Applied Sciences | An Open Access Journal from MDPI

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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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class="journal__description"> <h1> <em>Applied Sciences</em> </h1> <div class="journal__description__content"> <em>Applied Sciences</em> is an international, <a href="https://www.mdpi.com/editorial_process">peer-reviewed</a>, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.<br /> <ul> <li><strong><span class="label openaccess"><a title="Open Access" href="https://www.mdpi.com/openaccess">Open Access</a></span></strong>— free for readers, with <a href="https://www.mdpi.com/journal/applsci/apc">article processing charges (APC)</a> paid by authors or their institutions.</li> <li><strong>High Visibility:</strong> indexed within <a href="https://www.scopus.com/sourceid/21100829268?origin=sbrowse#tabs=0">Scopus</a>, <a href="https://mjl.clarivate.com/search-results?issn=2076-3417&hide_exact_match_fl=true&utm_source=mjl&utm_medium=share-by-link&utm_campaign=search-results-share-this-journal">SCIE (Web of Science)</a>, <a 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receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.</li> <li><strong>Testimonials:</strong> <a href="https://www.mdpi.com/testimonials?type=all&journal_id=90&page_count=20">See what our authors say about <em>Applied Sciences</em></a>.</li> <li><strong>Companion journals for <em>Applied Sciences</em> include: </strong><em><a href="https://www.mdpi.com/journal/applnano">Applied Nano</a></em>, <a style="font-style: italic;" href="https://www.mdpi.com/journal/AppliedChem">AppliedChem</a>, <em><a href="https://www.mdpi.com/journal/applbiosci">Applied Biosciences</a>, </em><em><a href="https://www.mdpi.com/journal/virtualworlds">Virtual Worlds</a>,</em> <em><a href="https://www.mdpi.com/journal/spectroscj">Spectroscopy Journal</a></em> and <em><a href="https://www.mdpi.com/journal/jeta">JETA</a></em>.</li> </ul> </div> <div style="margin-bottom: 15px;"> <strong>Impact Factor:</strong> 2.5 (2023); 5-Year Impact Factor: 2.7 (2023) </div> <div> <a href="/journal/applsci/imprint" class="UI_JournalImprintsInfoButton"> <i class="material-icons spaced-link">subject</i> Imprint Information </a>    <a href="/journal/applsci/applsci_flyer.pdf" class="UD_JournalFlyer"> <i class="material-icons spaced-link">get_app</i> Journal Flyer </a>     <a class="oa-link" href="https://www.mdpi.com/about/openaccess"> <i class="material icons spaced-link"></i> Open Access </a>     <strong> ISSN: 2076-3417 </strong> </div> <div style="clear: both;"></div> </div> </div> </div> <div class="content__container content__container--overflow-initial"> <div class="custom-accordion-for-small-screen-link active"> <h2 class="no-padding-left">Latest Articles</h2> </div> <div class="custom-accordion-for-small-screen-content"> <div class="expanding-div collapsed"> <div class="generic-item article-item no-border"> <div class="article-content"> <div class="label right label__btn"> <a data-dropdown="drop-supplementary-1527514" aria-controls="drop-supplementary-1527514" aria-expanded="false" title="Supplementary Material"> <i class="material-icons">attachment</i> </a> <div id="drop-supplementary-1527514" class="f-dropdown label__btn__dropdown label__btn__dropdown--wide" data-dropdown-content aria-hidden="true" tabindex="-1"> Supplementary material: <br/> <a href="/2076-3417/14/23/10842/s1?version=1732294107"> Supplementary File 1 (ZIP, 16952 KiB) </a><br/> </div> </div> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 30 pages, 26115 KiB   </span> <a href="/2076-3417/14/23/10842/pdf?version=1732294106" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Application of Active Soil Gas Screening for the Identification of Groundwater Contamination with Chlorinated Hydrocarbons at an Industrial Area—A Case Study of the Former Refrigerator Manufacturer Calex (City of Zlaté Moravce, Western Slovakia)" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10842">Application of Active Soil Gas Screening for the Identification of Groundwater Contamination with Chlorinated Hydrocarbons at an Industrial Area—A Case Study of the Former Refrigerator Manufacturer Calex (City of Zlaté Moravce, Western Slovakia)</a> <div class="authors"> by <span class="inlineblock "><strong>Roman Tóth</strong>, </span><span class="inlineblock "><strong>Edgar Hiller</strong>, </span><span class="inlineblock "><strong>Veronika Špirová</strong>, </span><span class="inlineblock "><strong>Ľubomír Jurkovič</strong>, </span><span class="inlineblock "><strong>Ľubica Ševčíková</strong>, </span><span class="inlineblock "><strong>Juraj Macek</strong>, </span><span class="inlineblock "><strong>Claudia Čičáková</strong>, </span><span class="inlineblock "><strong>Tibor Kovács</strong> and </span><span class="inlineblock "><strong>Anton Auxt</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10842; <a href="https://doi.org/10.3390/app142310842">https://doi.org/10.3390/app142310842</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Background: Groundwater contamination with chlorinated hydrocarbons (CLHCs), particularly with tetrachloroethylene (PCE) and trichloroethylene (TCE), which are used in industry for degreasing and cleaning, can be considered a serious problem concerning the entire world. In addition to conventional groundwater monitoring from a network of <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10842/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Background: Groundwater contamination with chlorinated hydrocarbons (CLHCs), particularly with tetrachloroethylene (PCE) and trichloroethylene (TCE), which are used in industry for degreasing and cleaning, can be considered a serious problem concerning the entire world. In addition to conventional groundwater monitoring from a network of wells, several screening methods have been proposed to identify and delineate groundwater contamination with volatile organic compounds (VOCs), such as soil gas measurement, bioindicators, direct-push technologies or geophysical techniques. The main objectives of this study were to confirm the feasibility of active soil gas screening for the characterisation of groundwater contamination with CLHCs under the wider area of the former refrigerator manufacturer (city of Zlaté Moravce, western Slovakia) and to evaluate the human health risks through exposure to CLHCs present in groundwater. Methods: a conventional site investigation based on concentration measurements using gas chromatography-mass spectrometry from monitoring wells and soil gas measurements using a portable photo-ionisation detector device were applied. Results: The chemical analyses showed the persistent contamination of groundwater, with PCE, TCE and other CLHCs, such as <i>cis</i>-1,2-dichloroethylene (<i>cis</i>-DCE) or 1,1,2-trichloroethane (TCA), being most severe in the zone of the former factory (up to 2690, 83,900, 6020 and 156 µg/L for PCE, TCE, <i>cis</i>-DCE and TCA, respectively), but also extended into the residential zone located 600 m along the groundwater flow line. Soil gas measurements of VOCs and other chemical parameters (methane (CH<sub>4</sub>), total petroleum (TP), carbon dioxide (CO<sub>2</sub>) and oxygen (O<sub>2</sub>)) from a densely designed network of sampling points (<i>n</i> = 300) helped trace the current state of groundwater contamination. Spatial distribution maps of VOCs concentrations in soil gas clearly marked the areas of the highest CLHCs concentrations in groundwater. Principal component analysis (PCA) confirmed a significant correlation of VOCs and CLHCs with the first principal component, PC1, explaining up to 84% of the total variability of the concentration data, suggesting that VOCs in soil gas were a suitable marker of the extent of groundwater contamination with CLHCs. Despite severe groundwater contamination with CLHCs reaching residential areas, local residents were not exposed to non-carcinogenic risks, but a potential carcinogenic risk was present. Conclusions: based on the results, it could be confirmed that soil gas screening is an efficient and quick tool for identifying the sources of groundwater contamination with CLHCs as well as the level of this contamination. <a href="/2076-3417/14/23/10842">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/6R56P81WIP ">Latest Advances in Environmental Engineering: Approaches to the Management and Treatment of Water, Air and Waste</a>)<br/> </div> </div> </div> </div> <div class="extending-content content-ready"> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 21 pages, 1991 KiB   </span> <a href="/2076-3417/14/23/10841/pdf?version=1732292833" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Advanced SDR-Based Custom OFDM Protocol for Improved Data Rates in HF-NVIS Links" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10841">Advanced SDR-Based Custom OFDM Protocol for Improved Data Rates in HF-NVIS Links</a> <div class="authors"> by <span class="inlineblock "><strong>Emil Șorecău</strong>, </span><span class="inlineblock "><strong>Mirela Șorecău</strong> and </span><span class="inlineblock "><strong>Paul Bechet</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10841; <a href="https://doi.org/10.3390/app142310841">https://doi.org/10.3390/app142310841</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In the current context of global communications, HF (High Frequency) NVIS (Near Vertical Incidence Skywave) data networks can be of strategic importance, providing short- and medium-range communication capabilities independent of terrestrial configuration and existing conventional communications infrastructure. They are essential in critical conditions, <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10841/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In the current context of global communications, HF (High Frequency) NVIS (Near Vertical Incidence Skywave) data networks can be of strategic importance, providing short- and medium-range communication capabilities independent of terrestrial configuration and existing conventional communications infrastructure. They are essential in critical conditions, such as natural disasters or conflicts, when terrestrial networks are unavailable. This paper investigates the development of such systems for HF NVIS data communications by introducing a customized Orthogonal Frequency Division Multiplexing (OFDM) protocol with parameters adapted to HF ionospheric propagation, implemented on Software-Defined Radio (SDR) systems, which provide extensive configurability and high adaptability to varying HF channel conditions. This work presents an innovative approach to the application of OFDM narrow-channel aggregation in the HF spectrum, a technique that significantly enhances system performance. The aggregation enables a more efficient utilization of the available spectrum and an increase in the data transmission rate, which represents a substantial advancement in NVIS communications. The implementation was realized using an SDR system, which allows flexible integration of the new OFDM protocol and dynamic adaptation of resources. The work also includes the development of a messaging application capable of using this enhanced HF communication system, taking advantage of the new features of channel aggregation and SDR flexibility. This application demonstrates the applicability of the protocol in real-world scenarios and provides a robust platform for data transmission under conditions of limited access to other means of communication. Thus, this study contributes to the technological advancement of NVIS communications and opens new research and deployment directions in HF communications. <a href="/2076-3417/14/23/10841">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/9ZHBI145N8 ">Cognitive Radio: Trends, Methods, Applications and Challenges</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 14 pages, 839 KiB   </span> <a href="/2076-3417/14/23/10840/pdf?version=1732292644" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Evaluation of the Wachtel Healing Index and Its Correlation with Early Implantation Success or Failured at Two Months" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10840">Evaluation of the Wachtel Healing