Applied Sciences

23 pages, 4646 KB

Open AccessArticle

Analysis of Vehicle Lateral Position in Curves Using a Driving Simulator: Road Markings, Human Factors and Road Features

by Santiago Martin-Castresana, Miriam Martinez-Garcia, Rafael Enriquez and Maria Castro

Appl. Sci. 2025, 15(17), 9851; https://doi.org/10.3390/app15179851 - 8 Sep 2025

Viewed by 1518

Abstract

The vehicle lateral position within a lane is critical in road safety, particularly on curved sections, where excessive deviations are often associated with crashes. This study analyses the effect of three traffic-calming measures on the lateral position of vehicles on curves with varying [...] Read more.

The vehicle lateral position within a lane is critical in road safety, particularly on curved sections, where excessive deviations are often associated with crashes. This study analyses the effect of three traffic-calming measures on the lateral position of vehicles on curves with varying radii and turning directions. The experiment was conducted using a driving simulator with the participation of 48 drivers, assessing two leading indicators: the vehicle’s mean lateral position (LP) and the standard deviation of that position (SDLP). The results showed that, in curves, male drivers tended to drive further from the centre of the lane compared to female drivers. Additionally, female drivers exhibited less weaving in their trajectories (lower SDLP). Older drivers adopted more centred trajectories; however, SDLP increased with age. Drivers with higher annual exposure tended to drive further from the lane centre in curves. Among the traffic-calming measures, red-coloured transverse bands (CTB) reduced the lateral position by approximately 0.12 m in left curves. In contrast, red peripheral transverse bars (PTB) proved most effective in lowering lateral variability (SDLP). Geometric differences were also observed: greater curve radii were associated with lower SDLP values. Full article

(This article belongs to the Special Issue Human–Vehicle Interactions)

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14 pages, 4655 KB

Open AccessArticle

Evaluation of Surface Roughness with Reduced Data of BRDF Pattern

by Jui-Hsiang Yen, Zih-Ying Fang and Cheng-Huan Chen

Appl. Sci. 2025, 15(17), 9850; https://doi.org/10.3390/app15179850 - 8 Sep 2025

Viewed by 1445

Abstract

Traditional non-destructive measurement of surface roughness exploits complete data of bidirectional reflective distribution function (BRDF). The instrument is normally bulky and the process should be conducted off-line, hence it is time-consuming. If only a part of BRDF data can be sufficient to determine [...] Read more.

Traditional non-destructive measurement of surface roughness exploits complete data of bidirectional reflective distribution function (BRDF). The instrument is normally bulky and the process should be conducted off-line, hence it is time-consuming. If only a part of BRDF data can be sufficient to determine the surface roughness, both the measurement equipment and processing time can be significantly reduced. This paper proposes a compact device capable of detecting multiple angular intensities of reflective scattering with different incident angles from different spatial points of the target object at the same time. It is used to evaluate the surface roughness of a standard specimen with arithmetic mean roughness (Ra) values ranging from 0.13 µm to 2.1 µm. The case of measuring two spatial points of the specimen is used for illustrating the calibration procedure of the device and how the data were searched and processed to increase the reliability and robustness for evaluating the surface roughness with reduced data of BRDF. Similar methodologies can be applicable for other real-time detection methods based on the scattering process. Full article

(This article belongs to the Topic Advances in Non-Destructive Testing Methods, 3rd Edition)

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17 pages, 3454 KB

Open AccessArticle

Design and Vibration Characteristic Analysis of Piezoelectric Micro Oil-Supply Device

by Zhaoliang Dou, Jianfang Da, Gang Zhou, Shaohua Zhang, Lu Gao and Fengbin Liu

Appl. Sci. 2025, 15(17), 9849; https://doi.org/10.3390/app15179849 - 8 Sep 2025

Viewed by 555

Abstract

In response to the lubrication failure problem during spacecraft operation, new requirements have been put forward for micro, precise, and dynamically adjustable lubrication and oil-supply technology for its key moving components. This article charts the design of a micro fuel-supply device structure based [...] Read more.

In response to the lubrication failure problem during spacecraft operation, new requirements have been put forward for micro, precise, and dynamically adjustable lubrication and oil-supply technology for its key moving components. This article charts the design of a micro fuel-supply device structure based on a piezoelectric oscillator. Through finite-element simulation, the influence of the vibration mode and excitation parameters (waveform, frequency, voltage amplitude) of the piezoelectric oscillator on the displacement response amplitude and period of the oscillator is analyzed in depth. Research on waveform characteristics shows that sine waves can maintain frequency and phase stability due to their single-frequency nature, with an amplitude of 0.21615 mm between the two; The study of frequency characteristics shows that the displacement response amplitude of the piezoelectric oscillator is the largest at a 4914.2 Hz resonant state, which is about 10 times that of the non-resonant state; the study on voltage amplitude characteristics shows that the vibration displacement amplitude is significantly positively correlated with the driving voltage. When the excitation voltage is 220 V, the displacement response amplitude is 0.21615 mm and the period is 3960 µs. This study provides important theoretical support for optimizing the performance of piezoelectric oscillators. Full article

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21 pages, 5368 KB

Open AccessArticle

Predicting Urban Traffic Under Extreme Weather by Deep Learning Method with Disaster Knowledge

by Jiting Tang, Yuyao Zhu, Saini Yang and Carlo Jaeger

Appl. Sci. 2025, 15(17), 9848; https://doi.org/10.3390/app15179848 - 8 Sep 2025

Viewed by 1342

Abstract

Meteorological and climatological trends are surely changing the way urban infrastructure systems need to be operated and maintained. Urban road traffic fluctuates more significantly under the interference of strong wind–rain weather, especially during tropical cyclones. Deep learning-based methods have significantly improved the accuracy [...] Read more.

Meteorological and climatological trends are surely changing the way urban infrastructure systems need to be operated and maintained. Urban road traffic fluctuates more significantly under the interference of strong wind–rain weather, especially during tropical cyclones. Deep learning-based methods have significantly improved the accuracy of traffic prediction under extreme weather, but their robustness still has much room for improvement. As the frequency of extreme weather events increases due to climate change, accurately predicting spatiotemporal patterns of urban road traffic is crucial for a resilient transportation system. The compounding effects of the hazards, environments, and urban road network determine the spatiotemporal distribution of urban road traffic during an extreme weather event. In this paper, a novel Knowledge-driven Attribute-Augmented Attention Spatiotemporal Graph Convolutional Network (KA3STGCN) framework is proposed to predict urban road traffic under compound hazards. We design a disaster-knowledge attribute-augmented unit to enhance the model’s ability to perceive real-time hazard intensity and road vulnerability. The attribute-augmented unit includes the dynamic hazard attributes and static environment attributes besides the road traffic information. In addition, we improve feature extraction by combining Graph Convolutional Network, Gated Recurrent Unit, and the attention mechanism. A real-world dataset in Shenzhen City, China, was employed to validate the proposed framework. The findings show that the prediction accuracy of traffic speed can be significantly increased by 12.16%~31.67% with disaster information supplemented, and the framework performs robustly on different road vulnerabilities and hazard intensities. The framework can be migrated to other regions and disaster scenarios in order to strengthen city resilience. Full article

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13 pages, 2375 KB

Open AccessArticle

The Impact of Process Variations on the Thermo-Mechanical Behavior of 3D Integrated Circuits

by Yi-Cheng Chan, Ming-Han Liao and Chun-Wei Yao

Appl. Sci. 2025, 15(17), 9847; https://doi.org/10.3390/app15179847 - 8 Sep 2025

Viewed by 709

Abstract

The use of vertically stacked architectures in three-dimensional integrated circuits (3DICs) offers a transformative path for advancing Moore’s Law by significantly boosting computational density. A key obstacle arises from the integration of heterogeneous materials, which introduces critical thermo-mechanical challenges, particularly due to the [...] Read more.

