Applied Sciences

18 pages, 3430 KB

Open AccessArticle

Real-Time Inertia Estimation and Adaptive-Model-Predictive-Control-Based Virtual Inertia Support for Frequency Control in Low-Inertia Systems

by Cenk Andiç

Appl. Sci. 2026, 16(4), 2161; https://doi.org/10.3390/app16042161 - 23 Feb 2026

Viewed by 653

Abstract

This study presents adaptive virtual inertia strategy supported by a model-predictive-control (MPC)-based real-time inertia estimation method. The proposed approach aims to mitigate frequency stability problems caused by low inertia in isolated power systems with high penetration of photovoltaics. The system inertia is estimated [...] Read more.

This study presents adaptive virtual inertia strategy supported by a model-predictive-control (MPC)-based real-time inertia estimation method. The proposed approach aims to mitigate frequency stability problems caused by low inertia in isolated power systems with high penetration of photovoltaics. The system inertia is estimated using frequency measurements obtained from phasor measurement unit. Based on the obtained real-time inertia information, the PI gains (

K_{p}

and

K_{i}

) in load frequency control unit and virtual inertia gain (

K_{v i}

) are updated simultaneously via MPC-based adaptive mechanism. In the first scenario, it was shown that under 10% PV penetration, the system inertia decreased from 5.00 s to 4.54 s, and the system became more sensitive to load changes. The proposed adaptive battery energy storage system support shows that a load change of 0.1 p.u. results in a response of 0.079 p.u. in 0.17 s. The adaptive BESS response raises frequency nadir from 49.6892 Hz to 49.9635 Hz, improving maximum frequency deviation by 88.25%. In the second scenario, it was observed that method maintained its stability even when the system inertia dropped to 3.33 s in 10–50% PV penetration range. This study presents integrated and innovative frequency control strategy for modern isolated power systems. Full article

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16 pages, 9682 KB

Open AccessArticle

Quasi-Static and Fatigue Strength of Copper-Brazed Stainless Steel

by Srečko Glodež, Tonica Bončina, Žan Dvoršak, Branko Nečemer and Franc Zupanič

Appl. Sci. 2026, 16(4), 2160; https://doi.org/10.3390/app16042160 - 23 Feb 2026

Viewed by 394

Abstract

This study investigates the quasi-static and fatigue strength of copper-brazed 316L stainless steel. Quasi-static and fatigue tests were conducted at room temperature (20 °C) using a Zwick/Roell Vibrophore 100 testing machine and specially designed copper-brazed specimens. Two types of specimens were prepared—tensile and [...] Read more.

This study investigates the quasi-static and fatigue strength of copper-brazed 316L stainless steel. Quasi-static and fatigue tests were conducted at room temperature (20 °C) using a Zwick/Roell Vibrophore 100 testing machine and specially designed copper-brazed specimens. Two types of specimens were prepared—tensile and shear specimens—to obtain the stress–strain relationships (σ–ε and τ–ε) and the fatigue life (S–N) curves. Based on the experimental results, the quasi-static and fatigue strengths of the copper-brazed joints under external tensile and shear loading were evaluated. The fatigue tests reveal that the shear fatigue strength is significantly lower than the tensile fatigue strength. Furthermore, a comprehensive investigation was conducted, focusing on the metallographic characterisation of the brazed joint and fractographic analyses of fracture surfaces obtained under quasi-static and fatigue loading, with particular emphasis on the shear strength of the investigated brazed joint. The experimental results obtained may be crucial for designing engineering structures (e.g., plate heat exchangers), where copper-brazed joints are the weakest members of the structure. Full article

(This article belongs to the Special Issue Fatigue and Fracture Behavior of Engineering Materials)

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21 pages, 767 KB

Open AccessReview

Accuracy and Clinical Relevance of Robot-Assisted Implant Surgery: An Umbrella Review

by Javier Basualdo Allende, Vanessa Campos-Bijit, Constanza Morales-Gómez, Leonardo Díaz, Cristian Bersezio and Eduardo Fernández

Appl. Sci. 2026, 16(4), 2159; https://doi.org/10.3390/app16042159 - 23 Feb 2026

Viewed by 469

Abstract

Robot-assisted implant surgery (RAIS) represents the most advanced form of digitally guided implant placement, integrating virtual planning with mechanically constrained execution and real-time control. Although multiple systematic reviews suggest superior accuracy with robotic systems, the magnitude and clinical relevance of these gains remain [...] Read more.

Robot-assisted implant surgery (RAIS) represents the most advanced form of digitally guided implant placement, integrating virtual planning with mechanically constrained execution and real-time control. Although multiple systematic reviews suggest superior accuracy with robotic systems, the magnitude and clinical relevance of these gains remain uncertain at the highest level of evidence. This umbrella review, conducted according to PRISMA 2020 and Joanna Briggs Institute guidelines, aimed to synthesize and critically appraise systematic reviews and meta-analyses evaluating the accuracy of Robot-assisted implant surgery (RAIS) in dental implantology. Search across five major databases identified seven eligible reviews published between 2023 and 2025, including clinical, cadaveric, and in vitro evidence. Across reviews, RAIS consistently demonstrated the highest placement accuracy, with pooled mean coronal deviations of 0.60–0.73 mm, apical deviations of 0.63–0.70 mm, and angular deviations typically between 1.4° and 1.7°. Comparative meta-analyses reported significant reductions in linear (−0.15 to −0.21 mm) and angular deviations (−1.2° to −1.4°) compared with dynamic navigation. Despite these technical advantages, evidence linking improved accuracy to enhanced implant survival, reduced complications, or superior patient-reported outcomes was limited. Robotic workflows were associated with longer setup times, while safety profiles were comparable to other guided techniques. Overall, RAIS provides the highest placement accuracy currently reported; however, further high-quality clinical trials are needed to clarify its impact on long-term clinical outcomes and cost-effectiveness. Full article

(This article belongs to the Special Issue Innovations in Dental Implants and Prosthodontics)

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21 pages, 2586 KB

Open AccessArticle

Multi-Agent Reinforcement Learning Model Simulation for Attention-Deficit Hyperactivity Disorder Children

by Zineb Namasse, Zineb Hidila, Mohamed Tabaa, Mounia Elhaddadi and Samar Mouchawrab

Appl. Sci. 2026, 16(4), 2158; https://doi.org/10.3390/app16042158 - 23 Feb 2026

Viewed by 480

Abstract

Background: A child with Attention-Deficit Hyperactivity Disorder (ADHD) faces two issues: inattention and hyperactivity/impulsivity. These two symptoms make the child’s life more challenging compared to non-ADHD individuals. Therefore, one of the steps toward better quality of life involves cooperation with and contact with [...] Read more.

