Applied Sciences

25 pages, 2423 KB

Open AccessArticle

Solar-to-Hydrogen Production Potential Across Romania’s Hydrogen Ecosystems: Integrated PV-Electrolysis Modelling and Techno-Environmental Assessment

by Raluca-Andreea Felseghi, Claudiu Ioan Oprea, Paula Veronica Ungureșan, Mihaela Ionela Bian and Ligia Mihaela Moga

Appl. Sci. 2026, 16(6), 3110; https://doi.org/10.3390/app16063110 - 23 Mar 2026

Viewed by 582

Abstract

This study develops and applies an integrated modeling framework to assess the solar-to-hydrogen-to-power potential across Romania’s five hydrogen ecosystems defined in the National Hydrogen Strategy. The methodology couples PVGIS-based photovoltaic yield simulations, based on hourly solar irradiation data and including system losses, with [...] Read more.

This study develops and applies an integrated modeling framework to assess the solar-to-hydrogen-to-power potential across Romania’s five hydrogen ecosystems defined in the National Hydrogen Strategy. The methodology couples PVGIS-based photovoltaic yield simulations, based on hourly solar irradiation data and including system losses, with MHOGA-based electrolysis simulation, enabling a quantitative-energetic-environmental (Q-E-E) system-level assessment. A 1 MW photovoltaic plant was simulated under three mounting configurations (15° fixed tilt, optimal tilt, and solar tracking) and interfaced with alkaline (AEL) and proton exchange membrane electrolysers (PEMEL). Specific photovoltaic yields reach up to 360 kWh/m²_PV·year under tracking conditions, producing up to 7.5 kg/m²_PV·year (AEL) and 6.8 kg/m²_PV·year (PEMEL), expressed per unit of photovoltaic surface area to enable consistent comparison across the configurations considered. The modeled round-trip efficiency of the full solar–electricity–hydrogen–electricity chain is 38.32% for AEL and 34.57% for PEMEL. Life-cycle-based emission modeling yields 0.92 kg CO₂/kg H₂ (AEL) and 1.03 kg CO₂/kg H₂ (PEMEL), while avoided emissions exceed 250 g CO₂/kWh relative to grid intensity. Land-use modeling indicates area requirements between 9402 and 18,804 m²/MW, depending on the Ground Coverage Ratio. Results demonstrate that system configuration exerts a stronger influence than regional solar variability in determining hydrogen yield, highlighting the need for integrated techno-environmental optimization for large-scale deployment. Full article

(This article belongs to the Special Issue 10th Anniversary of the Energy Science and Technology Section—the Future of Energy)

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24 pages, 5930 KB

Open AccessArticle

Style-Abstraction-Based Data Augmentation for Robust Affective Computing

by Xu Qiu, Taewan Kim and Bongjae Kim

Appl. Sci. 2026, 16(6), 3109; https://doi.org/10.3390/app16063109 - 23 Mar 2026

Viewed by 422

Abstract

Personality recognition and emotion recognition, two core tasks within affective computing, are fundamentally constrained by data scarcity as collecting and annotating human behavioral data is expensive and restricted by privacy concerns. Under these limited data conditions, existing models tend to rely on superficial [...] Read more.

Personality recognition and emotion recognition, two core tasks within affective computing, are fundamentally constrained by data scarcity as collecting and annotating human behavioral data is expensive and restricted by privacy concerns. Under these limited data conditions, existing models tend to rely on superficial shortcut features such as background appearance, lighting conditions, or color variations, rather than behavior-relevant cues including facial expressions, posture, and motion dynamics. To address this issue, we propose Style-Abstraction-based Data Augmentation, a style transfer-based augmentation strategy that reduces dependency on low-level appearance information while preserving high-level semantic cues. Specifically, we employ cartoonization to generate stylized variants of training videos that retain expressive characteristics but remove stylistic bias. We validate our approach on three diverse personality benchmarks (First Impression v2, UDIVA v0.5, and KETI) and emotion benchmark(Emotion Dataset) using state-of-the-art models including ViViT (Video Vision Transformer), TimeSformer, and VST (Video Swin Transformer). Our experiments indicate that increasing the proportion of style-abstracted data in the training set can improve performance on the evaluated datasets. Notably, our method yields consistent gains across all benchmarks: a 0.0893 reduction in MSE on UDIVA v0.5 (with VST), a 0.0023 improvement in 1-MAE on KETI (with TimeSformer), and a 0.0051 improvement on First Impression v2 (with TimeSformer). Furthermore, extending style-abstraction-based data augmentation to a four-class categorical emotion recognition task demonstrates similar performance gains, achieving up to a 3.44% accuracy increase with the TimeSformer backbone. These findings verify that our style-abstraction-based data augmentation facilitates learning of behavior-relevant features by reducing reliance on superficial shortcuts. Overall, cartoonization-based style abstraction for data augmentation functions as both an effective augmentation strategy and a regularization mechanism, encouraging the model to learn more stable and generalizable representations for affective computing applications. Full article

(This article belongs to the Special Issue Advances in Computer Vision and Digital Image Processing)

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23 pages, 5519 KB

Open AccessArticle

Experimental Investigation of a Low-Temperature Ejector-Based Air-Conditioning System Driven by CHP Heat

by Sarken Kapayeva, Jacek Cieślik and Marek Bergander

Appl. Sci. 2026, 16(6), 3108; https://doi.org/10.3390/app16063108 - 23 Mar 2026

Viewed by 386

Abstract

This paper presents an experimental investigation of a low-temperature ejector-based air-conditioning system designed to utilize waste heat from Combined Heat and Power (CHP) plants. The system operates with isobutane (R600a) as the working fluid and is driven by low-grade heat sources in the [...] Read more.

This paper presents an experimental investigation of a low-temperature ejector-based air-conditioning system designed to utilize waste heat from Combined Heat and Power (CHP) plants. The system operates with isobutane (R600a) as the working fluid and is driven by low-grade heat sources in the temperature range of 80–120 °C. A prototype experimental rig was developed to evaluate the influence of key operating parameters, including motive steam pressure and evaporator temperature, on the system’s Coefficient of Performance (COP) and entrainment ratio. The results demonstrate that the system can maintain stable operation even at ultra-low heat source temperatures, achieving a maximum COP of 0.35 under optimal conditions. The findings confirm the feasibility of using R600a in ejector-based systems for sustainable cooling applications. Furthermore, the study highlights the potential for integrating such systems into existing district heating networks to enhance overall energy efficiency. Overall, the presented results provide valuable experimental data supporting the development of sustainable, thermally driven cooling technologies that reduce reliance on grid electricity and high-GWP refrigerants. Full article

(This article belongs to the Section Mechanical Engineering)

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29 pages, 2702 KB

Open AccessArticle

PFMS-RRT*: A Progress-Aware Fused-Sampling RRT* with Multi-Level Strategy Extension for Path Planning

by Zhongwei Li, Jiaming Li and Cai Luo

Appl. Sci. 2026, 16(6), 3107; https://doi.org/10.3390/app16063107 - 23 Mar 2026

Viewed by 360

Abstract

Sampling-based planners such as RRT* are attractive for robot navigation in complex spaces, but they often suffer from high randomness, low efficiency, slow convergence, and suboptimal path quality in cluttered environments. To address these limitations, this paper proposes PFMS-RRT*, a progress-aware fused-sampling RRT* [...] Read more.

