Applied Sciences

16 pages, 11001 KB

Open AccessArticle

Influence of Cellular Structural Characteristics on Stem Mechanical Strength in Two Wheat Cultivars (Triticum aestivum L)

by Qingting Liu, Zhenghe Luo, Meimei Wang, Zhichao Lin, Yao Huang, Qing Zhou and Xueting Han

Appl. Sci. 2025, 15(19), 10424; https://doi.org/10.3390/app151910424 (registering DOI) - 25 Sep 2025

Abstract

The lodging of wheat has a significant impact on its yield, and its resistance is intricately associated with the mechanical strength of its stem. The majority of existing studies on this issue have been conducted at the macroscale, and the quantitative relationship between [...] Read more.

The lodging of wheat has a significant impact on its yield, and its resistance is intricately associated with the mechanical strength of its stem. The majority of existing studies on this issue have been conducted at the macroscale, and the quantitative relationship between cellular structural characteristics and the mechanical strength of the wheat stem remains poorly understood. This study aimed to investigate this relationship in two wheat cultivars: ‘Zhoumai 36’ and ‘Angong 38’. Samples were collected from the second basal internode of stems at three growth stages: anthesis, grain filling, and maturity. Transmission Electron Microscopy (TEM) and X-Ray Diffraction (XRD) were utilized to examine cellular morphology, measure cell wall thickness, and analyze microfibril angles and crystallite sizes within the cell walls. Tensile tests were conducted to determine the tensile strength and elastic modulus of the stem samples. The relationship between cellular structural characteristics and stem mechanical strength was systematically investigated. The results demonstrated that during the developmental transition from anthesis to maturity, the elastic modulus of the stems in the two wheat varieties exhibited divergent trends: a decrease from 1.60 ± 0.08 GPa to 1.25 ± 0.04 GPa (mean ± SEM) in ‘Zhoumai 36’and an increase from 1.15 ± 0.07 GPa to 1.48 ± 0.18 GPa (mean ± SEM) in ‘Angong 38’ These differences were accompanied by variations in water content between the two varieties. Furthermore, it was observed that the thickness of the S2 layer (the middle layers of the secondary cell wall) in both sclerenchyma and vessel cells showed a positive correlation with stem elastic modulus. Conversely, the microfibril angle of the S2 layer displayed a negative correlation with elastic modulus. Cellulose crystallite size varied across the growth stages, ranging from 1.22 ± 0.10 nm to 1.83 ± 0.30 nm (mean ± SEM) in ‘Zhoumai 36’ and from 1.42 ± 0.11 nm to 1.85 ± 0.23 nm (mean ± SEM) in ‘Angong 38’, respectively, and this parameter also exhibited a positive correlation with elastic modulus. This study clarified the variation trends of stem elastic modulus in wheat cultivars ‘Zhoumai 36’ and ‘Angong 38’ from anthesis to maturity and revealed, through experimental determination and correlation analysis, the microscale quantitative relationships between the stem cellular structural characteristics (S2 layer thickness, S2 layer microfibril angle, and cellulose crystallite size) and mechanical strength (characterized by elastic modulus) in the two cultivars. Full article

13 pages, 488 KB

Open AccessArticle

Determinants of Quality of Life in Older Adults: The Role of Sarcopenia, Physical Fitness, and Lifestyle Factors

by Jun-Young Sung, Su-Yeon Roh, Moon Jin Lee and Jiyoun Kim

Appl. Sci. 2025, 15(19), 10423; https://doi.org/10.3390/app151910423 (registering DOI) - 25 Sep 2025

Abstract

To examine the relationship between the applicability of the Sarcopenia & Quality of Life (SarQoL) questionnaire and physical fitness factors including living environment, body composition, and exercise function of individuals aged over 60 years were analyzed. The study included 551 older adults (129 [...] Read more.

To examine the relationship between the applicability of the Sarcopenia & Quality of Life (SarQoL) questionnaire and physical fitness factors including living environment, body composition, and exercise function of individuals aged over 60 years were analyzed. The study included 551 older adults (129 men, 422 women) aged 60–100 years, residing in South Korea, and without serious diseases, disabilities, or physical activity limitations. Demographics, health indicators, living conditions, SarQoL survey results, and physical fitness were assessed. Group differences were evaluated using one-way ANOVA with Tukey’s post hoc test. Hierarchical regression analysis explored the relationship between SarQoL scores and various factors, and multiple logistic regression analyzed the association between SarQoL and exercise variables. Statistical significance was set at p < 0.05. Sex (B, 0.200; t, 2.233; p < 0.001), age (B, −0.031; t, −6.574; p < 0.001), education level (B, −0.110; t, 3.067; p = 0.002), and regular physical activity influenced SarQoL scores. Physical fitness measures were significantly correlated with SarQoL scores. Additionally, conditions such as diabetes, sleep disorders, and sarcopenia affected SarQoL outcomes. High SarQoL in older adults is associated with higher educational attainment, presence of medical conditions, and improved physical fitness. This underscores the complex interplay of physical and mental health in the aging process and highlights the importance of preventing and managing sarcopenia to improve the quality of life for older adults. Full article