Background: A child with Attention-Deficit Hyperactivity Disorder (ADHD) faces two issues: inattention and hyperactivity/impulsivity. These two symptoms make the child’s life more challenging compared to non-ADHD individuals. Therefore, one of the steps toward better quality of life involves cooperation with and contact with the environment to better address this condition. Thanks to Artificial Intelligence (AI), doctors, caregivers, and parents are increasingly better able to understand the hardships these children face. One AI technique is Reinforcement Learning (RL). Methods: We propose an RL model simulation with 44 child agents with or without ADHD, using the Independent Deep Q Network (IDQN), Value Decomposition Network (VDN), and QMIX algorithms. Results: Comparing the results obtained with these three algorithms, children with ADHD find it more challenging to choose the maximum rewards than neurotypical children (395 at episode 300 for non-ADHD compared to 340 at episode 120 for ADHD using IDQN, 69 from episode 90 for ADHD compared to 82 for non-ADHD via VDN, and 31 at episode 110 for ADHD versus 28 at episode 110 for non-ADHD with QMIX). Conclusions: The simulated ADHD agents struggle to aim for the maximum rewards as much as neurotypical children. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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42 pages, 1834 KB

Open AccessArticle

Privacy-by-Design in AI-Assisted Systems for Caregivers of Children with Autism: A Secure Multi-Agent Architecture

by Ionuț Croitoru, Cristina Elena Turcu and Corneliu Octavian Turcu

Appl. Sci. 2026, 16(4), 2157; https://doi.org/10.3390/app16042157 - 23 Feb 2026

Viewed by 830

Abstract

Caregivers of children with Autism Spectrum Disorder (ASD) frequently experience chronic psychological stress, thereby necessitating accessible support. Although artificial intelligence (AI)-based assisted technologies have the potential to reduce caregiver workload, most existing solutions lack robust privacy control and clinical interoperability, which significantly limits [...] Read more.

Caregivers of children with Autism Spectrum Disorder (ASD) frequently experience chronic psychological stress, thereby necessitating accessible support. Although artificial intelligence (AI)-based assisted technologies have the potential to reduce caregiver workload, most existing solutions lack robust privacy control and clinical interoperability, which significantly limits their adoption in regulated healthcare environments. To address these challenges, this paper proposes a Privacy-by-Design (PbD) multi-agent architecture that enables consent-aware, auditable, and privacy-preserving AI-assisted support for caregivers of children with ASD. The effectiveness of the proposed architecture was evaluated using two datasets: one focusing on clinically grounded autism-related knowledge and another reflecting naturalistic caregiver observation language. System performance was assessed using a Retrieval-Augmented Generation Assessment (RAGAs)-based framework with a Large Language Model (LLM)-as-a-Judge approach implemented via a locally deployed Llama 3 8B model. The system achieved answer relevancy scores of 0.767 for the clinical dataset and 0.750 for the observational dataset, with corresponding Recall@K values of 0.400 and 0.742, respectively. Context precision ranged from 0.599 to 0.631, and no harmful content was detected. Overall, the proposed architecture demonstrates secure caregiver–specialist collaboration through consent-aware routing, anonymised data storage, and controlled data reconstruction, providing a regulation-aligned design option for privacy-preserving AI integration in assisted care platforms. Full article

(This article belongs to the Special Issue Intelligent Data Processing and Management: Technologies and Applications)

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15 pages, 886 KB

Open AccessArticle

Modeling and Control of a Nonlinear Dual-Pendulum Energy Harvester Using BLDC Motors and MPPT Algorithm

by Marcin Fronc, Marek Borowiec, Grzegorz Litak, Krzysztof Kolano and Mateusz Waśkowicz

Appl. Sci. 2026, 16(4), 2156; https://doi.org/10.3390/app16042156 - 23 Feb 2026

Viewed by 362

Abstract

Nonlinear energy harvesting systems based on multibody structures constitute a promising solution for autonomous devices powered by ambient vibrations. This paper presents the modeling and control of a nonlinear energy harvester employing a double pendulum configuration and BLDC motors operating as generators. The [...] Read more.

Nonlinear energy harvesting systems based on multibody structures constitute a promising solution for autonomous devices powered by ambient vibrations. This paper presents the modeling and control of a nonlinear energy harvester employing a double pendulum configuration and BLDC motors operating as generators. The primary objective of the study was to develop a control strategy that enables the maximization of harvested power while simultaneously improving the energy conversion efficiency during the charging of the battery supplying the target system. The developed model incorporates the mechanical equations of motion of the double pendulum, an electrical model of the BLDC motors, and two independently controlled buck–boost converters, each connected to one joint of the pendulum. In addition, a perturb-and-observe (P&O) maximum power point tracking (MPPT) algorithm was implemented, which utilizes a portion of the computational resources of the target system’s microcontroller and allows for dynamic adjustment of the electrical loads seen by the generators. Simulation results obtained in the Simulink environment confirm that the application of independent power converters combined with local MPPT control leads to an increase in the total harvested power and ensures more stable battery charging under conditions of variable mechanical excitation. The obtained results demonstrate the effectiveness of the proposed approach and indicate its potential applicability in self-powered systems operating in environments characterized by irregular and stochastic vibrations. Full article

(This article belongs to the Special Issue Nonlinear Dynamics in Mechanical Engineering and Thermal Engineering)

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28 pages, 3863 KB

Open AccessArticle

Synergistic Optimization of Yangshan Port’s Collection-Distribution Network with Application of Electric Autonomous Container Truck Configuration Under Carbon Constraints

by You Kong, Lingye Xu, Qile Wu and Zhihong Yao

Appl. Sci. 2026, 16(4), 2155; https://doi.org/10.3390/app16042155 - 23 Feb 2026

Viewed by 391

Abstract

Decarbonization has emerged as a crucial objective in the optimization of port collection and distribution networks. To investigate the synergistic effects of carbon trading mechanisms and the implementation of electric autonomous container trucks (EACTs), this study develops a multi-objective bi-level programming model that [...] Read more.

Decarbonization has emerged as a crucial objective in the optimization of port collection and distribution networks. To investigate the synergistic effects of carbon trading mechanisms and the implementation of electric autonomous container trucks (EACTs), this study develops a multi-objective bi-level programming model that simultaneously minimizes transportation cost, carbon trading cost, and transportation time. The model is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), generating a Pareto-optimal solution set, from which the optimal solution is selected using a normalized ideal point method. Simulation-based case studies validate the feasibility and practical applicability of the proposed model. The results show that the optimized network significantly outperforms the traditional road-dominant mode. Under the baseline carbon price of 70 CNY/ton, the optimal deployment rate of EACTs reaches 25.03% and 33.87%. Sensitivity analysis reveals a distinct non-linear threshold effect: increasing the carbon price to 90 CNY/ton drives the EACT adoption rate to 32.76% and 45.38%, resulting in a 6.98% reduction in carbon emissions and a 12.75% decrease in total operational costs compared to the baseline scenario. Additionally, strict carbon quotas (e.g., 3000 tons) are found to further compel a modal shift, peaking EACT usage at 35.08% and 46.71%. These quantitative findings offer actionable insights for optimizing multimodal transport structures and refining carbon trading policies. Full article

(This article belongs to the Special Issue Advanced, Smart, and Sustainable Transportation)

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22 pages, 22771 KB

Open AccessArticle

Accurate Finite Element Simulation of Unidirectional and Alternate Multi-Pass Drawings, Focusing on Residual Stresses

by Yeongbin Shin, Boseung Hong, Sukhwan Chung, Wanjin Chung and Mansoo Joun

Appl. Sci. 2026, 16(4), 2154; https://doi.org/10.3390/app16042154 - 23 Feb 2026

Viewed by 338

Abstract

An optimized numerical model is proposed, accompanied by an in-depth investigation of the characteristics of rod and tube drawing processes and a critical review of previous studies on tube drawing from the perspectives of practicality and accuracy. An automatic simulation framework, specifically a [...] Read more.