Sampling-based planners such as RRT* are attractive for robot navigation in complex spaces, but they often suffer from high randomness, low efficiency, slow convergence, and suboptimal path quality in cluttered environments. To address these limitations, this paper proposes PFMS-RRT*, a progress-aware fused-sampling RRT* with a multi-level strategy extension. The method builds on a bidirectional RRT* framework and introduces three main components: (i) a progress-aware fused sampling scheme that adapts an oriented elliptical sampling region based on inter-tree progress and stagnation, mixes locally guided elliptical samples with globally explorative Halton-sequence samples, and dynamically balances exploration and exploitation; (ii) a three-level goal-guided extension mechanism that escalates from direct steering to local probing and then multi-direction detours to maintain forward progress when obstacles block expansion; and (iii) a smooth tangential artificial potential field (APF) extension used as a fallback, with a failure-driven probabilistic switching rule that increases APF usage after repeated extension failures. Simulations in four representative 2D environments (sparse, corridor-like dense, random dense, and narrow passage) show that PFMS-RRT* consistently yields shorter paths, lower and more stable runtime, and fewer nodes than several RRT* variants while maintaining competitive or improved obstacle clearance. Full article

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13 pages, 1166 KB

Open AccessArticle

Am I Top of the Pops? Does Feedback of Live GPS Between Sets of Hurling-Specific Small-Sided Games Improve Subsequent Running and Physiological Performance?

by Shane Malone, John Keane, Tom Hargroves, Conor P. Clancy, John David Duggan, Damien Young and Kieran D. Collins

Appl. Sci. 2026, 16(6), 3106; https://doi.org/10.3390/app16063106 - 23 Mar 2026

Viewed by 435

Abstract

The investigation aimed to determine if live feedback of team- and player-specific global positioning system (GPS) running performance data between bouts of hurling small-sided games (SSGs) altered the physical and physiological responses during subsequent bouts of SSGs during a 6-week hurling pre-season period. [...] Read more.

The investigation aimed to determine if live feedback of team- and player-specific global positioning system (GPS) running performance data between bouts of hurling small-sided games (SSGs) altered the physical and physiological responses during subsequent bouts of SSGs during a 6-week hurling pre-season period. Twenty-four (n = 24) hurling players (age 25.5 ± 3.2 years; height 177.9 ± 3.2 cm; body mass 83.5 ± 4.5 kg) received either feedback or no feedback during hurling-specific SSGs across a 6-week pre-season period. Teams were assigned to two specific groups, a) GPS live feedback or b) no GPS live feedback (control) for each session, with feedback provided during the SSG rest interval. Running performance (10-Hz, STATSports, Apex, Northern Ireland), heart rate (Polar T31 coded, Polar Electro, Finland), and rating of perceived exertion (RPE) were measured. Data was analyzed using linear mixed-effect models with the effect size (Cohen’s d) used to determine the size of the effect between feedback and non-feedback conditions. Trivial-o-small differences at all time points were observed in heart rate and RPE measures during SSGs, respectively. Trivial-to-moderate effects were observed between feedback and non-feedback conditions for total distance (p = 0.04; ES = 0.25; small) high-speed running (p = 0.043; ES = 0.59; moderate), maximal speed (p = 0.345; ES = 0.11; trivial) and accelerations (p = 0.03; ES = 0.55; moderate). The current data suggests that coaches and applied practitioners may use live GPS feedback to alter the running and physiological performance within hurling-specific SSGs during a pre-season period. Full article

(This article belongs to the Special Issue Innovation in Sports and Exercise Performance)

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21 pages, 1911 KB

Open AccessArticle

Research on Multi-Objective Optimization Model and Algorithm for Reliability Location of Emergency Facilities

by Mingyuan Liu, Lintao Liu, Futai Liang and Guocheng Wang

Appl. Sci. 2026, 16(6), 3105; https://doi.org/10.3390/app16063105 - 23 Mar 2026

Viewed by 344

Abstract

The issue of emergency facility location is a long-term strategic issue, and the complexity and diversity of the decision-making environment force decision-makers to focus on multiple objectives when making location decisions. We develop a multi-objective optimization system centered on cost-effectiveness, service balance, and [...] Read more.

The issue of emergency facility location is a long-term strategic issue, and the complexity and diversity of the decision-making environment force decision-makers to focus on multiple objectives when making location decisions. We develop a multi-objective optimization system centered on cost-effectiveness, service balance, and fairness, targeting three core objectives: minimizing total costs, minimizing differences in service quality among demand points, and minimizing material shortage gaps between demand points. To address the issue of limited facility service capacity induced by material shortages, we establish a multi-objective optimization model for the reliable location of emergency facilities. By combining the model’s characteristics with the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and an elite retention strategy, the Pareto frontier solution set of the multi-objective model is obtained, and the model’s feasibility is verified through various examples of different scales. Finally, sensitivity analysis was conducted on the reliability location model of emergency facilities under different disruption risks using the control variable method, and the topology structure of the reliability location allocation network for emergency facilities under different disruption situations is obtained. The research findings provide decision-makers with actionable references and technical support for selecting reliable locations for emergency facilities amid disruption risks. Full article

(This article belongs to the Section Transportation and Future Mobility)

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15 pages, 1225 KB

Open AccessArticle

Quantitative Assessment of Aerosol Leakage in Protective Clothing During Nursing Tasks: The Impact of Body Morphology and Pumping Effects

by Chin-Hsiang Luo, Shinhao Yang and Hsiao-Chien Huang

Appl. Sci. 2026, 16(6), 3104; https://doi.org/10.3390/app16063104 - 23 Mar 2026

Viewed by 325

Abstract

Personal protective equipment (PPE) is critical for defending against airborne biological hazards; however, current standard testing protocols often rely on “black-box” aggregate metrics or qualitative visual inspections that fail to pinpoint localized vulnerabilities. This study proposes a novel, spatially resolved quantitative methodology combining [...] Read more.

Personal protective equipment (PPE) is critical for defending against airborne biological hazards; however, current standard testing protocols often rely on “black-box” aggregate metrics or qualitative visual inspections that fail to pinpoint localized vulnerabilities. This study proposes a novel, spatially resolved quantitative methodology combining a whole-body fluorescent aerosol exposure chamber with an entropy-based image processing algorithm. By establishing a robust linear calibration mode, we accurately mapped and quantified localized aerosol ingress through protective clothing interfaces. Dynamic human-in-simulant tests were conducted using three suit models on two subjects with distinct body morphologies over 2- and 5-min exposure durations. Quantitative results revealed two distinct morphological failure mechanisms. A well-fitted suit resulted in steady “ Steady Accumulation,” where the total body leakage mass increased consistently (e.g., from 3.29 to 4.19 μg/cm²) while maintaining stable standard deviation, indicating preserved structural integrity. Conversely, an oversized fit induced “Structural Instability” and an erratic “Bellows Effect.” This mismatch was characterized by a dramatic inflation in aerosol leakage standard deviation during extended dynamic movements, rather than a simple increase in the mean leakage. Ultimately, this study empirically proves that protective clothing efficacy is highly morphology-dependent. The proposed quantitative methodology provides a rigorous scientific tool for diagnosing localized interface failures, thereby facilitating targeted improvements in PPE design and occupational safety. Full article

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29 pages, 3177 KB

Open AccessArticle

Dual-Distillation Vision-Language Model for Multimodal Emotion Recognition in Conversation with Quantized Edge Deployment

by DeogHwa Kim, Yu il Lee, Da Hyun Yoon, Byeong Jun Kim and Deok-Hwan Kim

Appl. Sci. 2026, 16(6), 3103; https://doi.org/10.3390/app16063103 - 23 Mar 2026

Viewed by 630

Abstract

Multimodal Emotion Recognition in Conversation (ERC) has attracted attention as a key technology in human–computer interaction, mental healthcare, and intelligent services. However, deploying ERC in real-world settings remains challenging due to reliability gaps across modalities, instability in visual representations, and the high computational [...] Read more.