(This article belongs to the Special Issue Sports, Exercise and Healthcare)

29 pages, 2410 KB

Open AccessArticle

Inverse Procedure to Initial Parameter Estimation for Air-Dropped Packages Using Neural Networks

by Beata Potrzeszcz-Sut and Marta Grzyb

Appl. Sci. 2025, 15(19), 10422; https://doi.org/10.3390/app151910422 (registering DOI) - 25 Sep 2025

Abstract

This paper presents a neural network–driven framework for solving the inverse problem of initial parameter estimation in air-dropped package missions. Unlike traditional analytical methods, which are computationally intensive and often impractical in real time, the proposed system leverages the flexibility of multilayer perceptrons [...] Read more.

This paper presents a neural network–driven framework for solving the inverse problem of initial parameter estimation in air-dropped package missions. Unlike traditional analytical methods, which are computationally intensive and often impractical in real time, the proposed system leverages the flexibility of multilayer perceptrons to model both forward and inverse relationships between drop conditions and flight outcomes. In the forward stage, a trained network predicts range, flight time, and impact velocity from predefined release parameters. In the inverse stage, a deeper neural model reconstructs the required release velocity, angle, and altitude directly from the desired operational outcomes. By employing a hybrid workflow—combining physics-based simulation with neural approximation—our approach generates large, high-quality datasets at low computational cost. Results demonstrate that the inverse network achieves high accuracy across deterministic and stochastic tests, with minimal error when operating within the training domain. The study confirms the suitability of neural architectures for tackling complex, nonlinear identification tasks in precision airdrop operations. Beyond their technical efficiency, such models enable agile, GPS-independent mission planning, offering a reliable and low-cost decision support tool for humanitarian aid, scientific research, and defense logistics. This work highlights how artificial intelligence can transform conventional trajectory design into a fast, adaptive, and autonomous capability. Full article

(This article belongs to the Special Issue Application of Neural Computation in Artificial Intelligence)

28 pages, 3723 KB

Open AccessArticle

A Practical Classification Approach for Chemical, Biological, Radiological and Nuclear (CBRN) Hazards Based on Toxicological and Situational Parameters

by Leslaw Gorniak, Natalia Cichon, Maksymilian Stela, Marcin Niemcewicz, Marcin Podogrocki, Adrian Siadkowski, Michal Ceremuga and Michal Bijak

Appl. Sci. 2025, 15(19), 10421; https://doi.org/10.3390/app151910421 (registering DOI) - 25 Sep 2025

Abstract

CBRN incidents are characterized by high uncertainty in terms of agent identity, dissemination methods, and situational context. This unpredictability complicates effective and timely response, especially in the initial phase before specialist services arrive, and lays the burden of applying protection and response measures [...] Read more.

CBRN incidents are characterized by high uncertainty in terms of agent identity, dissemination methods, and situational context. This unpredictability complicates effective and timely response, especially in the initial phase before specialist services arrive, and lays the burden of applying protection and response measures on members of civil society participating in the incident. This paper proposes a structured classification framework for CBRN hazards to address this gap, integrating key characteristics from existing systems such as the GHS (Globally Harmonized System), WHO (World Health Organization) biosafety levels, and radiological exposure guidelines. The system emphasizes properties relevant for first responders and non-specialists, including observable effects, exposure routes, and hazard endpoints such as toxicity, virulence, and radiation dose. The goal is to enable rapid hazard recognition, improve communication, and support situational decision-making in public security scenarios. Full article

11 pages, 1538 KB

Open AccessArticle

The Gas Migration During the Drainage Process of Ultra-Long Directional Boreholes in Coal Seams

by Shuaiyin He, Mingyao Wei and Yingke Liu

Appl. Sci. 2025, 15(19), 10420; https://doi.org/10.3390/app151910420 (registering DOI) - 25 Sep 2025

Abstract

The use of ultra-long directional drilling holes for large-scale pre-drainage of gas in coal seams offers advantages such as extensive coverage and high efficiency, but its effectiveness in deep coal seams remains unclear. Focusing on the seepage characteristics of the No. 8 coal [...] Read more.