The use of vertically stacked architectures in three-dimensional integrated circuits (3DICs) offers a transformative path for advancing Moore’s Law by significantly boosting computational density. A key obstacle arises from the integration of heterogeneous materials, which introduces critical thermo-mechanical challenges, particularly due to the mismatch in the coefficients of thermal expansion (CTE) of silicon (Si) and copper (Cu). Such mismatches can compromise mechanical reliability and complicate the definition of the keep-out zone (KOZ) in dense systems. This paper provides a detailed analysis of the thermo-mechanical behavior of stacked 3DICs, exploring a range of device geometries and process conditions. The findings reveal that CTE-induced stress is the dominant factor influencing mechanical integrity, surpassing other mechanical forces. It is concluded that the KOZ must be no less than 1.5 times the feature diameter to adequately mitigate stress-related risks. Additionally, thermal stress interactions in configurations with adjacent structures can increase the KOZ requirement by up to 33.3% relative to isolated instances. Yet, multi-layered designs show enhanced thermal performance, a benefit attributed to the high thermal conductivity of copper. The knowledge gained from this study provides a valuable framework for optimizing the reliability and thermal management of 3DIC systems and is especially relevant for high-performance sensor devices where both mechanical stability and efficient heat dissipation are vital. Full article

(This article belongs to the Special Issue Applied Electronics and Functional Materials)

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29 pages, 5969 KB

Open AccessArticle

Integrated Digital Twin and BIM Approach to Minimize Environmental Loads for In-Situ Production and Yard-Stock Management of Precast Concrete Components

by Junyoung Park, Sunkuk Kim and Jeeyoung Lim

Appl. Sci. 2025, 15(17), 9846; https://doi.org/10.3390/app15179846 - 8 Sep 2025

Viewed by 1622

Abstract

Digital twin (DT) technology, integrated with building information modeling (BIM), enables real-time feedback and predictive analytics in construction. This study presents a BIM-enabled DT framework to optimize in situ production and yard-stock management of precast concrete (PC) components with a focus on minimizing [...] Read more.

Digital twin (DT) technology, integrated with building information modeling (BIM), enables real-time feedback and predictive analytics in construction. This study presents a BIM-enabled DT framework to optimize in situ production and yard-stock management of precast concrete (PC) components with a focus on minimizing CO₂ emissions. Using Oracle Crystal Ball, scenario-based simulations revealed up to an 8.9% reduction in environmental impact. Distinct from prior research that largely emphasized cost or off-site strategies, this study uniquely addresses on-site sustainability by embedding carbon metrics into the decision-making process. The framework was validated through a large-scale logistics warehouse project that showcased its practical utility. This research contributes a replicable method for enhancing sustainability in precast construction through digital technologies. Full article

(This article belongs to the Special Issue Development, Characterization, Application and Recycling of Novel Construction Materials, 2nd Edition)

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16 pages, 3014 KB

Open AccessArticle

Research on the Internal Flow Characteristics of Single- and Coaxial-Nozzle Ejectors for Hydrogen Recirculation in PEMFC

by Jaewoong Han, Seongjae Won and Jinwook Lee

Appl. Sci. 2025, 15(17), 9845; https://doi.org/10.3390/app15179845 - 8 Sep 2025

Viewed by 413

Abstract

Hydrogen proton exchange membrane fuel cells (PEMFCs) are a promising clean energy technology for automotive applications owing to their high efficiency and environmentally friendly characteristics. Efficient hydrogen recirculation is critical for sustaining the PEMFC performance, and ejector-based systems offer a passive, energy-efficient solution. [...] Read more.

Hydrogen proton exchange membrane fuel cells (PEMFCs) are a promising clean energy technology for automotive applications owing to their high efficiency and environmentally friendly characteristics. Efficient hydrogen recirculation is critical for sustaining the PEMFC performance, and ejector-based systems offer a passive, energy-efficient solution. However, traditional ejectors suffer from performance degradation across varying fuel-cell loads owing to their limited adaptability. To address this limitation, this study investigated the internal flow behavior and recirculation performance of single- and coaxial-nozzle ejectors, focusing on the influence of the diameter ratio between the mixing chamber and nozzle throat. Numerical simulations were performed to evaluate the flow structures and recirculation ratios under various operating conditions. The diameter ratio between the mixing chamber and the nozzle throat played a crucial role in determining the flow uniformity and recirculation efficiency. Specifically, lower diameter ratios reduce the recirculation ratio across all operating conditions, whereas higher diameter ratios exhibit diminished performance only under very low power outputs (≤4 bar) but show enhanced performance at medium-to-high outputs. These findings suggest that tailoring the geometric parameters of coaxial-nozzle ejectors can significantly improve hydrogen recirculation adaptability in PEMFC systems, thereby supporting more stable and efficient operation across a wide range of vehicle load conditions. Full article

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15 pages, 2802 KB

Open AccessArticle

Influence of Hot Isostatic Pressing on the Microstructure and Mechanical Properties of Hastelloy X Samples Manufactured via Laser Powder Bed Fusion

by Piotr Maj, Konstanty Jonak, Dorota Moszczynska, Rafał Molak, Ryszard Sitek and Jarosław Mizera

Appl. Sci. 2025, 15(17), 9844; https://doi.org/10.3390/app15179844 - 8 Sep 2025

Viewed by 680

Abstract

This study investigates the effects of Hot Isostatic Pressing (HIP) treatment on the microstructural evolution and mechanical properties of Laser Powder Bed Fusion (LPBF)-manufactured Hastelloy H. This research evaluates the trade-offs between defect elimination, anisotropy reduction, and strength retention in well-optimized LPBF components. [...] Read more.

