Applied Sciences | Editor’s choice Articles

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<option value="geomatics" > Geomatics </option> <option value="geometry" > Geometry </option> <option value="geosciences" > Geosciences </option> <option value="geotechnics" > Geotechnics </option> <option value="geriatrics" > Geriatrics </option> <option value="glacies" > Glacies </option> <option value="gucdd" > Gout, Urate, and Crystal Deposition Disease (GUCDD) </option> <option value="grasses" > Grasses </option> <option value="hardware" > Hardware </option> <option value="healthcare" > Healthcare </option> <option value="hearts" > Hearts </option> <option value="hemato" > Hemato </option> <option value="hematolrep" > Hematology Reports </option> <option value="heritage" > Heritage </option> <option value="histories" > Histories </option> <option value="horticulturae" > Horticulturae </option> <option value="hospitals" > Hospitals </option> <option value="humanities" > Humanities </option> <option value="humans" > Humans </option> <option value="hydrobiology" > Hydrobiology </option> <option value="hydrogen" > Hydrogen </option> <option value="hydrology" > Hydrology </option> <option value="hygiene" > Hygiene </option> <option value="immuno" > Immuno </option> <option value="idr" > Infectious Disease Reports </option> <option value="informatics" > Informatics </option> <option value="information" > Information </option> <option value="infrastructures" > Infrastructures </option> <option value="inorganics" > Inorganics </option> <option value="insects" > Insects </option> <option value="instruments" > Instruments </option> <option value="iic" > Intelligent Infrastructure and Construction </option> <option value="ijerph" > International Journal of Environmental Research and Public Health (IJERPH) </option> <option value="ijfs" > International Journal of Financial Studies (IJFS) </option> <option value="ijms" > International Journal of Molecular Sciences (IJMS) </option> <option value="IJNS" > International Journal of Neonatal Screening (IJNS) </option> <option value="ijpb" > International Journal of Plant Biology (IJPB) </option> <option value="ijt" > International Journal of Topology </option> <option value="ijtm" > International Journal of Translational Medicine (IJTM) </option> <option value="ijtpp" > International Journal of Turbomachinery, Propulsion and Power (IJTPP) </option> <option value="ime" > International Medical Education (IME) </option> <option value="inventions" > Inventions </option> <option value="IoT" > IoT </option> <option value="ijgi" > ISPRS International Journal of Geo-Information (IJGI) </option> <option value="J" > J </option> <option value="jal" > Journal of Ageing and Longevity (JAL) </option> <option value="jcdd" > Journal of Cardiovascular Development and Disease (JCDD) </option> <option value="jcto" > Journal of Clinical & Translational Ophthalmology (JCTO) </option> <option value="jcm" > Journal of Clinical Medicine (JCM) </option> <option value="jcs" > Journal of Composites Science (J. 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Intell.) </option> <option value="jlpea" > Journal of Low Power Electronics and Applications (JLPEA) </option> <option value="jmmp" > Journal of Manufacturing and Materials Processing (JMMP) </option> <option value="jmse" > Journal of Marine Science and Engineering (JMSE) </option> <option value="jmahp" > Journal of Market Access & Health Policy (JMAHP) </option> <option value="jmp" > Journal of Molecular Pathology (JMP) </option> <option value="jnt" > Journal of Nanotheranostics (JNT) </option> <option value="jne" > Journal of Nuclear Engineering (JNE) </option> <option value="ohbm" > Journal of Otorhinolaryngology, Hearing and Balance Medicine (JOHBM) </option> <option value="jop" > Journal of Parks </option> <option value="jpm" > Journal of Personalized Medicine (JPM) </option> <option value="jpbi" > Journal of Pharmaceutical and BioTech Industry (JPBI) </option> <option value="jor" > Journal of Respiration (JoR) </option> <option value="jrfm" > Journal of Risk and Financial Management (JRFM) </option> <option value="jsan" > Journal of Sensor and Actuator Networks (JSAN) </option> <option value="joma" > Journal of the Oman Medical Association (JOMA) </option> <option value="jtaer" > Journal of Theoretical and Applied Electronic Commerce Research (JTAER) </option> <option value="jvd" > Journal of Vascular Diseases (JVD) </option> <option value="jox" > Journal of Xenobiotics (JoX) </option> <option value="jzbg" > Journal of Zoological and Botanical Gardens (JZBG) </option> <option value="journalmedia" > Journalism and Media </option> <option value="kidneydial" > Kidney and Dialysis </option> <option value="kinasesphosphatases" > Kinases and Phosphatases </option> <option value="knowledge" > Knowledge </option> <option value="labmed" > LabMed </option> <option value="laboratories" > Laboratories </option> <option value="land" > Land </option> <option value="languages" > Languages </option> <option value="laws" > Laws </option> <option value="life" > Life 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Mathematics </option> <option value="medsci" > Medical Sciences </option> <option value="msf" > Medical Sciences Forum </option> <option value="medicina" > Medicina </option> <option value="medicines" > Medicines </option> <option value="membranes" > Membranes </option> <option value="merits" > Merits </option> <option value="metabolites" > Metabolites </option> <option value="metals" > Metals </option> <option value="meteorology" > Meteorology </option> <option value="methane" > Methane </option> <option value="mps" > Methods and Protocols (MPs) </option> <option value="metrics" > Metrics </option> <option value="metrology" > Metrology </option> <option value="micro" > Micro </option> <option value="microbiolres" > Microbiology Research </option> <option value="micromachines" > Micromachines </option> <option value="microorganisms" > Microorganisms </option> <option value="microplastics" > Microplastics </option> <option value="minerals" > Minerals </option> <option value="mining" > Mining </option> <option value="modelling" > Modelling </option> <option value="mmphys" > Modern Mathematical Physics </option> <option value="molbank" > Molbank </option> <option value="molecules" > Molecules </option> <option value="mti" > Multimodal Technologies and Interaction (MTI) </option> <option value="muscles" > Muscles </option> <option value="nanoenergyadv" > Nanoenergy Advances </option> <option value="nanomanufacturing" > Nanomanufacturing </option> <option value="nanomaterials" > Nanomaterials </option> <option value="ndt" > NDT </option> <option value="network" > Network </option> <option value="neuroglia" > Neuroglia </option> <option value="neurolint" > Neurology International </option> <option value="neurosci" > NeuroSci </option> <option value="nitrogen" > Nitrogen </option> <option value="ncrna" > Non-Coding RNA (ncRNA) </option> <option value="nursrep" > Nursing Reports </option> <option value="nutraceuticals" > Nutraceuticals </option> <option value="nutrients" > Nutrients </option> <option value="obesities" > Obesities </option> <option value="oceans" > Oceans </option> <option value="onco" > Onco </option> <option value="optics" > Optics </option> <option value="oral" > Oral </option> <option value="organics" > Organics </option> <option value="organoids" > Organoids </option> <option value="osteology" > Osteology </option> <option value="oxygen" > Oxygen </option> <option value="parasitologia" > Parasitologia </option> <option value="particles" > Particles </option> <option value="pathogens" > Pathogens </option> <option value="pathophysiology" > Pathophysiology </option> <option value="pediatrrep" > Pediatric Reports </option> <option value="pets" > Pets </option> <option value="pharmaceuticals" > Pharmaceuticals </option> <option value="pharmaceutics" > Pharmaceutics </option> <option value="pharmacoepidemiology" > Pharmacoepidemiology </option> <option value="pharmacy" > Pharmacy </option> <option value="philosophies" > Philosophies </option> <option value="photochem" > Photochem </option> <option value="photonics" > Photonics </option> <option value="phycology" > Phycology </option> <option value="physchem" > Physchem </option> <option value="psf" > Physical Sciences Forum </option> <option value="physics" > Physics </option> <option value="physiologia" > Physiologia </option> <option value="plants" > Plants </option> <option value="plasma" > Plasma </option> <option value="platforms" > Platforms </option> <option value="pollutants" > Pollutants </option> <option value="polymers" > Polymers </option> <option value="polysaccharides" > Polysaccharides </option> <option value="populations" > Populations </option> <option value="poultry" > Poultry </option> <option value="powders" > Powders </option> <option value="proceedings" > Proceedings </option> <option value="processes" > Processes </option> <option value="prosthesis" > Prosthesis </option> <option value="proteomes" > Proteomes </option> <option 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<option value="toxins" > Toxins </option> <option value="transplantology" > Transplantology </option> <option value="traumacare" > Trauma Care </option> <option value="higheredu" > Trends in Higher Education </option> <option value="tropicalmed" > Tropical Medicine and Infectious Disease (TropicalMed) </option> <option value="universe" > Universe </option> <option value="urbansci" > Urban Science </option> <option value="uro" > Uro </option> <option value="vaccines" > Vaccines </option> <option value="vehicles" > Vehicles </option> <option value="venereology" > Venereology </option> <option value="vetsci" > Veterinary Sciences </option> <option value="vibration" > Vibration </option> <option value="virtualworlds" > Virtual Worlds </option> <option value="viruses" > Viruses </option> <option value="vision" > Vision </option> <option value="waste" > Waste </option> <option value="water" > Water </option> <option value="wild" > Wild </option> <option value="wind" > Wind </option> <option 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Sci.</em> <b>2023</b>, <em>13</em>(18), 10399; <a href="https://doi.org/10.3390/app131810399">https://doi.org/10.3390/app131810399</a> - 17 Sep 2023 </div> <a href="/2076-3417/13/18/10399#metrics">Cited by 51</a> | Viewed by 39148 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Wearable technology is increasingly vital for improving sports performance through real-time data analysis and tracking. Both professional and amateur athletes rely on wearable sensors to enhance training efficiency and competition outcomes. However, further research is needed to fully understand and optimize their potential <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/18/10399/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Wearable technology is increasingly vital for improving sports performance through real-time data analysis and tracking. Both professional and amateur athletes rely on wearable sensors to enhance training efficiency and competition outcomes. However, further research is needed to fully understand and optimize their potential in sports. This comprehensive review explores the measurement and monitoring of athletic performance, injury prevention, rehabilitation, and overall performance optimization using body wearable sensors. By analyzing wearables’ structure, research articles across various sports, and commercial sensors, the review provides a thorough analysis of wearable sensors in sports. Its findings benefit athletes, coaches, healthcare professionals, conditioners, managers, and researchers, offering a detailed summary of wearable technology in sports. The review is expected to contribute to future advancements in wearable sensors and biometric data analysis, ultimately improving sports performance. Limitations such as privacy concerns, accuracy issues, and costs are acknowledged, stressing the need for legal regulations, ethical principles, and technical measures for safe and fair use. The importance of personalized devices and further research on athlete comfort and performance impact is emphasized. The emergence of wearable imaging devices holds promise for sports rehabilitation and performance monitoring, enabling enhanced athlete health, recovery, and performance in the sports industry. <a href="/2076-3417/13/18/10399">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/13A921H9OV ">Advances in Wearable Devices for Sports</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/18/10399/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1241681"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1241681"><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="#next1241681" data-cycle-prev="#prev1241681" data-cycle-progressive="#images1241681" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1241681-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g001-550.jpg?1694941380" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1241681" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1241681-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g002-550.jpg?1694941381'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1241681-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g003-550.jpg?1694941383'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1241681-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g004-550.jpg?1694941384'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1241681-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g005-550.jpg?1694941387'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1241681-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g006-550.jpg?1694941389'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1241681-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g007-550.jpg?1694941392'><p>Figure 7</p></div></script></div></div><div id="article-1241681-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g001-550.jpg?1694941380" title=" <strong>Figure 1</strong><br/> <p>Number of Article by Journals.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/18/10399'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g002-550.jpg?1694941381" title=" <strong>Figure 2</strong><br/> <p>Number of Articles by Year.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/18/10399'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g003-550.jpg?1694941383" title=" <strong>Figure 3</strong><br/> <p>Number of Articles by Research Areas.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/18/10399'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g004-550.jpg?1694941384" title=" <strong>Figure 4</strong><br/> <p>Number of Articles by Country.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/18/10399'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g005-550.jpg?1694941387" title=" <strong>Figure 5</strong><br/> <p>Keyword Co-occurrence and Cluster Network.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/18/10399'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g006-550.jpg?1694941389" title=" <strong>Figure 6</strong><br/> <p>Layered Structure of Wearable Computing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/18/10399'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-10399/article_deploy/html/images/applsci-13-10399-g007-550.jpg?1694941392" title=" <strong>Figure 7</strong><br/> <p>Anatomical parts of the human body.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/18/10399'>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="1218142" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 20 pages, 1441 KiB   </span> <a href="/2076-3417/13/16/9288/pdf?version=1692167252" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Crop Prediction Model Using Machine Learning Algorithms" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/16/9288">Crop Prediction Model Using Machine Learning Algorithms</a> <div class="authors"> by <span class="inlineblock "><strong>Ersin Elbasi</strong>, </span><span class="inlineblock "><strong>Chamseddine Zaki</strong>, </span><span class="inlineblock "><strong>Ahmet E. Topcu</strong>, </span><span class="inlineblock "><strong>Wiem Abdelbaki</strong>, </span><span class="inlineblock "><strong>Aymen I. Zreikat</strong>, </span><span class="inlineblock "><strong>Elda Cina</strong>, </span><span class="inlineblock "><strong>Ahmed Shdefat</strong> and </span><span class="inlineblock "><strong>Louai Saker</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(16), 9288; <a href="https://doi.org/10.3390/app13169288">https://doi.org/10.3390/app13169288</a> - 16 Aug 2023 </div> <a href="/2076-3417/13/16/9288#metrics">Cited by 57</a> | Viewed by 35376 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/16/9288/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential benefits of integrating machine learning algorithms in modern agriculture. The main focus of these algorithms is to help optimize crop production and reduce waste through informed decisions regarding planting, watering, and harvesting crops. This paper includes a discussion on the current state of machine learning in agriculture, highlighting key challenges and opportunities, and presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, incorporating online IoT sensor data that were obtained in a real-time manner, farmers can make more informed verdicts about factors that affect crop growth. Eventually, integrating these technologies can transform modern agriculture by increasing crop yields while minimizing waste. Fifteen different algorithms have been considered to evaluate the most appropriate algorithms to use in agriculture, and a new feature combination scheme-enhanced algorithm is presented. The results show that we can achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99.46% using Naïve Bayes Classifier and Hoeffding Tree algorithms. These results will indicate an increase in production rates and reduce the effective cost for the farms, leading to more resilient infrastructure and sustainable environments. Moreover, the findings we obtained in this study can also help future farmers detect diseases early, increase crop production efficiency, and reduce prices when the world is experiencing food shortages. <a href="/2076-3417/13/16/9288">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/X31AV753JD ">Advances in Technology Applied in Agricultural Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/16/9288/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1218142"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1218142"><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="#next1218142" data-cycle-prev="#prev1218142" data-cycle-progressive="#images1218142" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1218142-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-09288/article_deploy/html/images/applsci-13-09288-g001-550.jpg?1692167447" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1218142" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1218142-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-09288/article_deploy/html/images/applsci-13-09288-g002-550.jpg?1692167448'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1218142-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-09288/article_deploy/html/images/applsci-13-09288-g003-550.jpg?1692167449'><p>Figure 3</p></div></script></div></div><div id="article-1218142-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-09288/article_deploy/html/images/applsci-13-09288-g001-550.jpg?1692167447" title=" <strong>Figure 1</strong><br/> <p>IoT and machine learning-based crop analysis and prediction process.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9288'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-09288/article_deploy/html/images/applsci-13-09288-g002-550.jpg?1692167448" title=" <strong>Figure 2</strong><br/> <p>AI-based crop analysis and prediction.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9288'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-09288/article_deploy/html/images/applsci-13-09288-g003-550.jpg?1692167449" title=" <strong>Figure 3</strong><br/> <p>Methodology for crop prediction using IoT and ML.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9288'>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="1206416" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 20 pages, 3740 KiB   </span> <a href="/2076-3417/13/15/8793/pdf?version=1690699236" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Variational Autoencoders for Data Augmentation in Clinical Studies" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/15/8793">Variational Autoencoders for Data Augmentation in Clinical Studies</a> <div class="authors"> by <span class="inlineblock "><strong>Dimitris Papadopoulos</strong> and </span><span class="inlineblock "><strong>Vangelis D. Karalis</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(15), 8793; <a href="https://doi.org/10.3390/app13158793">https://doi.org/10.3390/app13158793</a> - 30 Jul 2023 </div> <a href="/2076-3417/13/15/8793#metrics">Cited by 14</a> | Viewed by 3655 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/15/8793/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data augmentation approach in the field of clinical trials by employing variational autoencoders (VAEs). Several forms of VAEs were developed and used for the generation of virtual subjects. Various types of VAEs were explored and employed in the production of virtual individuals, and several different scenarios were investigated. The VAE-generated data exhibited similar performance to the original data, even in cases where a small proportion of them (e.g., 30–40%) was used for the reconstruction of the generated data. Additionally, the generated data showed even higher statistical power than the original data in cases of high variability. This represents an additional advantage for the use of VAEs in situations of high variability, as they can act as noise reduction. The application of VAEs in clinical trials can be a useful tool for decreasing the required sample size and, consequently, reducing the costs and time involved. Furthermore, it aligns with ethical concerns surrounding human participation in trials. <a href="/2076-3417/13/15/8793">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/WT1338FO98 ">Advanced Artificial Intelligence in Medicine and Bioinformatics</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/15/8793/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1206416"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1206416"><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="#next1206416" data-cycle-prev="#prev1206416" data-cycle-progressive="#images1206416" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1206416-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g001-550.jpg?1690699323" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1206416" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1206416-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g002-550.jpg?1690699315'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1206416-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g003-550.jpg?1690699314'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1206416-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g004-550.jpg?1690699321'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1206416-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g005-550.jpg?1690699322'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1206416-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g006-550.jpg?1690699313'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1206416-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g007-550.jpg?1690699319'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1206416-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g008-550.jpg?1690699317'><p>Figure 8</p></div></script></div></div><div id="article-1206416-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g001-550.jpg?1690699323" title=" <strong>Figure 1</strong><br/> <p>Visual representation of a variational autoencoder. The process of encoding involves compressing data from their original space to a latent space, while the decoding process involves decompressing the data. The methodology involves the utilization of neural networks as both an encoder and a decoder, with the aim of acquiring an optimal encoding–decoding scheme through an iterative optimization process. Variational autoencoders aim to establish mapping between the input data and a probability distribution across the latent space.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/15/8793'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g002-550.jpg?1690699315" title=" <strong>Figure 2</strong><br/> <p>Schematic representation of the analysis strategy in this study. Initially, two randomly generated datasets were generated for the test (T) and reference (R) groups. Then followed subsampling to draw parts of the original population. Finally, the variational autoencoder was applied to the subsampled data in order to produce the generated datasets. The aim of the generated datasets was to exhibit the same properties as the original data. In this study, comparisons were made among the three datasets (original vs. subsampled vs. generated), as well as between the T and R groups of all datasets.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/15/8793'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g003-550.jpg?1690699314" title=" <strong>Figure 3</strong><br/> <p>Distribution of the generated data for both R and T groups using the “softplus” (<b>a</b>) and linear (<b>b</b>) activation functions for the output layer.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/15/8793'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g004-550.jpg?1690699321" title=" <strong>Figure 4</strong><br/> <p>Distribution of the generated data for both the R and T groups using variational autoencoders with 100 (<b>a</b>), 500 (<b>b</b>), 1000 (<b>c</b>), 5000 (<b>d</b>), and 10,000 (<b>e</b>) epochs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/15/8793'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g005-550.jpg?1690699322" title=" <strong>Figure 5</strong><br/> <p>Distribution of the generated data for both the R and T groups using variational autoencoders with 2 (<b>a</b>), 3 (<b>b</b>), and 4 (<b>c</b>) hidden layers for the encoder and the decoder.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/15/8793'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g006-550.jpg?1690699313" title=" <strong>Figure 6</strong><br/> <p>Probability of accepting equivalence between the original and the generated datasets for three levels of variability (CV): (<b>a</b>) 10%, (<b>b</b>) 20%, and (<b>c</b>) 40%. The results are shown separately for the test and reference groups, as well as the two types of activation functions (“softplus” and linear) used for the hidden layers.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/15/8793'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g007-550.jpg?1690699319" title=" <strong>Figure 7</strong><br/> <p>Probability of accepting equivalence between the test and reference groups for the original (<b>a</b>), subsampled (<b>b</b>), and generated (<b>c</b>) datasets Three levels of variability (coefficient of variation, CV) were used: 10%, 20%, and 40%. In all cases, the “softplus” activation was used for the hidden layers, while both the test and reference groups were assumed to exhibit identical average performances.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/15/8793'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08793/article_deploy/html/images/applsci-13-08793-g008-550.jpg?1690699317" title=" <strong>Figure 8</strong><br/> <p>Probability of accepting equivalence between the test and reference groups for several ratios (1, 1.10, 1.25, 1.50) of the average test (T)/reference (R) performance. The comparisons were made separately for the original (<b>a</b>), subsampled (<b>b</b>), and generated datasets by the variational autoencoder (<b>c</b>). In all cases, the “softplus” activation function was used for the hidden layers and two levels of variability (coefficient of variation, CV) were used: 10% and 20%.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/15/8793'>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="1195017" 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, 1433 KiB   </span> <a href="/2076-3417/13/14/8234/pdf?version=1689567923" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry" 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 feature" data-dropdown="drop-article-label-feature" aria-expanded="false">Feature Paper</span><span class='label choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/14/8234">Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry</a> <div class="authors"> by <span class="inlineblock "><strong>Surya Sasikumar Nair</strong>, </span><span class="inlineblock "><strong>Joanna Trafiałek</strong> and </span><span class="inlineblock "><strong>Wojciech Kolanowski</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(14), 8234; <a href="https://doi.org/10.3390/app13148234">https://doi.org/10.3390/app13148234</a> - 15 Jul 2023 </div> <a href="/2076-3417/13/14/8234#metrics">Cited by 17</a> | Viewed by 14790 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This review aims to address the current data on edible packaging systems used in food production. The growing global population, changes in the climate and dietary patterns, and the increasing need for environmental protection, have created an increasing demand for waste-free food production. <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/14/8234/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This review aims to address the current data on edible packaging systems used in food production. The growing global population, changes in the climate and dietary patterns, and the increasing need for environmental protection, have created an increasing demand for waste-free food production. The need for durable and sustainable packaging materials has become significant in order to avoid food waste and environmental pollution. Edible packaging has emerged as a promising solution to extend the shelf life of food products and reduce dependence on petroleum-based resources. This review analyzes the history, production methods, barrier properties, types, and additives of edible packaging systems. The review highlights the advantages and importance of edible packaging materials and describes how they can improve sustainability measures. The market value of edible packaging materials is expanding. Further research on and developments in edible food packaging materials are needed to increase sustainable, eco-friendly packaging practices that are significant for environmental protection and food safety. <a href="/2076-3417/13/14/8234">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/16HO7VJ777 ">Feature Review Papers in ‘Food Science and Technology’ Section</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/14/8234/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1195017"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1195017"><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="#next1195017" data-cycle-prev="#prev1195017" data-cycle-progressive="#images1195017" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1195017-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-08234/article_deploy/html/images/applsci-13-08234-g001-550.jpg?1689567987" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1195017" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1195017-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08234/article_deploy/html/images/applsci-13-08234-g002-550.jpg?1689567990'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1195017-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08234/article_deploy/html/images/applsci-13-08234-g003-550.jpg?1689567989'><p>Figure 3</p></div></script></div></div><div id="article-1195017-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-08234/article_deploy/html/images/applsci-13-08234-g001-550.jpg?1689567987" title=" <strong>Figure 1</strong><br/> <p>Schematic representation of the production of edible films (Casting method) and coatings.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8234'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08234/article_deploy/html/images/applsci-13-08234-g002-550.jpg?1689567990" title=" <strong>Figure 2</strong><br/> <p>Schematic representation of the production of edible films via the extrusion method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8234'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08234/article_deploy/html/images/applsci-13-08234-g003-550.jpg?1689567989" title=" <strong>Figure 3</strong><br/> <p>A compositional overview of edible films and coatings (according to the authors of [<a href="#B4-applsci-13-08234" class="html-bibr">4</a>,<a href="#B32-applsci-13-08234" class="html-bibr">32</a>,<a href="#B33-applsci-13-08234" class="html-bibr">33</a>,<a href="#B34-applsci-13-08234" class="html-bibr">34</a>,<a href="#B35-applsci-13-08234" class="html-bibr">35</a>,<a href="#B36-applsci-13-08234" class="html-bibr">36</a>,<a href="#B37-applsci-13-08234" class="html-bibr">37</a>,<a href="#B38-applsci-13-08234" class="html-bibr">38</a>,<a href="#B39-applsci-13-08234" class="html-bibr">39</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8234'>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="1188579" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 14 pages, 2847 KiB   </span> <a href="/2076-3417/13/13/7947/pdf?version=1689063500" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/13/7947">Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network</a> <div class="authors"> by <span class="inlineblock "><strong>Giulia Rubiu</strong>, </span><span class="inlineblock "><strong>Marco Bologna</strong>, </span><span class="inlineblock "><strong>Michaela Cellina</strong>, </span><span class="inlineblock "><strong>Maurizio Cè</strong>, </span><span class="inlineblock "><strong>Davide Sala</strong>, </span><span class="inlineblock "><strong>Roberto Pagani</strong>, </span><span class="inlineblock "><strong>Elisa Mattavelli</strong>, </span><span class="inlineblock "><strong>Deborah Fazzini</strong>, </span><span class="inlineblock "><strong>Simona Ibba</strong>, </span><span class="inlineblock "><strong>Sergio Papa</strong> and </span><span class="inlineblock "><strong>Marco Alì</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(13), 7947; <a href="https://doi.org/10.3390/app13137947">https://doi.org/10.3390/app13137947</a> - 6 Jul 2023 </div> <a href="/2076-3417/13/13/7947#metrics">Cited by 12</a> | Viewed by 5666 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/13/7947/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays. Methods: An instance segmentation model was trained using the architecture of a Mask Region-based Convolutional Neural Network (Mask-RCNN). The data for the training, validation, and testing were taken from the Tuft dental database (1000 panoramic dental radiographs). The number of the predicted label was 52 (20 deciduous and 32 permanent). The size of the training, validation, and test sets were 760, 190, and 70 images, respectively, and the split was performed randomly. The model was trained for 300 epochs, using a batch size of 10, a base learning rate of 0.001, and a warm-up multistep learning rate scheduler (gamma = 0.1). Data augmentation was performed by changing the brightness, contrast, crop, and image size. The percentage of correctly detected teeth and Dice in the test set were used as the quality metrics for the model. Results: In the test set, the percentage of correctly classified teeth was 98.4%, while the Dice score was 0.87. For both the left mandibular central and lateral incisor permanent teeth, the Dice index result was 0.91 and the accuracy was 100%. For the permanent teeth right mandibular first molar, mandibular second molar, and third molar, the Dice indexes were 0.92, 0.93, and 0.78, respectively, with an accuracy of 100% for all three different teeth. For deciduous teeth, the Dice indexes for the right mandibular lateral incisor, right mandibular canine, and right mandibular first molar were 0.89, 0.91, and 0.85, respectively, with an accuracy of 100%. Conclusions: A successful instance segmentation model for teeth identification in panoramic dental X-ray was developed and validated. This model may help speed up and automate tasks like teeth counting and identifying specific missing teeth, improving the current clinical practice. <a href="/2076-3417/13/13/7947">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/DCX7M51E3M ">Advanced Materials and Technology in Dental, Oral and Maxillofacial Health</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/13/7947/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1188579"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1188579"><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="#next1188579" data-cycle-prev="#prev1188579" data-cycle-progressive="#images1188579" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1188579-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g001-550.jpg?1689063583" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1188579" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1188579-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g002-550.jpg?1689063579'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1188579-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g003-550.jpg?1689063589'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1188579-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g004-550.jpg?1689063584'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1188579-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g005-550.jpg?1689063582'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1188579-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g006-550.jpg?1689063590'><p>Figure 6</p></div></script></div></div><div id="article-1188579-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g001-550.jpg?1689063583" title=" <strong>Figure 1</strong><br/> <p>Schematic representation of the Mask-RCNN implemented in the detectron2 library and the 3 main components: Convolutional backbone, Region Proposal Network (RPN), and Head part (ROI/box head).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7947'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g002-550.jpg?1689063579" title=" <strong>Figure 2</strong><br/> <p>Example of output of the Mask-RCNN on a panoramic RX.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7947'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g003-550.jpg?1689063589" title=" <strong>Figure 3</strong><br/> <p>Example of ground truths (<b>A</b>,<b>B</b>) and corresponding predictions (<b>C</b>,<b>D</b>) for two panoramic RX images.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7947'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g004-550.jpg?1689063584" title=" <strong>Figure 4</strong><br/> <p>Distribution of the Dice index in the 50 patients of the test set.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7947'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g005-550.jpg?1689063582" title=" <strong>Figure 5</strong><br/> <p>Example of a patient with a low Dice score (=0.19), original radiograph (<b>A</b>), the corresponding ground truths (<b>B</b>), and predictions (<b>C</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7947'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07947/article_deploy/html/images/applsci-13-07947-g006-550.jpg?1689063590" title=" <strong>Figure 6</strong><br/> <p>Distribution of the number of false negatives for the 50 patients of the test set.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7947'>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="1188485" 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, 1713 KiB   </span> <a href="/2076-3417/13/13/7940/pdf?version=1689235213" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Digital Twins: The New Frontier for Personalized Medicine?" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/13/7940">Digital Twins: The New Frontier for Personalized Medicine?</a> <div class="authors"> by <span class="inlineblock "><strong>Michaela Cellina</strong>, </span><span class="inlineblock "><strong>Maurizio Cè</strong>, </span><span class="inlineblock "><strong>Marco Alì</strong>, </span><span class="inlineblock "><strong>Giovanni Irmici</strong>, </span><span class="inlineblock "><strong>Simona Ibba</strong>, </span><span class="inlineblock "><strong>Elena Caloro</strong>, </span><span class="inlineblock "><strong>Deborah Fazzini</strong>, </span><span class="inlineblock "><strong>Giancarlo Oliva</strong> and </span><span class="inlineblock "><strong>Sergio Papa</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(13), 7940; <a href="https://doi.org/10.3390/app13137940">https://doi.org/10.3390/app13137940</a> - 6 Jul 2023 </div> <a href="/2076-3417/13/13/7940#metrics">Cited by 37</a> | Viewed by 8545 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/13/7940/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, and physiological processes. Their application has the potential to transform patient care in the direction of increasingly personalized data-driven medicine. The use of DHTs can be integrated with digital twins of healthcare institutions to improve organizational management processes and resource allocation. By modeling the complex multi-omics interactions between genetic and environmental factors, DHTs help monitor disease progression and optimize treatment plans. Through digital simulation, DHT models enable the selection of the most appropriate molecular therapy and accurate 3D representation for precision surgical planning, together with augmented reality tools. Furthermore, they allow for the development of tailored early diagnosis protocols and new targeted drugs. Furthermore, digital twins can facilitate medical training and education. By creating virtual anatomy and physiology models, medical students can practice procedures, enhance their skills, and improve their understanding of the human body. Overall, digital twins have immense potential to revolutionize healthcare, improving patient care and outcomes, reducing costs, and enhancing medical research and education. However, challenges such as data security, data quality, and data interoperability must be addressed before the widespread adoption of digital twins in healthcare. We aim to propose a narrative review on this hot topic to provide an overview of the potential applications of digital twins to improve treatment and diagnostics, but also of the challenges related to their development and widespread diffusion. <a href="/2076-3417/13/13/7940">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/Biomedical_Info ">Methods, Applications and Developments in Biomedical Informatics</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/13/7940/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1188485"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1188485"><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="#next1188485" data-cycle-prev="#prev1188485" data-cycle-progressive="#images1188485" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1188485-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g001-550.jpg?1689235770" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1188485" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1188485-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g002-550.jpg?1689235774'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1188485-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g003-550.jpg?1689235772'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1188485-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g004-550.jpg?1689235768'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1188485-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g005-550.jpg?1689235769'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1188485-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g006-550.jpg?1689235771'><p>Figure 6</p></div></script></div></div><div id="article-1188485-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g001-550.jpg?1689235770" title=" <strong>Figure 1</strong><br/> <p>Graphical representation of the development of a digital twin of a physical entity from data collection and integration to perform prediction and establish an appropriate intervention.