>) yarn breakage, (<b>c</b>) fold, (<b>d</b>) hole-scale image, (<b>e</b>) yarn-break scale image, (<b>f</b>) fold-scale image, (<b>g</b>) the test results of the algorithm, (<b>h</b>) the test results of the algorithm, (<b>i</b>) the test results of the algorithm, (<b>j</b>) LCD test results, (<b>k</b>) LCD test results, (<b>l</b>) LCD test results, (<b>m</b>) LBPs test results, (<b>n</b>) LBPs test results, (<b>o</b>) LBPs test results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g011-550.jpg?1732111011" title=" <strong>Figure 11</strong><br/> <p>Accuracy and training loss of validation set under knowledge distillation. (<b>a</b>) Verification accuracy; (<b>b</b>) training loss.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g012-550.jpg?1732111012" title=" <strong>Figure 12</strong><br/> <p>Fabric defect detection platform. (<b>a</b>) Defect detection system, (<b>b</b>) defect detection view.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g013-550.jpg?1732111013" title=" <strong>Figure 13</strong><br/> <p>Detection results of plain weave defects.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g014-550.jpg?1732111013" title=" <strong>Figure 14</strong><br/> <p>Detection results of twill tissue defects.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10754/article_deploy/html/images/applsci-14-10754-g015-550.jpg?1732111014" title=" <strong>Figure 15</strong><br/> <p>Change the warp weave defect detection results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10754'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525309" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 19 pages, 3743 KiB   </span> <a href="/2076-3417/14/22/10753/pdf?version=1732245322" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10753">Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments</a> <div class="authors"> by <span class="inlineblock "><strong>Rong Xie</strong> and </span><span class="inlineblock "><strong>Chunjun Chen</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10753; <a href="https://doi.org/10.3390/app142210753">https://doi.org/10.3390/app142210753</a> - 20 Nov 2024 </div> Viewed by 243 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The vertical curve is designed to smooth sudden gradient changes in the longitudinal profile, enhancing train operational safety and passenger comfort. However, dynamic detection in these segments has consistently encountered issues with long-wavelength vertical irregularities exceeding tolerance limits. To investigate the root causes <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10753/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The vertical curve is designed to smooth sudden gradient changes in the longitudinal profile, enhancing train operational safety and passenger comfort. However, dynamic detection in these segments has consistently encountered issues with long-wavelength vertical irregularities exceeding tolerance limits. To investigate the root causes of this phenomenon and develop a targeted solution, a comprehensive vehicle-track dynamics simulation model was first constructed, based on the design principles for intercity railway vertical curves. The inertial reference method was then applied to process the acceleration and relative displacement data between the detection beam and the track, yielding virtual irregularities. These were compared with excitation irregularities to identify key factors affecting detection accuracy in vertical curve segments. Through further analysis of abnormal exceedances in detection data, the reference cancellation method was proposed. By employing smoothing filters and orthogonal least squares fitting, this method effectively removes track alignment components from the acceleration integration results. Detection errors under various conditions were then compared between the two methods to evaluate the feasibility and effectiveness of the reference cancellation approach. Results indicate that regions with increased longitudinal profile detection errors are primarily located at and near gradient transition points. The vertical curve radius was found to be the primary factor influencing the accuracy of long-wavelength irregularity detection. The proposed reference cancellation method effectively reduces detection errors in areas near gradient transition points to levels comparable to other track sections. Compared to the inertial reference method, the reference cancellation method reduces the maximum detection error by up to 71.77% and the root mean square error by up to 86.61%, effectively mitigating the abnormal exceedances associated with vertical curves. <a href="/2076-3417/14/22/10753">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10753/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525309"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525309"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525309" data-cycle-prev="#prev1525309" data-cycle-progressive="#images1525309" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525309-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g001-550.jpg?1732245578" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525309" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g002-550.jpg?1732245579'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g003-550.jpg?1732245581'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g004-550.jpg?1732245583'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g005-550.jpg?1732245586'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g006-550.jpg?1732245588'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g007-550.jpg?1732245590'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g008-550.jpg?1732245592'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g009-550.jpg?1732245593'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g010-550.jpg?1732245595'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g011-550.jpg?1732245596'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g012-550.jpg?1732245598'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1525309-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g013-550.jpg?1732245600'><p>Figure 13</p></div></script></div></div><div id="article-1525309-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g001-550.jpg?1732245578" title=" <strong>Figure 1</strong><br/> <p>Comprehensive vehicle multibody dynamics model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g002-550.jpg?1732245579" title=" <strong>Figure 2</strong><br/> <p>Comparison of virtual and incentive irregularity waveforms.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g003-550.jpg?1732245581" title=" <strong>Figure 3</strong><br/> <p>Comparison of time and frequency domain indexes of virtual and incentive irregularities at different speeds (<b>a</b>) Error standard deviation comparison (<b>b</b>) Power spectral density comparison.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g004-550.jpg?1732245583" title=" <strong>Figure 4</strong><br/> <p>Time–frequency domain comparison of virtual and excitation irregularity metrics at different detection speeds (<b>a</b>) Comparison of error standard deviation (<b>b</b>) Comparison of power spectral density.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g005-550.jpg?1732245586" title=" <strong>Figure 5</strong><br/> <p>Time–frequency domain comparison of virtual and excitation irregularity metrics under varying vertical curve gradients and lengths (<b>a</b>) Comparison of error standard deviation (<b>b</b>) Comparison of power spectral density.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g006-550.jpg?1732245588" title=" <strong>Figure 6</strong><br/> <p>Time–frequency domain comparison of virtual and excitation irregularity metrics under varying vertical curve gradients and radii (<b>a</b>) Comparison of error standard deviation (<b>b</b>) Comparison of power spectral density.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g007-550.jpg?1732245590" title=" <strong>Figure 7</strong><br/> <p>Time–frequency domain comparison of virtual and excitation irregularity metrics under varying vertical curve radii and lengths (<b>a</b>) Comparison of error standard deviation (<b>b</b>) Comparison of power spectral density.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g008-550.jpg?1732245592" title=" <strong>Figure 8</strong><br/> <p>Error analysis of the acceleration integral method (<b>a</b>) Track irregularity comparison (<b>b</b>) Detection result amplitude-spatial frequency spectrum.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g009-550.jpg?1732245593" title=" <strong>Figure 9</strong><br/> <p>Amplitude–frequency characteristics of H (s).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g010-550.jpg?1732245595" title=" <strong>Figure 10</strong><br/> <p>Comparison of detection beam pitch angle over mileage.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g011-550.jpg?1732245596" title=" <strong>Figure 11</strong><br/> <p>Flowchart of the reference cancellation method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g012-550.jpg?1732245598" title=" <strong>Figure 12</strong><br/> <p>Time domain comparison of acceleration integration and pitch angle integration results (<b>a</b>) Time domain comparison of integration results, (<b>b</b>) Time domain comparison of integration result differences.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10753/article_deploy/html/images/applsci-14-10753-g013-550.jpg?1732245600" title=" <strong>Figure 13</strong><br/> <p>Time–frequency domain comparison of different algorithms (<b>a</b>) Comparison of error standard deviation (<b>b</b>) Comparison of power spectral density.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10753'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525296" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 22 pages, 10049 KiB   </span> <a href="/2076-3417/14/22/10752/pdf?version=1732168107" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Failure Probability Analysis of the Transmission Line Considering Uncertainty Under Combined Ice and Wind Loads" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10752">Failure Probability Analysis of the Transmission Line Considering Uncertainty Under Combined Ice and Wind Loads</a> <div class="authors"> by <span class="inlineblock "><strong>Jiaxiang Li</strong>, </span><span class="inlineblock "><strong>Chao Zhang</strong>, </span><span class="inlineblock "><strong>Jian Zhang</strong>, </span><span class="inlineblock "><strong>Xuesheng Zhang</strong> and </span><span class="inlineblock "><strong>Wenrui Wang</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10752; <a href="https://doi.org/10.3390/app142210752">https://doi.org/10.3390/app142210752</a> - 20 Nov 2024 </div> Viewed by 266 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The probability of accidents, including conductor breakage and tower collapse, for the transmission tower-line system significantly increases under combined ice and wind loads. The existing research on the failure probability of the tower-line system under combined ice and wind loads is limited to <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10752/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The probability of accidents, including conductor breakage and tower collapse, for the transmission tower-line system significantly increases under combined ice and wind loads. The existing research on the failure probability of the tower-line system under combined ice and wind loads is limited to static calculation, ignoring the fluctuating effect of wind. In addition, uncertainty in the material strength and geometric dimensions of the structure due to the production process and other pertinent factors could affect the bearing capacity of the tower. To accurately assess the failure probability of transmission lines under combined ice and wind loads, this paper first established numerical models of the transmission tower-line system considering structural uncertainty based on the Latin Hypercube Sampling method. And then, the limit performance indexes of the uncertain models were determined by Pushover analysis. Subsequently, considering the joint probability distributions of ice thickness–wind speed and wind speed–wind direction, the failure probability of the tower-line system under ice and wind loads was calculated. Finally, the influence of structural uncertainty and fluctuating wind on the failure probability was discussed. The results showed that, compared with structural uncertainty, the fluctuating effect of wind had a more significant influence on the failure probability of the tower-line system under combined ice and wind loads. After considering the fluctuating effect of wind, the smaller ice loads can potentially lead to the failure of the transmission tower-line system. <a href="/2076-3417/14/22/10752">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/WLL3ELR852 ">Structural Dynamics and Risk Assessment of Structures</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10752/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525296"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525296"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525296" data-cycle-prev="#prev1525296" 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src='https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g010a-550.jpg?1732168180'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525296-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g010b-550.jpg?1732168181'><p>Figure 10 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1525296-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g011a-550.jpg?1732168182'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1525296-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g011b-550.jpg?1732168183'><p>Figure 11 Cont.</p></div> --- <div 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src='https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g014-550.jpg?1732168186'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='17' data-target='article-1525296-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g015-550.jpg?1732168187'><p>Figure 15</p></div> --- <div class='openpopupgallery' data-imgindex='18' data-target='article-1525296-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g016-550.jpg?1732168188'><p>Figure 16</p></div></script></div></div><div id="article-1525296-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g001-550.jpg?1732168171" title=" <strong>Figure 1</strong><br/> <p>The joint probability density distribution: (<b>a</b>) IT–WS; (<b>b</b>) WS–WD.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g002-550.jpg?1732168172" title=" <strong>Figure 2</strong><br/> <p>The size of the tower.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g003-550.jpg?1732168173" title=" <strong>Figure 3</strong><br/> <p>The sampling results: (<b>a</b>) samples of yield strength for Q235; (<b>b</b>) samples of elasticity modulus.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g004-550.jpg?1732168173" title=" <strong>Figure 4</strong><br/> <p>The FEM of transmission tower-line system.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g005-550.jpg?1732168174" title=" <strong>Figure 5</strong><br/> <p>The diagram of the simulation section and the observation point.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g006-550.jpg?1732168176" title=" <strong>Figure 6</strong><br/> <p>Tornado diagrams: (<b>a</b>) 5-mm ice thickness; (<b>b</b>) 10-mm ice thickness; (<b>c</b>) 15-mm ice thickness; (<b>d</b>) 20-mm ice thickness; (<b>e</b>) 25-mm ice thickness.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g007-550.jpg?1732168178" title=" <strong>Figure 7</strong><br/> <p>The pushover curve of the tower under different ice thicknesses: (<b>a</b>) 5-mm ice thickness; (<b>b</b>) 10-mm ice thickness; (<b>c</b>) 15-mm ice thickness; (<b>d</b>) 20-mm ice thickness; (<b>e</b>) 25-mm ice thickness.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g008-550.jpg?1732168178" title=" <strong>Figure 8</strong><br/> <p>Load-displacement curves with different ice thicknesses.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g009-550.jpg?1732168179" title=" <strong>Figure 9</strong><br/> <p>Random load samples.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g010a-550.jpg?1732168180" title=" <strong>Figure 10</strong><br/> <p>The fragility surfaces: (<b>a</b>) C1; (<b>b</b>) U1; (<b>c</b>) U6; (<b>d</b>) U21.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g010b-550.jpg?1732168181" title=" <strong>Figure 10 Cont.</strong><br/> <p>The fragility surfaces: (<b>a</b>) C1; (<b>b</b>) U1; (<b>c</b>) U6; (<b>d</b>) U21.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g011a-550.jpg?1732168182" title=" <strong>Figure 11</strong><br/> <p>The fragility curves under different ice thicknesses: (<b>a</b>) 5-mm ice thickness; (<b>b</b>) 10-mm ice thickness; (<b>c</b>) 15-mm ice thickness; (<b>d</b>) 20-mm ice thickness; (<b>e</b>) 25-mm ice thickness.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g011b-550.jpg?1732168183" title=" <strong>Figure 11 Cont.</strong><br/> <p>The fragility curves under different ice thicknesses: (<b>a</b>) 5-mm ice thickness; (<b>b</b>) 10-mm ice thickness; (<b>c</b>) 15-mm ice thickness; (<b>d</b>) 20-mm ice thickness; (<b>e</b>) 25-mm ice thickness.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g012a-550.jpg?1732168184" title=" <strong>Figure 12</strong><br/> <p>The failure probability density surfaces: (<b>a</b>) C1; (<b>b</b>) U1; (<b>c</b>) U6; (<b>d</b>) U21.