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Volume 15
Issue 11
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Appl. Sci., Volume 15, Issue 11 (June-1 2025) – 586 articles

Cover Story (view full-size image): This paper presents a review of the emerging trends in additive–subtractive manufacturing over the last five years. This review has been carried out by applying an adaptation of the PRISMA methodology to the field of manufacturing engineering. Specifically, open access papers published in English between 2020 and 2024, collected in prestigious journals (classified as Q1 and Q2 within the ranking of their respective categories according to the Journal Citation Report), and peer-reviewed conference proceedings of recognized prestige have been selected. From the analysis of the selected articles, it is concluded that hybrid additive and subtractive manufacturing is especially focused on the aerospace field, using titanium and nickel alloys, combining processes among which DED (directed energy deposition) and milling stand out. View this paper
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20 pages, 3901 KiB  
Article
Designing Social Robots with LLMs for Engaging Human Interaction
by Maria Pinto-Bernal, Matthijs Biondina and Tony Belpaeme
Appl. Sci. 2025, 15(11), 6377; https://doi.org/10.3390/app15116377 - 5 Jun 2025
Viewed by 302
Abstract
Large Language Models (LLMs), particularly those enhanced through Reinforcement Learning from Human Feedback, such as ChatGPT, have opened up new possibilities for natural and open-ended spoken interaction in social robotics. However, these models are not inherently designed for embodied, multimodal contexts. This paper [...] Read more.
Large Language Models (LLMs), particularly those enhanced through Reinforcement Learning from Human Feedback, such as ChatGPT, have opened up new possibilities for natural and open-ended spoken interaction in social robotics. However, these models are not inherently designed for embodied, multimodal contexts. This paper presents a user-centred approach to integrating an LLM into a humanoid robot, designed to engage in fluid, context-aware conversation with socially isolated older adults. We describe our system architecture, which combines real-time speech processing, layered memory summarisation, persona conditioning, and multilingual voice adaptation to support personalised, socially appropriate interactions. Through iterative development and evaluation, including in-home exploratory trials with older adults (n = 7) and a preliminary study with young adults (n = 43), we investigated the technical and experiential challenges of deploying LLMs in real-world human–robot dialogue. Our findings show that memory continuity, adaptive turn-taking, and culturally attuned voice design enhance user perceptions of trust, naturalness, and social presence. We also identify persistent limitations related to response latency, hallucinations, and expectation management. This work contributes design insights and architectural strategies for future LLM-integrated robots that aim to support meaningful, emotionally resonant companionship in socially assistive settings. Full article
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23 pages, 4110 KiB  
Article
Exploring CeO2-Doped Co/SBA-15 Catalysts for Acetic Acid Oxidative Steam Reforming
by Carlos A. Chirinos, Álvaro Moreno de la Calle, Pedro J. Megía, Arturo J. Vizcaíno, José A. Calles and Alicia Carrero
Appl. Sci. 2025, 15(11), 6376; https://doi.org/10.3390/app15116376 - 5 Jun 2025
Viewed by 226
Abstract
This work explores the effect of the incorporation of CeO2 into Co/SBA-15 catalysts in hydrogen production through acetic acid oxidative steam reforming as a bio-oil aqueous phase model compound. CeO2 was incorporated (5–30 wt.%) to improve the physicochemical properties of the [...] Read more.
This work explores the effect of the incorporation of CeO2 into Co/SBA-15 catalysts in hydrogen production through acetic acid oxidative steam reforming as a bio-oil aqueous phase model compound. CeO2 was incorporated (5–30 wt.%) to improve the physicochemical properties of the catalyst. XRD analysis confirmed that the addition of CeO2 resulted in smaller Co0 mean crystallite sizes, while H2-TPR showed enhanced reducibility properties. The catalytic performance was evaluated in the 400–700 °C range, S/C molar ratio = 2, O2/C molar ratio = 0.0375, WHSV = 30.2 h−1, and P = 1 atm. Catalysts containing 10 and 20 wt.% of CeO2 exhibited the best catalytic performance, achieving nearly complete conversions and H2 yield values, approaching thermodynamic equilibrium at 550 °C. Both samples maintained an acetic acid conversion above 90% after 30 h of time-on-stream, with H2 yields above 55% along the steam reforming tests. This agrees with their lower coke formation rates (7.2 and 12.0 mgcoke·gcat−1·h−1 for Co/10CeO2-SBA15 and Co/20CeO2-SBA15, respectively). Full article
(This article belongs to the Special Issue Advances in New Sources of Energy and Fuels)
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25 pages, 1220 KiB  
Review
Shedding Light on FIRE Syndrome: An Overview of a Novel Condition in Eosinophilic Esophagitis
by Selda Ali, Maria Cătălina Cernat, Mihaela Ruxandra Vintilă, Elena Camelia Berghea and Roxana Silvia Bumbăcea
Appl. Sci. 2025, 15(11), 6375; https://doi.org/10.3390/app15116375 - 5 Jun 2025
Viewed by 334
Abstract
Food-Induced Immediate Response of the Esophagus (FIRE) is a newly described syndrome observed in eosinophilic esophagitis (EoE) patients. It is defined by an immediate hypersensitivity reaction of the esophagus that occurs when specific foods and beverages interface with esophageal mucosa. The available data [...] Read more.
Food-Induced Immediate Response of the Esophagus (FIRE) is a newly described syndrome observed in eosinophilic esophagitis (EoE) patients. It is defined by an immediate hypersensitivity reaction of the esophagus that occurs when specific foods and beverages interface with esophageal mucosa. The available data regarding this topic is scarce. Therefore, we aimed to review relevant publications in order to better characterize the main aspects of this syndrome and hypothesize about potential mechanisms underlying FIRE syndrome and possible future therapeutic approaches. We searched PubMed, Embase, and Web of Science databases for relevant articles published before February 1st, 2025. The results were narrowed down to four articles describing a total of 105 cases of FIRE syndrome. These patients had a distinct clinical presentation, characterized by retrosternal discomfort or pain, differentiating it from solid food dysphagia or pollen-food allergy syndrome (PFAS). Currently, diagnosis is based on clinical presentation, with no diagnostic tests or biomarkers available. Emerging evidence suggests that IgE-mediated hypersensitivity, mast cells, and neuroimmune interactions may play a central role in the pathogenesis of FIRE syndrome. The therapeutic approaches remain speculative, with trigger avoidance being the main option. This article brings to the forefront the need for continued research to address current knowledge gaps regarding FIRE syndrome, which is important for optimizing patient management. Full article
(This article belongs to the Special Issue New Diagnostic and Therapeutic Approaches in Food Allergy)
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41 pages, 6794 KiB  
Article
Effectiveness of Electrode Design Methodologies for Fast EDM Slotting of Thick Silicon Wafers
by Mahmud Anjir Karim and Muhammad Pervej Jahan
Appl. Sci. 2025, 15(11), 6374; https://doi.org/10.3390/app15116374 - 5 Jun 2025
Viewed by 222
Abstract
Silicon is the most commonly used material in the electronic industries due to its unique properties, which also make it very difficult to machine using conventional machining. Electrical discharge machining (EDM) is a non-traditional process that is gaining popularity for machining silicon, although [...] Read more.