An optimized numerical model is proposed, accompanied by an in-depth investigation of the characteristics of rod and tube drawing processes and a critical review of previous studies on tube drawing from the perspectives of practicality and accuracy. An automatic simulation framework, specifically a dual-step simulation scheme incorporating a specialized remeshing function, is presented to enhance the applicability and accuracy of simulations for rod and tube drawing processes. The effectiveness of the proposed finite element (FE) analysis model is evaluated by comparing FE-predicted results with those reported in the literature. Using typical examples of various multi-pass drawing processes, including both unidirectional and alternately driven cases, the importance of the proposed FE model and its automatic analysis capability in improving engineering productivity is demonstrated. Full article

(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing, 2nd Edition)

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31 pages, 2629 KB

Open AccessArticle

Using EEG to Explore Teachers’ Emotional Responses to Problem Behaviours in Learners with Autism Spectrum Disorder

by Zekai Alper Alp, Veysel Aksoy, Fatma Latifoğlu, Şerife Gengeç Benli and Avşar Ardıç

Appl. Sci. 2026, 16(4), 2153; https://doi.org/10.3390/app16042153 - 23 Feb 2026

Viewed by 611

Abstract

This study aimed to investigate the emotional changes in the brain activity of 34 special education teachers using electroencephalography (EEG) signals in response to common problem behaviours observed in students with Autism Spectrum Disorder (ASD), such as self-harm, aggression, tantrums, and stereotyped behaviours. [...] Read more.

This study aimed to investigate the emotional changes in the brain activity of 34 special education teachers using electroencephalography (EEG) signals in response to common problem behaviours observed in students with Autism Spectrum Disorder (ASD), such as self-harm, aggression, tantrums, and stereotyped behaviours. Vignettes with Turkish narration and stimulus videos were used for each behaviour type to trigger emotions. EEG data were collected from the frontal, temporal, parietal, and occipital regions, and subjected to pre-processing steps such as band-pass filtering (0.5–40 Hz) and Independent Component Analysis (ICA), and various spectral and statistical features were extracted. To improve classification performance, feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method, and Support Vector Machine (SVM), Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), and Random Forest (RF) algorithms were used for classification. The machine learning techniques used achieved success rates of up to 97.66% F1 score in classifying teachers’ brain activity in response to different behavioural patterns. Teachers showed strong negative emotional responses to self-harm, aggression, and tantrums, while showing less response to the stereotypical behaviours. It is recommended that the study be replicated with different signals and teachers. Full article

(This article belongs to the Special Issue Improving Healthcare with Artificial Intelligence)

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15 pages, 3816 KB

Open AccessArticle

EW YOLO: Edge Computing IoT and YOLOv11 Setup for E-Waste

by Shubhyansh Rai, Rashmi Chawla, Munish Vashishath and Giancarlo Fortino

Appl. Sci. 2026, 16(4), 2152; https://doi.org/10.3390/app16042152 - 23 Feb 2026

Viewed by 488

Abstract

An industry 5.0 revolution is characterized by advanced automation and human-centric design resulting in an unprecedented growth in the electronics sector. This advancement comes at the cost of a surge in electronic waste (E-waste) generation. In the past, many researchers have reported on [...] Read more.

An industry 5.0 revolution is characterized by advanced automation and human-centric design resulting in an unprecedented growth in the electronics sector. This advancement comes at the cost of a surge in electronic waste (E-waste) generation. In the past, many researchers have reported on E-waste recycling and management; however, the efficient collection of domestic E-waste still remains a critical challenge. This research paper presents a novel approach to domestic E-waste management by developing a smart E-Bin equipped with an Electronic Waste Detection and Bin-Level Control System (EDBLCS), IoT setup, and a YOLOv11-powered (EW YOLO) computer vision system. This innovative solution selectively collects only E-waste, ensuring accurate identification and preventing contamination with other waste streams, with the mAP@0.50 score increased to 0.90074 by Epoch 50, while mAP@0.50–0.95 reached 0.73899 using YOLOv11. The primary contribution of this work is the integration of YOLOv11-based real-time detection with an IoT-enabled smart E-Bin framework to enable selective, edge-oriented domestic E-waste segregation. Full article

(This article belongs to the Section Green Sustainable Science and Technology)

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29 pages, 11858 KB

Open AccessArticle

Study on a Damage Constitutive Model for Surrounding Rock Under the Coupling Effects of Initial Damage and Cyclic Blasting

by Kaiyi Xie and Bo Wu

Appl. Sci. 2026, 16(4), 2151; https://doi.org/10.3390/app16042151 - 23 Feb 2026

Viewed by 419

Abstract

To reveal the cumulative damage mechanism of surrounding rock with initial damage under cyclic blasting loads during tunnel reconstruction and expansion, this study combines theoretical modeling, split Hopkinson pressure bar (SHPB) tests, and three-dimensional numerical simulation. First, based on the Z-W-T model framework, [...] Read more.

To reveal the cumulative damage mechanism of surrounding rock with initial damage under cyclic blasting loads during tunnel reconstruction and expansion, this study combines theoretical modeling, split Hopkinson pressure bar (SHPB) tests, and three-dimensional numerical simulation. First, based on the Z-W-T model framework, a dynamic damage constitutive model capable of uniformly describing the coupling effects of initial damage and dynamic disturbance is constructed by introducing a damage evolution equation based on the Weibull distribution and an initial damage variable D₀. Second, SHPB impact tests are conducted on sandstone specimens with different D₀ values under various strain rates to obtain their dynamic mechanical responses. The model parameters are calibrated and its validity is verified. Finally, the validated model is implemented in ABAQUS via a user material subroutine to establish a 3D finite element model of the tunnel reconstruction and expansion, and a numerical test with seven cyclic blasting events is performed. The results show that the dynamic compressive strength of the surrounding rock increases significantly with increasing strain rate, but D₀ has a clear weakening effect, which is amplified under high strain rates. Numerical simulation reveals that the damage in the surrounding rock accumulates nonlinearly with the number of blasts. The incremental expansion of the damage zone after the first blast is 1.51 m, decreasing to 0.03 m by the seventh blast, indicating a successively diminishing incremental expansion per blast. This reflects the saturation characteristics of damage accumulation and the diminishing driving effect of subsequent blasts due to energy dissipation and compaction within the already-damaged zone. The study provides key theoretical and analytical tools for evaluating the long-term stability of surrounding rock with initial damage under cyclic blasting. Full article

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15 pages, 4263 KB

Open AccessArticle

Driver Attention Prediction Based on Adaptive Fusion of Cross-Modal Features

by Mingfang Zhang, Tong Zhang, Congling Yan and Yiran Zhang

Appl. Sci. 2026, 16(4), 2150; https://doi.org/10.3390/app16042150 - 23 Feb 2026

Viewed by 374

Abstract

To investigate the dynamic changes in driver attention in complex road traffic scenarios, this paper proposes a driver attention prediction method based on cross-modal adaptive feature fusion (DAFNet). First, semantic segmentation is applied to the input image sequences, and a dual-branch encoder using [...] Read more.