This study investigates the effects of Hot Isostatic Pressing (HIP) treatment on the microstructural evolution and mechanical properties of Laser Powder Bed Fusion (LPBF)-manufactured Hastelloy H. This research evaluates the trade-offs between defect elimination, anisotropy reduction, and strength retention in well-optimized LPBF components. Specimens were manufactured using optimized LPBF parameters, achieving 99.85% density, and then subjected to HIP treatment at 1160 °C/100 MPa for 4 h. The analysis includes porosity analysis, grain size measurement, crystallographic texture evaluation, and tensile tests in two principal orientations. The results show that HIP treatment provides minimal benefits for defect elimination in already high-quality LPBF material, reducing porosity from 0.15% to <0.01%—a negligible improvement that does not translate to proportional mechanical enhancement. Tensile tests show that as-built specimens exhibited orientation-dependent strength, with XY-oriented samples reaching a yield strength (YS) of 682 MPa, ultimate tensile strength (UTS) of 864 MPa, and elongation of 17%, while XZ-oriented samples showed lower strength (YS = 621 MPa, UTS = 653 MPa) but superior ductility (elongation = 47%). After HIP treatment, anisotropy was largely removed, with both XY and XZ orientations showing comparable strength (YS ≈ 315–317 MPa, UTS ≈ 682–691 MPa) and elongation (38–41%). This indicates that HIP significantly improves ductility and isotropy at the cost of reduced strength. HIP treatment effectively eliminates the anisotropy of LPBF components, achieving uniform hardness across all orientations while reducing crystallographic texture intensity from 12.3× to 3.2× random orientation. This isotropy improvement occurs through grain-coarsening mechanisms that increase the average grain size from 7.5 μm to 13.5 μm, eliminating cellular–dendritic strengthening structures and reducing hardness by 32% (254 HV2 to 170 HV2) following Hall–Petch relationships. The conducted research confirms that HIP treatment allows for modification of the microstructure of Hastelloy X alloy, which may lead to the improvement of its mechanical properties in high-temperature applications and a significant increase in the isotropy of the material. Full article

(This article belongs to the Special Issue Mechanics of Advanced Composite Structures)

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14 pages, 855 KB

Open AccessArticle

Novel Machine Learning-Based Approach for Determining Milk Clotting Time Using Sheep Milk

by João Dias, Sandra Gomes, Karina S. Silvério, Daniela Freitas, Jaime Fernandes, João Martins, José Jasnau Caeiro, Manuela Lageiro and Nuno Alvarenga

Appl. Sci. 2025, 15(17), 9843; https://doi.org/10.3390/app15179843 - 8 Sep 2025

Viewed by 560

Abstract

The enzymatic coagulation of milk, crucial in cheese production, entails the hydrolysis of κ-casein and subsequent micelle aggregation. Conventional assessment standards, such as the Berridge method, depend on visual inspection and are susceptible to operator bias. Recent methods for the identification of milk-clotting [...] Read more.

The enzymatic coagulation of milk, crucial in cheese production, entails the hydrolysis of κ-casein and subsequent micelle aggregation. Conventional assessment standards, such as the Berridge method, depend on visual inspection and are susceptible to operator bias. Recent methods for the identification of milk-clotting time rely on optical, ultrasonic, and image-based technologies. In the present work, the composition of milk was evaluated through standard methods from ISO and AOAC. Milk coagulation time (MCT) was measured through viscosimetry, Berridge’s operator-driven technique, and a machine learning approach employing computer vision. Coagulation was additionally observed using the Optigraph, which measures micellar aggregation through near-infrared light attenuation for immediate analysis. Sheep milk samples were analysed for their composition and coagulation characteristics. Coagulation times, assessed via Berridge (BOB), demonstrated high correlation (R² = 0.9888) with viscosimetry (Visc) and machine learning (ML). Increased levels of protein and casein were linked to extended MCT, whereas lower pH levels sped up coagulation. The calcium content did not have a notable impact. Optigraph assessments validated variations in firmness and aggregation rate. Principal Component Analysis (PCA) identified significant correlations between total solids, casein, and MCT techniques. Estimates from ML-based MCT closely align with those from operator-based methods, confirming its dependability. This research emphasises ML as a powerful, automated method for evaluating milk coagulation, presenting a compelling substitute for conventional approaches. Full article

(This article belongs to the Special Issue Innovation in Dairy Products)

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23 pages, 5296 KB

Open AccessArticle

Research on the Lightweight Design of Aviation Generator Rear Cover Utilizing Topology Optimization

by Huazhong Zhang, Hongbiao Yin, Xu Deng, Hengxin Xu and Zhigang Yao

Appl. Sci. 2025, 15(17), 9842; https://doi.org/10.3390/app15179842 - 8 Sep 2025

Viewed by 604

Abstract

Topology optimization serves as a critical method for promoting lightweight structural design. Traditional methods predominantly focus on mechanical performance evaluation, often neglecting the critical correlation between modal characteristics and structural stiffness. The Evolutionary Structural Optimization (ESO) method is extensively employed in topology optimization; [...] Read more.

Topology optimization serves as a critical method for promoting lightweight structural design. Traditional methods predominantly focus on mechanical performance evaluation, often neglecting the critical correlation between modal characteristics and structural stiffness. The Evolutionary Structural Optimization (ESO) method is extensively employed in topology optimization; however, iterative oscillations lead to issues such as grid divergence and diminished solution quality. To address issues such as iterative oscillations and mesh divergence in the traditional Evolutionary Structural Optimization (ESO) method, this study applies a Simp Evolutionary Structural Optimization (SI-ESO) methodology. This method integrates intermediate density parameters and penalty factors into the progressive structural optimization process, thereby significantly enhancing iterative convergence and model quality. This work applied the optimized SI-ESO method to the lightweight redesign of an aviation generator’s rear cover, with validation conducted through additive manufacturing. Subsequently, the back cover of an aviation generator was redesigned and fabricated utilizing additive manufacturing technology. Empirical results indicate that under maximum stress conditions and employing the same additive process, the maximum deformation of the SI-ESO-optimized model is reduced compared to that of the ESO-designed model. Compared with the original design, the SI-ESO-optimized model achieved a 31% weight reduction, while relative to the ESO-optimized model, it exhibited a 27% lower maximum stress and a 10.53% higher first-order frequency, demonstrating both lightweighting and enhanced structural stiffness. Full article

(This article belongs to the Special Issue Structural Optimization Methods and Applications, 2nd Edition)

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17 pages, 2598 KB

Open AccessArticle

Evaluating the Performance Impact of Data Sovereignty Features on Data Spaces

by Stanisław Galij, Grzegorz Pawlak and Sławomir Grzyb

Appl. Sci. 2025, 15(17), 9841; https://doi.org/10.3390/app15179841 - 8 Sep 2025

Viewed by 562

Abstract

Data Spaces appear to offer a solution to data sovereignty concerns in public cloud environments, which are managed by third parties and must therefore be considered potentially untrusted. The IDS Connector, a key component of Data Space architecture, acts as a secure gateway, [...] Read more.

Data Spaces appear to offer a solution to data sovereignty concerns in public cloud environments, which are managed by third parties and must therefore be considered potentially untrusted. The IDS Connector, a key component of Data Space architecture, acts as a secure gateway, enforcing data sovereignty by controlling data usage and ensuring that data processing occurs within a trusted and verifiable environment. This study compares the performance of cloud-native data sharing services offered by major cloud providers—Amazon, Microsoft, and Google—with Data Spaces services delivered via two connector implementations: the Dataspace Connector and the Prometheus-X Dataspace Connector. An extensive set of experiments reveals significant differences in the performance of cloud-native managed services, as well as between connector implementations and hosting methods. The results indicate that the differences in the performance of data sharing services are unexpectedly substantial between providers, reaching up to 187%, and that the performance of different connector implementations also varies considerably, with an average difference of 56%. This indicates that the choice of cloud provider and data space Connector implementation has a major impact on the performance of the designed solution. Full article

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

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22 pages, 1740 KB

Open AccessArticle

MNATS: A Multi-Neighborhood Adaptive Tabu Search Algorithm for the Distributed No-Wait Flow Shop Scheduling Problem

by Zhaohui Zhang, Wanqiu Zhao, Hong Zhao and Xu Bian

Appl. Sci. 2025, 15(17), 9840; https://doi.org/10.3390/app15179840 - 8 Sep 2025

Viewed by 414

Abstract

The Distributed No-Wait Flow Shop Scheduling Problem (DNWFSP) arises in various manufacturing contexts, such as chemical production and electronic assembly, where strict no-wait constraints and multi-factory coordination are required. Solving the DNWFSP involves determining the allocation of jobs to factories and the no-wait [...] Read more.