Silicon is the most commonly used material in the electronic industries due to its unique properties, which also make it very difficult to machine using conventional machining. Electrical discharge machining (EDM) is a non-traditional process that is gaining popularity for machining silicon, although a slower machining rate is one of its limitations. This study investigates two electrode design strategies to enhance the efficiency of EDM by improving the material removal rates, reducing tool wear, and refining the quality of machined features. The first approach involves using graphite electrodes in various array configurations (1 × 4 to 6 × 4) and leg heights (0.2″ and 0.3″). The second approach employs hollow electrodes with differing wall thicknesses (0.04″, 0.08″, and 0.12″). The effects of these variables on performance were evaluated by maintaining constant EDM parameters. The results indicate that increasing the number of electrode legs improves the flushing conditions, resulting in shorter machining times. Meanwhile, the shorter electrode height outperforms the taller electrode, providing a higher machining speed. The thinnest wall thickness for hollow electrodes yielded the best performance due to the increased energy distribution. Both electrode design methodologies can be used for the mass fabrication of features with targeted profiles on silicon using the die-sinking EDM process. Full article
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23 pages, 1101 KiB  
Article
QELPS Algorithm: A Novel Dynamic Optimization Technology for Quantum Circuits Scheduling Engineering Problems
by Zuoqiang Du, Xingjie Li and Hui Li
Appl. Sci. 2025, 15(11), 6373; https://doi.org/10.3390/app15116373 - 5 Jun 2025
Viewed by 354
Abstract
In the noisy medium-scale quantum era, quantum computers are constrained by a limited number of qubits, restricted physical topological structures, and interference from environmental noise, making efficient and stable circuit scheduling a significant challenge. To improve the feasibility of quantum computing, it is [...] Read more.
In the noisy medium-scale quantum era, quantum computers are constrained by a limited number of qubits, restricted physical topological structures, and interference from environmental noise, making efficient and stable circuit scheduling a significant challenge. To improve the feasibility of quantum computing, it is essential to optimize the scheduling of quantum gates and the insertion of SWAP gates, reducing running time and enhancing computational efficiency. We propose a collaborative optimization framework that integrates the Quantum Exchange Lock Parallel Scheduler (QELPS) with the Full-level Joint Optimization SWAP Algorithm (FJOSA). In QELPS, SWAP conflict characteristics are used to adjust the layout of quantum gates across different levels while considering physical constraints and dynamically adapting to the circuit’s execution state. Quantum lock parallel technology enables the selective postponement of certain quantum gates, minimizing circuit depth and mitigating inefficiencies caused by excessive SWAP gate insertions. Meanwhile, FJOSA employs a cross-layer optimization strategy that combines heuristic algorithms with cost functions to improve gate scheduling at a global level. This approach effectively reduces quantum gate conflicts found in traditional methods and optimizes execution order, leading to better computational efficiency and circuit performance. Experimental results show that, compared to the traditional 2QAN algorithm, QELPS and FJOSA reduce additional gate insertions by 85.59% and 89.38%, respectively, while decreasing running time by 56.32% and 66.47%. These improvements confirm that the proposed method significantly enhances circuit scheduling efficiency and reduces resource consumption, making it a promising approach for optimizing quantum computation. Full article
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21 pages, 512 KiB  
Article
Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
by Taewan Kim and Bongjae Kim
Appl. Sci. 2025, 15(11), 6372; https://doi.org/10.3390/app15116372 - 5 Jun 2025
Viewed by 257
Abstract
Sign Language Recognition (SLR) has made substantial progress through advances in deep learning and video-based action recognition. Conventional SLR systems typically segment input videos into a fixed number of clips (e.g., five clips per video), regardless of the video’s actual length, to meet [...] Read more.
Sign Language Recognition (SLR) has made substantial progress through advances in deep learning and video-based action recognition. Conventional SLR systems typically segment input videos into a fixed number of clips (e.g., five clips per video), regardless of the video’s actual length, to meet the fixed-length input requirements of deep learning models. While this approach simplifies model design and training, it fails to account for temporal variations inherent in sign language videos. Specifically, applying a fixed number of clips to videos of varying lengths can lead to significant information loss: longer videos suffer from excessive frame skipping, causing the model to miss critical gestural cues, whereas shorter videos require frame duplication, introducing temporal redundancy that distorts motion dynamics. To address these limitations, we propose a dynamic clip generation method that adaptively adjusts the number of clips during inference based on a novel coverage metric. This metric quantifies how effectively a clip selection captures the temporal information in a given video, enabling the system to maintain both temporal fidelity and computational efficiency. Experimental results on benchmark SLR datasets using multiple models-including 3D CNNs, R(2+1)D, Video Swin Transformer, and Multiscale Vision Transformers demonstrate that our method consistently outperforms fixed clip generation methods. Notably, our approach achieves 98.67% accuracy with the Video Swin Transformer while reducing inference time by 28.57%. These findings highlight the effectiveness of coverage-based dynamic clip generation in improving both accuracy and efficiency, particularly for videos with high temporal variability. Full article
(This article belongs to the Topic Applied Computing and Machine Intelligence (ACMI))
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17 pages, 2006 KiB  
Article
Mitigating Long-Term Forecasting Bias in Time-Series Neural Networks via Ensemble of Short-Term Dependencies
by Jiahui Wang, Wenqian Zhou, Fangshu Chen, Liming Wang, Ruijun Pan and Chengcheng Yu
Appl. Sci. 2025, 15(11), 6371; https://doi.org/10.3390/app15116371 - 5 Jun 2025
Viewed by 275
Abstract
Time-series forecasting is essential for predicting future trends based on historical data, with significant applications in meteorology, transportation, and finance. However, existing models often exhibit unsatisfactory performance in long-term forecasting scenarios. To address this limitation, we propose the Time-Series Neural Networks via Ensemble [...] Read more.
Time-series forecasting is essential for predicting future trends based on historical data, with significant applications in meteorology, transportation, and finance. However, existing models often exhibit unsatisfactory performance in long-term forecasting scenarios. To address this limitation, we propose the Time-Series Neural Networks via Ensemble of Short-Term Dependencies (TSNN-ESTD). This model leverages iTransformer as the base predictor to simultaneously train short-term and long-term forecasting models. The vanilla iTransformer’s linear decoding layer is optimized by replacing it with an LSTM layer, and an additional long-term model is introduced to enhance stability. The ensemble strategy employs short-term predictions to correct the bias in long-term forecasts. Our extensive experiments demonstrate that TSNN-ESTD reduces the MSE and MAE by 9.17% and 2.3% on five benchmark datasets. Full article
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14 pages, 1003 KiB  
Article
A Linear Fitting Algorithm Based on Modified Random Sample Consensus
by Yujin Min, Yun Tang, Hao Chen and Faquan Zhang
Appl. Sci. 2025, 15(11), 6370; https://doi.org/10.3390/app15116370 - 5 Jun 2025
Viewed by 229
Abstract
When performing linear fitting on datasets containing outliers, common algorithms may face problems like inadequate fitting accuracy. We propose a linear fitting algorithm based on Locality-Sensitive Hashing (LSH) and Random Sample Consensus (RANSAC). Our algorithm combines the efficient similarity search capabilities of the [...] Read more.