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g012b-550.jpg?1732168185" title=" <strong>Figure 12 Cont.</strong><br/> <p>The failure probability density surfaces: (<b>a</b>) C1; (<b>b</b>) U1; (<b>c</b>) U6; (<b>d</b>) U21.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g013-550.jpg?1732168185" title=" <strong>Figure 13</strong><br/> <p>The wind at point 11 (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mn>10</mn> </mrow> </msub> <mo>=</mo> <mn>20</mn> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>): (<b>a</b>) wind speed; (<b>b</b>) wind spectra comparison.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g014-550.jpg?1732168186" title=" <strong>Figure 14</strong><br/> <p>The comparison of fragility surfaces.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g015-550.jpg?1732168187" title=" <strong>Figure 15</strong><br/> <p>The comparison of fragility curves.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10752/article_deploy/html/images/applsci-14-10752-g016-550.jpg?1732168188" title=" <strong>Figure 16</strong><br/> <p>The failure probability density surfaces: (<b>a</b>) static analysis; (<b>b</b>) dynamic analysis.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10752'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525285" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 18 pages, 582 KiB   </span> <a href="/2076-3417/14/22/10751/pdf?version=1732185817" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Analysis of the Severity of Heavy Truck Traffic Accidents Under Different Road Conditions" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10751">Analysis of the Severity of Heavy Truck Traffic Accidents Under Different Road Conditions</a> <div class="authors"> by <span class="inlineblock "><strong>Ziqun Tian</strong>, </span><span class="inlineblock "><strong>Facheng Chen</strong>, </span><span class="inlineblock "><strong>Sheqiang Ma</strong> and </span><span class="inlineblock "><strong>Mengzhu Guo</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10751; <a href="https://doi.org/10.3390/app142210751">https://doi.org/10.3390/app142210751</a> - 20 Nov 2024 </div> Viewed by 289 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The rising frequency of heavy truck accidents in China poses a significant public safety risk, endangering lives and property. However, current research based on data from heavy truck accidents in China remains limited, making it challenging to support the formulation of traffic management <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10751/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The rising frequency of heavy truck accidents in China poses a significant public safety risk, endangering lives and property. However, current research based on data from heavy truck accidents in China remains limited, making it challenging to support the formulation of traffic management measures. To mitigate the severity of these accidents, this study analyzed five years of heavy truck accident data from a specific region in China and developed logistic regression models for different road conditions. The aim was to identify the key factors influencing accident severity and understand the underlying mechanisms. The findings revealed that, under urban road conditions, the severity of heavy truck accidents is significantly impacted by factors such as lighting conditions, road safety attributes, driver age, and vehicle driving status. On highways, accident severity is largely influenced by visibility, roadside protection measures, intersection and section types, vehicle driving status, inter-vehicle accident types, and road safety features. On expressways, critical factors include inter-vehicle accident types, driver violations, visibility, and road alignment. In conclusion, the factors contributing to the severity of heavy truck accidents vary according to road conditions, which necessitates tailored traffic management strategies. The study’s findings offer theoretical support for more targeted approaches to preventing and controlling heavy truck traffic accident severity under different road conditions in China. <a href="/2076-3417/14/22/10751">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/K94T4443U8 ">Traffic Safety Measures and Assessment</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10751/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="absgraph cycle-slideshow"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525285-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10751/article_deploy/html/images/applsci-14-10751-g001-550.jpg?1732185948" alt="" style="border: 0;"><p>Figure 1</p></div></div></div><div id="article-1525285-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10751/article_deploy/html/images/applsci-14-10751-g001-550.jpg?1732185948" title=" <strong>Figure 1</strong><br/> <p>Data Flow Chart.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10751'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525326" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 18 pages, 7440 KiB   </span> <a href="/2076-3417/14/22/10750/pdf?version=1732161670" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10750">Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method</a> <div class="authors"> by <span class="inlineblock "><strong>Chengcheng Wang</strong>, </span><span class="inlineblock "><strong>Zhi Yan</strong>, </span><span class="inlineblock "><strong>Qifeng Li</strong>, </span><span class="inlineblock "><strong>Zhaopeng Zhu</strong> and </span><span class="inlineblock "><strong>Chengkai Zhang</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10750; <a href="https://doi.org/10.3390/app142210750">https://doi.org/10.3390/app142210750</a> - 20 Nov 2024 </div> Viewed by 276 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10750/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps has significant potential for energy savings. However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. This study proposes a novel Long Short-Term Memory Attention model for precise prediction of drilling pump energy consumption. By integrating Long Short-Term Memory (LSTM) networks with the Attention mechanism, the model effectively captures complex nonlinear relationships and long-term dependencies in energy consumption data. Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. The proposed model achieved Mean Absolute Error (MAE) values ranging from 5.19 to 10.20 and R<sup>2</sup> values close to one (0.95 to 0.98) in four test scenarios, demonstrating excellent predictive performance under complex conditions. The high-precision prediction of drilling pump energy consumption based on this method can support energy optimization and provide guidance for field operations. <a href="/2076-3417/14/22/10750">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/7B9WOFNV3N ">Development and Application of Intelligent Drilling Technology</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10750/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525326"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525326"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525326" data-cycle-prev="#prev1525326" data-cycle-progressive="#images1525326" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525326-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g001-550.jpg?1732161755" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525326" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525326-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g002-550.jpg?1732161756'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525326-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g003-550.jpg?1732161758'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525326-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g004-550.jpg?1732161759'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525326-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g005-550.jpg?1732161760'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525326-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g006-550.jpg?1732161762'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525326-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g007-550.jpg?1732161764'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525326-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g008-550.jpg?1732161765'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525326-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g009-550.jpg?1732161766'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525326-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g010-550.jpg?1732161768'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525326-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g011-550.jpg?1732161770'><p>Figure 11</p></div></script></div></div><div id="article-1525326-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g001-550.jpg?1732161755" title=" <strong>Figure 1</strong><br/> <p>The structure of the LSTM.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10750'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g002-550.jpg?1732161756" title=" <strong>Figure 2</strong><br/> <p>Attention mechanism.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10750'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g003-550.jpg?1732161758" title=" <strong>Figure 3</strong><br/> <p>Application of the RANSAC mechanism.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10750'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g004-550.jpg?1732161759" title=" <strong>Figure 4</strong><br/> <p>Independent sliding window approach for real-time pump power data.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10750'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g005-550.jpg?1732161760" title=" <strong>Figure 5</strong><br/> <p>LSTM-Attention model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10750'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g006-550.jpg?1732161762" title=" <strong>Figure 6</strong><br/> <p>Pump power prediction experiments: (<b>a</b>) Test 1; (<b>b</b>) Test 2; (<b>c</b>) Test 3; (<b>d</b>) Test 4.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10750'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g007-550.jpg?1732161764" title=" <strong>Figure 7</strong><br/> <p>Trends and fluctuations in Test 1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10750'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g008-550.jpg?1732161765" title=" <strong>Figure 8</strong><br/> <p>Trends and fluctuations in Test 2.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10750'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g009-550.jpg?1732161766" title=" <strong>Figure 9</strong><br/> <p>Trends and fluctuations in Test 3.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10750'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g010-550.jpg?1732161768" title=" <strong>Figure 10</strong><br/> <p>Trends and fluctuations in Test 4.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10750'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10750/article_deploy/html/images/applsci-14-10750-g011-550.jpg?1732161770" title=" <strong>Figure 11</strong><br/> <p>Performance comparison with different indices: (<b>a</b>) MAE; (<b>b</b>) RMSE; (<b>c</b>) MSE; (<b>d</b>) R<sup>2</sup>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10750'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525266" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 11 pages, 8283 KiB   </span> <a href="/2076-3417/14/22/10749/pdf?version=1732108693" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Repair Composite Adhesion Strength: A Comparison of Testing Methods" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10749">Repair Composite Adhesion Strength: A Comparison of Testing Methods</a> <div class="authors"> by <span class="inlineblock "><strong>Khrystyna Moskalova</strong>, </span><span class="inlineblock "><strong>Serhii Hedulian</strong>, </span><span class="inlineblock "><strong>Nadiia Antoniuk</strong> and </span><span class="inlineblock "><strong>Mario Šercer</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10749; <a href="https://doi.org/10.3390/app142210749">https://doi.org/10.3390/app142210749</a> - 20 Nov 2024 </div> Viewed by 292 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The adhesive strength of repair composites to concrete substrates was assessed through both Ukrainian and European standard test methods. The types of adhesion loss observed included adhesive failure along the contact layer (AF-S), and cohesion failure along the substrate (CF-S). The Ukrainian method <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10749/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The adhesive strength of repair composites to concrete substrates was assessed through both Ukrainian and European standard test methods. The types of adhesion loss observed included adhesive failure along the contact layer (AF-S), and cohesion failure along the substrate (CF-S). The Ukrainian method showed adhesive bond loss in 90.5% of samples (181 out of 200), while the European method showed loss in 76% (152 out of 200). However, under identical conditions, the EU standard showed greater consistency (standard deviation 0.25) than the Ukrainian standard (standard deviation 0.42 and 0.32). The effect of pull-off techniques on failure models varied depending on the epoxy thickness and the mechanical testing performed. Repair composites meeting the highest Ukrainian structural class criteria (PM1) were classified as R3 materials according to the European standard. This research highlights that statistical analysis shows a significant improvement in reliability with an increased number of pull-off tests. <a href="/2076-3417/14/22/10749">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/4950IA62UU ">Mechanical Properties and Characterization Technologies of Composite Materials</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10749/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525266"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525266"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525266" data-cycle-prev="#prev1525266" data-cycle-progressive="#images1525266" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525266-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g001-550.jpg?1732108759" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525266" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525266-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g002-550.jpg?1732108760'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525266-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g003-550.jpg?1732108762'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525266-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g004-550.jpg?1732108763'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525266-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g005-550.jpg?1732108763'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525266-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g006-550.jpg?1732108764'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525266-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g007-550.jpg?1732108765'><p>Figure 7</p></div></script></div></div><div id="article-1525266-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g001-550.jpg?1732108759" title=" <strong>Figure 1</strong><br/> <p>Types of adhesive bond loss: <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">σ</mi> </mrow> <mrow> <mi mathvariant="normal">t</mi> </mrow> </msub> <mo>—</mo> </mrow> </semantics></math>internal shrinkage stress; <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mi mathvariant="normal">f</mi> <mo> </mo> </mrow> </msub> </mrow> </semantics></math>—tensile strength of the repair material; <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mi mathvariant="normal">t</mi> </mrow> </msub> </mrow> </semantics></math>—tensile strength of the concrete substrate; <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mi mathvariant="normal">A</mi> </mrow> </msub> </mrow> </semantics></math>—adhesive strength.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10749'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g002-550.jpg?1732108760" title=" <strong>Figure 2</strong><br/> <p>Raw materials of the experiment.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10749'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g003-550.jpg?1732108762" title=" <strong>Figure 3</strong><br/> <p>Use of mechanical mixer for (<b>a</b>) mixing process and (<b>b</b>) determining the consistency by cone device: 1—bowl with mortar; 2—reference cone; 3—lock screw; 4—scale; 5—holders.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10749'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g004-550.jpg?1732108763" title=" <strong>Figure 4</strong><br/> <p>Pull–Off tester DYNA Z16: 1—canvas; 2—retainer; 3—load supply; 4—manometer; 5—a test steel dollies, Ø50 mm; 6—draw bolt, M8.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10749'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g005-550.jpg?1732108763" title=" <strong>Figure 5</strong><br/> <p>Reference concrete slabs prepared for pull-off testing with applied repair composite and cut-out specimens.