The use of ultra-long directional drilling holes for large-scale pre-drainage of gas in coal seams offers advantages such as extensive coverage and high efficiency, but its effectiveness in deep coal seams remains unclear. Focusing on the seepage characteristics of the No. 8 coal seam in the Baode Mining Area of Shanxi Province, experimental tests were conducted to investigate the evolution of dual-scale porosity permeability. The relationship between matrix/fracture permeability and effective stress were built. Utilizing numerical simulations, this study reveals the nonlinear mechanism in which permeability behavior during gas drainage is jointly influenced by pore pressure reduction and matrix shrinkage. Field measurements and simulation results demonstrated that in shallow borehole regions (<1500 m), permeability increased by up to 3.5 times, while in deeper regions (>2000 m), drainage efficiency significantly declined due to limited pressure drop propagation. These findings provide theoretical support for optimizing the layout of ultra-long directional drilling holes, enhancing gas drainage efficiency, and ensuring safe mining operations. Full article

(This article belongs to the Topic Advances in Mining and Geotechnical Engineering)

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26 pages, 2076 KB

Open AccessArticle

Analysis of the Interdependence of Surface-Induced Pilling and Electrical Resistance of Cotton Knitwear

by Juro Živičnjak and Antoneta Tomljenović

Appl. Sci. 2025, 15(19), 10419; https://doi.org/10.3390/app151910419 (registering DOI) - 25 Sep 2025

Abstract

The occurrence of pilling affects the appearance and aesthetic properties of knitwear and leads to a shortened lifespan of underwear, which is usually worn directly on the skin and under the outer layers of clothing and is exposed to direct contact with various [...] Read more.

The occurrence of pilling affects the appearance and aesthetic properties of knitwear and leads to a shortened lifespan of underwear, which is usually worn directly on the skin and under the outer layers of clothing and is exposed to direct contact with various textile materials in a dynamic microclimate. The interdependence of surface-induced pilling and electrical resistance (i.e., conductivity), which also affects wearing comfort, has not been sufficiently investigated. This paper therefore analyzes how surface-induced pilling of different intensities affects the surface resistivity and vertical resistance, physical properties and moisture content of double jersey cotton knitwear under different relative humidity conditions (25%, 40%, 65% and 80%) using Pearson’s correlation coefficient and coefficient of determination. Pilling was induced using the modified Martindale method and two types of abrasives, with higher intensity and larger pills obtained with a rougher wool reference abrasive. It was found that the surface resistivity and vertical resistance of cotton knitwear increased after prolonged wear due to surface-induced pilling and that mass, thickness and moisture content were not directly related to changes in electrical resistivity. The results of the Pearson correlation analysis showed a strong and quantifiable correlation between the intensity of surface pilling and surface resistivity at relative humidity up to 65%, despite their high moisture absorption. This statistically confirms that the occurrence of pilling reduces the electrical conductivity of cotton knitwear, resulting in a lower wearing comfort of cotton-based underwear. This finding can be useful in the development of underwear with high durability and comfort. Full article

(This article belongs to the Section Materials Science and Engineering)

27 pages, 4698 KB

Open AccessArticle

COA–VMPE–WD: A Novel Dual-Denoising Method for GPS Time Series Based on Permutation Entropy Constraint

by Ziyu Wang and Xiaoxing He

Appl. Sci. 2025, 15(19), 10418; https://doi.org/10.3390/app151910418 (registering DOI) - 25 Sep 2025

Abstract

To address the challenge of effectively filtering out noise components in GPS coordinate time series, we propose a denoising method based on parameter-optimized variational mode decomposition (VMD). The method combines permutation entropy with mutual information as the fitness function and uses the crayfish [...] Read more.

To address the challenge of effectively filtering out noise components in GPS coordinate time series, we propose a denoising method based on parameter-optimized variational mode decomposition (VMD). The method combines permutation entropy with mutual information as the fitness function and uses the crayfish (COA) algorithm to adaptively obtain the optimal parameter combination of the number of modal decompositions and quadratic penalty factors for VMD, and then, sample entropy is used to identify effective mode components (IMF), which are reconstructed into denoised signals to achieve effective separation of signal and noise The experiments were conducted using simulated signals and 52 GPS station data from CMONOC to compare and analyze the COA–VMPE–WD method with wavelet denoising (WD), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. The result shows that the COA–VMPE–WD method can effectively remove noise from GNSS coordinate time series and preserve the original features of the signal, with the most significant effect on the U component. The COA–VMPE–WD method reduced station velocity by an average of 50.00%, 59.09%, 18.18%, and 64.00% compared to the WD, EMD, EEMD, and CEEMDAN methods. The noise reduction effect is higher than the other four methods, providing reliable data for subsequent analysis and processing. Full article