Index and Its Correlation with Early Implantation Success or Failured at Two Months</a> <div class="authors"> by <span class="inlineblock "><strong>María José Moya-Villaescusa</strong>, </span><span class="inlineblock "><strong>Arturo Sánchez-Pérez</strong>, </span><span class="inlineblock "><strong>Nerea Lara-Hernández</strong>, </span><span class="inlineblock "><strong>Alfonso Jornet-García</strong> and </span><span class="inlineblock "><strong>José María Montoya-Carralero</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10840; <a href="https://doi.org/10.3390/app142310840">https://doi.org/10.3390/app142310840</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Implants are increasingly used in dentistry. Nevertheless, several factors can cause treatment failure. To assess initial wound healing, various indices have been developed. The purpose of this study was to assess the relationship between early wound healing and implant success. Fifty single implants <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10840/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Implants are increasingly used in dentistry. Nevertheless, several factors can cause treatment failure. To assess initial wound healing, various indices have been developed. The purpose of this study was to assess the relationship between early wound healing and implant success. Fifty single implants (25 in women and 25 in men) were placed in patients meeting the inclusion criteria. The implants (Ticare<sup>®</sup>) were placed via the two-stage technique. Patients were assessed at 24 h, one week, one month, and two months post-implantation. At the final evaluation, implantation failure or success was recorded. Postoperative pain in these patients at one week after placement was assessed with a visual analogue scale (VAS), and the Wachtel soft tissue early healing index was used to measure healing. Two months after implant placement, the success rate was 92%. No statistically significant relationship was found between the Wachtel index and short-term implant success or failure. Additionally, there was no relationship between implant success or failure and variables such as smoking, diabetes status, age, sex, or guided bone regeneration (GBR). Neither the Wachtel index nor any of the other variables studied is a predictor of early implantation success. <a href="/2076-3417/14/23/10840">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/applsci/sections/Applied_Dentistry">Applied Dentistry and Oral Sciences</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 14 pages, 4355 KiB   </span> <a href="/2076-3417/14/23/10839/pdf?version=1732291991" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Computational Fluid Dynamics Analysis of Gas Suction in Coaxial Flow Venturi Injector: Impact of Gas–Liquid Interface Structure in Mixing Section" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10839">Computational Fluid Dynamics Analysis of Gas Suction in Coaxial Flow Venturi Injector: Impact of Gas–Liquid Interface Structure in Mixing Section</a> <div class="authors"> by <span class="inlineblock "><strong>Yihan Zu</strong>, </span><span class="inlineblock "><strong>Wenchen Zhong</strong>, </span><span class="inlineblock "><strong>Enle Xu</strong> and </span><span class="inlineblock "><strong>Zhenyong Miao</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10839; <a href="https://doi.org/10.3390/app142310839">https://doi.org/10.3390/app142310839</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The gas–liquid Venturi injector has been widely applied in industrial production due to its advantages of high entrainment and low energy consumption. In this study, Computational Fluid Dynamics (CFD) was employed to investigate the effect of the gas–liquid interface structure within the mixing <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10839/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The gas–liquid Venturi injector has been widely applied in industrial production due to its advantages of high entrainment and low energy consumption. In this study, Computational Fluid Dynamics (CFD) was employed to investigate the effect of the gas–liquid interface structure within the mixing section on entrainment behavior by varying the geometry of the mixing section during gas–liquid coaxial flow. The simulation results indicate that along the jet direction, the gas–liquid interface generally transitions from a smooth cylindrical shape to a lobed structure in the mixing section. Surface waves mainly appear in the lobed region. Furthermore, lobed and surface wave structures reduce pressure loss and enhance entrainment. Additionally, the study found that longer mixing sections enhance entrainment under low flow resistance. This study provides valuable insights for achieving high jet entrainment and offers supplementary research on gas–liquid interface structures in jets constrained by solid boundaries. <a href="/2076-3417/14/23/10839">Full article</a> </div> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 16 pages, 806 KiB   </span> <a href="/2076-3417/14/23/10838/pdf?version=1732289605" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="NMR Analysis of Pulegone in Food Products" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10838">NMR Analysis of Pulegone in Food Products</a> <div class="authors"> by <span class="inlineblock "><strong>Yifei Yu</strong>, </span><span class="inlineblock "><strong>Thomas Kuballa</strong> and </span><span class="inlineblock "><strong>Dirk W. Lachenmeier</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10838; <a href="https://doi.org/10.3390/app142310838">https://doi.org/10.3390/app142310838</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Pulegone is a monoterpene ketone found in a variety of mint species. It has been classified as possibly carcinogenic to humans (Group 2B) by the International Agency for Research on Cancer (IARC). In previous studies, pulegone in food was analyzed exclusively via GC-MS, <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10838/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Pulegone is a monoterpene ketone found in a variety of mint species. It has been classified as possibly carcinogenic to humans (Group 2B) by the International Agency for Research on Cancer (IARC). In previous studies, pulegone in food was analyzed exclusively via GC-MS, while <sup>1</sup>H NMR methods were limited to essential oils. The aim of this study was to develop an NMR method for the detection and quantification of pulegone in essential oils and foods. A mixture of methanol-<i>d</i>₄/chloroform-<i>d</i>₁ in a 1:1 ratio (<i>v/v</i>) was identified as the most effective solvent for separating pulegone signals. The essential oils were subjected to analysis at this solvent-mixture ratio. The extraction of pulegone was required for food analysis, and the steam distillation method proved to be more effective than the ultrasonic-assisted extraction method. The highest pulegone concentrations were identified in pennyroyal oil and muña oil, whereas lower levels were observed in other matrices, including corn mint oil and select food items. A toxicological assessment showed that the amount consumed did not exert any adverse effects on human health. <a href="/2076-3417/14/23/10838">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/3JFF323903 ">Recent Applications of Plant Extracts in the Food Industry</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 16 pages, 10466 KiB   </span> <a href="/2076-3417/14/23/10837/pdf?version=1732288139" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Hierarchical Residual Attention Network for Musical Instrument Recognition Using Scaled Multi-Spectrogram" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10837">Hierarchical Residual Attention Network for Musical Instrument Recognition Using Scaled Multi-Spectrogram</a> <div class="authors"> by <span class="inlineblock "><strong>Rujia Chen</strong>, </span><span class="inlineblock "><strong>Akbar Ghobakhlou</strong> and </span><span class="inlineblock "><strong>Ajit Narayanan</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10837; <a href="https://doi.org/10.3390/app142310837">https://doi.org/10.3390/app142310837</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Musical instrument recognition is a relatively unexplored area of machine learning due to the need to analyze complex spatial–temporal audio features. Traditional methods using individual spectrograms, like STFT, Log-Mel, and MFCC, often miss the full range of features. Here, we propose a hierarchical <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10837/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Musical instrument recognition is a relatively unexplored area of machine learning due to the need to analyze complex spatial–temporal audio features. Traditional methods using individual spectrograms, like STFT, Log-Mel, and MFCC, often miss the full range of features. Here, we propose a hierarchical residual attention network using a scaled combination of multiple spectrograms, including STFT, Log-Mel, MFCC, and CST features (Chroma, Spectral contrast, and Tonnetz), to create a comprehensive sound representation. This model enhances the focus on relevant spectrogram parts through attention mechanisms. Experimental results with the OpenMIC-2018 dataset show significant improvement in classification accuracy, especially with the “Magnified 1/4 Size” configuration. Future work will optimize CST feature scaling, explore advanced attention mechanisms, and apply the model to other audio tasks to assess its generalizability. <a href="/2076-3417/14/23/10837">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/VG6UL55U91 ">AI in Audio Analysis: Spectrogram-Based Recognition</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10837/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1527403"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1527403"><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="#next1527403" data-cycle-prev="#prev1527403" data-cycle-progressive="#images1527403" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1527403-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g001-550.jpg?1732288206" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1527403" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1527403-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g002-550.jpg?1732288208'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1527403-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g003-550.jpg?1732288210'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1527403-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g004-550.jpg?1732288212'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1527403-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g005-550.jpg?1732288215'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1527403-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g006-550.jpg?1732288224'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1527403-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g007-550.jpg?1732288232'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1527403-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g008-550.jpg?1732288240'><p>Figure 8</p></div></script></div></div><div id="article-1527403-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g001-550.jpg?1732288206" title=" <strong>Figure 1</strong><br/> <p>(<b>a</b>) From top to bottom: Log-Mel Spectrogram shows frequency (Hz) over time, where brighter regions represent higher intensity in decibels (dB). Chroma Features capture pitch class profiles (C, C#, D, D#, etc.), with “#” indicating a sharp note in musical notation. Spectral Contrast represents the difference in intensity between spectral peaks and valleys across frequency bands (e.g., 6400+ Hz to 200–400 Hz), providing complementary information to the Log-Mel Spectrogram. Tonnetz encodes harmonic relationships such as tonic, minor third, major third, perfect fifth, minor seventh, and major seventh, which represent musical intervals over time. (<b>b</b>) Combined Spectrogram Features (all same size), (<b>c</b>) Combined Spectrogram Features (CST—1/4 Size), (<b>d</b>) Combined Spectrogram Features (CST—Half Size), and (<b>e</b>) Combined Spectrogram Features (CST—3/4 Size) illustrate normalized feature values scaled between 0 and 1, where brighter colors indicate values closer to 1, and darker colors indicate values closer to 0. This normalization ensures consistent visualization despite differences in the original scales (e.g., frequency reaching 8192 Hz in Log-Mel, compared to the Tonnetz scale with a maximum value of 6 (Major Seventh)).