When performing linear fitting on datasets containing outliers, common algorithms may face problems like inadequate fitting accuracy. We propose a linear fitting algorithm based on Locality-Sensitive Hashing (LSH) and Random Sample Consensus (RANSAC). Our algorithm combines the efficient similarity search capabilities of the LSH algorithm with the robust fitting mechanism of RANSAC. With proper hash functions designed, similar data points are mapped to the same hash bucket, thereby enabling the efficient identification and removal of outliers. RANSAC is then used to fit the model parameters of the processed dataset. The optimal parameters for the linear model are obtained after multiple iterative processes. This algorithm significantly reduces the influence of outliers on the dataset, resulting in improved fitting accuracy and enhanced robustness. Experimental results demonstrate that the proposed improved RANSAC linear fitting algorithm outperforms the Weighted Least Squares, traditional RANSAC, and Maximum Likelihood Estimation methods, achieving a reduction in the sum of squared residuals by 29%, 16%, and 8%, respectively. Full article
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18 pages, 3095 KiB  
Article
Study on the Evolution Law of Overlying Rock Collapse Induced by Mining Based on BOTDR
by Chenrui Huang, Chaomin Mu, Hui Zhou and Quanmin Xie
Appl. Sci. 2025, 15(11), 6369; https://doi.org/10.3390/app15116369 - 5 Jun 2025
Viewed by 248
Abstract
Based on Brillouin optical time-domain reflectometry (BOTDR) technology, this study integrates laboratory tensile tests and similarity simulation experiments to systematically investigate the relationship between overlying strata collapse and fiber strain during coal seam mining. An analytical expression was established to describe the correlation [...] Read more.
Based on Brillouin optical time-domain reflectometry (BOTDR) technology, this study integrates laboratory tensile tests and similarity simulation experiments to systematically investigate the relationship between overlying strata collapse and fiber strain during coal seam mining. An analytical expression was established to describe the correlation between overlying strata displacement and fiber strain. The horizontal fiber monitoring results indicate that fiber strain accurately captures the evolution of overlying strata collapse and exhibits strong agreement with actual displacement height. When the working face advanced to 115 m and 155 m, the rock strata primarily underwent stress adjustment with minimal failure. At 195 m, the collapse zone expanded significantly, resulting in a notable increase in fiber strain. By 240 m, severe roof failure occurred, forming a complete caving zone in the goaf. The fiber strain curve exhibited a characteristic “double convex peak” pattern, with peak positions closely corresponding to rock fracture locations, further validating the feasibility of fiber monitoring in coal seam mining. Vertical fiber monitoring clearly delineated the evolution of the “three-zone” structure (caving zone, fracture zone, and bending subsidence zone) in the overlying strata. The fiber strain underwent a staged transformation from compressive strain to tensile strain, followed by stable compaction. The “stepped” characteristics of the strain curve effectively represented the heights of the three zones, highlighting the progressive and synchronized nature of rock failure. These findings demonstrate that fiber strain effectively characterizes the collapse height and evolution of overlying strata, enabling precise identification of rock fracture locations. This research provides scientific insights and technical support for roof stability assessment and mine safety management in coal seam mining. Full article
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19 pages, 5578 KiB  
Article
Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
by Hanxi Xu, Wenhua Li, Zhongjiu Zheng and Yunhe Wang
Appl. Sci. 2025, 15(11), 6368; https://doi.org/10.3390/app15116368 - 5 Jun 2025
Viewed by 220
Abstract
This paper proposes a method for array design and optimal transmitting coil selection of underwater wireless power transfer systems. This method is divided into three steps. Firstly, by analyzing the influence of different ratio side lengths of the transmitting coil and receiving coil [...] Read more.
This paper proposes a method for array design and optimal transmitting coil selection of underwater wireless power transfer systems. This method is divided into three steps. Firstly, by analyzing the influence of different ratio side lengths of the transmitting coil and receiving coil on mutual inductance, the optimal ratio side length coil is selected. Secondly, by analyzing the relative size of the reflection impedance of the power supply coil and its surrounding coils, the optimal coil activation criterion is derived. Finally, by estimating the position of the receiving coil without communication, the switching of the power supply coil is realized. According to the proposed method, it was verified on the experimental platform. Under a rated power of 300 W with a load resistance of 20 Ω, the system maintains efficiency ≥ 80% even under horizontal offsets up to 150 mm (75% of the transmitting coil side length) and two-dimensional offsets up to 200 mm (100% of the transmitting coil side length). Full article
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14 pages, 1894 KiB  
Article
Peri-Implantitis Causal Therapy with and Without Doxycycline: Retrospective Cohort Clinical Study
by Bianca D’Orto and Elisabetta Polizzi
Appl. Sci. 2025, 15(11), 6367; https://doi.org/10.3390/app15116367 - 5 Jun 2025
Viewed by 276
Abstract
Background: Topical application within peri-implant pockets ensures high drug concentrations at the infection site while minimizing systemic exposure. However, the comparative effectiveness of non-surgical causal therapy alone versus its combination with doxycycline remains unclear. This retrospective observational clinical study aimed to evaluate the [...] Read more.
Background: Topical application within peri-implant pockets ensures high drug concentrations at the infection site while minimizing systemic exposure. However, the comparative effectiveness of non-surgical causal therapy alone versus its combination with doxycycline remains unclear. This retrospective observational clinical study aimed to evaluate the impact of adjunctive doxycycline on peri-implant parameters, considering smoking, systemic conditions, and implant–prosthetic rehabilitation (single implant, implant-supported bridge, or full-arch). Methods: Patients were retrospectively assigned to a control group (CG), receiving non-surgical causal therapy alone, or a test group (TG), which is also treated with topical doxycycline. Peri-implant parameters, including Peri-implant Probing Depht (PPD), Bleeding on Probing (BoP), Plaque Index (PI), and suppuration, were assessed at baseline (T0) and follow-up (T1). Multivariate logistic regression and stratified subgroup analyses were conducted to adjust for confounders such as smoking, systemic conditions, and implant–prosthetic rehabilitation types. Results: Two hundred nine patients were included in the study, of whom 97 were in the CG and 112 were in the TG. At T1, the TG exhibited a statistically significant reduction in PPD, BoP, PI, and suppuration compared to the CG (p < 0.05). Conclusions: The adjunctive use of topical doxycycline significantly enhances clinical outcomes in non-surgical peri-implantitis treatment. Further longitudinal studies are needed to confirm these findings and assess long-term stability. Full article
(This article belongs to the Special Issue Dental Implants: Latest Advances and Prospects)
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21 pages, 3949 KiB  
Article
A Heuristic Algorithm for Locating Line-to-Line Faults in Photovoltaic Systems
by Jia-Zhang Jhan, Bo-Hong Li, Hsun-Tsung Chiu, Hong-Chan Chang and Cheng-Chien Kuo
Appl. Sci. 2025, 15(11), 6366; https://doi.org/10.3390/app15116366 - 5 Jun 2025
Viewed by 208
Abstract
Photovoltaic (PV) systems have experienced rapid global deployment. However, line-to-line short-circuit faults pose serious safety risks and can lead to significant power losses or fire hazards. While existing fault detection methods can identify fault types, they cannot precisely locate fault positions, resulting in [...] Read more.