The Distributed No-Wait Flow Shop Scheduling Problem (DNWFSP) arises in various manufacturing contexts, such as chemical production and electronic assembly, where strict no-wait constraints and multi-factory coordination are required. Solving the DNWFSP involves determining the allocation of jobs to factories and the no-wait processing sequences within each factory, making it a highly complex combinatorial problem. To address the limitations of existing methods—including poor initial solution quality, limited neighborhood exploration, and a tendency to converge prematurely—this paper proposes a Multi-Neighborhood Adaptive Tabu Search Algorithm (MNATS). The MNATS integrates a balance–lookahead NEH initializer (BL-NEH), an adaptive neighborhood local search (ANLS) strategy, and an Adaptive Tabu-Guided Perturbation (ATP) strategy. Experimental results on multiple benchmark instances demonstrate that MNATS algorithm significantly outperforms several state-of-the-art algorithms in terms of solution quality and robustness. Full article

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15 pages, 4240 KB

Open AccessArticle

High Accuracy Compensation of Straightness Errors in Linear Guideways Under Controlled Thermal and Vibrational Loads

by Zelong Li, Yifan Dai, Tao Lai, Saichen Li and Yufang Zhou

Appl. Sci. 2025, 15(17), 9839; https://doi.org/10.3390/app15179839 - 8 Sep 2025

Viewed by 432

Abstract

On-machine measurement is a highly effective approach for enhancing machining accuracy and efficiency. A critical factor influencing the accuracy of on-machine measurements is the straightness error of the linear guideway. However, this error is significantly affected by environmental factors such as temperature, vibration, [...] Read more.

On-machine measurement is a highly effective approach for enhancing machining accuracy and efficiency. A critical factor influencing the accuracy of on-machine measurements is the straightness error of the linear guideway. However, this error is significantly affected by environmental factors such as temperature, vibration, and gravity deformation. To improve the measurement accuracy of machine tools, this study investigates the impacts of these factors on straightness errors and proposes an innovative separation and compensation model for linear guideway straightness. A thermo-mechanical coupling simulation is employed to establish a model that quantifies the influence of thermal errors on straightness. The results demonstrate that thermal gradients cause the straightness error to bend to varying degrees, depending on the temperature distribution. Furthermore, a vibration error model is developed, revealing that the vibration period is approximately twice the ball diameter. Notably, vibration errors can be effectively mitigated using a band-stop filter to eliminate the corresponding frequency components. The study also addresses the effect of gravity deformation, comparing the deformation under different support conditions, highlighting the significance of precise support positioning. Through experimental validation of the straightness error separation and compensation model, it is shown that the straightness error of a conventional linear guideway can be reduced by 95%, and the compensated straightness error is less than 0.2 μm. This novel approach not only improves the accuracy of on-machine measurement but also provides valuable insights for optimizing machine tool performance under dynamic operating conditions. Full article

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20 pages, 2418 KB

Open AccessArticle

Optimal Efficiency and Automatic Current Commands Map Generator for an Interior Permanent Magnet Synchronous Motor in Electric Vehicles

by Shin-Hung Chang and Hsing-Yu Yeh

Appl. Sci. 2025, 15(17), 9838; https://doi.org/10.3390/app15179838 - 8 Sep 2025

Viewed by 622

Abstract

A systematic and highly efficient current commands generator for an interior permanent magnet synchronous motor (IPMSM) in electric vehicles is proposed. This paper integrates maximum torque per ampere (MTPA), maximum power control (MPC), and maximum torque per voltage (MTPV) criteria for optimal efficiency, [...] Read more.

A systematic and highly efficient current commands generator for an interior permanent magnet synchronous motor (IPMSM) in electric vehicles is proposed. This paper integrates maximum torque per ampere (MTPA), maximum power control (MPC), and maximum torque per voltage (MTPV) criteria for optimal efficiency, and systematically establishes an optimal current control commands workflow. A rapid current commands mapping technique and an automatic high efficiency of all speed range current command generator are proposed. The automatically generated commands table can be effectively applied in a motor controller to reduce the energy consumption of an electric vehicle for all operating speed range. A graphical user interface (GUI) tool for the generator, which can automatically produce the current command (look-up tables, LUT) in an Excel format, is designed. High-speed field-weakening and zero-torque cruising (ZTC) in electric vehicles are also thoughtfully considered. By using the proposed method, motor controller designers can more rapidly adjust required motor current command tables and speed up the development period. Both GUI simulation and experimental results show the effectiveness and feasibility of the proposed method. Full article

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15 pages, 3677 KB

Open AccessArticle

Contextual Feature Expansion with Superordinate Concept for Compositional Zero-Shot Learning

by Soohyeong Kim and Yong Suk Choi

Appl. Sci. 2025, 15(17), 9837; https://doi.org/10.3390/app15179837 - 8 Sep 2025

Viewed by 454

Abstract

Compositional Zero-Shot Learning (CZSL) seeks to enable machines to recognize objects and attributes (i.e., primitives),learn their associations, and generalize to novel compositions, enabling systems to exhibit a human-like ability to infer and generalize. The existing approaches, multi-label and multi-class classification, face inherent trade-offs: [...] Read more.

Compositional Zero-Shot Learning (CZSL) seeks to enable machines to recognize objects and attributes (i.e., primitives),learn their associations, and generalize to novel compositions, enabling systems to exhibit a human-like ability to infer and generalize. The existing approaches, multi-label and multi-class classification, face inherent trade-offs: the former suffers from biases against unrelated compositions, while the latter struggles with exponentially growing search spaces as the number of objects and attributes increases. To overcome these limitations and address the exponential complexity in CZSL, we introduce Concept-oriented Feature ADjustment (CoFAD), a novel method that extracts superordinate conceptual features based on primitive relationships and expands label feature boundaries. By incorporating spectral clustering and membership function in fuzzy logic, CoFAD achieves state-of-the-art performance while using 2×–4× less GPU memory and reducing training time by up to 50× on large-scale dataset. Full article

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28 pages, 1797 KB

Open AccessArticle

Sensor-Based Analysis of Upper Limb Motor Coordination After Stroke: Insights from EMG, ROM, and Motion Data During the Wolf Motor Function Test

by Ji-Yong Jung and Jung-Ja Kim

Appl. Sci. 2025, 15(17), 9836; https://doi.org/10.3390/app15179836 - 8 Sep 2025

Viewed by 541

Abstract

The Wolf Motor Function Test (WMFT) is widely used to evaluate upper limb motor performance after stroke. However, conventional approaches may overlook domain-specific neuromuscular and kinematic differences during task execution. This study classified WMFT tasks into three functional domains: proximal reaching and transport [...] Read more.

