Applied Sciences

20 pages, 362 KB

Open AccessArticle

Patch-Based Transformer–Graph Framework (PTSTG) for Traffic Forecasting in Transportation Systems

by Grach Mkrtchian and Mikhail Gorodnichev

Appl. Sci. 2025, 15(19), 10468; https://doi.org/10.3390/app151910468 (registering DOI) - 26 Sep 2025

Accurate traffic forecasting underpins intelligent transportation systems. We present PTSTG, a compact spatio-temporal forecaster that couples a patch-based Transformer encoder with a data-driven adaptive adjacency and lightweight node graph blocks. The temporal module tokenizes multivariate series into fixed-length patches to capture short- and [...] Read more.

Accurate traffic forecasting underpins intelligent transportation systems. We present PTSTG, a compact spatio-temporal forecaster that couples a patch-based Transformer encoder with a data-driven adaptive adjacency and lightweight node graph blocks. The temporal module tokenizes multivariate series into fixed-length patches to capture short- and long-range patterns in a single pass, while the graph module refines node embeddings via learned inter-node aggregation. A horizon-specific head emits all steps simultaneously. On standard benchmarks (METR-LA, PEMS-BAY) and the LargeST (SD) split with horizons

{3, 6, 12} \to {15, 30, 60}

minutes, PTSTG delivers competitive point-estimate results relative to recent temporal graph models. On METR-LA/PEMS-BAY, it remains close to strong baselines (e.g., DCRNN) without surpassing them; on LargeST, it attains favorable average RMSE/MAE while trailing the strongest hybrids on some horizons. The design preserves a compact footprint and single-pass, multi-horizon inference, and offers clear capacity-driven headroom without architectural changes. Full article

(This article belongs to the Special Issue Computer Vision of Edge AI on Automobile)

23 pages, 1916 KB

Open AccessArticle

Research on Position Tracking Performance Optimization of Permanent Magnet Synchronous Motors Based on Improved Active Disturbance Rejection Control

by Yu Xu, Zihao Huang and Dejun Liu

Appl. Sci. 2025, 15(19), 10467; https://doi.org/10.3390/app151910467 (registering DOI) - 26 Sep 2025

Abstract

This study tackles the challenges associated with permanent magnet synchronous motor (PMSM) position control under complex operating conditions—characterized by strong coupling, nonlinearity, and time-varying parameters—which often lead to slow response, low control accuracy, and weak disturbance rejection capability. A high-performance control system is [...] Read more.

This study tackles the challenges associated with permanent magnet synchronous motor (PMSM) position control under complex operating conditions—characterized by strong coupling, nonlinearity, and time-varying parameters—which often lead to slow response, low control accuracy, and weak disturbance rejection capability. A high-performance control system is developed based on an active disturbance rejection controller (ADRC), with three key improvements proposed. Firstly, a modified nonlinear function is designed to suppress chattering. Secondly, a delay compensation module is integrated to synchronize the input signals of the extended state observer (ESO). Finally, an automated parameter tuning method is introduced using the Newton-Raphson optimization algorithm. Comparative simulations are conducted to validate the effectiveness of the proposed system, demonstrating its advantages of rapid response, minimal overshoot, and enhanced disturbance rejection capability. For the proposed strategy, the maximum position tracking error is 0.1 rad, the adjustment time is 0.15 s, the dynamic speed drop is 0.025 rad, and the recovery time is 0.15 s—all comprehensive performance indicators outperform those of other control strategies. Additionally, automated parameter tuning eliminates the need for manual adjustments, reduces operational complexity, and improves tuning accuracy, thereby significantly advancing the position control performance of PMSMs. Full article

(This article belongs to the Special Issue Power Electronics and Motor Control)

13 pages, 1076 KB

Open AccessArticle

Eccentric Exercise-Induced Muscle Damage Is Independent of Limb Dominance in Young Women

by Natalia Prokopiou, Dimitris Mandalidis, Gerasimos Terzis and Vassilis Paschalis

Appl. Sci. 2025, 15(19), 10466; https://doi.org/10.3390/app151910466 (registering DOI) - 26 Sep 2025

Abstract

Unaccustomed eccentric exercise is well established to induce exercise-induced muscle damage (EIMD), characterized by transient strength loss, delayed onset muscle soreness (DOMS), reduced range of motion, and proprioceptive disturbances. While limb dominance has been proposed as a potential modulator of susceptibility to EIMD, [...] Read more.

Unaccustomed eccentric exercise is well established to induce exercise-induced muscle damage (EIMD), characterized by transient strength loss, delayed onset muscle soreness (DOMS), reduced range of motion, and proprioceptive disturbances. While limb dominance has been proposed as a potential modulator of susceptibility to EIMD, evidence remains inconclusive. This exploratory study aimed to compare alterations in muscle damage indices between dominant and non-dominant knee extensors 48 h after eccentric isokinetic exercise. Eighteen physically active young women (23 ± 2 years) completed two eccentric exercise sessions (5 × 15 maximal contractions at 60°/s), one per limb, with sessions separated by 24–30 days. For all participants, testing was conducted during the early follicular phase. Muscle strength (isometric and eccentric peak torque), DOMS (palpation and pain pressure threshold), range of motion, fatigue index, and position sense were assessed pre- and 48 h post-exercise. Significant reductions in isometric and eccentric peak torque, increased DOMS, impaired position sense, and altered fatigue index were observed 48 h post-exercise in the exercised limb (p < 0.001), with no differences between dominant and non-dominant limbs across all indices. These findings demonstrate that limb dominance does not influence the magnitude of EIMD in knee extensors of young women. Practical implications include equal consideration of both limbs in eccentric training, rehabilitation, and injury prevention programs. Full article

(This article belongs to the Special Issue Resistance Training: Acute Responses, Short and Long-Term Adaptations in Athletic Performance and Health)

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23 pages, 6052 KB

Open AccessArticle

Research on Deterioration Characteristics of Tuffaceous Sandstone Under Acidic Wet–Dry Cycles

by Dunwen Liu, Mengzhao Wang, Chengtao Yang and Xiaofei Sun

Appl. Sci. 2025, 15(19), 10465; https://doi.org/10.3390/app151910465 (registering DOI) - 26 Sep 2025

Abstract

Conducted against the background of a highway project in Zhuji, Zhejiang Province, this study investigates the deterioration behavior of tuffaceous sandstone under the combined action of acid rain and wet–dry cycles. Laboratory experiments were carried out to explore its mechanical properties and damage [...] Read more.