To investigate the dynamic changes in driver attention in complex road traffic scenarios, this paper proposes a driver attention prediction method based on cross-modal adaptive feature fusion (DAFNet). First, semantic segmentation is applied to the input image sequences, and a dual-branch encoder using a 3D residual network is designed to extract spatio-temporal features from both RGB images and semantic information in parallel. Next, a 3D deformable attention mechanism is introduced to enhance the traditional Transformer algorithm, which focuses on the key salient regions through spatio-temporal offset prediction and adaptive fusion of cross-modal features. Subsequently, a predictive recurrent neural network is employed to forecast the fused spatio-temporal features and improve the stability of long-term sequence prediction. Finally, the driver attention results are predicted by a lightweight decoder. Experimental results demonstrate that the proposed method outperforms the comparative methods in overall performance. The predictions not only capture salient regions in driving scenes in a bottom-up manner but also track the driver’s intent in a top-down manner. Thus, our method exhibits strong adaptability to various complex traffic scenarios. Additionally, the method achieves an inference speed of 53.73 frames per second, satisfying the real-time performance requirement of on-vehicle systems. Full article

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27 pages, 2718 KB

Open AccessArticle

Quantifying Hidden Carbon Emissions Induced from Curbside Capacity Loss in Urban Freight Operations

by Angel Gil Gallego, María Pilar Lambán, Jesús Royo Sánchez, Juan Carlos Sánchez Catalán and Paula Morella Avinzano

Appl. Sci. 2026, 16(4), 2149; https://doi.org/10.3390/app16042149 - 23 Feb 2026

Viewed by 319

Abstract

Urban curbside loading and unloading zones are increasingly affected by competing non-logistics uses, such as outdoor terraces or resident parking, leading to reductions in effective curbside length. These design decisions can significantly alter service capacity and generate environmental externalities in urban freight operations [...] Read more.

Urban curbside loading and unloading zones are increasingly affected by competing non-logistics uses, such as outdoor terraces or resident parking, leading to reductions in effective curbside length. These design decisions can significantly alter service capacity and generate environmental externalities in urban freight operations that are rarely quantified. This study introduces the Factor of Occupancy (F_o) as a space–time design indicator for curbside unloading zones, defined as the product of effective curbside length and the maximum authorised dwell time. Using direct observational data from an urban block in Zaragoza (Spain), the analysis focuses on a loading and unloading zone whose effective length was reduced by approximately 6 m due to the installation of a restaurant terrace. Two curbside configurations are compared: a reduced configuration (8 m) and a restored configuration (14 m), keeping demand and temporal constraints constant. F_o is integrated into a loss-based queueing model (M/M/1/1) to estimate blocking probabilities and the number of served and rejected freight operations. To capture the environmental implications of curbside capacity loss, the paper proposes the Hidden Carbon Emissions (HCE) indicator, which quantifies the additional CO₂ emissions generated by rejected vehicles through block recirculation and idling during illegal occupancy, based on observed behaviour and publicly available emission factors. The results show that restoring curbside length substantially increases effective service capacity and reduces rejected vehicles, leading to a marked decrease in hidden CO₂ emissions per operation. The findings highlight that minor curbside design decisions can produce measurable impacts on both urban freight efficiency and environmental performance. Full article

(This article belongs to the Special Issue Green Transportation and Pollution Control)

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22 pages, 4777 KB

Open AccessArticle

Defect-Aware RGB Representation and Resolution-Efficient Deep Learning for Photovoltaic Failure Detection in Electroluminescence Images

by Damian Grzechca, Fatima Ez-Zahiri, Łukasz Chruszczyk and Fei Bian

Appl. Sci. 2026, 16(4), 2148; https://doi.org/10.3390/app16042148 - 23 Feb 2026

Viewed by 381

Abstract

Electroluminescence (EL) imaging is widely used for non-destructive inspection of photovoltaic (PV) cells; however, the low contrast of grayscale EL images limits the performance of automated defect detection methods. This manuscript proposes a defect-aware EL image classification framework that enhances defect visibility through [...] Read more.

Electroluminescence (EL) imaging is widely used for non-destructive inspection of photovoltaic (PV) cells; however, the low contrast of grayscale EL images limits the performance of automated defect detection methods. This manuscript proposes a defect-aware EL image classification framework that enhances defect visibility through local contrast enhancement and physically motivated RGB false-color mapping. Instead of simple channel replication, grayscale intensities are segmented into defect-related ranges and encoded to emphasize cracks, inactive regions, healthy silicon emission, and conductive pathways. The approach is evaluated on the public ELPV benchmark dataset proposing ResNet–50, EfficientNet–B0, and EfficientNet–B3 architectures at two input resolutions. The proposed representation consistently improves defect discrimination and achieves a maximum classification accuracy, outperforming previously reported CNN-based results on the same dataset. Notably, comparable accuracy is obtained at lower resolution, significantly reducing computational cost and inference time, which supports deployment with cheaper sensors and faster inspection pipelines. Class imbalance is addressed using focal loss, class weighting, and threshold calibration without artificial resampling, preserving realistic operating conditions. The results confirm that combining defect-aware RGB representation with resolution-efficient learning provides an accurate and computationally practical solution for EL-based PV defect detection. Full article

(This article belongs to the Special Issue AI-Based Machinery Health Monitoring)

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29 pages, 5106 KB

Open AccessArticle

Mechanical Behavior of Grouted Sleeve Butt and Lap Joints with Anti-Deflection Measures Under Uniaxial Tension and High-Stress Cyclic Loading

by Qiong Yu, Zuorui Wen, Ziming Tang, Hua Wei, Fangjun Zheng, Zhi Zhang, Zhenhai Chen and Jiaqiu Sun

Appl. Sci. 2026, 16(4), 2147; https://doi.org/10.3390/app16042147 - 23 Feb 2026

Viewed by 341

Abstract

To compare the mechanical performance differences between sleeve grouting lap and butt joints, a total of 41 lap joints, including 27 standard lap joints and 14 anti-deflection lap joints, and 20 butt joints were subjected to tensile and high-stress cyclic tensile-compression tests. The [...] Read more.

To compare the mechanical performance differences between sleeve grouting lap and butt joints, a total of 41 lap joints, including 27 standard lap joints and 14 anti-deflection lap joints, and 20 butt joints were subjected to tensile and high-stress cyclic tensile-compression tests. The results indicate that both types of joints failed by tensile fracture of the rebars in uniaxial tension, with the load-bearing capacity, total elongation at maximum force, and ductility generally meeting the code requirements. In high-stress cyclic tests, the load-bearing capacity of both types of joints increased, while the initial stiffness and ductility decreased. The residual deformation of the anti-deflection lap joints and butt joints generally met the specification requirements. Anti-deflection measures can reduce the residual deformation of lap joints; however, their constraint stiffness is limited, resulting in slightly greater residual deformation of lap joints compared to butt joints. After the completion of the high-stress cyclic tensile–compression tests, the maximum longitudinal strain near the reinforcement side of the sleeve’s middle cross-section in lap joints and the absolute value of the maximum circumferential compressive strain were both less than those in butt joints, indicating that lap joints have lower tensile performance requirements for the sleeve. Based on experimental results, a lap length of 12.5 d is recommended, with an additional 4–6 d allowance to enhance splice reliability under high-stress cyclic loading. Full article

(This article belongs to the Section Civil Engineering)

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22 pages, 1466 KB

Open AccessArticle

Brazilian Microalgae-Derived Bioactives: Antioxidant and Antibacterial Properties for Skin Care Application

by Édina A. R. Blasi, Jamili S. Hofstetter, Patrícia Susano, Susete Pinteus, Alice Martins, Helena Gaspar, Margarida Matias, Katie Shiels, Patrick Murray, Thainá I. Lamb, Emílio Berghahn, Giseli Buffon, Anja Reppner, Joana Silva, Celso Alves and João A. P. Henriques

Appl. Sci. 2026, 16(4), 2146; https://doi.org/10.3390/app16042146 - 23 Feb 2026

Cited by 1 | Viewed by 426

Abstract

Brazilian microalgae represent an underexplored reservoir of bioactive compounds with promising biotechnological and dermocosmetic applications. In this study, eight native Brazilian microalgae strains were cultivated under control (C) and stress conditions, nitrogen depletion (N) and salt stress (S), to modulate their bioactive profiles. [...] Read more.

