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Appl. Sci., Volume 16, Issue 16 (August-2 2026) – 486 articles

Cover Story (view full-size image): Aero-propulsive interactions in flapped configurations are a critical consideration for Short Take-Off and Landing (stol) aircraft. This study develops a surrogate modeling framework that predicts the section-level aerodynamic response of a propeller–airfoil–flap configuration across a multi-dimensional space of propeller positioning, flap geometry, and operational conditions. A paired powered and unpowered design of experiments isolates the propulsive contribution to lift, drag, and pitching moment, while a virtual-disk propeller model parameterized by volumetric thrust decouples the prediction from any specific blade design. The resulting surrogate enables rapid, optimization-ready exploration of propeller–airfoil–flap configurations, providing actionable trade-off information for the preliminary design of stol aircraft. View this paper
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29 pages, 15392 KB  
Article
Assessing Power Boiler Degradation: Thermography Combined with Machine Learning for Wall Thickness Estimation
by Rafał Gasz, Mirosław Lasar, Michał Tomaszewski and Sławomir Zator
Appl. Sci. 2026, 16(16), 8349; https://doi.org/10.3390/app16168349 - 21 Aug 2026
Viewed by 258
Abstract
Power boiler tubes are exposed to severe operating conditions that lead to wall thinning and material degradation. Reliable assessment of tube wall thickness is therefore essential for ensuring safe and efficient boiler operation. This exploratory laboratory study investigates the applicability of active thermography [...] Read more.
Power boiler tubes are exposed to severe operating conditions that lead to wall thinning and material degradation. Reliable assessment of tube wall thickness is therefore essential for ensuring safe and efficient boiler operation. This exploratory laboratory study investigates the applicability of active thermography combined with analytical and machine learning (ML) approaches for non-contact wall thickness estimation in power boiler tubes. Experimental investigations were performed on a single boiler tube specimen with artificially introduced wall-thickness reductions. Thermal responses were recorded using an infrared camera under both heating and cooling excitation conditions. Based on the acquired thermographic data, analytical models and machine learning algorithms were developed to estimate tube wall thickness. The machine learning approach was implemented using Random Forest and Support Vector Regression models and compared with conventional analytical modeling techniques. For separately analyzed and relatively homogeneous measurement series, the machine learning models produced lower descriptive errors than the analytical models, with the estimated three-RMSE error envelope decreasing from 0.51 mm to 0.17 mm. However, when heating and cooling datasets were aggregated, the analytical models achieved lower root mean square error values and demonstrated greater stability than the machine learning methods. These findings indicate that model performance strongly depends on the size, characteristics, and homogeneity of the available training data. Owing to the limited number of independent measurement series, the reported results should be interpreted as a small-sample feasibility assessment rather than as evidence of the general superiority of machine learning modeling. The results support the potential of active thermography for non-contact assessment of boiler tube wall thickness under controlled laboratory conditions. Further validation using additional specimens, grouped cross-validation, and physics-based synthetic data is required before the methodology can be considered for industrial implementation. Full article
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32 pages, 7952 KB  
Article
Overburden Strata Synchronous Breaking and Dynamic Load Mine Pressure Mechanism of Cross-Ditch Mining in Close-Distance Coal Seams
by Jie Zhang, Yiming Zhang, Tao Yang, Dong Liu, Hui Liu, Jianping Sun, Guang Qin, Longqian Zhang, Shuqi Zhang, Quanxin Wang, Yichao Zhou, Jiahao Zhao and Runyuan Song
Appl. Sci. 2026, 16(16), 8348; https://doi.org/10.3390/app16168348 - 21 Aug 2026
Viewed by 211
Abstract
Repeated mining of shallow-buried close-distance coal seams can disturb the fractured strata remaining in the goaf of the upper coal seam. Under gully terrain, mining disturbance is coupled with surface-relief effects, which may reactivate the overburden structure and induce dynamic strata-pressure behavior. In [...] Read more.
Repeated mining of shallow-buried close-distance coal seams can disturb the fractured strata remaining in the goaf of the upper coal seam. Under gully terrain, mining disturbance is coupled with surface-relief effects, which may reactivate the overburden structure and induce dynamic strata-pressure behavior. In particular, when the working face advances across gullies, the change in surface slope alters the spatial distribution of roof load, while lower-seam extraction further disturbs the fractured rock mass formed by upper-seam mining, increasing the risk of severe strata-pressure behavior and support-crushing accidents. Taking the cross-ditch mining of the 2−2 and 3−1 coal seams in Anshan Coal Mine as the research object, this study integrates field geological investigation, theoretical calculation, physical similarity simulation, and field engineering verification to analyze overburden structural evolution, key-stratum breaking characteristics, and support-load variation under gully terrain. The results show that gully landforms generate obvious nonuniform loading above the working face. During upslope advance, the roof load gradually increases from the goaf side to the solid-coal side, causing tensile stress concentration at the fixed end of the key stratum and accelerating rock-stratum failure. A cantilever rock-beam mechanical model subjected to parabolic nonuniform loading was established, and the maximum breaking interval of the key stratum was calculated as 24.09 m. With increasing gully slope angle, the load gradient intensifies, the rock-beam breaking interval decreases, and the risk of overburden instability increases. Physical similarity simulation indicates that, when the 2−2 coal seam working face passes through the 45° steep-slope section, the fractured overburden is more likely to form a stepped rock-beam structure, accompanied by slope rotation, stepped surface subsidence, and a sharp increase in support pressure. Under the 30° gentle-slope condition, The lateral confinement effect is stronger, roof movement is more gradual, and support-pressure fluctuation is reduced. During subsequent extraction of the lower 3−1 coal seam, repeated mining disturbance reactivates the overlying goaf structure, and the upper stepped rock beam and lower hinged rock beam couple to form a double composite structure. When the fracture lines of the upper and lower key strata are staggered, the instability load of the upper structure is mainly buffered by caved gangue and interburden strata. The calculated support resistance in the asynchronous breaking stage is 8248.04 kN, which agrees well with the field-measured value of 8273 kN. When the fracture lines tend to coincide and synchronous breaking occurs, the unstable load of the upper key block is transferred downward and superimposed on the structural load of the lower key block, increasing the required support resistance to 15,165.55 kN, far exceeding the rated working resistance of the ZY9200/15/29 hydraulic support. Sensitivity analysis indicates that gully slope angle is the dominant factor affecting support resistance. As the slope angle increases from 30° to 60°, the support resistance increases from 13,228.65 kN to 18,278.43 kN, and the normalized support-resistance index increases from 0.872 to 1.205. Therefore, synchronous breaking of double key strata is the main mechanical cause of sudden support-load increase and support-crushing risk during cross-ditch mining of shallow-buried close-distance coal seams. The results can provide a basis for hydraulic support selection, roof weakening, weighting-interval control, and dynamic strata-pressure prevention under similar conditions. Full article
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22 pages, 15404 KB  
Article
Numerical Analysis of the Mechanical Performance of a Precast Hollow-Slab Girder Bridge with Hinge-Joint Damage Under Interfacial Bond Degradation
by Wei Hou, Zhuolong Zhang, Xiaobo Zheng, Baojun Zhao and Zhuang Li
Appl. Sci. 2026, 16(16), 8347; https://doi.org/10.3390/app16168347 - 21 Aug 2026
Viewed by 221
Abstract
Hinge joints are critical structural components that connect precast girders and enhance the load-bearing capacity and serviceability of multi-girder bridges. Damage to hinge joints can significantly reduce the structural integrity of a bridge and may even lead to bridge collapse. This study numerically [...] Read more.