The Wolf Motor Function Test (WMFT) is widely used to evaluate upper limb motor performance after stroke. However, conventional approaches may overlook domain-specific neuromuscular and kinematic differences during task execution. This study classified WMFT tasks into three functional domains: proximal reaching and transport (PRT), fine motor manipulation (FMM), and gross motor functional control (GMFC). Interlimb differences in muscle activation, joint mobility, and movement amplitude were examined using sensor-based measurements. Twelve individuals with chronic stroke performed 16 WMFT tasks. Surface electromyography (EMG) and inertial measurement units (IMUs) recorded upper limb muscle activity, joint angles, and segmental displacement. Wilcoxon signed-rank tests and Spearman correlations were conducted for each functional domain. Significant asymmetries in EMG, range of motion (ROM), and root mean square (RMS) acceleration were found in PRT and FMM tasks. These results reflect increased proximal muscle activation and reduced distal engagement on the paretic side. GMFC tasks elicited more symmetrical patterns but still showed subtle deficits in distal control. Correlation analyses demonstrated strong interdependencies among neuromuscular and kinematic measures. This finding underscores the integrated nature of compensatory strategies. Categorizing WMFT tasks by functional domain and integrating multimodal sensor analysis revealed nuanced impairment patterns. These patterns were not detectable by conventional observational scoring. These findings support the use of sensor-based, domain-specific assessment to guide individualized rehabilitation strategies. Such approaches may ultimately enhance long-term functional recovery in stroke survivors. Full article

(This article belongs to the Special Issue Applications of Wearable Sensors in Healthcare, Rehabilitation and Sports Biomechanics)

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17 pages, 1992 KB

Open AccessArticle

Probabilistic Framework for Ground Movement Induced by Shield Tunnelling in Soft Soil Based on Gap Parameter

by Wenyu Yang, Lan Cui, Hemeng Tan and Luqi Wang

Appl. Sci. 2025, 15(17), 9835; https://doi.org/10.3390/app15179835 - 8 Sep 2025

Viewed by 499

Abstract

Numerical simulation and machine learning-based methods are frequently adopted when performing ground movement probabilistic analyses, considering the various uncertainties during shield tunnelling. However, numerical simulation takes time, while machine learning lacks interpretation somehow. New methods fully reflecting mechanisms and taking advantage of field [...] Read more.

Numerical simulation and machine learning-based methods are frequently adopted when performing ground movement probabilistic analyses, considering the various uncertainties during shield tunnelling. However, numerical simulation takes time, while machine learning lacks interpretation somehow. New methods fully reflecting mechanisms and taking advantage of field data should be proposed and applied in probabilistic analysis. This study proposes a probabilistic framework from the mechanism and data aspect based on the GAP parameter. Solutions for three components of the GAP parameter are first improved through different methods. Coupling the uncertainty of the input parameters, a probabilistic framework estimating the risks from both mechanistic and data insights is then established. Furthermore, the spatial variability in soft soil is considered in the framework by calculating the equivalent parameters. Through an analysis of a practical case, the results show that the measured data can fall within the 95% confidence interval of the predicted displacement samples. The median of the predicted samples is highly consistent with the measured value, and by considering the spatial variability in soil, the results can be more accurate. As a result, the proposed probabilistic framework is verified as practically applicable when predicting ground movement while considering multiple uncertainties. Full article

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22 pages, 4432 KB

Open AccessArticle

The Impact of Weather on Shared Bikes

by Peng Liu, Zhicheng Pan, Zhenlong Fan and Xiaoxia Wang

Appl. Sci. 2025, 15(17), 9834; https://doi.org/10.3390/app15179834 - 8 Sep 2025

Viewed by 710

Abstract

This article explores the impact of weather and environment on shared bicycles. Using a random forest model combined with explanatory machine learning methods, the relationship, threshold effect, and interaction effect between weather factors and the transfer volume of shared bicycles at subway stations [...] Read more.

This article explores the impact of weather and environment on shared bicycles. Using a random forest model combined with explanatory machine learning methods, the relationship, threshold effect, and interaction effect between weather factors and the transfer volume of shared bicycles at subway stations are analyzed. Research has shown that using the RF+IML method to study the impact of weather variables on shared bicycle transfer volume is feasible. There is a significant nonlinear relationship between various weather factors and shared bicycle transfers. Temperature, humidity, and rainfall have specific activation and threshold effects on the number of shared bicycle transfers. When humidity is below 60%, the variation in transfer volume remains relatively stable; however, once it exceeds 60%, the transfer volume drops sharply. When the temperature exceeds 17 °C, its impact tends to reach saturation. Similarly, when rainfall reaches around 20 mm, its adverse effect also approaches the threshold. Temperature is the most important factor affecting the prediction of shared bicycle transfer volume, with temperature, cold weather, and cold forecasts contributing over 35% to the total effect. The interaction effect between temperature and other weather factors accounts for 22% of the total effect. Full article

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31 pages, 9617 KB

Open AccessArticle

Alleviate Data Scarcity in Remanufacturing: Classifying the Reusability of Parts with Data-Efficient Generative Adversarial Networks (DE-GANs)

by Maximilian Herold, Engjëll Ahmeti, Naga Sai Teja Kolakaleti, Cagatay Odabasi, Jan Koller and Frank Döpper

Appl. Sci. 2025, 15(17), 9833; https://doi.org/10.3390/app15179833 - 8 Sep 2025

Viewed by 627

Abstract

Remanufacturing, a key element of the circular economy, enables products and parts to have new life cycles through a systematic process. Initially, used products (cores) are visually inspected and categorized according to their manufacturer and variant before being disassembled and cleaned. Subsequently, parts [...] Read more.

Remanufacturing, a key element of the circular economy, enables products and parts to have new life cycles through a systematic process. Initially, used products (cores) are visually inspected and categorized according to their manufacturer and variant before being disassembled and cleaned. Subsequently, parts are manually classified as directly reusable, reusable after reconditioning, or recyclable. As demand for remanufactured parts increases, automated classification becomes crucial. However, current Deep Learning (DL) methods, constrained by the scarcity of unique parts, often suffer from insufficient datasets, leading to overfitting. This research explores the effectiveness of Data-Efficient Generative Adversarial Network (DE-GAN) optimization approaches like FastGAN, APA, and InsGen in enhancing dataset diversity. These methods were evaluated against the State-of-the-Art (SOTA) Deep Convolutional Generative Adversarial Network (DCGAN) using metrics such as the Inception Score (IS), Fréchet Inception Distance (FID), and the classification accuracy of ResNet18 models trained with partially synthetic data. FastGAN achieved the lowest FID values among all models and led to a statistically significant improvement in ResNet18 classification accuracy. At a [1:1] real-to-synthetic ratio, the mean accuracy increased from 72% ± 4% (real-data-only) to 87% ± 3% (p < 0.001), and reached 94% ± 3% after hyperparameter optimization. In contrast, synthetic data generated by the SOTA DCGAN did not yield statistically significant improvements. Full article

(This article belongs to the Special Issue Emerging Applications of Artificial Intelligence in Mechanical, Control, Geological, Sensing, and Intelligent Detection Engineering: Integration and Innovations)

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21 pages, 8215 KB

Open AccessArticle

Erosion Behavior of Cohesive Deep-Sea Sediments Under Submerged Water Jets: Numerical Simulation and Experimental Validation

by Gang Wang, Chenglong Liu, Yangrui Cheng, Bingzheng Chen, Xiang Zhu, Yanyang Zhang and Yu Dai

Appl. Sci. 2025, 15(17), 9832; https://doi.org/10.3390/app15179832 - 8 Sep 2025

Viewed by 602

Abstract

Understanding the interaction between submerged water jets and cohesive deep-sea sediment is critical for optimizing deep-sea polymetallic nodule hydraulic mining techniques. This research investigated the distinct erosion behavior of cohesive sediments through laboratory experiments and numerical simulations. Cohesive deep-sea sediments were simulated using [...] Read more.