Brazilian microalgae represent an underexplored reservoir of bioactive compounds with promising biotechnological and dermocosmetic applications. In this study, eight native Brazilian microalgae strains were cultivated under control (C) and stress conditions, nitrogen depletion (N) and salt stress (S), to modulate their bioactive profiles. Derived acetone extracts (24 samples) were evaluated for their antioxidant and antibacterial activities relevant to skin health. The antioxidant capacity of extracts was assessed by three complementary methods: ferric reducing antioxidant power (FRAP), 2,2-diphenyl-1-picryl-hydrazyl (DPPH) and superoxide anion radicals scavenging. Additionally, the antibacterial effects against four skin microorganisms (Staphylococcus epidermidis, Staphylococcus hominis, Staphylococcus aureus, and Cutibacterium acnes) were also assessed. Among the tested samples, extracts from Scenedesmus armatus (Extract 40C) and from Chlorella sorokiniana (Extract 198C) displayed the highest antioxidant potential, with DPPH radical reduction of 22.6 ± 1.6% and 20.7 ± 1.9% and FRAP values of 178.3 and 156.8 μmol FeSO₄/g extract, respectively. Superoxide scavenging assays showed IC₅₀ values of 150.9 μg/mL for sample 40C and 139.6 μg/mL for sample 198C. Regarding the antibacterial assay, the IC₅₀ values for S. epidermidis were notable, with sample 198C exhibiting the highest potency (10.3 µg/mL), closely matching the standard drug (12.4 µg/mL). The inhibitory capacity against C. acnes showed that samples 40C (58.4 µg/mL) and 198C (83.5 µg/mL) demonstrated antimicrobial relevance. Mechanistic assays suggested that the antibacterial effects of both samples may involve alterations in bacterial membrane integrity and DNA damage. Overall, these findings highlight the dermocosmetic potential of native Brazilian microalgae, still largely untapped in biotechnology, as natural sources of multifunctional ingredients for the development of sustainable skin care formulations. Full article

(This article belongs to the Special Issue The Role of Bioactive Natural Products in Health and Disease, 2nd Edition)

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33 pages, 5698 KB

Open AccessArticle

Research on Energy-Saving Optimization of Central Air-Conditioning Systems Based on Photovoltaic and Energy Storage Coordination Under Time-of-Use Pricing

by Dezhong Qi, Longteng Xu, Lu Ding, Bin Fan and Honghong Wang

Appl. Sci. 2026, 16(4), 2145; https://doi.org/10.3390/app16042145 - 23 Feb 2026

Viewed by 294

Abstract

With the expansion of power grids, the increasing peak-valley load difference threatens grid security, a challenge addressed by time-of-use pricing which incentivizes load shifting. Since central air-conditioning systems account for over 60% of building energy use, optimizing them for efficiency and cost under [...] Read more.

With the expansion of power grids, the increasing peak-valley load difference threatens grid security, a challenge addressed by time-of-use pricing which incentivizes load shifting. Since central air-conditioning systems account for over 60% of building energy use, optimizing them for efficiency and cost under time-of-use pricing is crucial. This study presents an integrated optimization framework that coordinates photovoltaic generation, battery storage, and grid power. The approach develops a BES-LSTM forecasting model by using the Bald Eagle Search (BES) algorithm to tune Long Short-Term Memory (LSTM) network parameters for accurate cooling-load prediction. A central air-conditioning water-system energy-minimization model is then formulated and solved with an improved BES algorithm that incorporates adaptive opposition-based learning, logistic chaotic mapping, and Lévy flight. Finally, a daily schedule is optimized by partitioning time according to time-of-use price intervals and treating generation output, battery charge/discharge, and grid draw as decision variables. Simulations demonstrate that the framework reduces the central air-conditioning water system’s total energy consumption by an average of 28.7% and lowers energy costs under time-of-use pricing by 22.38%, achieving both significant energy savings and economic benefits. Full article

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19 pages, 580 KB

Open AccessArticle

VERA: A Privacy-Preserving Framework for Deep Learning Data Collection and Object Detection in Private Settings

by Manuel H. Jimenez, Onur Toker and Luis G. Jaimes

Appl. Sci. 2026, 16(4), 2144; https://doi.org/10.3390/app16042144 - 23 Feb 2026

Viewed by 421

Abstract

This paper introduces VERA (Vision Expert Real Analysis), a privacy-supporting cyber-physical framework designed for real-time data collection and visual analysis in healthcare environments. VERA limits exposure to identifiable RGB content by ensuring that annotators interact only with non-identifiable edge-based representations, while original images [...] Read more.

This paper introduces VERA (Vision Expert Real Analysis), a privacy-supporting cyber-physical framework designed for real-time data collection and visual analysis in healthcare environments. VERA limits exposure to identifiable RGB content by ensuring that annotators interact only with non-identifiable edge-based representations, while original images remain encrypted at rest using AES-CFB, with integrity verification performed before in-memory decryption. The system integrates edge-based obfuscation, secure annotation, in-memory decryption, and dynamic data augmentation to train YOLO-based person detection models without compromising patient privacy. Experimental results on a curated COCO subset show that VERA enables effective person detection, improving mean Average Precision (mAP) from an intentionally minimal baseline of 0.61 percent to 99.94 percent after full training and augmentation. This baseline is used solely to illustrate the contribution of the secure data preparation pipeline and is not intended to represent a fully optimized YOLO configuration. The results demonstrate that privacy-supportive workflows can maintain strong model performance while aligning with data protection practices common in regulated environments. Although this work focuses on person detection as a foundational stage, the VERA architecture is designed to support future extensions toward privacy-preserving Human Activity Recognition (HAR) tasks in clinical and assisted-living settings. Full article

(This article belongs to the Special Issue Human Activity Recognition (HAR) in Healthcare, 3rd Edition)

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29 pages, 2152 KB

Open AccessArticle

Transformer-Autoencoder-Based Unsupervised Temporal Anomaly Detection for Network Traffic with Dual Prediction and Reconstruction

by Jieke Lu, Xinyi Yang, Yang Liu, Haoran Zuo, Feng Zhou, Tong Yu, Dengmu Liu, Tianping Deng and Lijun Luo

Appl. Sci. 2026, 16(4), 2143; https://doi.org/10.3390/app16042143 - 23 Feb 2026

Viewed by 518

Abstract

With the rapid growth of large-scale networks, traditional rule-based and supervised anomaly detection methods struggle with heavy reliance on labeled data, slow response to rapidly changing patterns, and difficulty in capturing complex temporal anomalies. At the same time, real-world traffic exhibits strong class [...] Read more.