Hinge joints are critical structural components that connect precast girders and enhance the load-bearing capacity and serviceability of multi-girder bridges. Damage to hinge joints can significantly reduce the structural integrity of a bridge and may even lead to bridge collapse. This study numerically investigated the effects of hinge-joint damage on the mechanical performance and inter-girder connections of a hollow-slab girder bridge using a surface-based cohesive behavior model. Hinge-joint damage was simulated in the finite element software ABAQUS (version 2022) using bond-performance degradation at the slab–hinge joint interfaces. The effects of damage location and severity on the stress distribution within the hinge joints were evaluated. The results reveal that damage in two hinge joints produces stresses 15% higher than those caused by damage in a single hinge joint. This indicates a weak superposition effect among multiple damaged joints. Additionally, stress fluctuations at key points Nos. 1 and 2 are significantly greater than those at key points Nos. 3 and 4. These findings provide valuable guidance for improving the durability and crack resistance of hinge joints and designing and maintaining multi-girder bridges. Full article
(This article belongs to the Section Civil Engineering)
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45 pages, 12967 KB  
Article
Multi-Source Operational Feature-Driven Cutterhead Torque Prediction in Shield Tunnelling Using an IALA-Optimized Fuzzy Ensemble Deep RVFL Model
by Tianxing Ma, Liangxu Shen, Hang Sun, Keying Guo, Jingkun Su, Pu Wang, Junjun Zhang, Fengzhou Wang, Ping Lyu, Haowen Teng and Zhijing Shen
Appl. Sci. 2026, 16(16), 8346; https://doi.org/10.3390/app16168346 - 21 Aug 2026
Viewed by 376
Abstract
Cutterhead driving torque is the primary load indicator of earth-pressure-balance shield machines, yet its dependence on strongly coupled multi-source operating parameters limits the reliability of empirical formulations. This study proposes IALA-edRVFL-FIS-Reg, a fuzzy ensemble deep random vector functional link regression model optimized by [...] Read more.
Cutterhead driving torque is the primary load indicator of earth-pressure-balance shield machines, yet its dependence on strongly coupled multi-source operating parameters limits the reliability of empirical formulations. This study proposes IALA-edRVFL-FIS-Reg, a fuzzy ensemble deep random vector functional link regression model optimized by an improved artificial lemming algorithm (IALA). The base learner maps continuous operating parameters into fuzzy-state features through a Gaussian-membership Sugeno inference layer, propagates the concatenated raw and fuzzified inputs through stacked randomized hidden layers with direct input links, and obtains layer-wise output weights by regularized closed-form least squares before ensembling, thereby combining fuzzy-state representation with deep random feature mapping without gradient back-propagation. Distinct from the standard ALA, IALA introduces three explicitly defined mechanisms: an error-feedback exploration–exploitation transition factor normalized by the initial-population loss, which replaces the fixed energy factor; an adaptive step size coupling sigmoid error-gating with cosine annealing to preserve jumping capability while refining local search; and a stagnation-counter-triggered directional-disturbance jump for escaping local optima. Using 48,646 valid tunnelling records from 301 rings of Beijing Metro Line 22 and 65 raw and mechanism-based engineered features, the model attains R2 = 0.9555, RMSE = 382.52 kN·m, MAE = 302.95 kN·m and MAPE = 9.18%, outperforming eleven benchmarks on a ring-disjoint holdout, previously unseen rings of the same section, IALA yields an R2 gain of 0.0104 over ALA. Full article
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17 pages, 746 KB  
Article
Neuromuscular and Biomechanical Adaptations Following a 15-Week CrossFit-Based Functional Training Program in Competitive Sambo Athletes
by Cristian Dumitru Stanciu, Valentina Stefanica, Nicoleta Zaharie, Delia Badescu, Iulian Stoian, Ioan Teodor Hășmășan, Virgil Ene-Voiculescu, Denisa-Elena Hășmășan and Daniel Rosu
Appl. Sci. 2026, 16(16), 8345; https://doi.org/10.3390/app16168345 - 21 Aug 2026
Viewed by 246
Abstract
Background: Combat sports require athletes to repeatedly perform explosive actions under conditions of fatigue, demanding high levels of neuromuscular coordination, reactive agility, and intermittent effort capacity. This study examined changes in biomechanical and neuromuscular performance over a 15-week period during which competitive Sambo [...] Read more.
Background: Combat sports require athletes to repeatedly perform explosive actions under conditions of fatigue, demanding high levels of neuromuscular coordination, reactive agility, and intermittent effort capacity. This study examined changes in biomechanical and neuromuscular performance over a 15-week period during which competitive Sambo athletes completed a CrossFit-based functional training program alongside their regular sport-specific training. Methods: Forty male Sambo athletes (22.4 ± 2.6 years) completed a longitudinal repeated-measures intervention consisting of three weekly CrossFit-based sessions integrated into regular sport-specific training. The program was periodized across four progressive mesocycles emphasizing functional strength, explosive power, reactive agility, and fatigue resistance. Pre- and post-intervention assessments included upper- and lower-limb reaction speed, vertical jump height, repeated jump endurance, reactive agility, grip endurance, body-weight squat performance, Romanian deadlift repetitions, and the Special Judo Fitness Test (SJFT). Statistical analyses included paired-samples t-tests or Wilcoxon signed-rank tests with Benjamini–Hochberg false discovery rate correction (α = 0.05). Results: Significant post-intervention improvements were observed in most performance variables. Upper- and lower-limb reaction performance improved by 22.9% and 9.7%, respectively (p < 0.001), while vertical jump height increased by 11.4% and repeated jump endurance by 16.6% (p < 0.001). Reactive agility time decreased by 6.6% (p < 0.001), indicating faster cognitive-motor responsiveness. Functional strength improved substantially, with increases of 43.7% in body-weight squat performance and 71.9% in Romanian deadlift repetitions (p ≤ 0.001). Grip endurance improved by 6.3% (p = 0.007), whereas maximal handgrip strength showed no significant changes. The SJFT index decreased by 5.0% (p < 0.001), reflecting enhanced intermittent effort efficiency and fatigue tolerance. Large effect sizes were identified for upper-limb reaction speed (d = 1.42), explosive endurance (d = 1.46), and reactive agility (d = 0.96). Conclusions: Over the 15-week study period, participation in a CrossFit-based functional training program integrated into regular Sambo practice was associated with improvements in several biomechanical and neuromuscular performance outcomes, including reaction performance, explosive power, agility, muscular endurance, and sport-specific work capacity. However, because the study did not include a control group, these changes cannot be attributed exclusively to the CrossFit-based intervention. Controlled studies are required to distinguish intervention-specific effects from seasonal training adaptations, continued sport-specific practice, and test familiarization. Full article
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23 pages, 38140 KB  
Article
Electromyographic Characterization of Core Muscle Activation During Steel Club Exercises in Trained Males
by Jorge Gutiérrez-Hellín, Clara Larnaudie-Faura, Mario Matías-López, Arturo Franco-Andrés, Julio Martín-López, Jorge Sánchez-Infante, Pablo Casado-Martínez and Juan Del Coso
Appl. Sci. 2026, 16(16), 8344; https://doi.org/10.3390/app16168344 - 21 Aug 2026
Viewed by 342
Abstract
Background: Steel club training is a form of strength and conditioning exercise that involves swinging weighted clubs through circular and pendular movements, thereby imposing substantial rotational and multiplanar demands on the trunk. However, the core muscle activation patterns elicited by these exercises remain [...] Read more.
Background: Steel club training is a form of strength and conditioning exercise that involves swinging weighted clubs through circular and pendular movements, thereby imposing substantial rotational and multiplanar demands on the trunk. However, the core muscle activation patterns elicited by these exercises remain poorly characterized. Purpose: This study aimed to characterize global, muscle-specific, and side-to-side core muscle activation across a battery of steel club exercises and compare it with that of conventional plank variations. Methods: A within-subject experimental design was used in a single-session laboratory setting. Fourteen trained males (n = 14) performed ten steel club exercise conditions (comprising seven distinct movement patterns, with unilateral variations performed bilaterally) using 8 and 16 kg clubs and four isometric plank variations; data from n = 13 were available for the Shield Cast 16 kg condition and the omnibus ANOVA due to one excluded signal. Surface electromyography was bilaterally recorded from the rectus abdominis, external oblique, internal oblique, and multifidus muscles and normalized to maximal voluntary contraction (% MVC). Repeated-measures ANOVA was used to assess global and muscle-specific differences across exercise conditions, and paired-samples t-tests were used to evaluate side-to-side differences. Results: Global core activation significantly differed across exercises (p < 0.001, ηp2 = 0.798) and was generally greater during steel club exercises than during conventional planks. The Lateral Pendulum to Shoulder Cast (54.77 ± 9.32% MVC), Gamma Cast (47.79 ± 8.96% MVC), and Shield Cast (43.96 ± 11.25% MVC), all performed with 16 kg, elicited greater global activation than each of the four plank variations (all adjusted p < 0.05). The internal oblique showed the greatest activation across most steel club exercises, reaching 69.40 ± 12.18% MVC during the Lateral Pendulum to Shoulder Cast. Several unilateral exercises also elicited significant side-to-side differences, particularly Mills and Reverse Mills (all p < 0.05). Conclusions: Among experienced steel club practitioners, these exercises elicited exercise-specific, dynamic, and multiplanar core activation patterns, with several exercises—particularly those performed with 16 kg—producing greater global activation than conventional plank variations. These findings support steel club training as a complementary modality for dynamic core training, although the independent contributions of movement pattern and external load, as well as long-term adaptations, remain to be established. Full article
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30 pages, 5936 KB  
Article
Introducing MEGO and PDC: Novel Indicators for Quantifying Market Rigidity and Cross-Border Price Divergence in Central European Electricity Markets
by Marek Pavlík
Appl. Sci. 2026, 16(16), 8343; https://doi.org/10.3390/app16168343 - 21 Aug 2026
Viewed by 252
Abstract
The massive integration of variable renewable energy sources (vRES) in Central Europe is fundamentally transforming electricity price formation and straining transmission grids. However, existing academic metrics, such as the RES Capture Price, offer only a static view of investor revenues and fail to [...] Read more.