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10749'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g006-550.jpg?1732108764" title=" <strong>Figure 6</strong><br/> <p>Relations between the calculated deviation and the experimental concrete plates made according to the Ukrainian standard.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10749'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10749/article_deploy/html/images/applsci-14-10749-g007-550.jpg?1732108765" title=" <strong>Figure 7</strong><br/> <p>Relations between the calculated deviation and the experimental concrete plates made according to the EU standard.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10749'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525263" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 27 pages, 2383 KiB   </span> <a href="/2076-3417/14/22/10748/pdf?version=1732173953" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Integrating a Virtual Assistant by Using the RAG Method and VERTEX AI Framework at Algebra University" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10748">Integrating a Virtual Assistant by Using the RAG Method and VERTEX AI Framework at Algebra University</a> <div class="authors"> by <span class="inlineblock "><strong>Zlatan Morić</strong>, </span><span class="inlineblock "><strong>Leo Mršić</strong>, </span><span class="inlineblock "><strong>Mario Filjak</strong> and </span><span class="inlineblock "><strong>Goran Đambić</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10748; <a href="https://doi.org/10.3390/app142210748">https://doi.org/10.3390/app142210748</a> - 20 Nov 2024 </div> Viewed by 307 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The development and testing of a virtual assistant (VA) designed to enhance information retrieval and support in an academic environment are presented in this paper, with the Retrieval-Augmented Generation (RAG) approach being utilized alongside Google’s VERTEX AI Palm-2 model. A novel integration of <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10748/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The development and testing of a virtual assistant (VA) designed to enhance information retrieval and support in an academic environment are presented in this paper, with the Retrieval-Augmented Generation (RAG) approach being utilized alongside Google’s VERTEX AI Palm-2 model. A novel integration of RAG with contextual learning is introduced in this study, specifically for applications in university contact centers, where accuracy and relevance are considered paramount. The effectiveness of the VA was evaluated through user testing, focusing on two primary hypotheses: first, that the VA can achieve accurate interpretation and response to queries with context-based information, and second, that the VA minimizes potential harm from erroneous responses. In total, 187 participants were involved in the testing, and a diverse set of inquiries was utilized, resulting in 561 query–response interactions that were analyzed. It was shown that contextual data significantly reduced hallucinations and increased response accuracy, thereby underscoring the value of the RAG method in applications requiring high levels of specificity. Furthermore, the study provides empirical insights into the impact of AI-generated hallucinations and response inconsistencies, particularly about structured or procedural data. A framework for mitigating these challenges in future implementations is also offered. The scalability and adaptability of the RAG method in specialized academic contexts are demonstrated in this work, with broader implications for integrating AI-driven VAs across educational and professional domains being highlighted. <a href="/2076-3417/14/22/10748">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10748/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525263"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525263"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525263" data-cycle-prev="#prev1525263" data-cycle-progressive="#images1525263" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525263-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g001-550.jpg?1732174081" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525263" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525263-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g002-550.jpg?1732174082'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525263-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g003-550.jpg?1732174085'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525263-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g004-550.jpg?1732174088'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525263-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g005-550.jpg?1732174090'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525263-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g006-550.jpg?1732174093'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525263-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g007-550.jpg?1732174095'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525263-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g008-550.jpg?1732174097'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525263-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g009-550.jpg?1732174098'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525263-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g010-550.jpg?1732174101'><p>Figure 10</p></div></script></div></div><div id="article-1525263-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g001-550.jpg?1732174081" title=" <strong>Figure 1</strong><br/> <p>System Architecture for Scalable and Accurate Contextual Response Generation Using Palm-2 Model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10748'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g002-550.jpg?1732174082" title=" <strong>Figure 2</strong><br/> <p>SMAC Application Interface: Key Functional Elements and User Interaction Workflow.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10748'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g003-550.jpg?1732174085" title=" <strong>Figure 3</strong><br/> <p>Workflow for Query Formation and Contextual Response Generation in the RAG Method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10748'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g004-550.jpg?1732174088" title=" <strong>Figure 4</strong><br/> <p>Data Preparation and Vectorization Workflow for Semantic Similarity Retrieval.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10748'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g005-550.jpg?1732174090" title=" <strong>Figure 5</strong><br/> <p>Prediction Accuracy with and without Context.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10748'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g006-550.jpg?1732174093" title=" <strong>Figure 6</strong><br/> <p>Hallucination Occurrence by Context Relevance.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10748'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g007-550.jpg?1732174095" title=" <strong>Figure 7</strong><br/> <p>User Satisfaction Rating Distribution Across Metrics.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10748'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g008-550.jpg?1732174097" title=" <strong>Figure 8</strong><br/> <p>Overall User Satisfaction Ratings Across Metrics.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10748'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g009-550.jpg?1732174098" title=" <strong>Figure 9</strong><br/> <p>Proportion of Accurate vs. Hallucinated Responses.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10748'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10748/article_deploy/html/images/applsci-14-10748-g010-550.jpg?1732174101" title=" <strong>Figure 10</strong><br/> <p>High-Risk vs. Low-Risk Errors.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10748'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525271" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 13 pages, 275 KiB   </span> <a href="/2076-3417/14/22/10747/pdf?version=1732108608" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10747">Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System</a> <div class="authors"> by <span class="inlineblock "><strong>Woncheol Park</strong>, </span><span class="inlineblock "><strong>Seungmin Oh</strong> and </span><span class="inlineblock "><strong>Seonghyun Park</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10747; <a href="https://doi.org/10.3390/app142210747">https://doi.org/10.3390/app142210747</a> - 20 Nov 2024 </div> Viewed by 305 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This study proposes a system for analyzing non-face-to-face counseling data using text-mining techniques to assess psychological states and automatically classify them into predefined categories. The system addresses the challenge of understanding internal issues that may be difficult to express in traditional face-to-face counseling. <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10747/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This study proposes a system for analyzing non-face-to-face counseling data using text-mining techniques to assess psychological states and automatically classify them into predefined categories. The system addresses the challenge of understanding internal issues that may be difficult to express in traditional face-to-face counseling. To solve this problem, a counseling management system based on text mining was developed. In the experiment, we combined TF-IDF and Word Embedding techniques to process and classify client counseling data into five major categories: school, friends, personality, appearance, and family. The classification performance achieved high accuracy and F1-Score, demonstrating the system’s effectiveness in understanding and categorizing clients’ emotions and psychological states. This system offers a structured approach to analyzing counseling data, providing counselors with a foundation for recommending personalized counseling treatments. The findings of this study suggest that in-depth analysis and classification of counseling data can enhance the quality of counseling, even in non-face-to-face environments, offering more efficient and tailored solutions. <a href="/2076-3417/14/22/10747">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10747/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525271"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525271"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525271" data-cycle-prev="#prev1525271" data-cycle-progressive="#images1525271" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525271-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10747/article_deploy/html/images/applsci-14-10747-g001-550.jpg?1732108746" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525271" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525271-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10747/article_deploy/html/images/applsci-14-10747-g002-550.jpg?1732108747'><p>Figure 2</p></div></script></div></div><div id="article-1525271-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10747/article_deploy/html/images/applsci-14-10747-g001-550.jpg?1732108746" title=" <strong>Figure 1</strong><br/> <p>System configuration diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10747'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10747/article_deploy/html/images/applsci-14-10747-g002-550.jpg?1732108747" title=" <strong>Figure 2</strong><br/> <p>Process of the proposed system.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10747'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525273" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 4979 KiB   </span> <a href="/2076-3417/14/22/10746/pdf?version=1732108680" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Experimental Study on Fluid Dissipation Effects in Core Samples by NMR Measurement" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10746">Experimental Study on Fluid Dissipation Effects in Core Samples by NMR Measurement</a> <div class="authors"> by <span class="inlineblock "><strong>Zhongshu Liao</strong>, </span><span class="inlineblock "><strong>Gong Zhang</strong> and </span><span class="inlineblock "><strong>Yingying Ma</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10746; <a href="https://doi.org/10.3390/app142210746">https://doi.org/10.3390/app142210746</a> - 20 Nov 2024 </div> Viewed by 281 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Laboratory core nuclear magnetic resonance (NMR) relaxation measurements offer geological information, including rock porosity and oil saturation, relevant to logging. When core samples drilled from wells are exposed to air, the fluids within their pores inevitably dissipate. This phenomenon may lead to discrepancies <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10746/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Laboratory core nuclear magnetic resonance (NMR) relaxation measurements offer geological information, including rock porosity and oil saturation, relevant to logging. When core samples drilled from wells are exposed to air, the fluids within their pores inevitably dissipate. This phenomenon may lead to discrepancies between the results of nuclear magnetic resonance relaxation experiments and the actual situation underground. To deeply explore the impact of fluid dissipation on NMR core analysis experimental results, a series of simulated dissipation experiments were designed under constant temperature and humidity conditions. Variations in one-dimensional and two-dimensional NMR measurement results of oil-saturated samples were examined under varying crude oil viscosities and dissipation times. The experimental results indicate that as exposure time increases, the T<sub>2</sub> distribution of oil-saturated cores decreases, and the amplitude of the T<sub>2</sub> distribution peaks decreases. Both oil and water relaxation components show a decreasing trend; however, the dissipation rate of the bounding water component significantly exceeds that of the crude oil component. By employing two-dimensional NMR relaxation time distribution fluid quantitative analysis technology, the relationship between the dissipation rates of various phase fluids and exposure time during the stable dissipation stage was analyzed. This offers a reference for adjusting the oil saturation of exposed cores based on NMR measurements. <a href="/2076-3417/14/22/10746">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10746/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525273"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525273"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525273" data-cycle-prev="#prev1525273" data-cycle-progressive="#images1525273" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525273-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g001-550.jpg?1732108747" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525273" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525273-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g002-550.jpg?1732108748'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525273-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g003-550.jpg?1732108749'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525273-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g004-550.jpg?1732108750'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525273-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g005-550.jpg?1732108751'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525273-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g006a-550.jpg?1732108754'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525273-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g006b-550.jpg?1732108756'><p>Figure 6 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525273-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g007-550.jpg?1732108757'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525273-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g008-550.jpg?1732108758'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525273-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g009-550.jpg?1732108759'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525273-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g010-550.jpg?1732108760'><p>Figure 10</p></div></script></div></div><div id="article-1525273-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g001-550.jpg?1732108747" title=" <strong>Figure 1</strong><br/> <p>Photos of cores after cleaning and drying.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g002-550.jpg?1732108748" title=" <strong>Figure 2</strong><br/> <p>Mass change rate of sample A and sample B. Light blue and Green line represents the second stage of sample’s dispersion.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g003-550.jpg?1732108749" title=" <strong>Figure 3</strong><br/> <p>The rate of fluid change in sample A and sample B.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g004-550.jpg?1732108750" title=" <strong>Figure 4</strong><br/> <p>T<sub>2</sub> distribution measurement results at different dissipation stages: (<b>a</b>) T<sub>2</sub> relaxation time of sample A; (<b>b</b>) T<sub>2</sub> relaxation time of sample B.