Conducted against the background of a highway project in Zhuji, Zhejiang Province, this study investigates the deterioration behavior of tuffaceous sandstone under the combined action of acid rain and wet–dry cycles. Laboratory experiments were carried out to explore its mechanical properties and damage evolution mechanisms. Standard specimens prepared from field rock samples were subjected to wet–dry cycles using an acidic solution with pH ≈ 5.0. By integrating uniaxial compression, Brazilian splitting, ultrasonic wave monitoring, and acoustic emission techniques, a systematic analysis was carried out to evaluate the degradation of mechanical parameters, the evolution of wave velocity, and the underlying damage and failure mechanisms. The results indicate the following: (1) With the increase in the number of acidic dry–wet cycles, the compressive and tensile strengths of tuffaceous sandstone decrease significantly; the deterioration rate first decreases and then increases, with 150 cycles identified as the critical threshold for strength deterioration, beyond which the material enters a stage of rapid degradation. (2) The evolution of ultrasonic wave velocity shows a significant negative correlation with strength deterioration, and the attenuation rate of wave velocity exhibits a consistent trend with the number of cycles as that of strength deterioration. (3) Acoustic emission RA-AF analysis reveals that tensile cracks in tuffaceous sandstone gradually decrease while shear cracks slowly increase, with cracks primarily developing along the weakly cemented tuffaceous areas. (4) This study established fitting formulas for the deterioration of compressive and tensile strengths with the number of cycles, as well as a damage calculation formula based on changes in wave velocity. (5) This study provides practical support for mitigating natural disasters, such as slope instability, induced by this type of combined weathering. Full article

25 pages, 5161 KB

Open AccessArticle

Non-Destructive Classification of Sweetness and Firmness in Oranges Using ANFIS and a Novel CCI–GLCM Image Descriptor

by David Granados-Lieberman, Alejandro Israel Barranco-Gutiérrez, Adolfo R. Lopez, Horacio Rostro-Gonzalez, Miroslava Cano-Lara, Carlos Gustavo Manriquez-Padilla and Marcos J. Villaseñor-Aguilar

Appl. Sci. 2025, 15(19), 10464; https://doi.org/10.3390/app151910464 - 26 Sep 2025

Abstract

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed [...] Read more.

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed by integrating the Citrus Color Index (CCI) with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). By combining contrast, correlation, energy, and homogeneity across multiscale regions of interest and applying geometric calibration to correct image acquisition distortions, the descriptor effectively captures both chromatic and structural information from RGB images. These features served as input to an Adaptive Neuro-Fuzzy Inference System (ANFIS), selected for its ability to model nonlinear relationships and gradual transitions in citrus ripening. The proposed ANFIS models achieved R-squared values greater than or equal to 0.81 and root mean square error values less than or equal to 1.1 across all quality parameters, confirming their predictive robustness. Notably, representative models (ANFIS 2, 4, 6, and 8) demonstrated superior performance, supporting the extension of this approach to full-surface exploration of citrus fruits. The results outperform methods relying solely on color features, underscoring the importance of combining spectral and textural descriptors. This work highlights the potential of the CCI–GLCM-TF descriptor, in conjunction with ANFIS, for accurate, real-time, and non-invasive assessment of citrus quality, with practical implications for automated classification, postharvest process optimization, and cost reduction in the citrus industry. Full article

(This article belongs to the Special Issue Sensory Evaluation and Flavor Analysis in Food Science)

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15 pages, 3302 KB

Open AccessArticle

The Effect of Surface Properties of Steel Sections on Bond Strength in Soil-Cement Mix

by Maciej Szczygielski and Przemysław Fiołek

Appl. Sci. 2025, 15(19), 10463; https://doi.org/10.3390/app151910463 - 26 Sep 2025

Abstract

Soil strengthening with hydraulic binders has gained popularity in recent years and provides an alternative to traditional methods, both for foundation reinforcement and for retaining walls. In many cases, columns, walls, or soil-cement mix blocks require reinforcement with steel sections. Correctly assessing the [...] Read more.

Soil strengthening with hydraulic binders has gained popularity in recent years and provides an alternative to traditional methods, both for foundation reinforcement and for retaining walls. In many cases, columns, walls, or soil-cement mix blocks require reinforcement with steel sections. Correctly assessing the load-bearing capacity of a reinforced element requires an understanding of the bonding forces between the steel and the soil-cement mix. This article presents the results of pull-out tests conducted on steel flat bars embedded in a soil-cement mix. A soil-cement mix containing sand, silt, and clay fractions was prepared. The surfaces of the flat bars were treated in three different ways, and their roughness was subsequently measured. The pull-out strength of steel flat bars embedded in a soil-cement mix with compressive strength in the range of 1–2 MPa was determined. The tests revealed a correlation between surface roughness and bond strength. The conducted tests provided the basis for developing new research directions and for formulating a new bonding model for the interaction between steel profiles and soil-cement. Full article

23 pages, 1136 KB

Open AccessArticle

Phytochemical Profile and Biological Activities of Biscutella laevigata: A Comparative Study of Leaves, Seeds, and Microshoot Cultures

by Marta Klimek-Szczykutowicz, Magdalena Anna Malinowska, Anna Śliwa, Ivica Blažević, Azra Ðulović, Karolina Wiśniewska, Renata Piwowarczyk, Paulina Paprocka, Małgorzata Wrzosek and Agnieszka Szopa

Appl. Sci. 2025, 15(19), 10462; https://doi.org/10.3390/app151910462 - 26 Sep 2025

Abstract

Biscutella laevigata (Brassicaceae) is an endemic species confined to European mountain regions, with a distribution range extending from the Iberian Peninsula through the Carpathians to the Balkans. The objective of this study was to investigate the phytochemical composition and biological properties of extracts [...] Read more.

Biscutella laevigata (Brassicaceae) is an endemic species confined to European mountain regions, with a distribution range extending from the Iberian Peninsula through the Carpathians to the Balkans. The objective of this study was to investigate the phytochemical composition and biological properties of extracts obtained from leaves, seeds, and in vitro-derived microshoot cultures. UHPLC-DAD-MS/MS profiling of glucosinolates (GSLs) revealed six compounds exclusively present in seed extracts, with glucohirsutin identified as the predominant constituent (15.06 mg/100 g DW). No glucosinolates were detected in either leaf or microshoot extracts. Notably, 8-(methylsulfonyl)octyl GSL was reported in B. laevigata for the first time. The seed extract exhibited the highest total polyphenol content (TPC, 25,701.00 mg GAE/100 g DW), while leaf and microshoot extracts contained similar amounts (16,244.00 and 16,552.00 mg GAE/100 g DW, respectively). Among phenolic compounds, rutin was the most abundant, reaching up to 1609.21 mg/100 g DW in leaf extracts. Antioxidant capacity, assessed by ABTS and DPPH assays, was strongest in the seed extract (90.56% and 69.24% inhibition, respectively). The same extract demonstrated the greatest anti-elastase activity (12.68%), whereas the microshoot extract displayed a considerable Fe²⁺-chelating ability (12.48%). All tested extracts showed antimicrobial potential against Staphylococcus aureus, Escherichia coli, Cutibacterium acnes, and the fungus Candida albicans. Full article

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

23 pages, 2134 KB

Open AccessArticle

Influence of Water Level Change on Vibration Response and Isolation of Saturated Soil Under Moving Loads

by Jinbao Yao, Yueyue Chen and Longhua Dong

Appl. Sci. 2025, 15(19), 10461; https://doi.org/10.3390/app151910461 - 26 Sep 2025

Abstract

This paper investigates the influence of groundwater level fluctuations on the vibration response and isolation performance of saturated soil foundations under moving loads. A coupled model consisting of an overlying elastic layer and a saturated half-space is established, with water level variation simulated [...] Read more.