The massive integration of variable renewable energy sources (vRES) in Central Europe is fundamentally transforming electricity price formation and straining transmission grids. However, existing academic metrics, such as the RES Capture Price, offer only a static view of investor revenues and fail to capture dynamic market rigidity and systemic risks during periods of high instantaneous vRES penetration. This study addresses this literature gap by introducing two novel and transparent methodological parameters: Market Exposure to Green Overproduction (MEGO) and the Price Divergence Coefficient (PDC). Formulated as conditional non-parametric indicators, the MEGO index quantifies the conditional probability of price collapse and the loss of market elasticity during hours when vRES penetration exceeds critical thresholds (α = 0.50 to 0.80) of systemic load. Conversely, the PDC index measures the frequency of substantial price non-convergence across neighbouring bidding zones (CZ, PL, FR) relative to the German reference market (DE). Based on an extensive dataset spanning from 2015 to mid-2026—capturing the transition to 15 min market time units— the empirical results reveal a distinct change in market behaviour. While the frequency of price collapse during high-vRES periods was lower in earlier years and temporarily reduced during the 2022 energy crisis, the post-crisis period (2024–2026) exhibits substantially higher MEGO values, with periods in which wind and solar generation exceeded 80% of instantaneous system load being associated with prices at or below 0 EUR/MWh in up to 60% of the evaluated intervals. Concurrently, the PDC analysis reveals persistent spatial price non-convergence, particularly in France and Poland. These patterns coincided with major changes in European electricity-market conditions, including the implementation of Core Flow-Based Market Coupling, variations in nuclear availability and evolving cross-border network conditions; however, the PDC indicator alone does not permit causal attribution to any individual factor. The proposed MEGO and PDC parameters provide policymakers, transmission system operators (TSOs), and investors with an intuitive diagnostic framework for dimensioning grid flexibility, energy storage, and cross-border infrastructure in the decarbonization era. Full article
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51 pages, 9382 KB  
Article
A Novel Lightweight Transformer-Free Neuro-Scattering Mamba-KAN Architecture for Respiratory Sound Classification
by Florin Bogdan and Mihaela-Ruxandra Lascu
Appl. Sci. 2026, 16(16), 8342; https://doi.org/10.3390/app16168342 - 21 Aug 2026
Viewed by 203
Abstract
Automated pulmonary auscultation demands rapid and reliable anomaly detection. Contemporary deep learning frameworks frequently face deployment barriers due to their reliance on memory-intensive Transformer mechanisms and high-cost processing hardware. Addressing this limitation, the present study introduces the Neuro Scatter Mamba Kolmogorov–Arnold Neural Network [...] Read more.
Automated pulmonary auscultation demands rapid and reliable anomaly detection. Contemporary deep learning frameworks frequently face deployment barriers due to their reliance on memory-intensive Transformer mechanisms and high-cost processing hardware. Addressing this limitation, the present study introduces the Neuro Scatter Mamba Kolmogorov–Arnold Neural Network (NSMK-Net), a lightweight, Transformer-free architecture. The model integrates 1D Wavelet Scattering, bi-directional Selective State Space Models (Mamba), and Kolmogorov–Arnold Networks (KAN). By substituting quadratic self-attention with continuous-time differential discretization, the framework achieves very good computational efficiency under severe hardware constraints. Model optimization followed an eco-friendly “Green-AI” methodology, successfully converging on a standard 4 GB VRAM graphics unit. Regarding real-world deployment, the finalized architecture can be considered as a possible candidate for future “Edge-AI” applications, because it requires only 0.34 MB of parameter storage (89,342 parameters) and executes inference in approximately 48 milliseconds per respiratory cycle. Evaluated on the SPRSound dataset, the proposed model achieved a cycle-level accuracy of 84.67% (Macro-F1: 0.48). When tested under the strict official 60/40 partition of the ICBHI 2017 dataset, the network delivered a global accuracy of 41.56% (Macro-F1: 0.31) alongside an official reported ICBHI Score of 49.38%. These metrics indicate a stable detection capability when processing highly imbalanced clinical data. By replacing fixed activation nodes with learnable edge non-linearities and utilizing linear sequence memory, this new structural approach reduces the dependency on high-end hardware for medical acoustic processing. Full article
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33 pages, 1820 KB  
Article
Speech Signal Preprocessing and Feature Extraction for Biomarker Identification in Acute Heart Failure: A Pilot Study
by Andrzej Majkowski, Tomasz Rywik, Paweł Irzmański, Jakub Czapnik, Marcin Kołodziej and Anna Drohomirecka
Appl. Sci. 2026, 16(16), 8341; https://doi.org/10.3390/app16168341 - 21 Aug 2026
Viewed by 208
Abstract
This study presents a configurable speech signal preprocessing and feature-extraction workflow for identifying candidate acoustic biomarkers in acute heart failure. The workflow was evaluated in 12 patients hospitalized with acute heart failure. Short recordings of repeated vowels /a/, /i/, and /o/ were acquired [...] Read more.
This study presents a configurable speech signal preprocessing and feature-extraction workflow for identifying candidate acoustic biomarkers in acute heart failure. The workflow was evaluated in 12 patients hospitalized with acute heart failure. Short recordings of repeated vowels /a/, /i/, and /o/ were acquired shortly after admission and again before discharge following treatment and clinical stabilization. The pipeline included active-RMS normalization, automatic segmentation of repeated vowels, optional edge trimming, alternative pitch-estimation variants, and extraction of three feature families: phonatory and temporal measures, spectral-shape descriptors, and MFCC-based cepstral features. Within-patient admission-to-discharge differences were evaluated using two-sided Wilcoxon signed-rank tests, with nominal p-values interpreted as exploratory. Phonatory and temporal measures produced the most consistent exploratory findings. The pause-duration trend for /a/ decreased between admission and discharge and was the most configuration-stable individual candidate. CPP maximum and CPP range for /o/ increased consistently across the evaluated phonatory configurations, indicating systematic changes in cepstral prominence. Shimmer-related measures provided additional exploratory findings. MFCC measures showed complementary changes, particularly in MFCC11 variability for /o/, whereas spectral-shape effects were generally weaker and less consistent. Because the study involved a small, single-center cohort without a control group or external validation, the findings should be regarded as hypothesis-generating candidate acoustic measures rather than clinically validated biomarkers. Full article
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31 pages, 4058 KB  
Review
From Artifact to Decision Instrument: A Critical Review of Prototyping in Engineering Design
by Rafael Landaeta, Abolghassem Zabihollah and Reza Jazar
Appl. Sci. 2026, 16(16), 8340; https://doi.org/10.3390/app16168340 - 21 Aug 2026
Viewed by 283
Abstract
Prototyping has evolved from a simple representational artifact into a central mechanism for learning, communication, risk reduction, and decision-making in engineering design. Despite its widespread adoption across engineering disciplines, existing research remains fragmented across domains, methodologies, and application contexts, making it difficult to [...] Read more.