Photovoltaic (PV) systems have experienced rapid global deployment. However, line-to-line short-circuit faults pose serious safety risks and can lead to significant power losses or fire hazards. While existing fault detection methods can identify fault types, they cannot precisely locate fault positions, resulting in time-consuming and costly maintenance. This paper proposes a heuristic algorithm for accurately locating such faults in PV arrays based on module group voltage measurements. The algorithm employs a two-phase approach: fault candidate marking and fault location determination, capable of handling both intra-string and cross-string faults. Simulation tests on a 21 × 2 PV array configuration demonstrate a 97.56% fault location success rate, reducing the troubleshooting scope to within a single-module group. The proposed method offers a simple, fast, and cost-effective solution for PV system maintenance, potentially saving significant labor costs and reducing system downtime. Full article
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30 pages, 10731 KiB  
Article
Real-Time 3D Vision-Based Robotic Path Planning for Automated Adhesive Spraying on Lasted Uppers in Footwear Manufacturing
by Ya-Yung Huang, Jun-Ting Lai and Hsien-Huang Wu
Appl. Sci. 2025, 15(11), 6365; https://doi.org/10.3390/app15116365 - 5 Jun 2025
Viewed by 211
Abstract
The automation of adhesive application in footwear manufacturing is challenging due to complex surface geometries and model variability. This study presents an integrated 3D vision-based robotic system for adhesive spraying on lasted uppers. A triangulation-based scanning setup reconstructs each upper into a high-resolution [...] Read more.
The automation of adhesive application in footwear manufacturing is challenging due to complex surface geometries and model variability. This study presents an integrated 3D vision-based robotic system for adhesive spraying on lasted uppers. A triangulation-based scanning setup reconstructs each upper into a high-resolution point cloud, enabling customized spraying path planning. A six-axis robotic arm executes the path using an adaptive transformation matrix that aligns with surface normals. UV fluorescent dye and inspection are used to verify adhesive coverage. Experimental results confirm high repeatability and precision, with most deviations within the industry-accepted ±1 mm range. While localized glue-deficient areas were observed around high-curvature regions such as the toe cap, these remain limited and serve as a basis for further system enhancement. The system significantly reduces labor dependency and material waste, as observed through the replacement of four manual operators and the elimination of adhesive over-application in the tested production line. It has been successfully installed and validated on a production line in Hanoi, Vietnam, meeting real-world industrial requirements. This research contributes to advancing intelligent footwear manufacturing by integrating 3D vision, robotic motion control, and automation technologies. Full article
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13 pages, 1432 KiB  
Article
Effect of Ion Release on Color Stability of Zirconia: A Comparative Study
by Alqarama Mahardhika Thalib, Khanisyah Erza Gumilar, Israyani, Shang-Ming Wang, Li-Rong Kuo, Fang-Yu Fan and Chung-Ming Liu
Appl. Sci. 2025, 15(11), 6364; https://doi.org/10.3390/app15116364 - 5 Jun 2025
Viewed by 230
Abstract
Zirconia ceramics are widely used in dentistry, but maintaining long-term color stability remains challenging. This study investigated the combined effects of specimen thickness, immersion duration, and aging of coloring solutions on the color stability of two multilayer commercial zirconia materials: TT ONE Multilayer [...] Read more.
Zirconia ceramics are widely used in dentistry, but maintaining long-term color stability remains challenging. This study investigated the combined effects of specimen thickness, immersion duration, and aging of coloring solutions on the color stability of two multilayer commercial zirconia materials: TT ONE Multilayer (TT) and DD cubeX2 ML (DD). Discs (1.0–2.5 mm thick) were immersed in A2-shade coloring liquids for 30 s, 1 min, 3 min, and 5 min and evaluated after three months of solution aging. Color parameters (L*, a*, b*, C*, ΔE) were assessed, along with pH variation and Fe/Er ion concentrations using ICP-MS. Thinner specimens showed higher ΔE values and greater chromatic shifts than thicker ones. Aging of the coloring solutions increased L* values and discoloration, particularly in TT. ICP-MS revealed rising Fe and declining Er levels, correlating with observed optical changes. DD showed greater chemical and optical stability under identical conditions. These findings highlight the need to control zirconia thickness and coloring solution aging to preserve long-term esthetics. Full article
(This article belongs to the Special Issue Oral Diseases and Clinical Dentistry)
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12 pages, 505 KiB  
Article
Musculoskeletal Pain and Compensatory Mechanisms in Posture and Adaptation to Sport in Players from the Polish Men’s Goalball National Team—Cross Sectional Study
by Barbara Rosołek, Dan Iulian Alexe, Diana Celebańska and Anna Zwierzchowska
Appl. Sci. 2025, 15(11), 6363; https://doi.org/10.3390/app15116363 - 5 Jun 2025
Viewed by 203
Abstract
The aim of the study was to verify the relationship between musculoskeletal pain of elite Polish goalball players and selected physique and posture characteristics. We examined 12 players. The mean age was 21.8 ± 6.0 years, and a mean training experience of 6.3 [...] Read more.
The aim of the study was to verify the relationship between musculoskeletal pain of elite Polish goalball players and selected physique and posture characteristics. We examined 12 players. The mean age was 21.8 ± 6.0 years, and a mean training experience of 6.3 ± 3.4 years. Physique (body mass, body height, waist circumference, fat tissue, fat-free soft tissue) and posture (thoracic kyphosis and lumbar lordosis) and range of motion (in the thoracic and lumbar regions) were assessed. The incidences and locations of musculoskeletal pain were identified using the Nordic Musculoskeletal Questionnaire, covering the period from the last seven days (NMQ-7) and six months (NMQ-6). Due to the small group size, non-parametric tests (Spearman’s rank correlation) were used. The significance level was set at p < 0.05. Players were more likely to report musculoskeletal pain in the last six months than in the previous week. Pain reported in both NMQ6 and NMQ7 was most common in the wrists/hands and lower back, and, in NMQ6, also in the shoulders and ankles/feet. There were significant negative correlations of total NMQ7 with lumbar lordosis angle in the habitual standing position (R = −0.6; p = 0.04), trunk flexion (R = −0.8, p = 0.002), and trunk extension (R = −0.6; p = 0.03), and a positive correlation with thoracic kyphosis angle in trunk flexion (R = 0.8, p = 0.005). There was a statistically significant, inversely proportional relationship of thoracic kyphosis angle values in the habitual position (R = −0.58; p = 0.049) and thoracic kyphosis angle THA in trunk flexion (R = −0.6; p = 0.038) with time of disability. Relationships between some body posture parameters and musculoskeletal pain in the studied athletes were also noted. Full article
(This article belongs to the Special Issue Physiology and Biomechanical Monitoring in Sport)
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27 pages, 1976 KiB  
Article
Balancing Efficiency and Efficacy: A Contextual Bandit-Driven Framework for Multi-Tier Cyber Threat Detection
by Ibrahim Mutambik and Abdullah Almuqrin
Appl. Sci. 2025, 15(11), 6362; https://doi.org/10.3390/app15116362 - 5 Jun 2025
Viewed by 204
Abstract
In response to the rising volume and sophistication of cyber intrusions, data-oriented methods have emerged as critical defensive measures. While machine learning—including neural network-based solutions—has demonstrated strong capabilities in identifying malicious activities, several fundamental challenges remain. Chief among these difficulties are the substantial [...] Read more.