Multimodal Emotion Recognition in Conversation (ERC) has attracted attention as a key technology in human–computer interaction, mental healthcare, and intelligent services. However, deploying ERC in real-world settings remains challenging due to reliability gaps across modalities, instability in visual representations, and the high computational cost of large pretrained models. In particular, on resource-constrained edge devices, it is difficult to reduce model size and inference latency while preserving accuracy. To address these challenges, we jointly propose a knowledge-distillation-based multimodal ERC model, called DDVLM, with an edge-optimized Weight-Only Quantization (WOQ) pipeline for efficient edge deployment. DDVLM assigns the textual modality as the teacher and the visual modality as the student, transferring emotion-distribution knowledge to improve non-verbal representations and stabilize multimodal learning. In addition, Exponential Moving Average (EMA)-based self-distillation enhances the consistency and generalization capability of text features. Meanwhile, the proposed WOQ pipeline quantizes linear-layer weights to INT8 while preserving precision-sensitive operations in mixed precision, thereby minimizing accuracy loss and reducing model size, memory usage, and inference latency. Experiments on the MELD dataset demonstrated that the proposed approach achieves state-of-the-art performance while also enabling real-time inference on edge devices such as NVIDIA Jetson. Overall, this work presents a practical ERC framework that jointly considers accuracy and deployability. Full article

(This article belongs to the Special Issue Multimodal Emotion Recognition and Affective Computing)

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39 pages, 701 KB

Open AccessReview

Presence Assessment in Virtual Reality: A Systematic Literature Review

by Fernando Ojeda de Ocampo, Gustavo Hernández-Melgarejo, Antonio Ramírez-Treviño and Rita Q. Fuentes-Aguilar

Appl. Sci. 2026, 16(6), 3102; https://doi.org/10.3390/app16063102 - 23 Mar 2026

Viewed by 938

Abstract

A critical aspect of virtual reality is the extent to which the user forgets their real surroundings and becomes completely engaged within the virtual environment. Diverse factors affect this user perception, which are grouped into two main concepts: immersion and presence. Although the [...] Read more.

A critical aspect of virtual reality is the extent to which the user forgets their real surroundings and becomes completely engaged within the virtual environment. Diverse factors affect this user perception, which are grouped into two main concepts: immersion and presence. Although the study of presence is extensive, researchers have not reached a consensus on a protocol with specific instruments and stages to evaluate it. This leads to a wide variety of results with different assessment methods, experimental setups, stimuli implemented, and applications. Therefore, this article aims to provide an analysis of the state-of-the-art methods for assessing presence in VR systems during the last few years. This study seeks to determine and improve the understanding of current techniques used for presence assessment, human data collected, data analysis methods, and the technologies and virtual environments implemented. In addition, four opportunities are discussed to provide researchers guidelines that can lead to enhanced presence assessments and personalized VR experiences. Full article

(This article belongs to the Special Issue Recent Advances and Application of Virtual Reality)

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19 pages, 1481 KB

Open AccessArticle

Technological and Energy-Related Implications of Extending the Hydraulic Retention Time in a Rotating Electrobiological Disc Contactor (REBDC)

by Joanna Rodziewicz, Karolina Kłobukowska, Kamil Bryszewski and Wojciech Janczukowicz

Appl. Sci. 2026, 16(6), 3101; https://doi.org/10.3390/app16063101 - 23 Mar 2026

Viewed by 262

Abstract

The removal of nitrogen and phosphorus from wastewater with low organic carbon content requires the addition of an external carbon source. The objective of this study was to assess the influence of hydraulic retention time (HRT) on the efficiency of external carbon source [...] Read more.

The removal of nitrogen and phosphorus from wastewater with low organic carbon content requires the addition of an external carbon source. The objective of this study was to assess the influence of hydraulic retention time (HRT) on the efficiency of external carbon source utilization and on nitrogen and phosphorus removal in a Rotating Electro-Biological Disc Contactor (REBDC). The energy demand was evaluated based on energy consumption (E) and current efficiency (CE). Hydroponic tomato wastewater was treated in the REBDC at a constant current density of 2.5 A/m². Sodium acetate was used as the carbon source. Two C/N ratios were tested, 2.0 and 3.0, under HRT conditions of 24 h and 48 h. For both C/N ratios, extending the HRT resulted in decreased nitrogen removal efficiency. At HRT = 48 h and C/N = 3.0, the nitrogen concentration in the effluent was more than three times lower compared with C/N = 2.0. The highest phosphorus removal efficiency was achieved at C/N = 3.0 and HRT = 48 h (98.8%). Increasing the HRT led to reduced TOC utilization for both C/N ratios. As a consequence of extended HRT, lower CE values and higher E values were observed, indicating increased energy demand for nutrient removal. Full article

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29 pages, 7545 KB

Open AccessArticle

AI-Enhanced IoT Mechatronic Platform for Assisted Mobility and Safety Monitoring in Small Dogs Based on Laser-Induced Graphene Contact Temperature Sensing

by Alan Cuenca-Sánchez, Fernando Pantoja-Suárez and Diego Segovia

Appl. Sci. 2026, 16(6), 3100; https://doi.org/10.3390/app16063100 - 23 Mar 2026

Viewed by 366

Abstract

Assistive mobility devices for small animals require reliable monitoring to ensure safe and comfortable operation without increasing system complexity or invasiveness. This study presents a low-cost monitoring platform that integrates a laser-induced graphene (LIG) contact-temperature sensor into a passive mobility device for small [...] Read more.

Assistive mobility devices for small animals require reliable monitoring to ensure safe and comfortable operation without increasing system complexity or invasiveness. This study presents a low-cost monitoring platform that integrates a laser-induced graphene (LIG) contact-temperature sensor into a passive mobility device for small dogs, supported by a lightweight Internet of Things (IoT) architecture. The system combines contact temperature, ambient temperature, speed, and obstacle distance using an energy-aware acquisition strategy and prioritized wireless transmission for near-real-time monitoring. An unsupervised anomaly detection framework based on Isolation Forest identifies potentially unsafe operating conditions without labeled pathological data by leveraging absolute temperature and the differential feature

Δ T

between contact and ambient measurements. Experimental validation was conducted under controlled indoor conditions across six independent sessions with a small-breed dog, including static and dynamic phases to ensure repeatability. The system achieved packet delivery ratios of approximately 95%, with typical end-to-end latencies below 500 ms and worst-case delays below 850 ms. The proposed approach detected localized thermal deviations associated with friction or prolonged contact while remaining robust to normal activity- and environment-driven variations. These results demonstrate the feasibility of integrating LIG-based sensing and unsupervised analytics into assistive animal mobility platforms to enhance safety through continuous, non-invasive monitoring. Full article

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21 pages, 3274 KB

Open AccessArticle

Deep Reinforcement Learning-Based Water Jet Control for Robotic Manipulators Using an Improved Experience Replay Mechanism

by Rong Zhang, Jianjun Qin, Luyang Wang and Guotong Li

Appl. Sci. 2026, 16(6), 3099; https://doi.org/10.3390/app16063099 - 23 Mar 2026

Cited by 1 | Viewed by 364

Abstract

Existing robotic water jet control methods are limited by fixed spray configurations and low adaptability to complex or dynamic environments. These constraints hinder precise targeting in three-dimensional spaces. To overcome this, we propose a reinforcement learning-based water jet control framework that achieves accurate [...] Read more.