Prototyping has evolved from a simple representational artifact into a central mechanism for learning, communication, risk reduction, and decision-making in engineering design. Despite its widespread adoption across engineering disciplines, existing research remains fragmented across domains, methodologies, and application contexts, making it difficult to distinguish broadly applicable principles from context-specific practices. This paper presents a critical review of prototyping research in engineering design, synthesizing evidence from peer-reviewed journal articles and conference papers from foundational studies of the 1980s to recent developments in rapid prototyping, additive manufacturing, digital engineering, and Industry 4.0 systems. A thematic literature review was conducted to identify recurring principles, domain-dependent variations, emerging trends, and persistent limitations in current prototyping practices. The review examines key factors influencing prototyping effectiveness, including purpose, fidelity, timing, stakeholder involvement, modeling and analysis, risk management, economic considerations, and learning-oriented iteration. Particular attention is given to how uncertainty influences prototyping decisions and the ways in which different uncertainty conditions influence the selection, scope, and implementation of prototyping activities. The findings indicate that prototyping is best understood as a context-dependent decision-support activity whose effectiveness depends on the uncertainties, constraints, stakeholders, and design objectives associated with a specific engineering problem. Although several common principles emerge across engineering domains, substantial differences exist in how prototypes are used to support design decisions and system validation. The review identifies research gaps related to uncertainty-driven fidelity selection, integration of modeling, experimentation, and verification activities, and the limited availability of systematic guidance for selecting prototyping strategies across diverse engineering contexts. Future research should focus on generalized prototyping frameworks, quantitative decision-support methods for uncertainty management, enhanced stakeholder integration, and the continued convergence of physical and virtual prototyping environments in next-generation engineering systems. Full article
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29 pages, 1508 KB  
Article
Manual Diaphragm Therapy and Masseter Muscle Activity in Healthy Young Women: An Exploratory Randomized Study of sEMG, Mandibular Motor Function, and Perceived Stress
by Joanna Golec, Monika Nowak, Joanna Balicka-Bom, Sara Gamrot, Iwona Sulowska-Daszyk, Monika Michalik, Jędrzej Golec and Aneta Wieczorek
Appl. Sci. 2026, 16(16), 8339; https://doi.org/10.3390/app16168339 - 21 Aug 2026
Viewed by 244
Abstract
This exploratory randomized study examined whether manual diaphragm therapy is associated with masseter muscle activity, mandibular motor function, and perceived stress in healthy women. Forty-two asymptomatic women aged 20–30 years were randomized to an intervention group (n = 20), receiving six sessions [...] Read more.
This exploratory randomized study examined whether manual diaphragm therapy is associated with masseter muscle activity, mandibular motor function, and perceived stress in healthy women. Forty-two asymptomatic women aged 20–30 years were randomized to an intervention group (n = 20), receiving six sessions over two weeks, or a no-intervention control group (n = 22). Before and after the intervention period, bilateral masseter surface electromyography (sEMG) was recorded at rest and during standardized mandibular motor tasks; mandibular range of motion was measured with an electronic caliper, and perceived stress was assessed using the Perceived Stress Questionnaire. Non-parametric tests with Bonferroni correction were applied. No significant within-group changes were observed in resting masseter sEMG. The only task-related within-group result that remained significant after correction was a reduction in normalized right masseter mean RMS amplitude during protrusion in the intervention group (p = 0.001; adjusted p = 0.032; r = 0.685). Mandibular range of motion increased in both groups, but change scores did not differ between groups. Stress-related outcomes and correlations with sEMG or mandibular motion were not significant after adjustment. These findings do not demonstrate consistent group-specific effects. The isolated within-group finding should be regarded as preliminary and hypothesis-generating and cannot be attributed specifically to manual diaphragm therapy. Full article
(This article belongs to the Special Issue Physical Therapy Treatments for Musculoskeletal Pain)
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16 pages, 277 KB  
Article
Physicians Retain Moral Responsibility but Endorse Institutional Co-Responsibility in AI-Assisted Decisions: An Exploratory Vignette Study
by Florian Berghea, Alexandra Ligia Dinca, Diana Mihaela Ciuc and Gabi Valeriu Dinca
Appl. Sci. 2026, 16(16), 8338; https://doi.org/10.3390/app16168338 - 21 Aug 2026
Viewed by 280
Abstract
Background: Artificial intelligence (AI) systems, including large language models, are increasingly used in clinical practice, whether consulted informally by clinicians or introduced by employers into decision workflows. It remains unclear how physicians attribute moral responsibility when a decision follows an AI recommendation and [...] Read more.
Background: Artificial intelligence (AI) systems, including large language models, are increasingly used in clinical practice, whether consulted informally by clinicians or introduced by employers into decision workflows. It remains unclear how physicians attribute moral responsibility when a decision follows an AI recommendation and whether that attribution varies with the type of decision at stake. Methods: We conducted a cross-sectional, within-subject vignette survey of physicians in Romania. Each respondent rated the same three scenarios—urgent clinical, elective clinical, and administrative—in which a physician followed an AI recommendation under two extenuating institutional constraints. Five-point Likert items addressed the mitigation of blame by circumstances, physician responsibility despite the AI recommendation, and institutional co-responsibility. Analyses were non-parametric, with corrections for multiple testing. Results: Among 72 physicians from 17 specialties, respondents endorsed full personal responsibility in every scenario, including the administrative one, with no significant difference between scenarios. They rejected extenuating circumstances as mitigating in both clinical scenarios but were divided about them in the administrative scenario, which had the largest effect. Institutional co-responsibility was endorsed alongside personal responsibility rather than in place of it, and the two attributions were largely uncorrelated. No demographic association survived correction, although the study was not powered to detect small-effect sizes. Conclusions: Physicians treated AI as an instrument rather than a bearer of responsibility, which is unsurprising. The substantive findings lie elsewhere: personal responsibility was retained across all decision contexts, while what varied was the admissibility of institutional constraints as excuses and the emphasis placed on the institution’s share. Because respondents did not treat responsibility as a fixed quantity to be divided, the pattern is consistent with distributed-responsibility accounts rather than with a responsibility gap, though attitudinal data cannot adjudicate between normative accounts. The findings are exploratory and require confirmation in larger, more representative samples. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Biomedicine)
23 pages, 11731 KB  
Article
A Physics-Guided Raw-Dominant Gated Fusion Method for Fine-Grained Bearing Fault Diagnosis
by Chuanbo Wu, Guoao Jiao, Yongdi Zhang, Kangning Jin, Zihang Zhang and Zeming Li
Appl. Sci. 2026, 16(16), 8337; https://doi.org/10.3390/app16168337 - 21 Aug 2026
Viewed by 272
Abstract
Fine-grained bearing condition diagnosis is challenging because different bearing states within the same fault location often exhibit similar fault-characteristic-frequency responses, making them difficult to distinguish using envelope-spectrum information alone. To address this problem, a physics-guided raw-dominant gated fusion network (PG-RDGFN) is proposed for [...] Read more.
Fine-grained bearing condition diagnosis is challenging because different bearing states within the same fault location often exhibit similar fault-characteristic-frequency responses, making them difficult to distinguish using envelope-spectrum information alone. To address this problem, a physics-guided raw-dominant gated fusion network (PG-RDGFN) is proposed for fine-grained bearing fault diagnosis. In the proposed framework, the raw vibration signal is retained as the dominant information source, while the envelope spectrum provides complementary fault-modulation evidence. A physics-aware descriptor derived from bearing characteristic-frequency responses is incorporated into a sample-wise gating mechanism to adaptively regulate the contribution of the envelope-spectrum features. Distinct from conventional direct multi-branch fusion or physics-informed schemes that mainly use physical knowledge as an auxiliary input or regularization constraint, PG-RDGFN uses mechanism-derived physical confidence to regulate how much auxiliary envelope-spectrum evidence participates in the fusion, rather than directly using the physical prior as a fine-grained classification cue. Meanwhile, a physical-consistency loss constrains the learned gate using a scalar physical-confidence target, and a raw-branch auxiliary loss preserves the discriminative capability of the dominant raw representation. Experiments are conducted on an eight-class diagnosis task constructed from the Paderborn University bearing dataset. Compared with SVM, MLP, 1D-CNN, CNN-LSTM, and TCN, the PG-RDGFN achieves the highest accuracy of 98.87%. Ablation and gate-consistency analyses further verify the effectiveness of the envelope branch, gated fusion, physics guidance, and auxiliary supervision. These results demonstrate that PG-RDGFN provides an accurate and physically interpretable solution for fine-grained bearing condition diagnosis. Full article
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20 pages, 2647 KB  
Article
Student-t QPSO-Optimized Extended Kalman Filter for Robust Nonlinear GPS State Estimation Under Heavy-Tailed Noise
by Ilayat Ali Mir and Dah-Jing Jwo
Appl. Sci. 2026, 16(16), 8336; https://doi.org/10.3390/app16168336 - 21 Aug 2026
Viewed by 265
Abstract
Global Positioning System (GPS) positioning accuracy is strongly affected by inaccurate noise modeling and non-Gaussian pseudorange measurement errors, including heavy-tailed disturbances and abnormal outliers caused by multipath propagation and signal degradation. Conventional extended Kalman filters (EKFs) generally assume Gaussian measurement noise with fixed [...] Read more.