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10837'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g002-550.jpg?1732288208" title=" <strong>Figure 2</strong><br/> <p>Overview of the neural network architecture used for musical instrument classification. The input is an example sample of combined spectrogram features (CST) with all components scaled to the same size and normalized to a range between 0 and 1, where brighter colors indicate values closer to 1. The model includes three residual blocks (Res-Block-1, Res-Block-2, Res-Block-3), shaded in blue and dark blue represent operations such as convolution, batch normalization, and activation functions. Each residual block is followed by a coordinate attention mechanism (Early Attention, Mid Attention, Late Attention) to enhance feature representation. After the three residual blocks and attention layers, the final fully connected (FC) layer performs the classification to generate the output.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10837'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g003-550.jpg?1732288210" title=" <strong>Figure 3</strong><br/> <p>Mean average precision (mAP) comparison across various methods on the Open-MIC dataset [<a href="#B31-applsci-14-10837" class="html-bibr">31</a>,<a href="#B32-applsci-14-10837" class="html-bibr">32</a>,<a href="#B33-applsci-14-10837" class="html-bibr">33</a>,<a href="#B34-applsci-14-10837" class="html-bibr">34</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10837'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g004-550.jpg?1732288212" title=" <strong>Figure 4</strong><br/> <p>Precision, recall, and F1 score comparisons for different spectrogram scaled sizes.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10837'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g005-550.jpg?1732288215" title=" <strong>Figure 5</strong><br/> <p>Multilabel confusion matrices for the best-performing model on each instrument. The rows represent the ground truth labels, while the columns indicate the predicted classifications. The matrices display true positives, true negatives, false positives, and false negatives for every instrument classification. Darker shades represent higher values, illustrating areas of strong classification accuracy and confusion points between certain instruments.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10837'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g006-550.jpg?1732288224" title=" <strong>Figure 6</strong><br/> <p>Early feature and attention maps for a trumpet and bass sample showing the initial layer outputs of the Log-Mel and scaled CST (Chroma, Spectral contrast, and Tonnetz) features. The maps highlight the fundamental frequency components and outline basic musical structures through a combination of Log-Mel’s high-resolution representation and the CST features scaled to 1/4 of the Log-Mel size. The grayscale shades represent values in the feature maps and attention maps, where darker shades indicate lower values, and lighter shades indicate higher values, providing a visual representation of the extracted features.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10837'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g007-550.jpg?1732288232" title=" <strong>Figure 7</strong><br/> <p>Mid-level feature and attention maps for a trumpet and bass sample, capturing intermediate representations with heightened detail through the combination of Log-Mel spectrogram and CST features at 1/4 scale. The mid layers reveal more refined frequency and harmonic structures, enhancing the model’s ability to distinguish between complex timbral characteristics. The grayscale shades represent values in the feature maps and attention maps, where darker shades indicate lower values, and lighter shades indicate higher values, providing a visual representation of the extracted features.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10837'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10837/article_deploy/html/images/applsci-14-10837-g008-550.jpg?1732288240" title=" <strong>Figure 8</strong><br/> <p>Late feature and attention maps for a trumpet and bass sample, illustrating high-level abstracted representations formed by deeper layers. The Log-Mel spectrogram and scaled CST features at 1/4 size contribute to an enriched final classification layer, enhancing the recognition of instrument-specific patterns through focused, high-contrast attention maps. The grayscale shades represent values in the feature maps and attention maps, where darker shades indicate lower values, and lighter shades indicate higher values, providing a visual representation of the extracted features.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10837'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 19 pages, 1147 KiB   </span> <a href="/2076-3417/14/23/10836/pdf?version=1732287542" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Prediction of Corrosion Rate for Carbon Steel Using Regression Model with Commercial LPR Sensor Data" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10836">Prediction of Corrosion Rate for Carbon Steel Using Regression Model with Commercial LPR Sensor Data</a> <div class="authors"> by <span class="inlineblock "><strong>Kwang-Hu Jung</strong> and </span><span class="inlineblock "><strong>Jung-Hyung Lee</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10836; <a href="https://doi.org/10.3390/app142310836">https://doi.org/10.3390/app142310836</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In this study, a model was proposed to predict the corrosion rate (Mils per Year, MPY) of carbon steel in a 3.5% NaCl solution, with the objective of comparing the effectiveness of a commercial LPR sensor against traditional electrochemical methods, using potentiostat-based LPR <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10836/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In this study, a model was proposed to predict the corrosion rate (Mils per Year, MPY) of carbon steel in a 3.5% NaCl solution, with the objective of comparing the effectiveness of a commercial LPR sensor against traditional electrochemical methods, using potentiostat-based LPR techniques. The primary factors considered in the experiments were temperature, flow velocity, and pH, tested through a full factorial design to identify the most influential variables. Statistical analysis showed that temperature and flow velocity had a significant effect on corrosion rate, with their interaction having the most substantial impact. In contrast, pH had no statistically significant influence within the tested conditions, likely due to the dominant effects of temperature and flow velocity in the high-salinity environment. The MPY data were validated through Tafel plots, immersion coupon tests, and other electrochemical techniques to confirm the reliability of the measurements. A regression model trained on 54 MPY data points demonstrated high accuracy, achieving a coefficient of determination (R<sup>2</sup>) of 0.9733. The model also provided reliable predictions for factor combinations excluded from the training dataset. Additionally, scenario-based evaluations highlighted the model’s performance under simulated operating conditions, while revealing challenges related to sensor contamination during long-term use. These findings emphasize the potential of commercial LPR sensors as effective tools for real-time corrosion monitoring and demonstrate the utility of the regression model in marine environments. <a href="/2076-3417/14/23/10836">Full article</a> </div> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 27 pages, 1340 KiB   </span> <a href="/2076-3417/14/23/10835/pdf?version=1732287713" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Fusing Machine Learning and AI to Create a Framework for Employee Well-Being in the Era of Industry 5.0" 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 articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10835">Fusing Machine Learning and AI to Create a Framework for Employee Well-Being in the Era of Industry 5.0</a> <div class="authors"> by <span class="inlineblock "><strong>Cosmina-Mihaela Rosca</strong> and </span><span class="inlineblock "><strong>Adrian Stancu</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10835; <a href="https://doi.org/10.3390/app142310835">https://doi.org/10.3390/app142310835</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Employees are the most valuable resources in any company, and their well-being directly influences work productivity. This research investigates integrating health parameters and sentiment analysis expressed in sent messages to enhance employee well-being within organizations in the context of Industry 5.0. Our primary <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10835/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Employees are the most valuable resources in any company, and their well-being directly influences work productivity. This research investigates integrating health parameters and sentiment analysis expressed in sent messages to enhance employee well-being within organizations in the context of Industry 5.0. Our primary aim is to develop a Well-Being Index (WBI) that quantifies employee health through various physiological and psychological parameters. A new methodology combining data collection from wearable devices from 1 January 2023 to 18 October 2024 and advanced text analytics was employed to achieve the WBI. This study uses the LbfgsMaximumEntropy ML classification algorithm to construct the Well-Being Model (WBM) and Azure Text Analytics for sentiment evaluation to assess negative messages among employees. The findings reveal a correlation between physiological metrics and self-reported well-being, highlighting the utility of the WBI in identifying areas of concern within employee behavior. We propose that the employee global indicator (EGI) is calculated based on the WBI and the dissatisfaction score component (DSC) to measure the overall state of mind of employees. The WBM exhibited a <i>MacroAccuracy</i> of 91.81% and a <i>MicroAccuracy</i> of 95.95% after 384 configurations were analyzed. Azure Text Analytics evaluated 2000 text messages, resulting in a <i>Precision</i> of 99.59% and an <i>Accuracy</i> of 99.7%. In this case, the <i>Recall</i> was 99.89% and <i>F<sub>1</sub>-score</i> was 99.73%. In the Industry 5.0 environment, which focuses on the employee, a new protocol, the Employee KPI Algorithm (EKA), is integrated to prevent and identify employee stress. This study underscores the synergy between quantitative health metrics and qualitative sentiment analysis, offering organizations a framework to address employee needs proactively. <a href="/2076-3417/14/23/10835">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/X0VY7V3GVS ">AI Horizons: Present Status and Visions for the Next Era</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 18 pages, 11053 KiB   </span> <a href="/2076-3417/14/23/10834/pdf?version=1732287063" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Enhanced Wide-Area Glacier Velocity Monitoring in Svalbard via Synthetic Aperture Radar Offset Tracking Noise Suppression" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10834">Enhanced Wide-Area Glacier Velocity Monitoring in Svalbard via Synthetic Aperture Radar Offset Tracking Noise Suppression</a> <div class="authors"> by <span class="inlineblock "><strong>Honglei Yang</strong>, </span><span class="inlineblock "><strong>Songxue Zhao</strong>, </span><span class="inlineblock "><strong>Zeping Wang</strong>, </span><span class="inlineblock "><strong>Ao Yan</strong> and </span><span class="inlineblock "><strong>Zhenhan Shi</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10834; <a href="https://doi.org/10.3390/app142310834">https://doi.org/10.3390/app142310834</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Glacier