(This article belongs to the Special Issue Advanced GNSS Technologies: Measurement, Analysis, and Applications)

19 pages, 1046 KB

Open AccessArticle

A Hybrid Approach to Investigating Factors Associated with Crash Injury Severity: Integrating Interpretable Machine Learning with Logit Model

by Chenxi Wang and Thierry Serre

Appl. Sci. 2025, 15(19), 10417; https://doi.org/10.3390/app151910417 - 25 Sep 2025

Abstract

Understanding the determinants of crash injury severity is essential for developing effective safety strategies and reducing traffic-related losses. This study proposes a hybrid analytical framework that integrates interpretable machine learning with statistical modeling to address the limitations of existing approaches. A Random Forest [...] Read more.

Understanding the determinants of crash injury severity is essential for developing effective safety strategies and reducing traffic-related losses. This study proposes a hybrid analytical framework that integrates interpretable machine learning with statistical modeling to address the limitations of existing approaches. A Random Forest (RF) classifier, combined with Shapley Additive Explanations (SHAP), was first employed to capture nonlinear relationships and identify key predictors of injury outcomes, including safety equipment, age, gender, and the presence of fixed obstacles. Random Forest was chosen for its strong predictive performance in capturing nonlinear relationships, while SHAP provides transparent explanations of model predictions. To ensure statistical rigor and quantify associations, a Partial Proportional Odds (PPO) model was subsequently applied, allowing for the relaxation of the proportional odds assumption (POA) and enabling the estimation of marginal effects. The results consistently highlight the protective role of safety equipment and the increased risks associated with fixed obstacles, adverse weather, and nighttime conditions. For instance, seatbelt use is associated with a 29.61% higher probability of no injury, whereas fixed obstacles are associated with a 29.36% lower probability and a higher risk of severe injury. These findings support safety campaigns that encourage protective equipment use and infrastructure policies aimed at reducing roadside obstacles and improving nighttime visibility. Future research will focus on accounting unobserved heterogeneity and validating the framework across multi-regional datasets to improve its generalizability and policy relevance. Full article

(This article belongs to the Special Issue Advances in Land, Rail and Maritime Transport and in City Logistics)

17 pages, 5801 KB

Open AccessArticle

Research on the Deviatoric Stress Mode and Control of the Surrounding Rock in Close-Distance Double-Thick Coal Seam Roadways

by Dongdong Chen, Jiachen Tang, Wenrui He, Changxiang Gao and Chenjie Wang

Appl. Sci. 2025, 15(19), 10416; https://doi.org/10.3390/app151910416 - 25 Sep 2025

Abstract

To address the issue of sustained deformation in the main roadway surrounding rock triggered by intense movement of overlying strata following the reduction of width of the stopping pillar (WSP) in closely spaced double extra-thick coal seams (CSDECS). Analyze the evolution patterns of [...] Read more.

To address the issue of sustained deformation in the main roadway surrounding rock triggered by intense movement of overlying strata following the reduction of width of the stopping pillar (WSP) in closely spaced double extra-thick coal seams (CSDECS). Analyze the evolution patterns of abutment pressure, principal stress vector lines, and zones of deviatoric stress concentration (ZDSC) of the main roadways using multi-method approaches. The findings are as follows: As the WSP is reduced, the maximum abutment pressure (MAP) on both sides of the gate roadways’ surrounding rock becomes significantly more asymmetric and intense. The deflection trajectory of the maximum principal stress line (MPSL) in the two coal seams, induced by the advancing underlying panel, follows an approximate inverted ︺ shape. The evolution of the ZDSC and the main roadways in the adjacent working faces all shows three-stage characteristics. For the upper coal seam, it is characterized by crescent-shaped symmetry → slow and asymmetric increase of the peak value and the offset of the ZDSC → the ZDSC on the non-mining side (NM-S) reaches the maximum while the mining side (M-S) shows the reverse trend. For the lower coal seam, it is characterized by crescent-shaped symmetry → quasi-annular distribution with a slight increase in the peak value → significant and asymmetric increase of the peak values. Based on the identification of the key control zones in the ZDSC, an asymmetric reinforcement segmented control method was proposed. The findings provide useful guidance for analogous engineering projects. Full article

(This article belongs to the Topic Advances in Mining and Geotechnical Engineering)

21 pages, 6275 KB

Open AccessArticle

Influence of Bedding Angle on Mechanical Behavior and Grouting Reinforcement in Argillaceous Slate: Insights from Laboratory Tests and Field Experiments

by Xinfa Zeng, Chao Deng, Quan Yin, Yi Chen, Junying Rao, Yi Zhou and Wenqin Yan

Appl. Sci. 2025, 15(19), 10415; https://doi.org/10.3390/app151910415 - 25 Sep 2025

Abstract

Argillaceous slate (AS) is a typical metamorphic rock with well-developed bedding, widely distributed globally. Its bedding structure significantly impacts slope stability assessment, and the challenges associated with slope anchoring and support arising from bedding characteristics have become a focal point in the engineering [...] Read more.