This paper investigates the influence of groundwater level fluctuations on the vibration response and isolation performance of saturated soil foundations under moving loads. A coupled model consisting of an overlying elastic layer and a saturated half-space is established, with water level variation simulated by adjusting the elastic layer thickness. Using Biot’s theory and Fourier transforms, the dynamic response is solved analytically and validated numerically via COMSOL6.0 simulations with perfectly matched layers. Results indicate that the groundwater level significantly affects wave propagation: deeper water levels lead to responses resembling an elastic half-space, while rising water levels amplify surface displacement due to wave reflection at the saturation interface. As water levels approach the surface, behavior converges to that of a fully saturated foundation. P-wave resonance at certain water levels reduces isolation effectiveness. Furthermore, isolation performance is sensitive to load frequency, soil permeability, and trench dimensions. These findings offer valuable insights for designing vibration mitigation measures in environments with variable groundwater conditions. Full article

11 pages, 878 KB

Open AccessArticle

Data-Driven Prediction of Kinematic Transmission Error and Tonal Noise Risk in EV Gearboxes Based on Manufacturing Tolerances

by Krisztian Horvath and Martin Kaszab

Appl. Sci. 2025, 15(19), 10460; https://doi.org/10.3390/app151910460 - 26 Sep 2025

Abstract

Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary [...] Read more.

Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary excitation source of tonal gear noise in electric vehicle drivetrains. This study introduces the TRI, a novel, dimensionless indicator that quantifies relative tonal noise risk directly from predicted KTE values. We employ a large-scale dataset of 39,984 Monte Carlo simulations comprising 15 manufacturing tolerance and process-shift variables, with KTE values as the target. Baseline linear regression failed to capture the strongly non-linear relationships between tolerances and KTE (R² ≈ 0), whereas non-linear models—Random Forest and XGBoost—achieved high predictive accuracy (R² ≈ 0.82). Feature importance analysis revealed that pitch error, radial run-out, and misalignment are consistently the most influential parameters, with notable interaction effects such as pitch error × run-out and misalignment × form-defect shift. The TRI normalises predicted KTE values to a 0–1 scale, enabling rapid comparison of tolerance configurations in terms of tonal excitation risk. This approach supports early-stage design decision-making, reduces reliance on high-fidelity simulations and physical prototypes, and aligns with sustainability objectives by lowering material usage and energy consumption. The results demonstrate that data-driven surrogate models, combined with the TRI metric, can effectively bridge the gap between manufacturing tolerances and NVH performance assessment. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis and Prognosis in Electric Motors and Energy Storage Systems)

19 pages, 861 KB

Open AccessReview

The Impact of Virtual Reality on Employee Training and Learning in Organisations: A Systematic Literature Review

by Sofia Azevedo Carvalho, Ema Simões Conceição and Isabel C. P. Marques

Appl. Sci. 2025, 15(19), 10459; https://doi.org/10.3390/app151910459 - 26 Sep 2025

Abstract

This study analyses the literature on virtual reality and employee training and development in organisations, contributing to the advancement of knowledge in this area, as well as proposing a conceptual model of analysis and an agenda for future research. This is a systematic review, [...] Read more.

This study analyses the literature on virtual reality and employee training and development in organisations, contributing to the advancement of knowledge in this area, as well as proposing a conceptual model of analysis and an agenda for future research. This is a systematic review, based on the PRISMA checklist, stratifying the different thematic groups, using the VOSviewer software, version 1.6.19, and content analysis to establish a systematised and integrated structure, registered on the INPLASY platform and based on a sample of 201 studies published and indexed in the Web of Science and SCOPUS databases between 1998 and 2025. The results show four main groups: (1) Opportunities and sectoral applications in the use of virtual reality; (2) challenges in the use of virtual reality; (3) skills developed with virtual reality; (4) integration of virtual reality into organisational strategies. A conceptual model of analysis is presented to better integrate the themes. The study provides a new and solid systematization of the literature and supports the argument that virtual reality enables the acquisition of new technical and behavioural skills and offers personalised and safe training, contributing to the achievement of organisational strategy. Full article

(This article belongs to the Special Issue Advanced Virtual, Augmented, and Mixed Reality: Immersive Applications and Innovative Techniques: 2nd Edition)

21 pages, 2914 KB

Open AccessArticle

Moderate Physical Activity Generates Changes in Retina and Choroid in Low-Fit Adults

by Inés López-Cuenca, Rosa de Hoz, Lorena Elvira-Hurtado, José A. Matamoros, Lidia Sanchez-Puebla, José A. Fernandez-Albarral, Ana I. Ramírez, Juan J. Salazar, José M. Ramirez, Francisco Miguel-Tobal and Elena Salobrar-Garcia

Appl. Sci. 2025, 15(19), 10458; https://doi.org/10.3390/app151910458 - 26 Sep 2025

Abstract

Physical activity has been shown to influence ocular health, yet the acute effects of exercise on retinal and choroidal structures remain underexplored. This prospective pre-post study evaluated 30 low-fit adults without diagnosed cardiovascular disease who underwent comprehensive ophthalmologic assessments, including OCT and OCTA [...] Read more.

Physical activity has been shown to influence ocular health, yet the acute effects of exercise on retinal and choroidal structures remain underexplored. This prospective pre-post study evaluated 30 low-fit adults without diagnosed cardiovascular disease who underwent comprehensive ophthalmologic assessments, including OCT and OCTA imaging, before and after a submaximal aerobic capacity test. Statistically significant thinning was observed in specific retinal sectors, affecting both inner and outer layers, including the retinal pigment epithelium (RPE). Vascular analysis using the OCTAVA toolbox revealed a significant post-exercise reduction in vessel length density, total vessel length, branchpoint density and fractal dimension in the peripapillary plexus; and mean tortuosity in the macular superficial vascular complex (SVC). Choroidal thickness also showed a significant reduction in several regions. No significant changes were found in the foveal avascular zone (FAZ). These findings suggest that acute submaximal physical activity induces transient yet measurable changes in retinal and choroidal microvasculature. The results have potential implications for understanding ocular vascular dynamics and for evaluating ocular health in clinical and sports medicine contexts. Full article

(This article belongs to the Special Issue The Effects of Exercise on Physical Characteristics)

24 pages, 1904 KB

Open AccessArticle

Watermarking Fine-Tuning Datasets for Robust Provenance

by Ivo Gergov and Georgi Tsochev

Appl. Sci. 2025, 15(19), 10457; https://doi.org/10.3390/app151910457 - 26 Sep 2025

Abstract

Large Language Models are often fine-tuned on proprietary corpora, motivating reliable provenance signals. A corpus-level watermark method is proposed for fine-tuning datasets that survives training and common text transformations. The method subtly biases synonym choices according to a secret key (PRF) and encodes [...] Read more.