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7940'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g002-550.jpg?1689235774" title=" <strong>Figure 2</strong><br/> <p>Graphic representation of all the aspects required for the development of digital twins.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7940'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g003-550.jpg?1689235772" title=" <strong>Figure 3</strong><br/> <p>The correct creation of a digital twin requires multi-omics data.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7940'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g004-550.jpg?1689235768" title=" <strong>Figure 4</strong><br/> <p>The development of digital twin requires a huge amount of different data.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7940'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g005-550.jpg?1689235769" title=" <strong>Figure 5</strong><br/> <p>Possible applications of digital twins.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7940'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07940/article_deploy/html/images/applsci-13-07940-g006-550.jpg?1689235771" title=" <strong>Figure 6</strong><br/> <p>Therapies testing on DHT.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7940'>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="1184012" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 37 pages, 11612 KiB   </span> <a href="/2076-3417/13/13/7744/pdf?version=1688117858" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="New Trends in 4D Printing: A Critical Review" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/13/7744">New Trends in 4D Printing: A Critical Review</a> <div class="authors"> by <span class="inlineblock "><strong>Somayeh Vatanparast</strong>, </span><span class="inlineblock "><strong>Alberto Boschetto</strong>, </span><span class="inlineblock "><strong>Luana Bottini</strong> and </span><span class="inlineblock "><strong>Paolo Gaudenzi</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(13), 7744; <a href="https://doi.org/10.3390/app13137744">https://doi.org/10.3390/app13137744</a> - 30 Jun 2023 </div> <a href="/2076-3417/13/13/7744#metrics">Cited by 32</a> | Viewed by 5911 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In a variety of industries, Additive Manufacturing has revolutionized the whole design–fabrication cycle. Traditional 3D printing is typically employed to produce static components, which are not able to fulfill dynamic structural requirements and are inappropriate for applications such as soft grippers, self-assembly systems, <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/13/7744/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In a variety of industries, Additive Manufacturing has revolutionized the whole design–fabrication cycle. Traditional 3D printing is typically employed to produce static components, which are not able to fulfill dynamic structural requirements and are inappropriate for applications such as soft grippers, self-assembly systems, and smart actuators. To address this limitation, an innovative technology has emerged, known as “4D printing”. It processes smart materials by using 3D printing for fabricating smart structures that can be reconfigured by applying different inputs, such as heat, humidity, magnetism, electricity, light, etc. At present, 4D printing is still a growing technology, and it presents numerous challenges regarding materials, design, simulation, fabrication processes, applied strategies, and reversibility. In this work a critical review of 4D printing technologies, materials, and applications is provided. <a href="/2076-3417/13/13/7744">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/9BOT9A6NMK ">Recent Advances in Design of Additive Manufacturing: Materials and Production Processes</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/13/7744/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1184012"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1184012"><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="#next1184012" data-cycle-prev="#prev1184012" data-cycle-progressive="#images1184012" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1184012-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g001-550.jpg?1688118095" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1184012" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1184012-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g002-550.jpg?1688118099'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1184012-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g003-550.jpg?1688118091'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1184012-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g004-550.jpg?1688118087'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1184012-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g005-550.jpg?1688118090'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1184012-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g006-550.jpg?1688118088'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1184012-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g007-550.jpg?1688118096'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1184012-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g008-550.jpg?1688118084'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1184012-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g009-550.jpg?1688118094'><p>Figure 9</p></div></script></div></div><div id="article-1184012-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g001-550.jpg?1688118095" title=" <strong>Figure 1</strong><br/> <p>4DP structural elements.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g002-550.jpg?1688118099" title=" <strong>Figure 2</strong><br/> <p>Distribution of reviewed articles related to 4D printing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g003-550.jpg?1688118091" title=" <strong>Figure 3</strong><br/> <p>Schematic of shape-morphing effects: one-way (<b>a</b>), two-way (<b>b</b>), and multiway SMEs (<b>c</b>) [<a href="#B125-applsci-13-07744" class="html-bibr">125</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g004-550.jpg?1688118087" title=" <strong>Figure 4</strong><br/> <p>Shape memory behavior of stimulus-responsive materials: The SME in PLA (<b>a</b>) in [<a href="#B128-applsci-13-07744" class="html-bibr">128</a>]; architected mechanical metamaterials (<b>b</b>) [<a href="#B110-applsci-13-07744" class="html-bibr">110</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g005-550.jpg?1688118090" title=" <strong>Figure 5</strong><br/> <p>4D-printed cross-shape specimen route design (<b>a</b>); schematic of the heterogeneous lamination (<b>b</b>); shape transformation for different heating times (<b>c</b>) [<a href="#B14-applsci-13-07744" class="html-bibr">14</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g006-550.jpg?1688118088" title=" <strong>Figure 6</strong><br/> <p>A-line design: three varieties of segment composition (<b>a</b>); eight different bending orientations (<b>b</b>); after heating, a straight line may be transformed into a helix by combining distinct bending directions for separate segments [<a href="#B57-applsci-13-07744" class="html-bibr">57</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g007-550.jpg?1688118096" title=" <strong>Figure 7</strong><br/> <p>Analysis of the multi-material 4D construction before (<b>a</b>) and after (<b>b</b>) a stimulus is applied and (<b>c</b>) cross-sectional image of a bilayer element after a stimulus is applied [<a href="#B51-applsci-13-07744" class="html-bibr">51</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g008-550.jpg?1688118084" title=" <strong>Figure 8</strong><br/> <p>4DP multi-materials: complex origami structure composed by several hybrid hinges (<b>a</b>) [<a href="#B22-applsci-13-07744" class="html-bibr">22</a>]; self-locking mechanism of multilateral structure (<b>b</b>) [<a href="#B107-applsci-13-07744" class="html-bibr">107</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07744/article_deploy/html/images/applsci-13-07744-g009-550.jpg?1688118094" title=" <strong>Figure 9</strong><br/> <p>4DP multi-material: direct 4DP of structural parts with architecture-driven deformation modes (<b>a</b>) [<a href="#B151-applsci-13-07744" class="html-bibr">151</a>]; protein hydrogel cation programming and morphing through mechanochemical alterations (<b>b</b>) [<a href="#B163-applsci-13-07744" class="html-bibr">163</a>]; magnetic cantilevers sensitive to the magnetic field (<b>c</b>) [<a href="#B164-applsci-13-07744" class="html-bibr">164</a>]; shape-shifting cycle of a PLA rectangular multi-ply structure and TPU (<b>d</b>) [<a href="#B166-applsci-13-07744" class="html-bibr">166</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/13/7744'>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="1171587" 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, 4939 KiB   </span> <a href="/2076-3417/13/12/7155/pdf?version=1686816267" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/12/7155">Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges</a> <div class="authors"> by <span class="inlineblock "><strong>Quanyu Wang</strong>, </span><span class="inlineblock "><strong>Jin He</strong>, </span><span class="inlineblock "><strong>Caiyun Lu</strong>, </span><span class="inlineblock "><strong>Chao Wang</strong>, </span><span class="inlineblock "><strong>Han Lin</strong>, </span><span class="inlineblock "><strong>Hanyu Yang</strong>, </span><span class="inlineblock "><strong>Hang Li</strong> and </span><span class="inlineblock "><strong>Zhengyang Wu</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(12), 7155; <a href="https://doi.org/10.3390/app13127155">https://doi.org/10.3390/app13127155</a> - 15 Jun 2023 </div> <a href="/2076-3417/13/12/7155#metrics">Cited by 10</a> | Viewed by 3912 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/12/7155/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, including wheeled tractors, tracked tractors, rice transplanters, high clearance sprays, agricultural robots, agricultural tractor–trailers, etc. The application and improvement of path-tracking control methods are summarized based on the different working scenes and types of agricultural machinery. This study explores each of these methods in terms of accuracy, stability, robustness, and disadvantages/advantages. The main challenges in the field of agricultural vehicle path tracking control are defined, and future research directions are offered based on critical reviews. This review aims to provide a reference for determining which controllers to use in path-tracking control development for an autonomous agricultural vehicle. <a href="/2076-3417/13/12/7155">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/Agricultural_Review_Papers ">Feature Review Papers in Agricultural Science and Technology</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/12/7155/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1171587"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1171587"><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="#next1171587" data-cycle-prev="#prev1171587" data-cycle-progressive="#images1171587" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1171587-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g001-550.jpg?1686816379" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1171587" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1171587-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g002-550.jpg?1686816376'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1171587-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g003-550.jpg?1686816372'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1171587-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g004-550.jpg?1686816373'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1171587-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g005-550.jpg?1686816380'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1171587-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g006-550.jpg?1686816382'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1171587-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g007-550.jpg?1686816379'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1171587-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g008-550.jpg?1686816381'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1171587-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g009-550.jpg?1686816373'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1171587-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g010-550.jpg?1686816377'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1171587-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g011-550.jpg?1686816375'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1171587-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g012-550.jpg?1686816381'><p>Figure 12</p></div></script></div></div><div id="article-1171587-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g001-550.jpg?1686816379" title=" <strong>Figure 1</strong><br/> <p>Composition of agricultural machinery automatic navigation system.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g002-550.jpg?1686816376" title=" <strong>Figure 2</strong><br/> <p>Bicycle kinematic model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g003-550.jpg?1686816372" title=" <strong>Figure 3</strong><br/> <p>Agricultural tracked vehicle kinematic model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g004-550.jpg?1686816373" title=" <strong>Figure 4</strong><br/> <p>Agricultural articulated vehicle kinematic model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g005-550.jpg?1686816380" title=" <strong>Figure 5</strong><br/> <p>Tracked robot dynamic model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g006-550.jpg?1686816382" title=" <strong>Figure 6</strong><br/> <p>Block diagram of the path tracking control system.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g007-550.jpg?1686816379" title=" <strong>Figure 7</strong><br/> <p>Tractor automatic navigation control system test platform.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g008-550.jpg?1686816381" title=" <strong>Figure 8</strong><br/> <p>Automatic navigation system for crawler-type rape seeder.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g009-550.jpg?1686816373" title=" <strong>Figure 9</strong><br/> <p>Automatic driving control system for rice seeding machine.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g010-550.jpg?1686816377" title=" <strong>Figure 10</strong><br/> <p>Orchard vehicle automatic test platform.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g011-550.jpg?1686816375" title=" <strong>Figure 11</strong><br/> <p>Test prototype and test environment.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07155/article_deploy/html/images/applsci-13-07155-g012-550.jpg?1686816381" title=" <strong>Figure 12</strong><br/> <p>The tractor-trailer system.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7155'>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="1170084" 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, 9277 KiB   </span> <a href="/2076-3417/13/12/7078/pdf?version=1686653030" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Solar Sail Orbit Raising with Electro-Optically Controlled Diffractive Film" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/12/7078">Solar Sail Orbit Raising with Electro-Optically Controlled Diffractive Film</a> <div class="authors"> by <span class="inlineblock "><strong>Alessandro A. Quarta</strong> and </span><span class="inlineblock "><strong>Giovanni Mengali</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(12), 7078; <a href="https://doi.org/10.3390/app13127078">https://doi.org/10.3390/app13127078</a> - 13 Jun 2023 </div> <a href="/2076-3417/13/12/7078#metrics">Cited by 12</a> | Viewed by 1891 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The aim of this paper is to analyze the transfer performance of a spacecraft whose primary propulsion system is a diffractive solar sail with active, switchable panels. The spacecraft uses a propellantless thruster that converts the solar radiation pressure into propulsive acceleration by <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/12/7078/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The aim of this paper is to analyze the transfer performance of a spacecraft whose primary propulsion system is a diffractive solar sail with active, switchable panels. The spacecraft uses a propellantless thruster that converts the solar radiation pressure into propulsive acceleration by taking advantage of the diffractive property of an electro-optically controlled (binary) metamaterial. The proposed analysis considers a heliocentric mission scenario where the spacecraft is required to perform a two-dimensional transfer between two concentric and coplanar circular orbits. The sail attitude is assumed to be Sun-facing, that is, with its sail nominal plane perpendicular to the incoming sunlight. This is possible since, unlike a more conventional solar sail concept that uses metalized highly reflective thin films to reflect the photons, a diffractive sail is theoretically able to generate a component of the thrust vector along the sail nominal plane also in a Sun-facing configuration. The electro-optically controlled sail film is used to change the in-plane component of the thrust vector to accomplish the transfer by minimizing the total flight time without changing the sail attitude with respect to an orbital reference frame. This work extends the mathematical model recently proposed by the authors by including the potential offered by an active control of the diffractive sail film. The paper also thoroughly analyzes the diffractive sail-based spacecraft performance in a set of classical circle-to-circle heliocentric trajectories that model transfers from Earth to Mars, Venus and Jupiter. <a href="/2076-3417/13/12/7078">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/Recent_Advances_in_Space_Propulsion_Technology ">Recent Advances in Space Propulsion Technology</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/12/7078/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1170084"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1170084"><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="#next1170084" data-cycle-prev="#prev1170084" data-cycle-progressive="#images1170084" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1170084-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g001-550.jpg?1686653119" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1170084" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g002-550.jpg?1686653104'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g003-550.jpg?1686653117'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g004-550.jpg?1686653112'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g005-550.jpg?1686653108'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g006-550.jpg?1686653098'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g007-550.jpg?1686653099'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g008-550.jpg?1686653097'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g009-550.jpg?1686653106'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g010-550.jpg?1686653113'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g011-550.jpg?1686653114'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g012a-550.jpg?1686653110'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g012b-550.jpg?1686653103'><p>Figure 12 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g013a-550.jpg?1686653119'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1170084-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g013b-550.jpg?1686653116'><p>Figure 13 Cont.</p></div></script></div></div><div id="article-1170084-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g001-550.jpg?1686653119" title=" <strong>Figure 1</strong><br/> <p>Conceptual scheme of the thrust vector direction in a Sun-facing solar sail with a reflective or a diffractive film.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g002-550.jpg?1686653104" title=" <strong>Figure 2</strong><br/> <p>Single degree-of-freedom diffractive sail considered in the trajectory analysis discussed in Ref. [<a href="#B53-applsci-13-07078" class="html-bibr">53</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g003-550.jpg?1686653117" title=" <strong>Figure 3</strong><br/> <p>Conceptual scheme of JAXA’s Interplanetary Kite-craft Accelerated by Radiation Of the Sun (IKAROS) spacecraft, showing active LCD panels to execute attitude control maneuvers.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g004-550.jpg?1686653112" title=" <strong>Figure 4</strong><br/> <p>Diffractive sail equipped with EOCPs: conceptual sketch of the thrust vector variation as a function of EOCPs state.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g005-550.jpg?1686653108" title=" <strong>Figure 5</strong><br/> <p>Propulsive acceleration vector (and its components in a body reference frame) for an ideal diffractive sail, without EOCPs, in a Sun-facing configuration.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g006-550.jpg?1686653098" title=" <strong>Figure 6</strong><br/> <p>Propulsive acceleration components as a function of <math display="inline"><semantics> <mi>τ</mi> </semantics></math> and the state of EOCPs; see also Equations (<a href="#FD2-applsci-13-07078" class="html-disp-formula">2</a>) and (<a href="#FD3-applsci-13-07078" class="html-disp-formula">3</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g007-550.jpg?1686653099" title=" <strong>Figure 7</strong><br/> <p>Polar reference frame, parking orbit, and spacecraft state variables <span class="html-italic">r</span> and <math display="inline"><semantics> <mi>θ</mi> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g008-550.jpg?1686653097" title=" <strong>Figure 8</strong><br/> <p>Sequence of trajectory arcs related to the value of the adjoint variable <math display="inline"><semantics> <msub> <mi>λ</mi> <mi>v</mi> </msub> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g009-550.jpg?1686653106" title=" <strong>Figure 9</strong><br/> <p>Minimum flight time as a function of the target orbit radius when <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mspace width="0.166667em"/> <mi>mm</mi> <mo>/</mo> <msup> <mi mathvariant="normal">s</mi> <mn>2</mn> </msup> </mrow> </semantics></math>: comparison between an ideal and unconstrained reflective sail (red dashed line) and a Sun-facing diffractive sail with EOCPs (solid black line).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g010-550.jpg?1686653113" title=" <strong>Figure 10</strong><br/> <p>Force bubble of a reflective sail without attitude constraint and a diffractive sail with a Sun-facing configuration.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g011-550.jpg?1686653114" title=" <strong>Figure 11</strong><br/> <p>Final value of the polar angle as a function of the target orbit radius for an optimal transfer using an ideal and unconstrained reflective sail (red dashed line) or a Sun-facing diffractive sail with EOCPs (solid black line) when <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mspace width="0.166667em"/> <mi>mm</mi> <mo>/</mo> <msup> <mi mathvariant="normal">s</mi> <mn>2</mn> </msup> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g012a-550.jpg?1686653110" title=" <strong>Figure 12</strong><br/> <p>Optimal transfer trajectory for the three mission scenarios. Black circle → start, black square → arrival, blue line → parking orbit, red line → target orbit, black line → optimal transfer trajectory.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g012b-550.jpg?1686653103" title=" <strong>Figure 12 Cont.</strong><br/> <p>Optimal transfer trajectory for the three mission scenarios. Black circle → start, black square → arrival, blue line → parking orbit, red line → target orbit, black line → optimal transfer trajectory.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g013a-550.jpg?1686653119" title=" <strong>Figure 13</strong><br/> <p>Time variation of the control variable <math display="inline"><semantics> <mi>τ</mi> </semantics></math> for the three mission cases using a Sun-facing diffractive sail. Black circle → start, black square → arrival.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-07078/article_deploy/html/images/applsci-13-07078-g013b-550.jpg?1686653116" title=" <strong>Figure 13 Cont.</strong><br/> <p>Time variation of the control variable <math display="inline"><semantics> <mi>τ</mi> </semantics></math> for the three mission cases using a Sun-facing diffractive sail. Black circle → start, black square → arrival.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/12/7078'>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="1164218" 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, 9511 KiB   </span> <a href="/2076-3417/13/11/6842/pdf?version=1685960388" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Materials and Technique: The First Look at Saturnino Gatti" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/11/6842">Materials and Technique: The First Look at Saturnino Gatti</a> <div class="authors"> by <span class="inlineblock "><strong>Letizia Bonizzoni</strong>, </span><span class="inlineblock "><strong>Simone Caglio</strong>, </span><span class="inlineblock "><strong>Anna Galli</strong>, </span><span class="inlineblock "><strong>Luca Lanteri</strong> and </span><span class="inlineblock "><strong>Claudia Pelosi</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(11), 6842; <a href="https://doi.org/10.3390/app13116842">https://doi.org/10.3390/app13116842</a> - 5 Jun 2023 </div> <a href="/2076-3417/13/11/6842#metrics">Cited by 11</a> | Viewed by 2309 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> As part of the study project of the pictorial cycle, attributed to Saturnino Gatti, in the church of San Panfilo at Villagrande di Tornimparte (AQ), image analyses were performed in order to document the general conservation conditions of the surfaces, and to map <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/11/6842/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> As part of the study project of the pictorial cycle, attributed to Saturnino Gatti, in the church of San Panfilo at Villagrande di Tornimparte (AQ), image analyses were performed in order to document the general conservation conditions of the surfaces, and to map the different painting materials to be subsequently examined using spectroscopic techniques. To acquire the images, radiation sources, ranging from ultraviolet to near infrared, were used; analyses of ultraviolet fluorescence (UVF), infrared reflectography (IRR), infrared false colors (IRFC), and optical microscopy in visible light (OM) were carried out on all the panels of the mural painting of the apsidal conch. The Hypercolorimetric Multispectral Imaging (HMI) technique was also applied in selected areas of two panels. Due to the accurate calibration system, this technique is able to obtain high-precision colorimetric and reflectance measurements, which can be repeated for proper surface monitoring. The integrated analysis of the different wavelengths’ images—in particular, the ones processed in false colors—made it possible to distinguish the portions affected by retouching or repainting and to recover the legibility of some figures that showed chromatic alterations of the original pictorial layers. The IR reflectography, in addition to highlighting the portions that lost materials and were subject to non-original interventions, emphasized the presence of the underdrawing, which was detected using the <i>spolvero</i> technique. UVF photography led to a preliminary mapping of the organic and inorganic materials that exhibited characteristic induced fluorescence, such as a binder in correspondence with the original azurite painting or the wide use of white zinc in the retouched areas. The collected data made it possible to form a better iconographic interpretation. Moreover, it also enabled us to accurately select the areas to be investigated using spectroscopic analyses, both <i>in situ</i> and on micro-samples, in order to deepen our knowledge of the techniques used by the artist to create the original painting, and to detect subsequent interventions. <a href="/2076-3417/13/11/6842">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/72762643J2 ">Results of the II National Research Project of AIAr: Archaeometric Study of the Frescoes by Saturnino Gatti and Workshop at the Church of San Panfilo in Tornimparte (AQ, Italy)</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/11/6842/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1164218"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1164218"><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="#next1164218" data-cycle-prev="#prev1164218" data-cycle-progressive="#images1164218" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1164218-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g001-550.jpg?1685960651" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1164218" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1164218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g002-550.jpg?1685960664'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1164218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g003-550.jpg?1685960666'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1164218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g004-550.jpg?1685960668'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1164218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g005-550.jpg?1685960653'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1164218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g006-550.jpg?1685960655'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1164218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g007-550.jpg?1685960660'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1164218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g008-550.jpg?1685960659'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1164218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g009-550.jpg?1685960648'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1164218-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g010-550.jpg?1685960656'><p>Figure 10</p></div></script></div></div><div id="article-1164218-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g001-550.jpg?1685960651" title=" <strong>Figure 1</strong><br/> <p>Spectra of cut filters, A, B, and UV-IR, used in HMI and UVF.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6842'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g002-550.jpg?1685960664" title=" <strong>Figure 2</strong><br/> <p>The UVF image of panel A, Garden of Olives.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6842'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g003-550.jpg?1685960666" title=" <strong>Figure 3</strong><br/> <p>The UVF image of panel E, the Resurrection: (<b>A</b>) general view; the white rectangle indicates the detail shown in (<b>B</b>). (<b>C</b>) Detail of the golden traces found via OM in the rays.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6842'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g004-550.jpg?1685960668" title=" <strong>Figure 4</strong><br/> <p>Detail of Panel A, Garden of Olives (<b>A</b>) in visible light, (<b>B</b>) IRR, and (<b>C</b>) IRFC.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6842'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g005-550.jpg?1685960653" title=" <strong>Figure 5</strong><br/> <p>Detail of panel D, Deposition of Christ (<b>A</b>) in visible light, (<b>B</b>) IRR, and (<b>C</b>) IRFC.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6842'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g006-550.jpg?1685960655" title=" <strong>Figure 6</strong><br/> <p>The graphic user interface (GUI) of the PickViewer<sup>®</sup>, shown on the <b>left</b> of the RGB image, and on the <b>right</b>, the IRFC result is shown. Detail of panel A.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6842'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g007-550.jpg?1685960660" title=" <strong>Figure 7</strong><br/> <p>The GUI of the PickViewer<sup>®</sup> shows the RGB image on the <b>left</b>, with the selected point (white dot) for the application of the chromatic similarity tool, and on the <b>right</b>, the result is shown. Detail of panel A.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6842'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g008-550.jpg?1685960659" title=" <strong>Figure 8</strong><br/> <p>The GUI of the PickViewer<sup>®</sup> shows the RGB image on the <b>left</b>, and the PC1 obtained by applying the PCA to the three IR bands is shown on the <b>right</b>. Detail of panel A.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6842'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g009-550.jpg?1685960648" title=" <strong>Figure 9</strong><br/> <p>The GUI of the PickViewer<sup>®</sup> shows the RGB image with the selected point (white dot on the leg of the character that is lying down, probably non-original panting) for the application of the chromatic similarity tool on the <b>left</b>, and the result is shown on the <b>right</b>. Detail of panel E.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6842'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06842/article_deploy/html/images/applsci-13-06842-g010-550.jpg?1685960656" title=" <strong>Figure 10</strong><br/> <p>The GUI of the PickViewer<sup>®</sup> shows the RGB image with the selected point (white dot in the upper part of the green area, probably original) for the application of the chromatic similarity tool on the <b>left</b>, and the result is shown on the <b>right</b>. Detail of panel E.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6842'>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="1155457" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 37 pages, 6325 KiB   </span> <a href="/2076-3417/13/11/6450/pdf?version=1685003428" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Structural Health Monitoring and Management of Cultural Heritage Structures: A State-of-the-Art Review" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/11/6450">Structural Health Monitoring and Management of Cultural Heritage Structures: A State-of-the-Art Review</a> <div class="authors"> by <span class="inlineblock "><strong>Michela Rossi</strong> and </span><span class="inlineblock "><strong>Dionysios Bournas</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(11), 6450; <a href="https://doi.org/10.3390/app13116450">https://doi.org/10.3390/app13116450</a> - 25 May 2023 </div> <a href="/2076-3417/13/11/6450#metrics">Cited by 23</a> | Viewed by 4548 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In recent decades, the urgency to protect and upgrade cultural heritage structures (CHS) has become of primary importance due to their unique value and potential areas of impact (economic, social, cultural, and environmental). Structural health monitoring (SHM) and the management of CHS are <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/11/6450/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In recent decades, the urgency to protect and upgrade cultural heritage structures (CHS) has become of primary importance due to their unique value and potential areas of impact (economic, social, cultural, and environmental). Structural health monitoring (SHM) and the management of CHS are emerging as decisive safeguard measures aimed at assessing the actual state of the conservation and integrity of the structure. Moreover, the data collected from SHM are essential to plan cost-effective and sustainable maintenance solutions, in compliance with the basic preservation principles for historic buildings, such as minimum intervention. It is evident that, compared to new buildings, the application of SHM to CHS is even more challenging because of the uniqueness of each monitored structure and the need to respect its architectural and historical value. This paper aims to present a state-of-the-art evaluation of the current traditional and innovative SHM techniques adopted for CHS and to identify future research trends. First, a general introduction regarding the use of monitoring strategies and technologies for CHS is presented. Next, various traditional SHM techniques currently used in CHS are described. Then, attention is focused on the most recent technologies, such as fibre optic sensors and smart-sensing materials. Finally, an overview of innovative methods and tools for managing and analysing SHM data, including IoT-SHM systems and the integration of BIM in heritage structures, is provided. <a href="/2076-3417/13/11/6450">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Collection <a href=" /journal/applsci/topical_collections/nondestructive_testing_collection ">Nondestructive Testing (NDT)</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/11/6450/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1155457"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1155457"><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="#next1155457" data-cycle-prev="#prev1155457" data-cycle-progressive="#images1155457" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1155457-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g001-550.jpg?1685003654" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1155457" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g002-550.jpg?1685003650'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g003-550.jpg?1685003662'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g004-550.jpg?1685003665'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g005-550.jpg?1685003655'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g006-550.jpg?1685003664'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g007-550.jpg?1685003660'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g008-550.jpg?1685003649'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g009-550.jpg?1685003663'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g010-550.jpg?1685003652'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g011-550.jpg?1685003657'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g012-550.jpg?1685003651'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1155457-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g013-550.jpg?1685003658'><p>Figure 13</p></div></script></div></div><div id="article-1155457-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g001-550.jpg?1685003654" title=" <strong>Figure 1</strong><br/> <p>Static-dynamic SHM system installed in Consoli Palace, with crack meters (LVDTs), temperature sensors (T1, T2) and uni-axial piezoelectric accelerometers (A1–A3) (modified from [<a href="#B45-applsci-13-06450" class="html-bibr">45</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g002-550.jpg?1685003650" title=" <strong>Figure 2</strong><br/> <p>Monitoring of cracks employing: (<b>a</b>) plastic Tell-Tale crack meter, and (<b>b</b>) an LVDT sensor (from [<a href="#B48-applsci-13-06450" class="html-bibr">48</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g003-550.jpg?1685003662" title=" <strong>Figure 3</strong><br/> <p>Biaxial tiltmeters installed on eight capitals of the Milan Cathedral (from [<a href="#B37-applsci-13-06450" class="html-bibr">37</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g004-550.jpg?1685003665" title=" <strong>Figure 4</strong><br/> <p>Different types of dynamic sensors: force-balance accelerometers (<b>a</b>), from [<a href="#B38-applsci-13-06450" class="html-bibr">38</a>], and MEMS accelerometers (<b>b</b>), from [<a href="#B40-applsci-13-06450" class="html-bibr">40</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g005-550.jpg?1685003655" title=" <strong>Figure 5</strong><br/> <p>Typical optical fibre cross–section.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g006-550.jpg?1685003664" title=" <strong>Figure 6</strong><br/> <p>FBG operating principle.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g007-550.jpg?1685003660" title=" <strong>Figure 7</strong><br/> <p>Application of the multiaxial textile with integrated SHM sensors on a stone masonry building within the POLYMAST project (from [<a href="#B127-applsci-13-06450" class="html-bibr">127</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g008-550.jpg?1685003649" title=" <strong>Figure 8</strong><br/> <p>FBG sensors in a TRM coupon tested in tensile: configuration of FBG sensors (<b>a</b>); preparation of TRM coupon (<b>b</b>); stress versus strain curves (<b>c</b>) (from [<a href="#B129-applsci-13-06450" class="html-bibr">129</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g009-550.jpg?1685003663" title=" <strong>Figure 9</strong><br/> <p>An RFID sensor equipped with a piezoelectric sensor for crack detection ((<b>a</b>), from [<a href="#B136-applsci-13-06450" class="html-bibr">136</a>]; capacity piezoelectric sensors embedded in mortar joints (<b>b</b>), from [<a href="#B137-applsci-13-06450" class="html-bibr">137</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g010-550.jpg?1685003652" title=" <strong>Figure 10</strong><br/> <p>Concept of smart brick technology (<b>a</b>) and an example of its application for strain monitoring within a damaged masonry building using smart brick ((<b>b</b>), from [<a href="#B142-applsci-13-06450" class="html-bibr">142</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g011-550.jpg?1685003657" title=" <strong>Figure 11</strong><br/> <p>Orthophoto from close-range photogrammetry acquisition (<b>a</b>), from [<a href="#B155-applsci-13-06450" class="html-bibr">155</a>]; UAV image acquisition for marker-based structural defects monitoring (<b>b</b>), from [<a href="#B156-applsci-13-06450" class="html-bibr">156</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g012-550.jpg?1685003651" title=" <strong>Figure 12</strong><br/> <p>SHM-IoT system schematic (from [<a href="#B188-applsci-13-06450" class="html-bibr">188</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-06450/article_deploy/html/images/applsci-13-06450-g013-550.jpg?1685003658" title=" <strong>Figure 13</strong><br/> <p>Workflow towards HBIM creation and integration proposed by [<a href="#B207-applsci-13-06450" class="html-bibr">207</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/11/6450'>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="1136135" 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, 1171 KiB   </span> <a href="/2076-3417/13/9/5521/pdf?version=1683174100" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/9/5521">Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review</a> <div class="authors"> by <span class="inlineblock "><strong>José Maurício</strong>, </span><span class="inlineblock "><strong>Inês Domingues</strong> and </span><span class="inlineblock "><strong>Jorge Bernardino</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(9), 5521; <a href="https://doi.org/10.3390/app13095521">https://doi.org/10.3390/app13095521</a> - 28 Apr 2023 </div> <a href="/2076-3417/13/9/5521#metrics">Cited by 110</a> | Viewed by 26108 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/9/5521/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and transformers were more targeted to Natural Language Processing (NLP) tasks. Therefore, this paper presents a literature review that shows the differences between Vision Transformers (ViT) and Convolutional Neural Networks. The state of the art that used the two architectures for image classification was reviewed and an attempt was made to understand what factors may influence the performance of the two deep learning architectures based on the datasets used, image size, number of target classes (for the classification problems), hardware, and evaluated architectures and top results. The objective of this work is to identify which of the architectures is the best for image classification and under what conditions. This paper also describes the importance of the Multi-Head Attention mechanism for improving the performance of ViT in image classification. <a href="/2076-3417/13/9/5521">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/AI_Complex_Network ">Artificial Intelligence in Complex Networks</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/9/5521/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1136135"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1136135"><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="#next1136135" data-cycle-prev="#prev1136135" data-cycle-progressive="#images1136135" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1136135-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-05521/article_deploy/html/images/applsci-13-05521-g001-550.jpg?1683174171" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1136135" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1136135-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-05521/article_deploy/html/images/applsci-13-05521-g002-550.jpg?1683174173'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1136135-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-05521/article_deploy/html/images/applsci-13-05521-g003-550.jpg?1683174171'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1136135-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-05521/article_deploy/html/images/applsci-13-05521-g004-550.jpg?1683174169'><p>Figure 4</p></div></script></div></div><div id="article-1136135-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-05521/article_deploy/html/images/applsci-13-05521-g001-550.jpg?1683174171" title=" <strong>Figure 1</strong><br/> <p>Example of an architecture of the ViT, based on [<a href="#B1-applsci-13-05521" class="html-bibr">1</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/9/5521'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-05521/article_deploy/html/images/applsci-13-05521-g002-550.jpg?1683174173" title=" <strong>Figure 2</strong><br/> <p>Example of an architecture of a CNN, based on [<a href="#B2-applsci-13-05521" class="html-bibr">2</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/9/5521'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-05521/article_deploy/html/images/applsci-13-05521-g003-550.jpg?1683174171" title=" <strong>Figure 3</strong><br/> <p>Distribution of the selected studies by years.