With the rapid growth of large-scale networks, traditional rule-based and supervised anomaly detection methods struggle with heavy reliance on labeled data, slow response to rapidly changing patterns, and difficulty in capturing complex temporal anomalies. At the same time, real-world traffic exhibits strong class imbalance, where normal samples overwhelmingly dominate, causing many existing models to miss subtle but critical abnormal behaviors. To address these challenges, this paper proposes an unsupervised temporal anomaly detection framework for network traffic based on a Transformer-autoencoder bidirectional prediction and reconstruction model. The framework combines the advantages of autoencoders and regression models, using multi-head self-attention and positional encoding to capture long-range temporal dependencies in traffic sequences. A masked decoding mechanism is further employed to prevent information leakage from future time steps. The model jointly generates forward and backward predictions as well as reconstructed sequences, and designs multiple anomaly scoring strategies that integrate prediction and reconstruction errors to enhance the sensitivity to point, contextual, and collective anomalies under highly imbalanced data. Experiments on three public benchmark datasets demonstrate that the proposed method significantly improves detection performance, achieving up to an F1 score of 0.960 and a precision of 0.949, with recall approaching 1.0, while reducing false alarms, thereby showing strong applicability to practical network security scenarios. Full article

(This article belongs to the Special Issue Deep Learning and Its Applications in Natural Language Processing)

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23 pages, 6295 KB

Open AccessArticle

Influence of Transmitter Arrangement on Localization Accuracy in Radio–Ultrasonic RTLS in Underground Roadways

by Sławomir Bartoszek, Grzegorz Ćwikła, Gabriel Kost, Artur Dylong, Dominik Bałaga and Sebastian Jendrysik

Appl. Sci. 2026, 16(4), 2142; https://doi.org/10.3390/app16042142 - 23 Feb 2026

Viewed by 323

Abstract

This paper presents a sensitivity analysis of positioning accuracy in a localization system based on signal time-of-flight measurements, intended for operation in underground roadway workings. The underground environment is characterized by limited installation space, numerous obstacles causing multipath propagation, and the presence of [...] Read more.

This paper presents a sensitivity analysis of positioning accuracy in a localization system based on signal time-of-flight measurements, intended for operation in underground roadway workings. The underground environment is characterized by limited installation space, numerous obstacles causing multipath propagation, and the presence of sections with non-uniform geometry, which in practice leads to a “flattening” of the transmitter constellation and a deterioration of the conditioning of the trilateration problem. As a result, even small changes in input parameters (e.g., related to infrastructure geometry, distance-measurement quality, or the adopted model) may cause a significant change in the position-estimation error, thereby reducing the reliability of roadheader localization across the entire working area. In this study, a local sensitivity analysis is employed to identify the parameters that dominate the positioning outcome. Sensitivity coefficients are defined in a normalized form and are determined numerically using a perturbation approach (changing a given input parameter by a prescribed percentage), which avoids analytical differentiation of the complex relationships arising from the trilateration equations. The analysis is performed for a roadway scenario supported by an ŁP10 steel arch yielding support, with transmitters installed under the support arch and the roadheader trajectory represented by a sequence of consecutive position vectors. The obtained results allow the solution’s susceptibility to errors and uncertainties in the parameters to be assessed and indicate which parameters require priority control in practical implementation. On this basis, recommendations are formulated for the design and maintenance of the localization infrastructure, including transmitter placement and reconfiguration rules (relocation or adding an additional transmitter), to maintain stable positioning quality under operational mining conditions. Full article

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22 pages, 4602 KB

Open AccessArticle

Peak Strain Prediction and Fragility Assessment of Buried Pipelines Subjected to Normal-Slip and Reverse-Slip Faulting

by Hongyuan Jing, Peng Luo, Shuxin Zhang and Qinglu Deng

Appl. Sci. 2026, 16(4), 2141; https://doi.org/10.3390/app16042141 - 23 Feb 2026

Viewed by 262

Abstract

Permanent ground deformation caused by fault movement threatens the safe operation of buried pipelines. Accurate fragility assessment of buried pipelines subjected to faulting is essential for pipeline design and risk management. However, buried pipelines exhibit nonlinear mechanical responses due to the coupled effects [...] Read more.

Permanent ground deformation caused by fault movement threatens the safe operation of buried pipelines. Accurate fragility assessment of buried pipelines subjected to faulting is essential for pipeline design and risk management. However, buried pipelines exhibit nonlinear mechanical responses due to the coupled effects of multiple factors. Moreover, the effects of key parameters remain insufficiently quantified, limiting the accuracy and engineering applicability of existing fragility assessments. In this study, a three-dimensional finite element model incorporating large deformation and nonlinear pipe–soil interaction is developed and validated against representative experimental data. Using this model, numerical simulations are performed for 352 parameter combinations covering fault type, dip angle, burial depth, soil type, and pipe material. Nonlinear regression of the simulation results yielded predictive models for pipeline peak axial strain under normal-slip and reverse-slip faulting. A fragility framework is then established with fault displacement as the intensity measure, and fragility curves are derived for both faulting modes. The predicted peak axial strains agree with the finite element results: 78.6% (normal-slip) and 72.5% (reverse-slip) of predictions fall within ±20% error. The fragility curves enable quantitative estimation of fault-displacement thresholds. In the case study, the intact-to-damage displacement threshold is approximately 0.6 m for normal-slip faults but approximately 0.2 m for reverse-slip faults, indicating a higher failure likelihood under reverse-slip faulting. Within the investigated parameter ranges, the fault dip angle is the most significant factor affecting the pipeline failure probability for both normal-slip and reverse-slip faulting. Sandy soil and greater burial depth substantially increase the probability of moderate-to-severe damage, whereas higher steel grade increases the displacement threshold for transition from intact to failure. This study provides a rapid quantitative tool and a theoretical basis for pipeline design and risk quantification of buried pipelines in fault zones. Full article

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20 pages, 1527 KB

Open AccessArticle

“How Many Minutes Does the Player Have in His Legs?” Answering One of Football’s Oldest Coaching Questions Through a Mathematical Model

by Mauro Mandorino, Ronan Kavanagh, Antonio Tessitore, Valerio Persichetti, Manuel Morabito and Mathieu Lacome

Appl. Sci. 2026, 16(4), 2139; https://doi.org/10.3390/app16042139 - 23 Feb 2026

Viewed by 1558

Abstract

Coaches in professional football need to estimate how many minutes a player can tolerate in a match before relevant fatigue occurs. This study aimed to develop a framework to translate monitoring information into individualised, minute-based fatigue thresholds. Over four seasons in an elite [...] Read more.

Coaches in professional football need to estimate how many minutes a player can tolerate in a match before relevant fatigue occurs. This study aimed to develop a framework to translate monitoring information into individualised, minute-based fatigue thresholds. Over four seasons in an elite club, external load (total distance, high-speed running, mechanical work) and heart rate were collected in training. Machine-learning-derived fitness and fatigue indices were computed and combined with 7- and 28-day load variables in a Random Forest regression model predicting match minutes. The trained model was then used to simulate four fatigue conditions by fixing the match-day fatigue index (z-FA_match = 0, −1, −2, −3). In an independent test season, the model showed a mean absolute error of 22.5 min and R² = 0.17 for playing time prediction, with z-FA_match as the most influential predictor. Simulated fatigue thresholds occurred in an ordered way (0 = 57.1, −1 = 64.9, −2 = 84.8, −3 = 84.4) and differed across season period, playing position, overall seasonal minutes, and return-to-play status. Integrating external load with fitness and fatigue indices via machine learning can provide individualised estimates of when players are likely to reach fatigue states, supporting decisions on selection, substitutions, and return-to-play management. Full article

(This article belongs to the Special Issue Modern Approaches to Sport Physiology: From Monitoring to Decision-Making)

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20 pages, 5517 KB

Open AccessArticle

Experimental Research on the Supercooling and Freezing Temperatures of Unsaturated Soil

by Jihao Sun, Xiaojie Yang and Yilin Yue

Appl. Sci. 2026, 16(4), 2140; https://doi.org/10.3390/app16042140 - 22 Feb 2026

Viewed by 425

Abstract

With the development of polar regions and the deepening utilization of cold region resources, a large number of infrastructure projects are continuously being carried out. The freezing temperature of unsaturated soil is a critical factor governing the freezing depth and stability of foundations [...] Read more.