Large Language Models are often fine-tuned on proprietary corpora, motivating reliable provenance signals. A corpus-level watermark method is proposed for fine-tuning datasets that survives training and common text transformations. The method subtly biases synonym choices according to a secret key (PRF) and encodes a multi-bit payload with an error-correcting code, enabling keyed detection via a generalized likelihood ratio test with permutation-calibrated p-values. For short offline passages (~100 words), the channel is valid but statistically underpowered: the average density is ~0.0165, and the median p-value is close to 1.0. In generative tests with Mistral 7B across 12 configurations and 12,720 texts, 0.00% detection was observed at very high quality (~99.8%). As limited base cases, positive detection was reported for other setups: 8.9% (offline), 5.0% (Mistral 7B), and 3.0% (Llama2-13B). A permutation test (R = 5000), confidence intervals, and power analysis were added. Quality impact statements were refined, with “minimal impact” used instead of “imperceptible.” In this study, limitations and ethical use are discussed, and directions for stronger semantic channels and model-based detectors are outlined. Full article

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

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23 pages, 19884 KB

Open AccessArticle

Stress Bias Load Response of Different Roadway Layers in 20 m Extra-Thick Coal Seams

by Dongdong Chen, Changxiang Gao, Jiachen Tang, Shengrong Xie, Chenjie Wang, Hao Pan and Hao Sun

Appl. Sci. 2025, 15(19), 10456; https://doi.org/10.3390/app151910456 - 26 Sep 2025

Abstract

To address the challenge of asymmetric deformation and failure in the surrounding rock of main roadways within extra-thick coal seams caused by level differences under intense mining disturbance, this study systematically analyzed the evolution laws of principal stress fields, deviatoric stress fields, and [...] Read more.

To address the challenge of asymmetric deformation and failure in the surrounding rock of main roadways within extra-thick coal seams caused by level differences under intense mining disturbance, this study systematically analyzed the evolution laws of principal stress fields, deviatoric stress fields, and their impact on surrounding rock stability in upper-, middle-, and lower-level roadways within a 20 m extra-thick coal seam during mining retreat. The analysis employed numerical simulation, similarity simulation, and field monitoring. Key findings include the following: ① As the working face advances, the principal stress vector lines deflect following a bias-unloading pattern, while the peak value of the deviatoric stress field (PVDSF) exhibits asymmetric bias-loading characteristics. The lower-layer roadway emerges as the primary load-bearing layer controlling surrounding rock stability. ② The evolution trend of the maximum principal stress vector orientation is consistent across different layers. The deflection trajectory manifests as “the deflection of the goaf side → the near layer orientation → the deflection of the solid coal side”. ③ The deviatoric stress peak zones (DSPZs) at all layers exhibit a characteristic “three-stage” evolution. The deviatoric loading pattern for the lower-layer roadway surrounding rock is the following: initial state double peak region crescent-shaped non-layer distribution type → the range of the bimodal region and the extreme value increased simultaneously, distributed in a non-layer manner → the asymmetrical distribution type of steep drop in the peak area of non-mining deviator stress. ④ The junctions between the mining-side rib and floor and the non-mining-side rib and roof were identified as critical control zones. An innovative zonal asymmetric directional anchoring control technology, “anchor cable foundation support + concrete floor + asymmetric reinforcing anchor cable support”, along with a “One Directional Penetration and Three Synergies” control methodology, was proposed. Field monitoring confirmed the significant effectiveness of the optimized support system. Full article

20 pages, 4886 KB

Open AccessArticle

GPU-Driven Acceleration of Wavelet-Based Autofocus for Practical Applications in Digital Imaging

by HyungTae Kim, Duk-Yeon Lee, Dongwoon Choi and Dong-Wook Lee

Appl. Sci. 2025, 15(19), 10455; https://doi.org/10.3390/app151910455 - 26 Sep 2025

Abstract

A parallel implementation of wavelet-based autofocus (WBA) was presented to accelerate recursive operations and reduce computational costs. WBA evaluates digital focus indices (DFIs) using first- or second-order moments of the wavelet coefficients in high-frequency subbands. WBA is generally accurate and reliable; however, its [...] Read more.

A parallel implementation of wavelet-based autofocus (WBA) was presented to accelerate recursive operations and reduce computational costs. WBA evaluates digital focus indices (DFIs) using first- or second-order moments of the wavelet coefficients in high-frequency subbands. WBA is generally accurate and reliable; however, its computational cost is high owing to biorthogonal decomposition. Thus, this study parallelized the Daubechies-6 wavelet and norms of the high-frequency subbands for the DFI. The kernels of the DFI computation were constructed using open sources for driving multicore processors (MCPs) and general processing units (GPUs). The standard C++, OpenCV, OpenMP, OpenCL, and CUDA open-source platforms were selected to construct the DFI kernels, considering hardware compatibility. The experiment was conducted using the MCP, peripheral GPUs, and CPU-resident GPUs on desktops for advanced users and compact devices for industrial applications. The results demonstrated that the GPUs provided sufficient performance to achieve WBA even when using budget GPUs, indicating that the GPUs are advantageous for practical applications of WBA. This study also implies that although budget GPUs are left unused, they can potentially be great resources for wavelet-based processing. Full article

(This article belongs to the Special Issue Data Structures for Graphics Processing Units (GPUs))

14 pages, 2778 KB

Open AccessArticle

Evaluation of Fluoride Adsorptive Removal by Metallic Phosphates

by Ruijie Wang, Yingpeng Gu, Mengfei Ma and Yue Sun

Appl. Sci. 2025, 15(19), 10454; https://doi.org/10.3390/app151910454 - 26 Sep 2025

Abstract

Currently, various techniques are efficient in eliminating high quantities of fluoride from water, while the deep treatment of a low concentration of fluoridated water is inadequate. In this work, four metallic phosphates were synthesized, including YP, ZrP, CeP, and LaP, to enhance the [...] Read more.

Currently, various techniques are efficient in eliminating high quantities of fluoride from water, while the deep treatment of a low concentration of fluoridated water is inadequate. In this work, four metallic phosphates were synthesized, including YP, ZrP, CeP, and LaP, to enhance the elimination of fluoride. The X-ray diffractometer data demonstrated that ZrP was amorphous, while CeP, LaP, and YP were highly crystalline. YP had a strong fluoride removal ability in a neutral environment, and ZrP exhibited a superior fluoride adsorption effect in acidic media. The adsorption kinetic results suggested that YP, CeP, and LaP could achieve the adsorption equilibrium within 150 min, which was faster than ZrP. YP had the largest fluoride adsorption capacity fitted by Langmuir of 31.61 mg/g at 298 K, followed by ZrP, which was greater than those of CeP and LaP. All four metallic phosphates showed high selectivity in the interference of competing anions and organics, with YP and ZrP exhibiting superior selectivity than CeP and LaP. The adsorption mechanism was ligand exchange between metallic phosphate particles and fluoride, which was validated by Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy. The adsorption rate of metallic phosphates remained essentially stable in five consecutive adsorption–desorption cycles. Overall, metallic phosphates, especially YP and ZrP, have enormous potential in enhancing fluoride removal in the treatment of fluoridated water. Full article

(This article belongs to the Special Issue Innovative Approaches and Materials for Water Treatment)

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37 pages, 964 KB

Open AccessArticle

Linear Optimization Model with Nonlinear Constraints to Maximize Biogas Production from Organic Waste: A Practical Approach

by Juan Carlos Vesga Ferreira, Alexander Florez Martinez and Jhon Erickson Barbosa Jaimes

Appl. Sci. 2025, 15(19), 10453; https://doi.org/10.3390/app151910453 - 26 Sep 2025

Abstract

The excessive use of fossil fuels and the increasing generation of solid waste, driven by population growth, industrialization, and economic development, have led to serious environmental, energy, and public health issues. In light of this problem, it is crucial to adopt sustainable solutions [...] Read more.