Global Positioning System (GPS) positioning accuracy is strongly affected by inaccurate noise modeling and non-Gaussian pseudorange measurement errors, including heavy-tailed disturbances and abnormal outliers caused by multipath propagation and signal degradation. Conventional extended Kalman filters (EKFs) generally assume Gaussian measurement noise with fixed covariance matrices, which limits their robustness under degraded measurement conditions. This study proposes a Student-t robust quantum-behaved particle swarm optimization-based extended Kalman filter (ST-QPSO-EKF) for adaptive GPS state estimation. The proposed framework combines quantum-behaved particle swarm optimization (QPSO) with a Student-t-based robust measurement update, where the process-noise scaling factor, measurement-noise scaling factor, and Student-t degrees-of-freedom parameter are jointly optimized. The optimized parameters are obtained through an offline calibration stage and subsequently applied in the recursive GPS filtering process. A nonlinear GPS navigation simulation was conducted using Gaussian, Student-t heavy-tailed, and outlier-contaminated pseudorange measurement scenarios. The proposed method was compared with conventional EKF, QPSO-EKF, and Student-t EKF using 20 independent Monte Carlo realizations. The results demonstrate that QPSO-EKF provides improved accuracy under nominal Gaussian conditions, whereas ST-QPSO-EKF achieves superior performance under non-Gaussian measurement environments. Under Student-t heavy-tailed noise, ST-QPSO-EKF reduced the position RMSE to 3.814 m, while under outlier-contaminated noise it achieved a position RMSE of 3.952 m, outperforming the other compared methods. In addition, the proposed method maintained comparable online computational cost because the QPSO optimization was performed offline. The results indicate that jointly optimizing covariance parameters and Student-t robustness provides an effective strategy for improving GPS positioning reliability under complex pseudorange measurement conditions. Full article
(This article belongs to the Special Issue Advances in GNSS Technologies for Precision Navigation)
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16 pages, 541 KB  
Article
Clinical Decision-Making in Contemporary Prosthodontics: A Pilot Survey Comparing Romanian and Foreign Dentists
by Alfred Mark Sallai, Codruţa-Eliza Ille, Ioana-Cristina Talpoș-Niculescu, Flavio Santoro and Elisabeta Maria Vasca
Appl. Sci. 2026, 16(16), 8335; https://doi.org/10.3390/app16168335 - 21 Aug 2026
Viewed by 264
Abstract
(1) Background: This pilot study compared restorative practices, material-selection criteria, digital workflow adoption, and perceptions of hybrid restorative materials among 100 Romanian and foreign dentists; (2) Methods: Participants completed a 15-item questionnaire covering three domains: knowledge and clinical practice, material selection and professional [...] Read more.
(1) Background: This pilot study compared restorative practices, material-selection criteria, digital workflow adoption, and perceptions of hybrid restorative materials among 100 Romanian and foreign dentists; (2) Methods: Participants completed a 15-item questionnaire covering three domains: knowledge and clinical practice, material selection and professional perspectives, and Katana Avencia evaluation. Statistical analysis examined both item-level differences and multivariate response patterns; (3) Results: Although foreign dentists tended to favor zirconia restorations, responses within the knowledge and clinical practice domain were comparable between groups. Within the material selection and professional perspectives domain, professional perceptions of glass-ceramic restorations differed significantly between groups (p = 0.005, V = 0.45). Romanian dentists were more likely to identify translucency and durability as their main advantages, whereas foreign dentists emphasized optical and adhesive performance. Perceived barriers to CAD/CAM adoption also varied significantly (p < 0.001, V = 0.50), with Romanian dentists reporting structural barriers and foreign dentists reporting educational barriers. Significant differences in the perceived role of continuing professional education (p = 0.012, V = 0.38) reflected the greater importance assigned to continuing professional education by foreign dentists. The remaining items in this domain showed no significant differences. Both groups similarly evaluated the clinical reliability and esthetic performance of Katana Avencia restorations. However, they differed significantly in their preferred clinical indications (p < 0.001, V = 0.50), with foreign dentists favoring definitive anterior restorations and Romanian dentists favoring provisional and stress-absorbing applications. Multivariate analysis revealed distinct response profiles, with Romanian dentists associated with structural barriers to digital adoption and foreign dentists associated with continuing professional education and esthetic-oriented practice; (4) Conclusions: These findings suggest broad professional convergence within this pilot cohort, with the observed differences being associated with respondents’ perceptions of practice-related barriers, digital infrastructure, and continuing professional education. Given the exploratory, cross-sectional, and self-reported nature of the study, these associations should not be interpreted as evidence of causal or objectively measured differences between healthcare environments. Full article
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25 pages, 4685 KB  
Article
Near and Far Fields of a Dipole Antenna: A Unified Model
by Daniele Funaro, Lorella Fatone and Gianmarco Manzini
Appl. Sci. 2026, 16(16), 8334; https://doi.org/10.3390/app16168334 - 21 Aug 2026
Viewed by 221
Abstract
The dipole antenna is one of the oldest and most widely used devices in electromagnetic engineering, yet the behavior of its near-field during emission remains only partially captured by classical models. In the source-free region surrounding the arms, the vacuum Maxwell–Heaviside equations provide [...] Read more.
The dipole antenna is one of the oldest and most widely used devices in electromagnetic engineering, yet the behavior of its near-field during emission remains only partially captured by classical models. In the source-free region surrounding the arms, the vacuum Maxwell–Heaviside equations provide an insufficient number of configurations to describe the transient through which a bound signal becomes a freely propagating wave. We revisit the model equations, introducing an extended formulation in which an auxiliary velocity field complements the electromagnetic fields. Similarly to plasma physics, the outgoing signal is treated as an electromagnetic fluid carrying a charge density. As the far field is concerned, the resulting system admits an exact family of spherical free-wave solutions that follow the rules of geometrical optics. The near-to-far field transition also acquires a concrete dynamical description, thanks to the introduction of the pseudocharge, which is a charge-like density identified with the divergence of the electric field. In addition, a pressure-like potential, vanishing in the far field, tracks the conversion between bound and radiating energy. The approach is illustrated on a standard dipole antenna through direct numerical simulation of the full coupled system. The results suggest a unified analytical and computational pathway for antenna modeling, with natural extensions to more complex geometries and other radiating devices. Full article
(This article belongs to the Section Applied Physics General)
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26 pages, 3265 KB  
Article
How Reliable Is Automatic Emotion Classification in Children’s Drawings? A Reproducible Benchmark on a Public Corpus with Calibration and Selective Prediction
by Hoonhee Lee, Min-woo Kim, Jaewon Kim and Jungsup Oh
Appl. Sci. 2026, 16(16), 8333; https://doi.org/10.3390/app16168333 - 21 Aug 2026
Viewed by 319
Abstract
Emotion recognition in children’s drawings is difficult because affect is carried by sparse strokes, symbolic objects, and overall composition rather than by the stable appearance statistics of photographs. We built a reproducible four-class benchmark (Angry, Fear, Happy, Sad) on a single public corpus [...] Read more.