Existing robotic water jet control methods are limited by fixed spray configurations and low adaptability to complex or dynamic environments. These constraints hinder precise targeting in three-dimensional spaces. To overcome this, we propose a reinforcement learning-based water jet control framework that achieves accurate targeting without pose or angle restrictions. Specifically, we introduce Goal-Priority Hindsight Experience Replay (GPHER), a replay strategy that integrates the principles of Hindsight Experience Replay (HER), Prioritized Experience Replay (PER), and curriculum learning. GPHER dynamically adjusts sampling priorities based on goal-space distance, guiding training from simple to complex goals. Combined with Truncated Quantile Critics (TQCs), this approach accelerates convergence and enhances success rates. Both simulation and real-world experiments validate the robustness and adaptability of the proposed method, demonstrating its effectiveness for real-time robotic fluid control. Full article

(This article belongs to the Special Issue Motion Control for Robots and Automation)

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33 pages, 6453 KB

Open AccessArticle

Design of Optimized Time-Shifted Sine Motion Profiles for High-Speed, Low-Vibration Motion

by Chang-Wan Ha and Dongwook Lee

Appl. Sci. 2026, 16(6), 3098; https://doi.org/10.3390/app16063098 - 23 Mar 2026

Viewed by 297

Abstract

High-speed precision positioning systems require motion profiles that achieve rapid transfer while suppressing motion-induced vibration. Conventional time-optimal trajectories often minimize travel time at the expense of residual vibration, which prolongs settling and degrades positioning accuracy. This paper proposes a systematic framework for designing [...] Read more.

High-speed precision positioning systems require motion profiles that achieve rapid transfer while suppressing motion-induced vibration. Conventional time-optimal trajectories often minimize travel time at the expense of residual vibration, which prolongs settling and degrades positioning accuracy. This paper proposes a systematic framework for designing optimized time-shifted sine motion profiles that explicitly incorporate vibration suppression in the frequency domain. By integrating time-domain profile construction with Laplace-domain analysis, motion profiles are derived in a unified manner from 1^st-order to generalized

n^{th}

-order forms. A key theoretical result shows that the residual vibration amplitude after motion completion is proportional to the magnitude of

| s X (s) |

evaluated at the system poles, providing a clear analytical basis for a closed-form zero placement strategy. Explicit algebraic design conditions are obtained without iterative numerical optimization. Simulation-based case studies demonstrate that the proposed approach drastically reduces transient and residual vibrations while maintaining competitive motion completion times compared with time-optimal designs. Robustness is quantitatively evaluated using insensitivity and high-frequency roll-off metrics, revealing that increasing the profile order improves uncertainty tolerance by approximately

- 20

dB/decade per order. Furthermore, a short-stroke scenario shows that lower-order sine profiles can be advantageous under moderate uncertainty. The proposed framework provides a practical guideline for vibration-aware high-speed motion control. Full article

(This article belongs to the Special Issue Advanced Control Systems and Control Engineering)

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23 pages, 2238 KB

Open AccessArticle

High-Efficiency Digital Filters for Spectral Parameter Approximation in SDR

by Subahar Arivalagan, Britto Pari James and Man-Fai Leung

Appl. Sci. 2026, 16(6), 3097; https://doi.org/10.3390/app16063097 - 23 Mar 2026

Viewed by 352

Abstract

Filters supporting dynamic reconfiguration that use the spectral parameter approximation (SPA) technique, together with other methodologies, and the interpolated spectral parameter approximation (ISPA) technique offer dynamic adjustment of the cutoff frequency (f_c) with a narrow transition bandwidth and a very wide [...] Read more.

Filters supporting dynamic reconfiguration that use the spectral parameter approximation (SPA) technique, together with other methodologies, and the interpolated spectral parameter approximation (ISPA) technique offer dynamic adjustment of the cutoff frequency (f_c) with a narrow transition bandwidth and a very wide f_c range. However, they suffer from a high multiplier requirement, leading to increased hardware resource usage. With fewer multipliers, we suggest the Multiply and Accumulate (MAC)-based SPA (MAC-SPA) and MAC-based interpolated SPA (MAC-ISPA) filter in this article. This article describes a unified MAC structure utilizing Time-Division Multiplexing (TDM) that uses the resource-sharing concept to implement an MAC-SPA and MAC-ISPA filter. The developed dynamically reconfigurable filter is implemented and realized using a 0.18 µm CMOS process. Additional testing was done on the Xilinx xc6vlx760-1ff1760 FPGA device. Relative to the filter that incorporates SPA along with the modified coefficient decimation method (MCDM), the obtained results reveal that the proposed MAC-SPA and MAC-ISPA channel filters, synthesized on FPGA, achieve a reduction in occupied slice count by approximately 7% and 4.76%, respectively. Although their operating speeds are slightly lower by about 9.4% for the MAC-SPA filter and 13.89% for the MAC-ISPA filter, this tradeoff is offset by significant savings in hardware resources, making both designs more area-efficient with only a modest reduction in speed. Full article

(This article belongs to the Section Electrical, Electronics and Communications Engineering)

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24 pages, 4424 KB

Open AccessArticle

Hybrid Attribution-Based Interpretable Deep Reinforcement Learning for Autonomous Driving Behavior Decision-Making

by Yaxuan Liu, Jiakun Huang, Mingjun Li, Qing Ye and Xiaolin Song

Appl. Sci. 2026, 16(6), 3096; https://doi.org/10.3390/app16063096 - 23 Mar 2026

Viewed by 383

Abstract

With the increasing deployment of autonomous driving systems, the opaque nature of deep reinforcement learning (DRL) decision models hinders understanding and validation of driving decisions. To address this challenge, we propose a Hybrid Attribution-based Interpretable Deep Reinforcement Learning framework (HA-IDRL) for autonomous driving [...] Read more.

With the increasing deployment of autonomous driving systems, the opaque nature of deep reinforcement learning (DRL) decision models hinders understanding and validation of driving decisions. To address this challenge, we propose a Hybrid Attribution-based Interpretable Deep Reinforcement Learning framework (HA-IDRL) for autonomous driving behavior decision-making. The framework introduces a Hybrid Gradient–LRP (HGL) attribution mechanism that integrates gradient-based attribution and Layer-wise Relevance Propagation (LRP) to capture complementary sensitivity and contribution information, producing more consistent and comprehensive post hoc explanations. In addition to post hoc interpretability, we enhance structural interpretability by replacing the conventional multilayer perceptron (MLP) in the Dueling Deep Q-Network (Dueling DQN) architecture with Kolmogorov–Arnold Networks (KAN). By representing nonlinear interactions through learnable univariate functions and explicit summation structures, KAN provides inherently interpretable functional decompositions. The proposed framework is evaluated on a highway lane-changing task using the highway-env simulator. Experimental results show that HA-IDRL achieves decision-making performance comparable to representative DRL baselines, including Dueling DQN and Soft Actor-Critic (SAC), while providing explanations that are more stable and better aligned with human driving semantics. Moreover, the proposed method produces explanations with low computational overhead, enabling efficient and real-time interpretability in practical autonomous driving applications. Overall, HA-IDRL advances trustworthy autonomous driving by enabling high-performance decision-making and rigorous, multi-level interpretability, thereby improving the transparency and operational reliability of DRL-based driving policies. Full article

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

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28 pages, 1665 KB

Open AccessArticle

The Use of Social Media as Bibliographic Citations in Open Access Education Journals

by Dimitris Rousidis, Emmanouel Garoufallou, Paraskevas Koukaras, Ilias Nitsos and Christos Tjortjis

Appl. Sci. 2026, 16(6), 3095; https://doi.org/10.3390/app16063095 - 23 Mar 2026

Viewed by 528

Abstract

There has been a recent increase in the use of social media platforms (SMPs), as well as a large increase in scientific journals and academic article publications. We need to study if and how much academics, scholars and researchers trust SMPs as sources, [...] Read more.