The excessive use of fossil fuels and the increasing generation of solid waste, driven by population growth, industrialization, and economic development, have led to serious environmental, energy, and public health issues. In light of this problem, it is crucial to adopt sustainable solutions that promote the transition to renewable energy sources, such as biogas. Although progress has been made in optimizing biogas production, there is still no adaptable model for various environments that allows for the determination of optimal quantities of different organic wastes, simultaneously considering their composition, moisture content, and control of critical factors for biogas production, as well as the biodigester’s capacity and other relevant elements. In practice, the dosing of waste is conducted empirically, leading to inefficiencies that limit the potential for biogas production in real scenarios. The objective of this article is to propose a linear optimization model with nonlinear constraints that maximizes biogas production, considering fundamental parameters such as the moisture percentage, pH, carbon/nitrogen ratio (C/N), substrate volume, organic matter, volatile solids (VS), and biogas production potential from different wastes. The model estimates the optimal waste composition based on the biodigester capacity to ensure balanced substrates. The results for the proposed scenarios demonstrate its effectiveness: Scenario 1 achieved 3.42 m³ (3418.67 L) of biogas, while Scenario 2, with a greater diversity of waste, reached 8.06 m³ (8061.43 L). The model maintained pH (6.49–6.50), C/N ratio (20.00), and moisture (60.00%) within optimal ranges. Additionally, a Monte Carlo sensitivity analysis (1000 simulations) validated its robustness with a 95% confidence level. This model provides an efficient tool for optimizing biogas production and waste dosing in rural contexts, promoting clean and sustainable technologies for renewable energy generation. Full article

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

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26 pages, 3363 KB

Open AccessArticle

Energy-Efficient Coaxial Electrocoagulation for Integrated Treatment of Urban Wastewater and Acid Mine Drainage: A Response-Surface Approach

by Katherin Quispe-Ramos, Edilberto Melgar-Izaguirre, José Rivera-Rodríguez, César Gutiérrez-Cuba, Luis Carrasco-Venegas, Cesar Rodriguez-Aburto, Yone Ramos-Balcázar and Alex Pilco-Nuñez

Appl. Sci. 2025, 15(19), 10452; https://doi.org/10.3390/app151910452 - 26 Sep 2025

Abstract

This study determined the influence of experimental factors such as current density, surface-to-volume ratio (S/V), and contact time on the removal of Chemical Oxygen Demand (COD) and energy consumption during electrocoagulation, aiming to optimize the efficiency of a coaxial electrocoagulator for the co-treatment [...] Read more.

This study determined the influence of experimental factors such as current density, surface-to-volume ratio (S/V), and contact time on the removal of Chemical Oxygen Demand (COD) and energy consumption during electrocoagulation, aiming to optimize the efficiency of a coaxial electrocoagulator for the co-treatment of municipal wastewater and acid mine drainage. After identifying the optimal volumetric ratio between both types of effluents, a Box–Behnken design and response-surface methodology were employed to identify the conditions that maximize COD removal while minimizing energy consumption. Under optimal conditions (current density of 2.42 A·m⁻², S/V = 300 m²·m⁻³, 60 min), a COD removal of 91.13% was achieved with a specific energy of =2.59 kWh·kgCOD⁻¹. The statistical model for COD removal demonstrated a good fit (R² = 0.87), though its predictive power was limited (predicted R² = 0.53). In contrast, the model for energy consumption exhibited an outstanding fit (R² = 0.99) and high predictive consistency (predicted R² = 0.98), confirming the decisive influence of current density on energy demand. Additionally, the S/V ratio emerged as the most impactful factor in COD removal variability. Overall, the findings highlight the importance of balancing removal efficiency with the economic feasibility of the process, contributing to the design of more sustainable and effective strategies for integrated wastewater treatment. Full article

(This article belongs to the Special Issue Environmental Pollution and Wastewater Treatment Strategies)

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20 pages, 4712 KB

Open AccessArticle

Waste Marble Slurry as Partial Substitution for Cement: Effect of Water-to-Cement Ratio

by Zoi S. Metaxa, Sevasti Koryfidou, Lazaros Grigoriadis, Effrosyni Christodoulou, Athanasios Ekmektsis and Athanasios C. Mitropoulos

Appl. Sci. 2025, 15(19), 10451; https://doi.org/10.3390/app151910451 - 26 Sep 2025

Abstract

This study investigates the potential of waste marble slurry as a partial replacement for ordinary Portland cement, with particular emphases on the influence of the water-to-cement (w/c) ratio and the objectives of determining the effect of water content and the optimum marble slurry [...] Read more.

This study investigates the potential of waste marble slurry as a partial replacement for ordinary Portland cement, with particular emphases on the influence of the water-to-cement (w/c) ratio and the objectives of determining the effect of water content and the optimum marble slurry concentration. Cement pastes were prepared with three w/c ratios (0.3, 0.4, and 0.5) and five substitution levels of marble slurry (0%, 5%, 10%, 15%, and 20%). Workability was assessed through mini slump flow tests, while mechanical performance was evaluated via compressive and flexural mechanical tests. The initial and final setting times were also investigated. Electrical resistivity measurements, combined with X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM), were used to examine chemical composition and microstructure. Results showed that marble slurry behaves as an inert filler, rather than a reactive component. Its incorporation, up to 10%, significantly improves the fresh properties and mechanical performance of mixes with higher w/c ratios (0.4 and 0.5). At lower w/c ratios (0.3), strength was adversely affected due to insufficient hydration. Electrical resistivity measurements indicated that pastes with w/c = 0.5 and up to 10% slurry replacement became slightly more resistant to electrical current, whereas mixes with lower w/c ratios (0.3 and 0.4) showed only minor reductions at 5% and 10% cement substitution. SEM imaging demonstrated a denser microstructure when marble slurry was incorporated, consistent with a filler effect. Marble slurry was also found to accelerate the setting of cement pastes, an effect most evident at lower w/c ratios and higher substitution levels. Overall, the findings highlight that waste marble slurry can be effectively utilized at moderate replacement levels in cement-based materials, contributing to sustainable construction practices by reducing cement consumption and marble waste disposal. 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, 15460 KB

Open AccessArticle

Visual Hull-Based Approach for Coronary Vessel Three-Dimensional Reconstruction

by Dominik Bernard Lau and Tomasz Dziubich

Appl. Sci. 2025, 15(19), 10450; https://doi.org/10.3390/app151910450 - 26 Sep 2025

Abstract

This paper addresses the problem of automatically reconstructing three-dimensional coronary vessel trees from a series of X-ray angiography images, a task which remains difficult, particularly with respect to solutions requiring no additional user input. This study analyses the performance of a visual hull-based [...] Read more.