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g005-550.jpg?1732108751" title=" <strong>Figure 5</strong><br/> <p>T<sub>1</sub>-T<sub>2</sub> fluid identification plate (adapted from Fleury M, 2016 [<a href="#B24-applsci-14-10746" class="html-bibr">24</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g006a-550.jpg?1732108754" title=" <strong>Figure 6</strong><br/> <p>The results of twodimensional NMR experiments conducted at various dissipation stages of sample A.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g006b-550.jpg?1732108756" title=" <strong>Figure 6 Cont.</strong><br/> <p>The results of twodimensional NMR experiments conducted at various dissipation stages of sample A.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g007-550.jpg?1732108757" title=" <strong>Figure 7</strong><br/> <p>Schematic diagram of calculating the percentages of different fluid components using the twodimensional nuclear magnetic area integration method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g008-550.jpg?1732108758" title=" <strong>Figure 8</strong><br/> <p>The relationship between the remaining mass percentage of irreducible water and exposure time.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g009-550.jpg?1732108759" title=" <strong>Figure 9</strong><br/> <p>The relationship between the remaining mass percentage of crude oil and exposure time.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10746/article_deploy/html/images/applsci-14-10746-g010-550.jpg?1732108760" title=" <strong>Figure 10</strong><br/> <p>Two-dimensional NMR experimental results of each core fluid component in the second stage of fluid dissipation: (<b>a</b>) the NMR measurement result after 4 h of exposure treatment for Sample A; (<b>b</b>) the NMR measurement result after 96 h of exposure treatment for Sample A; (<b>c</b>) the NMR measurement result after 4 h of exposure treatment for Sample B; and (<b>d</b>) the NMR measurement result after 96 h of exposure treatment for Sample B.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10746'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525229" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 2747 KiB   </span> <a href="/2076-3417/14/22/10745/pdf?version=1732105052" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A Short-Circuit Current Calculation Model for Renewable Power Plants Considering Internal Topology" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10745">A Short-Circuit Current Calculation Model for Renewable Power Plants Considering Internal Topology</a> <div class="authors"> by <span class="inlineblock "><strong>Po Li</strong>, </span><span class="inlineblock "><strong>Ying Huang</strong>, </span><span class="inlineblock "><strong>Guoteng Wang</strong>, </span><span class="inlineblock "><strong>Jianhua Li</strong> and </span><span class="inlineblock "><strong>Jianyu Lu</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10745; <a href="https://doi.org/10.3390/app142210745">https://doi.org/10.3390/app142210745</a> - 20 Nov 2024 </div> Viewed by 314 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> With the large-scale integration of renewable energy into the grid, traditional short-circuit current (SCC) calculation methods for synchronous generators are no longer applicable to inverter-based non-synchronous machine sources (N-SMSs). Current SCC calculation methods for N-SMSs often use a single-machine multiplication method, which tends <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10745/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> With the large-scale integration of renewable energy into the grid, traditional short-circuit current (SCC) calculation methods for synchronous generators are no longer applicable to inverter-based non-synchronous machine sources (N-SMSs). Current SCC calculation methods for N-SMSs often use a single-machine multiplication method, which tends to overlook the internal variability of N-SMSs within power plants, leading to low calculation accuracy. To address this issue, this paper first derives an analytical expression for SCC in grid-connected inverters under low voltage ride through (LVRT) control strategies. Then, a single-machine steady-state SCC calculation model is proposed. Based on the classification of N-SMSs, a practical SCC calculation model for renewable power plants is introduced, balancing accuracy and computational speed. The feasibility of the model is validated through simulations. The proposed method enables simple calculations to obtain the steady-state voltage and SCC at the machine terminal, offering strong engineering practicality. <a href="/2076-3417/14/22/10745">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10745/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525229"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525229"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525229" data-cycle-prev="#prev1525229" data-cycle-progressive="#images1525229" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525229-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g001-550.jpg?1732105117" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525229" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525229-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g002-550.jpg?1732105119'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525229-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g003-550.jpg?1732105119'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525229-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g004-550.jpg?1732105120'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525229-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g005-550.jpg?1732105120'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525229-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g006-550.jpg?1732105121'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525229-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g007-550.jpg?1732105122'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525229-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g008-550.jpg?1732105122'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525229-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g009-550.jpg?1732105124'><p>Figure 9</p></div></script></div></div><div id="article-1525229-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g001-550.jpg?1732105117" title=" <strong>Figure 1</strong><br/> <p>Typical structure of N-SMSs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g002-550.jpg?1732105119" title=" <strong>Figure 2</strong><br/> <p>Diagram of grid connected inverter and its control system.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g003-550.jpg?1732105119" title=" <strong>Figure 3</strong><br/> <p>The positive-sequence <span class="html-italic">q</span>-axis current inner loop.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g004-550.jpg?1732105120" title=" <strong>Figure 4</strong><br/> <p>Current inner loop unit step response curve.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g005-550.jpg?1732105120" title=" <strong>Figure 5</strong><br/> <p>SCC calculation flowchart.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g006-550.jpg?1732105121" title=" <strong>Figure 6</strong><br/> <p>External fault of renewable power plant.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g007-550.jpg?1732105122" title=" <strong>Figure 7</strong><br/> <p>Renewable power plant internal loop.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g008-550.jpg?1732105122" title=" <strong>Figure 8</strong><br/> <p>Sketch diagram of the detailed wind farm model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10745/article_deploy/html/images/applsci-14-10745-g009-550.jpg?1732105124" title=" <strong>Figure 9</strong><br/> <p>Fault steady-state voltage of each wind turbine in the different fault situations. (<b>a</b>) γ = 0.25; (<b>b</b>) γ = 0.50; (<b>c</b>) γ = 0.75.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10745'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525218" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 21 pages, 3958 KiB   </span> <a href="/2076-3417/14/22/10744/pdf?version=1732104358" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="PHR-NFT: Decentralized Blockchain Framework with Hyperledger and NFTs for Secure and Transparent Patient Health Records" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10744">PHR-NFT: Decentralized Blockchain Framework with Hyperledger and NFTs for Secure and Transparent Patient Health Records</a> <div class="authors"> by <span class="inlineblock "><strong>Huwida E. Said</strong>, </span><span class="inlineblock "><strong>Nedaa B. Al Barghuthi</strong>, </span><span class="inlineblock "><strong>Sulafa M. Badi</strong>, </span><span class="inlineblock "><strong>Faiza Hashim</strong> and </span><span class="inlineblock "><strong>Shini Girija</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10744; <a href="https://doi.org/10.3390/app142210744">https://doi.org/10.3390/app142210744</a> - 20 Nov 2024 </div> Viewed by 333 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Blockchain technology holds significant promise for healthcare by enhancing the security and integrity of patient health records (PHRs) through decentralized storage and transparent access. However, it has substantial limitations, including problems with scalability, high transaction costs, privacy concerns, and intricate stakeholder access management. <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10744/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Blockchain technology holds significant promise for healthcare by enhancing the security and integrity of patient health records (PHRs) through decentralized storage and transparent access. However, it has substantial limitations, including problems with scalability, high transaction costs, privacy concerns, and intricate stakeholder access management. This study presents PHR-NFT, a novel framework that strengthens PHR privacy by utilizing Hyperledger Fabric and non-fungible tokens (NFTs) to address these issues. PHR-NFT improves privacy and communication by letting patients keep control of their medical records while permitting temporary, permission-based access by medical professionals. PHR-NFT offers a transparent solution that increases trust among healthcare stakeholders through the robust and decentralized architecture of the Hyperledger Fabric. This study demonstrates the viability and effectiveness of the PHR-NFT framework through performance evaluations focused on transaction latency, throughput, and security. This research has valuable implications for enhancing data privacy and security in healthcare practices and insightful information about blockchain-based healthcare systems. <a href="/2076-3417/14/22/10744">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/8549SYU7QT ">Future Security of NFT-Blockchain</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10744/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525218"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525218"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525218" data-cycle-prev="#prev1525218" data-cycle-progressive="#images1525218" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525218-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g001-550.jpg?1732104471" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525218" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g002-550.jpg?1732104475'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g003-550.jpg?1732104477'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g004-550.jpg?1732104478'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g005-550.jpg?1732104480'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g006-550.jpg?1732104482'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g007-550.jpg?1732104485'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g008-550.jpg?1732104487'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g009-550.jpg?1732104487'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g010-550.jpg?1732104488'><p>Figure 10</p></div></script></div></div><div id="article-1525218-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g001-550.jpg?1732104471" title=" <strong>Figure 1</strong><br/> <p>PHR-NFT system workflow.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g002-550.jpg?1732104475" title=" <strong>Figure 2</strong><br/> <p>PHR-NFT system architecture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g003-550.jpg?1732104477" title=" <strong>Figure 3</strong><br/> <p>Maximum, minimum, and average latency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g004-550.jpg?1732104478" title=" <strong>Figure 4</strong><br/> <p>Throughput paired with network send rate.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g005-550.jpg?1732104480" title=" <strong>Figure 5</strong><br/> <p>Network throughput vs. transaction latency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g006-550.jpg?1732104482" title=" <strong>Figure 6</strong><br/> <p>Total execution time.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g007-550.jpg?1732104485" title=" <strong>Figure 7</strong><br/> <p>Comparing Create Patient, Update Patient, and Query Patient chain codes. (<b>a</b>) Latency comparison, (<b>b</b>) throughput comparison, (<b>c</b>) fail count comparison and (<b>d</b>) success rate comparison.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g008-550.jpg?1732104487" title=" <strong>Figure 8</strong><br/> <p>Patient record retrieval from Hospital 1 and Hospital 2.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g009-550.jpg?1732104487" title=" <strong>Figure 9</strong><br/> <p>Transaction failure by varying worker nodes (transaction load = 8000).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10744/article_deploy/html/images/applsci-14-10744-g010-550.jpg?1732104488" title=" <strong>Figure 10</strong><br/> <p>Fail transaction rates across the configuration.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10744'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525199" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 22 pages, 6230 KiB   </span> <a href="/2076-3417/14/22/10743/pdf?version=1732103081" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="FEA-Based Design Procedure for IPMSM and IM for a Hybrid Electric Vehicle" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10743">FEA-Based Design Procedure for IPMSM and IM for a Hybrid Electric Vehicle</a> <div class="authors"> by <span class="inlineblock "><strong>Emad Roshandel</strong>, </span><span class="inlineblock "><strong>Amin Mahmoudi</strong>, </span><span class="inlineblock "><strong>Wen L. Soong</strong>, </span><span class="inlineblock "><strong>Solmaz Kahourzade</strong> and </span><span class="inlineblock "><strong>Nathan Kalisch</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10743; <a href="https://doi.org/10.3390/app142210743">https://doi.org/10.3390/app142210743</a> - 20 Nov 2024 </div> Viewed by 270 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This paper describes the detailed design procedure of electric machines using finite element analysis (FEA). The proposed method uses the available findings from the literature and FEA results for the design procedure. In addition to electromagnetic analysis, thermal analysis is executed to examine <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10743/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This paper describes the detailed design procedure of electric machines using finite element analysis (FEA). The proposed method uses the available findings from the literature and FEA results for the design procedure. In addition to electromagnetic analysis, thermal analysis is executed to examine the capability of the designed machines for handling the load in terms of thermal limits. It allows for considering the normal and overload performance of the electric machines during design. The proposed design procedure is used for designing a 100 kW induction machine (IM) and interior permanent magnet synchronous machine (IPMSM) for a parallel hybrid electric vehicle (HEV). The differences between the performance parameters of the studied machines are discussed, and the advantages and disadvantages of each design are highlighted. The designed machines are compared with commercially available electrical machines in terms of performance and power density. The comparison demonstrates that the developed machines can offer comparable performance to other designs. <a href="/2076-3417/14/22/10743">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/Developments_Electric_Vehicles ">Recent Developments in Electric Vehicles</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10743/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525199"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525199"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525199" data-cycle-prev="#prev1525199" data-cycle-progressive="#images1525199" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525199-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g001-550.jpg?1732103172" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525199" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g002-550.jpg?1732103172'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g003-550.jpg?1732103175'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g004-550.jpg?1732103178'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g005-550.jpg?1732103179'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g006-550.jpg?1732103182'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g007-550.jpg?1732103183'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g008-550.jpg?1732103184'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g009-550.jpg?1732103185'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g010-550.jpg?1732103186'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g011-550.jpg?1732103188'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g012-550.jpg?1732103190'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g013-550.jpg?1732103193'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1525199-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g014-550.jpg?1732103194'><p>Figure 14</p></div></script></div></div><div id="article-1525199-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g001-550.jpg?1732103172" title=" <strong>Figure 1</strong><br/> <p>The schematic of the proposed HEV transmission system, reprinted with permission from IEEE.