In response to the rising volume and sophistication of cyber intrusions, data-oriented methods have emerged as critical defensive measures. While machine learning—including neural network-based solutions—has demonstrated strong capabilities in identifying malicious activities, several fundamental challenges remain. Chief among these difficulties are the substantial resource demands related to data preprocessing and inference procedures, limited scalability beyond centralized environments, and the necessity of deploying multiple specialized detection models to address diverse stages of the cyber kill chain. This paper introduces a contextual bandit-based reinforcement learning approach, designed to reduce operational expenditures and enhance detection cost-efficiency by introducing an adaptive decision boundary within a layered detection scheme. The proposed framework continually measures the confidence of each participating detection model, applying a reward-driven mechanism to balance cost and accuracy. Specifically, each potential action, representing a particular decision boundary, earns a reward reflecting its overall cost-to-effectiveness ratio, thereby prioritizing reduced overheads. We validated our method using two highly representative datasets that capture prevalent modern-day threats: phishing and malware. Our findings show that this contextual bandit-based strategy adeptly regulates the frequency of resource-intensive detection tasks, significantly lowering both inference and processing expenses. Remarkably, it achieves this reduction with minimal compromise to overall detection accuracy and efficacy. Full article
(This article belongs to the Special Issue Advances in Internet of Things (IoT) Technologies and Cybersecurity)
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23 pages, 2069 KiB  
Article
Evaluating the Odor Mitigation Effects of Biochar-Enhanced Bedding Materials in a Simulated Bedded Pack Dairy Barn Environment: A Laboratory-Scale Study
by Jinho Shin, Daehun Kim, Yangjoon Lee, Seunghun Lee, Riuh Wardhani and Heekwon Ahn
Appl. Sci. 2025, 15(11), 6361; https://doi.org/10.3390/app15116361 - 5 Jun 2025
Viewed by 214
Abstract
This study evaluated the odor mitigation potential of rice husk biochar in a simulated dairy bedded pack over 21 days. Biochar was incorporated into a dairy manure–sawdust mixture at 5% and 10% dry weight. Emissions of key odorous compounds—ammonia (NH3), sulfur [...] Read more.
This study evaluated the odor mitigation potential of rice husk biochar in a simulated dairy bedded pack over 21 days. Biochar was incorporated into a dairy manure–sawdust mixture at 5% and 10% dry weight. Emissions of key odorous compounds—ammonia (NH3), sulfur compounds, volatile fatty acids, phenol, p-cresol, and indole—were evaluated. Odor units were assessed to determine perceived odor reduction. Biochar significantly reduced NH3 and dimethyl sulfide (DMS) emissions: NH3 by 27% and 43%, and DMS by 53% and 75%, at 5% and 10% application, respectively. The NH3 reduction was attributed to ammoniacal nitrogen adsorption, while the DMS reduction likely resulted from enhanced air permeability suppressing anaerobic bacterial activity. The 5% biochar treatment, achieving 63% and 70% of the NH3 and DMS reductions attained by the 10% treatment, respectively, offers a more practical and cost-effective option. Other odorous compounds were not significantly affected. A temporary reduction in odor units was observed on day 7. Rice husk biochar contains 14.5% atomic Si, primarily as silica, which supports structural stability but hinders pore development, reducing adsorption efficiency. These findings demonstrate the importance of biochar’s physicochemical properties in odor mitigation. Future research should evaluate long-term field performance, microbial interactions, and silica modification strategies. Full article
(This article belongs to the Section Agricultural Science and Technology)
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24 pages, 5441 KiB  
Article
Upgoing and Downgoing Wavefield Separation in Vertical Seismic Profiling Guided by Signal Knowledge Representation
by Cai Lu, Liyuan Qu, Jijun Liu and Jianbo Gao
Appl. Sci. 2025, 15(11), 6360; https://doi.org/10.3390/app15116360 - 5 Jun 2025
Viewed by 222
Abstract
Effective vertical seismic profiling (VSP) of upgoing and downgoing wave separation is essential for high-quality imaging. However, VSP wavefield separation is particularly challenging under complex geological conditions. Existing solutions encompass one derived from the mathematical characteristics of upgoing and downgoing waves, employing signal [...] Read more.
Effective vertical seismic profiling (VSP) of upgoing and downgoing wave separation is essential for high-quality imaging. However, VSP wavefield separation is particularly challenging under complex geological conditions. Existing solutions encompass one derived from the mathematical characteristics of upgoing and downgoing waves, employing signal decomposition methodologies, and another that utilizes data-driven machine learning techniques, achieving wavefield separation by training sample data to identify the distinct characteristics of upgoing and downgoing waves. This study introduces a VSP wave-separation method using signal knowledge representation, primarily by constructing knowledge representations of upgoing and downgoing waves. Physics-informed recurrent neural network FWI and Poynting vector physical knowledge representation yielded accurate velocity models. Axial gradient information was utilized to construct morphological knowledge representations of upgoing and downgoing waves. Directional differentiation knowledge representations were established based on kinematic characteristic disparities between upgoing and downgoing waves in the time-depth domain. These wave knowledge representations (KRs) built a dual convolutional autoencoder. Its distinct branches extracted up/down wave information, while the KRs, transformed into loss functions, enabled knowledge-driven unsupervised VSP wave separation. The proposed methodology was validated using a homogeneous layer and Marmousi models, demonstrating the effective separation of upgoing and downgoing waves from the VSP seismic records. Full article
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18 pages, 3409 KiB  
Article
Machine-Learning-Based Optimal Feed Rate Determination in Machining: Integrating GA-Calibrated Cutting Force Modeling and Vibration Analysis
by Yu-Peng Yeh, Han-Hao Tsai and Jen-Yuan Chang
Appl. Sci. 2025, 15(11), 6359; https://doi.org/10.3390/app15116359 - 5 Jun 2025
Viewed by 277
Abstract
Machining efficiency and stability are crucial for achieving high-quality manufacturing outcomes. One of the primary challenges in machining is the suppression of chatter, which negatively impacts surface finish, tool longevity, and overall process reliability. This study proposes a machine learning-based approach to optimize [...] Read more.