Understanding the interaction between submerged water jets and cohesive deep-sea sediment is critical for optimizing deep-sea polymetallic nodule hydraulic mining techniques. This research investigated the distinct erosion behavior of cohesive sediments through laboratory experiments and numerical simulations. Cohesive deep-sea sediments were simulated using bentonite–kaolinite mixtures. A series of laboratory experiments, including vane shear tests and viscosity tests under varying moisture content, were conducted to assess the sediments’ mechanical properties. Experimental submerged water jet erosion tests provided basic data for validating the numerical simulations. A Eulerian multi-fluid (EMF) model was implemented to capture sediment–water jet interactions under varying operational parameters, including jet velocities and nozzle heights. The erosion process was found to comprise three distinct stages, including rapid erosion, steady erosion, and stabilization. Two distinct erosion mechanisms were identified, depending on the jet intensity, which affected the depth and shape of the erosion pits. Quantitative analysis revealed that erosion depth exhibits an approximately linear relationship with jet velocity and nozzle height, whereas the erosion diameter shows nonlinear characteristics. These findings enhance the fundamental understanding of cohesive sediment responses under hydraulic disturbances, providing crucial insights for the design and optimization of efficient deep-sea mining systems. Full article

(This article belongs to the Special Issue Advances in Marine Geotechnics)

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25 pages, 17509 KB

Open AccessArticle

Assessment of Vegetation Cover and Rainfall Infiltration Effects on Slope Stability

by Gaoliang Tao, Lingsan Guo, Henglin Xiao, Qingsheng Chen, Sanjay Nimbalkar, Shiju Feng and Zhijia Wu

Appl. Sci. 2025, 15(17), 9831; https://doi.org/10.3390/app15179831 - 8 Sep 2025

Viewed by 592

Abstract

Investigating rainfall infiltration mechanisms and slope stability dynamics under varying vegetation cover conditions is essential for advancing ecological slope protection methodologies. This research focuses on large-scale outdoor slope models, with the objective of monitoring soil moisture variations in real-time during rainfall events on [...] Read more.

Investigating rainfall infiltration mechanisms and slope stability dynamics under varying vegetation cover conditions is essential for advancing ecological slope protection methodologies. This research focuses on large-scale outdoor slope models, with the objective of monitoring soil moisture variations in real-time during rainfall events on four types of slopes: bare, herbaceous, shrub, and mixed herb–shrub planting. Combining direct shear tests for unsaturated soil with numerical simulations, and considering the weakening effect of water on shear strength, this study analyzes slope stability. The findings reveal significant spatial variations in rainfall infiltration rates, with maximum values recorded at a burial depth of 0.2 m, declining as the burial depth increases. Different types of vegetation have distinct impacts on slope infiltration patterns: herbaceous increases cumulative infiltration by 21.32%, while shrub reduces it by 61.06%. The numerically simulated moisture content values demonstrate strong congruence with field-measured data. Compared with monoculture herbaceous or shrub root systems, the mixed herb–shrub root system exhibits the most significant enhancement effects on shear strength parameters. Under high water content conditions, root systems demonstrate substantially greater improvement in cohesion than in internal friction angle. Before rainfall, shrub vegetation contributed the most significant improvement to the safety factor, increasing it from 2.766 to 3.046, followed by herbaceous and mixed herb–shrub vegetation, which raised it to 2.81 and 2.948. After rainfall, mixed herb–shrub vegetation demonstrated the greatest enhancement of the safety factor, elevating it from 1.139 to 1.361, followed by herbaceous and shrub vegetation, which increased it to 1.192 and 1.275. The study offers preliminary insights and a scientific basis for the specific conditions tested for selecting and optimizing eco-friendly slope protection measures. Full article

(This article belongs to the Special Issue Advances in Failure Mechanism and Numerical Methods for Geomaterials)

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32 pages, 5663 KB

Open AccessArticle

Static and Dynamic Malware Analysis Using CycleGAN Data Augmentation and Deep Learning Techniques

by Moses Ashawa, Robert McGregor, Nsikak Pius Owoh, Jude Osamor and John Adejoh

Appl. Sci. 2025, 15(17), 9830; https://doi.org/10.3390/app15179830 - 8 Sep 2025

Viewed by 711

Abstract

The increasing sophistication of malware and the use of evasive techniques such as obfuscation pose significant challenges to traditional detection methods. This paper presents a deep convolutional neural network (CNN) framework that integrates static and dynamic analysis for malware classification using RGB image [...] Read more.

The increasing sophistication of malware and the use of evasive techniques such as obfuscation pose significant challenges to traditional detection methods. This paper presents a deep convolutional neural network (CNN) framework that integrates static and dynamic analysis for malware classification using RGB image representations. Binary and memory dump files are transformed into images to capture structural and behavioural patterns often missed in raw formats. The proposed system comprises two tailored CNN architectures: a static model with four convolutional blocks designed for binary-derived images and a dynamic model with three blocks optimised for noisy memory dump data. To enhance generalisation, we employed Cycle-Consistent Generative Adversarial Networks (CycleGANs) for cross-domain image augmentation, expanding the dataset to over 74,000 RGB images sourced from benchmark repositories (MaleVis and Dumpware10). The static model achieved 99.45% accuracy and perfect recall, demonstrating high sensitivity with minimal false positives. The dynamic model achieved 99.21% accuracy. Experimental results demonstrate that the fused approach effectively detects malware variants by learning discriminative visual patterns from both structural and runtime perspectives. This research contributes to a scalable and robust solution for malware classification unlike a single approach. Full article

(This article belongs to the Special Issue Machine Learning and Data Analysis: Bridging Theory and Real-World Solutions)

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22 pages, 1682 KB

Open AccessArticle

Unsupervised Domain Adaptation for Automatic Polyp Segmentation Using Synthetic Data

by Ioanna Malli, Ioannis A. Vezakis, Ioannis Kakkos, Theodosis Kalamatianos and George K. Matsopoulos

Appl. Sci. 2025, 15(17), 9829; https://doi.org/10.3390/app15179829 - 8 Sep 2025

Viewed by 591

Abstract

Colorectal cancer is a significant health concern that can often be prevented through early detection of precancerous polyps during routine screenings. Although artificial intelligence (AI) methods have shown potential in reducing polyp miss rates, clinical adoption remains limited due to concerns over patient [...] Read more.