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/9/5521'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-05521/article_deploy/html/images/applsci-13-05521-g004-550.jpg?1683174169" title=" <strong>Figure 4</strong><br/> <p>Distribution of the selected studies by application area.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/9/5521'>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="1123624" 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, 928 KiB   </span> <a href="/2076-3417/13/8/4921/pdf?version=1681462310" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/8/4921">HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System</a> <div class="authors"> by <span class="inlineblock "><strong>Emad Ul Haq Qazi</strong>, </span><span class="inlineblock "><strong>Muhammad Hamza Faheem</strong> and </span><span class="inlineblock "><strong>Tanveer Zia</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(8), 4921; <a href="https://doi.org/10.3390/app13084921">https://doi.org/10.3390/app13084921</a> - 14 Apr 2023 </div> <a href="/2076-3417/13/8/4921#metrics">Cited by 39</a> | Viewed by 6690 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/8/4921/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks. <a href="/2076-3417/13/8/4921">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Collection <a href=" /journal/applsci/topical_collections/innovation_information_security ">Innovation in Information Security</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/8/4921/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1123624"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1123624"><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="#next1123624" data-cycle-prev="#prev1123624" data-cycle-progressive="#images1123624" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1123624-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-04921/article_deploy/html/images/applsci-13-04921-g001-550.jpg?1681462380" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1123624" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1123624-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-04921/article_deploy/html/images/applsci-13-04921-g002-550.jpg?1681462384'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1123624-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-04921/article_deploy/html/images/applsci-13-04921-g003-550.jpg?1681462382'><p>Figure 3</p></div></script></div></div><div id="article-1123624-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-04921/article_deploy/html/images/applsci-13-04921-g001-550.jpg?1681462380" title=" <strong>Figure 1</strong><br/> <p>Proposed HDLNIDS model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/8/4921'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-04921/article_deploy/html/images/applsci-13-04921-g002-550.jpg?1681462384" title=" <strong>Figure 2</strong><br/> <p>Accuracy of proposed deep learning model with respect to epochs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/8/4921'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-04921/article_deploy/html/images/applsci-13-04921-g003-550.jpg?1681462382" title=" <strong>Figure 3</strong><br/> <p>Loss graph of proposed deep learning model with respect to epochs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/8/4921'>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="1093127" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <a data-dropdown="drop-supplementary-1093127" aria-controls="drop-supplementary-1093127" aria-expanded="false" title="Supplementary Material"> <i class="material-icons">attachment</i> </a> <div id="drop-supplementary-1093127" class="f-dropdown label__btn__dropdown label__btn__dropdown--wide" data-dropdown-content aria-hidden="true" tabindex="-1"> Supplementary material: <br/> <a href="/2076-3417/13/6/3533/s1?version=1678427237"> Supplementary File 1 (ZIP, 216 KiB) </a><br/> </div> </div> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 21 pages, 5162 KiB   </span> <a href="/2076-3417/13/6/3533/pdf?version=1678427236" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/6/3533">Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis</a> <div class="authors"> by <span class="inlineblock "><strong>Vassilia J. Sinanoglou</strong>, </span><span class="inlineblock "><strong>Thalia Tsiaka</strong>, </span><span class="inlineblock "><strong>Konstantinos Aouant</strong>, </span><span class="inlineblock "><strong>Elizabeth Mouka</strong>, </span><span class="inlineblock "><strong>Georgia Ladika</strong>, </span><span class="inlineblock "><strong>Eftichia Kritsi</strong>, </span><span class="inlineblock "><strong>Spyros J. Konteles</strong>, </span><span class="inlineblock "><strong>Alexandros-George Ioannou</strong>, </span><span class="inlineblock "><strong>Panagiotis Zoumpoulakis</strong>, </span><span class="inlineblock "><strong>Irini F. Strati</strong> and </span><span class="inlineblock "><strong>Dionisis Cavouras</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(6), 3533; <a href="https://doi.org/10.3390/app13063533">https://doi.org/10.3390/app13063533</a> - 10 Mar 2023 </div> <a href="/2076-3417/13/6/3533#metrics">Cited by 16</a> | Viewed by 9456 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/6/3533/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess the quality of banana peels and flesh in terms of ripening and during storage in controlled temperatures and humidity. For this purpose, we implemented various analytical techniques, such as attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for texture, colorimetrics, and physicochemical features, along with image-analysis methods and discriminant as well as statistical analysis. Image-analysis outcomes showed that storage provoked significant degradation of banana peels based on the increased image-texture dissimilarity and the loss of the structural order of the texture. In addition, the computed features were sufficient to discriminate four ripening stages with high accuracy. Moreover, the results revealed that storage led to significant changes in the color parameters and dramatic decreases in the texture attributes of banana flesh. The combination of image and chemical analyses pinpointed that storage caused water migration to the flesh and significant starch decomposition, which was then converted into soluble sugars. The redness and yellowness of the peel; the flesh moisture content; the texture attributes; Brix; and the storage time were all strongly interrelated. The combination of these techniques, coupled with statistical tools, to monitor the physicochemical and organoleptic quality of bananas during storage could be further applied for assessing the quality of other fruits and vegetables under similar conditions. <a href="/2076-3417/13/6/3533">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/7AMOD1V140 ">Innovative Technologies in Food Detection</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/6/3533/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1093127"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1093127"><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="#next1093127" data-cycle-prev="#prev1093127" data-cycle-progressive="#images1093127" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1093127-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g001-550.jpg?1678427303" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1093127" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1093127-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g002a-550.jpg?1678427301'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1093127-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g002b-550.jpg?1678427313'><p>Figure 2 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1093127-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g002c-550.jpg?1678427309'><p>Figure 2 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1093127-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g002d-550.jpg?1678427323'><p>Figure 2 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1093127-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g003-550.jpg?1678427320'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1093127-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g004-550.jpg?1678427310'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1093127-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g005-550.jpg?1678427325'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1093127-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g006a-550.jpg?1678427304'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1093127-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g006b-550.jpg?1678427305'><p>Figure 6 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1093127-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g007-550.jpg?1678427319'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1093127-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g008-550.jpg?1678427315'><p>Figure 8</p></div></script></div></div><div id="article-1093127-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g001-550.jpg?1678427303" title=" <strong>Figure 1</strong><br/> <p>Representative images of the banana peel over a period of 21 days (i.e., days 2, 4, 7, 9, 11, 14, 17, 21) during fruit storage and ripening.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g002a-550.jpg?1678427301" title=" <strong>Figure 2</strong><br/> <p>Variations of image analysis computed features (lightness L*, a* parameter, b* parameter, mean, standard deviation, skewness, kurtosis, contrast, dissimilarity, energy, homogeneity, correlation, angular second moment (ASM), short-run emphasis (SRE), long-run emphasis (LRE), gray-level non-uniformity (GLN), run-length non-uniformity (RLN) and run percentage (RP)) of the banana peel samples, according to storage intervals of 2, 4, 7, 9, 11, 14, 17, 21 days.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g002b-550.jpg?1678427313" title=" <strong>Figure 2 Cont.</strong><br/> <p>Variations of image analysis computed features (lightness L*, a* parameter, b* parameter, mean, standard deviation, skewness, kurtosis, contrast, dissimilarity, energy, homogeneity, correlation, angular second moment (ASM), short-run emphasis (SRE), long-run emphasis (LRE), gray-level non-uniformity (GLN), run-length non-uniformity (RLN) and run percentage (RP)) of the banana peel samples, according to storage intervals of 2, 4, 7, 9, 11, 14, 17, 21 days.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g002c-550.jpg?1678427309" title=" <strong>Figure 2 Cont.</strong><br/> <p>Variations of image analysis computed features (lightness L*, a* parameter, b* parameter, mean, standard deviation, skewness, kurtosis, contrast, dissimilarity, energy, homogeneity, correlation, angular second moment (ASM), short-run emphasis (SRE), long-run emphasis (LRE), gray-level non-uniformity (GLN), run-length non-uniformity (RLN) and run percentage (RP)) of the banana peel samples, according to storage intervals of 2, 4, 7, 9, 11, 14, 17, 21 days.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g002d-550.jpg?1678427323" title=" <strong>Figure 2 Cont.</strong><br/> <p>Variations of image analysis computed features (lightness L*, a* parameter, b* parameter, mean, standard deviation, skewness, kurtosis, contrast, dissimilarity, energy, homogeneity, correlation, angular second moment (ASM), short-run emphasis (SRE), long-run emphasis (LRE), gray-level non-uniformity (GLN), run-length non-uniformity (RLN) and run percentage (RP)) of the banana peel samples, according to storage intervals of 2, 4, 7, 9, 11, 14, 17, 21 days.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g003-550.jpg?1678427320" title=" <strong>Figure 3</strong><br/> <p>Scatter diagram of the discrimination, including textural features, amongst banana peel samples from days 2, 7, 11, and 21.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g004-550.jpg?1678427310" title=" <strong>Figure 4</strong><br/> <p>Colored images of the fruit ripening of the iodine-stained banana flesh during the 21-day storage period (i.e., days 2, 4, 7, 9, 11, 14, 17, 21).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g005-550.jpg?1678427325" title=" <strong>Figure 5</strong><br/> <p>Water activity (aw), moisture content (%), Brix, and titratable acidity (%) of the banana-flesh samples during storage at 18.0 ± 0.5 °C.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g006a-550.jpg?1678427304" title=" <strong>Figure 6</strong><br/> <p>Lightness (L*), redness/greenness (a*), yellowness/blueness (b*), and hue angle (h) of the banana-flesh samples during storage at 18.0 ± 0.5 °C.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g006b-550.jpg?1678427305" title=" <strong>Figure 6 Cont.</strong><br/> <p>Lightness (L*), redness/greenness (a*), yellowness/blueness (b*), and hue angle (h) of the banana-flesh samples during storage at 18.0 ± 0.5 °C.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g007-550.jpg?1678427319" title=" <strong>Figure 7</strong><br/> <p>Firmness, springiness, adhesiveness, cohesiveness, and chewiness of the banana-flesh samples during storage, at 18.0 ± 0.5 °C.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03533/article_deploy/html/images/applsci-13-03533-g008-550.jpg?1678427315" title=" <strong>Figure 8</strong><br/> <p>Pairwise correlation matrix between physicochemical parameters of the banana flesh and the features L*, a*, b* of the banana peel, during storage.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/6/3533'>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="1087258" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 20 pages, 1537 KiB   </span> <a href="/2076-3417/13/5/3268/pdf?version=1678076054" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/5/3268">Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature</a> <div class="authors"> by <span class="inlineblock "><strong>Chrysovalantis-Antonios D. Tsiakos</strong> and </span><span class="inlineblock "><strong>Christos Chalkias</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(5), 3268; <a href="https://doi.org/10.3390/app13053268">https://doi.org/10.3390/app13053268</a> - 3 Mar 2023 </div> <a href="/2076-3417/13/5/3268#metrics">Cited by 25</a> | Viewed by 5992 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/5/3268/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and different geomorphological features, while exhibiting different scales and spectral responses. Thus, the monitoring of changes in the coastal land classes and the extraction of coastlines/shorelines can be a challenging task. Earth Observation data and the application of spatiotemporal analysis methods can facilitate shoreline change analysis and detection. Apart from remote sensing methods, the advent of machine learning-based techniques presents an emerging trend, being capable of supporting the monitoring and modeling of coastal ecosystems at large scales. In this context, this study aims to provide a review of the relevant literature falling within the period of 2015–2022, where different machine learning approaches were applied for cases of coast-line/shoreline extraction and change analysis, and/or coastal dynamic monitoring. Particular emphasis is given on the analysis of the selected studies, including details about their performances, as well as their advantages and weaknesses, and information about the different environmental data employed. <a href="/2076-3417/13/5/3268">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/GIS_spatial_planning_hazards ">GIS and Spatial Planning for Natural Hazards Mitigation</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/5/3268/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1087258"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1087258"><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="#next1087258" data-cycle-prev="#prev1087258" data-cycle-progressive="#images1087258" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1087258-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-03268/article_deploy/html/images/applsci-13-03268-g001-550.jpg?1678076123" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1087258" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1087258-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03268/article_deploy/html/images/applsci-13-03268-g002-550.jpg?1678076124'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1087258-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03268/article_deploy/html/images/applsci-13-03268-g003-550.jpg?1678076126'><p>Figure 3</p></div></script></div></div><div id="article-1087258-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-03268/article_deploy/html/images/applsci-13-03268-g001-550.jpg?1678076123" title=" <strong>Figure 1</strong><br/> <p>Categorization of reviewed papers and studies, based on their geographical scales.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/5/3268'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03268/article_deploy/html/images/applsci-13-03268-g002-550.jpg?1678076124" title=" <strong>Figure 2</strong><br/> <p>Categorization of reviewed papers and studies via satellite sensor.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/5/3268'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03268/article_deploy/html/images/applsci-13-03268-g003-550.jpg?1678076126" title=" <strong>Figure 3</strong><br/> <p>Schematic representation of the machine learning techniques investigated in the literature for shoreline/coastline extraction and monitoring of coastal dynamics [<a href="#B33-applsci-13-03268" class="html-bibr">33</a>,<a href="#B74-applsci-13-03268" class="html-bibr">74</a>,<a href="#B75-applsci-13-03268" class="html-bibr">75</a>,<a href="#B76-applsci-13-03268" class="html-bibr">76</a>,<a href="#B77-applsci-13-03268" class="html-bibr">77</a>,<a href="#B78-applsci-13-03268" class="html-bibr">78</a>,<a href="#B79-applsci-13-03268" class="html-bibr">79</a>,<a href="#B80-applsci-13-03268" class="html-bibr">80</a>,<a href="#B81-applsci-13-03268" class="html-bibr">81</a>,<a href="#B82-applsci-13-03268" class="html-bibr">82</a>,<a href="#B83-applsci-13-03268" class="html-bibr">83</a>,<a href="#B84-applsci-13-03268" class="html-bibr">84</a>,<a href="#B85-applsci-13-03268" class="html-bibr">85</a>,<a href="#B86-applsci-13-03268" class="html-bibr">86</a>,<a href="#B87-applsci-13-03268" class="html-bibr">87</a>,<a href="#B88-applsci-13-03268" class="html-bibr">88</a>,<a href="#B89-applsci-13-03268" class="html-bibr">89</a>,<a href="#B90-applsci-13-03268" class="html-bibr">90</a>,<a href="#B91-applsci-13-03268" class="html-bibr">91</a>,<a href="#B92-applsci-13-03268" class="html-bibr">92</a>,<a href="#B93-applsci-13-03268" class="html-bibr">93</a>,<a href="#B94-applsci-13-03268" class="html-bibr">94</a>,<a href="#B95-applsci-13-03268" class="html-bibr">95</a>,<a href="#B96-applsci-13-03268" class="html-bibr">96</a>,<a href="#B97-applsci-13-03268" class="html-bibr">97</a>,<a href="#B98-applsci-13-03268" class="html-bibr">98</a>,<a href="#B99-applsci-13-03268" class="html-bibr">99</a>,<a href="#B100-applsci-13-03268" class="html-bibr">100</a>,<a href="#B101-applsci-13-03268" class="html-bibr">101</a>,<a href="#B102-applsci-13-03268" class="html-bibr">102</a>,<a href="#B103-applsci-13-03268" class="html-bibr">103</a>,<a href="#B104-applsci-13-03268" class="html-bibr">104</a>,<a href="#B105-applsci-13-03268" class="html-bibr">105</a>,<a href="#B107-applsci-13-03268" class="html-bibr">107</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/5/3268'>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="1082407" 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, 7110 KiB   </span> <a href="/2076-3417/13/5/3078/pdf?version=1677722402" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Comparative Analysis of Primary Photosynthetic Reactions Assessed by OJIP Kinetics in Three Brassica Crops after Drought and Recovery" 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 feature" data-dropdown="drop-article-label-feature" aria-expanded="false">Feature Paper</span><span class='label choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/5/3078">Comparative Analysis of Primary Photosynthetic Reactions Assessed by OJIP Kinetics in Three <i>Brassica</i> Crops after Drought and Recovery</a> <div class="authors"> by <span class="inlineblock "><strong>Jasenka Antunović Dunić</strong>, </span><span class="inlineblock "><strong>Selma Mlinarić</strong>, </span><span class="inlineblock "><strong>Iva Pavlović</strong>, </span><span class="inlineblock "><strong>Hrvoje Lepeduš</strong> and </span><span class="inlineblock "><strong>Branka Salopek-Sondi</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(5), 3078; <a href="https://doi.org/10.3390/app13053078">https://doi.org/10.3390/app13053078</a> - 27 Feb 2023 </div> <a href="/2076-3417/13/5/3078#metrics">Cited by 12</a> | Viewed by 2108 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Plant drought tolerance depends on adaptations of the photosynthetic apparatus to changing environments triggered by water deficit. The seedlings of three <i>Brassica</i> crops differing in drought sensitivity, <i>Brassica oleracea</i> L. var. <i>capitata</i>—white cabbage, <i>Brassica oleracea</i> L. var. <i>acephala</i>—kale, and <i>Brassica rapa</i> <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/5/3078/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Plant drought tolerance depends on adaptations of the photosynthetic apparatus to changing environments triggered by water deficit. The seedlings of three <i>Brassica</i> crops differing in drought sensitivity, <i>Brassica oleracea</i> L. var. <i>capitata</i>—white cabbage, <i>Brassica oleracea</i> L. var. <i>acephala</i>—kale, and <i>Brassica rapa</i> L. var. <i>pekinensis</i>—Chinese cabbage, were exposed to drought by withholding water. Detailed insight into the photosynthetic machinery was carried out when the seedling reached a relative water content of about 45% and after re-watering by analyzing the OJIP kinetics. The key objective of this study was to find reliable parameters for distinguishing drought−tolerant and drought-sensitive varieties before permanent structural and functional changes in the photosynthetic apparatus occur. According to our findings, an increase in the total performance index (PI<sub>total</sub>) and structure–function index (SFI), positive L and K bands, total driving forces (ΔDF), and drought resistance index (DRI) suggest drought tolerance. At the same time, susceptible varieties can be distinguished based on negative L and K bands, PI<sub>total</sub>, SFI, and the density of reaction centers (RC/CS<sub>0</sub>). Kale proved to be the most tolerant, Chinese cabbage was moderately susceptible, and white cabbage showed high sensitivity to the investigated drought stress. The genetic variation revealed among the selected <i>Brassica</i> crops could be used in breeding programs and high-precision crop management. <a href="/2076-3417/13/5/3078">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/X5382CCS5X ">Biophysical Properties of Agricultural Crops</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/5/3078/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1082407"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1082407"><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="#next1082407" data-cycle-prev="#prev1082407" data-cycle-progressive="#images1082407" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1082407-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g001-550.jpg?1677722480" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1082407" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1082407-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g002-550.jpg?1677722481'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1082407-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g003-550.jpg?1677722482'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1082407-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g004-550.jpg?1677722480'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1082407-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g005-550.jpg?1677722483'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1082407-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g006-550.jpg?1677722478'><p>Figure 6</p></div></script></div></div><div id="article-1082407-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g001-550.jpg?1677722480" title=" <strong>Figure 1</strong><br/> <p>Shapes and amplitudes of OJIP transient curves determined in three <span class="html-italic">Brassica</span> seedlings after exposure to drought and subsequent recovery are shown as kinetics of relative variable fluorescence Vt and as difference kinetics ΔVOP (<b>a</b>,<b>f</b>,<b>k</b>). Difference kinetics ΔVt for the individual bands L (<b>b</b>,<b>g</b>,<b>l</b>), K (<b>c</b>,<b>h</b>,<b>m</b>), H (<b>d</b>,<b>i</b>,<b>n</b>), and G (<b>e</b>,<b>j</b>,<b>o</b>) are plotted at different time ranges. The O, J, I, and P steps are indicated in V<sub>t</sub> curves.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/5/3078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g002-550.jpg?1677722481" title=" <strong>Figure 2</strong><br/> <p>Spider plots represent selected JIP-test parameters that characterize PSII functioning in three <span class="html-italic">Brassica</span> seedlings: Chinese cabbage (<b>a</b>), white cabbage (<b>b</b>) and kale (<b>c</b>), subjected to drought followed by recovery. Each dataset is normalized to the respective controls (watered seedlings) separately for each variety (control = 1). Asterisks (*) signify differences between the treatments and the corresponding control, while double-asterisks (**) represent significant differences between both the control and recovery at <span class="html-italic">p</span> ≤ 0.05 (ANOVA, HSD).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/5/3078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g003-550.jpg?1677722482" title=" <strong>Figure 3</strong><br/> <p>Chlorophyll <span class="html-italic">a</span> fluorescence parameters characterizing the PSII functioning: minimal fluorescence intensity, F<sub>0</sub> (<b>a</b>), normalized area, S<sub>m</sub> (<b>b</b>), structure – function index, SFI (<b>c</b>), fraction of inactivated OEC, V<sub>K</sub>/V<sub>J</sub> (<b>d</b>), density of reaction centers per excited cross section, RC/CS<sub>0</sub> (<b>e</b>) and overall connectivity parameter, <span class="html-italic">p</span> (<b>f</b>) measured in three <span class="html-italic">Brassica</span> seedlings subjected to drought and subsequent recovery. Normalized data are presented as the mean ± SD; <span class="html-italic">n</span> = 7; asterisk (*) represents a significant difference at <span class="html-italic">p</span> ≤ 0.05 (ANOVA, HSD).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/5/3078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g004-550.jpg?1677722480" title=" <strong>Figure 4</strong><br/> <p>Difference in the driving forces (ΔDF) of three <span class="html-italic">Brassica</span> seedlings after exposure to drought and subsequent recovery. Stacked columns represent differences in DFs in treated seedlings minus the corresponding control separately for each variety. Each DF is calculated by summing up their partial driving forces.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/5/3078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g005-550.jpg?1677722483" title=" <strong>Figure 5</strong><br/> <p>Linear model between logarithms of relative ET<sub>0</sub>/ABS and PI<sub>ABS</sub> in three <span class="html-italic">Brassica</span> seedlings: Chinese cabbage (<b>a</b>), white cabbage (<b>b</b>) and kale (<b>c</b>), subjected to drought (filled circles) and subsequent recovery (empty circles) relative to corresponding controls.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/5/3078'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-03078/article_deploy/html/images/applsci-13-03078-g006-550.jpg?1677722478" title=" <strong>Figure 6</strong><br/> <p>Drought resistance index (DRI) (<b>a</b>) of three <span class="html-italic">Brassica</span> seedlings subjected to drought relative to corresponding controls. Bars represent the means ± SD of seven measurements (<span class="html-italic">n</span> = 7); different letters represent significant differences at <span class="html-italic">p</span> ≤ 0.05 (ANOVA, HSD). Principal component analysis (PCA) (<b>b</b>) shows variation within and among three <span class="html-italic">Brassica</span> seedlings (blue dots) in the control (C) and after drought (D) and recovery (R) in relation to the PSII functioning parameters, performance index, quantum efficiencies, and flux ratios shown as red dots.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/5/3078'>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="1071905" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 24 pages, 2558 KiB   </span> <a href="/2076-3417/13/4/2600/pdf?version=1676624250" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Comparison of the Spreadability of Butter and Butter Substitutes" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/4/2600">Comparison of the Spreadability of Butter and Butter Substitutes</a> <div class="authors"> by <span class="inlineblock "><strong>Małgorzata Ziarno</strong>, </span><span class="inlineblock "><strong>Dorota Derewiaka</strong>, </span><span class="inlineblock "><strong>Anna Florowska</strong> and </span><span class="inlineblock "><strong>Iwona Szymańska</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(4), 2600; <a href="https://doi.org/10.3390/app13042600">https://doi.org/10.3390/app13042600</a> - 17 Feb 2023 </div> <a href="/2076-3417/13/4/2600#metrics">Cited by 13</a> | Viewed by 6006 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> There are many types of butter, soft margarine, and blends, e.g., a mixture of butter and vegetable fats, on the market as bread spreads. Among these, butter and blends of butter with vegetable fats are very popular. The consumer’s choice of product is <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/4/2600/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> There are many types of butter, soft margarine, and blends, e.g., a mixture of butter and vegetable fats, on the market as bread spreads. Among these, butter and blends of butter with vegetable fats are very popular. The consumer’s choice of product is often determined by functional properties, such as texture, and the physicochemical composition of butter and butter substitutes. The aim of this study was to compare sixteen market samples of butter and butter substitutes in terms of spreadability and other selected structural (spreadability, hardness, adhesive force, and adhesiveness) and physicochemical parameters (water content, water distribution, plasma pH, color, acid value, peroxide number, saponification number, and instrumentally measured fatty acid profile) to investigate their correlation with spreadability. The parameters determined here were correlated with factors such as the type of sample, measuring temperature, and physicochemical composition. The statistical analysis revealed a very strong positive correlation between hardness and spreadability for all samples tested at 4 °C, as well as between hardness and spreadability for all samples tested 30 min after removal from the refrigerator; however, the interpretation of the results was different if the butter and butter substitute samples were subjected to a multivariate analysis separately. <a href="/2076-3417/13/4/2600">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/unconventional_raw_materials ">Unconventional Raw Materials for Food Products</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/4/2600/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1071905"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1071905"><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="#next1071905" data-cycle-prev="#prev1071905" data-cycle-progressive="#images1071905" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1071905-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-02600/article_deploy/html/images/applsci-13-02600-g001-550.jpg?1676624337" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1071905" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1071905-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02600/article_deploy/html/images/applsci-13-02600-g002-550.jpg?1676624328'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1071905-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02600/article_deploy/html/images/applsci-13-02600-g003a-550.jpg?1676624333'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1071905-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02600/article_deploy/html/images/applsci-13-02600-g003b-550.jpg?1676624331'><p>Figure 3 Cont.</p></div></script></div></div><div id="article-1071905-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-02600/article_deploy/html/images/applsci-13-02600-g001-550.jpg?1676624337" title=" <strong>Figure 1</strong><br/> <p>Graphs of correlation matrix showing the relationship between the spreadability, hardness, adhesive force and adhesiveness of butter and butter substitute samples (<b>a</b>) and butter substitute samples alone (<b>b</b>) measured at different temperatures (with a confidence level of 95.0%).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2600'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02600/article_deploy/html/images/applsci-13-02600-g002-550.jpg?1676624328" title=" <strong>Figure 2</strong><br/> <p>Graphs of correlation matrix showing the relationship between the spreadability, selected physicochemical properties, and the color components of butter and butter substitute samples (<b>a</b>) and butter substitute samples alone (<b>b</b>) measured at different temperatures (with a confidence level of 95.0%).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2600'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02600/article_deploy/html/images/applsci-13-02600-g003a-550.jpg?1676624333" title=" <strong>Figure 3</strong><br/> <p>Graphs of correlation matrix showing the relationship between spreadability, acid value, saponification number and SFA, MUFA, and PUFA fatty acid profile (<b>a</b>), as well as between spreadability, and the fatty acid percentage share (the percentage for each fatty acid determined) of butter samples (<b>b</b>) measured at different temperatures (with a confidence level of 95.0%).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2600'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02600/article_deploy/html/images/applsci-13-02600-g003b-550.jpg?1676624331" title=" <strong>Figure 3 Cont.</strong><br/> <p>Graphs of correlation matrix showing the relationship between spreadability, acid value, saponification number and SFA, MUFA, and PUFA fatty acid profile (<b>a</b>), as well as between spreadability, and the fatty acid percentage share (the percentage for each fatty acid determined) of butter samples (<b>b</b>) measured at different temperatures (with a confidence level of 95.0%).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2600'>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="1071150" 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, 1225 KiB   </span> <a href="/2076-3417/13/4/2573/pdf?version=1677048608" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/4/2573">Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals</a> <div class="authors"> by <span class="inlineblock "><strong>Wenqian Lin</strong> and </span><span class="inlineblock "><strong>Chao Li</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(4), 2573; <a href="https://doi.org/10.3390/app13042573">https://doi.org/10.3390/app13042573</a> - 16 Feb 2023 </div> <a href="/2076-3417/13/4/2573#metrics">Cited by 44</a> | Viewed by 7229 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> People’s emotions play an important part in our daily life and can not only reflect psychological and physical states, but also play a vital role in people’s communication, cognition and decision-making. Variations in people’s emotions induced by external conditions are accompanied by variations <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/4/2573/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> People’s emotions play an important part in our daily life and can not only reflect psychological and physical states, but also play a vital role in people’s communication, cognition and decision-making. Variations in people’s emotions induced by external conditions are accompanied by variations in physiological signals that can be measured and identified. People’s psychological signals are mainly measured with electroencephalograms (EEGs), electrodermal activity (EDA), electrocardiograms (ECGs), electromyography (EMG), pulse waves, etc. EEG signals are a comprehensive embodiment of the operation of numerous neurons in the cerebral cortex and can immediately express brain activity. EDA measures the electrical features of skin through skin conductance response, skin potential, skin conductance level or skin potential response. ECG technology uses an electrocardiograph to record changes in electrical activity in each cardiac cycle of the heart from the body surface. EMG is a technique that uses electronic instruments to evaluate and record the electrical activity of muscles, which is usually referred to as myoelectric activity. EEG, EDA, ECG and EMG have been widely used to recognize and judge people’s emotions in various situations. Different physiological signals have their own characteristics and are suitable for different occasions. Therefore, a review of the research work and application of emotion recognition and judgment based on the four physiological signals mentioned above is offered. The content covers the technologies adopted, the objects of application and the effects achieved. Finally, the application scenarios for different physiological signals are compared, and issues for attention are explored to provide reference and a basis for further investigation. <a href="/2076-3417/13/4/2573">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/biological_science ">Recent Advances in Biological Science and Technology</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/4/2573/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1071150"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1071150"><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="#next1071150" data-cycle-prev="#prev1071150" data-cycle-progressive="#images1071150" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1071150-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-02573/article_deploy/html/images/applsci-13-02573-g001-550.jpg?1677048678" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1071150" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1071150-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02573/article_deploy/html/images/applsci-13-02573-g002-550.jpg?1677048677'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1071150-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02573/article_deploy/html/images/applsci-13-02573-g003-550.jpg?1677048680'><p>Figure 3</p></div></script></div></div><div id="article-1071150-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-02573/article_deploy/html/images/applsci-13-02573-g001-550.jpg?1677048678" title=" <strong>Figure 1</strong><br/> <p>System of virtual gesture control and a manipulator propelled by emotion [<a href="#B48-applsci-13-02573" class="html-bibr">48</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2573'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02573/article_deploy/html/images/applsci-13-02573-g002-550.jpg?1677048677" title=" <strong>Figure 2</strong><br/> <p>Steering change driven by emotions [<a href="#B48-applsci-13-02573" class="html-bibr">48</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2573'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02573/article_deploy/html/images/applsci-13-02573-g003-550.jpg?1677048680" title=" <strong>Figure 3</strong><br/> <p>Process of emotion judgment model [<a href="#B82-applsci-13-02573" class="html-bibr">82</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2573'>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="1069423" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 26 pages, 6738 KiB   </span> <a href="/2076-3417/13/4/2494/pdf?version=1676466487" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Tannin Extraction from Chestnut Wood Waste: From Lab Scale to Semi-Industrial Plant" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/4/2494">Tannin Extraction from Chestnut Wood Waste: From Lab Scale to Semi-Industrial Plant</a> <div class="authors"> by <span class="inlineblock "><strong>Clelia Aimone</strong>, </span><span class="inlineblock "><strong>Giorgio Grillo</strong>, </span><span class="inlineblock "><strong>Luisa Boffa</strong>, </span><span class="inlineblock "><strong>Samuele Giovando</strong> and </span><span class="inlineblock "><strong>Giancarlo Cravotto</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(4), 2494; <a href="https://doi.org/10.3390/app13042494">https://doi.org/10.3390/app13042494</a> - 15 Feb 2023 </div> <a href="/2076-3417/13/4/2494#metrics">Cited by 16</a> | Viewed by 5838 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The chestnut tree (<i>Castanea sativa</i>, Mill.) is a widespread plant in Europe whose fruits and wood has a relevant economic impact. Chestnut wood (CW) is rich in high-value compounds that exhibit various biological activities, such as antioxidant as well as anticarcinogenic <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/4/2494/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The chestnut tree (<i>Castanea sativa</i>, Mill.) is a widespread plant in Europe whose fruits and wood has a relevant economic impact. Chestnut wood (CW) is rich in high-value compounds that exhibit various biological activities, such as antioxidant as well as anticarcinogenic and antimicrobial properties. These metabolites can be mainly divided into monomeric polyphenols and tannins. In this piece of work, we investigated a sustainable protocol to isolate enriched fractions of the above-mentioned compounds from CW residues. Specifically, a sequential extraction protocol, using subcritical water, was used as a pre-fractionation step, recovering approximately 88% of tannins and 40% of monomeric polyphenols in the first and second steps, respectively. The optimized protocol was also tested at pre-industrial levels, treating up to 13.5 kg CW and 160 L of solution with encouraging results. Ultra- and nanofiltrations were used to further enrich the recovered fractions, achieving more than 98% of the tannin content in the heavy fraction, whilst the removed permeate achieved up to 752.71 mg GAE/g<sub>ext</sub> after the concentration (75.3%). Samples were characterized by means of total phenolic content (TPC), antioxidant activity (DPPH· and ABTS·), and tannin composition (hydrolysable and condensed). In addition, LC-MS-DAD was used for semiqualitative purposes to detect vescalagin/castalagin and vescalin/castalin, as well as gallic acid and ellagic acid. The developed valorization protocol allows the efficient fractionation and recovery of the major polyphenolic components of CW with a sustainable approach that also evaluates pre-industrial scaling-up. <a href="/2076-3417/13/4/2494">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/applsci/sections/green_sustainable_science_technology">Green Sustainable Science and Technology</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/4/2494/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1069423"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1069423"><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="#next1069423" data-cycle-prev="#prev1069423" data-cycle-progressive="#images1069423" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1069423-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g001-550.jpg?1676466566" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1069423" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g002-550.jpg?1676466577'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g003-550.jpg?1676466581'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g004-550.jpg?1676466567'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g005-550.jpg?1676466580'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g006-550.jpg?1676466562'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g007-550.jpg?1676466559'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g008-550.jpg?1676466569'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g009-550.jpg?1676466575'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g010-550.jpg?1676466562'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g011-550.jpg?1676466572'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g012-550.jpg?1676466575'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g0A1-550.jpg?1676466568'><p>Figure A1</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1069423-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g0A2-550.jpg?1676466564'><p>Figure A2</p></div></script></div></div><div id="article-1069423-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g001-550.jpg?1676466566" title=" <strong>Figure 1</strong><br/> <p>Comparison of the overall process yield and the total polyphenols’ extraction yield, between different investigated treatments.