Machining efficiency and stability are crucial for achieving high-quality manufacturing outcomes. One of the primary challenges in machining is the suppression of chatter, which negatively impacts surface finish, tool longevity, and overall process reliability. This study proposes a machine learning-based approach to optimize feed rate in machining operations by integrating a genetic algorithm (GA)-calibrated cutting force model with vibration analysis. A theoretical cutting force dataset is generated under varying machining conditions, followed by frequency-domain analysis using Fast Fourier Transform (FFT) to identify feed rates that minimize chatter. These optimal feed rates are then used to train an Extreme Gradient Boosting (XGBoost) regression model, with Bayesian optimization employed for hyperparameter tuning. The trained model achieves an R2 score of 0.7887, indicating strong prediction accuracy. To verify the model’s effectiveness, robotic milling experiments were conducted using a UR10e manipulator. Surface quality evaluations showed that the model-predicted feed rates consistently resulted in better surface finish and reduced chatter effects compared to conventional settings. These findings validate the model’s ability to enhance machining performance and demonstrate the practical value of integrating simulated dynamics and machine learning for data-driven parameter optimization in robotic systems. Full article
(This article belongs to the Topic Innovation, Communication and Engineering)
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12 pages, 494 KiB  
Article
Design of a Dual-Path Speech Enhancement Model
by Seorim Hwang, Sung Wook Park and Youngcheol Park
Appl. Sci. 2025, 15(11), 6358; https://doi.org/10.3390/app15116358 - 5 Jun 2025
Viewed by 179
Abstract
Although both noise suppression and speech restoration are fundamental to speech enhancement, many Deep neural network (DNN)-based approaches tend to focus disproportionately on one, often overlooking the importance of their joint handling. In this study, we propose a dual-path architecture designed to balance [...] Read more.
Although both noise suppression and speech restoration are fundamental to speech enhancement, many Deep neural network (DNN)-based approaches tend to focus disproportionately on one, often overlooking the importance of their joint handling. In this study, we propose a dual-path architecture designed to balance noise suppression and speech restoration. The main path consists of an encoder and two specialized decoders: one dedicated to estimating the clean speech spectrum and the other to predicting a noise suppression mask. To reinforce the joint modeling of noise suppression and speech restoration, we introduce an auxiliary refinement path. This path consists of a separate encoder–decoder structure and is designed to further refine the enhanced speech by incorporating complementary information, learned independently from the main path. By using this dual-path architecture, the model better preserves fine speech details while reducing residual noise. Experimental results on the VoiceBank + DEMAND dataset show that our model surpasses conventional methods across multiple evaluation metrics in the causal setup. Specifically, it achieves a PESQ score of 3.33, reflecting improved speech quality, and a CSIG score of 4.48, indicating enhanced intelligibility. Furthermore, it demonstrates superior noise suppression, achieving an SNRseg of 10.44 and a CBAK score of 3.75. Full article
(This article belongs to the Special Issue Application of Deep Learning in Speech Enhancement Technology)
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20 pages, 11164 KiB  
Article
Assessment of Transformer Fault Severity from Online Dissolved Gas Analysis Using Positive CUSUM
by Naris Chattranont, Sakhon Woothipatanapan and Nattachote Rugthaicharoencheep
Appl. Sci. 2025, 15(11), 6357; https://doi.org/10.3390/app15116357 - 5 Jun 2025
Viewed by 248
Abstract
Dissolved gas analysis (DGA) is one of the transformer testing methods that has been widely used for a long time because it does not require the transformer to be offline for testing. The inspection can be conducted while the transformer is operating normally, [...] Read more.
Dissolved gas analysis (DGA) is one of the transformer testing methods that has been widely used for a long time because it does not require the transformer to be offline for testing. The inspection can be conducted while the transformer is operating normally, and it is cost-effective. In research on fault severity, thermodynamic theory has been applied to find the energy index of the hydrocarbon gases generated by faults, CH4, C2H6, C2H4 and C2H2, referred to as the Normalised Energy Intensity (NEI). This study examines the application of the Positive Cumulative Sum of Difference (CUSUM) method to hydrocarbon NEI and carbon dioxide NEI for fault severity assessment. The study demonstrates the effectiveness of this approach by means of case studies on the basis of DGA results from two steel plants. In the case of NEI from hydrocarbon gases (NEIHC), the positive CUSUM is compared with the NEI, the NEI score, and the cumulative NEI. In the case of NEI from carbon oxide gases (NEICO), Positive CUSUM is compared with NEI and the ratio of CO2 to CO. It was found that Positive CUSUM for NEIHC is a much better indicator of the severity of defects than the NEI, the NEI score, and the cumulative NEI. In contrast, Positive CUSUM for NEICO gave excessively high values. However, combining NEI with the CO2/CO ratio gave better monitoring results. Therefore, from an online DGA perspective, Positive CUSUM can be used to effectively monitor fault severity. Full article
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17 pages, 5002 KiB  
Article
Research on Blueberry Maturity Detection Based on Receptive Field Attention Convolution and Adaptive Spatial Feature Fusion
by Bingqiang Huang, Zongyi Xie, Hanno Homann, Zhengshun Fei, Xinjian Xiang, Yongping Zheng, Guolong Zhang and Siqi Sun
Appl. Sci. 2025, 15(11), 6356; https://doi.org/10.3390/app15116356 - 5 Jun 2025
Viewed by 157
Abstract
Detecting small objects in complex outdoor conditions remains challenging. This paper proposes an improved version of YOLOv8n for the detection of blueberry in challenging outdoor scenarios. In this context, this article addresses feature extraction, small-target detection, and multi-scale feature fusion. Specifically, the C2F-RFAConv [...] Read more.
Detecting small objects in complex outdoor conditions remains challenging. This paper proposes an improved version of YOLOv8n for the detection of blueberry in challenging outdoor scenarios. In this context, this article addresses feature extraction, small-target detection, and multi-scale feature fusion. Specifically, the C2F-RFAConv module is introduced to enhance spatial receptive field learning and a P2-level detection layer is introduced for small and distant targets and fused by a four-head adaptive spatial feature fusion detection head (Detect-FASFF). Additionally, the Focaler-CIoU loss is chosen to mitigate sample imbalance, accelerate convergence, and improve overall model performance. Experiments on our blueberry maturity dataset show that the proposed model outperforms YOLOv8n, achieving 2.8% higher precision, 4% higher recall, and a 4.5% increase in mAP@0.5, with an FPS of 80. It achieves 89.1%, 91.0%, and 85.5% AP for ripe, semi-ripe, and unripe blueberries, demonstrating robustness under varying lighting, occlusion, and distance conditions. Compared to other lightweight networks, the model offers superior accuracy and efficiency. Future work will focus on model compression for real-world deployment. Full article
(This article belongs to the Special Issue Application, Optimization and Architecture of Deep Learning Neural Network)
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17 pages, 2735 KiB  
Article
Influence of Crossing Cable Arrangement on the Static Performance of Long-Span Three-Tower Cable-Stayed Bridges
by Shengbo Chai, Kaijie Huang and Xiulan Wang
Appl. Sci. 2025, 15(11), 6355; https://doi.org/10.3390/app15116355 - 5 Jun 2025
Viewed by 160
Abstract
Insufficient structural stiffness is a key technical challenge that restricts the increase in span of multi-tower cable-stayed bridges. In order to clarify the application effect of crossing cables in long-span, multi-tower cable-stayed bridges, theoretical analysis and the finite element method were used to [...] Read more.