movement is an important indicator of climate change, reflecting the quality and state changes in glacier migration and mass balance in the context of global warming. Although accurately estimating glacier surface flow velocity is crucial for various applications, achieving this is challenging <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10834/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Glacier movement is an important indicator of climate change, reflecting the quality and state changes in glacier migration and mass balance in the context of global warming. Although accurately estimating glacier surface flow velocity is crucial for various applications, achieving this is challenging due to factors such as low temporal correlation and high noise effects. This paper presents the pixel offset tracking (POT) technology based on Synthetic Aperture Radar (SAR) data for glacier velocity monitoring, with enhanced cross-correlation matching window and noise suppression approaches. In particular, a noise suppression optimization method and a matching window optimization index suitable for wide-area glacier velocity monitoring are proposed. The inter-annual wide-area two-dimensional plane flow velocity of glaciers in the Svalbard archipelago was obtained by using a total of seven Sentinel-1 data sets from two orbits covering the entire Svalbard archipelago in 2021. The results indicate that 25 large glaciers in Svalbard destabilized in 2021, with a peak flow velocity of 6.18 m/day. At the same time, the influence of climate, topography, and other factors on glacier surface velocity is discussed. The wide-area glacier velocity monitoring method and its application demonstrated in this paper will serve as a valuable reference for studying glacier migration in the Arctic Svalbard archipelago and for other large-scale wide-area deformation monitoring efforts. <a href="/2076-3417/14/23/10834">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/8QC079LM10 ">Latest Advances in Radar Remote Sensing Technologies</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 17 pages, 2390 KiB   </span> <a href="/2076-3417/14/23/10833/pdf?version=1732285429" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="An Attempt to Establish a Mathematical Model for an Unconventional Worm Gear with Bearings" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10833">An Attempt to Establish a Mathematical Model for an Unconventional Worm Gear with Bearings</a> <div class="authors"> by <span class="inlineblock "><strong>Simion Haragâș</strong>, </span><span class="inlineblock "><strong>Roland Ninacs</strong>, </span><span class="inlineblock "><strong>Ovidiu Buiga</strong>, </span><span class="inlineblock "><strong>Lucian Tudose</strong>, </span><span class="inlineblock "><strong>Alexandru Haragâș</strong>, </span><span class="inlineblock "><strong>Ioana Monica Sas-Boca</strong> and </span><span class="inlineblock "><strong>Felicia Aurora Cristea</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10833; <a href="https://doi.org/10.3390/app142310833">https://doi.org/10.3390/app142310833</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The aim of this paper is to develop a mathematical model for an unconventional worm gear consisting of a globoid worm and a worm wheel where the teeth are bearings. Using rolling elements such the teeth of the worm wheel (ball bearings) transforms <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10833/more" onclick="$(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 develop a mathematical model for an unconventional worm gear consisting of a globoid worm and a worm wheel where the teeth are bearings. Using rolling elements such the teeth of the worm wheel (ball bearings) transforms the sliding friction to rolling friction during the process of worm gear meshing, improving power. The geometry of the component elements of the gear is analyzed in correlation with its kinematics. After the creation of the mathematical model, it is validated both analytically (through complex graphic representations) and experimentally (by creating, for a particular case, the 3D model and the concrete physical model (prototype) of the gear through 3D printing). <a href="/2076-3417/14/23/10833">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/P7NC7924OX ">Machine Tools, Advanced Manufacturing and Precision Manufacturing</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 4989 KiB   </span> <a href="/2076-3417/14/23/10832/pdf?version=1732285944" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10832">End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms</a> <div class="authors"> by <span class="inlineblock "><strong>Jeongho Kim</strong>, </span><span class="inlineblock "><strong>Joonho Seon</strong>, </span><span class="inlineblock "><strong>Soohyun Kim</strong>, </span><span class="inlineblock "><strong>Seongwoo Lee</strong>, </span><span class="inlineblock "><strong>Jinwook Kim</strong>, </span><span class="inlineblock "><strong>Byungsun Hwang</strong>, </span><span class="inlineblock "><strong>Youngghyu Sun</strong> and </span><span class="inlineblock "><strong>Jinyoung Kim</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10832; <a href="https://doi.org/10.3390/app142310832">https://doi.org/10.3390/app142310832</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> An unmanned aerial vehicle (UAV) swarm has emerged as a powerful tool for mission execution in a variety of applications supported by deep neural networks (DNNs). In the context of UAV swarms, conventional methods for efficient data processing involve transmitting data to cloud <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10832/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> An unmanned aerial vehicle (UAV) swarm has emerged as a powerful tool for mission execution in a variety of applications supported by deep neural networks (DNNs). In the context of UAV swarms, conventional methods for efficient data processing involve transmitting data to cloud and edge servers. However, these methods often face limitations in adapting to real-time applications due to the low latency of cloud-based approaches and weak mobility of edge-based approaches. In this paper, a new system called deep reinforcement learning-based resilient layer distribution (DRL-RLD) for distributed inference is designed to minimize end-to-end latency in UAV swarm, considering the resource constraints of UAVs. The proposed system dynamically allocates CNN layers based on UAV-to-UAV and UAV-to-ground communication links to minimize end-to-end latency. It can also enhance resilience to maintain mission continuity by reallocating layers when inoperable UAVs occur. The performance of the proposed system was verified through simulations in terms of latency compared to the comparison baselines, and its robustness was demonstrated in the presence of inoperable UAVs. <a href="/2076-3417/14/23/10832">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/UM8TUDD1R3 ">Novel Advances in Internet of Vehicles</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 29 pages, 9575 KiB   </span> <a href="/2076-3417/14/23/10831/pdf?version=1732284400" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Design and Multi-Objective Optimization of Auxetic Sandwich Panels for Blastworthy Structures Using Machine Learning Method" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10831">Design and Multi-Objective Optimization of Auxetic Sandwich Panels for Blastworthy Structures Using Machine Learning Method</a> <div class="authors"> by <span class="inlineblock "><strong>Andika</strong>, </span><span class="inlineblock "><strong>Sigit Puji Santosa</strong>, </span><span class="inlineblock "><strong>Djarot Widagdo</strong> and </span><span class="inlineblock "><strong>Arief Nur Pratomo</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10831; <a href="https://doi.org/10.3390/app142310831">https://doi.org/10.3390/app142310831</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The design and multi-objective optimization of auxetic sandwich panels (ASPs) are performed to enhance the blastworthiness of armored fighting vehicles (AFVs). Various metastructures in the form of four auxetic geometries are proposed as the sandwich core: re-entrant honeycomb (REH), double-arrow honeycomb (DAH), star <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10831/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The design and multi-objective optimization of auxetic sandwich panels (ASPs) are performed to enhance the blastworthiness of armored fighting vehicles (AFVs). Various metastructures in the form of four auxetic geometries are proposed as the sandwich core: re-entrant honeycomb (REH), double-arrow honeycomb (DAH), star honeycomb (SH), and tetra-chiral honeycomb (CH). This paper employs a combination of finite element and machine learning methodologies to evaluate blastworthiness performance. Optimization is carried out using the nondominated sorting genetic algorithm II (NSGA-II) method. The optimization results show significant improvements in blastworthiness performance, with notable reductions in permanent displacement and enhancements in specific energy absorption (SEA). Global sensitivity analysis using SHapley Additive exPlanations (SHAP) reveals that cell thickness is the most critical factor affecting blastworthiness performance, followed by the number of cells and corner angle or radius for CH. The application of optimized ASP on AFVs shows promising results, with no failure occurring in the occupant floor. Furthermore, AFVs equipped with the optimized ASP DAH significantly reduce maximum displacement and acceleration by 39.00% and 43.56%, respectively, and enhance SEA by 48.30% compared to optimized aluminum foam sandwich panels. This study concludes that ASPs have potential applications in broader engineering fields. <a href="/2076-3417/14/23/10831">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/2L70GY60R5 ">Structural Dynamics and Protective Materials</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 23 pages, 1514 KiB   </span> <a href="/2076-3417/14/23/10830/pdf?version=1732283243" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10830">Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG</a> <div class="authors"> by <span class="inlineblock "><strong>Adhe Rahmatullah Sugiharto Suwito P</strong>, </span><span class="inlineblock "><strong>Ayumi Ohnishi</strong>, </span><span class="inlineblock "><strong>Yudith Dian Prawitri</strong>, </span><span class="inlineblock "><strong>Riries Rulaningtyas</strong>, </span><span class="inlineblock "><strong>Tsutomu Terada</strong> and </span><span class="inlineblock "><strong>Masahiko Tsukamoto</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10830; <a href="https://doi.org/10.3390/app142310830">https://doi.org/10.3390/app142310830</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Subjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of impairment-level recognition in several studies. This study continues our previous work on the use of EMG to recognize finger movement impairment levels. In contrast to our <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10830/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Subjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of impairment-level recognition in several studies. This study continues our previous work on the use of EMG to recognize finger movement impairment levels. In contrast to our previous work, this study provided a better and more reliable recognition result with improved experimental settings, such as an increased sampling frequency, EMG channels, and extensive patient data. This study employed two data processing mechanisms, inter-subject cross-validation (ISCV) and data-scaled inter-subject cross-validation (DS-ISCV), resulting in two evaluation methods. The machine learning algorithms employed in this study were SVM, random forest (RF), and multi-layer perceptron (MLP). MLP_ISCV achieved the highest average recall score of 0.73 across impairment levels in the spherical grasp task. Subsequently, the highest average recall score of 0.72 among non-majority classes was achieved by SVM_DS-ISCV in the mass extension task. The cross-validation