Emotion recognition in children’s drawings is difficult because affect is carried by sparse strokes, symbolic objects, and overall composition rather than by the stable appearance statistics of photographs. We built a reproducible four-class benchmark (Angry, Fear, Happy, Sad) on a single public corpus of 818 children’s drawings and compared three transfer-learning regimes under identical stratified five-fold splits with nested model selection: ResNet-50, ViT-B/16, and an end-to-end fine-tuned SigLIP image encoder (SigLIP-FT). SigLIP-FT reached the highest macro-F1 (0.773 ± 0.028), ahead of ViT-B/16 (0.700 ± 0.040) and ResNet-50 (0.598 ± 0.038), and was the best calibrated (ECE 0.119). Frozen-feature linear probes preserve this ordering (0.541, 0.648, 0.731), locating the advantage in the pretrained representations rather than in fine-tuning, while zero-shot SigLIP reaches only 0.510, so task-specific supervision remains necessary. Margin-based abstention raised retained-set macro-F1 to 0.810 at 78.0% coverage and 0.844 at 61.4% coverage—post hoc operating points computed on the pooled out-of-fold predictions; deployment thresholds must be fixed on independent data—and SigLIP-FT attains the lowest area under the risk–coverage curve (AURC 0.126 versus 0.199 and 0.278). Residual errors are highly structured: 87.0% lie within the negative-emotion triad, and Fear is the hardest category for all three architectures. All findings are established on this single corpus; their transfer to other collections is an open question. Coverage–performance behavior, rather than a single full-coverage score, is the appropriate reporting standard for ambiguous visual domains of this kind. Full article
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43 pages, 11529 KB  
Article
Enhancing End-to-End Graphite Ore Grade Detection via Boundary-Aware Refinement, Bidirectional Fusion, and Difficulty-Aware Distillation
by Yanwu Yi, Binghui Wei, Zeyang Qiu, Chen Yang and Xueyu Huang
Appl. Sci. 2026, 16(16), 8332; https://doi.org/10.3390/app16168332 - 21 Aug 2026
Viewed by 360
Abstract
Graphite ore grade sorting is a key step toward intelligent mineral processing; however, it faces three representational contradictions: ambiguous classification posteriors at grade boundaries, asymmetric multi-scale feature interaction, and the mismatch between class-agnostic self-distillation assignment and sample-level difficulty. Targeting these, this paper adopts [...] Read more.
Graphite ore grade sorting is a key step toward intelligent mineral processing; however, it faces three representational contradictions: ambiguous classification posteriors at grade boundaries, asymmetric multi-scale feature interaction, and the mismatch between class-agnostic self-distillation assignment and sample-level difficulty. Targeting these, this paper adopts D-FINE as the baseline and introduces three decoupled improvements at its decoder, encoder, and criterion layers. (1) Boundary-Grade-aware Distribution Refinement (BG-FDR) online identifies boundary samples via the Top-2 classification score gap and modulates regression-distribution refinement, yielding +2.69 percentage points in mAP@0.5 with zero additional trainable parameters. (2) Bidirectional Feature Pyramid with Global–Local Spatial Attention (BiFPN-GLSA) builds a learnable weighted bidirectional multi-scale fusion path. (3) Difficulty-Aware Decoupled Distillation with Wise-Inner-Shape-IoU (DADD+Wise-IoU) imposes class- and sample-level difficulty-aware constraints. In the integrated full model, this increases Precision from 66.21% to 71.43% (+5.22 pp), F1 from 73.57% to 77.57%, and mean IoU from 97.81% to 98.35%, while false positives drop by 19.6%; the only parameter overhead (+3.84M) comes from BiFPN-GLSA, with BG-FDR and DADD adding effectively no network weights. Ablation on a self-built 3800-image dataset reveals a non-monotonic AP–Precision relationship: the mAP-optimal configuration (BG-FDR+BiFPN-GLSA, 94.17%) and the Precision-optimal one (DADD+Wise-IoU, 77.54%) do not coincide, providing a quantitative basis for objective-driven module selection in industrial sorting. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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16 pages, 6913 KB  
Article
Alkoxyl Derivatives of 7-Hydroxyflavone: Synthesis, Physicochemical Properties, Antioxidant, and Antihyperglycemic Activities
by Patryk Mruczek, Andrzej Grudzień and Monika Kadela-Tomanek
Appl. Sci. 2026, 16(16), 8331; https://doi.org/10.3390/app16168331 - 21 Aug 2026
Viewed by 272
Abstract
Flavones are natural substances widely distributed in fruits and vegetable. Their compounds are characterized by a broad spectrum of activities, including antioxidant activity. Their most interesting biological effect, however, is antihyperglycemic activity, mediated by the inhibition of α-glucosidase. A series of 7-hydroxyflavone derivatives [...] Read more.
Flavones are natural substances widely distributed in fruits and vegetable. Their compounds are characterized by a broad spectrum of activities, including antioxidant activity. Their most interesting biological effect, however, is antihyperglycemic activity, mediated by the inhibition of α-glucosidase. A series of 7-hydroxyflavone derivatives was synthesized and comprehensively characterized using nuclear resonance magnetic spectroscopy. The biological potential of the synthesized derivatives was assessed through antioxidant (DPPH) and α-glucosidase inhibitory assays. The physicochemical and pharmacokinetic properties were analyzed using in silico methods. Finally, molecular docking studies were performed to elucidate the binding mode of compounds within the catalytic site of α-glucosidase. Full article
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18 pages, 1865 KB  
Article
Data-Driven Disturbance-Observer-Based Actuator-Space Control of a Dual-Axis Thrust-Vectoring Platform
by Connor Calme, Lundon Salley, Luis F. Zapata-Rivera and Aldo J. Muñoz-Vázquez
Appl. Sci. 2026, 16(16), 8330; https://doi.org/10.3390/app16168330 - 21 Aug 2026
Viewed by 267
Abstract
Electromechanical thrust-vector control systems are subject to friction, backlash, and configuration-dependent dynamics that are difficult to model explicitly, motivating controllers that adapt from operational data without requiring an identified plant. This paper proposes a data-driven, disturbance-observer-based controller in actuator space for a dual-axis [...] Read more.
Electromechanical thrust-vector control systems are subject to friction, backlash, and configuration-dependent dynamics that are difficult to model explicitly, motivating controllers that adapt from operational data without requiring an identified plant. This paper proposes a data-driven, disturbance-observer-based controller in actuator space for a dual-axis thrust-vectoring platform. The controller operates on a sliding surface defined over the actuator tracking error and uses an adaptive input matrix gain together with a lumped disturbance observer, both of which are updated from encoder and control data through gradient descent applied to a joint identification loss. The Newton–Euler equations of the mechanical system are projected onto the actuator coordinates through the angular-velocity map, yielding a structurally well-posed actuator-space model, which is used to design the adaptive gain; nonetheless, the dynamic model is never evaluated online. The resulting controller requires only encoder measurements of actuator displacements, a reference trajectory computed from the platform geometry, and bounded normalized commands; the mechanism Jacobian and the inertia and Coriolis matrices are not evaluated online. Boundedness of the adaptive gain and disturbance estimate is established, and uniform ultimate boundedness of the tracking error follows under a mild alignment condition. Simulation results on a coupled nonlinear plant and hardware-in-the-loop experiments on a dual-channel actuator testbed are presented, comparing the proposed controller against PID, super-twisting, unit-vector sliding mode, and MFAC baselines on a circular thrust-vector reference. Full article
(This article belongs to the Special Issue Recent Developments in 3D Mechatronics Design)
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16 pages, 21529 KB  
Article
Zero-Shot Low-Light Image Enhancement via Diffusion with Joint Frequency and Spatial Guidance
by Jinghui Chu, Xiaoyi Yu and Wei Lu
Appl. Sci. 2026, 16(16), 8329; https://doi.org/10.3390/app16168329 - 21 Aug 2026
Viewed by 216
Abstract
Existing zero-shot low-light image enhancement methods often underutilize image priors, leading to noise amplification and color distortion. To address these issues, we propose a zero-shot framework for low-light image enhancement. The framework first performs illumination-aware self-supervised denoising to generate a cleaner reference image, [...] Read more.
Existing zero-shot low-light image enhancement methods often underutilize image priors, leading to noise amplification and color distortion. To address these issues, we propose a zero-shot framework for low-light image enhancement. The framework first performs illumination-aware self-supervised denoising to generate a cleaner reference image, which is then used to guide diffusion-based enhancement with a pre-trained backbone. Specifically, the denoising module uses pairwise downsampling together with the proposed illumination prior to suppress noise in dark regions. We then guide the reverse sampling of the pre-trained diffusion model with a refinement strategy operating in both the frequency and spatial domains, so that illumination enhancement and local detail refinement can be jointly achieved during sampling. At each step, Fourier-based reconstruction contributes to illumination enhancement while preserving structural information, and illumination-guided spatial adjustment further refines local brightness. Experiments on multiple benchmark datasets show that the proposed method improves illumination while preserving structural details. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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13 pages, 801 KB  
Article
Electromyographic Analysis of Gesture Execution and Actual Tool Use in Healthy Adults
by Se-Ra Min, Jun-Seok Kim and Tae-Hoon Kim
Appl. Sci. 2026, 16(16), 8328; https://doi.org/10.3390/app16168328 - 21 Aug 2026
Viewed by 216
Abstract
Assessments based solely on gesture execution may not reproduce the sensory and biomechanical demands of actual tool use. This study compared upper-limb muscle activation during gesture execution (GE) and actual tool use (AU) in 52 healthy adults in their twenties performing five tasks [...] Read more.