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g002-550.jpg?1676466577" title=" <strong>Figure 2</strong><br/> <p>Extraction yield and TPC: comparison with the commercial benchmark (SilvaFEED ENC).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g003-550.jpg?1676466581" title=" <strong>Figure 3</strong><br/> <p>Evaluation of the antioxidant activity, with DPPH· and ABTS· assays, expressed as Trolox equivalents.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g004-550.jpg?1676466567" title=" <strong>Figure 4</strong><br/> <p>Subdivision of TPC-responding molecules, between tannin fraction and free polyphenol fraction, expressed as percentage on TPC.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g005-550.jpg?1676466580" title=" <strong>Figure 5</strong><br/> <p>Pre-treatment influence on extract population of tannins and polyphenols.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g006-550.jpg?1676466562" title=" <strong>Figure 6</strong><br/> <p>(<b>a</b>) Single diafiltration with ¼ of removed water; (<b>b</b>) triple diafiltration with complete water replacement.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g007-550.jpg?1676466559" title=" <strong>Figure 7</strong><br/> <p>Tannin enrichment with the triple diafiltration (UF).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g008-550.jpg?1676466569" title=" <strong>Figure 8</strong><br/> <p>Tannin percentage of diafiltration retentates; labels and sphere radius indicate the polyphenol percentage. Sum of tannins and monomeric polyphenols is normalized to 100.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g009-550.jpg?1676466575" title=" <strong>Figure 9</strong><br/> <p>Mass balance of NF process, expressed as percentage on dry yield (%).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g010-550.jpg?1676466562" title=" <strong>Figure 10</strong><br/> <p>LC-MS analysis, vescalagin/castalagin (m/z: 934.6) and vescalin/castalin (m/z: 632.4) spectra. Masses are selected +0.5 m/z due to detector calibration.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g011-550.jpg?1676466572" title=" <strong>Figure 11</strong><br/> <p>LC-MS analysis, detail for gallocatechins signal region (m/z: 306). (<b>A</b>) Mass spectra; (<b>B</b>) DAD chromatogram. Masses are selected +0.5 m/z due to detector calibration.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g012-550.jpg?1676466575" title=" <strong>Figure 12</strong><br/> <p>LC-MS analysis, detail for ellagic (m/z: 302) and gallic acid signals (m/z: 170). Masses are selected +0.5 m/z due to detector calibration.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g0A1-550.jpg?1676466568" title=" <strong>Figure A1</strong><br/> <p>Experimental procedure for the tannin analyses.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02494/article_deploy/html/images/applsci-13-02494-g0A2-550.jpg?1676466564" title=" <strong>Figure A2</strong><br/> <p>LC-MS analysis, comparison for cinchonine precipitation of tannins.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2494'>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="1061504" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 14 pages, 5185 KiB   </span> <a href="/2076-3417/13/4/2167/pdf?version=1675846634" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Speech Emotion Recognition Based on Two-Stream Deep Learning Model Using Korean Audio Information" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/4/2167">Speech Emotion Recognition Based on Two-Stream Deep Learning Model Using Korean Audio Information</a> <div class="authors"> by <span class="inlineblock "><strong>A-Hyeon Jo</strong> and </span><span class="inlineblock "><strong>Keun-Chang Kwak</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(4), 2167; <a href="https://doi.org/10.3390/app13042167">https://doi.org/10.3390/app13042167</a> - 8 Feb 2023 </div> <a href="/2076-3417/13/4/2167#metrics">Cited by 17</a> | Viewed by 4177 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Identifying a person’s emotions is an important element in communication. In particular, voice is a means of communication for easily and naturally expressing emotions. Speech emotion recognition technology is a crucial component of human–computer interaction (HCI), in which accurately identifying emotions is key. <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/4/2167/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Identifying a person’s emotions is an important element in communication. In particular, voice is a means of communication for easily and naturally expressing emotions. Speech emotion recognition technology is a crucial component of human–computer interaction (HCI), in which accurately identifying emotions is key. Therefore, this study presents a two-stream-based emotion recognition model based on bidirectional long short-term memory (Bi-LSTM) and convolutional neural networks (CNNs) using a Korean speech emotion database, and the performance is comparatively analyzed. The data used in the experiment were obtained from the Korean speech emotion recognition database built by Chosun University. Two deep learning models, Bi-LSTM and YAMNet, which is a CNN-based transfer learning model, were connected in a two-stream architecture to design an emotion recognition model. Various speech feature extraction methods and deep learning models were compared in terms of performance. Consequently, the speech emotion recognition performance of Bi-LSTM and YAMNet was 90.38% and 94.91%, respectively. However, the performance of the two-stream model was 96%, which was a minimum of 1.09% and up to 5.62% improved compared with a single model. <a href="/2076-3417/13/4/2167">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/08B9GNS8AV ">Deep Learning and Machine Learning in Biomedical Signal and Image Processing</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/4/2167/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1061504"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1061504"><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="#next1061504" data-cycle-prev="#prev1061504" data-cycle-progressive="#images1061504" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1061504-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g001-550.jpg?1675846713" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1061504" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1061504-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g002-550.jpg?1675846718'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1061504-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g003-550.jpg?1675846716'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1061504-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g004-550.jpg?1675846717'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1061504-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g005-550.jpg?1675846703'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1061504-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g006-550.jpg?1675846704'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1061504-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g007-550.jpg?1675846710'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1061504-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g008-550.jpg?1675846714'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1061504-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g009-550.jpg?1675846707'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1061504-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g010-550.jpg?1675846705'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1061504-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g011-550.jpg?1675846712'><p>Figure 11</p></div></script></div></div><div id="article-1061504-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g001-550.jpg?1675846713" title=" <strong>Figure 1</strong><br/> <p>Visualization of Bark spectrograms for eight emotions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2167'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g002-550.jpg?1675846718" title=" <strong>Figure 2</strong><br/> <p>Visualization of ERB spectrograms for eight emotions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2167'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g003-550.jpg?1675846716" title=" <strong>Figure 3</strong><br/> <p>Visualization of log-mel spectrograms for eight emotions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2167'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g004-550.jpg?1675846717" title=" <strong>Figure 4</strong><br/> <p>LSTM architecture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2167'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g005-550.jpg?1675846703" title=" <strong>Figure 5</strong><br/> <p>Bi-LSTM architecture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2167'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g006-550.jpg?1675846704" title=" <strong>Figure 6</strong><br/> <p>Structure of the Bi-LSTM and CNN two-stream-based SER model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2167'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g007-550.jpg?1675846710" title=" <strong>Figure 7</strong><br/> <p>Visualization of speech data for eight emotions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2167'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g008-550.jpg?1675846714" title=" <strong>Figure 8</strong><br/> <p>Confusion matrix of Bi-LSTM SER model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2167'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g009-550.jpg?1675846707" title=" <strong>Figure 9</strong><br/> <p>Confusion matrix of YAMNet SER model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2167'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g010-550.jpg?1675846705" title=" <strong>Figure 10</strong><br/> <p>Accuracy of Bi-LSTM and CNN two-stream-based SER model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2167'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02167/article_deploy/html/images/applsci-13-02167-g011-550.jpg?1675846712" title=" <strong>Figure 11</strong><br/> <p>Confusion matrix of Bi-LSTM and CNN two-stream-based SER model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2167'>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="1061095" 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, 12841 KiB   </span> <a href="/2076-3417/13/4/2156/pdf?version=1675827974" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Design of a Smart Factory Based on Cyber-Physical Systems and Internet of Things towards Industry 4.0" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/4/2156">Design of a Smart Factory Based on Cyber-Physical Systems and Internet of Things towards Industry 4.0</a> <div class="authors"> by <span class="inlineblock "><strong>Mutaz Ryalat</strong>, </span><span class="inlineblock "><strong>Hisham ElMoaqet</strong> and </span><span class="inlineblock "><strong>Marwa AlFaouri</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(4), 2156; <a href="https://doi.org/10.3390/app13042156">https://doi.org/10.3390/app13042156</a> - 8 Feb 2023 </div> <a href="/2076-3417/13/4/2156#metrics">Cited by 97</a> | Viewed by 13029 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The rise of Industry 4.0, which employs emerging powerful and intelligent technologies and represents the digital transformation of manufacturing, has a significant impact on society, industry, and other production sectors. The industrial scene is witnessing ever-increasing pressure to improve its agility and versatility <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/4/2156/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The rise of Industry 4.0, which employs emerging powerful and intelligent technologies and represents the digital transformation of manufacturing, has a significant impact on society, industry, and other production sectors. The industrial scene is witnessing ever-increasing pressure to improve its agility and versatility to accommodate the highly modularized, customized, and dynamic demands of production. One of the key concepts within Industry 4.0 is the smart factory, which represents a manufacturing/production system with interconnected processes and operations via cyber-physical systems, the Internet of Things, and state-of-the-art digital technologies. This paper outlines the design of a smart cyber-physical system that complies with the innovative smart factory framework for Industry 4.0 and implements the core industrial, computing, information, and communication technologies of the smart factory. It discusses how to combine the key components (pillars) of a smart factory to create an intelligent manufacturing system. As a demonstration of a simplified smart factory model, a smart manufacturing case study with a drilling process is implemented, and the feasibility of the proposed method is demonstrated and verified with experiments. <a href="/2076-3417/13/4/2156">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/applsci/sections/mechanical_engineering">Mechanical Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/4/2156/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1061095"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1061095"><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="#next1061095" data-cycle-prev="#prev1061095" data-cycle-progressive="#images1061095" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1061095-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g001-550.jpg?1675828058" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1061095" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1061095-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g002-550.jpg?1675828051'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1061095-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g003-550.jpg?1675828054'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1061095-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g004-550.jpg?1675828050'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1061095-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g005-550.jpg?1675828049'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1061095-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g006-550.jpg?1675828052'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1061095-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g007-550.jpg?1675828063'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1061095-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g008-550.jpg?1675828062'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1061095-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g009-550.jpg?1675828056'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1061095-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g010-550.jpg?1675828061'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1061095-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g011-550.jpg?1675828047'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1061095-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g012-550.jpg?1675828048'><p>Figure 12</p></div></script></div></div><div id="article-1061095-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g001-550.jpg?1675828058" title=" <strong>Figure 1</strong><br/> <p>Pillars of Industry 4.0.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g002-550.jpg?1675828051" title=" <strong>Figure 2</strong><br/> <p>The architecture of a smart factory in an intelligent automation pyramid.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g003-550.jpg?1675828054" title=" <strong>Figure 3</strong><br/> <p>Experimental platform of the proposed smart factory.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g004-550.jpg?1675828050" title=" <strong>Figure 4</strong><br/> <p>The proposed CAD model of the drilling unit.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g005-550.jpg?1675828049" title=" <strong>Figure 5</strong><br/> <p>IBM Watson menu.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g006-550.jpg?1675828052" title=" <strong>Figure 6</strong><br/> <p>System interface using microcontroller.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g007-550.jpg?1675828063" title=" <strong>Figure 7</strong><br/> <p>(<b>a</b>). The API architecture of JOpenShowVar; (<b>b</b>). The API architecture of CrossComClient.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g008-550.jpg?1675828062" title=" <strong>Figure 8</strong><br/> <p>Producer–consumer loops.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g009-550.jpg?1675828056" title=" <strong>Figure 9</strong><br/> <p>Producer loop state diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g010-550.jpg?1675828061" title=" <strong>Figure 10</strong><br/> <p>Interfacing network.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g011-550.jpg?1675828047" title=" <strong>Figure 11</strong><br/> <p>Program data flow.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-02156/article_deploy/html/images/applsci-13-02156-g012-550.jpg?1675828048" title=" <strong>Figure 12</strong><br/> <p>Data visualization in IBM Watson IoT.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/4/2156'>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="1050499" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 51 pages, 3550 KiB   </span> <a href="/2076-3417/13/3/1726/pdf?version=1675043046" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Solid Lipid Nanoparticles (SLNs) and Nanostructured Lipid Carriers (NLCs) as Food-Grade Nanovehicles for Hydrophobic Nutraceuticals or Bioactives" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/3/1726">Solid Lipid Nanoparticles (SLNs) and Nanostructured Lipid Carriers (NLCs) as Food-Grade Nanovehicles for Hydrophobic Nutraceuticals or Bioactives</a> <div class="authors"> by <span class="inlineblock "><strong>Chuan-He Tang</strong>, </span><span class="inlineblock "><strong>Huan-Le Chen</strong> and </span><span class="inlineblock "><strong>Jin-Ru Dong</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(3), 1726; <a href="https://doi.org/10.3390/app13031726">https://doi.org/10.3390/app13031726</a> - 29 Jan 2023 </div> <a href="/2076-3417/13/3/1726#metrics">Cited by 36</a> | Viewed by 6635 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Although solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been successfully used as drug delivery systems for about 30 years, the usage of these nanoparticles as food-grade nanovehicles for nutraceuticals or bioactive compounds has been, relatively speaking, scarcely investigated. With fast-increasing <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/3/1726/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Although solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been successfully used as drug delivery systems for about 30 years, the usage of these nanoparticles as food-grade nanovehicles for nutraceuticals or bioactive compounds has been, relatively speaking, scarcely investigated. With fast-increasing interest in the incorporation of a wide range of bioactives in food formulations, as well as health awareness of consumers, there has been a renewed urge for the development of food-compatible SLNs and/or NLCs as nanovehicles for improving water dispersibility, stability, bioavailability, and bioactivities of many lipophilic nutraceuticals or poorly soluble bioactives. In this review, the development of food-grade SLNs and NLCs, as well as their utilization as nanosized delivery systems for lipophilic or hydrophobic nutraceuticals, was comprehensively reviewed. First, the structural composition and preparation methods of food-grade SLNs and NLCs were simply summarized. Next, some key issues about the usage of such nanoparticles as oral nanovehicles, e.g., incorporation and release of bioactives, oxidative stability, lipid digestion and absorption, and intestinal transport, were critically discussed. Then, recent advances in the utilization of SLNs and NLCs as nanovehicles for encapsulation and delivery of different liposoluble or poorly soluble nutraceuticals or bioactives were comprehensively reviewed. The performance of such nanoparticles as nanovehicles for improving stability, bioavailability, and bioactivities of curcuminoids (and curcumin in particular) was also highlighted. Lastly, some strategies to improve the oral bioavailability and delivery of loaded nutraceuticals in such nanoparticles were presented. The review will be relevant, providing state-of-the-art knowledge about the development of food-grade lipid-based nanovehicles for improving the stability and bioavailability of many nutraceuticals. <a href="/2076-3417/13/3/1726">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/G923JELARZ ">Editorial Board Members' Collection Series: Functional Foods</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/3/1726/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1050499"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1050499"><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="#next1050499" data-cycle-prev="#prev1050499" data-cycle-progressive="#images1050499" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1050499-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-ag-550.jpg?1675043137" alt="" style="border: 0;"><p>Graphical abstract</p></div><script id="images1050499" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1050499-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g001-550.jpg?1675043136'><p>Figure 1</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1050499-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g002-550.jpg?1675043131'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1050499-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g003-550.jpg?1675043135'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1050499-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g004-550.jpg?1675043134'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1050499-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g005-550.jpg?1675043133'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1050499-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g006-550.jpg?1675043130'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1050499-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g007-550.jpg?1675043129'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1050499-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g008-550.jpg?1675043129'><p>Figure 8</p></div></script></div></div><div id="article-1050499-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-ag-550.jpg?1675043137" title=" <strong>Graphical abstract</strong><br/><strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g001-550.jpg?1675043136" title=" <strong>Figure 1</strong><br/> <p>Schematic representation of the composition structures of lipid nanoparticles: SLNs and NLCs. (Adapted from [<a href="#B19-applsci-13-01726" class="html-bibr">19</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g002-550.jpg?1675043131" title=" <strong>Figure 2</strong><br/> <p>Schematic representation of triacylglycerol molecules and lipid polymorphism with three common types of subcellular cells, referring to the polymorphs <span class="html-italic">α</span>, <span class="html-italic">β′</span>, and <span class="html-italic">β</span>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g003-550.jpg?1675043135" title=" <strong>Figure 3</strong><br/> <p>(<b>a</b>) The the hot and cold homogenization procedures used to obtain bioactive-loaded solid lipid nanoparticles (SLNs). (<b>b</b>) The melt micro-emulsification process used to obtain bioactive-loaded SLNs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g004-550.jpg?1675043134" title=" <strong>Figure 4</strong><br/> <p>(<b>a</b>) Three models for the incorporation of bioactives into SLNs: homogenous matrix model (left), core-shell models with bioactive-enriched shell (middle), and bioactive-enriched core (right). (<b>b</b>) The release kinetics of bioactives from the SLNs with the corresponding three models (above).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g005-550.jpg?1675043133" title=" <strong>Figure 5</strong><br/> <p>Schematic illustration for lipid digestion of lipid nanoparticles (SLNs and NLCs) and bioactives (B) solubilization in the small intestine. (Adapted from [<a href="#B25-applsci-13-01726" class="html-bibr">25</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g006-550.jpg?1675043130" title=" <strong>Figure 6</strong><br/> <p>Schematic illustration for the influence of high-melting and low-melting surfactants on the nucleation and crystallization of lipid nanoparticles. Using high-melting lecithin (80H) as the surfactant, core-shell lipid nanoparticles, with <span class="html-italic">ω</span>-3 PUFAs as the cores, are formed, while, in the case of low-melting lecithin (PC75), a fraction of <span class="html-italic">ω</span>-3 PUFAs might exclude from the crystallized solid lipid matrix upon cooling. (Adapted from [<a href="#B30-applsci-13-01726" class="html-bibr">30</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g007-550.jpg?1675043129" title=" <strong>Figure 7</strong><br/> <p>(<b>a</b>) Photographs of <span class="html-italic">curcuma longa</span> L. and its dried rhizomes. (<b>b</b>) Chemical structure of curcuminoids (curcumin, demethoxycurcumin and disdemethoxycurcumin) from turmeric.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01726/article_deploy/html/images/applsci-13-01726-g008-550.jpg?1675043129" title=" <strong>Figure 8</strong><br/> <p>Schematic representation of several types of surface-modified SLNs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1726'>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="1050534" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <a data-dropdown="drop-supplementary-1050534" aria-controls="drop-supplementary-1050534" aria-expanded="false" title="Supplementary Material"> <i class="material-icons">attachment</i> </a> <div id="drop-supplementary-1050534" class="f-dropdown label__btn__dropdown label__btn__dropdown--wide" data-dropdown-content aria-hidden="true" tabindex="-1"> Supplementary material: <br/> <a href="/2076-3417/13/3/1728/s1?version=1674986451"> Supplementary File 1 (ZIP, 133 KiB) </a><br/> </div> </div> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 18 pages, 1715 KiB   </span> <a href="/2076-3417/13/3/1728/pdf?version=1675400905" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="TeleFE: A New Tool for the Tele-Assessment of Executive Functions in Children" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/3/1728">TeleFE: A New Tool for the Tele-Assessment of Executive Functions in Children</a> <div class="authors"> by <span class="inlineblock "><strong>Carlotta Rivella</strong>, </span><span class="inlineblock "><strong>Costanza Ruffini</strong>, </span><span class="inlineblock "><strong>Clara Bombonato</strong>, </span><span class="inlineblock "><strong>Agnese Capodieci</strong>, </span><span class="inlineblock "><strong>Andrea Frascari</strong>, </span><span class="inlineblock "><strong>Gian Marco Marzocchi</strong>, </span><span class="inlineblock "><strong>Alessandra Mingozzi</strong>, </span><span class="inlineblock "><strong>Chiara Pecini</strong>, </span><span class="inlineblock "><strong>Laura Traverso</strong>, </span><span class="inlineblock "><strong>Maria Carmen Usai</strong> and </span><span class="inlineblock "><strong>Paola Viterbori</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(3), 1728; <a href="https://doi.org/10.3390/app13031728">https://doi.org/10.3390/app13031728</a> - 29 Jan 2023 </div> <a href="/2076-3417/13/3/1728#metrics">Cited by 11</a> | Viewed by 2913 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In recent decades, the utility of cognitive tele-assessment has increasingly been highlighted, both in adults and in children. The present study aimed to present TeleFE, a new tool for the tele-assessment of EF in children aged 6–13. TeleFE consists of a web platform <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/3/1728/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In recent decades, the utility of cognitive tele-assessment has increasingly been highlighted, both in adults and in children. The present study aimed to present TeleFE, a new tool for the tele-assessment of EF in children aged 6–13. TeleFE consists of a web platform including four tasks based on robust neuropsychological paradigms to evaluate inhibition, interference suppression, working memory, cognitive flexibility, and planning. It also includes questionnaires on EF for teachers and parents, to obtain information on the everyday functioning of the children. As TeleFE allows the assessment of EF both remotely and in-person, a comparison of the two modalities was conducted by administering TeleFE to 1288 Italian primary school children. A series of ANOVA was conducted, showing no significant effect of assessment modality (<i>p</i> > 0.05 for all the measures). In addition, significant differences by class emerged for all the measures (<i>p</i> < 0.001 for all the measures except <i>p</i> = 0.008 for planning). Finally, a significant sex effect emerged for inhibition (<i>p</i> < 0.001) and for the reaction times in both interference control (<i>p</i> = 0.013) and cognitive flexibility (<i>p</i> < 0.001), with boys showing a lower inhibition and faster reaction times. The implications of these results along with the indications for the choice of remote assessment are discussed. <a href="/2076-3417/13/3/1728">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/Diagnosis_Disorders ">New Digital Technologies for Diagnosis and Rehabilitation of Neurodevelopmental Disorders</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/3/1728/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1050534"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1050534"><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="#next1050534" data-cycle-prev="#prev1050534" data-cycle-progressive="#images1050534" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1050534-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-01728/article_deploy/html/images/applsci-13-01728-g001-550.jpg?1675400984" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1050534" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1050534-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01728/article_deploy/html/images/applsci-13-01728-g002-550.jpg?1675400986'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1050534-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01728/article_deploy/html/images/applsci-13-01728-g003-550.jpg?1675400985'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1050534-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01728/article_deploy/html/images/applsci-13-01728-g004-550.jpg?1675400983'><p>Figure 4</p></div></script></div></div><div id="article-1050534-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-01728/article_deploy/html/images/applsci-13-01728-g001-550.jpg?1675400984" title=" <strong>Figure 1</strong><br/> <p>Exemplification of Go/NoGo task.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01728/article_deploy/html/images/applsci-13-01728-g002-550.jpg?1675400986" title=" <strong>Figure 2</strong><br/> <p>Flanker task, exemplification of the 2nd block, peripheral congruent condition.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01728/article_deploy/html/images/applsci-13-01728-g003-550.jpg?1675400985" title=" <strong>Figure 3</strong><br/> <p>Exemplification of the 2-back task.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01728/article_deploy/html/images/applsci-13-01728-g004-550.jpg?1675400983" title=" <strong>Figure 4</strong><br/> <p>DPT map.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1728'>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="1044205" 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, 7391 KiB   </span> <a href="/2076-3417/13/3/1465/pdf?version=1675071685" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Data Augmentation Method for Plant Leaf Disease Recognition" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/3/1465">Data Augmentation Method for Plant Leaf Disease Recognition</a> <div class="authors"> by <span class="inlineblock "><strong>Byeongjun Min</strong>, </span><span class="inlineblock "><strong>Taehyun Kim</strong>, </span><span class="inlineblock "><strong>Dongil Shin</strong> and </span><span class="inlineblock "><strong>Dongkyoo Shin</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(3), 1465; <a href="https://doi.org/10.3390/app13031465">https://doi.org/10.3390/app13031465</a> - 22 Jan 2023 </div> <a href="/2076-3417/13/3/1465#metrics">Cited by 17</a> | Viewed by 4025 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage to various crops. If climate change continues, it will likely be very difficult to maintain current crop production, and the problem of a <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/3/1465/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage to various crops. If climate change continues, it will likely be very difficult to maintain current crop production, and the problem of a shortage of expert manpower is also deepening. Fortunately, research on various early diagnosis systems based on deep learning is actively underway to solve these problems, but the problem of lack of diversity in some hard-to-collect disease samples remains. This imbalanced data increases the bias of machine learning models, causing overfitting problems. In this paper, we propose a data augmentation method based on an image-to-image translation model to solve the bias problem by supplementing these insufficient diseased leaf images. The proposed augmentation method performs translation between healthy and diseased leaf images and utilizes attention mechanisms to create images that reflect more evident disease textures. Through these improvements, we generated a more plausible diseased leaf image compared to existing methods and conducted an experiment to verify whether this data augmentation method could further improve the performance of a classification model for early diagnosis of plants. In the experiment, the PlantVillage dataset was used, and the extended dataset was built using the generated images and original images, and the performance of the classification models was evaluated through the test set. <a href="/2076-3417/13/3/1465">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/GA84M5W036 ">Applications of Machine Learning in Agriculture</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/3/1465/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1044205"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1044205"><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="#next1044205" data-cycle-prev="#prev1044205" data-cycle-progressive="#images1044205" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1044205-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g001-550.jpg?1675071962" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1044205" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1044205-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g002-550.jpg?1675071964'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1044205-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g003-550.jpg?1675071968'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1044205-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g004-550.jpg?1675071966'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1044205-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g005-550.jpg?1675071958'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1044205-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g006-550.jpg?1675071967'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1044205-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g007-550.jpg?1675071960'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1044205-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g008-550.jpg?1675071964'><p>Figure 8</p></div></script></div></div><div id="article-1044205-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g001-550.jpg?1675071962" title=" <strong>Figure 1</strong><br/> <p>Data Augmentation Method for Plant Leaf Disease Recognition.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1465'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g002-550.jpg?1675071964" title=" <strong>Figure 2</strong><br/> <p>Pre-activation Residual Attention Block (PaRAB).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1465'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g003-550.jpg?1675071968" title=" <strong>Figure 3</strong><br/> <p>Channel-Spatial Attention module.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1465'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g004-550.jpg?1675071966" title=" <strong>Figure 4</strong><br/> <p>Sample image of disease-infected grapes (Black rot, Scab, Rust).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1465'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g005-550.jpg?1675071958" title=" <strong>Figure 5</strong><br/> <p>Sample image of disease-infected grapes (Black rot, Esca, Blight).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1465'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g006-550.jpg?1675071967" title=" <strong>Figure 6</strong><br/> <p>Sample image of disease-infected potatoes (Early blight, Late blight).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1465'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g007-550.jpg?1675071960" title=" <strong>Figure 7</strong><br/> <p>Comparison of disease-infected leaf image results generated by the proposed method and CycleGAN.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1465'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01465/article_deploy/html/images/applsci-13-01465-g008-550.jpg?1675071964" title=" <strong>Figure 8</strong><br/> <p>t-SNE visualization of feature vectors extracted from EfficientNet trained on the augmented apple rust dataset.