Colorectal cancer is a significant health concern that can often be prevented through early detection of precancerous polyps during routine screenings. Although artificial intelligence (AI) methods have shown potential in reducing polyp miss rates, clinical adoption remains limited due to concerns over patient privacy, limited access to annotated data, and the high cost of expert labeling. To address these challenges, we propose an unsupervised domain adaptation (UDA) approach that leverages a fully synthetic colonoscopy dataset, SynthColon, and adapts it to real-world, unlabeled data. Our method builds on the DAFormer framework and integrates a Transformer-based hierarchical encoder, a context-aware feature fusion decoder, and a self-training strategy. We evaluate our approach on the Kvasir-SEG and CVC-ClinicDB datasets. Results show that our method achieves improved segmentation performance of 69% mIoU compared to the baseline approach from the original SynthColon study and remains competitive with models trained on enhanced versions of the dataset. Full article

(This article belongs to the Special Issue AI-Based Biomedical Signal and Image Processing)

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28 pages, 48754 KB

Open AccessArticle

Advances in Geological Resource Calculations, Incorporating New Parameters for Optimal Classification

by Gonzalo Ares, Isidro Diego Álvarez, Alicja Krzemień and César Castañón Fernández

Appl. Sci. 2025, 15(17), 9828; https://doi.org/10.3390/app15179828 - 8 Sep 2025

Viewed by 686

Abstract

A fundamental aspect in the evaluation of mining projects is the classification of mineral resources, as it directly influences the definition of mineral reserves and affects both the planning and operational phases of the mine. Traditional methods employed in the industry are based [...] Read more.

A fundamental aspect in the evaluation of mining projects is the classification of mineral resources, as it directly influences the definition of mineral reserves and affects both the planning and operational phases of the mine. Traditional methods employed in the industry are based on geometric or geostatistical criteria which, while constituting the fundamental basis of the process, may prove insufficient when applied in isolation to reflect the uncertainty inherent in the databases used for the evaluation of mineral deposits. As discussed throughout the article, this limitation can lead to an incorrect or imprecise assignment of resource categories. This work presents a methodology to integrate variables related to sample quality as an additional criterion in resource classification. This allows for the identification of areas with greater uncertainty and the adjustment of their categories more consistently with data reliability. The effectiveness of the proposed method is demonstrated through its application to a real case study, complemented by a comprehensive analysis of its implications and results. Full article

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14 pages, 2528 KB

Open AccessArticle

Application of the Expectation-Maximization Clustering Method for Identifying Li Geochemical Anomalies in Stream Sediments in Southeastern Hunan Province, China

by Weiming Dai, Qinghao Zhang and Xinyun Zhao

Appl. Sci. 2025, 15(17), 9827; https://doi.org/10.3390/app15179827 - 8 Sep 2025

Viewed by 399

Abstract

The identification of lithium (Li) geochemical anomalies is crucial for the exploration of Li mineral resources. However, variations in lithological backgrounds in lithologically complex regions often hinder the accurate identification of these anomalies. In this study, we employ an unsupervised Expectation-Maximization (EM) clustering [...] Read more.

The identification of lithium (Li) geochemical anomalies is crucial for the exploration of Li mineral resources. However, variations in lithological backgrounds in lithologically complex regions often hinder the accurate identification of these anomalies. In this study, we employ an unsupervised Expectation-Maximization (EM) clustering algorithm to tackle this issue. Using 1:200,000 scale geochemical data from 2559 stream sediment samples in Chenzhou, Hunan Province, China, we selected seven major elements—SiO₂, Al₂O₃, Fe₂O₃, MgO, CaO, Na₂O, and K₂O—as clustering indicators. This approach allowed us to classify the samples into six distinct groups, significantly reducing the influence of lithological background on the detection of Li anomalies. After applying the 3σ technique to eliminate 122 outliers and conducting Z-score normalization on Li concentration data within each group, Li anomalies were identified using a uniform threshold of the mean + two standard deviations. The results indicate that the EM clustering method effectively suppresses pronounced yet spurious anomalies in high-background areas where granitic intrusions are present, accounting for approximately 0.6% of the total study area, while simultaneously uncovering subtle but significant anomalies in low-background regions characterized by slightly metamorphic and siliceous rocks, accounting for approximately 1.7% of the total study area. This approach substantially improves the reliability of anomalies, offering a robust tool for Li exploration in lithologically complex regions. Full article

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17 pages, 472 KB

Open AccessSystematic Review

Embedding Digital Technologies (AI and ICT) into Physical Education: A Systematic Review of Innovations, Pedagogical Impact, and Challenges

by Dragoș Ioan Tohănean, Ana Maria Vulpe, Raluca Mijaica and Dan Iulian Alexe

Appl. Sci. 2025, 15(17), 9826; https://doi.org/10.3390/app15179826 - 8 Sep 2025

Viewed by 1127

Abstract

This systematic review investigates the integration of artificial intelligence (AI) and information and communication technologies (ICT) in physical education across all educational levels. Physical education is uniquely centered on motor skill development, physical activity engagement, and health promotion—outcomes that require tailored technological approaches. [...] Read more.

This systematic review investigates the integration of artificial intelligence (AI) and information and communication technologies (ICT) in physical education across all educational levels. Physical education is uniquely centered on motor skill development, physical activity engagement, and health promotion—outcomes that require tailored technological approaches. Through the analysis of recent empirical studies, the main areas where digital technologies contribute to pedagogical innovation are highlighted—such as personalized learning, real-time feedback, student motivation, and educational inclusion. The findings show that AI-assisted tools facilitate differentiated instruction and self-regulated learning by adapting to students’ individual performance levels. Technologies such as wearables and augmented reality (AR)/virtual reality (VR) systems increase engagement and support the participation of students with special educational needs. Furthermore, AI contributes to more efficient and objective assessment of motor performance, coordination, and movement quality. However, significant structural and ethical challenges persist, such as unequal access to digital infrastructure, lack of teacher training, and concerns related to personal data protection. Teachers’ perceptions reflect both openness to the educational potential of AI and caution regarding its practical implementation. The review concludes that AI and ICT can substantially transform physical education, provided that coherent policies, clear ethical frameworks, and investments in teachers’ professional development are in place. Full article

(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence)

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25 pages, 8561 KB

Open AccessArticle

CFD-Driven Enhancement for Supersonic Aircraft Variable Geometry Inlet

by Abdullah Ezzeldin and Zhenlong Wu

Appl. Sci. 2025, 15(17), 9825; https://doi.org/10.3390/app15179825 - 8 Sep 2025

Viewed by 588

Abstract

High-speed propulsion systems require supersonic inlets for operation; however, these inlets lose efficiency when the flight speed range is wide. Fixed-geometry inlets designed for particular conditions encounter operational difficulties when running at supercritical speeds, including shockwave instabilities and pressure reduction, limiting their operational [...] Read more.