This paper addresses the problem of automatically reconstructing three-dimensional coronary vessel trees from a series of X-ray angiography images, a task which remains difficult, particularly with respect to solutions requiring no additional user input. This study analyses the performance of a visual hull-based algorithm, producing the actual positions of heart arteries in the coordinate system, which is an approach not sufficiently explored in XRA images analysis. The proposed algorithm first creates a bounding cube using a novel heuristic and then iteratively projects the cube onto preprocessed 2D images, removing points too far from the depicted arteries. The method performance is first evaluated on a synthetic dataset through a series of experiments, and for a set of common clinical angles, 3D Dice of 75.25% and 78.61% reprojection Dice is obtained, which rivals the state-of-the-art machine learning methods. The findings suggest that the method offers a promising and interpretable alternative to black box methods on the synthethic dataset in question. Full article

(This article belongs to the Special Issue Novel Advances in Biomedical Signal and Image Processing)

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15 pages, 3920 KB

Open AccessArticle

Identification of Rubber Belt Damages Using Machine Learning Algorithms

by Miroslaw Rucki, Arturas Kilikevicius, Damian Bzinkowski and Tomasz Ryba

Appl. Sci. 2025, 15(19), 10449; https://doi.org/10.3390/app151910449 - 26 Sep 2025

Abstract

This paper presents the experimental results of a Machine Learning application for the health monitoring of a conveyor belt. The real-time analysis of the rubber belt condition is a crucial issue in achieving safety and avoiding critical failures and related expenses. The measuring [...] Read more.

This paper presents the experimental results of a Machine Learning application for the health monitoring of a conveyor belt. The real-time analysis of the rubber belt condition is a crucial issue in achieving safety and avoiding critical failures and related expenses. The measuring system based on strain gauges was applied to identify the actual state of the belt. Using the Classification Lerner application from MATLAB platform, 22 algorithms were tested, and using the Diagnostic Feature Designer application, the analysis was performed. Three tested ML algorithms were able to classify the states of the conveyor belt with preset damages correctly, exhibiting 100% prediction accuracy. The k-nearest neighbors (KNN) classifiers and neural networks failed to achieve that level of accuracy. Full article

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

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17 pages, 1576 KB

Open AccessArticle

Assessment of the Possible Inhibitory Effect of PFAS-Containing Aqueous Wastes on Aerobic Biomasses

by Maria Cristina Collivignarelli, Roberta Pedrazzani, Stefano Bellazzi, Giorgia Grecchi, Marco Baldi, Alessandro Abbà and Giorgio Bertanza

Appl. Sci. 2025, 15(19), 10448; https://doi.org/10.3390/app151910448 - 26 Sep 2025

Abstract

Per- and polyfluoroalkyl substances (PFASs), known as “forever chemicals,” are synthetic organofluorine compounds widely used since the 1940s due to their chemical and thermal stability. However, growing concerns about their environmental and human health risks have emerged. Although the toxicity of PFASs to [...] Read more.

Per- and polyfluoroalkyl substances (PFASs), known as “forever chemicals,” are synthetic organofluorine compounds widely used since the 1940s due to their chemical and thermal stability. However, growing concerns about their environmental and human health risks have emerged. Although the toxicity of PFASs to humans has been extensively researched, their effects on microbial consortia in wastewater treatment plants (WWTPs) have not been as thoroughly investigated. This study evaluates whether aqueous wastes (AWs) containing PFASs inhibit aerobic biomasses from various WWTPs. Approximately 400 respirometric tests showed no acute toxicity. However, biomass tolerance varied based on acclimatization. Biomass from a municipal WWTP was more tolerant to AWs with short-chain PFASs, whereas biomass from a WWTP authorized to receive AWs was less inhibited by AWs rich in long-chain PFASs. These findings highlight the potential role of municipal WWTPs in treating PFAS-contaminated AWs and emphasize the need for tailored treatment strategies to minimize environmental risks. Full article

(This article belongs to the Special Issue PFAS Removal: Challenges and Solutions)

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22 pages, 701 KB

Open AccessArticle

CuBE: A Customizable Bounds Evaluation Framework for Automated Assessment of RAG Systems in Government Services

by Bolun Yang, Xuhong Yu, Xin Zheng, Jing Nong, Zhentao Liu, Xinmin Dai and Xiaoyao Xie

Appl. Sci. 2025, 15(19), 10447; https://doi.org/10.3390/app151910447 - 26 Sep 2025

Abstract

Retrieval-Augmented Generation (RAG) systems are increasingly adopted in government services, yet different administrations have varying customization needs and lack standardized methods to evaluate performance. In particular, general-purpose evaluation approaches fail to show how well a system meets domain-specific expectations. This paper presents CuBE [...] Read more.

Retrieval-Augmented Generation (RAG) systems are increasingly adopted in government services, yet different administrations have varying customization needs and lack standardized methods to evaluate performance. In particular, general-purpose evaluation approaches fail to show how well a system meets domain-specific expectations. This paper presents CuBE (Customizable Bounds Evaluation), a tailored evaluation framework for RAG systems in public administration. CuBE integrates large language model (LLM) scoring, customizable evaluation dimensions, and a bounded scoring paradigm with baseline and upper-bound reference sets, enhancing fairness, consistency, and interpretability. We further introduce Lightweight Targeted Assessment (LTA) to support efficient customization. CuBE is validated on GSIA (Guizhou Provincial Government Service Center Intelligent Assistant) by using four state-of-the-art language models. The results show that CuBE produces robust, stable, and model-agnostic evaluations while reducing reliance on manual annotation and facilitating system optimization and rapid iteration. Moreover, CuBE informs parameter settings, enabling developers to design RAG systems that better meet customizer needs. This study establishes a replicable paradigm for trustworthy and efficient evaluation of RAG systems in complex government service scenarios. Full article

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14 pages, 2823 KB

Open AccessArticle

Reactive Oxygen Species-Mediated Oral Cancer Cells Treatment Using Photosensitizer-Combined Carbon Dots via Apoptosis–Ferroptosis Synergistic Therapy

by So-Young Park, Mi-Heon Ryu, Wooil Kim, Franklin Garcia-Godoy, Yong Hoon Kwon and Hye-Ock Jang

Appl. Sci. 2025, 15(19), 10446; https://doi.org/10.3390/app151910446 - 26 Sep 2025

Abstract

In this study, the applicability of carbon dots (CDs) for the treatment of oral cancer cells in vitro was assessed under laser irradiation. For the study, CDs were synthesized using an amino acid via heat treatment and then combined with a photosensitizer. The [...] Read more.