Argillaceous slate (AS) is a typical metamorphic rock with well-developed bedding, widely distributed globally. Its bedding structure significantly impacts slope stability assessment, and the challenges associated with slope anchoring and support arising from bedding characteristics have become a focal point in the engineering field. In this study, with bedding dip angle as the key variable, mechanical tests such as uniaxial compression, triaxial compression, direct shear, and Brazilian splitting tests were conducted on AS. Additionally, field anchoring grouting diffusion tests on AS slopes were carried out. The aim is to investigate the basic mechanical properties of AS and the grout diffusion law under different bedding dip angles. The research results indicate that the bedding dip angle has a remarkable influence on the failure mode, stress–strain curve, and mechanical indices such as compressive strength and elastic modulus of AS specimens. The stress–strain curves in uniaxial and triaxial tests, as well as the stress-displacement curve in the Brazilian splitting test, all undergo four stages: crack closure, elastic deformation, crack propagation, and post-peak failure. As the bedding dip angle increases, the uniaxial and triaxial compressive strengths and elastic modulus first decrease and then increase, while the splitting tensile strength continuously decreases. The consistency of the bedding in AS causes the grout to diffuse in a near-circular pattern on the bedding plane centered around the borehole. Among the factors affecting the diffusion range of the grout, the bedding dip angle and grouting angle have a relatively minor impact, while the grouting pressure has a significant impact. A correct understanding and grasp of the anisotropic characteristics of AS and the anchoring grouting diffusion law are of great significance for slope stability assessment and anchoring design in AS areas. Full article

(This article belongs to the Special Issue Advances in Slope Stability and Rock Fracture Mechanisms)

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20 pages, 3746 KB

Open AccessArticle

Perception of Audio–Visual Synchronization in Olfactory-Enhanced 360-Degree Video

by Aleph Campos da Silveira, Roope Raisamo, Fotios Spyridonis, Alexandra Covaci, Gheorghita Ghinea and Celso Alberto Saibel Santos

Appl. Sci. 2025, 15(19), 10414; https://doi.org/10.3390/app151910414 - 25 Sep 2025

Abstract

This study examines the impact of olfactory stimuli on user experience (UX) metrics in 360-degree videos under varying levels of audio–visual (AV) skew. Subjective responses and questionnaire results revealed that scents helped stabilize enjoyment and artifact tolerance scores, particularly under severe AV skews, [...] Read more.

This study examines the impact of olfactory stimuli on user experience (UX) metrics in 360-degree videos under varying levels of audio–visual (AV) skew. Subjective responses and questionnaire results revealed that scents helped stabilize enjoyment and artifact tolerance scores, particularly under severe AV skews, compared to non-olfactory conditions. However, the stationary nature of the scent delivery device decreased the intensity of olfactory stimuli, limiting their potential impact. Objective analyses highlighted a masking effect in 360-degree videos, where participant visual exploration reduced sensitivity to AV skews. Despite these challenges, olfactory stimuli demonstrated resilience to AV skews, suggesting their potential to buffer negative effects and enhance immersive experiences. However, they did not significantly improve overall video quality ratings. The study underscores the need for advances in olfactory display technology, such as head-mounted scent emitters and dynamic sensory integration, to enhance multimedia experiences. Full article

22 pages, 2267 KB

Open AccessArticle

Multi-Camera Machine Vision for Detecting and Analyzing Vehicle–Pedestrian Conflicts at Signalized Intersections: Deep Neural-Based Pose Estimation Algorithms

by Ahmed Mohamed and Mohamed M. Ahmed

Appl. Sci. 2025, 15(19), 10413; https://doi.org/10.3390/app151910413 - 25 Sep 2025

Abstract

Over the past decade, researchers have advanced traffic monitoring using surveillance cameras, unmanned aerial vehicles (UAVs), loop detectors, LiDAR, microwave sensors, and sensor fusion. These technologies effectively detect and track vehicles, enabling robust safety assessments. However, pedestrian detection remains challenging due to diverse [...] Read more.