Insufficient structural stiffness is a key technical challenge that restricts the increase in span of multi-tower cable-stayed bridges. In order to clarify the application effect of crossing cables in long-span, multi-tower cable-stayed bridges, theoretical analysis and the finite element method were used to study the influence of the cable sag effect on the longitudinal constraint stiffness of crossing cables. The longitudinal constraint stiffness formula of the crossing cable was modified by introducing the equivalent elastic modulus to consider the cable sag effect. Based on the stiffness formula, the influence of the main span, initial stress of the crossing cable, and the ratio of the crossing cable area on its restraining effect was analyzed. The finite element model of a three-tower cable-stayed bridge with main span length of 1000 m and 1500 m is established to verify the accuracy of the formula, and the influence of the number of crossing cables and the tower height on the restraining effect of crossing cables is explored. The research results indicate that as the main span length increases, the location of maximum restraining stiffness of crossing cables moves closer to the mid span; increasing the area of crossing cables connected to the mid tower can effectively suppress the deviation of the tower. In addition, increasing the main span length will reduce the restraining effect of the crossing cables, while changes in the height of the towers do not affect the enhancement effect of the crossing cables on structural rigidity. Full article
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15 pages, 880 KiB  
Article
Comparative Analysis of Lower Limb Muscle Activity During Isometric External Rotation in Static and Dynamic Modeling Approaches
by Miłosz Chrzan, Robert Michnik, Sławomir Suchoń, Michał Burkacki and Katarzyna Nowakowska-Lipiec
Appl. Sci. 2025, 15(11), 6354; https://doi.org/10.3390/app15116354 - 5 Jun 2025
Viewed by 230
Abstract
This study investigates differences in lower limb muscle activity during isometric external hip rotation while standing using static and dynamic models within the AnyBody Modeling System. Thirty-three participants performed controlled isometric rotations using a custom-designed device capable of simultaneously measuring rotational moments and [...] Read more.
This study investigates differences in lower limb muscle activity during isometric external hip rotation while standing using static and dynamic models within the AnyBody Modeling System. Thirty-three participants performed controlled isometric rotations using a custom-designed device capable of simultaneously measuring rotational moments and ground reaction forces. Both static and dynamic simulations were conducted for each subject using personalized biomechanical models. Muscle activity values at the point of peak rotational moment were analyzed for twelve key muscles involved in hip rotation and stabilization of the knee joint, and statistical differences were assessed for significance. Muscles from the gluteal group (Gluteus minimus, medius, and maximus) generally showed lower activation in dynamic simulations, although this trend was not statistically significant for all muscles or test conditions. The mean difference in muscle activity values between static and dynamic simulations was between 0.03 and 0.08 for the gluteal group muscles and up to 0.15 for the Iliopsoas. Static models overestimated the role of stabilizers. Significant differences (p ≤ 0.05, Wilcoxon signed-rank test) were observed between the two approaches in terms of predicted muscle activation. In conclusion, discrepancies in muscle activity predictions between static and dynamic simulations highlight the need for task-specific simulation design and careful result interpretation. Full article
(This article belongs to the Special Issue Numerical Simulation in Biomechanics and Biomedical Engineering-3rd Edition)
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18 pages, 4011 KiB  
Article
Effect of Marine Coolant Additives on Cavitation Erosion–Corrosion of Diesel Engine Cylinder Liner
by Woo-Seck Jeon and Il-Cho Park
Appl. Sci. 2025, 15(11), 6353; https://doi.org/10.3390/app15116353 - 5 Jun 2025
Viewed by 170
Abstract
In this study, cavitation erosion tests were conducted to investigate the effects of the presence of coolant additives and chlorides on the corrosion and cavitation erosion of cylinder liners in marine diesel engines. Electrochemical experiments were conducted to evaluate the corrosion characteristics of [...] Read more.
In this study, cavitation erosion tests were conducted to investigate the effects of the presence of coolant additives and chlorides on the corrosion and cavitation erosion of cylinder liners in marine diesel engines. Electrochemical experiments were conducted to evaluate the corrosion characteristics of ductile cast iron (DCI), and the corrosion potential and corrosion current density were measured. In addition, weight loss, surface roughness, and maximum surface damage depth were quantified as a function of cavitation exposure time. Furthermore, to investigate the erosion and erosion–corrosion characteristics induced by cavitation attack, the damaged surface morphology was closely examined using a scanning electron microscope (SEM) after the cavitation erosion tests. The results revealed that the coolant additive effectively protected the DCI from corrosion caused by aggressive chlorides. In particular, when an appropriate amount of additive was added to a coolant containing 100 ppm of chloride, the corrosion current density of DCI was reduced by approximately 31.7 times, significantly improving corrosion resistance. Therefore, different surface damage mechanisms corresponding to cavitation erosion and cavitation erosion–corrosion were identified depending on the presence or absence of the coolant additive during the cavitation erosion tests. Full article
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21 pages, 860 KiB  
Review
Fuzzy Adaptive Dynamic Surface Control with Constant Gain for Non-Affine Pure-Feedback Systems
by Lian Chen, Yixu Wang, Jianjun Cui, Daiyue Wang and Song Ling
Appl. Sci. 2025, 15(11), 6352; https://doi.org/10.3390/app15116352 - 5 Jun 2025
Viewed by 163
Abstract
This paper considers tracking control of non-affine pure-feedback systems with uncertain disturbances. The “explosion of complexity” in the process of backstepping design is eliminated by introducing the output of first-order filter to replace the derivative of virtual control signal. The mean-value theorem is [...] Read more.
This paper considers tracking control of non-affine pure-feedback systems with uncertain disturbances. The “explosion of complexity” in the process of backstepping design is eliminated by introducing the output of first-order filter to replace the derivative of virtual control signal. The mean-value theorem is utilized to overcome the non-affine difficulties appearing from the pure-feedback systems. By employing fuzzy logic systems (FLSs) and dynamic surface approach, an adaptive fuzzy controller with only one adaptive parameter is presented. Stability analysis shows that the proposed control method guarantees that all closed-loop system signals are semi-globally uniformly ultimately bounded. The main innovations of this paper are that the final gain function is viewed as an adjustable constant gain in the procedure of actual control signal design and the FLSs only handle state errors and disturbances. The new controller not only has the advantages of tracking performance, but it also reduces the computational burden in comparison with traditional adaptive backstepping method whose FLSs need to tackle with all gain function and system dynamics. A comparison simulation example shows the effectiveness of the proposed controller. Full article
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19 pages, 998 KiB  
Article
Neural Network Method for Distance Prediction and Impedance Matching of a Wireless Power Transfer System
by Lorenzo Sabino, Davide Milillo, Fabio Crescimbini and Francesco Riganti Fulginei
Appl. Sci. 2025, 15(11), 6351; https://doi.org/10.3390/app15116351 - 5 Jun 2025
Viewed by 212
Abstract
This study introduces a novel and versatile application of neural networks (NNs) to enhance two distinct aspects of Wireless Power Transfer (WPT) systems. First, a compact NN architecture is presented for accurate distance estimation and automated impedance matching in a WPT system. Trained [...] Read more.