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/3/1465'>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="1037189" 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, 951 KiB   </span> <a href="/2076-3417/13/2/1193/pdf?version=1673855873" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Pasteurization of Food and Beverages by High Pressure Processing (HPP) at Room Temperature: Inactivation of Staphylococcus aureus, Escherichia coli, Listeria monocytogenes, Salmonella, and Other Microbial Pathogens" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/2/1193">Pasteurization of Food and Beverages by High Pressure Processing (HPP) at Room Temperature: Inactivation of <i>Staphylococcus aureus, Escherichia coli, Listeria monocytogenes, Salmonella,</i> and Other Microbial Pathogens</a> <div class="authors"> by <span class="inlineblock "><strong>Filipa Vinagre M. Silva</strong> and </span><span class="inlineblock "><strong>Evelyn</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(2), 1193; <a href="https://doi.org/10.3390/app13021193">https://doi.org/10.3390/app13021193</a> - 16 Jan 2023 </div> <a href="/2076-3417/13/2/1193#metrics">Cited by 28</a> | Viewed by 10128 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Vegetative pathogens actively grow in foods, metabolizing and dividing their cells. They have consequently become a focus of concern for the food industry, food regulators and food control agencies. Although much has been done by the food industry and food regulatory agencies, foodborne <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/2/1193/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Vegetative pathogens actively grow in foods, metabolizing and dividing their cells. They have consequently become a focus of concern for the food industry, food regulators and food control agencies. Although much has been done by the food industry and food regulatory agencies, foodborne outbreaks are still reported globally, causing illnesses, hospitalizations, and in certain cases, deaths, together with product recalls and subsequent economic losses. Major bacterial infections from raw and processed foods are caused by <i>Escherichia coli</i> serotype O157:H7, <i>Salmonella enteritidis,</i> and <i>Listeria monocytogenes</i>. High pressure processing (HPP) (also referred to as high hydrostatic pressure, HHP) is a non-thermal pasteurization technology that relies on very high pressures (400–600 MPa) to inactivate pathogens, instead of heat, thus causing less negative impact in the food nutrients and quality. HPP can be used to preserve foods, instead of chemical food additives. In this study, a review of the effect of HPP treatments on major vegetative bacteria in specific foods was carried out. HPP at 600 MPa, commonly used by the food industry, can achieve the recommended 5–8-log reductions in <i>E. coli</i>, <i>S. enteritidis</i>, <i>L. monocytogenes</i>, and <i>Vibrio</i>. <i>Staphylococcus aureus</i> presented the highest resistance to HPP among the foodborne vegetative pathogens investigated, followed by <i>E. coli</i>. More susceptible <i>L. monocytogenes</i> and <i>Salmonella</i> spp. bacteria were reduced by 6 logs at pressures within 500–600 MPa. <i>Vibrio</i> spp. (e.g., raw oysters), <i>Campylobacter jejuni</i>, <i>Yersinia enterocolitica</i>, <i>Citrobacter freundii</i> and <i>Aeromonas hydrophila</i> generally required lower pressures (300–400 MPa) for inactivation. Bacterial species and strain, as well as the food itself, with a characteristic composition, affect the microbial inactivation. This review demonstrates that HPP is a safe pasteurization technology, which is able to achieve at least 5-log reduction in major food bacterial pathogens, without the application of heat. <a href="/2076-3417/13/2/1193">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/Non_Thermal_Technologies ">Non-thermal Technologies for Food Processing</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/2/1193/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1037189"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1037189"><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="#next1037189" data-cycle-prev="#prev1037189" data-cycle-progressive="#images1037189" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1037189-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-01193/article_deploy/html/images/applsci-13-01193-g001-550.jpg?1673855946" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1037189" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1037189-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01193/article_deploy/html/images/applsci-13-01193-g002-550.jpg?1673855944'><p>Figure 2</p></div></script></div></div><div id="article-1037189-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-01193/article_deploy/html/images/applsci-13-01193-g001-550.jpg?1673855946" title=" <strong>Figure 1</strong><br/> <p>Diagram representing high-pressure processing (HPP) treatment of a pre-packed food (extracted from EFSA BIOHAZ Panel, European Food Safety Authority Panel on Biological Hazards, 2022) [<a href="#B5-applsci-13-01193" class="html-bibr">5</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/1193'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01193/article_deploy/html/images/applsci-13-01193-g002-550.jpg?1673855944" title=" <strong>Figure 2</strong><br/> <p>Diagram showing the high-pressure processing (HPP) inactivation of microorganisms in a beverage (extracted from Silva and van Wyk, 2021) [<a href="#B21-applsci-13-01193" class="html-bibr">21</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/1193'>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="1036757" 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, 1989 KiB   </span> <a href="/2076-3417/13/2/1169/pdf?version=1673835149" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/2/1169">A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China</a> <div class="authors"> by <span class="inlineblock "><strong>Jia Yao</strong>, </span><span class="inlineblock "><strong>Hongdou Han</strong>, </span><span class="inlineblock "><strong>Yang Yang</strong>, </span><span class="inlineblock "><strong>Yiming Song</strong> and </span><span class="inlineblock "><strong>Guihe Li</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(2), 1169; <a href="https://doi.org/10.3390/app13021169">https://doi.org/10.3390/app13021169</a> - 15 Jan 2023 </div> <a href="/2076-3417/13/2/1169#metrics">Cited by 55</a> | Viewed by 8369 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The continuous temperature rise has raised global concerns about CO<sub>2</sub> emissions. As the country with the largest CO<sub>2</sub> emissions, China is facing the challenge of achieving large CO<sub>2</sub> emission reductions (or even net-zero CO<sub>2</sub> emissions) in a short period. <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/2/1169/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The continuous temperature rise has raised global concerns about CO<sub>2</sub> emissions. As the country with the largest CO<sub>2</sub> emissions, China is facing the challenge of achieving large CO<sub>2</sub> emission reductions (or even net-zero CO<sub>2</sub> emissions) in a short period. With the strong support and encouragement of the Chinese government, technological breakthroughs and practical applications of carbon capture, utilization, and storage (CCUS) are being aggressively pursued, and some outstanding accomplishments have been realized. Based on the numerous information from a wide variety of sources including publications and news reports only available in Chinese, this paper highlights the latest CCUS progress in China after 2019 by providing an overview of known technologies and typical projects, aiming to provide theoretical and practical guidance for achieving net-zero CO<sub>2</sub> emissions in the future. <a href="/2076-3417/13/2/1169">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/Applications_Carbon_Capture_Utilization_Storage ">Applications and Challenges in Carbon Capture, Utilization and Storage</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/2/1169/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1036757"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1036757"><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="#next1036757" data-cycle-prev="#prev1036757" data-cycle-progressive="#images1036757" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1036757-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g001-550.jpg?1673835218" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1036757" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1036757-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g002a-550.jpg?1673835216'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1036757-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g002b-550.jpg?1673835219'><p>Figure 2 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1036757-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g003-550.jpg?1673835219'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1036757-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g004-550.jpg?1673835214'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1036757-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g005-550.jpg?1673835216'><p>Figure 5</p></div></script></div></div><div id="article-1036757-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g001-550.jpg?1673835218" title=" <strong>Figure 1</strong><br/> <p>Annual CO<sub>2</sub> emissions of some countries from 1980 to 2020 [<a href="#B55-applsci-13-01169" class="html-bibr">55</a>]. Data were derived from the Global Carbon Project.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/1169'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g002a-550.jpg?1673835216" title=" <strong>Figure 2</strong><br/> <p>Distribution of CCUS projects in China [<a href="#B50-applsci-13-01169" class="html-bibr">50</a>]. (<b>a</b>) CCUS project type distribution in China; (<b>b</b>) CCUS project location distribution in China.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/1169'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g002b-550.jpg?1673835219" title=" <strong>Figure 2 Cont.</strong><br/> <p>Distribution of CCUS projects in China [<a href="#B50-applsci-13-01169" class="html-bibr">50</a>]. (<b>a</b>) CCUS project type distribution in China; (<b>b</b>) CCUS project location distribution in China.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/1169'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g003-550.jpg?1673835219" title=" <strong>Figure 3</strong><br/> <p>Schematic flow diagrams of post-combustion capture, pre-combustion capture and oxy-fuel combustion capture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/1169'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g004-550.jpg?1673835214" title=" <strong>Figure 4</strong><br/> <p>CO<sub>2</sub> utilization classification [<a href="#B97-applsci-13-01169" class="html-bibr">97</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/1169'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-01169/article_deploy/html/images/applsci-13-01169-g005-550.jpg?1673835216" title=" <strong>Figure 5</strong><br/> <p>CO<sub>2</sub> storage classifications.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/1169'>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="1028235" 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, 1011 KiB   </span> <a href="/2076-3417/13/2/837/pdf?version=1673955370" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/2/837">A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection</a> <div class="authors"> by <span class="inlineblock "><strong>Marta Catillo</strong>, </span><span class="inlineblock "><strong>Antonio Pecchia</strong> and </span><span class="inlineblock "><strong>Umberto Villano</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(2), 837; <a href="https://doi.org/10.3390/app13020837">https://doi.org/10.3390/app13020837</a> - 7 Jan 2023 </div> <a href="/2076-3417/13/2/837#metrics">Cited by 19</a> | Viewed by 4016 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/2/837/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, which often provide one separate model per device or per attack. These solutions are not suited to the scale and dynamism of modern IoT networks. This paper proposes a novel IoT-driven cross-device method, which allows learning a single IDS model instead of many separate models atop the traffic of different IoT devices. A semi-supervised approach is adopted due to its wider applicability for unanticipated attacks. The solution is based on an all-in-one deep autoencoder, which consists of training a single deep neural network with the normal traffic from different IoT devices. Extensive experimentation performed with a widely used benchmarking dataset indicates that the all-in-one approach achieves within 0.9994–0.9997 recall, 0.9999–1.0 precision, 0.0–0.0071 false positive rate and 0.9996–0.9998 F1 score, depending on the device. The results obtained demonstrate the validity of the proposal, which represents a lightweight and device-independent solution with considerable advantages in terms of transferability and adaptability. <a href="/2076-3417/13/2/837">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Collection <a href=" /journal/applsci/topical_collections/innovation_information_security ">Innovation in Information Security</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/2/837/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1028235"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1028235"><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="#next1028235" data-cycle-prev="#prev1028235" data-cycle-progressive="#images1028235" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1028235-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-00837/article_deploy/html/images/applsci-13-00837-g001-550.jpg?1673955436" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1028235" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1028235-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00837/article_deploy/html/images/applsci-13-00837-g002-550.jpg?1673955436'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1028235-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00837/article_deploy/html/images/applsci-13-00837-g003-550.jpg?1673955438'><p>Figure 3</p></div></script></div></div><div id="article-1028235-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-00837/article_deploy/html/images/applsci-13-00837-g001-550.jpg?1673955436" title=" <strong>Figure 1</strong><br/> <p>Representation of an autoencoder (three hidden layers).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/837'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00837/article_deploy/html/images/applsci-13-00837-g002-550.jpg?1673955436" title=" <strong>Figure 2</strong><br/> <p>Steps underlying the threshold selection method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/837'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00837/article_deploy/html/images/applsci-13-00837-g003-550.jpg?1673955438" title=" <strong>Figure 3</strong><br/> <p>RE of the test sets for the <tt>Danmini</tt> doorbell and <tt>Provision PT-838</tt> security camera devices: (<b>a</b>) <tt>Danmini</tt>–Separate autoencoding; (<b>b</b>) <tt>Danmini</tt>–All-in-one autoencoding; (<b>c</b>) <tt>Provision PT-838</tt>–Separate autoencoding; (<b>d</b>) <tt>Provision PT-838</tt>–All-in-one autoencoding.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/837'>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="1027526" 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, 33375 KiB   </span> <a href="/2076-3417/13/2/812/pdf?version=1673003028" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Using UAS-Aided Photogrammetry to Monitor and Quantify the Geomorphic Effects of Extreme Weather Events in Tectonically Active Mass Waste-Prone Areas: The Case of Medicane Ianos" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/2/812">Using UAS-Aided Photogrammetry to Monitor and Quantify the Geomorphic Effects of Extreme Weather Events in Tectonically Active Mass Waste-Prone Areas: The Case of Medicane Ianos</a> <div class="authors"> by <span class="inlineblock "><strong>Evelina Kotsi</strong>, </span><span class="inlineblock "><strong>Emmanuel Vassilakis</strong>, </span><span class="inlineblock "><strong>Michalis Diakakis</strong>, </span><span class="inlineblock "><strong>Spyridon Mavroulis</strong>, </span><span class="inlineblock "><strong>Aliki Konsolaki</strong>, </span><span class="inlineblock "><strong>Christos Filis</strong>, </span><span class="inlineblock "><strong>Stylianos Lozios</strong> and </span><span class="inlineblock "><strong>Efthymis Lekkas</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(2), 812; <a href="https://doi.org/10.3390/app13020812">https://doi.org/10.3390/app13020812</a> - 6 Jan 2023 </div> <a href="/2076-3417/13/2/812#metrics">Cited by 10</a> | Viewed by 2193 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Extreme weather events can trigger various hydrogeomorphic phenomena and processes including slope failures. These shallow instabilities are difficult to monitor and measure due to the spatial and temporal scales in which they occur. New technologies such as unmanned aerial systems (UAS), photogrammetry and <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/2/812/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Extreme weather events can trigger various hydrogeomorphic phenomena and processes including slope failures. These shallow instabilities are difficult to monitor and measure due to the spatial and temporal scales in which they occur. New technologies such as unmanned aerial systems (UAS), photogrammetry and the structure-from-motion (SfM) technique have recently demonstrated capabilities useful in performing accurate terrain observations that have the potential to provide insights into these geomorphic processes. This study explores the use of UAS-aided photogrammetry and change detection, using specialized techniques such as the digital elevation model (DEM) of differences (DoD) and cloud-to-cloud distance (C2C) to monitor and quantify geomorphic changes before and after an extreme medicane event in Myrtos, a highly visited touristic site on Cephalonia Island, Greece. The application demonstrates that the combination of UAS with photogrammetry allows accurate delineation of instabilities, volumetric estimates of morphometric changes, insights into erosion and deposition processes and the delineation of higher-risk areas in a rapid, safe and practical way. Overall, the study illustrates that the combination of tools facilitates continuous monitoring and provides key insights into geomorphic processes that are otherwise difficult to observe. Through this deeper understanding, this approach can be a stepping stone to risk management of this type of highly-visited sites, which in turn is a key ingredient to sustainable development in high-risk areas. <a href="/2076-3417/13/2/812">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/applsci/sections/earth_sciences_geography">Earth Sciences</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/2/812/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1027526"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1027526"><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="#next1027526" data-cycle-prev="#prev1027526" data-cycle-progressive="#images1027526" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1027526-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g001-550.jpg?1673003122" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1027526" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g002-550.jpg?1673003102'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g003-550.jpg?1673003115'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g004-550.jpg?1673003105'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g005-550.jpg?1673003131'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g006-550.jpg?1673003107'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g007-550.jpg?1673003127'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g008-550.jpg?1673003118'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g009-550.jpg?1673003113'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g010-550.jpg?1673003111'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g011-550.jpg?1673003124'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g012-550.jpg?1673003130'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1027526-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g013-550.jpg?1673003099'><p>Figure 13</p></div></script></div></div><div id="article-1027526-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g001-550.jpg?1673003122" title=" <strong>Figure 1</strong><br/> <p>Location map of Myrtos beach. The insets show the study site at different scales (projected coordinate system GGRS’87, EPSG:2100).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g002-550.jpg?1673003102" title=" <strong>Figure 2</strong><br/> <p>The morphological, geological, tectonic, and geotechnical context of the generation of destructive slope failures on the steep slopes of the Myrtos coastal area, based on geological field surveys in the area.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g003-550.jpg?1673003115" title=" <strong>Figure 3</strong><br/> <p>The geological, tectonic, and geotechnical setting of the steep slopes of the Myrtos coastal area, based on geological field surveys.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g004-550.jpg?1673003105" title=" <strong>Figure 4</strong><br/> <p>Spatial distribution of sites affected by Medicane Ianos in Cephalonia Island. <span class="html-italic">EP</span>: Erissos Peninsula; <span class="html-italic">CS</span>: Charakas site; <span class="html-italic">KM</span>: Kalon Mt; <span class="html-italic">PV</span>: Pylaros valley; <span class="html-italic">TV</span>: Thinia Valley; <span class="html-italic">ADM</span>: Agia Dynati Mt; <span class="html-italic">AM</span>: Aenos Mt; <span class="html-italic">AP</span>: Argostoli peninsula. The study area of Myrtos is located at the western end of Pylaros valley corresponding to the transition zone from the Erissos peninsula to the mountainous central Cephalonia.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g005-550.jpg?1673003131" title=" <strong>Figure 5</strong><br/> <p>Typical views of the impact of Medicane Ianos on Cephalonia Island: (<b>a</b>–<b>d</b>) debris inundated public and private areas of Assos village causing minor non-structural damage to buildings and blockage of ground floor entrances. (<b>e</b>,<b>f</b>) The port of Assos village was also affected by the generated debris flows. Debris flooded the coastal road, part of the port, and the adjacent beaches, resulting in morphological changes. (<b>f</b>–<b>i</b>) Debris flows were also triggered in several sites in the Agia Efimia area, resulting in temporary traffic disruption due to accumulation of debris on adjacent parts of the road network. Assos is located in the western part of the Erissos peninsula, while Agia Efimia is located in the eastern part of the transition zone from the Erissos peninsula in the north to the Aenos Mt fault block to the south.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g006-550.jpg?1673003107" title=" <strong>Figure 6</strong><br/> <p>Characteristic views of (<b>a</b>–<b>g</b>) the impact of the debris and mud flows generated by Medicane Ianos on the rockfall protection net fences (<b>a</b>–<b>d</b>) and the visitor facilities along the beach (<b>d</b>–<b>g</b>). The slope protection works were disabled in several sites due to the large quantity of mobilized material, which partially covered large segments of the road (<b>d</b>) and also the tourist facilities and infrastructure at the base of the slope (<b>e</b>–<b>g</b>), which were almost buried in the deposits.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g007-550.jpg?1673003127" title=" <strong>Figure 7</strong><br/> <p>Views of the equipment used for detecting the geomorphological changes triggered by the 2020 Medicane Ianos in the Myrtos coastal area: (<b>a</b>) a commercial multi-rotary UAS (DJI Phantom 4 RTK) with an on-board GNSS antenna with the ability of RTK processing was used as a rover antenna, (<b>b</b>) the DJI D-RTK-2 was used as the base station antenna.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g008-550.jpg?1673003118" title=" <strong>Figure 8</strong><br/> <p>Overview of the geomorphic changes at the popular Myrtos beach, through comparison of the constructed orthoimages before (<b>a</b>) and after (<b>b</b>) the Medicane Ianos disaster.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g009-550.jpg?1673003113" title=" <strong>Figure 9</strong><br/> <p>Sparse point cloud showing the UAS camera locations during the image acquisition and the position of the seven pseudo-GCPs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g010-550.jpg?1673003111" title=" <strong>Figure 10</strong><br/> <p>Comparison of digital surface models created from imagery captured (<b>a</b>) before and (<b>b</b>) after the destructive event.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g011-550.jpg?1673003124" title=" <strong>Figure 11</strong><br/> <p>The results of the processing of the affected area over Myrtos beach with the DoD technique. The color scale shows the calculated vertical changes. Blue areas show elevation loss (erosion) and therefore the sources of material, whereas red areas of the figure show elevation gain (deposition), which in many locations exceeds a thickness of 5 m.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g012-550.jpg?1673003130" title=" <strong>Figure 12</strong><br/> <p>A perspective pseudo-3D view of the affected area, with a color scale showing vertical changes (erosion and deposition), and the distinctly affected elements (including high-visitation areas, infrastructure, and buildings), parts of the access road that suffered deposition from debris flows and rock avalanches, indicating the higher risk parts of the site, as well as the unaffected areas of Myrtos.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00812/article_deploy/html/images/applsci-13-00812-g013-550.jpg?1673003099" title=" <strong>Figure 13</strong><br/> <p>Graph showing the percentage of the points from the point cloud that have either increased their elevation (red bars) showing deposition or decreased their elevation (blue bars) showing erosion.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/812'>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="1024364" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 10 pages, 626 KiB   </span> <a href="/2076-3417/13/2/677/pdf?version=1672810508" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Knowing Knowledge: Epistemological Study of Knowledge in Transformers" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/2/677">Knowing Knowledge: Epistemological Study of Knowledge in Transformers</a> <div class="authors"> by <span class="inlineblock "><strong>Leonardo Ranaldi</strong> and </span><span class="inlineblock "><strong>Giulia Pucci</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(2), 677; <a href="https://doi.org/10.3390/app13020677">https://doi.org/10.3390/app13020677</a> - 4 Jan 2023 </div> <a href="/2076-3417/13/2/677#metrics">Cited by 46</a> | Viewed by 3362 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/2/677/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search for strategic reference points evoke essential issues in the analysis of AI. Studying how knowledge can be represented through methods of theoretical generalization and empirical observation is only the latest step in a long process of evolution. In this paper, we try to outline the origin of knowledge and how modern artificial minds have inherited it. <a href="/2076-3417/13/2/677">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/Deep_Learning_Based_On_Neural_Network_Design ">Deep Learning Based on Neural Network Design</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/2/677/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-1024364-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-00677/article_deploy/html/images/applsci-13-00677-g001-550.jpg?1672810583" alt="" style="border: 0;"><p>Figure 1</p></div></div></div><div id="article-1024364-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-00677/article_deploy/html/images/applsci-13-00677-g001-550.jpg?1672810583" title=" <strong>Figure 1</strong><br/> <p>Proposed architecture formed by the modules of BERT and KERMIT.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/2/677'>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="1022780" 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, 925 KiB   </span> <a href="/2076-3417/13/1/610/pdf?version=1672916838" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A Review of the Relationship between Gut Microbiome and Obesity" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/1/610">A Review of the Relationship between Gut Microbiome and Obesity</a> <div class="authors"> by <span class="inlineblock "><strong>Dorottya Zsálig</strong>, </span><span class="inlineblock "><strong>Anikó Berta</strong>, </span><span class="inlineblock "><strong>Vivien Tóth</strong>, </span><span class="inlineblock "><strong>Zoltán Szabó</strong>, </span><span class="inlineblock "><strong>Klára Simon</strong>, </span><span class="inlineblock "><strong>Mária Figler</strong>, </span><span class="inlineblock "><strong>Henriette Pusztafalvi</strong> and </span><span class="inlineblock "><strong>Éva Polyák</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(1), 610; <a href="https://doi.org/10.3390/app13010610">https://doi.org/10.3390/app13010610</a> - 2 Jan 2023 </div> <a href="/2076-3417/13/1/610#metrics">Cited by 28</a> | Viewed by 15927 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Obesity is a rapidly growing problem of public health on a worldwide scale, responsible for more than 60% of deaths associated with high body mass index. Recent studies underpinned the augmenting importance of the gut microbiota in obesity. Gut microbiota alterations affect the <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/1/610/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Obesity is a rapidly growing problem of public health on a worldwide scale, responsible for more than 60% of deaths associated with high body mass index. Recent studies underpinned the augmenting importance of the gut microbiota in obesity. Gut microbiota alterations affect the energy balance of the host organism; namely, as a factor affecting energy production from the diet and as a factor affecting host genes regulating energy expenditure and storage. Gut microbiota composition is characterised by constant variability, and is affected by several dietary factors, suggesting the probability that manipulation of the gut microbiota may promote leaning or prevent obesity. Our narrative review summarizes the results of recent years that stress the effect of gut microbiota in the development of obesity. It investigates the factors (diet, dietary components, lifestyle, and environment) that might affect the gut microbiota composition. Possible strategies for the prevention and/or treatment of obesity include restoring or modifying the composition of the microbiota by consuming prebiotics and probiotics, fermented foods, fruits, vegetables, and avoiding foods of animal origin high in saturated fat and sugar. <a href="/2076-3417/13/1/610">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/S315BBP841 ">Functional Foods in Disease Prevention and Health Promotion: Volume II</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/1/610/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-1022780-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-00610/article_deploy/html/images/applsci-13-00610-g001-550.jpg?1672916916" alt="" style="border: 0;"><p>Figure 1</p></div></div></div><div id="article-1022780-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-00610/article_deploy/html/images/applsci-13-00610-g001-550.jpg?1672916916" title=" <strong>Figure 1</strong><br/> <p>Dietary and environmental factors influencing the growth of some bacteria responsible for normobiosis or dysbiosis, causing an increase or decrease in body weight through various mechanisms.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/610'>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="1017756" 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, 939 KiB   </span> <a href="/2076-3417/13/1/388/pdf?version=1672221696" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Review: Renewable Energy in an Increasingly Uncertain Future" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/13/1/388">Review: Renewable Energy in an Increasingly Uncertain Future</a> <div class="authors"> by <span class="inlineblock "><strong>Patrick Moriarty</strong> and </span><span class="inlineblock "><strong>Damon Honnery</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(1), 388; <a href="https://doi.org/10.3390/app13010388">https://doi.org/10.3390/app13010388</a> - 28 Dec 2022 </div> <a href="/2076-3417/13/1/388#metrics">Cited by 16</a> | Viewed by 4365 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> A number of technical solutions have been proposed for tackling global climate change. However, global climate change is not the only serious global environmental challenge we face demanding an urgent response, even though atmospheric CO<sub>2</sub> ppm have risen from 354 in 1990 <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/1/388/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> A number of technical solutions have been proposed for tackling global climate change. However, global climate change is not the only serious global environmental challenge we face demanding an urgent response, even though atmospheric CO<sub>2</sub> ppm have risen from 354 in 1990 to 416 in 2020. The rise of multiple global environmental challenges makes the search for solutions more difficult, because all technological solutions give rise to some unwanted environmental effects. Further, not only must these various problems be solved in the same short time frame, but they will need to be tackled in a time of rising international tensions, and steady global population increase. This review looks particularly at how all these environmental problems impact the future prospects for renewable energy (RE), given that RE growth must not exacerbate the other equally urgent problems, and must make a major difference in a decade or so. The key finding is that, while the world must shift to RE in the longer run, in the short term what is more important is to improve Earth’s ecological sustainability by the most effective means possible. It is shown that reducing both the global transport task and agricultural production (while still providing an adequate diet for all) can be far more effective than converting the energy used in these sectors to RE. <a href="/2076-3417/13/1/388">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/CR_Energy ">New Developments and Prospects in Clean and Renewable Energies</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/1/388/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1017756"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1017756"><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="#next1017756" data-cycle-prev="#prev1017756" data-cycle-progressive="#images1017756" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1017756-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-00388/article_deploy/html/images/applsci-13-00388-g001-550.jpg?1672221769" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1017756" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1017756-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00388/article_deploy/html/images/applsci-13-00388-g002-550.jpg?1672221768'><p>Figure 2</p></div></script></div></div><div id="article-1017756-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-00388/article_deploy/html/images/applsci-13-00388-g001-550.jpg?1672221769" title=" <strong>Figure 1</strong><br/> <p>Scopus counts from 1970 to 2022 for articles on climate change (CC), biodiversity loss (Bio) and ocean sustainability (Ocean), normalized by all environment articles. Note left-hand scale for CC papers.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/388'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00388/article_deploy/html/images/applsci-13-00388-g002-550.jpg?1672221768" title=" <strong>Figure 2</strong><br/> <p>Distribution of nominal GDP (USD 2017), and 2019 CO<sub>2</sub>-eq emissions from transport, agriculture and energy. Note: Distribution presented as cumulative fractions of the global total (190 countries) for each sector, all ranked in order of increasing nominal GDP/capita. GDP/capita for the six most populated countries shown. The data are drawn from [<a href="#B82-applsci-13-00388" class="html-bibr">82</a>,<a href="#B92-applsci-13-00388" class="html-bibr">92</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/388'>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="1012509" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 26 pages, 2225 KiB   </span> <a href="/2076-3417/13/1/168/pdf?version=1671786347" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Nature-Based Solutions in Urban Areas: A European Analysis" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/1/168">Nature-Based Solutions in Urban Areas: A European Analysis</a> <div class="authors"> by <span class="inlineblock "><strong>Sara Bona</strong>, </span><span class="inlineblock "><strong>Armando Silva-Afonso</strong>, </span><span class="inlineblock "><strong>Ricardo Gomes</strong>, </span><span class="inlineblock "><strong>Raquel Matos</strong> and </span><span class="inlineblock "><strong>Fernanda Rodrigues</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(1), 168; <a href="https://doi.org/10.3390/app13010168">https://doi.org/10.3390/app13010168</a> - 23 Dec 2022 </div> <a href="/2076-3417/13/1/168#metrics">Cited by 22</a> | Viewed by 6721 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Currently, the world is facing resource scarcity as the environmental impacts of human intervention continue to intensify. To facilitate the conservation and recovery of ecosystems and to transform cities into more sustainable, intelligent, regenerative, and resilient environments, the concepts of circularity and nature-based <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/1/168/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Currently, the world is facing resource scarcity as the environmental impacts of human intervention continue to intensify. To facilitate the conservation and recovery of ecosystems and to transform cities into more sustainable, intelligent, regenerative, and resilient environments, the concepts of circularity and nature-based solutions (NbS) are applied. The role of NbS within green infrastructure in urban resilience is recognised, and considerable efforts are being made by the European Commission (EC) to achieve the European sustainability goals. However, it is not fully evidenced, in an integrated way, which are the main NbS implemented in the urban environment and their effects. This article aims to identify the main and most recent NbS applied in urban environments at the European level and to analyse the integration of different measures as an innovative analysis based on real cases. For this purpose, this work presents a literature review of 69 projects implemented in 24 European cities, as well as 8 urban actions and 3 spatial scales of implementation at the district level. Therefore, there is great potential for NbS adoption in buildings and their surroundings, which are still not prioritized, given the lack of effective monitoring of the effects of NbS. <a href="/2076-3417/13/1/168">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/sustainability_energy_buildings ">Sustainability in Energy and Buildings: Future Perspectives and Challenges</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/1/168/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1012509"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1012509"><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="#next1012509" data-cycle-prev="#prev1012509" data-cycle-progressive="#images1012509" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1012509-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g001-550.jpg?1671786428" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1012509" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1012509-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g002-550.jpg?1671786422'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1012509-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g003-550.jpg?1671786437'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1012509-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g004-550.jpg?1671786427'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1012509-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g005-550.jpg?1671786423'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1012509-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g006-550.jpg?1671786419'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1012509-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g007-550.jpg?1671786425'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1012509-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g008-550.jpg?1671786443'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1012509-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g009-550.jpg?1671786435'><p>Figure 9</p></div></script></div></div><div id="article-1012509-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g001-550.jpg?1671786428" title=" <strong>Figure 1</strong><br/> <p>Overview of nature-based concepts and their relationship to existing key concepts. Adapted from [<a href="#B25-applsci-13-00168" class="html-bibr">25</a>,<a href="#B28-applsci-13-00168" class="html-bibr">28</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/168'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g002-550.jpg?1671786422" title=" <strong>Figure 2</strong><br/> <p>Spatial distribution of cities by country.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/168'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g003-550.jpg?1671786437" title=" <strong>Figure 3</strong><br/> <p>Number of NbS Projects by European countries.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/168'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g004-550.jpg?1671786427" title=" <strong>Figure 4</strong><br/> <p>Distribution of NbS urban actions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/168'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g005-550.jpg?1671786423" title=" <strong>Figure 5</strong><br/> <p>Spatial scale NbS of implementation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/168'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g006-550.jpg?1671786419" title=" <strong>Figure 6</strong><br/> <p>Distribution of the multiple benefits of NbS.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/168'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g007-550.jpg?1671786425" title=" <strong>Figure 7</strong><br/> <p>Distribution of NbS ecosystem services.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/168'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g008-550.jpg?1671786443" title=" <strong>Figure 8</strong><br/> <p>Multiple benefits NbS.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/168'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-00168/article_deploy/html/images/applsci-13-00168-g009-550.jpg?1671786435" title=" <strong>Figure 9</strong><br/> <p>NbS ecosystem services.