Assessments based solely on gesture execution may not reproduce the sensory and biomechanical demands of actual tool use. This study compared upper-limb muscle activation during gesture execution (GE) and actual tool use (AU) in 52 healthy adults in their twenties performing five tasks derived from the Florida Apraxia Screening Test-Revised: sprinkling salt, drinking water, stirring coffee, combing hair, and writing. Surface electromyography was recorded from eight muscles of the dominant right upper limb. Because GE and AU were obtained from the same participants, primary comparisons were performed using two-sided paired-samples t tests. Family-wise error across 40 muscle-by-task comparisons was controlled using the Holm procedure, and mean paired differences, 95% confidence intervals, p values, and Cohen’s dz were reported. After Holm adjustment, 18 of 40 comparisons were significant (absolute dz = 0.52–1.62). GE showed greater anterior deltoid activation in Tasks 1, 2, 3, and 5 and greater biceps and triceps activation in Task 3. AU showed greater extensor carpi radialis and extensor digitorum activation in Task 2; greater activation of all measured wrist and finger muscles in Task 4; and greater posterior deltoid, triceps, wrist, and finger muscle activation in Task 5. These findings demonstrate task- and muscle-specific differences in normalized EMG amplitude between GE and AU. The observed differences may be associated with physical interaction during AU, but the underlying biomechanical and sensorimotor mechanisms were not directly measured. The results provide exploratory reference data from healthy young adults for future studies comparing representational and actual tool-use conditions. Full article
(This article belongs to the Special Issue Advanced Physical Therapy for Rehabilitation)
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17 pages, 2607 KB  
Article
A Hybrid Genetic Algorithm–Particle Filter for Fatigue Crack Propagation Prediction
by Mei Li, Xiao Wu, Yuexi Liu, Jue Wang and Beng Ma
Appl. Sci. 2026, 16(16), 8327; https://doi.org/10.3390/app16168327 - 21 Aug 2026
Viewed by 179
Abstract
Fatigue crack propagation prediction plays a critical role in structural health monitoring and remaining useful life (RUL) assessment of engineering structures. However, conventional particle filter (PF) algorithms may suffer from particle impoverishment and insufficient particle diversity, which can adversely affect prediction accuracy and [...] Read more.
Fatigue crack propagation prediction plays a critical role in structural health monitoring and remaining useful life (RUL) assessment of engineering structures. However, conventional particle filter (PF) algorithms may suffer from particle impoverishment and insufficient particle diversity, which can adversely affect prediction accuracy and stability. To address these limitations, a hybrid genetic algorithm–particle filter (GA-PF) is developed for fatigue crack propagation prediction by incorporating genetic operations, including selection, crossover, and mutation, into the PF framework to optimize particle distribution and enhance global search capability. The proposed method is evaluated using fatigue crack growth experimental data, and its performance is compared with that of the conventional PF algorithm. The results show that the GA-PF method achieves improved prediction performance for fatigue crack propagation and remaining useful life estimation. At 255,000 cycles, the GA-PF algorithm predicted a median RUL of 22,500 cycles, with a relative RUL error of 9.04%, whereas the conventional PF algorithm resulted in a relative RUL error of 45.41%. These results indicate the potential benefit of introducing genetic optimization into the particle filter framework for fatigue crack propagation prediction. The findings further suggest that the hybrid GA-PF method can improve predictive performance compared with conventional PF on the tested dataset. Full article
(This article belongs to the Section Mechanical Engineering)
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11 pages, 3493 KB  
Article
Span-Length Optimization of Random DFB Raman Amplifiers for Long-Haul WDM Transmission up to 7500 km
by Paweł Rosa
Appl. Sci. 2026, 16(16), 8326; https://doi.org/10.3390/app16168326 - 21 Aug 2026
Viewed by 212
Abstract
We present a simulation study of bidirectionally pumped distributed Raman amplification (DRA) noise performance for two recirculating-loop span configurations—50 km and 75 km—evaluated over a 50-channel C-band grid (191,200–196,100 GHz, 100 GHz spacing) representative of dense WDM transmission. For each span length, the [...] Read more.
We present a simulation study of bidirectionally pumped distributed Raman amplification (DRA) noise performance for two recirculating-loop span configurations—50 km and 75 km—evaluated over a 50-channel C-band grid (191,200–196,100 GHz, 100 GHz spacing) representative of dense WDM transmission. For each span length, the forward pump power was first optimized on a central channel (193,600 GHz) to minimize signal power variation (SPV), yielding optimum values of 1.2 W for the 50 km span and 2 W for the 75 km span; the backward pump power was then set to achieve 0 dB net gain on the same central channel. These optimized pump conditions were applied uniformly across all 50 channels to characterize on–off gain variation for each single-span configuration. Using an input OSNR of 30 dB, recirculating-loop simulations were then performed for both spans across 1 to 60 (50 km) and 1 to 40 (75 km) round trips, covering a common reach of up to 7500 km. The OSNR degrades more slowly with distance for the 50 km than for the 75 km span up to 7500 km. Under the conditions studied, shorter DRA spans give better noise performance, even though more recirculations are required for the same reach.  Full article
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18 pages, 23282 KB  
Article
Research on an Improved YOLOv8-Based Object Detection Algorithm for Flame and Smoke Detection in Factory Environments
by Linlin Cao, Xinxin Chen, Sitong Guo, Jiaqi Wang, Duowen Chen, Fengyan Lun, Haoyu Zhang, Kaibao Wang and Jianyong Li
Appl. Sci. 2026, 16(16), 8325; https://doi.org/10.3390/app16168325 - 21 Aug 2026
Viewed by 189
Abstract
Overcoming complex background noise and poor small-target detection in industrial settings, this paper introduces YOLOv8-BBP2, an enhanced YOLOv8 model. To better extract dynamic features, the backbone integrates a BiFormer dual-level routing attention mechanism. Moreover, a learnable Bi-directional Feature Pyramid Network (BiFPN) replaces the [...] Read more.
Overcoming complex background noise and poor small-target detection in industrial settings, this paper introduces YOLOv8-BBP2, an enhanced YOLOv8 model. To better extract dynamic features, the backbone integrates a BiFormer dual-level routing attention mechanism. Moreover, a learnable Bi-directional Feature Pyramid Network (BiFPN) replaces the standard module, optimizing multi-scale feature integration. A P2 detection head is also added to accurately identify tiny objects, such as early flames and thin smoke. Tested on a custom factory fire dataset, YOLOv8-BBP2 yields 95.231% precision, 94.612% recall, and 89.677% mean average precision (mAP@0.5). These metrics represent respective gains of 3.31%, 4.934%, and 7.451% over the baseline YOLOv8s. Ultimately, with an inference speed of 20 ms per frame, the proposed network ensures highly robust, real-time performance. Full article
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39 pages, 17741 KB  
Article
Design Element Analysis Driven by Attribute Networks: A Hybrid Method for Identifying Critical Product Design Attributes
by Guanlong Li, Jiawei Wang, Shutao Zhang and Zhiqiang Yang
Appl. Sci. 2026, 16(16), 8324; https://doi.org/10.3390/app16168324 - 21 Aug 2026
Viewed by 207
Abstract
In today’s consumer-oriented market environment, enterprises face increasingly intense competition, making the identification of key factors in demand-driven product design critically important. Identifying critical product design elements facilitates the optimal allocation of design resources and enhances product competitiveness to better meet user expectations. [...] Read more.
In today’s consumer-oriented market environment, enterprises face increasingly intense competition, making the identification of key factors in demand-driven product design critically important. Identifying critical product design elements facilitates the optimal allocation of design resources and enhances product competitiveness to better meet user expectations. This study proposes an attribute-network-based method for identifying key product design elements grounded in complex network theory, providing a novel quantitative approach for investigating the mapping relationship between user cognition and design elements within the field of Kansei engineering. First, products are modeled as nodes, and edges are established based on thresholded attribute similarity between products to construct a product attribute network. Second, community detection algorithms are applied to partition the constructed network, revealing the intrinsic association strength among product attributes and enabling the identification of key products within each cluster. Subsequently, a random forest method is employed to extract the core product attributes that influence cluster formation, yielding attribute importance rankings, which are further validated using the XGBoost algorithm to ensure robustness. Finally, a Bluetooth speaker case study is conducted to verify the effectiveness and feasibility of the proposed method for identifying key product design factors. The results demonstrate that the proposed approach can effectively identify key product design factors and rank the critical factors influencing the product design process, achieving high accuracy and practical applicability. Full article
(This article belongs to the Section Applied Industrial Technologies)
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33 pages, 1120 KB  
Article
Investigating Contrastive Learning for Conditional Variational Autoencoders in Network Intrusion Detection
by Huy Minh Dinh, Wei Zong, Yang-Wai Chow and Willy Susilo
Appl. Sci. 2026, 16(16), 8323; https://doi.org/10.3390/app16168323 - 21 Aug 2026
Viewed by 234
Abstract
Class imbalance, where majority-class samples vastly outnumber minority-class samples, remains a persistent challenge in network intrusion detection systems (NIDS), often causing classifiers to overlook rare but critical attack types while yielding misleadingly optimistic performance metrics. Synthetic data generation is a common mitigation strategy. [...] Read more.