result shows that the proposed method effectively handled the imbalanced dataset without being biased toward the majority class. The proposed method demonstrated the potential to assist doctors in clarifying the subjective assessment of finger movement impairment levels. <a href="/2076-3417/14/23/10830">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/N78HQ3KUE7 ">Human Biomechanics and EMG Signal Processing</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 9384 KiB   </span> <a href="/2076-3417/14/23/10829/pdf?version=1732284368" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks 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 articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10829">BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection</a> <div class="authors"> by <span class="inlineblock "><strong>Long Guo</strong>, </span><span class="inlineblock "><strong>Lubin Wang</strong>, </span><span class="inlineblock "><strong>Qiang Yu</strong> and </span><span class="inlineblock "><strong>Xiaolan Xie</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10829; <a href="https://doi.org/10.3390/app142310829">https://doi.org/10.3390/app142310829</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In the field of bookbinding, accurately and efficiently detecting signature sequences during the binding process is crucial for enhancing quality, improving production efficiency, and advancing industrial automation. Despite significant advancements in object detection technology, verifying the correctness of signature sequences remains challenging due <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10829/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In the field of bookbinding, accurately and efficiently detecting signature sequences during the binding process is crucial for enhancing quality, improving production efficiency, and advancing industrial automation. Despite significant advancements in object detection technology, verifying the correctness of signature sequences remains challenging due to the small size, dense distribution, and abundance of low-quality signature marks. To tackle these challenges, we introduce the Book Signature Marks Detection (BSMD-YOLOv8) model, specifically designed for scenarios involving small, closely spaced objects such as signature marks. Our proposed backbone, the Lightweight Multi-scale Residual Network (LMRNet), achieves a lightweight network while enhancing the accuracy of small object detection. To address the issue of insufficient fusion of local and global feature information in PANet, we design the Low-stage gather-and-distribute (Low-GD) module and the High-stage gather-and-distribute (High-GD) module to enhance the model’s multi-scale feature fusion capabilities, thereby refining the integration of local and global features of signature marks. Furthermore, we introduce Wise-IoU (WIoU) as a replacement for CIoU, prioritizing anchor boxes with moderate quality and mitigating harmful gradients from low-quality examples. Experimental results demonstrate that, compared to YOLOv8n, BSMD-YOLOv8 reduces the number of parameters by 65%, increases the frame rate by 7 FPS, and enhances accuracy, recall, and mAP50 by 2.2%, 8.6%, and 3.9% respectively, achieving rapid and accurate detection of signature marks. <a href="/2076-3417/14/23/10829">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10829/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1527289"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1527289"><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="#next1527289" data-cycle-prev="#prev1527289" data-cycle-progressive="#images1527289" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1527289-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g001-550.jpg?1732284445" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1527289" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1527289-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g002-550.jpg?1732284447'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1527289-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g003-550.jpg?1732284447'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1527289-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g004-550.jpg?1732284448'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1527289-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g005-550.jpg?1732284449'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1527289-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g006-550.jpg?1732284450'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1527289-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g007-550.jpg?1732284451'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1527289-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g008-550.jpg?1732284451'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1527289-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g009-550.jpg?1732284452'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1527289-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g010-550.jpg?1732284457'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1527289-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g011-550.jpg?1732284458'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1527289-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g012-550.jpg?1732284460'><p>Figure 12</p></div></script></div></div><div id="article-1527289-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g001-550.jpg?1732284445" title=" <strong>Figure 1</strong><br/> <p>Examples of mismatched detections and accurate detections.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g002-550.jpg?1732284447" title=" <strong>Figure 2</strong><br/> <p>Examples of the dataset: (<b>a</b>) before data augmentation; (<b>b</b>) after data augmentation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g003-550.jpg?1732284447" title=" <strong>Figure 3</strong><br/> <p>Analysis results of the dataset: (<b>a</b>) Information regarding the manual annotation process for objects in the dataset; (<b>b</b>) Low-quality signature mark examples (The areas circled in red).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g004-550.jpg?1732284448" title=" <strong>Figure 4</strong><br/> <p>BSMD-YOLOv8 network structure diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g005-550.jpg?1732284449" title=" <strong>Figure 5</strong><br/> <p>Different backbone network designs: (<b>a</b>) CSP-Darknet53; (<b>b</b>) LMRNet.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g006-550.jpg?1732284450" title=" <strong>Figure 6</strong><br/> <p>Multi-scale residual convolution module structure diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g007-550.jpg?1732284451" title=" <strong>Figure 7</strong><br/> <p>PANet and Improved PANet structure diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g008-550.jpg?1732284451" title=" <strong>Figure 8</strong><br/> <p>Low-stage gather-and-distribute module structure diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g009-550.jpg?1732284452" title=" <strong>Figure 9</strong><br/> <p>High-stage gather-and-distribute module structure diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g010-550.jpg?1732284457" title=" <strong>Figure 10</strong><br/> <p>Comparison of actual test results: (<b>a</b>) original image; (<b>b</b>) inference results for YOLOv8n; (<b>c</b>) inference results for Improved-YOLOv5s; (<b>d</b>) inference results for BSMD-YOLOv8.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g011-550.jpg?1732284458" title=" <strong>Figure 11</strong><br/> <p>Training progress plot comparing ablation experiments based on mAP50 and Recall: (<b>a</b>) mAP50 vs. Epochs; (<b>b</b>) Recall vs. Epochs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10829/article_deploy/html/images/applsci-14-10829-g012-550.jpg?1732284460" title=" <strong>Figure 12</strong><br/> <p>Comparison of detection results between YOLOv8_L_I and BSMD-YOLOv8: (<b>a</b>) YOLOv8_L_I (using CIoU); (<b>b</b>) BSMD-YOLOv8 (using WIoU).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10829'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 28 pages, 3058 KiB   </span> <a href="/2076-3417/14/23/10828/pdf?version=1732282537" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10828">Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values</a> <div class="authors"> by <span class="inlineblock "><strong>Gabriela Bastos Porsani</strong>, </span><span class="inlineblock "><strong>Juan Bautista Echeverría Trueba</strong> and </span><span class="inlineblock "><strong>Carlos Fernández Bandera</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10828; <a href="https://doi.org/10.3390/app142310828">https://doi.org/10.3390/app142310828</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Model calibration refines design-stage inputs to align with real-world building performance. Accurate parameter selection, especially for highly sensitive variables like air leakage, is crucial. This study compared two building energy model calibration methods. The “classic” method adjusted indoor air capacitance, internal mass, and <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10828/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Model calibration refines design-stage inputs to align with real-world building performance. Accurate parameter selection, especially for highly sensitive variables like air leakage, is crucial. This study compared two building energy model calibration methods. The “classic” method adjusted indoor air capacitance, internal mass, and air infiltration, while a novel method focused on capacitance and internal mass, using empirical data for infiltration. The infiltration values were calculated using the decay equation and the EnergyPlus equations with site-specific coefficients. A triple validation assessed model performance in terms of temperature (CIBSE TM63), energy consumption (minimization), and indoor air quality (represented by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>CO</mi><mn>2</mn></msub></semantics></math></inline-formula> levels in accordance with the ASTM D5157 Standard). Results demonstrated the novel method’s superiority across all three performance metrics. All calibrated models met the CIBSE TM63 criteria even during the validation period, which was five times longer than the training period. Compared to the classic method, models incorporating dynamic empirical infiltration showed a 29% and 26% improvement in MAE and RMSE, respectively, in temperature prediction. In energy consumption results, the novel method models presented a 31% reduction, and for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>CO</mi><mn>2</mn></msub></semantics></math></inline-formula> level agreement, these models achieved a 130% higher R² value than the classic model. In addition, the classic method’s infiltration values failed to meet ASTM D5157 requirements, suggesting reliance on unrealistic parameter values for accurate temperature representation. The incorporation of calculated air leakage data into the BEM allowed a more realistic estimation of capacitance and internal mass values, emphasizing the importance of accurate air infiltration modeling for overall model reliability. <a href="/2076-3417/14/23/10828">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/RU4XJ8C683 ">Energy Efficiency and Thermal Comfort in Buildings</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <a data-dropdown="drop-supplementary-1527220" aria-controls="drop-supplementary-1527220" aria-expanded="false" title="Supplementary Material"> <i class="material-icons">attachment</i> </a> <div id="drop-supplementary-1527220" class="f-dropdown label__btn__dropdown label__btn__dropdown--wide" data-dropdown-content aria-hidden="true" tabindex="-1"> Supplementary material: <br/> <a href="/2076-3417/14/23/10827/s1?version=1732281724"> Supplementary File 1 (ZIP, 2073 KiB) </a><br/> </div> </div> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 21 pages, 9713 KiB   </span> <a href="/2076-3417/14/23/10827/pdf?version=1732281723" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="CFD Study of the Impact of an Electrical Power Transformer on a Historical Building: Assessment and Solutions" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10827">CFD