There has been a recent increase in the use of social media platforms (SMPs), as well as a large increase in scientific journals and academic article publications. We need to study if and how much academics, scholars and researchers trust SMPs as sources, i.e., citations, for writing their research articles. The purpose of this research is to explore the relationship between SMPs and bibliographic article citations for ten years between 2010 and 2019, with 31 December marking the official identification of COVID-19, a milestone that affected the whole world, including academic publishing. By using a citation retrieval tool written in Java, the citations referring to the URLs of 6432 articles from 14 Q1 open access education journals ranked by the SCImago platform were extracted. The retrieved URLs were stored in a relational database, preprocessed and cleaned, and analyzed using SQL queries to identify and quantify citations originating from SMPs. The findings showed that there were 112 instances, which corresponds to 1.8% of the articles, of an SMP post being used as a citation. Out of the 17 SMPs checked, eight were used, with the most popular being YouTube, having a percentage of 68% of the aforementioned 112 citations, followed by Twitter (now X) with approximately 13.5% and then by Facebook with around 7%. Most of these in-text citations were found at the Introduction and the Design/Methodology sections of the papers. Other important findings of this study were that about 2% of the URL citations referred to blogs and wikis and that one in 100 articles used Wikipedia in the bibliography. Also, for a 26-year period from 1999 to 2024, it was observed that the number of journals increased by 82.8%, while the number of open access journals showed an impressive 552.14% increase. The findings of this study could lead to changes in the metadata design of bibliographic databases, like the way of searching them, and to a review of the life cycle duration of sustainable access to the content of the cited SMPs. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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25 pages, 47875 KB

Open AccessArticle

Early Warning and Risk Assessment for Rainfall-Induced Shallow Loess Landslides

by Feng Gao, Yonghui Meng, Qingbing Wang, Jing He, Fanqi Meng, Jian Guo and Chao Yin

Appl. Sci. 2026, 16(6), 3094; https://doi.org/10.3390/app16063094 - 23 Mar 2026

Viewed by 336

Abstract

Rainfall-induced shallow loess landslides pose a significant threat to human life and property. Early warning and risk assessment of these landslides are critical prerequisites for engineering control and disaster loss reduction. The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS)-Three-dimensional Slope Stability [...] Read more.

Rainfall-induced shallow loess landslides pose a significant threat to human life and property. Early warning and risk assessment of these landslides are critical prerequisites for engineering control and disaster loss reduction. The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS)-Three-dimensional Slope Stability Analysis Tool (Scoops 3D) joint model can overcome the shortcomings of using a single TRIGRS model for hydrological analysis and a single Scoops 3D model for slope stability analysis. Landslide risk assessment based on expected economic loss, on the other hand, can overcome the issue of maintaining the risk level edge and sorting at the same level. In this paper, the TRIGRS model’s head pressures were put into the Scoops 3D model, with the southeast of Fangta, a town in Shaanxi province, China, as the study area. The relationship between the slope gradient and the number of grids in each stable grade was certified. The rainfall thresholds for landslides, based on both rainfall intensity and rainfall duration, were obtained by rerunning the TRIGRS-Scoops 3D joint model. The landslide range and land uses of each dangerous slope were determined by maximum likelihood classification, and then the expected economic loss was calculated. To verify the reliability of the TRIGRS-Scoops 3D joint model, the identified dangerous slopes were compared with the results from landslide susceptibility mapping. The results show that the unstable grids are concentrated within a slope gradient of 30° to 35°, and the landslide early warning levels are divided into Tier 3, Tier 2, and Tier 1 Warnings. The occurrence of shallow loess landslides is affected by both rainfall intensity and rainfall duration, and the combined effect should be considered in early warning. The distribution of both extreme susceptible grids and high susceptible grids across all 23 dangerous slopes demonstrates the reasonableness of the TRIGRS-Scoops 3D joint model. The landslide susceptible probability within some dangerous slopes exhibits spatial variability. The mapping relationship between the slope gradient and loess landslides is extremely complex. This paper can provide a theoretical basis for the early warning and risk management for rainfall-induced shallow loess landslides; the proposed method is also applicable to other regions with similar geological and meteorological conditions. Full article

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28 pages, 11377 KB

Open AccessArticle

Extended State Observer-Assisted Fast Adaptive Extremum-Seeking Searching Interval Type-2 Fuzzy PID Control of Permanent Magnet Synchronous Motors for Speed Ripple Mitigation at Low-Speed Operation

by Fuat Kılıç

Appl. Sci. 2026, 16(6), 3093; https://doi.org/10.3390/app16063093 - 23 Mar 2026

Cited by 1 | Viewed by 344

Abstract

Permanent magnet synchronous motors (PMSMs) are utilized in demanding conditions and applications requiring precision and accuracy, such as servo systems. Especially at low speeds, the effects of cogging torque, current measurement and offset errors, improper controller gains, mechanical resonance, and torque fluctuations caused [...] Read more.

Permanent magnet synchronous motors (PMSMs) are utilized in demanding conditions and applications requiring precision and accuracy, such as servo systems. Especially at low speeds, the effects of cogging torque, current measurement and offset errors, improper controller gains, mechanical resonance, and torque fluctuations caused by load torque and flux result in fluctuations at various frequencies in the motor output speed. This study, motivated by two factors, proposes an extended state observer (ESO)-based multivariable fast response extremum-seeking (FESC) interval type-2 fuzzy PID (IT2FPID) controller to improve dynamic response and reduce speed ripple at low speeds in situations where all these negative factors could arise. This approach enables the real-time adaptation of parameters to counteract the decline in controller performance caused by the nonlinear characteristics of PMSMs and parameter fluctuations while also optimizing disturbance rejection in the speed response under varying operating conditions and existing speed ripple. The experimental results from the prototype setup validate that the proposed control mechanism is functional, valid, and precise in diminishing speed ripples during low-speed operations. The simulation and test outcomes of the control scheme show that speed noise at low speeds is reduced from 26% to 3% compared to traditional proportional-integral (PI) controller and supertwisting (STW) sliding mode controller (SMC) responses and that the scheme exhibits a 16–23% reduction in undershoot amplitude and faster recovery in the presence of load torque variations. Full article

(This article belongs to the Special Issue Fuzzy Control Systems and Decision-Making)

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17 pages, 23332 KB

Open AccessArticle

Astronomically Forced Cyclicity and Cyclostratigraphic Framework of the Middle Jurassic Bath–Bajocian Formation in the West Siberian Basin

by Chengyu Song, Yefei Chen, Lun Zhao, Yunyang Liu and Yujie Gao

Appl. Sci. 2026, 16(6), 3092; https://doi.org/10.3390/app16063092 - 23 Mar 2026

Viewed by 277

Abstract

We aim to elucidate the sedimentary cyclicity of the Middle Jurassic Bath–Bajocian Formation in the northern S Oilfield of the West Siberian Basin, address the lack of high-resolution Milankovitch cycle research in this region, and support hydrocarbon exploration and development. This study employs [...] Read more.