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/1/168'>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="992519" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 30 pages, 3754 KiB   </span> <a href="/2076-3417/12/23/12377/pdf?version=1670056046" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/12/23/12377">A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework</a> <div class="authors"> by <span class="inlineblock "><strong>Alejandro del Real Torres</strong>, </span><span class="inlineblock "><strong>Doru Stefan Andreiana</strong>, </span><span class="inlineblock "><strong>Álvaro Ojeda Roldán</strong>, </span><span class="inlineblock "><strong>Alfonso Hernández Bustos</strong> and </span><span class="inlineblock "><strong>Luis Enrique Acevedo Galicia</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(23), 12377; <a href="https://doi.org/10.3390/app122312377">https://doi.org/10.3390/app122312377</a> - 3 Dec 2022 </div> <a href="/2076-3417/12/23/12377#metrics">Cited by 31</a> | Viewed by 7878 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In this review, the industry’s current issues regarding intelligent manufacture are presented. This work presents the status and the potential for the I4.0 and I5.0’s revolutionary technologies. AI and, in particular, the DRL algorithms, which are a perfect response to the unpredictability and <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/23/12377/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In this review, the industry’s current issues regarding intelligent manufacture are presented. This work presents the status and the potential for the I4.0 and I5.0’s revolutionary technologies. AI and, in particular, the DRL algorithms, which are a perfect response to the unpredictability and volatility of modern demand, are studied in detail. Through the introduction of RL concepts and the development of those with ANNs towards DRL, the potential and variety of these kinds of algorithms are highlighted. Moreover, because these algorithms are data based, their modification to meet the requirements of industry operations is also included. In addition, this review covers the inclusion of new concepts, such as digital twins, in response to an absent environment model and how it can improve the performance and application of DRL algorithms even more. This work highlights that DRL applicability is demonstrated across all manufacturing industry operations, outperforming conventional methodologies and, most notably, enhancing the manufacturing process’s resilience and adaptability. It is stated that there is still considerable work to be carried out in both academia and industry to fully leverage the promise of these disruptive tools, begin their deployment in industry, and take a step closer to the I5.0 industrial revolution. <a href="/2076-3417/12/23/12377">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/Smart_Machines_Intelligent_Manufacturing ">Smart Machines and Intelligent Manufacturing</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/23/12377/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev992519"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next992519"><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="#next992519" data-cycle-prev="#prev992519" data-cycle-progressive="#images992519" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-992519-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g001-550.jpg?1670056148" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images992519" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-992519-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g002-550.jpg?1670056155'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-992519-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g003-550.jpg?1670056152'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-992519-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g004-550.jpg?1670056145'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-992519-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g005-550.jpg?1670056154'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-992519-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g006-550.jpg?1670056147'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-992519-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g007-550.jpg?1670056157'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-992519-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g008-550.jpg?1670056150'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-992519-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g009-550.jpg?1670056146'><p>Figure 9</p></div></script></div></div><div id="article-992519-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g001-550.jpg?1670056148" title=" <strong>Figure 1</strong><br/> <p>Structure of Markov Decision Process.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/23/12377'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g002-550.jpg?1670056155" title=" <strong>Figure 2</strong><br/> <p>Annual scientific production of the query.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/23/12377'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g003-550.jpg?1670056152" title=" <strong>Figure 3</strong><br/> <p>Three-field plot: author keywords on the left, authors in the middle and affiliations on the right.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/23/12377'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g004-550.jpg?1670056145" title=" <strong>Figure 4</strong><br/> <p>Collaboration world map.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/23/12377'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g005-550.jpg?1670056154" title=" <strong>Figure 5</strong><br/> <p>Source clustering through Bradford’s Law.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/23/12377'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g006-550.jpg?1670056147" title=" <strong>Figure 6</strong><br/> <p>Venn diagram of DRL approaches occurrences in the research query.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/23/12377'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g007-550.jpg?1670056157" title=" <strong>Figure 7</strong><br/> <p>Citation network with colour and size legends.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/23/12377'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g008-550.jpg?1670056150" title=" <strong>Figure 8</strong><br/> <p>(<b>A</b>) Citation network coloured based on the appearance of selected words with size legend; (<b>B</b>) coloured Venn diagram of keywords. The asterisks are used to replace multiple characters anywhere in a word.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/23/12377'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-12377/article_deploy/html/images/applsci-12-12377-g009-550.jpg?1670056146" title=" <strong>Figure 9</strong><br/> <p>Classification of deep reinforcement learning algorithms.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/23/12377'>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="977540" 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, 2011 KiB   </span> <a href="/2076-3417/12/22/11752/pdf?version=1669100160" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/12/22/11752">A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning</a> <div class="authors"> by <span class="inlineblock "><strong>Patrick Vanin</strong>, </span><span class="inlineblock "><strong>Thomas Newe</strong>, </span><span class="inlineblock "><strong>Lubna Luxmi Dhirani</strong>, </span><span class="inlineblock "><strong>Eoin O’Connell</strong>, </span><span class="inlineblock "><strong>Donna O’Shea</strong>, </span><span class="inlineblock "><strong>Brian Lee</strong> and </span><span class="inlineblock "><strong>Muzaffar Rao</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(22), 11752; <a href="https://doi.org/10.3390/app122211752">https://doi.org/10.3390/app122211752</a> - 18 Nov 2022 </div> <a href="/2076-3417/12/22/11752#metrics">Cited by 43</a> | Viewed by 13441 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/22/11752/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is a tool that helps to detect intrusions by inspecting the network traffic. Although many researchers have studied and created new IDS solutions, IDS still needs improving in order to have good detection accuracy while reducing false alarm rates. In addition, many IDS struggle to detect zero-day attacks. Recently, machine learning algorithms have become popular with researchers to detect network intrusion in an efficient manner and with high accuracy. This paper presents the concept of IDS and provides a taxonomy of machine learning methods. The main metrics used to assess an IDS are presented and a review of recent IDS using machine learning is provided where the strengths and weaknesses of each solution is outlined. Then, details of the different datasets used in the studies are provided and the accuracy of the results from the reviewed work is discussed. Finally, observations, research challenges and future trends are discussed. <a href="/2076-3417/12/22/11752">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/4W0501CT27 ">Information Security and Privacy</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/22/11752/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev977540"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next977540"><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="#next977540" data-cycle-prev="#prev977540" data-cycle-progressive="#images977540" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-977540-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g001-550.jpg?1669100254" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images977540" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-977540-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g002-550.jpg?1669100253'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-977540-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g003-550.jpg?1669100256'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-977540-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g004-550.jpg?1669100258'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-977540-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g005-550.jpg?1669100255'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-977540-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g006-550.jpg?1669100260'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-977540-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g007-550.jpg?1669100253'><p>Figure 7</p></div></script></div></div><div id="article-977540-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g001-550.jpg?1669100254" title=" <strong>Figure 1</strong><br/> <p>ROC Curves for different IDSs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/22/11752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g002-550.jpg?1669100253" title=" <strong>Figure 2</strong><br/> <p>Main types of machine learning methods.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/22/11752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g003-550.jpg?1669100256" title=" <strong>Figure 3</strong><br/> <p>Unsupervised Machine Learning Categories.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/22/11752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g004-550.jpg?1669100258" title=" <strong>Figure 4</strong><br/> <p>Frequency of Classifier used (<a href="#sec6-applsci-12-11752" class="html-sec">Section 6</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/22/11752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g005-550.jpg?1669100255" title=" <strong>Figure 5</strong><br/> <p>Feature extraction distribution (from <a href="#sec6-applsci-12-11752" class="html-sec">Section 6</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/22/11752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g006-550.jpg?1669100260" title=" <strong>Figure 6</strong><br/> <p>Metrics used (<a href="#sec6-applsci-12-11752" class="html-sec">Section 6</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/22/11752'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-11752/article_deploy/html/images/applsci-12-11752-g007-550.jpg?1669100253" title=" <strong>Figure 7</strong><br/> <p>Public datasets percentage of use (<a href="#sec6-applsci-12-11752" class="html-sec">Section 6</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/22/11752'>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="957029" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <a data-dropdown="drop-supplementary-957029" aria-controls="drop-supplementary-957029" aria-expanded="false" title="Supplementary Material"> <i class="material-icons">attachment</i> </a> <div id="drop-supplementary-957029" class="f-dropdown label__btn__dropdown label__btn__dropdown--wide" data-dropdown-content aria-hidden="true" tabindex="-1"> Supplementary material: <br/> <a href="/2076-3417/12/21/10958/s1?version=1667025607"> Supplementary File 1 (ZIP, 21 KiB) </a><br/> </div> </div> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 18 pages, 7267 KiB   </span> <a href="/2076-3417/12/21/10958/pdf?version=1667025606" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Machine Learning-Assisted Prediction of Oil Production and CO2 Storage Effect in CO2-Water-Alternating-Gas Injection (CO2-WAG)" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/12/21/10958">Machine Learning-Assisted Prediction of Oil Production and CO<sub>2</sub> Storage Effect in CO<sub>2</sub>-Water-Alternating-Gas Injection (CO<sub>2</sub>-WAG)</a> <div class="authors"> by <span class="inlineblock "><strong>Hangyu Li</strong>, </span><span class="inlineblock "><strong>Changping Gong</strong>, </span><span class="inlineblock "><strong>Shuyang Liu</strong>, </span><span class="inlineblock "><strong>Jianchun Xu</strong> and </span><span class="inlineblock "><strong>Gloire Imani</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(21), 10958; <a href="https://doi.org/10.3390/app122110958">https://doi.org/10.3390/app122110958</a> - 29 Oct 2022 </div> <a href="/2076-3417/12/21/10958#metrics">Cited by 14</a> | Viewed by 3730 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In recent years, CO<sub>2</sub> flooding has emerged as an efficient method for improving oil recovery. It also has the advantage of storing CO<sub>2</sub> underground. As one of the promising types of CO<sub>2</sub> enhanced oil recovery (CO<sub>2</sub>-EOR), CO<sub>2</sub> <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/21/10958/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In recent years, CO<sub>2</sub> flooding has emerged as an efficient method for improving oil recovery. It also has the advantage of storing CO<sub>2</sub> underground. As one of the promising types of CO<sub>2</sub> enhanced oil recovery (CO<sub>2</sub>-EOR), CO<sub>2</sub> water-alternating-gas injection (CO<sub>2</sub>-WAG) can suppress CO<sub>2</sub> fingering and early breakthrough problems that occur during oil recovery by CO<sub>2</sub> flooding. However, the evaluation of CO<sub>2</sub>-WAG is strongly dependent on the injection parameters, which in turn renders numerical simulations computationally expensive. So, in this work, machine learning is used to help predict how well CO<sub>2</sub>-WAG will work when different injection parameters are used. A total of 216 models were built by using CMG numerical simulation software to represent CO<sub>2</sub>-WAG development scenarios of various injection parameters where 70% of them were used as training sets and 30% as testing sets. A random forest regression algorithm was used to predict CO<sub>2</sub>-WAG performance in terms of oil production, CO<sub>2</sub> storage amount, and CO<sub>2</sub> storage efficiency. The CO<sub>2</sub>-WAG period, CO<sub>2</sub> injection rate, and water–gas ratio were chosen as the three main characteristics of injection parameters. The prediction results showed that the predicted value of the test set was very close to the true value. The average absolute prediction deviations of cumulative oil production, CO<sub>2</sub> storage amount, and CO<sub>2</sub> storage efficiency were 1.10%, 3.04%, and 2.24%, respectively. Furthermore, it only takes about 10 s to predict the results of all 216 scenarios by using machine learning methods, while the CMG simulation method spends about 108 min. It demonstrated that the proposed machine-learning method can rapidly predict CO<sub>2</sub>-WAG performance with high accuracy and high computational efficiency under conditions of various injection parameters. This work gives more insights into the optimization of the injection parameters for CO<sub>2</sub>-EOR. <a href="/2076-3417/12/21/10958">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/AI_Petroleum ">Artificial Intelligence Applications in Petroleum Exploration and Production</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/21/10958/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev957029"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next957029"><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="#next957029" data-cycle-prev="#prev957029" data-cycle-progressive="#images957029" 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3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g004-550.jpg?1667025678'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g005-550.jpg?1667025692'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g006a-550.jpg?1667025675'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g006b-550.jpg?1667025690'><p>Figure 6 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g007-550.jpg?1667025676'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g008-550.jpg?1667025680'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g009-550.jpg?1667025685'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g010a-550.jpg?1667025673'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g010b-550.jpg?1667025687'><p>Figure 10 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g011a-550.jpg?1667025677'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g011b-550.jpg?1667025691'><p>Figure 11 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g012a-550.jpg?1667025674'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g012b-550.jpg?1667025689'><p>Figure 12 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='16' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g013a-550.jpg?1667025671'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='17' data-target='article-957029-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g013b-550.jpg?1667025670'><p>Figure 13 Cont.</p></div></script></div></div><div id="article-957029-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g001-550.jpg?1667025682" title=" <strong>Figure 1</strong><br/> <p>Three-deminsional diagram of the Water Alternating Gas model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g002-550.jpg?1667025686" title=" <strong>Figure 2</strong><br/> <p>Bottom-hole pressure in injection and production wells.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g003-550.jpg?1667025679" title=" <strong>Figure 3</strong><br/> <p>Oil cut and water cut of the produced fluid in the standard condition.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g004-550.jpg?1667025678" title=" <strong>Figure 4</strong><br/> <p>The training process of the random forest algorithm.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g005-550.jpg?1667025692" title=" <strong>Figure 5</strong><br/> <p>Cumulative oil production in the production process.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g006a-550.jpg?1667025675" title=" <strong>Figure 6</strong><br/> <p>CO<sub>2</sub> storage in the production process: (<b>a</b>) injection, production and storage amount of CO<sub>2</sub>, and (<b>b</b>) CO<sub>2</sub> storage efficiency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g006b-550.jpg?1667025690" title=" <strong>Figure 6 Cont.</strong><br/> <p>CO<sub>2</sub> storage in the production process: (<b>a</b>) injection, production and storage amount of CO<sub>2</sub>, and (<b>b</b>) CO<sub>2</sub> storage efficiency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g007-550.jpg?1667025676" title=" <strong>Figure 7</strong><br/> <p>Cumulative oil production of CO<sub>2</sub>-WAG, CO<sub>2</sub> flooding and water flooding.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g008-550.jpg?1667025680" title=" <strong>Figure 8</strong><br/> <p>Gas oil ratio of the production in the WAG and CO<sub>2</sub> flooding.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g009-550.jpg?1667025685" title=" <strong>Figure 9</strong><br/> <p>Effect of WAG cycle on (<b>a</b>) cumulative oil production, (<b>b</b>) CO<sub>2</sub> storage amount, and (<b>c</b>) CO<sub>2</sub> storage efficiency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g010a-550.jpg?1667025673" title=" <strong>Figure 10</strong><br/> <p>Effect of fluid injection rate on (<b>a</b>) cumulative oil production, (<b>b</b>) CO<sub>2</sub> storage amount, and (<b>c</b>) CO<sub>2</sub> storage efficiency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g010b-550.jpg?1667025687" title=" <strong>Figure 10 Cont.</strong><br/> <p>Effect of fluid injection rate on (<b>a</b>) cumulative oil production, (<b>b</b>) CO<sub>2</sub> storage amount, and (<b>c</b>) CO<sub>2</sub> storage efficiency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g011a-550.jpg?1667025677" title=" <strong>Figure 11</strong><br/> <p>Effect of water–gas ratio on (<b>a</b>) cumulative oil production, (<b>b</b>) CO<sub>2</sub> storage amount, and (<b>c</b>) CO<sub>2</sub> storage efficiency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g011b-550.jpg?1667025691" title=" <strong>Figure 11 Cont.</strong><br/> <p>Effect of water–gas ratio on (<b>a</b>) cumulative oil production, (<b>b</b>) CO<sub>2</sub> storage amount, and (<b>c</b>) CO<sub>2</sub> storage efficiency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g012a-550.jpg?1667025674" title=" <strong>Figure 12</strong><br/> <p>Comparison of the predicted values by random forest algorithm with the true values in (<b>a</b>) cumulative oil production, (<b>b</b>) CO<sub>2</sub> storage amount, and (<b>c</b>) CO<sub>2</sub> storage efficiency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g012b-550.jpg?1667025689" title=" <strong>Figure 12 Cont.</strong><br/> <p>Comparison of the predicted values by random forest algorithm with the true values in (<b>a</b>) cumulative oil production, (<b>b</b>) CO<sub>2</sub> storage amount, and (<b>c</b>) CO<sub>2</sub> storage efficiency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g013a-550.jpg?1667025671" title=" <strong>Figure 13</strong><br/> <p>Relative deviation between the predicted value and true value in (<b>a</b>) cumulative oil production, (<b>b</b>) CO<sub>2</sub> storage amount, and (<b>c</b>) CO<sub>2</sub> storage efficiency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-10958/article_deploy/html/images/applsci-12-10958-g013b-550.jpg?1667025670" title=" <strong>Figure 13 Cont.</strong><br/> <p>Relative deviation between the predicted value and true value in (<b>a</b>) cumulative oil production, (<b>b</b>) CO<sub>2</sub> storage amount, and (<b>c</b>) CO<sub>2</sub> storage efficiency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/21/10958'>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="928948" 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, 4661 KiB   </span> <a href="/2076-3417/12/19/9805/pdf?version=1664447318" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Forecast of Airblast Vibrations Induced by Blasting Using Support Vector Regression Optimized by the Grasshopper Optimization (SVR-GO) Technique" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/12/19/9805">Forecast of Airblast Vibrations Induced by Blasting Using Support Vector Regression Optimized by the Grasshopper Optimization (SVR-GO) Technique</a> <div class="authors"> by <span class="inlineblock "><strong>Lihua Chen</strong>, </span><span class="inlineblock "><strong>Panagiotis G. Asteris</strong>, </span><span class="inlineblock "><strong>Markos Z. Tsoukalas</strong>, </span><span class="inlineblock "><strong>Danial Jahed Armaghani</strong>, </span><span class="inlineblock "><strong>Dmitrii Vladimirovich Ulrikh</strong> and </span><span class="inlineblock "><strong>Mojtaba Yari</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(19), 9805; <a href="https://doi.org/10.3390/app12199805">https://doi.org/10.3390/app12199805</a> - 29 Sep 2022 </div> <a href="/2076-3417/12/19/9805#metrics">Cited by 17</a> | Viewed by 2300 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Air overpressure (AOp) is an undesirable environmental effect of blasting. To date, a variety of empirical equations have been developed to forecast this phenomenon and prevent its negative impacts with accuracy. However, the accuracy of these methods is not sufficient. In addition, they <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/19/9805/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Air overpressure (AOp) is an undesirable environmental effect of blasting. To date, a variety of empirical equations have been developed to forecast this phenomenon and prevent its negative impacts with accuracy. However, the accuracy of these methods is not sufficient. In addition, they are resource-consuming. This study employed support vector regression (SVR) optimized with the grasshopper optimizer (GO) algorithm to forecast AOp resulting from blasting. Additionally, a novel input selection technique, the Boruta algorithm (BFS), was applied. A new algorithm, the SVR-GA-BFS<sub>7</sub>, was developed by combining the models mentioned above. The findings showed that the SVR-GO-BFS<sub>7</sub> model was the best technique (R<sup>2</sup> = 0.983, RMSE = 1.332). The superiority of this model means that using the seven most important inputs was enough to forecast the AOp in the present investigation. Furthermore, the performance of SVR-GO-BFS<sub>7</sub> was compared with various machine learning techniques, and the model outperformed the base models. The GO was compared with some other optimization techniques, and the superiority of this algorithm over the others was confirmed. Therefore, the suggested method presents a framework for accurate AOp prediction that supports the resource-saving forecasting methods. <a href="/2076-3417/12/19/9805">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/blast_impact_engineering ">Blast and Impact Engineering on Structures and Materials</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/19/9805/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev928948"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next928948"><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="#next928948" data-cycle-prev="#prev928948" data-cycle-progressive="#images928948" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-928948-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g001-550.jpg?1664447394" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images928948" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-928948-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g002-550.jpg?1664447398'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-928948-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g003-550.jpg?1664447401'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-928948-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g004-550.jpg?1664447407'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-928948-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g005-550.jpg?1664447405'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-928948-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g006-550.jpg?1664447397'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-928948-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g007-550.jpg?1664447396'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-928948-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g008-550.jpg?1664447404'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-928948-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g009-550.jpg?1664447406'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-928948-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g010-550.jpg?1664447408'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-928948-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g011-550.jpg?1664447399'><p>Figure 11</p></div></script></div></div><div id="article-928948-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g001-550.jpg?1664447394" title=" <strong>Figure 1</strong><br/> <p>The SVR’s structure SVR.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9805'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g002-550.jpg?1664447398" title=" <strong>Figure 2</strong><br/> <p>The execution steps of BFS.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9805'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g003-550.jpg?1664447401" title=" <strong>Figure 3</strong><br/> <p>Flowchart of this study.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9805'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g004-550.jpg?1664447407" title=" <strong>Figure 4</strong><br/> <p>10-fold cross validation schematic view.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9805'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g005-550.jpg?1664447405" title=" <strong>Figure 5</strong><br/> <p>The results of the input selection study on the data.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9805'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g006-550.jpg?1664447397" title=" <strong>Figure 6</strong><br/> <p>SVR-GO-BFS<sub>7</sub> optimization model for different population sizes (PSs).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9805'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g007-550.jpg?1664447396" title=" <strong>Figure 7</strong><br/> <p>Performance of the SVR-GO-BFS<sub>n</sub> models with various inputs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9805'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g008-550.jpg?1664447404" title=" <strong>Figure 8</strong><br/> <p>Real and predicted AOp values by various developed models, SVR-GO-BFS<sub>7</sub> is the best model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9805'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g009-550.jpg?1664447406" title=" <strong>Figure 9</strong><br/> <p>Real and predicted AOp values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9805'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g010-550.jpg?1664447408" title=" <strong>Figure 10</strong><br/> <p>Results of predictions by various models.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9805'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09805/article_deploy/html/images/applsci-12-09805-g011-550.jpg?1664447399" title=" <strong>Figure 11</strong><br/> <p>The outcomes of the developed model with various optimization methods.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9805'>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="926013" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 26 pages, 5272 KiB   </span> <a href="/2076-3417/12/19/9677/pdf?version=1664533768" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Benchmarking 4G and 5G-Based Cellular-V2X for Vehicle-to-Infrastructure Communication and Urban Scenarios in Cooperative Intelligent Transportation Systems" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/12/19/9677">Benchmarking 4G and 5G-Based Cellular-V2X for Vehicle-to-Infrastructure Communication and Urban Scenarios in Cooperative Intelligent Transportation Systems</a> <div class="authors"> by <span class="inlineblock "><strong>Tibor Petrov</strong>, </span><span class="inlineblock "><strong>Peter Pocta</strong> and </span><span class="inlineblock "><strong>Tatiana Kovacikova</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(19), 9677; <a href="https://doi.org/10.3390/app12199677">https://doi.org/10.3390/app12199677</a> - 26 Sep 2022 </div> <a href="/2076-3417/12/19/9677#metrics">Cited by 17</a> | Viewed by 3911 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Vehicle-to-Infrastructure (V2I) communication is expected to bring tremendous benefits in terms of increased road safety, improved traffic efficiency and decreased environmental impact. In 2017, The 3rd Generation Partnership Project (3GPP) released 3GPP Release 14, which introduced Cellular Vehicle-to-Everything communication (C-V2X), bringing Vehicle-to-Everything (V2X) <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/19/9677/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Vehicle-to-Infrastructure (V2I) communication is expected to bring tremendous benefits in terms of increased road safety, improved traffic efficiency and decreased environmental impact. In 2017, The 3rd Generation Partnership Project (3GPP) released 3GPP Release 14, which introduced Cellular Vehicle-to-Everything communication (C-V2X), bringing Vehicle-to-Everything (V2X) communication capabilities to cellular networks, hence creating an alternative to Dedicated Short-Range Communications (DSRC) technology. Since then, every new 3GPP Release including Release 15, a first full set of 5G standards, offered V2X capabilities. In this paper, we present a complex simulation study, which benchmarks the performance of LTE-based and 5G-based C-V2X technologies deployed for V2I communication in an urban setting. The study compares LTE and 5G deployed both in the Device-to-Device in mode 3 and in infrastructural mode. Target performance indicators used for comparison are average end-to-end (E2E) latency and Packet Delivery Ratio (PDR). The performance of those technologies is studied under varying communication conditions realized by a variation of vehicle traffic intensity, communication perimeter and message generation frequency. Furthermore, the effects of infrastructure deployment density on the performance of selected C-V2X communication technologies are explored by comparing the performance of the investigated technologies for three infrastructure density scenarios, i.e., involving two, four and eight base stations (BSs). The performance results are put into a context of the connectivity requirements of the most popular V2I communication services. The results indicate that both C-V2X technologies can support all the considered V2I services without any limitations in terms of the communication perimeter, traffic intensity and message generation frequency. When it comes to the infrastructure density deployment, the results show that increasing the density of the infrastructure deployment from two BSs to four BSs offers a remarkable performance improvement for all the considered V2I services as well as investigated technologies and their modes. Further infrastructure density increase (from four BSs to eight BSs) does not yield any practical benefits in the investigated urban scenario. <a href="/2076-3417/12/19/9677">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/5G_Vehicle_2_Everything ">5G Vehicle-to-Everything (V2X): Latest Advances and Prospects</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/19/9677/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev926013"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next926013"><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="#next926013" data-cycle-prev="#prev926013" data-cycle-progressive="#images926013" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-926013-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g001-550.jpg?1664611175" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images926013" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g002-550.jpg?1664611174'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g003-550.jpg?1664611152'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g004-550.jpg?1664611177'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g005-550.jpg?1664611157'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g006-550.jpg?1664611170'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g007-550.jpg?1664611172'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g008-550.jpg?1664611166'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g009a-550.jpg?1664611153'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g009b-550.jpg?1664611158'><p>Figure 9 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g010-550.jpg?1664611187'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g011-550.jpg?1664611155'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g012-550.jpg?1664611185'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g013-550.jpg?1664611181'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g014-550.jpg?1664611190'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g015-550.jpg?1664611183'><p>Figure 15</p></div> --- <div class='openpopupgallery' data-imgindex='16' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g016-550.jpg?1664611164'><p>Figure 16</p></div> --- <div class='openpopupgallery' data-imgindex='17' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g017-550.jpg?1664611160'><p>Figure 17</p></div> --- <div class='openpopupgallery' data-imgindex='18' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g018-550.jpg?1664611168'><p>Figure 18</p></div> --- <div class='openpopupgallery' data-imgindex='19' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g019-550.jpg?1664611179'><p>Figure 19</p></div> --- <div class='openpopupgallery' data-imgindex='20' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g020-550.jpg?1664611162'><p>Figure 20</p></div> --- <div class='openpopupgallery' data-imgindex='21' data-target='article-926013-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g021-550.jpg?1664611189'><p>Figure 21</p></div></script></div></div><div id="article-926013-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g001-550.jpg?1664611175" title=" <strong>Figure 1</strong><br/> <p>Visualization of the simulation scenario. Base stations depicted in black represent the cellular infrastructure used to simulate the fixed infrastructure density scenario. To study the impact of infrastructure density on the performance of Cellular-V2X (C-V2X) technologies, i.e., the varying infrastructure density scenario, four additional base stations (in red) were added.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g002-550.jpg?1664611174" title=" <strong>Figure 2</strong><br/> <p>Packet Delivery Ratio (PDR) simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 250 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g003-550.jpg?1664611152" title=" <strong>Figure 3</strong><br/> <p>PDR simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 5O0 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g004-550.jpg?1664611177" title=" <strong>Figure 4</strong><br/> <p>PDR simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 750 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g005-550.jpg?1664611157" title=" <strong>Figure 5</strong><br/> <p>PDR simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 1000 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g006-550.jpg?1664611170" title=" <strong>Figure 6</strong><br/> <p>PDR simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 1250 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g007-550.jpg?1664611172" title=" <strong>Figure 7</strong><br/> <p>PDR simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 1500 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g008-550.jpg?1664611166" title=" <strong>Figure 8</strong><br/> <p>End-to-End (E2E) latency simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 250 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g009a-550.jpg?1664611153" title=" <strong>Figure 9</strong><br/> <p>E2E latency simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 500 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g009b-550.jpg?1664611158" title=" <strong>Figure 9 Cont.</strong><br/> <p>E2E latency simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 500 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g010-550.jpg?1664611187" title=" <strong>Figure 10</strong><br/> <p>E2E latency simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 750 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g011-550.jpg?1664611155" title=" <strong>Figure 11</strong><br/> <p>E2E latency simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 1000 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g012-550.jpg?1664611185" title=" <strong>Figure 12</strong><br/> <p>E2E latency simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 1250 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g013-550.jpg?1664611181" title=" <strong>Figure 13</strong><br/> <p>E2E latency simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 1500 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g014-550.jpg?1664611190" title=" <strong>Figure 14</strong><br/> <p>PDR simulated for the both modes of the 4G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 250 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g015-550.jpg?1664611183" title=" <strong>Figure 15</strong><br/> <p>PDR simulated for the both modes of the 4G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 1500 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g016-550.jpg?1664611164" title=" <strong>Figure 16</strong><br/> <p>PDR simulated for the both modes of the 5G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 250 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g017-550.jpg?1664611160" title=" <strong>Figure 17</strong><br/> <p>PDR simulated for the both modes of the 5G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 1500 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g018-550.jpg?1664611168" title=" <strong>Figure 18</strong><br/> <p>E2E latency simulated for the both modes of the 4G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 250 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g019-550.jpg?1664611179" title=" <strong>Figure 19</strong><br/> <p>E2E latency simulated for the both modes of the 4G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 1500 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g020-550.jpg?1664611162" title=" <strong>Figure 20</strong><br/> <p>E2E latency simulated for the both modes of the 5G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 250 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09677/article_deploy/html/images/applsci-12-09677-g021-550.jpg?1664611189" title=" <strong>Figure 21</strong><br/> <p>E2E latency simulated for the both modes of the 5G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 1500 vehicles per hour.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9677'>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="922513" 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, 2503 KiB   </span> <a href="/2076-3417/12/19/9529/pdf?version=1664164027" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="An Improved Algorithm of Drift Compensation for Olfactory Sensors" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/12/19/9529">An Improved Algorithm of Drift Compensation for Olfactory Sensors</a> <div class="authors"> by <span class="inlineblock "><strong>Siyu Lu</strong>, </span><span class="inlineblock "><strong>Jialiang Guo</strong>, </span><span class="inlineblock "><strong>Shan Liu</strong>, </span><span class="inlineblock "><strong>Bo Yang</strong>, </span><span class="inlineblock "><strong>Mingzhe Liu</strong>, </span><span class="inlineblock "><strong>Lirong Yin</strong> and </span><span class="inlineblock "><strong>Wenfeng Zheng</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(19), 9529; <a href="https://doi.org/10.3390/app12199529">https://doi.org/10.3390/app12199529</a> - 22 Sep 2022 </div> <a href="/2076-3417/12/19/9529#metrics">Cited by 83</a> | Viewed by 3296 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm. Usually for this kind of problem, it is difficult to obtain better recognition results by directly using the semi-supervised learning algorithm. <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/19/9529/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm. Usually for this kind of problem, it is difficult to obtain better recognition results by directly using the semi-supervised learning algorithm. For this reason, we propose a domain transformation semi-supervised weighted kernel extreme learning machine (DTSWKELM) algorithm, which converts the data through the domain and uses SWKELM algorithmic classification to transform the semi-supervised classification problem of different domain data into a semi-supervised classification problem of the same domain data. <a href="/2076-3417/12/19/9529">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/AAIPAR ">Advances in Artificial Intelligence for Perception Augmentation and Reasoning</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/19/9529/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev922513"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next922513"><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="#next922513" data-cycle-prev="#prev922513" data-cycle-progressive="#images922513" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-922513-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g001-550.jpg?1664179406" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images922513" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-922513-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g002-550.jpg?1664179408'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-922513-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g003-550.jpg?1664179405'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-922513-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g004-550.jpg?1664179407'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-922513-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g005-550.jpg?1664179409'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-922513-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g006-550.jpg?1664179406'><p>Figure 6</p></div></script></div></div><div id="article-922513-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g001-550.jpg?1664179406" title=" <strong>Figure 1</strong><br/> <p>DTSWKELM algorithm flow chart.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9529'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g002-550.jpg?1664179408" title=" <strong>Figure 2</strong><br/> <p>Dot diagram of 10 batches of data after PCA dimensionality reduction.