Over the past decade, researchers have advanced traffic monitoring using surveillance cameras, unmanned aerial vehicles (UAVs), loop detectors, LiDAR, microwave sensors, and sensor fusion. These technologies effectively detect and track vehicles, enabling robust safety assessments. However, pedestrian detection remains challenging due to diverse motion patterns, varying clothing colors, occlusions, and positional differences. This study introduces an innovative approach that integrates multiple surveillance cameras at signalized intersections, regardless of their types or resolutions. Two distinct convolutional neural network (CNN)-based detection algorithms accurately track road users across multiple views. The resulting trajectories undergo analysis, smoothing, and integration, enabling detailed traffic scene reconstruction and precise identification of vehicle–pedestrian conflicts. The proposed framework achieved 97.73% detection precision and an average intersection over union (IoU) of 0.912 for pedestrians, compared to 68.36% and 0.743 with a single camera. For vehicles, it achieved 98.2% detection precision and an average IoU of 0.955, versus 58.78% and 0.516 with a single camera. These findings highlight significant improvements in detecting and analyzing traffic conflicts, enhancing the identification and mitigation of potential hazards. Full article

20 pages, 6974 KB

Open AccessArticle

Investigation on the Effects of Operating Parameters on the Transient Thermal Behavior of the Wet Clutch in Helicopters

by Xiaokang Li, Dahuan Wei, Hao Wang, Yixiong Yan, Hongzhi Yan, Mei Yin and Yexin Xiao

Appl. Sci. 2025, 15(19), 10412; https://doi.org/10.3390/app151910412 - 25 Sep 2025

Abstract

The aviation wet clutch, as an indispensable component in helicopters, is particularly vulnerable to performance deterioration due to temperature rises, especially in high-power-density and high-torque conditions. Consequently, a comprehensive thermal-fluid-dynamic model, coupled with a dynamic model considering the spline friction and split spring [...] Read more.

The aviation wet clutch, as an indispensable component in helicopters, is particularly vulnerable to performance deterioration due to temperature rises, especially in high-power-density and high-torque conditions. Consequently, a comprehensive thermal-fluid-dynamic model, coupled with a dynamic model considering the spline friction and split spring and a thermal model considering the heat transfer parameters in friction pair gaps, was proposed in this work. The effects of operating parameters on the transient thermal behaviors of friction discs were investigated. A rise in rotation speed from 2000 rpm to 2400 rpm facilitates a 10.1% increase in the maximum temperature of the friction discs. An increase in control oil pressure from 1.5 MPa to 1.9 MPa rises the maximum temperature of the friction disc by 19.4%. Moreover, increased lubrication oil flow not only depresses the maximum temperature of the friction disc by 14.5% but also significantly narrows the temperature gradient by 16.7% and improves the temperature field uniformity. Therefore, reasonably increasing lubricant oil flow and decreasing control oil pressure can effectively reduce temperature rises and improve the temperature field uniformity. These results contribute to designing and developing optimal control strategies to enhance the comprehensive performance of helicopter transmission. Full article

22 pages, 893 KB

Open AccessArticle

Modelling Magnetisation and Transport AC Loss of HTS Tapes near Ferromagnetic Materials Using an Integral Equation Method

by Calvin C. T. Chow, K. T. Chau and Francesco Grilli

Appl. Sci. 2025, 15(19), 10411; https://doi.org/10.3390/app151910411 - 25 Sep 2025

Abstract

The integral equation formulation of Maxwell’s equations proposed by Brandt provides an alternative to the H and T-A formulations for modelling high-temperature superconducting (HTS) tapes. A modified version of Brandt’s method in the literature models ferromagnetic domains near the tapes by [...] Read more.

The integral equation formulation of Maxwell’s equations proposed by Brandt provides an alternative to the H and T-A formulations for modelling high-temperature superconducting (HTS) tapes. A modified version of Brandt’s method in the literature models ferromagnetic domains near the tapes by considering the ferromagnetic domains as equivalent surface current. This paper extends this method by including the effect of external magnetic field acting on the ferromagnetic and HTS domains. The proposed method is used on a benchmark problem, which considers an HTS tape with a ferromagnetic substrate under an external time-varying magnetic field. The results agree closely (error in average ac loss less than 3%) with the widely-used T-A formulation implemented in COMSOL down to 2 mT. In addition, the proposed method is also applied to HTS tapes carrying transport ac current in a slot of a machine’s stator iron core, and HTS tapes in a stator iron slot in a machine under working conditions. It is found that ac loss calculated by the proposed method increases as the discretization size of the ferromagnetic material’s boundary decreases, and overshoots the value calculated by the T-A formulation in COMSOL when using very fine discretization. Full article

(This article belongs to the Special Issue Applied Superconductivity: Material, Design, and Application)

30 pages, 1847 KB

Open AccessArticle

Damage Assessment and Fatigue Life Prediction in Exhaust Manifolds Through a Unified Method Using the FEM and XFEM

by Nouhaila Ouyoussef, Hassane Moustabchir, Maria Luminita Scutaru and Ovidiu Vasile

Appl. Sci. 2025, 15(19), 10410; https://doi.org/10.3390/app151910410 - 25 Sep 2025

Abstract

This study investigates the structural and fracture behavior of an automotive exhaust manifold with a predefined semi-elliptical surface crack under realistic thermo-mechanical loading. A combined FEM–XFEM workflow was applied; the FEM identified the critical stress concentration zone, where the maximum Von Mises stress [...] Read more.