Class imbalance, where majority-class samples vastly outnumber minority-class samples, remains a persistent challenge in network intrusion detection systems (NIDS), often causing classifiers to overlook rare but critical attack types while yielding misleadingly optimistic performance metrics. Synthetic data generation is a common mitigation strategy. However, existing methods often fail to capture the non-linear network traffic and neglect inter-class relationships with the majority class, resulting in inconsistent performance gains. This study investigates three contrastive learning loss functions, Contrastive Loss, Soft Nearest Neighbor Loss, and Supervised Contrastive Loss, integrated into a Conditional Variational Autoencoder (CVAE) regularised via the standard Kullback-Leibler divergence objective. Experiments were conducted on four widely used NIDS benchmark datasets (NSL-KDD, UNSW-NB15, CIC-IDS2017, and CSE-CIC-IDS2018), with synthetic data evaluated using four machine learning classifiers against the original imbalanced data, a non-contrastive CVAE, and conventional oversampling approaches. The results show that the effectiveness of integrating contrastive learning into the CVAE framework is dependent on the specific dataset, minority class, contrastive loss function, and distance metric, with the proposed approach outperforming traditional oversampling techniques in several settings without degrading majority-class performance or overall accuracy. These findings provide practical guidance for selecting contrastive learning objectives in class-imbalanced NIDS scenarios. Full article
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19 pages, 8406 KB  
Article
How Spatial Constraints Govern Aerodynamic Performance of Archimedes Spiral Wind Turbines: A CFD-Based Comparative Analysis
by Ziyun Zhou, Chenxi Feng, Jin Liu, Xilong Lu and Jianyong Ling
Appl. Sci. 2026, 16(16), 8322; https://doi.org/10.3390/app16168322 - 21 Aug 2026
Viewed by 266
Abstract
Archimedes Spiral Wind Turbines (ASWTs) are suitable for small-scale urban wind applications, but changing the blade angle can also change the rotor dimensions under different geometric constraints. This study examines these coupled effects by comparing five ASWT configurations with blade angles of 30°, [...] Read more.
Archimedes Spiral Wind Turbines (ASWTs) are suitable for small-scale urban wind applications, but changing the blade angle can also change the rotor dimensions under different geometric constraints. This study examines these coupled effects by comparing five ASWT configurations with blade angles of 30°, 45°, and 60° under fixed-diameter (Fixed D) and fixed-axial-length (Fixed L) conditions. Steady three-dimensional Reynolds-averaged Navier–Stokes simulations using the Multiple Reference Frame method were conducted to evaluate the power coefficient (Cp), torque coefficient (Ct), and mid-plane pressure and velocity fields. The numerical setup was assessed through grid-independence and reference-data comparisons. Under the Fixed D constraint, the 60° configuration achieved the highest Cp of 0.2915 at a tip-speed ratio (λ) of 1.9, whereas the 30° configuration reached a maximum Cp of 0.1792 at λ = 1.0. Under the fixed L constraint, the corresponding Cp values were 0.2869 at λ = 2.5 for the 60° configuration and 0.1713 at λ = 0.8 for the 30° configuration. The baseline 45° configuration achieved a maximum Cp of 0.2444 at λ = 1.5. The torque coefficient decreased with increasing λ for all configurations, while the pressure and velocity fields differed between the two constraints at the same blade angle. These results indicate that blade angle should be evaluated together with rotor diameter, axial length, and the installation envelope, because a larger rotor or swept area does not necessarily produce a proportional increase in normalized aerodynamic efficiency. Full article
(This article belongs to the Special Issue Fluid Dynamics Analysis of Wind Turbines)
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13 pages, 3130 KB  
Review
Rehabilitation of Shoulder Disorders: An ICF-Oriented Lexical Network Analysis of the Literature
by Daniele Coraci, Gianluca Regazzo, Gianpaolo Ronconi, Gabriele Santilli, Cristina Razzano, Lucrezia Tognolo and Stefano Masiero
Appl. Sci. 2026, 16(16), 8321; https://doi.org/10.3390/app16168321 - 21 Aug 2026
Viewed by 233
Abstract
(1) Background: The continuous growth of rehabilitation literature makes evidence synthesis increasingly challenging. Lexical network analysis may provide quantitative information on the organization of scientific knowledge. This study aimed to characterize the shoulder rehabilitation literature through an International Classification of Functioning, Disability and [...] Read more.
(1) Background: The continuous growth of rehabilitation literature makes evidence synthesis increasingly challenging. Lexical network analysis may provide quantitative information on the organization of scientific knowledge. This study aimed to characterize the shoulder rehabilitation literature through an International Classification of Functioning, Disability and Health (ICF)-oriented lexical network approach. (2) Methods: PubMed was searched for papers on shoulder rehabilitation published from 2016 to 2025. ICF-related lexical terms associated with the shoulder were selected from the official ICF framework, and their frequency inside the titles and abstracts of the found papers was calculated. A binary text-term matrix was generated and converted into a bipartite network. Graph-theoretical analysis, including feature reduction, was performed to identify representative topological descriptors and relationships. (3) Results: The analysis identified 6424 paper-word connections. SHOULDER, PAIN, and FUNCTION represented the principal lexical hubs of the network. Feature reduction enabled the characterization of the network by eigencentrality and neighborhood connectivity. Participation- and environment-related terms showed comparatively limited representation. PARTICIPATION showed the highest neighborhood connectivity. (4) Conclusions: The proposed ICF-oriented lexical network approach provides a quantitative framework for exploring the lexical organization of rehabilitation literature, providing complementary information to conventional literature reviews and supporting future evidence synthesis and research planning. Full article
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Article
Effects of an Eight-Week Combined Isometric and Elastic-Resistance Training Program on Body Composition and Functional Fitness in Older Women
by Sanghyun Lee and Byungkwan Kim
Appl. Sci. 2026, 16(16), 8320; https://doi.org/10.3390/app16168320 - 21 Aug 2026
Viewed by 267
Abstract
This exploratory randomized trial evaluated an eight-week combined isometric and elastic-resistance program in community-dwelling women aged 70 years or older. Forty-nine participants were randomized to exercise (EG; n = 24) or control (CG; n = 25). Twenty-two EG participants completed the intervention and [...] Read more.
This exploratory randomized trial evaluated an eight-week combined isometric and elastic-resistance program in community-dwelling women aged 70 years or older. Forty-nine participants were randomized to exercise (EG; n = 24) or control (CG; n = 25). Twenty-two EG participants completed the intervention and follow-up; two had no post-intervention measurements because of scheduling conflicts. All randomized participants were included in the intention-to-treat analysis using 50 data sets generated by predictive mean matching and baseline-adjusted analysis of covariance. Compared with the CG, the EG showed lower body weight (adjusted mean difference [AMD], −0.45 kg), BMI (−0.19 kg/m2), 4 m gait time (−0.96 s), and five-repetition chair-stand time (−1.60 s), and higher skeletal muscle mass (0.38 kg), calf circumference (0.80 cm), and handgrip strength (1.01 kg). The corresponding primary 95% confidence intervals excluded zero, although the skeletal-muscle-mass interval narrowly included zero under conservative imputation. Differences in body fat percentage (−0.33 percentage points; 95% CI, −1.24 to 0.58) and tandem-stance time (0.79 s; 95% CI, −0.42 to 2.00) were inconclusive. These preliminary findings suggest that the program may improve selected body-composition and functional outcomes. Larger prospectively powered trials are required to confirm their magnitude, clinical relevance, and durability. Full article
(This article belongs to the Special Issue Advances in Sport, Physical Activity, and Health)
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