With the development of polar regions and the deepening utilization of cold region resources, a large number of infrastructure projects are continuously being carried out. The freezing temperature of unsaturated soil is a critical factor governing the freezing depth and stability of foundations in cold regions or seasons. Concurrently, the supercooling state of soil significantly influences the assessment of its phase composition and physico-mechanical properties. This study employed physical experiments, theoretical formulas, and numerical simulations to reveal the influencing factors and underlying mechanisms of supercooling characteristics in unsaturated soils under controlled low-rate continuous cooling conditions. The results demonstrate that a reduced temperature gradient between the sample surface and the ambient environment correlates with a lower supercooling limit temperature and an extended supercooling duration. An excessively high cooling rate suppresses the supercooling phenomenon in the sample core due to boundary effects. In contrast, neither the temperature difference nor the external cooling rate exhibit a negligible influence on the freezing temperature. Analysis of the temperature–time curves reveals that the freezing process of silty clay is more stable, exhibiting fewer stepwise temperature declines during the phase change plateau, whereas mudstone shows heightened sensitivity to variations in the thermal gradient. Compared to conventional thermocouple measurements, the proposed methodology achieves an optimal balance between temporal efficiency and measurement accuracy. It not only enhances experimental controllability and data reliability, but also provides more scientific theoretical support and technical pathways for predicting freezing depth, designing foundation thermal systems, and preventing frozen ground disasters in cold region engineering. Full article

(This article belongs to the Topic Complex Rock Mechanics Problems and Solutions, 2nd Edition)

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21 pages, 635 KB

Open AccessArticle

A New Insight into Ancient Wheat Pasta: Physicochemical, Technological and Cooking Quality of Triticum dicoccum (Emmer)

by İzzet Özhamamcı

Appl. Sci. 2026, 16(4), 2138; https://doi.org/10.3390/app16042138 - 22 Feb 2026

Viewed by 437

Abstract

Emmer (Triticum turgidum ssp. dicoccum) is attracting renewed interest as a nutrient-dense ancient wheat for sustainable cereal foods; however, product-level evidence for region-specific landraces remains limited. This study characterizes pasta produced exclusively from 100% Triticum dicoccum semolina cultivated in Ardahan (Türkiye) [...] Read more.

Emmer (Triticum turgidum ssp. dicoccum) is attracting renewed interest as a nutrient-dense ancient wheat for sustainable cereal foods; however, product-level evidence for region-specific landraces remains limited. This study characterizes pasta produced exclusively from 100% Triticum dicoccum semolina cultivated in Ardahan (Türkiye) by integrating proximate composition, cooking performance, and instrumental texture (TPA). The emmer pasta contained 12.70% protein, 4.93% total dietary fiber, and 1.68% ash, with an energy value of 366.25 kcal/100 g. Cooking tests revealed 10.86% cooking loss, 219.98% water absorption, and 101.62% volume increase, indicating limited cooking tolerance consistent with a weaker starch–protein matrix. In comparison with conventional T. durum pasta, cooked emmer pasta exhibited comparable hardness, gumminess, and chewiness, but higher adhesiveness and springiness alongside lower resilience and cohesiveness. These results highlight Ardahan-grown T. dicoccum as a nutritionally valuable pasta raw material, albeit with technological constraints (particularly cooking loss) that warrant further optimization for industrial use. Full article

(This article belongs to the Section Food Science and Technology)

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16 pages, 2520 KB

Open AccessArticle

Flow-Integrated Efficiency Assessment of Shared Bicycles and Its Influencing Factors: A Case Study of Beijing

by Zhifang Yin, Yiqi Li, Shengyao Qin and Teqi Dai

Appl. Sci. 2026, 16(4), 2137; https://doi.org/10.3390/app16042137 - 22 Feb 2026

Viewed by 342

Abstract

As dockless bike-sharing systems rapidly expanded, this study aims to develop a flow-integrated framework for assessing bicycle usage efficiency, which addresses a critical gap in conventional static indicators. Existing studies rely primarily on big data to evaluate location-specific efficiency using Time-to-Booking (ToB). However, [...] Read more.

As dockless bike-sharing systems rapidly expanded, this study aims to develop a flow-integrated framework for assessing bicycle usage efficiency, which addresses a critical gap in conventional static indicators. Existing studies rely primarily on big data to evaluate location-specific efficiency using Time-to-Booking (ToB). However, ToB ignores network flow effects while bicycles departing from the same location may reach destinations with vastly different ToB values. To overcome this, we propose a flow-integrated ToB (FwToB) index that incorporates the idle time at both the trip origin and destination. Applying this index to central Beijing reveals significant spatial heterogeneity while maintaining the original core-periphery pattern, indicating that most bicycles flow to areas with similar efficiency. Geographically weighted regression further shows that factors like population density, healthcare, shopping facilities, and distance to metro stations influence efficiency with substantial spatial non-stationarity. These findings advance the understanding of bike-sharing efficiency and offer insights for operators and urban planners. Full article

(This article belongs to the Section Earth Sciences)

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7 pages, 173 KB

Open AccessEditorial

Editorial: Intelligent Manufacturing and Design Under Challenging Conditions

by Ling Chen, Zengfeng Duan, Yanyan Li and Guoqing Zhang

Appl. Sci. 2026, 16(4), 2136; https://doi.org/10.3390/app16042136 - 22 Feb 2026

Viewed by 281

Abstract

n/a n/a n/a Full article

(This article belongs to the Special Issue Intelligent Manufacturing and Design Under Challenging Conditions)

22 pages, 2918 KB

Open AccessArticle

MV-RiskNet: Multi-View Attention-Based Deep Learning Model for Regional Epidemic Risk Prediction and Mapping

by Beyzanur Okudan and Abdullah Ammar Karcioglu

Appl. Sci. 2026, 16(4), 2135; https://doi.org/10.3390/app16042135 - 22 Feb 2026

Viewed by 381

Abstract

Regional epidemic risk prediction requires holistic modeling of heterogeneous data sources such as demographic structure, health capacity, geographical features and human mobility. In this study, a unique and multi-modal epidemiological data set integrating demographic, health, geographic and mobility indicators of Türkiye and its [...] Read more.