Study of the Impact of an Electrical Power Transformer on a Historical Building: Assessment and Solutions</a> <div class="authors"> by <span class="inlineblock "><strong>Fabio Nardecchia</strong>, </span><span class="inlineblock "><strong>Luca Gugliermetti</strong>, </span><span class="inlineblock "><strong>Laura Pompei</strong> and </span><span class="inlineblock "><strong>Federico Cinquepalmi</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10827; <a href="https://doi.org/10.3390/app142310827">https://doi.org/10.3390/app142310827</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Historical building reuse is aimed at preservation, where buildings are recovered for new uses connected to cultural activities. This paper presents the analysis of the impact of thermo-fluid dynamics due to a 500 kW electrical power transformer installed inside a historical building. The <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10827/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Historical building reuse is aimed at preservation, where buildings are recovered for new uses connected to cultural activities. This paper presents the analysis of the impact of thermo-fluid dynamics due to a 500 kW electrical power transformer installed inside a historical building. The analysis is performed using computational fluid dynamics simulations validated through measurement campaigns carried out during the summer period. High temperatures and wide humidity variations can damage building plasters and cause malfunctions in power equipment. To avoid these situations, two different installation layouts were studied. One consists of the power transformer directly installed in the environment and cooled by an inlet fan, and the other consists of the power transformer being insulated from the external environment by an enclosure connected to a forced ventilation system. The second layout showed better results both inside and outside the transformer enclosure. The maximum indoor condition was about 4.3 °C, with a −7.2% RH and an airflow rate of 1100 m<sup>3</sup>/h, and the maximum outdoor air condition was 3.3 °C, with a −1.39% RH and a flow rate of 2200 m<sup>3</sup>/h. However, the temperatures and humidity inside the building and outside the transformer enclosure were almost the same. <a href="/2076-3417/14/23/10827">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/RU4XJ8C683 ">Energy Efficiency and Thermal Comfort in Buildings</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10827/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1527220"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1527220"><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="#next1527220" data-cycle-prev="#prev1527220" data-cycle-progressive="#images1527220" 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src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g003-550.jpg?1732281809'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g004-550.jpg?1732281811'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g005-550.jpg?1732281812'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g006-550.jpg?1732281814'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g007-550.jpg?1732281815'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g008-550.jpg?1732281817'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g009-550.jpg?1732281818'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g010-550.jpg?1732281820'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g011-550.jpg?1732281823'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g012-550.jpg?1732281824'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g013-550.jpg?1732281826'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g014-550.jpg?1732281827'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g015-550.jpg?1732281829'><p>Figure 15</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g016-550.jpg?1732281830'><p>Figure 16</p></div> --- <div class='openpopupgallery' data-imgindex='16' data-target='article-1527220-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g017-550.jpg?1732281832'><p>Figure 17</p></div></script></div></div><div id="article-1527220-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g001-550.jpg?1732281806" title=" <strong>Figure 1</strong><br/> <p>Museéto architectural plan with three sections at the beginning, middle and end of the room.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g002-550.jpg?1732281808" title=" <strong>Figure 2</strong><br/> <p>Environmental data (Temperature [°C], Relative Humidity [%RH] and Dew Point [°C]) collected by the weather station during the period from 24 July 2020 to 15 September 2020.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g003-550.jpg?1732281809" title=" <strong>Figure 3</strong><br/> <p>Solar radiation [W/m<sup>2</sup>] measured by the weather station during the period from 24 July 2020 to 15 September 2020.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g004-550.jpg?1732281811" title=" <strong>Figure 4</strong><br/> <p>(<b>a</b>) Wind Rose chart with principal wind components (in [km/h]) measured by the weather station during the period from 24 July 2020 to 15 September 2020; (<b>b</b>) Average wind speed components (in [km/h]) measured by the weather station during the period from 24 July 2020 to 15 September 2020.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g005-550.jpg?1732281812" title=" <strong>Figure 5</strong><br/> <p>Domain mesh and sensor locations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g006-550.jpg?1732281814" title=" <strong>Figure 6</strong><br/> <p>SENSOR 66: comparisons of temperature (<b>left</b>) and relative humidity (<b>right</b>) measured by the sensor, simulated, and registered outside the building.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g007-550.jpg?1732281815" title=" <strong>Figure 7</strong><br/> <p>SENSOR 72: comparisons of temperature (<b>left</b>) and relative humidity (<b>right</b>) measured by the sensor, simulated, and registered outside the building.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g008-550.jpg?1732281817" title=" <strong>Figure 8</strong><br/> <p>SENSOR 84: comparison of temperature (<b>left</b>) and relative humidity (<b>right</b>) measured by the sensor, simulated, and registered outside the building.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g009-550.jpg?1732281818" title=" <strong>Figure 9</strong><br/> <p>Temperature (<b>left</b>) and relative humidity (<b>right</b>) results on the longitudinal section.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g010-550.jpg?1732281820" title=" <strong>Figure 10</strong><br/> <p>(<b>a</b>) Technical system of air extraction in layout one (open transformer enclosure); (<b>b</b>) layout of the open transformer enclosure.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g011-550.jpg?1732281823" title=" <strong>Figure 11</strong><br/> <p>(<b>a</b>) Layout of the open transformer enclosure; (<b>b</b>) layout of the confined transformer enclosure.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g012-550.jpg?1732281824" title=" <strong>Figure 12</strong><br/> <p>Simulation O-1, Sensor 66: temperature (<b>left</b>) and relative humidity (<b>right</b>) results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g013-550.jpg?1732281826" title=" <strong>Figure 13</strong><br/> <p>Simulation O-1: Temperature and relative humidity fields on the lateral plane.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g014-550.jpg?1732281827" title=" <strong>Figure 14</strong><br/> <p>Simulation O-2, Sensor 66: temperature (<b>left</b>) and relative humidity (<b>right</b>) results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g015-550.jpg?1732281829" title=" <strong>Figure 15</strong><br/> <p>Simulation O-2: Temperature (<b>left</b>) and relative humidity (<b>right</b>) fields on the longitudinal plane.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g016-550.jpg?1732281830" title=" <strong>Figure 16</strong><br/> <p>Simulation C-1: Temperature (<b>left</b>) and relative humidity (<b>right</b>) fields on the longitudinal plan.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10827/article_deploy/html/images/applsci-14-10827-g017-550.jpg?1732281832" title=" <strong>Figure 17</strong><br/> <p>Simulation C-4: Temperature (<b>left</b>) and relative humidity (<b>right</b>) fields on the longitudinal plan.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10827'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 16 pages, 8754 KiB   </span> <a href="/2076-3417/14/23/10826/pdf?version=1732280260" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Evaluating Petrophysical Properties Using Digital Rock Physics Analysis: A CO2 Storage Feasibility Study of Lithuanian Reservoirs" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10826">Evaluating Petrophysical Properties Using Digital Rock Physics Analysis: A CO<sub>2</sub> Storage Feasibility Study of Lithuanian Reservoirs</a> <div class="authors"> by <span class="inlineblock "><strong>Shruti Malik</strong>, </span><span class="inlineblock "><strong>Pijus Makauskas</strong>, </span><span class="inlineblock "><strong>Ravi Sharma</strong> and </span><span class="inlineblock "><strong>Mayur Pal</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10826; <a href="https://doi.org/10.3390/app142310826">https://doi.org/10.3390/app142310826</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> As the global concern over greenhouse gas emissions grows, CO<sub>2</sub> storage in deep saline aquifers and depleted reservoirs has become crucial for climate change mitigation. This study evaluates the feasibility of Lithuanian deep saline aquifers, specifically, Syderiai and Vaskai, for effective CO<sub></sub> <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10826/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> As the global concern over greenhouse gas emissions grows, CO<sub>2</sub> storage in deep saline aquifers and depleted reservoirs has become crucial for climate change mitigation. This study evaluates the feasibility of Lithuanian deep saline aquifers, specifically, Syderiai and Vaskai, for effective CO<sub>2</sub> storage. Unlike previous theoretical analyses, it provides experimental data on static and dynamic reservoir parameters that impact CO<sub>2</sub> injection and retention. Using micro X-ray computed tomography (MXCT) and multi-resolution scanning at 8 µm and 22 µm, digital rock volumes (DRVs) from core samples were created to determine porosity and permeability. The method, validated against analogous samples, identified a representative element volume (REV) within sub-volumes, showing a homogeneous distribution of petrophysical properties in the Lithuanian samples. The results show that DRVs can accurately reflect pore-scale properties, achieving 90–95% agreement with lab measurements, and offer a rapid, efficient means for analyzing storage potentials. These insights confirm that Lithuanian aquifers are promising for CO<sub>2</sub> sequestration, with recommendations for further long-term monitoring and applications of this technique across the region. <a href="/2076-3417/14/23/10826">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/Z07535Q5VY ">CCUS: Paving the Way to Net Zero Emissions Technologies</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10826/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1527177"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1527177"><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="#next1527177" data-cycle-prev="#prev1527177" data-cycle-progressive="#images1527177" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1527177-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g001-550.jpg?1732280350" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1527177" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g002-550.jpg?1732280351'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g003-550.jpg?1732280352'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g004-550.jpg?1732280353'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g005-550.jpg?1732280354'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g006-550.jpg?1732280355'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g007-550.jpg?1732280357'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g008-550.jpg?1732280359'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g009-550.jpg?1732280360'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g010-550.jpg?1732280362'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g011-550.jpg?1732280363'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g012-550.jpg?1732280364'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g013-550.jpg?1732280366'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1527177-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g014-550.jpg?1732280367'><p>Figure 14</p></div></script></div></div><div id="article-1527177-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g001-550.jpg?1732280350" title=" <strong>Figure 1</strong><br/> <p>Locations of CO<sub>2</sub> emission sources, hydrocarbon fields, and saline aquifers in Baltic countries.