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g002-550.jpg?1732103172" title=" <strong>Figure 2</strong><br/> <p>The schematic of the proposed spiral cooling system for the stators of both machines.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g003-550.jpg?1732103175" title=" <strong>Figure 3</strong><br/> <p>The design flowchart of the studied 100 kW interior permanent magnet synchronous machine.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g004-550.jpg?1732103178" title=" <strong>Figure 4</strong><br/> <p>Two-dimensional FEA results of the studied PMSM V-shape rotors for different rotor magnet angles. The maximum continuous power (MCP) is reported @2800 rpm with Class F temperature rise. <span class="html-italic">I<sub>sc</sub></span> is the short circuit current.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g005-550.jpg?1732103179" title=" <strong>Figure 5</strong><br/> <p>Stacked bar graphs of the losses of the three PMSM designs obtained from 2D FEA.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g006-550.jpg?1732103182" title=" <strong>Figure 6</strong><br/> <p>The proposed step-by-step design process for the induction machine design.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g007-550.jpg?1732103183" title=" <strong>Figure 7</strong><br/> <p>Effect of the number of rotor bars: (<b>a</b>) 2D FEA results of torque ripple at rated torque, (<b>b</b>) 2D FEA results of performance parameters, (<b>c</b>) 3D FEA results of hot spot temperatures. The current, torque, and output power are per unit based on their rated values which are 120 A, 260 Nm, and 80 kW, respectively.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g008-550.jpg?1732103184" title=" <strong>Figure 8</strong><br/> <p>Two-dimensional FEA results of the IM loss breakdown for different numbers of rotor bars.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g009-550.jpg?1732103185" title=" <strong>Figure 9</strong><br/> <p>(<b>a</b>) Cross-section of the designed induction machine, (<b>b</b>) flux density distribution in the full-load operating condition.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g010-550.jpg?1732103186" title=" <strong>Figure 10</strong><br/> <p>Two-dimensional FEA output power results: (<b>a</b>) IPMSM, (<b>b</b>) IM.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g011-550.jpg?1732103188" title=" <strong>Figure 11</strong><br/> <p>Two-dimensional FEA efficiency map results: (<b>a</b>) IPMSM, (<b>b</b>) IM.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g012-550.jpg?1732103190" title=" <strong>Figure 12</strong><br/> <p>Two-dimensional FEA power factor results: (<b>a</b>) IPMSM, (<b>b</b>) IM.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g013-550.jpg?1732103193" title=" <strong>Figure 13</strong><br/> <p>Three-dimensional FEA results of the transient thermal analysis of the designed machines: (<b>a</b>) IPMSM, (<b>b</b>) IM.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10743/article_deploy/html/images/applsci-14-10743-g014-550.jpg?1732103194" title=" <strong>Figure 14</strong><br/> <p>Two-dimensional FEA results of induction machines with different numbers of poles designed using the proposed process: (<b>a</b>) output power considering the thermal limit, (<b>b</b>) core loss/weight (bars) and weight (line).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10743'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525133" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 18 pages, 3720 KiB   </span> <a href="/2076-3417/14/22/10742/pdf?version=1732100073" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Packaging Design Image Segmentation Based on Improved Full Convolutional Networks" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10742">Packaging Design Image Segmentation Based on Improved Full Convolutional Networks</a> <div class="authors"> by <span class="inlineblock "><strong>Chunxiao Zhang</strong>, </span><span class="inlineblock "><strong>Mengmeng Han</strong>, </span><span class="inlineblock "><strong>Jingjing Jia</strong> and </span><span class="inlineblock "><strong>Chulsoo Kim</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10742; <a href="https://doi.org/10.3390/app142210742">https://doi.org/10.3390/app142210742</a> - 20 Nov 2024 </div> Viewed by 263 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Packaging design plays a critical role in brand recognition and cultural dissemination, yet the traditional design process is time-consuming and dependent on the designer’s technical skills, making it difficult to quickly respond to market changes and consumer demands. In recent years, advancements in <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10742/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Packaging design plays a critical role in brand recognition and cultural dissemination, yet the traditional design process is time-consuming and dependent on the designer’s technical skills, making it difficult to quickly respond to market changes and consumer demands. In recent years, advancements in machine learning, particularly in the field of natural language processing (NLP), have paved the way for novel methods in other areas, such as image processing and packaging design. This study draws inspiration from advanced NLP techniques and proposes an improved fully convolutional network (FCN) model for image semantic segmentation, which is applied to packaging design. The model integrates superpixel technology, multi-branch networks, dual-attention mechanisms, and edge knowledge distillation in a manner analogous to the approach taken by NLP models in the context of semantic segmentation and context understanding. The experimental results showed that the model achieved significant improvements in accuracy, inference efficiency, and memory usage, with an average accuracy of 96.84% and a false-alarm rate of only 2.78%. Compared to traditional methods, the proposed model achieved over 96% accuracy across 50 packaging design images, with an average segmentation error rate of only 1.42%. By incorporating machine learning techniques from NLP into image processing, this study enhances the overall quality and efficiency of packaging design and provides new directions for the application of advanced technologies across different fields. <a href="/2076-3417/14/22/10742">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/applsci/sections/computing_artificial_intelligence">Computing and Artificial Intelligence</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10742/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525133"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525133"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525133" data-cycle-prev="#prev1525133" data-cycle-progressive="#images1525133" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525133-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g001-550.jpg?1732100197" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525133" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525133-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g002-550.jpg?1732100198'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525133-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g003-550.jpg?1732100199'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525133-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g004-550.jpg?1732100201'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525133-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g005-550.jpg?1732100203'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525133-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g006-550.jpg?1732100205'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525133-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g007-550.jpg?1732100206'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525133-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g008-550.jpg?1732100208'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525133-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g009-550.jpg?1732100210'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525133-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g010-550.jpg?1732100211'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525133-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g011-550.jpg?1732100213'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1525133-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g012-550.jpg?1732100215'><p>Figure 12</p></div></script></div></div><div id="article-1525133-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g001-550.jpg?1732100197" title=" <strong>Figure 1</strong><br/> <p>The Flow of Superpixel-Assisted ISS Model Based on FCN.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g002-550.jpg?1732100198" title=" <strong>Figure 2</strong><br/> <p>Schematic Diagram of Target Contour Recognition Process.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g003-550.jpg?1732100199" title=" <strong>Figure 3</strong><br/> <p>Main Process of Improving ISS Model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g004-550.jpg?1732100201" title=" <strong>Figure 4</strong><br/> <p>Schematic Diagram of the Coding Network.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g005-550.jpg?1732100203" title=" <strong>Figure 5</strong><br/> <p>Structure Diagram of Decoding Network.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g006-550.jpg?1732100205" title=" <strong>Figure 6</strong><br/> <p>Specific flow of lightweight Design Method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g007-550.jpg?1732100206" title=" <strong>Figure 7</strong><br/> <p>Structure of the Edge Knowledge Distillation Module.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g008-550.jpg?1732100208" title=" <strong>Figure 8</strong><br/> <p>Comparison of Precision and False-Positive Rate.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g009-550.jpg?1732100210" title=" <strong>Figure 9</strong><br/> <p>Comparison Results Before and After Model Improvement.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g010-550.jpg?1732100211" title=" <strong>Figure 10</strong><br/> <p>Results of Comparison between Training Set and Test Set.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g011-550.jpg?1732100213" title=" <strong>Figure 11</strong><br/> <p>Effect Verification of Edge Knowledge Distillation Module.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10742/article_deploy/html/images/applsci-14-10742-g012-550.jpg?1732100215" title=" <strong>Figure 12</strong><br/> <p>Semantic Segmentation Performance Comparison [<a href="#B6-applsci-14-10742" class="html-bibr">6</a>,<a href="#B8-applsci-14-10742" class="html-bibr">8</a>,<a href="#B10-applsci-14-10742" class="html-bibr">10</a>,<a href="#B11-applsci-14-10742" class="html-bibr">11</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10742'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525134" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <a data-dropdown="drop-supplementary-1525134" aria-controls="drop-supplementary-1525134" aria-expanded="false" title="Supplementary Material"> <i class="material-icons">attachment</i> </a> <div id="drop-supplementary-1525134" class="f-dropdown label__btn__dropdown label__btn__dropdown--wide" data-dropdown-content aria-hidden="true" tabindex="-1"> Supplementary material: <br/> <a href="/2076-3417/14/22/10741/s1?version=1732100077"> Supplementary File 1 (ZIP, 98 KiB) </a><br/> </div> </div> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 23 pages, 3012 KiB   </span> <a href="/2076-3417/14/22/10741/pdf?version=1732173483" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Novel In Silico Strategies to Model the In Vivo Nerve Scarring Around Implanted Parylene C Devices" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10741">Novel In Silico Strategies to Model the In Vivo Nerve Scarring Around Implanted Parylene C Devices</a> <div class="authors"> by <span class="inlineblock "><strong>Pier Nicola Sergi</strong>, </span><span class="inlineblock "><strong>Jaume del Valle</strong>, </span><span class="inlineblock "><strong>Thomas Stieglitz</strong>, </span><span class="inlineblock "><strong>Xavier Navarro</strong> and </span><span class="inlineblock "><strong>Silvestro Micera</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10741; <a href="https://doi.org/10.3390/app142210741">https://doi.org/10.3390/app142210741</a> - 20 Nov 2024 </div> Viewed by 273 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The implantation of materials into in vivo peripheral nerves triggers the production of scar tissue. A scar capsule progressively incorporates foreign bodies, which become insulated from the surrounding environment. This phenomenon is particularly detrimental in the case of electrical active sites enveloped within <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10741/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The implantation of materials into in vivo peripheral nerves triggers the production of scar tissue. A scar capsule progressively incorporates foreign bodies, which become insulated from the surrounding environment. This phenomenon is particularly detrimental in the case of electrical active sites enveloped within scar sheets, since the loss of contact with axons highly decreases the effectiveness of neural interfaces. As a consequence, the in silico modelling of scar capsule evolution may lead to improvements in the design of intraneural structures and enhancing their reliability over time. In this work, a novel theoretical framework is proposed to model the evolution of capsule thickness over time together with an improved optimisation procedure able to avoid apparently suitable choices resulting from standard procedures. This framework provides a fast, simple, and accurate modelling of experimental data (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.97</mn></mrow></semantics></math></inline-formula>), definitely improving on previous approaches. <a href="/2076-3417/14/22/10741">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/applsci/sections/electrical_electronics_communications_engineering">Electrical, Electronics and Communications Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10741/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525134"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525134"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525134" data-cycle-prev="#prev1525134" data-cycle-progressive="#images1525134" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525134-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g001-550.jpg?1732173651" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525134" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525134-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g002-550.jpg?1732173652'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525134-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g003-550.jpg?1732173654'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525134-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g004-550.jpg?1732173656'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525134-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g005-550.jpg?1732173658'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525134-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g006-550.jpg?1732173659'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525134-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g007-550.jpg?1732173660'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525134-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g008-550.jpg?1732173663'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525134-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g009a-550.jpg?1732173665'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525134-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g009b-550.jpg?1732173666'><p>Figure 9 Cont.