This study investigates the structural and fracture behavior of an automotive exhaust manifold with a predefined semi-elliptical surface crack under realistic thermo-mechanical loading. A combined FEM–XFEM workflow was applied; the FEM identified the critical stress concentration zone, where the maximum Von Mises stress reached 165.6 MPa at 700 °C, and the XFEM was used to model crack growth with a refined mesh. The computed Mode I stress intensity factors ranged from 21 to 24 MPa√m, remaining below the temperature-dependent fracture toughness of AISI 321 stainless steel, which confirmed stable crack behavior under service conditions. Fatigue life was assessed using the Smith–Watson–Topper (SWT) parameter. Two scenarios were considered: a quasi-pulsating case, giving a predicted life of 3.8 × 10⁸ cycles, and a fully reversed case, reducing the life to 6.7 × 10⁷ cycles. These results confirm that the manifold operates within the high-cycle fatigue regime, while also demonstrating the strong sensitivity of life predictions to the applied stress ratio. This combined FEM–XFEM methodology provides a reliable numerical framework for assessing crack driving forces and guiding durability-based design of exhaust manifolds. Full article

(This article belongs to the Special Issue Technical Advances in Vibration Analysis: Modeling, Simulation and Applications)

29 pages, 1197 KB

Open AccessArticle

A BIM-Oriented Framework for Integrating IoT-Based Air Quality Monitoring Systems Using the AllBIMclass Classification

by Eduardo J. Renard-Julián, José M. Olmos and M. Socorro García-Cascales

Appl. Sci. 2025, 15(19), 10409; https://doi.org/10.3390/app151910409 - 25 Sep 2025

Abstract

This paper presents a BIM-oriented methodological framework for integrating air quality monitoring systems based on IoT sensors into building and infrastructure projects. A set of low-cost environmental sensors capable of measuring PM1, PM2.5, PM10, temperature, and humidity was deployed in a real residential [...] Read more.

This paper presents a BIM-oriented methodological framework for integrating air quality monitoring systems based on IoT sensors into building and infrastructure projects. A set of low-cost environmental sensors capable of measuring PM1, PM2.5, PM10, temperature, and humidity was deployed in a real residential setting to illustrate the proposed approach. To enable semantic integration within BIM workflows, a structured classification system, AllBIMclass, was developed. It provides dedicated hierarchical codes for environmental sensors, defined by monitored parameters, installation location (indoor, outdoor, or mixed), power supply, and data handling mode. The pilot experience demonstrated how sensors can be registered, classified, and linked to BIM models, supporting data visualisation and basic management tasks. AllBIMclass is available in Revit 2026 (version 26.6.4.409, build 20250227_1515, 64-bit) (TXT) and Archicad 28 (version 28.0.0, build 3001, x86–64-bit) (XML) formats and is fully compatible with IFC schemas. Although the framework has not yet been applied to large-scale projects, its components are technically operational and ready for implementation. This research contributes to bridging the gap between environmental monitoring and digital construction workflows, paving the way for integration into digital twins, smart buildings, and sustainable infrastructure systems. Full article

(This article belongs to the Special Issue Advances in BIM-Based Architecture and Civil Infrastructure Systems)

15 pages, 1790 KB

Open AccessArticle

Identification of Poria Origin Based on Multi-Matrix Projection Discrimination of PCA

by Xinqiang Wang, Yawen Qin, Wei Xiong, Fangyuan Wang, Song Ye, Siqian Yang and Huiting Tao

Appl. Sci. 2025, 15(19), 10408; https://doi.org/10.3390/app151910408 - 25 Sep 2025

Abstract

This study proposes a rapid method for identifying the geographical origin of Poria by combining Raman spectroscopy with an improved PCA algorithm—multi-matrix projection discrimination analysis. Poria samples from four Chinese provinces—Yunnan, Anhui, Shaanxi, and Hubei—were analyzed. Four datasets were constructed, each containing 25 [...] Read more.