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g002-550.jpg?1732280351" title=" <strong>Figure 2</strong><br/> <p>Location of saline aquifers of Lithuania, Syderiai and Vaskai. The depth contours indicate the top depth of the Cambrian structures.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g003-550.jpg?1732280352" title=" <strong>Figure 3</strong><br/> <p>Set 1 of the rock samples.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g004-550.jpg?1732280353" title=" <strong>Figure 4</strong><br/> <p>Set 2 of the rock samples.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g005-550.jpg?1732280354" title=" <strong>Figure 5</strong><br/> <p>The samples used for low- and high-resolution scanning.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g006-550.jpg?1732280355" title=" <strong>Figure 6</strong><br/> <p>D3Q19 lattice arrangement.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g007-550.jpg?1732280357" title=" <strong>Figure 7</strong><br/> <p>First segmentation workflow applied to rock samples.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g008-550.jpg?1732280359" title=" <strong>Figure 8</strong><br/> <p>Second workflow applied to samples with comparatively higher clay content.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g009-550.jpg?1732280360" title=" <strong>Figure 9</strong><br/> <p>(<b>a</b>–<b>c</b>) Illustration of sub-volume extraction for fluid flow simulations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g010-550.jpg?1732280362" title=" <strong>Figure 10</strong><br/> <p>Segmented 3D digital sub-volume (<b>left</b>) and corresponding simulated flow volume (<b>right</b>) for sample S3. In the segmented volume (<b>left</b>), the solid matrix is represented in a dark (red) color, the pore space is depicted in a light (white) color, and the pink color shows the different mineral phases. In the simulated volume (<b>right</b>), the flow of fluid through the pore space is indicated by blue lines.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g011-550.jpg?1732280363" title=" <strong>Figure 11</strong><br/> <p>Permeability values for each sub-volume of sample S3 at 22 µm (<b>left</b>) and 8 µm (<b>right</b>) resolutions. The error bars represent the deviation of permeability for each sub-volume from the laboratory-derived values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g012-550.jpg?1732280364" title=" <strong>Figure 12</strong><br/> <p>Permeability values for each sub-volume of sample V4 at 22 µm (<b>left</b>) and 8 µm (<b>right</b>) resolutions. The error bars represent the deviation of permeability for each sub-volume from the laboratory-derived values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g013-550.jpg?1732280366" title=" <strong>Figure 13</strong><br/> <p>Porosity vs permeability plots comparing sub-volumes at 22 µm and 8 µm with laboratory values for samples S3 (<b>left</b>) and V4 (<b>right</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10826/article_deploy/html/images/applsci-14-10826-g014-550.jpg?1732280367" title=" <strong>Figure 14</strong><br/> <p>Mechanistic models for mid-case permeability distribution for Syderiai.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10826'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 24 pages, 6256 KiB   </span> <a href="/2076-3417/14/23/10825/pdf?version=1732280275" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Study on Optimization of Rice-Drying Process Parameters and Directional Regulation of Nutrient Quality" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10825">Study on Optimization of Rice-Drying Process Parameters and Directional Regulation of Nutrient Quality</a> <div class="authors"> by <span class="inlineblock "><strong>Jinquan Li</strong>, </span><span class="inlineblock "><strong>Kezhen Chang</strong>, </span><span class="inlineblock "><strong>Jun Yin</strong>, </span><span class="inlineblock "><strong>Yi Jin</strong>, </span><span class="inlineblock "><strong>Xiaokang Yi</strong>, </span><span class="inlineblock "><strong>Zhongjie Zhang</strong>, </span><span class="inlineblock "><strong>Yichuan He</strong>, </span><span class="inlineblock "><strong>Qiaonan Yang</strong>, </span><span class="inlineblock "><strong>Zhihui Tang</strong>, </span><span class="inlineblock "><strong>Xiaoyu Liu</strong> and </span><span class="inlineblock "><strong>Wenfu Wu</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10825; <a href="https://doi.org/10.3390/app142310825">https://doi.org/10.3390/app142310825</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The physicochemical components of rice such as starch, protein, fat and water have significant influence on its nutritional value, and the drying process can easily cause changes in these components. In this paper, the effect of technical parameters on the nutritional quality of <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10825/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The physicochemical components of rice such as starch, protein, fat and water have significant influence on its nutritional value, and the drying process can easily cause changes in these components. In this paper, the effect of technical parameters on the nutritional quality of rice during hot-air drying was studied, and a control method of the rice-drying process based on effective accumulated temperature was proposed to ensure the drying quality and improve the drying efficiency. Through thin-layer drying experiments, hot-air temperature (T), humidity (RH), initial moisture content (MC), wind speed (V) and tempering ratio (TR) were selected as control factors, and the central composite design was adopted to optimize the experimental scheme. The relationship between each factor and nutrient quality was revealed through response surface analysis, and the regression model and process optimization parameters were established. The results show that the optimization parameters are as follows: hot-air temperature, 48.87 °C; humidity, 30.12%; initial moisture content, 21.31%; wind speed, 0.62 m/s; tempering ratio, 2.87; the optimized total drying time is 4.23 h; the effective accumulated temperature is 214.44 °C·h. The contents of protein, fat, amylose and amylopectin were 8.47 g/100 g, 1.97 g/100 g, 15.33 g/100 g and 60.50 g/100 g, respectively. The relative error of the verification test was 4.17%. The optimized process can effectively maintain the nutritional quality of rice and improve drying efficiency. This paper provides a new way to deeply explore the mechanism of rice quality change, and the established process reference chart provides a scientific basis for actual drying operations and the development of an intelligent control system. <a href="/2076-3417/14/23/10825">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/SP2W74N3Q6 ">Chemistry, Quality, and Processing Technology of Different Cereals, Pseudocereals, Legumes and Oilseeds Grain Types</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 16 pages, 5987 KiB   </span> <a href="/2076-3417/14/23/10824/pdf?version=1732279332" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="From Single Shot to Structure: End-to-End Network-Based Deflectometry for Specular Free-Form Surface Reconstruction" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10824">From Single Shot to Structure: End-to-End Network-Based Deflectometry for Specular Free-Form Surface Reconstruction</a> <div class="authors"> by <span class="inlineblock "><strong>M.Hadi Sepanj</strong>, </span><span class="inlineblock "><strong>Saed Moradi</strong>, </span><span class="inlineblock "><strong>Amir Nazemi</strong>, </span><span class="inlineblock "><strong>Claire Preston</strong>, </span><span class="inlineblock "><strong>Anthony M. D. Lee</strong> and </span><span class="inlineblock "><strong>Paul Fieguth</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10824; <a href="https://doi.org/10.3390/app142310824">https://doi.org/10.3390/app142310824</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Deflectometry is a key component in the precise measurement of specular (mirrored) surfaces; however, traditional methods often lack an end-to-end approach that performs 3D reconstruction in a single shot with high accuracy and generalizes across different free-form surfaces. This paper introduces a novel <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10824/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Deflectometry is a key component in the precise measurement of specular (mirrored) surfaces; however, traditional methods often lack an end-to-end approach that performs 3D reconstruction in a single shot with high accuracy and generalizes across different free-form surfaces. This paper introduces a novel deep neural network (DNN)-based approach for end-to-end 3D reconstruction of free-form specular surfaces using single-shot deflectometry. Our proposed network, VUDNet, innovatively combines discriminative and generative components to accurately interpret orthogonal fringe patterns and generate high-fidelity 3D surface reconstructions. By leveraging a hybrid architecture integrating a Variational Autoencoder (VAE) and a modified U-Net, VUDNet excels in both depth estimation and detail refinement, achieving superior performance in challenging environments. Extensive data simulation using Blender leading to a dataset which we will make available, ensures robust training and enables the network to generalize across diverse scenarios. Experimental results demonstrate the strong performance of VUDNet, setting a new standard for 3D surface reconstruction. <a href="/2076-3417/14/23/10824">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/89J6D3671C ">Technical Advances in 3D Reconstruction</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10824/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1527156"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1527156"><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="#next1527156" data-cycle-prev="#prev1527156" data-cycle-progressive="#images1527156" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1527156-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g001-550.jpg?1732279437" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1527156" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1527156-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g002-550.jpg?1732279438'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1527156-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g003-550.jpg?1732279439'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1527156-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g004-550.jpg?1732279440'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1527156-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g005-550.jpg?1732279442'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1527156-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g006-550.jpg?1732279443'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1527156-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g007-550.jpg?1732279444'><p>Figure 7</p></div></script></div></div><div id="article-1527156-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g001-550.jpg?1732279437" title=" <strong>Figure 1</strong><br/> <p>Overview of the context faced by this paper: A known fringe pattern is reflected by some shape of interest, and the resulting reflection is captured by a camera. Our proposed method, VUDNet, reconstructs the estimated shape based on the observed image, trained on a dataset of simulated reflections.