</p></div></script></div></div><div id="article-1525134-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g001-550.jpg?1732173651" title=" <strong>Figure 1</strong><br/> <p>(<b>a</b>) The W color matrix [decimal logarithm of RMSE (Root Mean Square Error)] was able to select potentially suitable combinations of i and j indexes. (<b>b</b>) The block matrix of potentially suitable combinations was divided into five submatrices <math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>A</mi> <mn>1</mn> </mrow> </msub> </semantics></math>(4<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>A</mi> <mn>2</mn> </mrow> </msub> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>A</mi> <mn>3</mn> </mrow> </msub> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>A</mi> <mn>4</mn> </mrow> </msub> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), and <math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>A</mi> <mn>5</mn> </mrow> </msub> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1). (<b>c</b>) The block of not suitable combinations was divided into five submatrices <math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>B</mi> <mn>1</mn> </mrow> </msub> </semantics></math>(1<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>B</mi> <mn>2</mn> </mrow> </msub> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>B</mi> <mn>3</mn> </mrow> </msub> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>B</mi> <mn>4</mn> </mrow> </msub> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), and <math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>B</mi> <mn>5</mn> </mrow> </msub> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1). (<b>d</b>) The twin LRR color matrix [base 10 logarithm of <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math>]. (<b>e</b>) Potentially suitable combinations were divided into five submatrices <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>A</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>(4<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>A</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>A</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>A</mi> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), and <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>A</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1). (<b>f</b>) The block of not suitable combinations was divided into five submatrices <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>B</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>(1<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>B</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>B</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>B</mi> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), and <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>B</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1). (<b>g</b>) The same structure was applied to the RR matrix (<math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> values): potentially suitable combinations were divided into five submatrices <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>A</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>(4<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>A</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>A</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>A</mi> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), and <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>A</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics></math> (3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1). (<b>h</b>) Not suitable combinations were divided in block matrices <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>B</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>(1<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>B</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>B</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>B</mi> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1), and <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>B</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics></math> (2<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>1).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g002-550.jpg?1732173652" title=" <strong>Figure 2</strong><br/> <p>(<b>a</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>b</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.94</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>c</b>) Box plots of both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.86</mn> </mrow> </semantics></math>). (<b>d</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: two vertical asymptotes and three stationary points were detected (see inset). (<b>e</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>f</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.57</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>g</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.99</mn> </mrow> </semantics></math>). (<b>h</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: a vertical asymptote and a stationary point were detected (see inset). (<b>i</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>j</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>k</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.98</mn> </mrow> </semantics></math>). (<b>l</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: a vertical asymptote and a stationary point were detected (see inset).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g003-550.jpg?1732173654" title=" <strong>Figure 3</strong><br/> <p>(<b>a</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>b</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.69</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>c</b>) Box plots of both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.96</mn> </mrow> </semantics></math>). (<b>d</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: three vertical asymptotes together with two stationary point were detected (see inset). (<b>e</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>f</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.35</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>g</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.94</mn> </mrow> </semantics></math>). (<b>h</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: a vertical asymptote together two stationary points was detected (see inset). (<b>i</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>)</mo> </mrow> </semantics></math>. (<b>j</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.50</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>k</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.99</mn> </mrow> </semantics></math>). (<b>l</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: a vertical asymptote and a stationary point were detected (see inset).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g004-550.jpg?1732173656" title=" <strong>Figure 4</strong><br/> <p>(<b>a</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>b</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.88</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>c</b>) Box plots of both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.82</mn> </mrow> </semantics></math>). (<b>d</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: three vertical asymptotes together with two stationary points were detected (see inset). (<b>e</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>f</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.46</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>g</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.85</mn> </mrow> </semantics></math>). (<b>h</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: a vertical asymptote together two stationary points was detected (see inset). (<b>i</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>j</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.29</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>k</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.78</mn> </mrow> </semantics></math>). (<b>l</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: no vertical asymptotes and a stationary point were detected (see inset).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g005-550.jpg?1732173658" title=" <strong>Figure 5</strong><br/> <p>(<b>a</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>b</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.84</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>c</b>) Box plots of both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.85</mn> </mrow> </semantics></math>). (<b>d</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: no vertical asymptotes and no stationary points were detected (see inset). (<b>e</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>f</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.16</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>g</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.86</mn> </mrow> </semantics></math>). (<b>h</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>)</mo> </mrow> </semantics></math>: no vertical asymptotes and a stationary point were detected (see inset). (<b>i</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>j</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.11</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>k</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.78</mn> </mrow> </semantics></math>). (<b>l</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>)</mo> </mrow> </semantics></math>: no vertical asymptotes and a stationary point were detected (see inset).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g006-550.jpg?1732173659" title=" <strong>Figure 6</strong><br/> <p>(<b>a</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>b</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.26</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>c</b>) Box plots of both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.84</mn> </mrow> </semantics></math>). (<b>d</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: no vertical asymptotes and a stationary point were detected (see inset). (<b>e</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>f</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.15</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>g</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.85</mn> </mrow> </semantics></math>). (<b>h</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: no vertical asymptotes and a stationary point were detected (see inset). (<b>i</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>j</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.07</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>k</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.35</mn> </mrow> </semantics></math>). (<b>l</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: no vertical asymptotes and a stationary point were detected (see inset). (<b>m</b>) Correlation between experimental and predicted values of scar tissue thickness for the function <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>n</b>) QQ plot of experimental data and in silico prediction distributions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.44</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>o</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.92</mn> </mrow> </semantics></math>). (<b>p</b>) Continuum evolution over time of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math>: no vertical asymptotes and three stationary points were detected (see inset).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g007-550.jpg?1732173660" title=" <strong>Figure 7</strong><br/> <p>The <math display="inline"><semantics> <mi mathvariant="sans-serif">Ξ</mi> </semantics></math> matrix, which accounts for the novel metric, is represented by a 3<math display="inline"><semantics> <mrow> <mspace width="3.33333pt"/> <mo>×</mo> <mspace width="3.33333pt"/> </mrow> </semantics></math>5 matrix together with a singleton matrix for the combination <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. In particular, on the left side of the <math display="inline"><semantics> <mi mathvariant="sans-serif">Ξ</mi> </semantics></math> matrix, some suitable combinations are identified (in yellow) and, among them, the best one (light yellow, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>j</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>). On the contrary, on the right side of the matrix are grouped some unsuitable combinations, due to the presence of one or more vertical asymptotes in the continuum evolution of the candidate function. In addition, the candidate with <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> shows a low value of the RR matrix, while the combination <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> results in Runge instability.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g008-550.jpg?1732173663" title=" <strong>Figure 8</strong><br/> <p>Shape changes and sensitivity of the best functional form <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math> with respect to numerical changes in parameters <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>α</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> <mo>,</mo> <mi>γ</mi> </mrow> </semantics></math> (legends): (<b>a</b>) Change in shape of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math> for increasing values of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>α</mi> <mn>0</mn> </msub> </mrow> </semantics></math> (arrow). (<b>b</b>) Sensitivity of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math> for increasing values of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>α</mi> <mn>0</mn> </msub> </mrow> </semantics></math> (arrow) when <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> <mo>,</mo> <mi>γ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. (<b>c</b>) Shape change of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math> for increasing values of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>α</mi> <mn>1</mn> </msub> </mrow> </semantics></math> (arrow). (<b>d</b>) Sensitivity of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math> for increasing values of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>α</mi> <mn>1</mn> </msub> </mrow> </semantics></math> (arrow) when <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> <mo>,</mo> <mi>γ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. (<b>e</b>) Change in shape of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math> for increasing values of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> </mrow> </semantics></math> (arrow). (<b>f</b>) Sensitivity of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math> for increasing values of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> </mrow> </semantics></math> (arrow) when <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>α</mi> <mn>1</mn> </msub> <mo>,</mo> <mi>γ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. (<b>g</b>) Change in shape of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math> for increasing values of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> </mrow> </semantics></math> (arrow). (<b>h</b>) Sensitivity of <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">¯</mo> </mover> <mo>,</mo> <mi>γ</mi> <mo>)</mo> </mrow> </semantics></math> for increasing values of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> </mrow> </semantics></math> (arrow) when <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>α</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>β</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g009a-550.jpg?1732173665" title=" <strong>Figure 9</strong><br/> <p>(<b>a</b>) Dependence of the best candidate function of the parameter <math display="inline"><semantics> <mi>γ</mi> </semantics></math>: the influence of the change in <math display="inline"><semantics> <mi>γ</mi> </semantics></math> on the ability to model the experimental data was investigated. A global decreasing trend (from around 0.86 to around 0.75) was observed for increasing values of the <math display="inline"><semantics> <mi>γ</mi> </semantics></math> parameter, together with some sudden <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> transitions and plateau-like values. (<b>b</b>) Correlation between experimental and predicted values (<math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>) narrowed to experimental determinations. (<b>c</b>) QQ plot showing the form of distribution for both experimental data and in silico predictions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>d</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.77</mn> </mrow> </semantics></math>). (<b>e</b>) Comparison between experimental data and predictions for the continuum evolution over time of the best candidate function with <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>6.5575</mn> </mrow> </semantics></math>. Mean experimental data are shown with error bars (<math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> </mrow> </semantics></math> standard deviation), while the mean in silico prediction (red bold line) is shown with <math display="inline"><semantics> <mrow> <mn>99.95</mn> <mo>%</mo> </mrow> </semantics></math> confidence prediction bounds. (<b>f</b>) Increment in the <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> standard statistics in this work with respect to previous literature works (i.e., Refs. [<a href="#B57-applsci-14-10741" class="html-bibr">57</a>,<a href="#B58-applsci-14-10741" class="html-bibr">58</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10741/article_deploy/html/images/applsci-14-10741-g009b-550.jpg?1732173666" title=" <strong>Figure 9 Cont.