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9529'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g003-550.jpg?1664179405" title=" <strong>Figure 3</strong><br/> <p>Dot diagram of 10 batches of data after PCA dimensionality reduction after domain transformation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9529'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g004-550.jpg?1664179407" title=" <strong>Figure 4</strong><br/> <p>The histogram of the recognition effect of each algorithm in Experiment 1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9529'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g005-550.jpg?1664179409" title=" <strong>Figure 5</strong><br/> <p>The histogram of the recognition effect of each algorithm in Experiment 2.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9529'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09529/article_deploy/html/images/applsci-12-09529-g006-550.jpg?1664179406" title=" <strong>Figure 6</strong><br/> <p>The impact of the trade-off parameters <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>2</mn> </msub> </mrow> </semantics></math> on the algorithm recognition effect. (<b>a</b>) The influence of <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> on the algorithm; (<b>b</b>) the influence of <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>2</mn> </msub> </mrow> </semantics></math> on the algorithm.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9529'>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="921945" 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, 6052 KiB   </span> <a href="/2076-3417/12/19/9507/pdf?version=1663840993" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Scalability of Mach Number Effects on Noise Emitted by Side-by-Side Propellers" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/12/19/9507">Scalability of Mach Number Effects on Noise Emitted by Side-by-Side Propellers</a> <div class="authors"> by <span class="inlineblock "><strong>Caterina Poggi</strong>, </span><span class="inlineblock "><strong>Giovanni Bernardini</strong>, </span><span class="inlineblock "><strong>Massimo Gennaretti</strong> and </span><span class="inlineblock "><strong>Roberto Camussi</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(19), 9507; <a href="https://doi.org/10.3390/app12199507">https://doi.org/10.3390/app12199507</a> - 22 Sep 2022 </div> <a href="/2076-3417/12/19/9507#metrics">Cited by 14</a> | Viewed by 1715 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This paper presents a numerical investigation of noise radiated by two side-by-side propellers, suitable for Distributed-Electric-Propulsion concepts. The focus is on the assessment of the variation of the effects of blade tip Mach number on the radiated noise for variations of the direction <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/19/9507/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This paper presents a numerical investigation of noise radiated by two side-by-side propellers, suitable for Distributed-Electric-Propulsion concepts. The focus is on the assessment of the variation of the effects of blade tip Mach number on the radiated noise for variations of the direction of rotation, hub relative position, and the relative phase angle between the propeller blades. The aerodynamic analysis is performed through a potential-flow-based boundary integral formulation, which is able to model severe body–wake interactions.The noise field is evaluated through a boundary-integral formulation for the solution of the Ffowcs Williams and Hawkings equation. The numerical investigation shows that: the blade tip Mach number strongly affects the magnitude and directivity of the radiated noise; the increase of the tip-clearance increases the spatial frequency of the noise directivity at the two analyzed tip Mach numbers for both co-rotating and counter-rotating configurations; for counter-rotating propellers, the relative phase angle between the propeller blades provides a decrease of the averaged emitted noise, regardless the tip Mach number. One of the main results achieved is the scalability with the blade tip Mach number of the influence on the emitted noise of the considered design parameters. <a href="/2076-3417/12/19/9507">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/Aeroelasticity_and_Aeroacoustics ">Aerodynamic Aeroelasticity and Aeroacoustics of Rotorcraft</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/19/9507/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev921945"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next921945"><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="#next921945" data-cycle-prev="#prev921945" data-cycle-progressive="#images921945" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-921945-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g001-550.jpg?1663841079" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images921945" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-921945-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g002-550.jpg?1663841089'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-921945-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g003-550.jpg?1663841083'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-921945-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g004-550.jpg?1663841076'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-921945-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g005-550.jpg?1663841087'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-921945-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g006-550.jpg?1663841094'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-921945-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g007-550.jpg?1663841095'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-921945-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g008-550.jpg?1663841092'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-921945-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g009-550.jpg?1663841078'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-921945-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g010-550.jpg?1663841081'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-921945-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g011-550.jpg?1663841085'><p>Figure 11</p></div></script></div></div><div id="article-921945-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g001-550.jpg?1663841079" title=" <strong>Figure 1</strong><br/> <p>Sketch of the examined configurations, counter-rotating propellers.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9507'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g002-550.jpg?1663841089" title=" <strong>Figure 2</strong><br/> <p>Counter-rotating propellers, influence of TC on OASPL, for <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.52</mn> </mrow> </semantics></math> (upper) and <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math> (lower).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9507'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g003-550.jpg?1663841083" title=" <strong>Figure 3</strong><br/> <p>Counter-rotating configuration, OASPL difference vs. TC difference, for <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.52</mn> </mrow> </semantics></math> (upper) and <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math> (lower).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9507'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g004-550.jpg?1663841076" title=" <strong>Figure 4</strong><br/> <p>Co-rotating propellers, influence of TC on OASPL, for <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.52</mn> </mrow> </semantics></math> (upper) and <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math> (lower).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9507'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g005-550.jpg?1663841087" title=" <strong>Figure 5</strong><br/> <p>Co-rotating configuration, OASPL difference vs. TC difference, for <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.52</mn> </mrow> </semantics></math> (upper) and <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math> (lower).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9507'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g006-550.jpg?1663841094" title=" <strong>Figure 6</strong><br/> <p>Counter-rotating propellers, OASPL directivity pattern on rotor disk for different TCs and different phase shifts (black line <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>°</mo> </msup> </mrow> </semantics></math>, blue line <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msup> <mn>12</mn> <mo>°</mo> </msup> </mrow> </semantics></math>, red line <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msup> <mn>24</mn> <mo>°</mo> </msup> </mrow> </semantics></math>, green line <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msup> <mn>36</mn> <mo>°</mo> </msup> </mrow> </semantics></math>); upper <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.52</mn> </mrow> </semantics></math>, lower <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9507'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g007-550.jpg?1663841095" title=" <strong>Figure 7</strong><br/> <p>Co-rotating propellers, OASPL directivity pattern on rotor disk for different TCs and different phase shifts (black line <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>°</mo> </msup> </mrow> </semantics></math>, blue line <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msup> <mn>12</mn> <mo>°</mo> </msup> </mrow> </semantics></math>, red line <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msup> <mn>24</mn> <mo>°</mo> </msup> </mrow> </semantics></math>, green line <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msup> <mn>36</mn> <mo>°</mo> </msup> </mrow> </semantics></math>); upper <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.52</mn> </mrow> </semantics></math>, lower <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9507'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g008-550.jpg?1663841092" title=" <strong>Figure 8</strong><br/> <p>Counter-rotating configuration, difference between far-field and near-field OASPL for the defined TCs and for <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.52</mn> </mrow> </semantics></math> (upper) and <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math> (lower).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9507'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g009-550.jpg?1663841078" title=" <strong>Figure 9</strong><br/> <p>Co-rotating configuration, difference between far-field and near-field OASPL for the defined TCs and for <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.52</mn> </mrow> </semantics></math> (upper) and <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math> (lower).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9507'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g010-550.jpg?1663841081" title=" <strong>Figure 10</strong><br/> <p>Counter-rotating configuration, comparison between far-field and near-field OASPL directivity patterns; red line near field, black line far-field; TC <math display="inline"><semantics> <mrow> <mo>=</mo> <mn>0.05</mn> <mi>d</mi> </mrow> </semantics></math> (upper), TC <math display="inline"><semantics> <mrow> <mo>=</mo> <mi>d</mi> </mrow> </semantics></math> (lower).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9507'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09507/article_deploy/html/images/applsci-12-09507-g011-550.jpg?1663841085" title=" <strong>Figure 11</strong><br/> <p>Co-rotating configuration, comparison between far-field and near-field OASPL directivity patterns; red line near field, black line far-field; TC <math display="inline"><semantics> <mrow> <mo>=</mo> <mn>0.05</mn> <mi>d</mi> </mrow> </semantics></math> (upper), TC <math display="inline"><semantics> <mrow> <mo>=</mo> <mi>d</mi> </mrow> </semantics></math> (lower).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/19/9507'>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="912334" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 36 pages, 617 KiB   </span> <a href="/2076-3417/12/18/9124/pdf?version=1663207902" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Federated Learning for Edge Computing: A Survey" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/12/18/9124">Federated Learning for Edge Computing: A Survey</a> <div class="authors"> by <span class="inlineblock "><strong>Alexander Brecko</strong>, </span><span class="inlineblock "><strong>Erik Kajati</strong>, </span><span class="inlineblock "><strong>Jiri Koziorek</strong> and </span><span class="inlineblock "><strong>Iveta Zolotova</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(18), 9124; <a href="https://doi.org/10.3390/app12189124">https://doi.org/10.3390/app12189124</a> - 11 Sep 2022 </div> <a href="/2076-3417/12/18/9124#metrics">Cited by 45</a> | Viewed by 11964 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> New technologies bring opportunities to deploy AI and machine learning to the edge of the network, allowing edge devices to train simple models that can then be deployed in practice. Federated learning (FL) is a distributed machine learning technique to create a global <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/18/9124/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> New technologies bring opportunities to deploy AI and machine learning to the edge of the network, allowing edge devices to train simple models that can then be deployed in practice. Federated learning (FL) is a distributed machine learning technique to create a global model by learning from multiple decentralized edge clients. Although FL methods offer several advantages, including scalability and data privacy, they also introduce some risks and drawbacks in terms of computational complexity in the case of heterogeneous devices. Internet of Things (IoT) devices may have limited computing resources, poorer connection quality, or may use different operating systems. This paper provides an overview of the methods used in FL with a focus on edge devices with limited computational resources. This paper also presents FL frameworks that are currently popular and that provide communication between clients and servers. In this context, various topics are described, which include contributions and trends in the literature. This includes basic models and designs of system architecture, possibilities of application in practice, privacy and security, and resource management. Challenges related to the computational requirements of edge devices such as hardware heterogeneity, communication overload or limited resources of devices are discussed. <a href="/2076-3417/12/18/9124">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/Edge_Computing_Communications ">Edge Computing Communications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/18/9124/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev912334"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next912334"><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="#next912334" data-cycle-prev="#prev912334" data-cycle-progressive="#images912334" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-912334-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-09124/article_deploy/html/images/applsci-12-09124-g001-550.jpg?1663228896" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images912334" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-912334-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09124/article_deploy/html/images/applsci-12-09124-g002-550.jpg?1663228899'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-912334-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09124/article_deploy/html/images/applsci-12-09124-g003-550.jpg?1663228900'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-912334-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09124/article_deploy/html/images/applsci-12-09124-g004-550.jpg?1663228898'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-912334-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09124/article_deploy/html/images/applsci-12-09124-g005-550.jpg?1663228894'><p>Figure 5</p></div></script></div></div><div id="article-912334-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-09124/article_deploy/html/images/applsci-12-09124-g001-550.jpg?1663228896" title=" <strong>Figure 1</strong><br/> <p>Taxonomy of FL system.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/18/9124'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09124/article_deploy/html/images/applsci-12-09124-g002-550.jpg?1663228899" title=" <strong>Figure 2</strong><br/> <p>Architecture design of FL.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/18/9124'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09124/article_deploy/html/images/applsci-12-09124-g003-550.jpg?1663228900" title=" <strong>Figure 3</strong><br/> <p>Architecture design of centralized FL vs. decentralized FL [<a href="#B46-applsci-12-09124" class="html-bibr">46</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/18/9124'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09124/article_deploy/html/images/applsci-12-09124-g004-550.jpg?1663228898" title=" <strong>Figure 4</strong><br/> <p>Data partition of horizontal federated learning, vertical federated learning, and federated transfer learning.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/18/9124'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09124/article_deploy/html/images/applsci-12-09124-g005-550.jpg?1663228894" title=" <strong>Figure 5</strong><br/> <p>Synchronous and asynchronous FL communication.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/18/9124'>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="910312" 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, 1753 KiB   </span> <a href="/2076-3417/12/18/9038/pdf?version=1662689792" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A Novel Hybrid Method for Short-Term Wind Speed Prediction Based on Wind Probability Distribution Function and Machine Learning Models" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/12/18/9038">A Novel Hybrid Method for Short-Term Wind Speed Prediction Based on Wind Probability Distribution Function and Machine Learning Models</a> <div class="authors"> by <span class="inlineblock "><strong>Rabin Dhakal</strong>, </span><span class="inlineblock "><strong>Ashish Sedai</strong>, </span><span class="inlineblock "><strong>Suhas Pol</strong>, </span><span class="inlineblock "><strong>Siva Parameswaran</strong>, </span><span class="inlineblock "><strong>Ali Nejat</strong> and </span><span class="inlineblock "><strong>Hanna Moussa</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(18), 9038; <a href="https://doi.org/10.3390/app12189038">https://doi.org/10.3390/app12189038</a> - 8 Sep 2022 </div> <a href="/2076-3417/12/18/9038#metrics">Cited by 15</a> | Viewed by 2720 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/18/9038/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all these methods to achieve a higher WSP accuracy as, thus far, hybrid wind speed predictions are mainly made by using multivariate time series data. To do so, an error correction algorithm for the probability-density-based wind speed prediction model is introduced. Moreover, a comparative analysis of the performance of each method for accurately predicting wind speed for each time step of short-term forecast horizons is performed. All the models studied are used to form the prediction model by optimizing the weight function for each time step of a forecast horizon for each model that contributed to forming the proposed hybrid prediction model. The National Oceanic and Atmospheric Administration (NOAA) and System Advisory Module (SAM) databases were used to demonstrate the accuracy of the proposed models and conduct a comparative analysis. The results of the study show the significant improvement on the performance of wind speed prediction models through the development of a proposed hybrid prediction model. <a href="/2076-3417/12/18/9038">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/load_forecasting_renewables_forecasting ">Very Short/Short/Medium/Long Term Load Forecasting and Renewables Forecasting</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/18/9038/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev910312"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next910312"><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="#next910312" data-cycle-prev="#prev910312" data-cycle-progressive="#images910312" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-910312-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-09038/article_deploy/html/images/applsci-12-09038-g001-550.jpg?1662710839" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images910312" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-910312-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09038/article_deploy/html/images/applsci-12-09038-g002a-550.jpg?1662710845'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-910312-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09038/article_deploy/html/images/applsci-12-09038-g002b-550.jpg?1662710841'><p>Figure 2 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-910312-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-09038/article_deploy/html/images/applsci-12-09038-g003-550.jpg?1662710843'><p>Figure 3</p></div></script></div></div><div id="article-910312-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-09038/article_deploy/html/images/applsci-12-09038-g001-550.jpg?1662710839" title=" <strong>Figure 1</strong><br/> <p>LSTM network and detail structure of an LSTM cell.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/18/9038'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09038/article_deploy/html/images/applsci-12-09038-g002a-550.jpg?1662710845" title=" <strong>Figure 2</strong><br/> <p>Distribution of wind speed and direction at four different sites.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/18/9038'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09038/article_deploy/html/images/applsci-12-09038-g002b-550.jpg?1662710841" title=" <strong>Figure 2 Cont.</strong><br/> <p>Distribution of wind speed and direction at four different sites.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/18/9038'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-09038/article_deploy/html/images/applsci-12-09038-g003-550.jpg?1662710843" title=" <strong>Figure 3</strong><br/> <p>Error correction algorithm for simulated wind speed using Weibull and Rayleigh distribution function.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/18/9038'>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="903306" 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, 3237 KiB   </span> <a href="/2076-3417/12/17/8769/pdf?version=1662011667" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/12/17/8769">Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting</a> <div class="authors"> by <span class="inlineblock "><strong>Moisés Cordeiro-Costas</strong>, </span><span class="inlineblock "><strong>Daniel Villanueva</strong>, </span><span class="inlineblock "><strong>Pablo Eguía-Oller</strong> and </span><span class="inlineblock "><strong>Enrique Granada-Álvarez</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(17), 8769; <a href="https://doi.org/10.3390/app12178769">https://doi.org/10.3390/app12178769</a> - 31 Aug 2022 </div> <a href="/2076-3417/12/17/8769#metrics">Cited by 15</a> | Viewed by 2688 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/17/8769/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ is a common alternative to improve sustainability in this sector. An efficient trade-off of the photovoltaic source in the fields of Zero Energy Buildings (ZEB), nearly Zero Energy Buildings (nZEB) or MicroGrids (MG) requires an accurate forecast of photovoltaic production. These systems constantly generate data that are not used. Artificial Intelligence methods can take advantage of this missing information and provide accurate forecasts in real time. Thus, in this manuscript a comparative analysis is carried out to determine the most appropriate Artificial Intelligence methods to forecast photovoltaic production in buildings. On the one hand, the Machine Learning methods considered are Random Forest (RF), Extreme Gradient Boost (XGBoost), and Support Vector Regressor (SVR). On the other hand, Deep Learning techniques used are Standard Neural Network (SNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). The models are checked with data from a real building. The models are validated using normalized Mean Bias Error (nMBE), normalized Root Mean Squared Error (nRMSE), and the coefficient of variation (R<sup>2</sup>). Standard deviation is also used in conjunction with these metrics. The results show that the models forecast the test set with errors of less than 2.00% (nMBE) and 7.50% (nRMSE) in the case of considering nights, and 4.00% (nMBE) and 11.50% (nRMSE) if nights are not considered. In both situations, the R<sup>2</sup> is greater than 0.85 in all models. <a href="/2076-3417/12/17/8769">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Collection <a href=" /journal/applsci/topical_collections/photovoltaic_installations ">Improvements in the Production, Monitoring, Management and Impact on the Grid of Photovoltaic Installations</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/17/8769/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev903306"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next903306"><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="#next903306" data-cycle-prev="#prev903306" data-cycle-progressive="#images903306" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-903306-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g001-550.jpg?1662019967" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images903306" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-903306-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g002-550.jpg?1662019973'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-903306-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g003-550.jpg?1662019968'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-903306-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g004-550.jpg?1662019972'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-903306-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g005-550.jpg?1662019961'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-903306-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g006a-550.jpg?1662019963'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-903306-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g006b-550.jpg?1662019966'><p>Figure 6 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-903306-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g007a-550.jpg?1662019964'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-903306-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g007b-550.jpg?1662019965'><p>Figure 7 Cont.</p></div></script></div></div><div id="article-903306-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g001-550.jpg?1662019967" title=" <strong>Figure 1</strong><br/> <p>Phase diagram for the application of the Artificial Intelligence methods according to the optimization process.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8769'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g002-550.jpg?1662019973" title=" <strong>Figure 2</strong><br/> <p>Configuration of a Support Vector Regression model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8769'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g003-550.jpg?1662019968" title=" <strong>Figure 3</strong><br/> <p>Configuration of a Random Forest model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8769'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g004-550.jpg?1662019972" title=" <strong>Figure 4</strong><br/> <p>Configuration of a Deep Learning model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8769'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g005-550.jpg?1662019961" title=" <strong>Figure 5</strong><br/> <p>Photovoltaic production forecasting problem, where the Artificial Intelligence model corresponds to the considered Machine Learning or Deep Learning model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8769'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g006a-550.jpg?1662019963" title=" <strong>Figure 6</strong><br/> <p>Adjustments corresponding to the train split of the SVR, SNN and CNN models in the forecast of photovoltaic production: (<b>a</b>) Forecast on a day with good conditions, (<b>b</b>) Forecast on a day with variant conditions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8769'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g006b-550.jpg?1662019966" title=" <strong>Figure 6 Cont.</strong><br/> <p>Adjustments corresponding to the train split of the SVR, SNN and CNN models in the forecast of photovoltaic production: (<b>a</b>) Forecast on a day with good conditions, (<b>b</b>) Forecast on a day with variant conditions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8769'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g007a-550.jpg?1662019964" title=" <strong>Figure 7</strong><br/> <p>Adjustments corresponding to the test split of the SVR, SNN and CNN models in the forecast of photovoltaic production: (<b>a</b>) Forecast on a day with good conditions, (<b>b</b>) Forecast on a day with variant conditions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8769'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08769/article_deploy/html/images/applsci-12-08769-g007b-550.jpg?1662019965" title=" <strong>Figure 7 Cont.</strong><br/> <p>Adjustments corresponding to the test split of the SVR, SNN and CNN models in the forecast of photovoltaic production: (<b>a</b>) Forecast on a day with good conditions, (<b>b</b>) Forecast on a day with variant conditions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8769'>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="901148" 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, 3118 KiB   </span> <a href="/2076-3417/12/17/8662/pdf?version=1661833757" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/12/17/8662">Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning</a> <div class="authors"> by <span class="inlineblock "><strong>Senait Gebremichael Tesfagergish</strong>, </span><span class="inlineblock "><strong>Jurgita Kapočiūtė-Dzikienė</strong> and </span><span class="inlineblock "><strong>Robertas Damaševičius</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(17), 8662; <a href="https://doi.org/10.3390/app12178662">https://doi.org/10.3390/app12178662</a> - 29 Aug 2022 </div> <a href="/2076-3417/12/17/8662#metrics">Cited by 37</a> | Viewed by 5909 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> We live in a digitized era where our daily life depends on using online resources. Businesses consider the opinions of their customers, while people rely on the reviews/comments of other users before buying specific products or services. These reviews/comments are usually provided in <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/17/8662/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> We live in a digitized era where our daily life depends on using online resources. Businesses consider the opinions of their customers, while people rely on the reviews/comments of other users before buying specific products or services. These reviews/comments are usually provided in the non-normative natural language within different contexts and domains (in social media, forums, news, blogs, etc.). Sentiment classification plays an important role in analyzing such texts collected from users by assigning positive, negative, and sometimes neutral sentiment values to each of them. Moreover, these texts typically contain many expressed or hidden emotions (such as happiness, sadness, etc.) that could contribute significantly to identifying sentiments. We address the emotion detection problem as part of the sentiment analysis task and propose a two-stage emotion detection methodology. The first stage is the unsupervised zero-shot learning model based on a sentence transformer returning the probabilities for subsets of 34 emotions (anger, sadness, disgust, fear, joy, happiness, admiration, affection, anguish, caution, confusion, desire, disappointment, attraction, envy, excitement, grief, hope, horror, joy, love, loneliness, pleasure, fear, generosity, rage, relief, satisfaction, sorrow, wonder, sympathy, shame, terror, and panic). The output of the zero-shot model is used as an input for the second stage, which trains the machine learning classifier on the sentiment labels in a supervised manner using ensemble learning. The proposed hybrid semi-supervised method achieves the highest accuracy of 87.3% on the English SemEval 2017 dataset. <a href="/2076-3417/12/17/8662">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/Natural_Language_Processing_Applications ">Natural Language Processing (NLP) and Applications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/17/8662/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev901148"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next901148"><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="#next901148" data-cycle-prev="#prev901148" data-cycle-progressive="#images901148" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-901148-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g001-550.jpg?1661833828" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images901148" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-901148-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g002-550.jpg?1661833827'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-901148-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g003-550.jpg?1661833830'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-901148-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g004-550.jpg?1661833828'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-901148-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g005-550.jpg?1661833836'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-901148-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g006-550.jpg?1661833830'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-901148-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g007-550.jpg?1661833833'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-901148-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g008-550.jpg?1661833835'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-901148-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g009-550.jpg?1661833826'><p>Figure 9</p></div></script></div></div><div id="article-901148-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g001-550.jpg?1661833828" title=" <strong>Figure 1</strong><br/> <p>Workflow of the proposed semi-supervised method for sentiment classification. It includes two stages (phases), which combine unsupervised classification for recognizing emotions in a text, and supervised classification for detecting sentiments from emotions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8662'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g002-550.jpg?1661833827" title=" <strong>Figure 2</strong><br/> <p>Two stages of the proposed semi-supervised method. The first stage uses unsupervised zero-shot learning by sentence transformers to obtain emotion probabilities. The second stage uses supervised ensemble learning to learn sentiments from emotion probabilities.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8662'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g003-550.jpg?1661833830" title=" <strong>Figure 3</strong><br/> <p>A representation of the wheel of emotions (the Plutchik’s model).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8662'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g004-550.jpg?1661833828" title=" <strong>Figure 4</strong><br/> <p>Pipeline of the first stage classification.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8662'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g005-550.jpg?1661833836" title=" <strong>Figure 5</strong><br/> <p>The distribution of texts among positive/negative/neutral sentiment categories in the IMDB, Sentiment 140, and SemEval-2017 datasets.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8662'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g006-550.jpg?1661833830" title=" <strong>Figure 6</strong><br/> <p>Confusion matrix of 3-class (negative, neutral and positive) classification.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8662'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g007-550.jpg?1661833833" title=" <strong>Figure 7</strong><br/> <p>Accuracy vs. number of training instances for sentence transformer + machine learning classifiers and our proposed method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8662'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g008-550.jpg?1661833835" title=" <strong>Figure 8</strong><br/> <p>Results of statistical significance testing using non-parametric Wilcoxon test. Boxplots show an accuracy of classification using sentence transformers (Sent. Trans.) and the proposed method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8662'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08662/article_deploy/html/images/applsci-12-08662-g009-550.jpg?1661833826" title=" <strong>Figure 9</strong><br/> <p>Critical Distance diagram from post hoc Nemenyi test: (<b>a</b>) 2-class classification scenario, and (<b>b</b>) 3-class classification scenario showing mean ranks of the methods. The mean ranks falling within a grey box marking the critical distance are not statistically different. Red dot marks the mean rank. Blue dots mark the confidence interval.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8662'>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="899682" 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, 3152 KiB   </span> <a href="/2076-3417/12/17/8604/pdf?version=1662175674" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Malware Detection Using Memory Analysis Data in Big Data Environment" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/12/17/8604">Malware Detection Using Memory Analysis Data in Big Data Environment</a> <div class="authors"> by <span class="inlineblock "><strong>Murat Dener</strong>, </span><span class="inlineblock "><strong>Gökçe Ok</strong> and </span><span class="inlineblock "><strong>Abdullah Orman</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(17), 8604; <a href="https://doi.org/10.3390/app12178604">https://doi.org/10.3390/app12178604</a> - 27 Aug 2022 </div> <a href="/2076-3417/12/17/8604#metrics">Cited by 38</a> | Viewed by 7786 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection methods may fall short <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/17/8604/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection methods may fall short in advanced malware detection. Data obtained through memory analysis can provide important insights into the behavior and patterns of malware. This is because malwares leave various traces on memories. For this reason, the memory analysis method is one of the issues that should be studied in malware detection. In this study, the use of memory data in malware detection is suggested. Malware detection was carried out by using various deep learning and machine learning approaches in a big data environment with memory data. This study was carried out with Pyspark on Apache Spark big data platform in Google Colaboratory. Experiments were performed on the balanced CIC-MalMem-2022 dataset. Binary classification was made using Random Forest, Decision Tree, Gradient Boosted Tree, Logistic Regression, Naive Bayes, Linear Vector Support Machine, Multilayer Perceptron, Deep Feed Forward Neural Network, and Long Short-Term Memory algorithms. The performances of the algorithms used have been compared. The results were evaluated using the Accuracy, F1-score, Precision, Recall, and AUC performance metrics. As a result, the most successful malware detection was obtained with the Logistic Regression algorithm, with an accuracy level of 99.97% in malware detection by memory analysis. Gradient Boosted Tree follows the Logistic Regression algorithm with 99.94% accuracy. The Naive Bayes algorithm showed the lowest performance in malware analysis with memory data, with an accuracy of 98.41%. In addition, many of the algorithms used have achieved very successful results. According to the results obtained, the data obtained from memory analysis is very useful in detecting malware. In addition, deep learning and machine learning approaches were trained with memory datasets and achieved very successful results in malware detection. <a href="/2076-3417/12/17/8604">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/2076-3417/12/17/8604/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev899682"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next899682"><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="#next899682" data-cycle-prev="#prev899682" data-cycle-progressive="#images899682" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-899682-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g001-550.jpg?1662191920" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images899682" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-899682-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g002-550.jpg?1662191923'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-899682-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g003-550.jpg?1662191922'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-899682-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g004-550.jpg?1662191923'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-899682-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g005-550.jpg?1662191919'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-899682-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g006-550.jpg?1662191919'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-899682-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g007-550.jpg?1662191921'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-899682-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g008-550.jpg?1662191924'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-899682-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g009-550.jpg?1662191921'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-899682-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g010-550.jpg?1662191921'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-899682-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g011-550.jpg?1662191922'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-899682-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g012-550.jpg?1662191920'><p>Figure 12</p></div></script></div></div><div id="article-899682-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g001-550.jpg?1662191920" title=" <strong>Figure 1</strong><br/> <p>Random Forest Structure.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g002-550.jpg?1662191923" title=" <strong>Figure 2</strong><br/> <p>Decision Tree Structure.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g003-550.jpg?1662191922" title=" <strong>Figure 3</strong><br/> <p>Sigmoid Function.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g004-550.jpg?1662191923" title=" <strong>Figure 4</strong><br/> <p>Gradient Boosted Tree Iterations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g005-550.jpg?1662191919" title=" <strong>Figure 5</strong><br/> <p>Support Vector Machine Algorithm.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g006-550.jpg?1662191919" title=" <strong>Figure 6</strong><br/> <p>Feed-Forward Neural Network Architecture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g007-550.jpg?1662191921" title=" <strong>Figure 7</strong><br/> <p>LSTM Architecture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g008-550.jpg?1662191924" title=" <strong>Figure 8</strong><br/> <p>Malware Detection Flowchart.