Regional epidemic risk prediction requires holistic modeling of heterogeneous data sources such as demographic structure, health capacity, geographical features and human mobility. In this study, a unique and multi-modal epidemiological data set integrating demographic, health, geographic and mobility indicators of Türkiye and its neighboring countries was collected. Türkiye’s neighboring countries are Greece, Bulgaria, Georgia, Armenia, Iran, and Iraq. This dataset, created by combining raw data from these neighboring countries, provides a comprehensive regional representation that allows for both quantitative classification and spatial mapping of epidemiological risk. To address the class imbalance problem, Conditional GAN (CGAN), a class-conditional synthetic example generation approach that enhances high-risk category representation was used. In this study, we proposed a multi-view deep learning model named MV-RiskNet, which effectively models the multi-dimensional data structure by processing each view into independent subnetworks and integrating the representations with an attention-based fusion mechanism for regional epidemic risk prediction. Experimental studies were compared using Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Autoencoder classifier, and Graph Convolutional Network (GCN) models. The proposed MV-RiskNet with CGAN model achieved better results compared to other models, with 97.22% accuracy and 97.40% F1-score. The generated risk maps reveal regional clustering patterns in a spatially consistent manner, while attention analyses show that demographic and geographic features are the dominant determinants, while mobility plays a complementary role, especially in high-risk regions. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition)

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22 pages, 6038 KB

Open AccessArticle

Unilateral Flywheel Training Enhances Eccentric Braking Capacity, Change-of-Direction Performance, and Match Acceleration–Deceleration in Soccer Players

by Yue Dou, Wei Zhang, Hengquan Xu, Xinping Lyu, Yaqing Wang, Jiyao Zhang, Jiarong Lv, Yaotong Li, Yujie Hu, Bo Zhang and Dingmeng Ren

Appl. Sci. 2026, 16(4), 2134; https://doi.org/10.3390/app16042134 - 22 Feb 2026

Viewed by 630

Abstract

Objectives: This study examined whether 8 weeks of unilateral flywheel resistance training (FRT) enhances eccentric neuromuscular characteristics and change-of-direction (COD) performance in male soccer players, and whether these adaptations transfer to sport-specific dribbling and match-play demands. Methods: Twenty-four male soccer players were randomized [...] Read more.

Objectives: This study examined whether 8 weeks of unilateral flywheel resistance training (FRT) enhances eccentric neuromuscular characteristics and change-of-direction (COD) performance in male soccer players, and whether these adaptations transfer to sport-specific dribbling and match-play demands. Methods: Twenty-four male soccer players were randomized to a unilateral flywheel training group (EXT, n = 12) or a traditional resistance training control group (CON, n = 12). Both groups completed unilateral lower-limb strength training twice weekly for 8 weeks. Eccentric knee extensor and flexor peak torque (60°·s⁻¹), eccentric-to-concentric (E:C) ratio, and inter-limb asymmetry were assessed using isokinetic testing. Performance measures included a 10 m sprint, modified 505, COD deficit, a dribbling-based COD test (AFL), and GPS-derived high-intensity acceleration and deceleration metrics during matches. Results: Compared with CON, the EXT group showed greater increases in knee extensor (+0.54 Nm·kg⁻¹) and flexor (+0.46 Nm·kg⁻¹) eccentric peak torque, a higher E:C ratio, and reduced inter-limb asymmetry (all p < 0.05). While 10 m sprint performance remained unchanged, EXT improved modified 505 performance and reduced COD deficits (up to −0.06 s). In addition, AFL completion time decreased and match-play high-intensity acceleration and deceleration events increased in EXT compared with CON (p < 0.05). Conclusions: Unilateral FRT effectively enhances eccentric braking-related capacity and COD efficiency, with clear transfer to soccer-specific technical performance and high-intensity match-play demands. Full article

(This article belongs to the Special Issue Recent Research on Biomechanics and Sports)

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30 pages, 16905 KB

Open AccessArticle

Real-Time 2D Orthomosaic Mapping from UAV Video via Feature-Based Image Registration

by Se-Yun Hwang, Seunghoon Oh, Jae-Chul Lee, Soon-Sub Lee and Changsoo Ha

Appl. Sci. 2026, 16(4), 2133; https://doi.org/10.3390/app16042133 - 22 Feb 2026

Viewed by 508

Abstract

This study presents a real-time framework for generating two-dimensional (2D) orthomosaic maps directly from UAV video. The method targets operational scenarios in which a continuously updated 2D overview is required during flight or immediately after landing, without relying on time-consuming offline photogrammetry workflows [...] Read more.

This study presents a real-time framework for generating two-dimensional (2D) orthomosaic maps directly from UAV video. The method targets operational scenarios in which a continuously updated 2D overview is required during flight or immediately after landing, without relying on time-consuming offline photogrammetry workflows such as structure-from-motion (SfM) and multi-view stereo (MVS). The proposed procedure incrementally registers sparsely sampled video frames on standard CPU hardware using classical feature-based image registration. Each selected frame is converted to grayscale and processed under a fixed keypoint budget to maintain predictable runtime. Tentative correspondences are obtained through descriptor matching with ratio-test filtering, and outliers are removed using random sample consensus (RANSAC) to ensure geometric consistency. Inter-frame motion is modeled by a planar homography, enabling the mapping process to jointly account for rotation, scale variation, skew, and translation that commonly occur in UAV video due to yaw maneuvers, mild altitude variation, and platform motion. Sequential homographies are accumulated to warp incoming frames into a global mosaic canvas, which is updated incrementally using lightweight blending suitable for real-time visualization. Experimental results on three UAV video sequences with different durations, flight patterns, and scene targets report representative orthomosaic-style outputs and per-step CPU runtime statistics (mean, 95th percentile, and maximum), illustrating typical operating behavior under the tested settings. The framework produces visually coherent orthomosaic-style maps in real time for approximately planar scenes with sufficient overlap and texture, while clarifying practical failure modes under weak texture, motion blur, and strong parallax. Limitations include potential drift over long sequences and the absence of ground-truth references for absolute registration-error evaluation. Full article

(This article belongs to the Special Issue The Routing, Object Tracking, and Multiple Coordination of Unmanned Aerial Vehicles (UAVs))

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18 pages, 5605 KB

Open AccessArticle

Heat Transfer on an Internal Thermal Insulation Structure for a High-Temperature Device: Numerical Simulation and Experiment

by Yin Li, Haihua Li, Wanhua Chen, Wenguo Yang, Zhixu Gu and Bowen Liu

Appl. Sci. 2026, 16(4), 2132; https://doi.org/10.3390/app16042132 - 22 Feb 2026

Viewed by 315

Abstract

The internal thermal insulation structure serves as a vital subsystem within the thermal insulation system of high-temperature devices, playing a crucial role in effectively maintaining a high-temperature environment, reducing energy consumption, and enhancing testing efficiency. However, during the operation of these devices, the [...] Read more.

The internal thermal insulation structure serves as a vital subsystem within the thermal insulation system of high-temperature devices, playing a crucial role in effectively maintaining a high-temperature environment, reducing energy consumption, and enhancing testing efficiency. However, during the operation of these devices, the internal thermal insulation structure is inevitably subjected to high temperatures. Therefore, it is essential to focus on the heat transfer performance of this structure. Initially, the internal thermal insulation structure is designed, and the relative dimensions and materials of each component are determined. Subsequently, a finite element model of the internal thermal insulation structure is established, and numerical simulations of heat transfer are conducted under the device’s operating conditions to analyze the thermal insulation structure. This analysis is ultimately validated through high-temperature experiments conducted on specimens of the internal thermal insulation structure. The results indicate that the designed internal thermal insulation structure effectively maintains the high-temperature environment within the device and demonstrates excellent thermal insulation performance, with a maximum heat flux of 66.7 W/m² and an outer wall surface temperature of 25.98 °C. This work is significant as it lays the groundwork for the design and construction of such devices. Full article

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Appl. Sci., Volume 16, Issue 4 (February-2 2026) – 491 articles

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