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10824'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g002-550.jpg?1732279438" title=" <strong>Figure 2</strong><br/> <p>General architecture of the proposed VUDNet for end-to-end 3D reconstruction of specular free-form surfaces. The network integrates a Variational Autoencoder (VAE, <b>bottom</b>) for coarse depth estimation and a modified U-Net (<b>top</b>) for detail refinement. The ensemble approach leverages both generative and discriminative components, combining their outputs (<b>right</b>) to produce accurate depth maps from single-shot 2D images.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10824'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g003-550.jpg?1732279439" title=" <strong>Figure 3</strong><br/> <p>Simulation environment setup for generating the dataset. The environment includes a fixed camera, a fixed pattern, and various surface settings to replicate realistic deflectometry scenarios. An orthogonal sinusoidal fringe pattern is projected onto specular objects, and the reflected fringes are captured by the camera. This setup ensures the generation of a robust and varied dataset, essential for training the VUDNet to accurately reconstruct 3D surfaces from single-shot 2D images. Since this image is a direct screenshot from the Blender environment, the surface is reflecting the simulated world background. The reflection of the pattern plane on the surface is visible to the camera.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10824'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g004-550.jpg?1732279440" title=" <strong>Figure 4</strong><br/> <p>Mean absolute difference between the ground truth depth map and the predicted depth map from our VUDNet model for a selected sample. The error pattern demonstrates smoothness, effective regularization, and an overall minimal presence of outliers. The top region contains localized patterns of higher error values (illustrated as collection of red pixels), however despite these localized artifacts, the remainder of the image demonstrates the model’s accuracy with no visible orthogonal fringe patterns.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10824'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g005-550.jpg?1732279442" title=" <strong>Figure 5</strong><br/> <p>Comparison of ground truth (<b>left</b>) and VUDNet-estimated depth maps (<b>right</b>), showing effective noise reduction and fine detail retention. The top images showcase a result for a deformation sample, while the bottom images represent an example of the geometric case.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10824'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g006-550.jpg?1732279443" title=" <strong>Figure 6</strong><br/> <p>VAE representation (<b>left</b>) showcasing distinct separation of clusters in the latent space, visualized as the first and second components of t-SNE. The clustering indicates effective differentiation of surface characteristics and potent feature extraction. The four images on the right correspond to the selected points in the latent space (<b>right</b>). It is evident that images from a given cluster share related surface characteristics, highlighting the network’s ability to identify underlying similarities despite variations in surface characteristics.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10824'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10824/article_deploy/html/images/applsci-14-10824-g007-550.jpg?1732279444" title=" <strong>Figure 7</strong><br/> <p>The image panels here are organized the same as in <a href="#applsci-14-10824-f006" class="html-fig">Figure 6</a>, with the latent space (<b>left</b>) visualized from the first and second components of t-SNE, and the images (<b>right</b>) corresponding to the selected points in the latent space. The difference is that <a href="#applsci-14-10824-f006" class="html-fig">Figure 6</a> was trained on the entire dataset, whereas here it is trained exclusively on the deformation data. It is clear that images positioned closer in the latent space share more similarities in reflection shape, while those farther apart are less similar, even though they belong to the same overall category of deformation surfaces. This emphasizes the network’s capability to capture underlying similarities despite variations in surface characteristics within the same category.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10824'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 12 pages, 3803 KiB   </span> <a href="/2076-3417/14/23/10823/pdf?version=1732280383" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Design and Analysis of Additional Tendon Path for Determining Bending Shape in Hyper-Redundant Manipulator with Rolling Joints" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/23/10823">Design and Analysis of Additional Tendon Path for Determining Bending Shape in Hyper-Redundant Manipulator with Rolling Joints</a> <div class="authors"> by <span class="inlineblock "><strong>Hansoul Kim</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(23), 10823; <a href="https://doi.org/10.3390/app142310823">https://doi.org/10.3390/app142310823</a> - 22 Nov 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Flexible surgical robots are emerging as advanced tools for minimally invasive surgeries, offering greater versatility compared to traditional rigid robots. Unlike commercial endoscopes, the overtube should remain fixed to maintain stability, ensure a clear field of view, and allow surgical tools to perform <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10823/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Flexible surgical robots are emerging as advanced tools for minimally invasive surgeries, offering greater versatility compared to traditional rigid robots. Unlike commercial endoscopes, the overtube should remain fixed to maintain stability, ensure a clear field of view, and allow surgical tools to perform tasks efficiently. While constant curvature bending of the overtube is sufficient for some lesions, certain lesions require the overtube to bend into specific shapes to achieve appropriate positioning. Various methods for creating different bending shapes have been proposed in previous research, typically involving connecting multiple segments. However, this approach complicates control and reduces both space and cost efficiency. This study proposed a conceptual method for adding a shaping tendon to control the bending shape and mathematically analyzed the effect of this shaping tendon, inserted along an arbitrary path in addition to the main driving tendons for constant curvature bending, on the bending shape of the hyper-redundant manipulator with rolling joints. The overall system was modeled and analyzed from an energy perspective, and the validity of the proposed mathematical modeling was verified through comparison with results obtained from physical experiments. In addition, it was identified that the design parameter determining the tendon path is a significant element in defining the bending shape of the overtube. <a href="/2076-3417/14/23/10823">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/23/10823/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1527183"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1527183"><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="#next1527183" data-cycle-prev="#prev1527183" data-cycle-progressive="#images1527183" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1527183-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g001-550.jpg?1732280467" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1527183" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1527183-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g002-550.jpg?1732280469'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1527183-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g003-550.jpg?1732280471'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1527183-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g004-550.jpg?1732280473'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1527183-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g005-550.jpg?1732280475'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1527183-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g006-550.jpg?1732280476'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1527183-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g007-550.jpg?1732280477'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1527183-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g008-550.jpg?1732280479'><p>Figure 8</p></div></script></div></div><div id="article-1527183-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g001-550.jpg?1732280467" title=" <strong>Figure 1</strong><br/> <p>Description of situation requiring non-constant curvature bending for the overtube for efficient surgical tasks in confined and constrained spaces like gastrointestinal tract.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10823'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g002-550.jpg?1732280469" title=" <strong>Figure 2</strong><br/> <p>Overtube with <span class="html-italic">n</span> rolling joints bent into different shapes due to the tensions applied to the inserted tendons.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10823'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g003-550.jpg?1732280471" title=" <strong>Figure 3</strong><br/> <p>The arrangement of tendons within the joint and the moment arms of each tendon when the rotation angle between <span class="html-italic">joint<sub>t</sub></span> and <span class="html-italic">Joint<sub>t</sub></span><sub>+1</sub> is <span class="html-italic">θ<sub>i</sub></span>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10823'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g004-550.jpg?1732280473" title=" <strong>Figure 4</strong><br/> <p>(<b>a</b>–<b>c</b>) Overall mechanical design of three different types of overtubes for analyzing the effect of the shaping tendon on the bending shape; (<b>d</b>) the arrangement of all tendons.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10823'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g005-550.jpg?1732280475" title=" <strong>Figure 5</strong><br/> <p>The bending results estimated based on the mathematical modeling equations proposed in <a href="#sec2dot1-applsci-14-10823" class="html-sec">Section 2.1</a> for (<b>a</b>) Case 1, (<b>b</b>) Case 2, and (<b>c</b>) Case 3.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10823'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g006-550.jpg?1732280476" title=" <strong>Figure 6</strong><br/> <p>Overview of the physical experimental setup for measuring the bending shape according to the tensions applied to the tendons inserted in a Type-A overtube.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10823'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g007-550.jpg?1732280477" title=" <strong>Figure 7</strong><br/> <p>The actual shape of the Type-A overtube when different tensions are applied to the tendons inserted in (<b>a</b>) Case 1, (<b>b</b>) Case 2, and (<b>c</b>) Case 3.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10823'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-10823/article_deploy/html/images/applsci-14-10823-g008-550.jpg?1732280479" title=" <strong>Figure 8</strong><br/> <p>The actual shape of the Type-A overtube when different tensions are applied to the tendons inserted in (<b>a</b>) Case 1, (<b>b</b>) Case 2, and (<b>c</b>) Case 3.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/23/10823'>Full article</a></strong> "></a></div> </div> </div> </div> </div> <div class="generic-item last-item"> <a class="bold" href="/search?q=&journal=applsci&sort=pubdate&page_count=50">More Articles...</a> </div> </div> </div> </div> <div id="left-column" class="content__column large-3 large-pull-6 medium-3 medium-pull-6 small-12 columns"> <div id="js-large-main-top-container"> <div id="js-main-top-container" class="content__container"> <a href="/journal/applsci"> <img 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