We aim to elucidate the sedimentary cyclicity of the Middle Jurassic Bath–Bajocian Formation in the northern S Oilfield of the West Siberian Basin, address the lack of high-resolution Milankovitch cycle research in this region, and support hydrocarbon exploration and development. This study employs the gamma-ray (GR) logging data of Well 79 as the primary dataset. Using Acycle V2.8 software implemented on the MATLAB 2020b platform, we conducted a systematic astrochronological analysis. After improving data quality through preprocessing procedures—including outlier removal, linear interpolation, and detrending—we identified significant cyclic signals via spectral analysis. These cyclicities were subsequently validated using multitaper spectral analysis (MTM), sliding spectral analysis, COCO correlation testing, and wavelet analysis. Band-pass filtering was then applied to facilitate sequence subdivision and sedimentation rate estimation. The results reveal well-preserved Milankovitch cyclicity in the Bath–Bajocian Formation of Well 79. The observed cycle thicknesses corresponding to the 405 kyr long eccentricity, 100 kyr short eccentricity, 41 kyr obliquity, and 20 kyr precession are 34.57 m, 8.26 m, 3.44 m, and 1.73 m, respectively, with thickness ratios deviating by less than 5% from the theoretical 20:5:2:1 proportion. Sliding spectral analysis indicates an alternating pattern of increasing and decreasing sedimentation rates. Based on the identified orbital signals, 12 fourth-order sequences and 52 fifth-order cycles were recognized. Sedimentation rates among the three wells range from 6.49 to 12.08 cm/kyr, averaging 9.29 cm/kyr, and exhibit a decreasing trend from west to east. These findings provide a robust astrostratigraphic framework for refined stratigraphic division and reservoir prediction in the study area. Full article

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61 pages, 11232 KB

Open AccessArticle

A Contactless Deep Learning Framework for Quantitative Motor Assessment Aligned with the Movement Disorder Society Unified Parkinson’s Disease Rating Scale Part III: A Healthy Baseline Definition Study

by Andrea Zanela

Appl. Sci. 2026, 16(6), 3091; https://doi.org/10.3390/app16063091 - 23 Mar 2026

Viewed by 343

Abstract

The clinical evaluation of motor impairment in Parkinson’s disease is commonly based on the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III, which relies on visual assessment and is therefore subject to inter-rater variability. Existing technology-based solutions often require wearable [...] Read more.

The clinical evaluation of motor impairment in Parkinson’s disease is commonly based on the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III, which relies on visual assessment and is therefore subject to inter-rater variability. Existing technology-based solutions often require wearable sensors or lack structural alignment with the item-based architecture of the clinical examination. This study presents a fully automated and contactless framework designed to quantitatively describe motor performance in tasks explicitly aligned with MDS-UPDRS Part III. The system integrates stereo vision, deep learning-based pose estimation, and acoustic analysis to derive continuous, standardized quantitative descriptors. Objective Motor Item Indices were defined for 17 of the 18 motor items, excluding rigidity, which cannot be inferred from vision-based measurements. The framework was evaluated in a cohort of healthy subjects to establish an internal reference baseline for feature normalization and index construction. Within this cohort, descriptors exhibited coherent multivariate organization and internally consistent distributions, supporting methodological feasibility at this baseline definition stage. This work represents a methodological and baseline definition phase. Clinical validation in Parkinsonian populations, correlation with neurologist-rated scores, and longitudinal assessment remain necessary to determine diagnostic, severity-related, or early-stage applicability. Full article

(This article belongs to the Special Issue Emerging Technologies for Assistive Robotics)

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20 pages, 15544 KB

Open AccessArticle

The Potential Use of a Land Trend Algorithm for Regional Landslide Mapping in Indonesia

by Tubagus Nur Rahmat Putra, Muhammad Aufaristama, Khaled Ahmed, Mochamad Candra Wirawan Arief, Rahmihafiza Hanafi, Bambang Wijatmoko and Irwan Ary Dharmawan

Appl. Sci. 2026, 16(6), 3090; https://doi.org/10.3390/app16063090 - 23 Mar 2026

Viewed by 342

Abstract

Indonesia is among the most landslide-prone countries in the world, with thousands of fatalities and widespread infrastructure damage recorded over recent decades. Despite this high hazard level, regional-scale landslide monitoring remains constrained by the limitations of conventional bitemporal satellite imagery, which is susceptible [...] Read more.

Indonesia is among the most landslide-prone countries in the world, with thousands of fatalities and widespread infrastructure damage recorded over recent decades. Despite this high hazard level, regional-scale landslide monitoring remains constrained by the limitations of conventional bitemporal satellite imagery, which is susceptible to cloud contamination, dependent on precise acquisition timing, and unable to capture the full temporal dynamics of landslide occurrence and recovery. While the LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) algorithm has been widely applied for detecting vegetation disturbances such as forest loss and land-use change, its potential for landslide detection in tropical environments has not been sufficiently explored. This study aims to evaluate the applicability of LandTrendr applied to long-term Landsat time series imagery for automated regional-scale landslide detection and mapping in Indonesia. The method integrates temporal segmentation of the Normalized Difference Vegetation Index (NDVI) derived from Landsat imagery spanning 2000–2022 with slope information from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) to identify the characteristic drop-recovery spectral signature associated with landslide events. The algorithm was applied and evaluated in two geologically distinct study areas: Lombok, West Nusa Tenggara, and Pasaman, West Sumatra. Detection accuracies of 25.9% by location and 20.3% by area were achieved in Lombok and 76.3% by location and 85.3% by area in Pasaman. The lower accuracy in Lombok is primarily attributed to the predominance of small landslides below the sensor’s spatial resolution and rapid vegetation recovery. The proposed approach demonstrates the unique capability of LandTrendr to model the entire life cycle of a mass movement event, from pre-event stability through abrupt disturbance to ecological recovery within a single unified framework, providing a scalable and cost-effective tool for long-term landslide monitoring applicable to other tropical, landslide-prone regions. Full article

(This article belongs to the Section Environmental Sciences)

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29 pages, 7118 KB

Open AccessArticle

Improving Document Layout Analysis Using Synthetic Data Generation and Convolutional Models

by Olha Pronina, Tao Xia, Kyrylo Sheliah, Olena Piatykop, Vasily Efremenko and Elena Balalayeva

Appl. Sci. 2026, 16(6), 3089; https://doi.org/10.3390/app16063089 - 23 Mar 2026

Viewed by 587

Abstract

Document Layout Analysis (DLA) is a critical step in intelligent document processing and is essential for accurately reconstructing the hierarchical structure of pages. While modern convolutional neural networks exhibit high performance, their effectiveness heavily depends on the quality and representativeness of training data, [...] Read more.

Document Layout Analysis (DLA) is a critical step in intelligent document processing and is essential for accurately reconstructing the hierarchical structure of pages. While modern convolutional neural networks exhibit high performance, their effectiveness heavily depends on the quality and representativeness of training data, limiting their application in scenarios where labeled datasets are scarce. This paper proposes a method for enhancing DLA through synthetic generation of training data. A formalized mathematical model for generating document layouts has been developed, allowing control over element placement density, sizes, and spatial distribution. An experimental study investigated the impact of various data generation strategies on the training of the YOLO11m model, including median and threshold-based element splitting as well as different block sampling schemes. The experiments showed that employing median element splitting combined with random sampling from a large shuffled pool of synthetic data yields consistent improvements of 2–4% across all key metrics: precision, recall, mAP@50, and mAP@50:95, as compared with simple data generation strategies. These results demonstrate that targeted optimization of the data preparation process can enhance the performance of convolutional models in DLA tasks without increasing architectural complexity. The practical applicability of the method is validated through integration into the MinerU system. Future research will focus on extending the proposed model to complex layouts in scientific journals, technical reports, and handwritten documents. Full article

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

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22 pages, 3088 KB

Open AccessArticle

SLAR-Net: A Hierarchical Network with Spatial and Semantic Fusion for Fashion Attribute Recognition

by Yanxia Jin, Xiaozhu Zhang and Zhuangwei Zhang

Appl. Sci. 2026, 16(6), 3088; https://doi.org/10.3390/app16063088 - 23 Mar 2026

Viewed by 338

Abstract

With the rapid growth of fashion e-commerce, fashion attribute recognition has emerged as a critical research area in computer vision. Existing methods face two primary problems: (1) building multi-task models, leading to complex network architectures; (2) the overlooking of semantic relationships and spatial [...] Read more.