This study introduces a novel and versatile application of neural networks (NNs) to enhance two distinct aspects of Wireless Power Transfer (WPT) systems. First, a compact NN architecture is presented for accurate distance estimation and automated impedance matching in a WPT system. Trained on either impedance measurements or scattering parameters acquired from the transmitter side, this NN effectively predicts the inter-coil distance and identifies optimal capacitance values for maximizing power transfer. Validation using both simulated and experimental data demonstrates consistently low prediction error rates. Second, a separate NN is employed to predict the optimal transmission frequency for minimizing the phase angle between voltage and current, thereby maximizing the power factor. This NN, validated on experimental data spanning various load conditions and inter-coil distances, achieves performance comparable to traditional PI control, but with significantly faster prediction speeds. This speed advantage is crucial for real-time applications and directly contributes to improved power efficiency. The results presented in this study, including the high accuracy of distance and capacitance prediction and the rapid determination of optimal frequencies for power factor maximization, showcase the significant potential of NNs for optimizing WPT systems. These findings open the way for more efficient, adaptable, and intelligent wireless energy transfer solutions, with potential applications ranging from dynamic charging of electric vehicles to real-time optimization of implantable medical devices. Full article
(This article belongs to the Special Issue New Insights into Wireless Power Transmission Systems)
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11 pages, 2227 KiB  
Article
Relationship Between Ball Speed and Spin in Elite Youth Table Tennis Players Using Optical Sensors
by Thibault Delumeau, Christophe Plot, Eric Le Carpentier, Thibault Deschamps and Pierre Mousseau
Appl. Sci. 2025, 15(11), 6350; https://doi.org/10.3390/app15116350 - 5 Jun 2025
Viewed by 204
Abstract
This paper investigates the relationship between ball spin and linear speed in table tennis. This study uses a simple photodiode montage to introduce a methodology for measuring spin based on light reflection on the ball’s surface. Two optical-based measurement systems were developed to [...] Read more.
This paper investigates the relationship between ball spin and linear speed in table tennis. This study uses a simple photodiode montage to introduce a methodology for measuring spin based on light reflection on the ball’s surface. Two optical-based measurement systems were developed to measure either the ball’s speed or spin. This paper describes sensor calibration and error estimation. Those systems measured ball kinetic parameters from nine young elite players (aged 15 ± 1.5 years) who volunteered to perform 4 exercises. Results showed a strong positive linear relationship between the ball’s speed and spin (r = 0.96, R2 = 0.93, p < 0.001). The effect of exercise conditions on ball speed has been studied using a statistical test, ANOVA. Results showed a significant main effect of exercise conditions on ball speed (p < 0.05) with a very large effect size (η2 = 0.82). The study revealed significant variations in linear speed based on the type of stroke (backhand, forehand) and the incoming ball’s spin (topspin, backspin), with topspin forehand strokes achieving higher speeds compared to backhand strokes. These findings provide valuable knowledge for players to enhance performance in a competitive environment. Full article
(This article belongs to the Special Issue Applied Sports Performance Analysis)
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28 pages, 648 KiB  
Article
Telemedicine Queuing System Study: Integrating Queuing Theory, Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO)
by Deborah Tshiamala and Lagouge Tartibu
Appl. Sci. 2025, 15(11), 6349; https://doi.org/10.3390/app15116349 - 5 Jun 2025
Viewed by 154
Abstract
Telemedicine has emerged as a vital tool for expanding healthcare access, particularly in underserved areas, yet its effectiveness is often hindered by inefficient queuing systems, fluctuating patient demand, and limited resources. This study addresses these challenges by developing a hybrid Artificial Neural Network–Particle [...] Read more.
Telemedicine has emerged as a vital tool for expanding healthcare access, particularly in underserved areas, yet its effectiveness is often hindered by inefficient queuing systems, fluctuating patient demand, and limited resources. This study addresses these challenges by developing a hybrid Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) model aimed at improving the performance of telemedicine queuing systems. A simulation-based dataset was generated to represent patient arrivals, service rates, and queuing behaviors. An ANN was trained to predict key performance metrics, including queue intensity, system utilization, and delays. To further enhance the model’s predictive capabilities, PSO was applied to optimize critical ANN parameters, such as neuron count, swarm size, and acceleration factors. The optimized ANN-PSO model achieved high predictive accuracy, with correlation coefficients (R2) consistently exceeding 0.90 and low mean squared errors across most outputs. The findings show that optimal parameter configurations vary depending on the specific performance metric, reinforcing the value of adaptive optimization. The results confirm the ANN-PSO model’s ability to accurately predict queuing behavior and optimize system performance, providing a practical decision-support tool for telemedicine administrators to dynamically manage patient flow, reduce waiting times, and enhance resource utilization under variable demand conditions. Full article
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23 pages, 7660 KiB  
Article
Thermal Load Predictions in Low-Energy Buildings: A Hybrid AI-Based Approach Integrating Integral Feature Selection and Machine Learning Models
by Youness El Mghouchi and Mihaela Tinca Udristioiu
Appl. Sci. 2025, 15(11), 6348; https://doi.org/10.3390/app15116348 - 5 Jun 2025
Viewed by 207
Abstract
A hybrid Artificial Intelligence (AI) framework centered on metamodeling, integrating simulation data with hybrid data-driven techniques, was implemented to enhance the predictive accuracy and optimization of thermal load projections in three distinct climates in Morocco. Initially, 13 machine learning (ML) models were assessed [...] Read more.
A hybrid Artificial Intelligence (AI) framework centered on metamodeling, integrating simulation data with hybrid data-driven techniques, was implemented to enhance the predictive accuracy and optimization of thermal load projections in three distinct climates in Morocco. Initially, 13 machine learning (ML) models were assessed to predict heating and cooling loads. The best-performing models from this stage were then selected for the subsequent phase to find out the optimal combinations of inputs to predict thermal loads. In this phase, an Integral Feature Selection (IFS) method was employed in conjunction with the best ML models. An extensive evaluation using advanced statistical measures was performed during the evaluation stage. The results reveal that, for each climate, numerous high-accuracy prediction pathways were identified for thermal load prediction, surpassing the confidence level of 99% for R2. The results found here outperformed those reported by other researchers in thermal load predictions for Low-Energy Buildings (LEBs). Full article
(This article belongs to the Special Issue Renewable Energy in Smart Cities)
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