In this study, the applicability of carbon dots (CDs) for the treatment of oral cancer cells in vitro was assessed under laser irradiation. For the study, CDs were synthesized using an amino acid via heat treatment and then combined with a photosensitizer. The absorbance and photoluminescence of CDs were measured. The production of reactive oxygen species (ROS) was evaluated using assay agents. The glutathione (GSH) content of the test solutions was evaluated. The viability of normal and cancer cells was evaluated using CDs at different concentrations under laser irradiation. Live/dead cells and intracellular lipid peroxidation (LPO) were observed after treatment. According to the assays, the production of •OH, •O₂⁻, and ¹O₂ was spectroscopically observed, which was reflected by the change in their peak absorbance. GSH was depleted mostly during light irradiation. Cancer cells were eliminated without leaving visible live cells, whereas normal cells were minimally affected. Intracellular LPO was confirmed in cells by green fluorescence, which was emitted from an oxidized assay dye. Conclusively, the amino acid-based photosensitizer-combined CDs eliminated approximately 70% of the cancer cells in vitro under laser irradiation, with no visible live cells. Based on these assays, ROS production may induce cell death via synergistic apoptosis–ferroptosis therapy. Full article

(This article belongs to the Section Applied Dentistry and Oral Sciences)

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19 pages, 2445 KB

Open AccessArticle

Prediction of Multi-Hole Copper Electrodes’ Influence on Form Tolerance and Machinability Using Grey Relational Analysis and Adaptive Neuro-Fuzzy Inference System in Electrode Discharge Machining Process

by Sandeep Kumar, Subramanian Dhanabalan, Wilma Polini and Andrea Corrado

Appl. Sci. 2025, 15(19), 10445; https://doi.org/10.3390/app151910445 - 26 Sep 2025

Abstract

Electric discharge machining processes are prominent in the fastest-growing industries because of their accuracy, achievable complex workpiece shapes, and cost-effectiveness. Furthermore, the machining of high-quality difficult-to-machine alloys is becoming critical in the aerospace, manufacturing, and defence industries. While the optimisation of EDM parameters [...] Read more.

Electric discharge machining processes are prominent in the fastest-growing industries because of their accuracy, achievable complex workpiece shapes, and cost-effectiveness. Furthermore, the machining of high-quality difficult-to-machine alloys is becoming critical in the aerospace, manufacturing, and defence industries. While the optimisation of EDM parameters is essential for improving machining outcomes, it is also important to consider the trade-offs between different performances metrics, such as material removal rate and part accuracy. Part accuracy in terms of dimensional and geometric deviations from nominal values was rarely considered in the literature, if not by the authors. Balancing these factors remains a challenge in the field of EDM. Therefore, this work aims to carry out a multi-objective optimisation of both MRR and part accuracy. A Ni-based alloy (Inconel-625) was used that is widely used in creep-resistant turbine blades and vanes and turbine disks in gas turbine engines for aerospace and defence industries. Four performance indices were optimised simultaneously: two related to the performance of the EDM process and two connected with the form deviations of the manufactured surfaces. Multi-hole copper electrodes having different diameters and three process parameters were varied during the experimental tests. Grey relational analysis and the Adaptive Neuro-Fuzzy Inference System method were used for optimisation. Grey relational analysis found that the following values of the process parameter—0.16 mm of multi-hole electrode diameter, 12 Amperes of Peak current, 200 µs of pulse on time and 0.2 kg/m² as dielectric pressure—produce the optimal performance, i.e., a material removal rate of 0.099 mm³/min, an electrode wear rate of 0.0002 g/min, a circularity deviation of 0.0043 mm and a cylindricity deviation of 0.027 mm. From the experimental examination using multi-hole electrodes, it is concluded that the material removal rate increases and the electrode wear rate decreases because of the availability of higher spark discharge areas between the electrode and work material interface. The Adaptive Neuro-Fuzzy Inference System models showed minimum mean percentage error and, therefore, better performance in comparison with regression models. Full article

(This article belongs to the Special Issue Processing, Manufacturing and Machining of Advanced Alloy Materials: Latest Advances and Prospects)

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26 pages, 7215 KB

Open AccessArticle

Towards a Digital Twin for Buildings IAQ and Thermal Comfort Monitoring

by Eleonora Congiu, Giuseppe Desogus, Emanuela Quaquero, Giulia Rubiu and Francesca Poggi

Appl. Sci. 2025, 15(19), 10444; https://doi.org/10.3390/app151910444 - 26 Sep 2025

Abstract

Several studies have proven the impact of the quality of indoor environmental conditions on human professional and cognitive performances. Additionally, building energy efficiency and indoor comfort have attracted increasing interest, encouraging the implementation of advanced digital technologies and platforms for a more efficient [...] Read more.

Several studies have proven the impact of the quality of indoor environmental conditions on human professional and cognitive performances. Additionally, building energy efficiency and indoor comfort have attracted increasing interest, encouraging the implementation of advanced digital technologies and platforms for a more efficient management of buildings. In this context, this study proposes a new framework for an effective BIM-IoT integration leading to a nearly Digital Twin (DT) relying on a BIM model equipped with regularly-generated IEQ reports summarizing statistics from real-time collected data to support facility managers’ decision-making. Despite the relevant literature on the subject, the proposed methodology introduces some novelties, as monthly results of Indoor Air Quality (IAQ) and thermal comfort evaluation are provided by open HTML reports automatically generated through a Python 3.10 code from sensor data. These reports are easily readable without needing any external platform to be visualized and are directly accessible through BIM models. The proposed methodology has been validated on a pilot case study, thus proving its efficiency, effectiveness, and robustness in terms of automation level, interoperability, adaptability, reliability, accuracy in data visualization, and management. The study shows promising results but also some issues that could be addressed through further development of the research. Full article

(This article belongs to the Special Issue Building Information Modeling (BIM): Advanced Technology and Application)

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22 pages, 3551 KB

Open AccessArticle

Research on the Dynamic Response Characteristics of Soft Coal Under Impact Disturbance Based on Hamilton

by Feng Li, Tianyi Zhang, Chenchen Wang and Binchan Tian

Appl. Sci. 2025, 15(19), 10443; https://doi.org/10.3390/app151910443 - 26 Sep 2025

Abstract

To address the limitations of traditional elasticity theory in analyzing the dynamic response of soft coal under external impact, this study establishes a vibration control equation with an analytical solution based on Hamiltonian mechanics. Key control parameters within the equation were solved to [...] Read more.