With the rapid growth of fashion e-commerce, fashion attribute recognition has emerged as a critical research area in computer vision. Existing methods face two primary problems: (1) building multi-task models, leading to complex network architectures; (2) the overlooking of semantic relationships and spatial positional dependencies between fashion attributes. To address these issues, this paper proposes SLAR-Net, a novel hierarchical multi-label classification network that effectively fuses spatial and semantic information for improved recognition performance. Specifically, SLAR-Net adopts a progressive, hierarchical architecture. Firstly, we introduce a lightweight backbone network enhanced with a custom-designed attention mechanism to extract low-level image features. Secondly, we innovatively construct an adjacency matrix to represent the relative spatial orientations of attributes, which is then employed by a graph convolutional network to model mid-level spatial positional features. Thirdly, we design a graph embedding matrix that captures attribute dependency relationships, leveraging a neural network to learn high-level semantic representations. Finally, we propose a custom multi-head attention mechanism to fuse spatial and semantic features, facilitating enhanced feature interaction and improving recognition performance. Experimental results on fashion attribute and benchmark datasets demonstrate that SLAR-Net outperforms state-of-the-art methods in recognition accuracy, validating the effectiveness of the proposed hierarchical architecture and fusion strategy. Full article

(This article belongs to the Special Issue Advanced Technologies in Image Processing, Analysis, and Machine Vision)

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16 pages, 2150 KB

Open AccessArticle

In Search of Zurbarán’s Influence on the Óbidos Painting Workshop

by Vanessa Antunes, Sara Valadas, António Candeias, José Mirão, Ana Cardoso, Sofia Pessanha and Maria L. Carvalho

Appl. Sci. 2026, 16(6), 3087; https://doi.org/10.3390/app16063087 - 23 Mar 2026

Viewed by 282

Abstract

This study assesses indicative technical correspondences and divergences between Francisco de Zurbarán’s painting practices and those observed in the seventeenth-century Óbidos workshop (Baltazar Gomes Figueira and Josefa d’Óbidos). We focus on the composition and function of priming layers, the shadow-to-light painting sequence, and [...] Read more.

This study assesses indicative technical correspondences and divergences between Francisco de Zurbarán’s painting practices and those observed in the seventeenth-century Óbidos workshop (Baltazar Gomes Figueira and Josefa d’Óbidos). We focus on the composition and function of priming layers, the shadow-to-light painting sequence, and pigment/binder usage. A multi-analytical approach was employed: portable X-ray Fluorescence (XRF), Optical Microscopy on polished cross-sections (OM), Scanning Electron Microscopy in backscattered mode with Energy-Dispersive X-ray analysis (SEM-BSE/EDS), Micro-Confocal Raman Spectroscopy (µ-Raman), and Micro-Fourier Transform Infrared Spectroscopy (µ-FTIR). Rather than treating single pigments as diagnostic, we compare patterns of application and stratigraphic behaviour—notably a two-layer priming, in which a finer, Fe-rich upper layer is actively used to build shadows, and a consistent exploitation of the priming as a value layer in a shadow-to-light sequence. Materials largely overlap, while priming compositions differ, plausibly reflecting local resources. Given the small corpus (two works by Zurbarán, one by Baltazar, and one by Josefa), conclusions are presented as indicative and contextualized within Iberian workshop practice. Full article

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4 pages, 149 KB

Open AccessEditorial

Advances in Security, Trust and Privacy in Internet of Things

by Weidong Fang, Chunsheng Zhu and Andrew W. H. Ip

Appl. Sci. 2026, 16(6), 3086; https://doi.org/10.3390/app16063086 - 23 Mar 2026

Viewed by 347

Abstract

With the rapid development of information and communication technologies, the Internet of Things (IoT) has gradually become a key infrastructure supporting the development of the digital society [...] Full article

(This article belongs to the Special Issue Advances in Security, Trust and Privacy in Internet of Things)

20 pages, 5679 KB

Open AccessArticle

Study on the Cytotoxicity of Silver Nanoparticles in the Ligninolytic Fungus Phanerochaete chrysosporium

by Mihaela Racuciu, Lacramioara Oprica, Catalina Radu, Larisa Popescu-Lipan, Gabriel Ababei, Daniela Pricop, Laura Ursu, Daniel Timpu, Silvestru-Bogdanel Munteanu, Nicoleta Lupu and Dorina Creanga

Appl. Sci. 2026, 16(6), 3085; https://doi.org/10.3390/app16063085 - 23 Mar 2026

Viewed by 350

Abstract

Silver nanoparticles (AgNP), which have a wide range of applications in technical and biological fields, are produced in hundreds of tons annually and are eventually released into water, air, and soil. In this study, the effects of AgNPs on Phanerochaete chrysosporium, a [...] Read more.

Silver nanoparticles (AgNP), which have a wide range of applications in technical and biological fields, are produced in hundreds of tons annually and are eventually released into water, air, and soil. In this study, the effects of AgNPs on Phanerochaete chrysosporium, a white-rot fungus that plays a key role in wood waste degradation, were investigated. The AgNP were synthesized at high temperature with gallic acid under different pH conditions: near-neutral pH (~7.5), notation AgNP@GA-1, and alkaline pH (~10.5), notation AgNP@GA-2, focusing on their ability to cope with oxidative stress. The samples were characterized by fine granularity (particle diameter of 12 and 11 nm, respectively), specific plasmonic features (characteristic band at 425 and 408 nm), hydrodynamic diameter of 93 and 133 nm, respectively, and Zeta potential of −34 to −44 mV, which gave them stability over a period of three months. The fungal cultures exposed to AgNP concentrations of 40–100 µL/mL (approximately 4–11 µg/mL) presented superoxide dismutase (SOD) activity, which increased by approximately 45% at 40 µL/mL for AgNP@GA-1 after 7 days, whereas AgNP@GA-2 decreased SOD activity by up to 40% at 60 µL/mL. Both AgNP types strongly stimulated catalase (CAT) biosynthesis, with two- to three-fold increased activity on the 7th day at 100 µL/mL. CAT activity remained significantly elevated for AgNP@GA-1 on the 14th day at 60–80 µL/mL, whereas for AgNP@GA-2 it decreased by 40–60% compared with the control. Variations in malondialdehyde content indicated moderate lipid peroxidation, suggesting relatively low cytotoxic effects on fungal cells. Overall, the results demonstrate that P. chrysosporium exhibits adaptive biochemical responses to AgNP-induced oxidative stress while maintaining metabolic functionality, highlighting the potential compatibility of AgNPs with white-rot fungi involved in environmental wood waste biodegradation processes. Full article

(This article belongs to the Special Issue Applications of Nanoparticles in the Environmental Sciences: 2nd Edition)

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13 pages, 2245 KB

Open AccessArticle

Comparison of 45° and 90° Medial Row Anchor Insertion Angles in Double-Row Suture Bridge Rotator Cuff Repair: A Biomechanical and Finite Element Analysis

by Ali İhsan Kılıç, Samet Çıklaçandır, Mustafa Çeltik, Sercan Çapkin, Ali Ersen and Onur Başçı

Appl. Sci. 2026, 16(6), 3084; https://doi.org/10.3390/app16063084 - 23 Mar 2026

Viewed by 358

Abstract

Rotator cuff suture anchors have traditionally been inserted at the 45° “deadman” angle, but this recommendation was largely derived from single-row constructs and may not reflect the biomechanics of contemporary double-row suture bridge repairs. This study compared the biomechanical performance and stress distribution [...] Read more.