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g009-550.jpg?1662191921" title=" <strong>Figure 9</strong><br/> <p>Performance Comparison of ML and DL Models.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g010-550.jpg?1662191921" title=" <strong>Figure 10</strong><br/> <p>(<b>a</b>) ROC-AUC: comparison of ML and DL Models; (<b>b</b>) PRC-AUC: comparison of ML and DL Models.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g011-550.jpg?1662191922" title=" <strong>Figure 11</strong><br/> <p>Confusion Matrix of (<b>a</b>) Decision Tree; (<b>b</b>) Random Forest; (<b>c</b>) Naive Bayes; (<b>d</b>) Logistic Regression; (<b>e</b>) Gradient Boosted Tree; (<b>f</b>) Linear SVC; (<b>g</b>) MLP; (<b>h</b>) DNN; (<b>i</b>) LSTM.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08604/article_deploy/html/images/applsci-12-08604-g012-550.jpg?1662191920" title=" <strong>Figure 12</strong><br/> <p>Loss and Validation Loss Graphs for (<b>a</b>) DNN; and (<b>b</b>) LSTM.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8604'>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="896973" 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, 1657 KiB   </span> <a href="/2076-3417/12/17/8476/pdf?version=1661414798" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="VR Games in Cultural Heritage: A Systematic Review of the Emerging Fields of Virtual Reality and Culture Games" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/12/17/8476">VR Games in Cultural Heritage: A Systematic Review of the Emerging Fields of Virtual Reality and Culture Games</a> <div class="authors"> by <span class="inlineblock "><strong>Anastasios Theodoropoulos</strong> and </span><span class="inlineblock "><strong>Angeliki Antoniou</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(17), 8476; <a href="https://doi.org/10.3390/app12178476">https://doi.org/10.3390/app12178476</a> - 25 Aug 2022 </div> <a href="/2076-3417/12/17/8476#metrics">Cited by 42</a> | Viewed by 10283 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In recent years, the use of VR games in cultural heritage has been growing. VR Games have increasingly found their way into museums and exhibitions, highlighting the increasing cultural value associated with games and the institutionalization of game culture. In particular, serious VR <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/17/8476/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In recent years, the use of VR games in cultural heritage has been growing. VR Games have increasingly found their way into museums and exhibitions, highlighting the increasing cultural value associated with games and the institutionalization of game culture. In particular, serious VR games have a variety of benefits for educational purposes. There are several studies that deployed VR games to improve visitor experiences in several contexts. However, there are not sufficient studies in the field that examine the benefits and drawbacks of VR gaming. This lack of classification studies is regarded as an obstacle to developing more effective games and proposing guidance on the best way of using them in cultural heritage. This review aims to analyze how VR games are used in cultural heritage settings, to explore the evolution and opportunities of this emerging field, the challenges and tensions these innovations present, and to collectively advance this work to benefit visitor experiences. <a href="/2076-3417/12/17/8476">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/digitizing_cultural_heritage ">Advanced Technologies in Digitizing Cultural Heritage</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/17/8476/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev896973"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next896973"><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="#next896973" data-cycle-prev="#prev896973" data-cycle-progressive="#images896973" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-896973-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-08476/article_deploy/html/images/applsci-12-08476-g001-550.jpg?1661414980" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images896973" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-896973-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08476/article_deploy/html/images/applsci-12-08476-g002-550.jpg?1661414982'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-896973-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08476/article_deploy/html/images/applsci-12-08476-g003-550.jpg?1661414987'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-896973-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08476/article_deploy/html/images/applsci-12-08476-g004-550.jpg?1661414984'><p>Figure 4</p></div></script></div></div><div id="article-896973-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-08476/article_deploy/html/images/applsci-12-08476-g001-550.jpg?1661414980" title=" <strong>Figure 1</strong><br/> <p>Quality assessment of included studies (histogram).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8476'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08476/article_deploy/html/images/applsci-12-08476-g002-550.jpg?1661414982" title=" <strong>Figure 2</strong><br/> <p>Flow of information through the different phases of this review (Prisma flowchart [<a href="#B33-applsci-12-08476" class="html-bibr">33</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8476'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08476/article_deploy/html/images/applsci-12-08476-g003-550.jpg?1661414987" title=" <strong>Figure 3</strong><br/> <p>Publication venues of selected works.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8476'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08476/article_deploy/html/images/applsci-12-08476-g004-550.jpg?1661414984" title=" <strong>Figure 4</strong><br/> <p>Distribution of selected papers per year.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8476'>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="895941" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <a data-dropdown="drop-supplementary-895941" aria-controls="drop-supplementary-895941" aria-expanded="false" title="Supplementary Material"> <i class="material-icons">attachment</i> </a> <div id="drop-supplementary-895941" class="f-dropdown label__btn__dropdown label__btn__dropdown--wide" data-dropdown-content aria-hidden="true" tabindex="-1"> Supplementary material: <br/> <a href="/2076-3417/12/17/8427/s1?version=1661328696"> Supplementary File 1 (ZIP, 690 KiB) </a><br/> </div> </div> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 16 pages, 838 KiB   </span> <a href="/2076-3417/12/17/8427/pdf?version=1661328696" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Using Chatbots as AI Conversational Partners in Language Learning" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/12/17/8427">Using Chatbots as AI Conversational Partners in Language Learning</a> <div class="authors"> by <span class="inlineblock "><strong>Jose Belda-Medina</strong> and </span><span class="inlineblock "><strong>José Ramón Calvo-Ferrer</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(17), 8427; <a href="https://doi.org/10.3390/app12178427">https://doi.org/10.3390/app12178427</a> - 24 Aug 2022 </div> <a href="/2076-3417/12/17/8427#metrics">Cited by 74</a> | Viewed by 22631 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. This study <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/17/8427/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. This study aims to examine the knowledge, level of satisfaction and perceptions concerning the integration of conversational AI in language learning among future educators. In this mixed method research based on convenience sampling, 176 undergraduates from two educational settings, Spain (<i>n</i> = 115) and Poland (<i>n</i> = 61), interacted autonomously with three conversational agents (Replika, Kuki, Wysa) over a four-week period. A learning module about Artificial Intelligence and language learning was specifically designed for this research, including an ad hoc model named the Chatbot–Human Interaction Satisfaction Model (CHISM), which was used by teacher candidates to evaluate different linguistic and technological features of the three conversational agents. Quantitative and qualitative data were gathered through a pre-post-survey based on the CHISM and the TAM2 (technology acceptance) models and a template analysis (TA), and analyzed through IBM SPSS 22 and QDA Miner software. The analysis yielded positive results regarding perceptions concerning the integration of conversational agents in language learning, particularly in relation to perceived ease of use (PeU) and attitudes (AT), but the scores for behavioral intention (BI) were more moderate. The findings also unveiled some gender-related differences regarding participants’ satisfaction with chatbot design and topics of interaction. <a href="/2076-3417/12/17/8427">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/Environment_Intelligent_Education ">Technologies and Environments of Intelligent Education</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/17/8427/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev895941"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next895941"><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="#next895941" data-cycle-prev="#prev895941" data-cycle-progressive="#images895941" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-895941-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-08427/article_deploy/html/images/applsci-12-08427-g001-550.jpg?1661329057" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images895941" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-895941-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08427/article_deploy/html/images/applsci-12-08427-g002-550.jpg?1661329055'><p>Figure 2</p></div></script></div></div><div id="article-895941-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-08427/article_deploy/html/images/applsci-12-08427-g001-550.jpg?1661329057" title=" <strong>Figure 1</strong><br/> <p>Research stages.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8427'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08427/article_deploy/html/images/applsci-12-08427-g002-550.jpg?1661329055" title=" <strong>Figure 2</strong><br/> <p>Screenshots of the interaction with the three conversational agents (from left to right: Replika, Kuki, Wysa).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/17/8427'>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="893222" 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, 1132 KiB   </span> <a href="/2076-3417/12/16/8328/pdf?version=1660984190" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Where Are Smart Cities Heading? A Meta-Review and Guidelines for Future Research" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/12/16/8328">Where Are Smart Cities Heading? A Meta-Review and Guidelines for Future Research</a> <div class="authors"> by <span class="inlineblock "><strong>João Reis</strong>, </span><span class="inlineblock "><strong>Pedro Alexandre Marques</strong> and </span><span class="inlineblock "><strong>Pedro Carmona Marques</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(16), 8328; <a href="https://doi.org/10.3390/app12168328">https://doi.org/10.3390/app12168328</a> - 20 Aug 2022 </div> <a href="/2076-3417/12/16/8328#metrics">Cited by 27</a> | Viewed by 4307 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> (1) Background: Smart cities have been gaining attention in the community, both among researchers and professionals. Although this field of study is gaining some maturity, no academic manuscript yet offers a unique holistic view of the phenomenon. In fact, the existing systematic reviews <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/16/8328/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> (1) Background: Smart cities have been gaining attention in the community, both among researchers and professionals. Although this field of study is gaining some maturity, no academic manuscript yet offers a unique holistic view of the phenomenon. In fact, the existing systematic reviews make it possible to gather solid and relevant knowledge, but still dispersed; (2) Method: through a meta-review it was possible to provide a set of data, which allows the dissemination of the main theoretical and managerial contributions to enthusiasts and critics of the area; (3) Results: this research identified the most relevant topics for smart cities, namely, smart city dimensions, digital transformation, sustainability and resilience. In addition, this research emphasizes that the natural sciences have dominated scientific production, with greater attention being paid to megacities of developed nations. Recent empirical research also suggests that it is crucial to overcome key cybersecurity and privacy challenges in smart cities; (4) Conclusions: research on smart cities can be performed as multidisciplinary studies of small and medium-sized cities in developed or underdeveloped countries. Furthermore, future research should highlight the role played by cybersecurity in the development of smart cities and analyze the impact of smart city development on the link between the city and its stakeholders. <a href="/2076-3417/12/16/8328">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/Building_New_Services ">Sustainable Smart Cities: Building New Services and Products with Digital Technologies</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/16/8328/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev893222"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next893222"><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="#next893222" data-cycle-prev="#prev893222" data-cycle-progressive="#images893222" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-893222-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-08328/article_deploy/html/images/applsci-12-08328-g001-550.jpg?1660984256" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images893222" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-893222-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08328/article_deploy/html/images/applsci-12-08328-g002-550.jpg?1660984259'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-893222-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08328/article_deploy/html/images/applsci-12-08328-g003-550.jpg?1660984261'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-893222-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08328/article_deploy/html/images/applsci-12-08328-g004-550.jpg?1660984262'><p>Figure 4</p></div></script></div></div><div id="article-893222-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-08328/article_deploy/html/images/applsci-12-08328-g001-550.jpg?1660984256" title=" <strong>Figure 1</strong><br/> <p>PRISMA protocol.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8328'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08328/article_deploy/html/images/applsci-12-08328-g002-550.jpg?1660984259" title=" <strong>Figure 2</strong><br/> <p>Methodological process.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8328'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08328/article_deploy/html/images/applsci-12-08328-g003-550.jpg?1660984261" title=" <strong>Figure 3</strong><br/> <p>Conceptual framework.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8328'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08328/article_deploy/html/images/applsci-12-08328-g004-550.jpg?1660984262" title=" <strong>Figure 4</strong><br/> <p>VOSviewer 1.6.18 analysis.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8328'>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="893064" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 41 pages, 4861 KiB   </span> <a href="/2076-3417/12/16/8321/pdf?version=1661236940" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Analysis of Technologies for Carbon Dioxide Capture from the Air" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/12/16/8321">Analysis of Technologies for Carbon Dioxide Capture from the Air</a> <div class="authors"> by <span class="inlineblock "><strong>Grazia Leonzio</strong>, </span><span class="inlineblock "><strong>Paul S. Fennell</strong> and </span><span class="inlineblock "><strong>Nilay Shah</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(16), 8321; <a href="https://doi.org/10.3390/app12168321">https://doi.org/10.3390/app12168321</a> - 19 Aug 2022 </div> <a href="/2076-3417/12/16/8321#metrics">Cited by 24</a> | Viewed by 7784 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The increase in CO<sub>2</sub> concentration in the atmosphere has prompted the research community to find solutions for this environmental problem, which causes climate change and global warming. CO<sub>2</sub> removal through the use of negative emissions technologies could lead to global emission <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/16/8321/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The increase in CO<sub>2</sub> concentration in the atmosphere has prompted the research community to find solutions for this environmental problem, which causes climate change and global warming. CO<sub>2</sub> removal through the use of negative emissions technologies could lead to global emission levels becoming net negative towards the end of this century. Among these negative emissions technologies, direct air capture (DAC), in which CO<sub>2</sub> is captured directly from the atmosphere, could play an important role. The captured CO<sub>2</sub> can be removed in the long term and through its storage can be used for chemical processes, allowing closed carbon cycles in the short term. For DAC, different technologies have been suggested in the literature, and an overview of these is proposed in this work. Absorption and adsorption are the most studied and mature technologies, but others are also under investigation. An analysis of the main key performance indicators is also presented here and it is suggested that more efforts should be made to develop DAC at a large scale by reducing costs and improving efficiency. An additional discussion, addressing the social concern, is indicated as well. <a href="/2076-3417/12/16/8321">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/Carbon_Dioxide_Removal_Technologies ">Advances in Carbon Dioxide Removal Technologies</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/16/8321/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev893064"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next893064"><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="#next893064" data-cycle-prev="#prev893064" data-cycle-progressive="#images893064" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-893064-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g001-550.jpg?1661242671" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images893064" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g002-550.jpg?1661242668'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g003-550.jpg?1661242666'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g004-550.jpg?1661242684'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g005-550.jpg?1661242671'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g006-550.jpg?1661242679'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g007-550.jpg?1661242664'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g008-550.jpg?1661242685'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g009-550.jpg?1661242667'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g010-550.jpg?1661242676'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g011-550.jpg?1661242677'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g012-550.jpg?1661242682'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g013-550.jpg?1661242675'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g014-550.jpg?1661242665'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g015-550.jpg?1661242682'><p>Figure 15</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g016-550.jpg?1661242675'><p>Figure 16</p></div> --- <div class='openpopupgallery' data-imgindex='16' data-target='article-893064-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g017-550.jpg?1661242674'><p>Figure 17</p></div></script></div></div><div id="article-893064-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g001-550.jpg?1661242671" title=" <strong>Figure 1</strong><br/> <p>Diagram of the CO<sub>2</sub> separation process: CO<sub>2</sub> in the feed stream A is removed, resulting in a product stream B with more CO<sub>2</sub> and a product stream C with a small amount of CO<sub>2</sub>. Reproduced with permission from [<a href="#B30-applsci-12-08321" class="html-bibr">30</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g002-550.jpg?1661242668" title=" <strong>Figure 2</strong><br/> <p>Minimum thermodynamic work of CO<sub>2</sub> separation from (<b>a</b>) air, (<b>b</b>) natural gas combined cycle flue gas and (<b>c</b>) pulverized coal combustion flue gas. Reproduced under the Creative Commons attribution 3.0 license [<a href="#B32-applsci-12-08321" class="html-bibr">32</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g003-550.jpg?1661242666" title=" <strong>Figure 3</strong><br/> <p>Process scheme for the DAC absorption process with NaOH aqueous solution. Reproduced under the Standard ACS AuthorChoice/Editors’ Choice Usage Agreement [<a href="#B18-applsci-12-08321" class="html-bibr">18</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g004-550.jpg?1661242684" title=" <strong>Figure 4</strong><br/> <p>Scheme of the process analyzed by Keith et al. [<a href="#B34-applsci-12-08321" class="html-bibr">34</a>] using a KOH solution. Reproduced under a creative commons license.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g005-550.jpg?1661242671" title=" <strong>Figure 5</strong><br/> <p>Typical physisorbent materials. Adapted from [<a href="#B6-applsci-12-08321" class="html-bibr">6</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g006-550.jpg?1661242679" title=" <strong>Figure 6</strong><br/> <p>Classification of amine-functionalized sorbents. Reproduced with the permission from [<a href="#B6-applsci-12-08321" class="html-bibr">6</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g007-550.jpg?1661242664" title=" <strong>Figure 7</strong><br/> <p>Reaction scheme of CO<sub>2</sub> adsorption/desorption for an anion-exchange resin. Adapted from [<a href="#B109-applsci-12-08321" class="html-bibr">109</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g008-550.jpg?1661242685" title=" <strong>Figure 8</strong><br/> <p>Fuel cell concentrator scheme. Reproduced under the techconnect license [<a href="#B23-applsci-12-08321" class="html-bibr">23</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g009-550.jpg?1661242667" title=" <strong>Figure 9</strong><br/> <p>Schematic of a single electro-swing adsorption electrochemical. Reproduced under the Creative Commons attribution noncommercial 3.0 unported licence [<a href="#B120-applsci-12-08321" class="html-bibr">120</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g010-550.jpg?1661242676" title=" <strong>Figure 10</strong><br/> <p>Scheme of the experimental process (MEA = membrane electrode assembly, CEM = cation exchange membrane). Reproduced under the ACS AuthorChoice/Editors’ Choice via CC-BY-NC-ND Usage Agreement [<a href="#B121-applsci-12-08321" class="html-bibr">121</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g011-550.jpg?1661242677" title=" <strong>Figure 11</strong><br/> <p>Process scheme for CO<sub>2</sub> capture from the air using bipolar membrane electrodialysis. Reproduced under the ACS AuthorChoice/Editors’ Choice via CC-BY-NC-ND Usage Agreement [<a href="#B22-applsci-12-08321" class="html-bibr">22</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g012-550.jpg?1661242682" title=" <strong>Figure 12</strong><br/> <p>Configuration of BPMED cell (AEM = anion-exchange membrane, CEM = cation exchange membrane, BPM = bipolar membrane). Reproduced under the ACS AuthorChoice/Editors’ Choice via CC-BY-NC-ND Usage Agreement [<a href="#B22-applsci-12-08321" class="html-bibr">22</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g013-550.jpg?1661242675" title=" <strong>Figure 13</strong><br/> <p>CO<sub>2</sub> cryogenic separation process scheme. Adapted from [<a href="#B25-applsci-12-08321" class="html-bibr">25</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g014-550.jpg?1661242665" title=" <strong>Figure 14</strong><br/> <p>Carbon Engineering plant in <span class="html-italic">Texas</span>. Reproduced under a Creative Commons license [<a href="#B34-applsci-12-08321" class="html-bibr">34</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g015-550.jpg?1661242682" title=" <strong>Figure 15</strong><br/> <p>The Climeworks plant in Switzerland. Reproduced with permission granted by Climeworks AG [<a href="#B131-applsci-12-08321" class="html-bibr">131</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g016-550.jpg?1661242675" title=" <strong>Figure 16</strong><br/> <p>Global Thermostat plant [<a href="#B3-applsci-12-08321" class="html-bibr">3</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08321/article_deploy/html/images/applsci-12-08321-g017-550.jpg?1661242674" title=" <strong>Figure 17</strong><br/> <p>DAC cost breakdown and comparison: (<b>A</b>) liquid solvent SAC capital costs; (<b>B</b>) liquid solvent DAC operating costs; (<b>C</b>) solid sorbent DAC capital costs; (<b>D</b>) solid sorbent DAC operating costs. Reproduced with permission from [<a href="#B40-applsci-12-08321" class="html-bibr">40</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8321'>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="891814" 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, 2730 KiB   </span> <a href="/2076-3417/12/16/8261/pdf?version=1660825776" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="2D/3D Multimode Medical Image Alignment Based on Spatial Histograms" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/12/16/8261">2D/3D Multimode Medical Image Alignment Based on Spatial Histograms</a> <div class="authors"> by <span class="inlineblock "><strong>Yuxi Ban</strong>, </span><span class="inlineblock "><strong>Yang Wang</strong>, </span><span class="inlineblock "><strong>Shan Liu</strong>, </span><span class="inlineblock "><strong>Bo Yang</strong>, </span><span class="inlineblock "><strong>Mingzhe Liu</strong>, </span><span class="inlineblock "><strong>Lirong Yin</strong> and </span><span class="inlineblock "><strong>Wenfeng Zheng</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(16), 8261; <a href="https://doi.org/10.3390/app12168261">https://doi.org/10.3390/app12168261</a> - 18 Aug 2022 </div> <a href="/2076-3417/12/16/8261#metrics">Cited by 65</a> | Viewed by 3559 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The key to image-guided surgery (IGS) technology is to find the transformation relationship between preoperative 3D images and intraoperative 2D images, namely, 2D/3D image registration. A feature-based 2D/3D medical image registration algorithm is investigated in this study. We use a two-dimensional weighted spatial <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/16/8261/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The key to image-guided surgery (IGS) technology is to find the transformation relationship between preoperative 3D images and intraoperative 2D images, namely, 2D/3D image registration. A feature-based 2D/3D medical image registration algorithm is investigated in this study. We use a two-dimensional weighted spatial histogram of gradient directions to extract statistical features, overcome the algorithm’s limitations, and expand the applicable scenarios under the premise of ensuring accuracy. The proposed algorithm was tested on CT and synthetic X-ray images, and compared with existing algorithms. The results show that the proposed algorithm can improve accuracy and efficiency, and reduce the initial value’s sensitivity. <a href="/2076-3417/12/16/8261">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/AAIPAR ">Advances in Artificial Intelligence for Perception Augmentation and Reasoning</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/16/8261/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev891814"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next891814"><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="#next891814" data-cycle-prev="#prev891814" data-cycle-progressive="#images891814" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-891814-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g001-550.jpg?1660825846" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images891814" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-891814-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g002-550.jpg?1660825857'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-891814-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g003-550.jpg?1660825845'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-891814-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g004a-550.jpg?1660825860'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-891814-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g004b-550.jpg?1660825855'><p>Figure 4 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-891814-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g005-550.jpg?1660825865'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-891814-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g006-550.jpg?1660825852'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-891814-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g007-550.jpg?1660825856'><p>Figure 7</p></div></script></div></div><div id="article-891814-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g001-550.jpg?1660825846" title=" <strong>Figure 1</strong><br/> <p>Three dimensional CT image of the brain model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8261'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g002-550.jpg?1660825857" title=" <strong>Figure 2</strong><br/> <p>CT rendering of the brain model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8261'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g003-550.jpg?1660825845" title=" <strong>Figure 3</strong><br/> <p>DRR image at the initial value.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8261'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g004a-550.jpg?1660825860" title=" <strong>Figure 4</strong><br/> <p>Results of Experiment 1: (<b>a</b>) reference image 1; (<b>b</b>) DRR image based on WSHGD registration; (<b>c</b>) difference image after registration based on the WSHGD; (<b>d</b>) reference image 1; (<b>e</b>) DRR image registered based on WHGD; (<b>f</b>) difference image after registration based on the WHGD.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8261'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g004b-550.jpg?1660825855" title=" <strong>Figure 4 Cont.</strong><br/> <p>Results of Experiment 1: (<b>a</b>) reference image 1; (<b>b</b>) DRR image based on WSHGD registration; (<b>c</b>) difference image after registration based on the WSHGD; (<b>d</b>) reference image 1; (<b>e</b>) DRR image registered based on WHGD; (<b>f</b>) difference image after registration based on the WHGD.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8261'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g005-550.jpg?1660825865" title=" <strong>Figure 5</strong><br/> <p>The results of Experiment 2: (<b>a</b>) reference image 2; (<b>b</b>) DRR image based on WSHGD registration; (<b>c</b>) difference image after registration based on the WSHGD; (<b>d</b>) reference image 2; (<b>e</b>) DRR image registered based on WHGD; (<b>f</b>) difference image after registration based on the WHGD.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8261'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g006-550.jpg?1660825852" title=" <strong>Figure 6</strong><br/> <p>Result of Experiment 3: (<b>a</b>) reference image 3; (<b>b</b>) DRR image based on WSHGD registration; (<b>c</b>) difference image after registration based on the WSHGD; (<b>d</b>) reference image 3; (<b>e</b>) DRR image registered based on WHGD; (<b>f</b>) difference image after registration based on the WHGD.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8261'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08261/article_deploy/html/images/applsci-12-08261-g007-550.jpg?1660825856" title=" <strong>Figure 7</strong><br/> <p>Registration error and mean values of the weighted histogram of gradient directions (WHGD) and the weighted spatial histogram of gradient directions (WSHGD).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8261'>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="890942" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 40 pages, 42877 KiB   </span> <a href="/2076-3417/12/16/8240/pdf?version=1661332456" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Metal–Organic Frameworks as Powerful Heterogeneous Catalysts in Advanced Oxidation Processes for Wastewater Treatment" 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 choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/applsci/editors_choice">More Editor’s choice articles in journal <em>Applied Sciences</em>.</a>'>Editor’s Choice</span><span class="label articletype">Review</span></div> <a class="title-link" href="/2076-3417/12/16/8240">Metal–Organic Frameworks as Powerful Heterogeneous Catalysts in Advanced Oxidation Processes for Wastewater Treatment</a> <div class="authors"> by <span class="inlineblock "><strong>Antía Fdez-Sanromán</strong>, </span><span class="inlineblock "><strong>Emilio Rosales</strong>, </span><span class="inlineblock "><strong>Marta Pazos</strong> and </span><span class="inlineblock "><strong>Angeles Sanroman</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2022</b>, <em>12</em>(16), 8240; <a href="https://doi.org/10.3390/app12168240">https://doi.org/10.3390/app12168240</a> - 17 Aug 2022 </div> <a href="/2076-3417/12/16/8240#metrics">Cited by 14</a> | Viewed by 5096 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Nowadays, the contamination of wastewater by organic persistent pollutants is a reality. These pollutants are difficult to remove from wastewater with conventional techniques; hence, it is necessary to go on the hunt for new, innovative and environmentally sustainable ones. In this context, advanced <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/12/16/8240/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Nowadays, the contamination of wastewater by organic persistent pollutants is a reality. These pollutants are difficult to remove from wastewater with conventional techniques; hence, it is necessary to go on the hunt for new, innovative and environmentally sustainable ones. In this context, advanced oxidation processes have attracted great attention and have developed rapidly in recent years as promising technologies. The cornerstone of advanced oxidation processes is the selection of heterogeneous catalysts. In this sense, the possibility of using metal–organic frameworks as catalysts has been opened up given their countless physical–chemical characteristics, which can overcome several disadvantages of traditional catalysts. Thus, this review provides a brief review of recent progress in the research and practical application of metal–organic frameworks to advanced oxidation processes, with a special emphasis on the potential of Fe-based metal–organic frameworks to reduce the pollutants present in wastewater or to render them harmless. To do that, the work starts with a brief overview of the different types and pathways of synthesis. Moreover, the mechanisms of the generation of radicals, as well as their action on the organic pollutants and stability, are analysed. Finally, the challenges of this technology to open up new avenues of wastewater treatment in the future are sketched out. <a href="/2076-3417/12/16/8240">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/applsci/sections/Environmental_Sciences">Environmental Sciences</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/12/16/8240/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev890942"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next890942"><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="#next890942" data-cycle-prev="#prev890942" data-cycle-progressive="#images890942" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-890942-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g001-550.jpg?1661332551" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images890942" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-890942-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g002-550.jpg?1661332538'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-890942-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g003-550.jpg?1661332545'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-890942-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g004-550.jpg?1661332550'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-890942-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g005-550.jpg?1661332539'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-890942-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g006-550.jpg?1661332553'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-890942-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g007-550.jpg?1661332557'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-890942-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g008-550.jpg?1661332542'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-890942-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g009-550.jpg?1661332555'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-890942-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g010-550.jpg?1661332541'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-890942-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g011-550.jpg?1661332544'><p>Figure 11</p></div></script></div></div><div id="article-890942-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g001-550.jpg?1661332551" title=" <strong>Figure 1</strong><br/> <p>MOF structures (reproduced from CSD MOF Collection [<a href="#B19-applsci-12-08240" class="html-bibr">19</a>] under license CC BY-NC-SA 4.0).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8240'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g002-550.jpg?1661332538" title=" <strong>Figure 2</strong><br/> <p>Evolution of the number of published articles on wastewater treatment for each type of MOF in the last five years.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8240'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g003-550.jpg?1661332545" title=" <strong>Figure 3</strong><br/> <p>Most used synthesis methods to obtain Fe-MOFs [<a href="#B83-applsci-12-08240" class="html-bibr">83</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8240'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g004-550.jpg?1661332550" title=" <strong>Figure 4</strong><br/> <p>General scheme of (<b>a</b>) solvothermal/hydrothermal synthesis, (<b>b</b>) microwave-assisted synthesis, (<b>c</b>) electrochemical synthesis, (<b>d</b>) sonochemical synthesis, (<b>e</b>) mechanochemical synthesis, (<b>f</b>) dry-gel synthesis.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8240'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g005-550.jpg?1661332539" title=" <strong>Figure 5</strong><br/> <p>Schematic of the procedure found in papers using this synthesis technique.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8240'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g006-550.jpg?1661332553" title=" <strong>Figure 6</strong><br/> <p>Classification of AOP by the fundamentals of the process itself.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8240'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g007-550.jpg?1661332557" title=" <strong>Figure 7</strong><br/> <p>Keywords and overlay visualization co-occurrence analysis of AOPs using MOFs in wastewater research using VOS Viewer®.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8240'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g008-550.jpg?1661332542" title=" <strong>Figure 8</strong><br/> <p>Graphical scheme of the reactions occurring on the Fe<sub>x</sub>Cu<sub>1−x</sub>(BDC) surface for SMX degradation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8240'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g009-550.jpg?1661332555" title=" <strong>Figure 9</strong><br/> <p>Schematic of the proposed mechanism of PDS activation over Fe-MOFs under UV light towards persistent pollutants degradation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8240'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g010-550.jpg?1661332541" title=" <strong>Figure 10</strong><br/> <p>General scheme of the degradation electro-Fenton processes using (<b>a</b>) modified cathode with Fe-MOF and (<b>b</b>) Fe-MOF in bulk solution.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8240'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-12-08240/article_deploy/html/images/applsci-12-08240-g011-550.jpg?1661332544" title=" <strong>Figure 11</strong><br/> <p>Schematic representation of the photo-electro-Fenton process with Fe-MOF as suspended heterogeneous catalysts.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/12/16/8240'>Full article</a></strong> "></a></div> </div> </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 options <i class="material-icons">expand_more</i> </a> <a href="#" class="export-options-show export-element"> Show export options <i class="material-icons">expand_less</i> </a> </div> <div class="listing-export-options export-element"> <div class="export-element" style="margin-top: 10px; 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(e) { if (!Cookiebot.consent.preferences) { $(".js-toggle-desktop-layout-link").hide(); if ("" === "desktop") { window.location = "/toggle_desktop_layout_cookie"; } } }, false); var hash = $(location).attr('hash'); if ("#share" === hash) { if (1 === $("#main-share-modal").length) { $('#main-share-modal').foundation('reveal', 'open'); } } </script> <script src="https://pub.mdpi-res.com/assets/js/lib.js?f8d3d71b3a772f9d?1732615622"></script> <script src="https://pub.mdpi-res.com/assets/js/mdpi.js?c267ce58392b15da?1732615622"></script> <script>var banners_url = 'https://serve.mdpi.com';</script> <script type='text/javascript' src='https://pub.mdpi-res.com/assets/js/ifvisible.min.js?c621d19ecb761212?1732615622'></script> <script src="https://pub.mdpi-res.com/assets/js/xmltohtml/affix.js?ac4ea55275297c15?1732615622"></script> <script src="https://pub.mdpi-res.com/assets/js/clipboard.min.js?3f3688138a1b9fc4?1732615622"></script> <script type="text/javascript"> $(document).ready(function() { var helpFunctions = $(".middle-column__help__fixed"); var leftColumnAffix = $(".left-column__fixed"); var middleColumn = $("#middle-column"); var clone = null; helpFunctions.affix({ offset: { top: function() { return middleColumn.offset().top - 8 - (Foundation.utils.is_medium_only() ? 30 : 0); }, bottom: function() { return $("#footer").innerHeight() + 74 + (Foundation.utils.is_medium_only() ? 0 : 0); } } }); if (leftColumnAffix.length > 0) { clone = leftColumnAffix.clone(); clone.addClass("left-column__fixed__affix"); clone.insertBefore(leftColumnAffix); clone.css('width', leftColumnAffix.outerWidth() + 50); clone.affix({ offset: { top: function() { return leftColumnAffix.offset().top - 30 - (Foundation.utils.is_medium_only() ? 50 : 0); }, bottom: function() { return $("#footer").innerHeight() + 92 + (Foundation.utils.is_medium_only() ? 0 : 0); } } }); } $(window).on("resize", function() { if (clone !== null) { clone.css('width', leftColumnAffix.outerWidth() + 50); } }); new 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