High-speed propulsion systems require supersonic inlets for operation; however, these inlets lose efficiency when the flight speed range is wide. Fixed-geometry inlets designed for particular conditions encounter operational difficulties when running at supercritical speeds, including shockwave instabilities and pressure reduction, limiting their operational speed and altitude range. Increasing inlet flexibility is a critical requirement for aerospace systems that need multivariable propulsion capabilities for civilian and military operations. This study, based on a supersonic inlet whose design flight Mach number is 2.2, determines its operational performance when operating at a speed of Mach 3 and then investigates modifications for expanding its operational boundaries with variable geometry components. This study used computational fluid dynamics in ANSYS Fluent with the k-ω SST turbulence model for airflow analysis. The methodology starts with Mach 2.2 baseline validation before proceeding to the Mach 3 investigation at different upward cowl-lip deflection angles ranging from 5° to 16°. This study conducted tests with a bleed slot and a 6 mm semi-conical bump to practically diminish unstart occurrences and treatment of shock–boundary-layer interactions. The results showed that a lip deflection angle of 15° upward delivers maximum operational efficiency on Mach 3 in terms of compression efficiency, flow deceleration, and flow uniformity at the inlet exit, as it generates an exit Mach number of 1.9, identical to that of the unmodified baseline operating at Mach 2.2, while a 5° deflection upward has shown the best values for total pressure recovery. Bleed slot implementation with the bump shape decreased unstart effects at a backpressure 30 times bigger than ambient pressure and produced stable flow despite a total pressure recovery drop of 8.5%. At Mach 3, with 15 km altitude, these modifications allow the system to operate with similar effectiveness as the baseline design at lower speeds. This study introduces a method for modifying a fixed-geometry inlet and extending its limitations, offering a pathway for adaptable supersonic inlets. The findings contribute to propulsion systems design by introducing a simple method for applying geometrical variations with less mechanical complexity compared to traditional variable geometry inlets. They change the entire throat area, supporting the design of supersonic vehicles and sustainable supersonic travel. Full article

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16 pages, 2585 KB

Open AccessArticle

Betulin-Hippuric Acid Conjugates: Chemistry, Antiproliferative Activity and Mechanism of Action

by Marta Świtalska, Elwira Chrobak, Monika Kadela-Tomanek, Joanna Wietrzyk and Ewa Bębenek

Appl. Sci. 2025, 15(17), 9824; https://doi.org/10.3390/app15179824 - 8 Sep 2025

Viewed by 484

Abstract

The structure of betulin enables the formation of conjugates that offer improved activity, selectivity, or pharmacokinetic parameters. It was assumed that combining betulin with hippuric acid could produce a product with favorable biological properties. The bond connecting the conjugate elements was an ester [...] Read more.

The structure of betulin enables the formation of conjugates that offer improved activity, selectivity, or pharmacokinetic parameters. It was assumed that combining betulin with hippuric acid could produce a product with favorable biological properties. The bond connecting the conjugate elements was an ester group introduced using a method ensuring mild reaction conditions (Steglich method). In this way, betulin and its acetyl derivatives were converted into conjugates with hippuric acid, with good yields. The obtained compounds were assessed for their in vitro antiproliferative activity against seven different human cancer cell lines (MTT and SRB assays), preceded by in silico prediction (PASS online). Lipophilicity (logP_TLC), a significant parameter influencing all stages of the ADME process, was experimentally determined using RP-TLC. LogP_TLC values were compared with logP results obtained from available online computational programs. Antiproliferative activity studies demonstrated the significant sensitivity of MV4-11 cells to the tested compounds. The IC₅₀ values ranged from 4.2 to 31.4 µM. The mechanism of anticancer action was investigated for the most active derivatives 4, 5, and 7. For derivative 7, molecular docking revealed the highest affinity for the FLT3 protein binding site. Full article

(This article belongs to the Special Issue Advancements in Natural Compounds: Antimicrobial, Antioxidant, and Therapeutic Potentials)

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22 pages, 5570 KB

Open AccessArticle

A Bi-Directional Coupling Calibration Model and Adaptive Calibration Algorithm for a Redundant Serial Robot with Highly Elastic Joints

by Bin Wang and Zhouxiang Jiang

Appl. Sci. 2025, 15(17), 9823; https://doi.org/10.3390/app15179823 - 8 Sep 2025

Viewed by 420

Abstract

This paper proposes a calibration method for redundant robot arms with highly elastic joints. The method uses the second-order Chebyshev polynomial to characterize the variation in the error with the poses of all joints. This error model is consistent with the variation in [...] Read more.

This paper proposes a calibration method for redundant robot arms with highly elastic joints. The method uses the second-order Chebyshev polynomial to characterize the variation in the error with the poses of all joints. This error model is consistent with the variation in the gravitational torque on each joint and demonstrates good generalization. Based on this, the calibration model includes both kinematic errors and non-kinematic errors. For this high-dimensional model, an adaptive iterative identification algorithm is proposed for a large number of small error parameters of various types. The algorithm sets specific iteration rules for different types of error parameters and adjusts the convergence amplitude in each iteration, ensuring that the iterative algorithm converges to the global optimum. The simulation results show that for a redundant robot arm with 12 highly elastic joints, even with large linearization modeling errors, the new identification algorithm can gradually eliminate them during iteration, achieving an identification accuracy higher than 99.975% for all of the error parameters. The experimental results indicate that on a redundant robot arm with eight cable-driven elastic joints, the new model and identification algorithm reduce the 96.6% absolute positioning errors of the robot arm, enabling it to perform precise and flexible operations. It takes 40.534 s and 29.077 s to run the identification algorithm on MATLAB (R2023b, 2.10 GHz CPU) in the simulation and experiment, respectively. Full article

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19 pages, 5757 KB

Open AccessArticle

Machine Learning-Assisted Comparative Analysis of Fracture Propagation Mechanisms in CO₂ and Hydraulic Fracturing of Acid-Treated Tight Sandstone

by Jie Huang, Zhenlong Song, Weile Geng and Qinming Liang

Appl. Sci. 2025, 15(17), 9822; https://doi.org/10.3390/app15179822 - 8 Sep 2025

Viewed by 533

Abstract

Carbon dioxide (CO₂) fracturing and acid treatment are currently considered promising approaches to overcome the challenge of excessively high initiation pressure during conventional hydraulic fracturing in tight sandstone gas reservoirs. However, the mechanisms of these methods weaken the reservoir rock’s mechanical [...] Read more.

Carbon dioxide (CO₂) fracturing and acid treatment are currently considered promising approaches to overcome the challenge of excessively high initiation pressure during conventional hydraulic fracturing in tight sandstone gas reservoirs. However, the mechanisms of these methods weaken the reservoir rock’s mechanical properties, remain unclear. Using a machine learning approach, we elucidate the differences in initiation mechanisms between CO₂ fracturing and hydraulic fracturing under acid-treated conditions, thereby providing a mechanistic explanation for the lower initiation pressure observed in CO₂ fracturing compared to conventional hydraulic fracturing. The tensile fractures, shear fractures, and acid-modified fractures have been identified by a specially trained AI model, which achieved exceptional accuracy (95.4%). Acoustic emission source locations show that CO₂ fracturing mainly causes shear fracture along acid-weakened planes, which promotes the propagation of composite tensile-shear fractures in untreated reservoir areas. Due to the significantly lower diffusivity of water compared to CO₂, hydraulic fracturing predominantly induces non-acidic mixed-mode (tensile-shear) fractures. This fundamental difference in fracture patterns accounts for the higher initiation pressure observed in hydraulic fracturing compared to CO₂ fracturing. These findings offer crucial insights into pressurized fluid-driven fracturing mechanisms and propose an optimized technical pathway for enhancing hydrocarbon recovery in low-permeability sandstone formations. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Geotechnics and Engineering Geology—2nd Edition)

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