Rotator cuff suture anchors have traditionally been inserted at the 45° “deadman” angle, but this recommendation was largely derived from single-row constructs and may not reflect the biomechanics of contemporary double-row suture bridge repairs. This study compared the biomechanical performance and stress distribution of medial row anchors inserted at 45° versus 90° in a double-row suture bridge construct. Sixteen ovine humeri with intact infraspinatus tendons were randomized to 45° or 90° medial anchor insertion (n = 8 each), and double-row suture bridge repair was performed using 3.5 mm metallic and PEEK anchors. Specimens underwent uniaxial tensile testing (10-N preload, 5 mm/min) to failure, measuring yield load, failure load, displacement, stiffness, and energy absorption; additionally, a CT-based finite element model of the human humerus assessed von Mises stress, strain, and deformation under 200 N loading. Mean failure load was 161.96 ± 50.99 N for 45° and 185.61 ± 60.97 N for 90° (p = 0.447), and stiffness was 31.63 ± 8.18 N/mm versus 36.79 ± 9.26 N/mm (p = 0.291). Displacement at failure was greater with 90° insertion (8.11 ± 0.51 mm vs. 6.65 ± 0.83 mm; p = 0.002), while energy absorption was higher but not significantly different (p = 0.255). Finite element analysis demonstrated lower bone von Mises stress with 90° insertion (14.03 MPa) compared with 45° (24.77 MPa), with similar deformation. In double-row suture bridge repair, 90° medial anchor insertion provides comparable fixation strength to that at 45° while reducing bone stress, suggesting a biomechanical advantage. Full article

(This article belongs to the Special Issue Orthopaedic Biomechanics: Clinical Applications and Surgery)

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22 pages, 9667 KB

Open AccessArticle

A Transfer Learning System for Skin Disease Classification Using EfficientNet-B5 with Grad-CAM Explainability

by Daniel Turuta, Raul Robu and Ioan Filip

Appl. Sci. 2026, 16(6), 3083; https://doi.org/10.3390/app16063083 - 23 Mar 2026

Viewed by 639

Abstract

Accurate medical diagnostics for skin affections such as skin cancer, psoriasis, vascular tumors, or exanthems have become increasingly difficult due to the growing volume and visual variability of dermatological cases, as well as limited specialist availability. To address this, the present work introduces [...] Read more.

Accurate medical diagnostics for skin affections such as skin cancer, psoriasis, vascular tumors, or exanthems have become increasingly difficult due to the growing volume and visual variability of dermatological cases, as well as limited specialist availability. To address this, the present work introduces a complete and deployable deep-learning-based system capable of detecting ten distinct skin disease categories, trained using transfer learning with EfficientNet-B5 and enhanced with explainable AI through Grad-CAM. The proposed system achieves a top-3 accuracy of 95.96%, a weighted F1-score of 0.87, and class-specific F1-scores reaching 0.96 for acne and 0.95 for nail fungus. These results demonstrate strong predictive performance for the deep learning model trained, validated, and evaluated on a ten-class subset of the Dermnet dataset. The research conducted covers the visual explainability of the AI model classification process, including integration into a fully functional web application, usable as an expert system for image uploading, data processing and visualization of results. The AI visualizing technology based on Grad-CAM provides clear, class-specific heatmaps that highlight the most influential regions in each prediction, improving transparency and supporting clinical interpretability. Full article

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

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26 pages, 5957 KB

Open AccessArticle

Leakage-Aware Time-Based Top-K Start-Up Ranking for Venture Capital Investment Success Under Severe Class Imbalance Conditions: A Screening Evaluation Framework

by Mustafa Kellekci, Ufuk Cebeci and Onur Dogan

Appl. Sci. 2026, 16(6), 3082; https://doi.org/10.3390/app16063082 - 23 Mar 2026

Viewed by 288

Abstract

Many real-world screening tasks in venture capital must rank large start-up candidate pools under conditions of tight review capacity, time-varying information, and rare investment success outcomes. When datasets are constructed retrospectively, post-decision updates can leak into features and inflate performance, especially with random [...] Read more.

Many real-world screening tasks in venture capital must rank large start-up candidate pools under conditions of tight review capacity, time-varying information, and rare investment success outcomes. When datasets are constructed retrospectively, post-decision updates can leak into features and inflate performance, especially with random splits. This study proposes a leakage-aware, time-based evaluation framework for capacity-constrained screening formulated as a top-K ranking problem. Using a dataset of 117,141 early-stage firms as an empirical testbed, features were constructed strictly as of a reference time

t_{0}

, a 180-day temporal embargo was enforced around the train–test boundary, and generalization was assessed with time-ordered splits. Because venture capital decisions are made on a shortlist, evaluation emphasizes ranking quality using PR-AUC, Lift@K, Precision@K/Recall@K, and NDCG@K, reported with bootstrap confidence intervals. Under this leakage-aware protocol and with strong class imbalance, maturity-related signals achieve the strongest PR-AUC (0.0144), while team and combined signals yield the best top-50 shortlist concentration. Finally, probability calibration substantially improves reliability for threshold planning (Brier score reduced from 0.0972 to 0.0161 with sigmoid calibration) while leaving ranking essentially unchanged. Overall, the study provides a leakage-aware evaluation template and an interpretable baseline for time-dependent venture capital screening tasks involving start-up selection, investment success prediction, leakage risk, and limited review capacity. Full article

(This article belongs to the Special Issue Exploring AI: Methods and Applications for Data Mining)

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21 pages, 3438 KB

Open AccessArticle

IoT-Based Architecture with AI-Ready Analytics for Medical Waste Management: System Design and Pilot Validation

by Shynar Akhmetzhanova, Zhanar Oralbekova, Anuar Bayakhmetov, Ainur Abduvalova, Tamara Yeshmakhanova, Ainagul Berdygulova and Gulnara Toktarkozha

Appl. Sci. 2026, 16(6), 3081; https://doi.org/10.3390/app16063081 - 23 Mar 2026

Viewed by 603

Abstract

Internet-of-Things (IoT) sensing can improve traceability, safety, and efficiency of medical waste handling, yet many deployments remain fragmented, lack an end-to-end system architecture, and do not provide the structured data pipelines needed for artificial intelligence (AI) analytics. This paper presents a layered IoT-based [...] Read more.

Internet-of-Things (IoT) sensing can improve traceability, safety, and efficiency of medical waste handling, yet many deployments remain fragmented, lack an end-to-end system architecture, and do not provide the structured data pipelines needed for artificial intelligence (AI) analytics. This paper presents a layered IoT-based system design for medical waste management that integrates: (i) Espressif Systems 32 (ESP32)-based edge devices for fill-level and Global Positioning System (GPS) telemetry; (ii) secure network communication; (iii) a cloud backend for data ingestion, storage, and analytics; and (iv) operator dashboards with event-driven alerting. The architecture extends our prior GPS-enabled tracking and route optimization by adding sensor-driven state monitoring, threshold-based decision support, and a time-series data pipeline designed for future AI-driven predictive analytics. In a 30-day pilot with five containers, the system collected one reading every 15 min (14,400 total readings). The backend demonstrated efficient processing with an average Application Programming Interface (API) response time of 45 ms, sub-50 ms database write latency, and high uptime; alerts were delivered promptly upon threshold violations. Compared with a fixed-schedule baseline, the system enabled condition-based collection scheduling with zero data loss. The proposed design emphasizes modularity, fault tolerance, and integration readiness for hospital information systems, providing a practical blueprint for scalable smart-healthcare waste logistics and a foundation for machine learning-based predictive waste management. Full article

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