</strong><br/> <p>(<b>a</b>) Dependence of the best candidate function of the parameter <math display="inline"><semantics> <mi>γ</mi> </semantics></math>: the influence of the change in <math display="inline"><semantics> <mi>γ</mi> </semantics></math> on the ability to model the experimental data was investigated. A global decreasing trend (from around 0.86 to around 0.75) was observed for increasing values of the <math display="inline"><semantics> <mi>γ</mi> </semantics></math> parameter, together with some sudden <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> transitions and plateau-like values. (<b>b</b>) Correlation between experimental and predicted values (<math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>) narrowed to experimental determinations. (<b>c</b>) QQ plot showing the form of distribution for both experimental data and in silico predictions (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.48</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math>, Shapiro–Francia normality test with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, respectively). (<b>d</b>) Box plots for both experimental data and in silico predictions (unpaired Student <span class="html-italic">t</span>-test <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.77</mn> </mrow> </semantics></math>). (<b>e</b>) Comparison between experimental data and predictions for the continuum evolution over time of the best candidate function with <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>6.5575</mn> </mrow> </semantics></math>. Mean experimental data are shown with error bars (<math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> </mrow> </semantics></math> standard deviation), while the mean in silico prediction (red bold line) is shown with <math display="inline"><semantics> <mrow> <mn>99.95</mn> <mo>%</mo> </mrow> </semantics></math> confidence prediction bounds. (<b>f</b>) Increment in the <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> standard statistics in this work with respect to previous literature works (i.e., Refs. [<a href="#B57-applsci-14-10741" class="html-bibr">57</a>,<a href="#B58-applsci-14-10741" class="html-bibr">58</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10741'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525109" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 18 pages, 4410 KiB   </span> <a href="/2076-3417/14/22/10740/pdf?version=1732098991" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Evaluating Environmental Impacts of Urban Development Strategies: A Case Study of the Fontaine d’Ouche District" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10740">Evaluating Environmental Impacts of Urban Development Strategies: A Case Study of the Fontaine d’Ouche District</a> <div class="authors"> by <span class="inlineblock "><strong>Mohamad Achour</strong>, </span><span class="inlineblock "><strong>Mohamad Toufaily</strong>, </span><span class="inlineblock "><strong>Ludovic Avril</strong>, </span><span class="inlineblock "><strong>Gilles Betis</strong> and </span><span class="inlineblock "><strong>Nisrine Makhoul</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10740; <a href="https://doi.org/10.3390/app142210740">https://doi.org/10.3390/app142210740</a> - 20 Nov 2024 </div> Viewed by 330 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Life cycle assessment (LCA) is a methodology used to analyze the environmental impacts of a product. Initially, it was applied to buildings only, but recently, it has also been applied to entire neighborhood. This expansion from individual buildings to the neighborhood scale requires <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10740/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Life cycle assessment (LCA) is a methodology used to analyze the environmental impacts of a product. Initially, it was applied to buildings only, but recently, it has also been applied to entire neighborhood. This expansion from individual buildings to the neighborhood scale requires additional inputs, such as the impacts of public spaces, transportation, public network systems, and roads. Additionally, conducting an LCA for a neighborhood requires software tools to simulate the neighborhood and databases to store the environmental impacts of all the components integrated into the neighborhood. This paper presents an LCA case study of a neighborhood in Dijon, France. The study aims to analyze the thermal impacts of buildings and the neighborhood with single-, double-, and triple-pane windows. The second part of the study involves two LCA studies using the 1996 version of ecoinvent. The first study is a continuation of the thermal simulation analysis, while the second study compares concrete, masonry, and timber-based construction designs for the neighborhoods. The results show that the heating demands inside the buildings are substantially reduced when transitioning from single- to triple-pane, while the cooling demands show the opposite effect. Furthermore, doubling the width of double-pane windows resulted in less profound environmental damage for the construction, use, and demolition phases compared to single-pane windows, with only the renovation phase being more damaging. Additionally, the comparison between different construction scenarios shows that the timber-based variant is slightly advantageous from an environmental point of view compared to the concrete and masonry designs. <a href="/2076-3417/14/22/10740">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/D28D8TVT8T ">Enhancing Structural Sustainability: Data-Based Adaptive Solutions for the Built Environment</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10740/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525109"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525109"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525109" data-cycle-prev="#prev1525109" data-cycle-progressive="#images1525109" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525109-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g001-550.jpg?1732099126" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525109" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g002-550.jpg?1732099127'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g003-550.jpg?1732099128'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g004-550.jpg?1732099128'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g005-550.jpg?1732099130'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g006-550.jpg?1732099131'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g007-550.jpg?1732099132'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g008-550.jpg?1732099132'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g009-550.jpg?1732099134'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g010-550.jpg?1732099135'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g011-550.jpg?1732099136'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g012-550.jpg?1732099137'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1525109-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g013-550.jpg?1732099138'><p>Figure 13</p></div></script></div></div><div id="article-1525109-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g001-550.jpg?1732099126" title=" <strong>Figure 1</strong><br/> <p>Simplified explanation of the difference between the studied components in building LCA and neighborhood LCA.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g002-550.jpg?1732099127" title=" <strong>Figure 2</strong><br/> <p>Histogram representing the heating and cooling needs of Building 4 about the type of window pane installed.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g003-550.jpg?1732099128" title=" <strong>Figure 3</strong><br/> <p>Histogram representing the heating needs of Building 3 about in relation to the type of window installed and the type of scenarios adopted.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g004-550.jpg?1732099128" title=" <strong>Figure 4</strong><br/> <p>Histogram representing the cooling needs of Building 3 in relation to the type of window installed and type of scenarios adopted.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g005-550.jpg?1732099130" title=" <strong>Figure 5</strong><br/> <p>Comparative diagram of the environmental impacts between SV and DV neighborhoods for the construction phase.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g006-550.jpg?1732099131" title=" <strong>Figure 6</strong><br/> <p>Comparative diagram of the environmental impacts between SV and DV neighborhoods for the operation phase.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g007-550.jpg?1732099132" title=" <strong>Figure 7</strong><br/> <p>Comparative diagram of the environmental impacts between SV and DV neighborhoods for the renovation phase.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g008-550.jpg?1732099132" title=" <strong>Figure 8</strong><br/> <p>Comparative diagram of the environmental impacts between SV and DV neighborhoods for the deconstruction phase.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g009-550.jpg?1732099134" title=" <strong>Figure 9</strong><br/> <p>Histogram representing the normalized comparative values between the SV and DV neighborhoods for each environmental indicator.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g010-550.jpg?1732099135" title=" <strong>Figure 10</strong><br/> <p>Comparative diagram of the environmental impacts between the different construction designs for the construction phase.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g011-550.jpg?1732099136" title=" <strong>Figure 11</strong><br/> <p>Comparative diagram of the environmental impacts between the different construction designs for the operation phase.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g012-550.jpg?1732099137" title=" <strong>Figure 12</strong><br/> <p>Comparative diagram of the environmental impacts between the different construction designs for the deconstruction phase.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10740/article_deploy/html/images/applsci-14-10740-g013-550.jpg?1732099138" title=" <strong>Figure 13</strong><br/> <p>Comparative diagram of the environmental impacts between the different construction designs for the totality of the life cycle analysis.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10740'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1525099" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <a data-dropdown="drop-supplementary-1525099" aria-controls="drop-supplementary-1525099" aria-expanded="false" title="Supplementary Material"> <i class="material-icons">attachment</i> </a> <div id="drop-supplementary-1525099" class="f-dropdown label__btn__dropdown label__btn__dropdown--wide" data-dropdown-content aria-hidden="true" tabindex="-1"> Supplementary material: <br/> <a href="/2076-3417/14/22/10739/s1?version=1732098684"> Supplementary File 1 (ZIP, 2057 KiB) </a><br/> </div> </div> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 2098 KiB   </span> <a href="/2076-3417/14/22/10739/pdf?version=1732098684" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="The Value of Using Green Extraction Techniques to Enhance Polyphenol Content and Antioxidant Activity in Nasturtium officinale Leaves" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10739">The Value of Using Green Extraction Techniques to Enhance Polyphenol Content and Antioxidant Activity in <i>Nasturtium officinale</i> Leaves</a> <div class="authors"> by <span class="inlineblock "><strong>Eva Naoum</strong>, </span><span class="inlineblock "><strong>Aikaterini Xynopoulou</strong>, </span><span class="inlineblock "><strong>Konstantina Kotsou</strong>, </span><span class="inlineblock "><strong>Theodoros Chatzimitakos</strong>, </span><span class="inlineblock "><strong>Vassilis Athanasiadis</strong>, </span><span class="inlineblock "><strong>Eleni Bozinou</strong> and </span><span class="inlineblock "><strong>Stavros I. Lalas</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10739; <a href="https://doi.org/10.3390/app142210739">https://doi.org/10.3390/app142210739</a> - 20 Nov 2024 </div> Viewed by 429 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Increasing research is being directed toward the production of value-added products using plant extracts that are super-fortified with antioxidants. In this study, the extraction parameters for bioactive compounds (such as polyphenols) from <i>Nasturtium officinale</i> leaves and their antioxidant properties were optimized using response <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10739/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Increasing research is being directed toward the production of value-added products using plant extracts that are super-fortified with antioxidants. In this study, the extraction parameters for bioactive compounds (such as polyphenols) from <i>Nasturtium officinale</i> leaves and their antioxidant properties were optimized using response surface methodology. The optimization procedure examined the effects of the extraction temperature, time, and solvent composition on conventional magnetic stirring (ST). In addition, the impacts of two green techniques—pulsed electric field (PEF) and ultrasound (US)—were evaluated individually and in combination to assess their potential to enhance the extraction of the compounds. According to our findings, under the proposed extraction conditions (a combination of PEF, US, and ST as a extraction technique, 50% ethanolic solvent, for 30 min at 80 °C). <i>N. officinale</i> leaf extract proved to be an excellent source of bioactive compounds, with extracts containing rosmarinic acid (3.42 mg/g dried weight (dw)), chlorogenic acid (3.13 mg/g dw), total polyphenol content (28.82 mg of gallic acid equivalents (GAE)/g dw), and strong antioxidant properties. The FRAP method measured 57.15 μmol ascorbic acid equivalents (AAE)/g dw, while the DPPH radical scavenging activity method measured 47.55 μmol AAE/g dw. This study was carried out to evaluate and improve the concentration of bioactive compounds in <i>N. officinale</i> leaf extract, resulting in a product with multiple applications across the food, cosmetic, and pharmaceutical industries. <a href="/2076-3417/14/22/10739">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/applsci/sections/food_science_and_technology">Food Science and Technology</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10739/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1525099"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1525099"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1525099" data-cycle-prev="#prev1525099" data-cycle-progressive="#images1525099" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1525099-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10739/article_deploy/html/images/applsci-14-10739-g001-550.jpg?1732098790" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1525099" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1525099-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10739/article_deploy/html/images/applsci-14-10739-g002-550.jpg?1732098791'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1525099-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10739/article_deploy/html/images/applsci-14-10739-g003-550.jpg?1732098793'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1525099-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10739/article_deploy/html/images/applsci-14-10739-g004-550.jpg?1732098794'><p>Figure 4</p></div></script></div></div><div id="article-1525099-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10739/article_deploy/html/images/applsci-14-10739-g001-550.jpg?1732098790" title=" <strong>Figure 1</strong><br/> <p>Pareto plots represent transformed estimates for TPC (<b>A</b>), FRAP (<b>B</b>), and DPPH (<b>C</b>) assays. A gold dashed rectangular reference line is included in the plot to denote the significance level (<span class="html-italic">p</span> &lt; 0.05). Blue bars indicate positive values, while red bars represent negative values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10739'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10739/article_deploy/html/images/applsci-14-10739-g002-550.jpg?1732098791" title=" <strong>Figure 2</strong><br/> <p>Principal component analysis (PCA) was applied to the measured variables, with each <span class="html-italic">X</span> variable represented in blue.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10739'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10739/article_deploy/html/images/applsci-14-10739-g003-550.jpg?1732098793" title=" <strong>Figure 3</strong><br/> <p>Plot (<b>A</b>) illustrates the desirability function with extrapolation control and the partial least squares (PLS) prediction profiler for optimizing watercress plant. The variable importance plot (VIP) option in Plot (<b>B</b>) displays the VIP values for each predictor variable. The blue dashed line at the 0.8 mark on the VIT indicates the significance level for each variable.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10739'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10739/article_deploy/html/images/applsci-14-10739-g004-550.jpg?1732098794" title=" <strong>Figure 4</strong><br/> <p>Exemplary HPLC chromatogram at 320 nm of optimal extract of watercress demonstrating identified polyphenolic compounds. (1) Chlorogenic acid; (2) caffeic acid; (3) syringic acid; (4) <span class="html-italic">p</span>-coumaric acid; (5) ferulic acid; (6) rutin; (7) quercetin 3-β-<span class="html-italic">D</span>-glucoside; (8) luteolin-7-glucoside; (9) narirutin; (10) kaempferol-3-glucoside; (11) apigenin-7-<span class="html-italic">O</span>-glucoside; (12) myricetin; (13) rosmarinic acid.