To address the limitations of traditional elasticity theory in analyzing the dynamic response of soft coal under external impact, this study establishes a vibration control equation with an analytical solution based on Hamiltonian mechanics. Key control parameters within the equation were solved to determine the theoretical dominant vibration modes and natural frequencies of the weakest coal layer. Triangular and rectangular waves were transformed via FFT to analyze their harmonic components, and the superposition of the first four harmonics was selected as the input impact signal. The modal and natural frequency changes during the fragmentation of the central weak zone under external impact were simulated, and the dynamic displacement response was analyzed. The results indicate a strong response frequency range of 4.4–5.2 Hz, with the rectangular wave identified as the most effective response waveform. A similarity simulation platform was constructed, and experimental data showed that the velocity and displacement response trend of the coal mass aligned closely with theoretical predictions. Therefore, in actual underground operations, emphasis should be placed on monitoring low-frequency vibrations in mines, minimizing rectangular wave disturbances in the low-frequency range, and implementing pressure relief measures in high-risk zones to reduce the likelihood of coal and gas outbursts. By separately modeling high-risk zones and analyzing their dynamic response under external impact, this study explains the outburst mechanism of the weakest layer in soft coal from a dynamic perspective. Combining theoretical and experimental approaches, it provides a new theoretical basis for understanding and preventing coal and gas outbursts. Full article

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12 pages, 5228 KB

Open AccessArticle

Early Fault Detection in a Real Scenario of Hybrid Fiber–Coaxial Networks Using Machine Learning: An Approach Based on Decision Trees and Random Forests

by Christian Szcerba, Enrique Dávalos, Ariel Leiva and Juan Pinto-Ríos

Appl. Sci. 2025, 15(19), 10442; https://doi.org/10.3390/app151910442 - 26 Sep 2025

Abstract

Cable service providers face significant challenges in managing Hybrid Fiber–Coaxial (HFC) networks due to the growing demand for high-speed services. Ensuring high service availability is critical to preventing customer attrition. This study employs machine learning techniques, specifically Decision Tree and Random Forest models, [...] Read more.

Cable service providers face significant challenges in managing Hybrid Fiber–Coaxial (HFC) networks due to the growing demand for high-speed services. Ensuring high service availability is critical to preventing customer attrition. This study employs machine learning techniques, specifically Decision Tree and Random Forest models, for proactive fault detection in HFC networks using data from the Simple Network Management Protocol (SNMP). Two operational scenarios were considered: a network-wide model and node-specific models. The dataset for fault detection exhibited a severe class imbalance, with outage events being extremely rare. To address this, the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic samples of the minority class to balance the dataset, was applied. This significantly improved recall and F1-scores—the harmonic mean of precision and recall—while maintaining high precision. The results demonstrate that these machine learning algorithms achieve up to 98% accuracy, and the SMOTE-enhanced models provide more reliable detection of connectivity faults. This approach is highly effective for cable operators in maintaining quality of service, enabling proactive management of problems and enhancement of network performance. Full article

(This article belongs to the Topic Machine Learning in Communication Systems and Networks, 2nd Edition)

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24 pages, 9211 KB

Open AccessArticle

Design Assessment of Power Supply Systems for Divertor Coils in the Divertor Tokamak Test

by Giovanni Griva, Salvatore Musumeci, Radu Bojoi, Fausto Stella and Alessandro Lampasi

Appl. Sci. 2025, 15(19), 10441; https://doi.org/10.3390/app151910441 - 26 Sep 2025

Abstract

In tokamak-based nuclear fusion systems, powering the coils to control the plasma is a challenge that involves design choices that are a mix between advanced and traditional approaches. Each tokamak coil requires peculiar driving conditions and needs specific design activities. This paper deals [...] Read more.

In tokamak-based nuclear fusion systems, powering the coils to control the plasma is a challenge that involves design choices that are a mix between advanced and traditional approaches. Each tokamak coil requires peculiar driving conditions and needs specific design activities. This paper deals with power supply design assessment for the Divertor (DIV) Coils in the Divertor Tokamak Test (DTT) facility. The design constraints of high-current (5500 A) and relatively low-voltages lead to the comparison of an SCR-based AC–AC converter (cycloconverter) with an IGBT-based DC–AC inverter with devices in a parallel solution and with interleaved modulation. The design assessment of two converter solutions to drive the DIV coils with the control issues were explored and described. Several simulation results were carried out to define the DIV coils operative conditions. Furthermore, an electro-thermal analysis on the used IGBT or thyristor devices was carried out considering the losses and the highest temperatures obtained in the conditions of maximum stress for the components. Full article

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

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18 pages, 676 KB

Open AccessArticle

Node Classification of Imbalanced Data Using Ensemble Graph Neural Networks

by Yuan Liang

Appl. Sci. 2025, 15(19), 10440; https://doi.org/10.3390/app151910440 - 26 Sep 2025

Abstract

In real-world scenarios, many datasets suffer from class imbalance. For example, on online review platforms, the proportion of fake and genuine comments is often highly skewed. Although existing graph neural network (GNN) models have achieved notable progress in classification tasks, their performance tends [...] Read more.

In real-world scenarios, many datasets suffer from class imbalance. For example, on online review platforms, the proportion of fake and genuine comments is often highly skewed. Although existing graph neural network (GNN) models have achieved notable progress in classification tasks, their performance tends to rely on relatively balanced data distributions. To tackle this challenge, we propose an ensemble graph neural network framework designed for imbalanced node classification. Specifically, we employ spectral-based graph convolutional neural networks as base classifiers and train multiple models in parallel. We then adopt a bagging ensemble strategy to integrate the predictions of these classifiers and determine the final classification results through majority voting. Furthermore, we extend this approach to fake review detection tasks. Extensive experiments conducted on imbalanced node classification datasets (Cora and BlogCatalog), as well as fake review detection (YelpChi), demonstrate that our method consistently outperforms state-of-the-art baselines, achieving significant gains in accuracy, AUC, and Macro-F1. Notably, on the Cora dataset, our model improves accuracy and Macro-F1 by 3.4% and 2.3%, respectively, while on the BlogCatalog dataset, it achieves improvements of 2.5%, 1.8%, and 0.5% in accuracy, AUC, and Macro-F1, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts, 2nd Edition)

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14 pages, 696 KB

Open AccessArticle

Portfolio Management Strategies Based on Deep Temporal Clustering

by Eleftherios Kouloumpris, Panagiotis Doupidis, Konstantinos Moutsianas and Ioannis Vlahavas

Appl. Sci. 2025, 15(19), 10439; https://doi.org/10.3390/app151910439 - 26 Sep 2025

Abstract

Portfolio management (PM) facilitates optimal investing decisions and enables organizations to control risks and achieve stable financial growth. Advances in machine learning, mostly through supervised learning, are drastically changing the way in which PM is conducted. More recently, unsupervised learning is also emerging [...] Read more.

Portfolio management (PM) facilitates optimal investing decisions and enables organizations to control risks and achieve stable financial growth. Advances in machine learning, mostly through supervised learning, are drastically changing the way in which PM is conducted. More recently, unsupervised learning is also emerging as a paradigm that can support the creation of diversified and profitable portfolios through stock clustering. In the corresponding literature, there is significant evidence that cluster-informed methods can outperform both traditional and supervised approaches to PM. However, these works are few and have not considered state-of-the-art deep learning approaches for clustering, while stock allocation is often limited to equally weighted portfolios or mean-variance optimization (MVO). To address these issues, we propose a cluster-informed PM method based on deep temporal clustering (DTC) along with our recommended parameters for training convergence, combined with the conditional drawdown at risk (CDaR) portfolio allocation method. Unlike MVO, CDaR considers tail risk and can minimize extreme price drawdowns. Cluster validity metrics reveal that DTC outperforms previously proposed stock clustering methods. Furthermore, DTC enhanced by CDaR achieves a higher expected Sortino ratio (1.1) compared to previous works in clustering-based PM. Additional Brinson attribution and maximum drawdown analyses further confirm the robustness of our method. Full article

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

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