This study proposes a rapid method for identifying the geographical origin of Poria by combining Raman spectroscopy with an improved PCA algorithm—multi-matrix projection discrimination analysis. Poria samples from four Chinese provinces—Yunnan, Anhui, Shaanxi, and Hubei—were analyzed. Four datasets were constructed, each containing 25 Raman spectra per origin, with an additional 10 spectra per origin reserved as independent test sets. PCA was then separately applied to the spectral dataset of each origin to derive its respective eigenvector matrix. For each test spectrum, four reconstructed spectra were generated by projecting it onto the eigenvector matrices of the four origins. The origin was determined by identifying the one with the minimum Euclidean distance between the test spectrum and its reconstructions. When the first six principal components were used for model construction, the test set accuracy reached 97.5%, significantly outperforming the optimized PCA–SVM model, which achieved an accuracy of 85%. These results demonstrate that Raman spectroscopy, combined with the multi-matrix projection discrimination method based on PCA, can effectively capture the fingerprint information of Poria and accurately determine its geographical origin. Full article

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35 pages, 1729 KB

Open AccessArticle

Decision Framework for Asset Criticality and Maintenance Planning in Complex Systems: An Offshore Corrosion Management Case

by Marina Polonia Rios, Bruna Siqueira Kaiser, Rodrigo Goyannes Gusmão Caiado, Paulo Ivson and Deane Roehl

Appl. Sci. 2025, 15(19), 10407; https://doi.org/10.3390/app151910407 - 25 Sep 2025

Abstract

Asset maintenance management is critical in industries such as petrochemicals and oil and gas (O&G), where complex, interdependent systems heighten failure risks. Maintenance costs represent a significant portion of operational expenditures, emphasizing the need for effective risk-based strategies. A considerable gap exists in [...] Read more.

Asset maintenance management is critical in industries such as petrochemicals and oil and gas (O&G), where complex, interdependent systems heighten failure risks. Maintenance costs represent a significant portion of operational expenditures, emphasizing the need for effective risk-based strategies. A considerable gap exists in integrating uncertainty modelling into both criticality assessment and maintenance planning. Existing approaches often neglect combining expert-driven assessments with optimization models, limiting their applicability in real-world scenarios where cost-effective and risk-informed decision-making is crucial. Maintenance inefficiencies due to suboptimal asset selection result in substantial financial and safety-related consequences in asset-intensive industries. This study presents a framework integrating Reliability-Centered Maintenance (RCM) principles with fuzzy logic and decision-support methodologies to optimise maintenance portfolios for offshore O&G assets, particularly focusing on corrosion management. The framework evaluates asset criticality through comprehensive FMEA, employing MCDM and fuzzy logic to enhance maintenance planning and extend asset lifespan. A case study on offshore asset corrosion management demonstrates the framework’s effectiveness, selecting 60% of highly critical assets for maintenance, compared to 10% by current industry practices. This highlights the potential risk reduction and prevention of critical failures that might otherwise go unnoticed, providing actionable insights for asset integrity managers in the O&G sector. Full article

(This article belongs to the Special Issue Technology and Management Applied in Construction Engineering Projects II)

30 pages, 1957 KB

Open AccessArticle

Agent Systems and GIS Integration in Requirements Analysis and Selection of Optimal Locations for Energy Infrastructure Facilities

by Anna Kochanek, Tomasz Zacłona, Michał Szucki and Nikodem Bulanda

Appl. Sci. 2025, 15(19), 10406; https://doi.org/10.3390/app151910406 - 25 Sep 2025

Abstract

The dynamic development of agent systems and large language models opens up new possibilities for automating spatial and investment analyses. The study evaluated a reactive AI agent with an NLP interface, integrating Apache Spark for large-scale data processing with PostGIS as a reference [...] Read more.

The dynamic development of agent systems and large language models opens up new possibilities for automating spatial and investment analyses. The study evaluated a reactive AI agent with an NLP interface, integrating Apache Spark for large-scale data processing with PostGIS as a reference point. The analyses were carried out for two areas: Nowy Sącz (36,000 plots, 7 layers) and Ostrołęka (220,000 plots). For medium-sized datasets, both technologies produced similar results, but with large datasets, PostGIS exceeded time limits and was prone to failures. Spark maintained stable performance, analyzing 220,000 plots in approximately 240 s, confirming its suitability for interactive applications. In addition, clustering and spatial search algorithms were compared. The basic DFS required 530 s, while the improved one reduced the time almost tenfold to 54–62 s. The improved K-Means improved the spatial compactness of clusters (0.61–0.76 vs. <0.50 in most base cases) with a time of 56–64 s. Agglomerative clustering, although accurate, was too slow (3000–6000 s). The results show that the combination of Spark, improved algorithms, and agent systems with NLP significantly speeds up the selection of plots for renewable energy sources, supporting sustainable investment decisions. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Big Data)

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