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10739'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1524995" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 3765 KiB   </span> <a href="/2076-3417/14/22/10738/pdf?version=1732095812" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Drainage Troughs as a Protective Measure in Subway–Pedestrian Collisions: A Multibody Model Evaluation" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/22/10738">Drainage Troughs as a Protective Measure in Subway–Pedestrian Collisions: A Multibody Model Evaluation</a> <div class="authors"> by <span class="inlineblock "><strong>Daniel Hall</strong>, </span><span class="inlineblock "><strong>Kevin Gildea</strong> and </span><span class="inlineblock "><strong>Ciaran Simms</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10738; <a href="https://doi.org/10.3390/app142210738">https://doi.org/10.3390/app142210738</a> - 20 Nov 2024 </div> Viewed by 219 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Introduction: Subway–pedestrian collisions are a significant and growing problem, but they are poorly understood. This study presents the first subway–pedestrian collision model with the aim of evaluating the baseline safety performance of an R160 NYC train and track combination and the potential safety <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10738/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Introduction: Subway–pedestrian collisions are a significant and growing problem, but they are poorly understood. This study presents the first subway–pedestrian collision model with the aim of evaluating the baseline safety performance of an R160 NYC train and track combination and the potential safety effects of drainage trough depth. Methods: A baseline simulation test sample of 384 unique impacts (8 velocities (2–16 m/s), 24 positions (standing jumping and lying), and 2 track types (flat and crossties)) was created in MADYMO. The full simulation test sample (N = 1920) included with various depth drainage troughs (0–1 m). Head injuries and wheel and third rail contacts were evaluated. Results: Limb–wheel contact occurred in 60% of scenarios. Primary and secondary contact HIC<sub>15</sub> showed similar high severity, with an HIC<sub>15</sub> < 2000 (88% risk of AIS 4+) in 29% of results for both train and ground contact. Impact velocity strongly influences primary contact HIC<sub>15</sub> with limited effect on secondary contact. Impact velocities between 6 and 16 m/s showed little change in wheel contact. Increasing the trough depth up to 0.5 m showed a decrease in wheel contact probability with little increase in secondary contact. No further benefits were found above 0.5 m. Conclusions: A subway–pedestrian collision model is presented which predicts that wheel–pedestrian contact risk can be reduced with a 0.5 m drainage trough. The model suggests that slower impact velocities may reduce head injury risk for primary contact; however, this will have less effect on injuries caused by secondary and wheel contact. <a href="/2076-3417/14/22/10738">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/4A4M29DP74 ">Railway Dynamic Simulation: Recent Advances and Perspective, 2nd Edition</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10738/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1524995"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1524995"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1524995" data-cycle-prev="#prev1524995" data-cycle-progressive="#images1524995" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1524995-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g001-550.jpg?1732095886" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1524995" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1524995-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g002-550.jpg?1732095888'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1524995-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g003-550.jpg?1732095890'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1524995-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g004-550.jpg?1732095891'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1524995-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g005-550.jpg?1732095893'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1524995-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g006-550.jpg?1732095895'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1524995-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g007-550.jpg?1732095897'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1524995-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g008-550.jpg?1732095899'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1524995-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g009-550.jpg?1732095900'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1524995-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g010-550.jpg?1732095903'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1524995-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g011-550.jpg?1732095904'><p>Figure 11</p></div></script></div></div><div id="article-1524995-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g001-550.jpg?1732095886" title=" <strong>Figure 1</strong><br/> <p>MADYMO multibody model of an R160 subway train. A = anticlimber, B = coupler, and C = wheels.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g002-550.jpg?1732095888" title=" <strong>Figure 2</strong><br/> <p>NYC flat track bed with no crossties (image taken by the author) and multibody model. A—running rail, B—third rail (live), and C—third rail guard.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g003-550.jpg?1732095890" title=" <strong>Figure 3</strong><br/> <p>NYC track bed with drainage trough and crossties (image taken by the author) and multibody model. A—running rail, B—third rail (Live), C—third rail guard, D—drainage trough (suicide pit), and E—crosstie.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g004-550.jpg?1732095891" title=" <strong>Figure 4</strong><br/> <p>Representative stills from YouTube videos, illustrating 2D keypoints, 3D keypoints, and the subsequent MADYMO pedestrian modelling configuration via KinePose [<a href="#B41-applsci-14-10738" class="html-bibr">41</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g005-550.jpg?1732095893" title=" <strong>Figure 5</strong><br/> <p>Overall combined contact scores and weighted contact scores for the baseline simulation test sample.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g006-550.jpg?1732095895" title=" <strong>Figure 6</strong><br/> <p>Contact scores and weighted contact scores for the baseline model for each impact position.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g007-550.jpg?1732095897" title=" <strong>Figure 7</strong><br/> <p>Contact scores and weighted contact scores shown as a function of impact velocity (weighted *).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g008-550.jpg?1732095899" title=" <strong>Figure 8</strong><br/> <p>Baseline simulation test sample (BSTS) primary and secondary HIC<sub>15</sub> scores for jumping and standing impacts. The 2000 HIC<sub>15</sub> threshold is also shown.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g009-550.jpg?1732095900" title=" <strong>Figure 9</strong><br/> <p>Baseline simulation test sample proportion of jumping and standing HIC<sub>15</sub> &lt; 2000.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g010-550.jpg?1732095903" title=" <strong>Figure 10</strong><br/> <p>Full simulation test sample contact scores separated by impact position and drainage trough depth.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10738/article_deploy/html/images/applsci-14-10738-g011-550.jpg?1732095904" title=" <strong>Figure 11</strong><br/> <p>Relationship between drainage trough depth and secondary contact HIC<sub>15</sub> score for jumping and standing impact scenarios.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10738'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1524978" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 17 pages, 8959 KiB   </span> <a href="/2076-3417/14/22/10737/pdf?version=1732095124" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Laboratory Assessment of Manual Wheelchair Propulsion" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/14/22/10737">Laboratory Assessment of Manual Wheelchair Propulsion</a> <div class="authors"> by <span class="inlineblock "><strong>Bartosz Wieczorek</strong> and </span><span class="inlineblock "><strong>Maciej Sydor</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(22), 10737; <a href="https://doi.org/10.3390/app142210737">https://doi.org/10.3390/app142210737</a> - 20 Nov 2024 </div> Viewed by 198 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Self-propelled manual wheelchairs offer several advantages over electric wheelchairs, including promoting physical activity and requiring less maintenance due to their simple design. While theoretical analyses provide valuable insights, laboratory testing remains the most reliable method for evaluating and improving the efficiency of manual <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10737/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Self-propelled manual wheelchairs offer several advantages over electric wheelchairs, including promoting physical activity and requiring less maintenance due to their simple design. While theoretical analyses provide valuable insights, laboratory testing remains the most reliable method for evaluating and improving the efficiency of manual wheelchair drives. This article reviews and analyzes the laboratory methods for assessing the efficiency of wheelchair propulsion documented in the scientific literature: (1) A wheelchair dynamometer that replicates real-world driving scenarios, quantifies the wheelchair’s motion characteristics, and evaluates the physical exertion required for propulsion. (2) Simultaneous measurements of body position, motion, and upper limb EMG data to analyze biomechanics. (3) A method for determining the wheelchair’s trajectory based on data from the dynamometer. (4) Measurements of the dynamic center of mass (COM) of the human–wheelchair system to assess stability and efficiency; and (5) data analysis techniques for parameterizing large datasets and determining the COM. The key takeaways include the following: (1) manual wheelchairs offer benefits over electric ones but require customization to suit individual user biomechanics; (2) the necessity of laboratory-based ergometer testing for optimizing propulsion efficiency and safety; (3) the feasibility of replicating real-world driving scenarios in laboratory settings; and (4) the importance of efficient data analysis techniques for interpreting biomechanical studies. <a href="/2076-3417/14/22/10737">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/applsci/sections/biomedical_engineering">Biomedical Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/22/10737/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1524978"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1524978"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1524978" data-cycle-prev="#prev1524978" data-cycle-progressive="#images1524978" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1524978-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g001-550.jpg?1732095329" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1524978" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g002-550.jpg?1732095330'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g003-550.jpg?1732095332'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g004-550.jpg?1732095334'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g005-550.jpg?1732095336'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g006-550.jpg?1732095337'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g007-550.jpg?1732095338'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g008-550.jpg?1732095338'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g009-550.jpg?1732095340'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g010-550.jpg?1732095341'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g011-550.jpg?1732095342'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g012-550.jpg?1732095343'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1524978-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g013-550.jpg?1732095343'><p>Figure 13</p></div></script></div></div><div id="article-1524978-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g001-550.jpg?1732095329" title=" <strong>Figure 1</strong><br/> <p>Subsystems of a manual wheelchair (<span class="html-italic">Freeasy</span> model, manufactured by Cosmotech, Gliwice, Poland; source: own study).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g002-550.jpg?1732095330" title=" <strong>Figure 2</strong><br/> <p>Information flow diagram for wheelchair propulsion biomechanics study on the stationary roller dynamometer (source: [<a href="#B34-applsci-14-10737" class="html-bibr">34</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g003-550.jpg?1732095332" title=" <strong>Figure 3</strong><br/> <p>View of the stationary roller dynamometer with details of the most essential elements (own study): 1—support frame, 2—strain gauges, 3—weighing pan, 4—linear guides, 5—clamping system, 6—traction rollers, 7—BLDC motor, 8—encoder.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g004-550.jpg?1732095334" title=" <strong>Figure 4</strong><br/> <p>Wheelchair secured to the stationary roller dynamometer (source: own study): (<b>A</b>)—traction rollers, (<b>B</b>)—weight-scale lever, (<b>C</b>)—safety pin, (<b>D</b>)—clamps securing the wheelchair frame, (<b>E</b>)—lever arm height adjuster, (<b>F</b>)—fastened wheelchair frame.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g005-550.jpg?1732095336" title=" <strong>Figure 5</strong><br/> <p>Stroke patterns of the wheelchair propulsion (based on the methodology outlined in [<a href="#B35-applsci-14-10737" class="html-bibr">35</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g006-550.jpg?1732095337" title=" <strong>Figure 6</strong><br/> <p>Measurement apparatus used in the study: (<b>a</b>)—camera, (<b>b</b>)—illuminating lamp, (<b>c</b>)—boom, (<b>d</b>)—AruCo marker, (<b>e</b>)—EMG device (source: [<a href="#B42-applsci-14-10737" class="html-bibr">42</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g007-550.jpg?1732095338" title=" <strong>Figure 7</strong><br/> <p>Relative error (<b>A</b>) and number of detection points (<b>B</b>) as a function of marker speed (adapted from [<a href="#B45-applsci-14-10737" class="html-bibr">45</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g008-550.jpg?1732095338" title=" <strong>Figure 8</strong><br/> <p>Determination of the turning radius <span class="html-italic">R</span>, using the trapezoid method based on known values of left wheel speed <span class="html-italic">v</span><sub>L</sub>, right wheel speed <span class="html-italic">v</span><sub>P</sub>, and wheelbase <span class="html-italic">L</span> (source: [<a href="#B47-applsci-14-10737" class="html-bibr">47</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g009-550.jpg?1732095340" title=" <strong>Figure 9</strong><br/> <p>The partitioning of the wheelchair trajectory into trapezoidal segments (source: [<a href="#B47-applsci-14-10737" class="html-bibr">47</a>]): (<b>A</b>)—distance covered in the first iteration, (<b>B</b>)—distance covered in the second iteration, (<b>C</b>)—a combination of distances covered in the analyzed iterations, (<b>D</b>)—trajectory determined based on the specified iterations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g010-550.jpg?1732095341" title=" <strong>Figure 10</strong><br/> <p>Schematic diagram of the test stand with the reactions determined using strain gauge scales in four measurement planes (source: [<a href="#B34-applsci-14-10737" class="html-bibr">34</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g011-550.jpg?1732095342" title=" <strong>Figure 11</strong><br/> <p>Schematic diagrams of the beams showing the position of the center of mass (COM) of the person in the wheelchair on each of the four measurement planes (source: [<a href="#B34-applsci-14-10737" class="html-bibr">34</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g012-550.jpg?1732095343" title=" <strong>Figure 12</strong><br/> <p>Schematic diagram of the method for determining the position of the center of mass (COM) in the XY plane (source: [<a href="#B34-applsci-14-10737" class="html-bibr">34</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10737/article_deploy/html/images/applsci-14-10737-g013-550.jpg?1732095343" title=" <strong>Figure 13</strong><br/> <p>Schematic illustration of the method of replacing an arbitrary set of points (<b>a</b>) with an ellipse defining the area of points on the analyzed plane (<b>b</b>) (source: [<a href="#B57-applsci-14-10737" class="html-bibr">57</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/22/10737'>Full article</a></strong> "></a></div> </div> </div> <span class="more" style="display: none;"></span> </div> <div class="row footer"> <div class="listing-select-options"> <div class="columns small-12"> <div class="select generic-item"> <a href="#" class="export-options-show export-element export-expanded"> Show export 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Applied Sciences | November-2 2024 - Browse Articles