Applied Sciences doi: 10.3390/app16104828

Authors: Leonardo Ramos Ayomipo Israel Ojo Yasinthara Wadumesthri Ibrahim Almuhanna Humberto Rodriguez Gutierrez Darío A. Arena

Ultrathin ferrimagnetic heterostructures have emerged as promising platforms for next-generation spintronic devices, yet the role of two-dimensional substrates in modulating their magnetic properties remains underexplored. Here, we report a comprehensive study of the thickness- and temperature-dependent magnetic behavior of amorphous Fe73Co8Gd19 films (4–32 nm) deposited on Si, WSe2 bilayer, and WSe2 monolayer substrates. Structural integrity and stoichiometry were confirmed via X-Ray Diffraction (XRD), X-Ray Reflectivity (XRR), Raman spectroscopy, and Energy-Dispersive Spectroscopy (EDS) analysis. In-plane magnetometry from 10–300 K reveals that monolayer WSe2 promotes stronger interfacial spin alignment, with the 4 nm film exhibiting a sharp increase in coercivity below 50 K, where Hc exceeds 23 mT and even surpasses thicker counterparts, alongside enhanced saturation magnetization (∼790 kA/m at 100 K). This dramatic enhancement of coercivity is the most significant result of this work, underscoring the dominant role of interfacial coupling in governing low-temperature magnetic hardness. Conversely, films on bilayer exhibit suppressed magnetization and soft magnetic behavior (Hc < 10 mT) across all temperatures, making them attractive for ultralow-power and high-speed spintronic applications. These findings demonstrate that atomically thin WSe2 interfaces can modulate coercivity, magnetization, and squareness through proximity effects, establishing a tunable and thermally stable platform for spintronic device applications.

Applied Sciences doi: 10.3390/app16104827

Authors: Yiming Lou Zhuoyu Hu Guona Chen Shujin Wu

The inherent nonlinearity and uncertainty of wind power generation pose significant challenges to the security, stability, and economic operation of power grids. Therefore, accurate and reliable wind power forecasting is crucial for seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving point-prediction accuracy while overlooking effective multivariate dependency modeling and reliable uncertainty quantification, limiting both the informativeness and reliability of their forecasts. This study proposes a Fractional-order Momentum optimized Evidential iTransformer (FoM-EiT) for short-term wind power forecasting from multivariate time series. The proposed model integrates cyclic feature encoding for periodic variables, an inverted Transformer for variable-wise interaction learning, and an evidential output head that jointly produces point forecasts and uncertainty estimates from a shared representation. The proposed fractional-order momentum (FoM) optimization accumulates gradient history over an extended window, thereby smoothing oscillations caused by gradient competition and stabilizing the joint training process. Experiments on four real-world wind farms from different geographical regions show that FoM-EiT achieves competitive point forecasting performance, with R2 values of 0.6342, 0.8211, 0.7844, and 0.9161, and the Wilcoxon signed-rank test indicates that its advantages over the baselines are statistically significant in the vast majority of comparisons. For uncertainty quantification, FoM-EiT achieves Prediction Interval Coverage Probability (PICP) values of 0.9492, 0.9682, 0.9709, and 0.9498, while the Winkler score results further show that its prediction intervals outperform the conformal prediction and quantile regression baselines in terms of overall interval quality. These results indicate that FoM-EiT provides both accurate forecasts and trustworthy uncertainty information, making it a practical tool for dispatch, reserve allocation, and risk-aware short-term power system operation.

Applied Sciences doi: 10.3390/app16104826

Authors: Yumeng Xiao Yanling Wu Ruili Hu Minbo Zhang

Non-coal mines constitute a fundamental pillar of the global energy transition, providing essential raw materials across various sectors. Accidents in these facilities cause severe direct damage, including personal injuries and economic losses, while triggering broader systemic impacts, such as supply chain disruptions of essential energy materials. Taking China as an example, roof fall and rib spalling accidents account for 30% to 40% of all safety incidents in non-coal mines. Consequently, investigating the occurrence and evolutionary process of roof fall and rib spalling risks in these environments holds significant importance. To identify the fundamental factors of the accident, the 24Model and the fault tree are employed in this research. Expert elicitation and fuzzy set theory are used to determine the occurrence probability of each basic event. To overcome the limitations of traditional static risk assessment, a dynamic Bayesian network is introduced to capture the time-varying characteristics of risk factors, evaluating the overall accident probability over a 52-week period, equivalent to approximately one year. In this study, 30 basic events and 18 intermediate events were identified. Quantitative results show that effectively controlling the critical events (X26, X4, X20, X27, X2) reduces the accident probability by 56.39%. Furthermore, the dynamic Bayesian network analysis demonstrates that under the specific assumption of continuous risk accumulation across five dimensions (human, material, management, cultural, and environmental factors) without the prompt implementation of targeted interventions, the occurrence probability of the accident reaches 0.95023 after 52 weeks. The results demonstrate that this model surpasses static models by effectively identifying the critical causal factors of the accident and evaluating their occurrence probabilities through systematic causation analysis and dynamic accident evolution. This approach facilitates precise accident early warning, offering a practical reference for relevant enterprises and personnel involved in roof safety management.

Applied Sciences doi: 10.3390/app16104825

Authors: Nebojša Avramović Aleksandar Marković Tijana Čomić Sava Čavoški Nikola Zornić Vladimir Vujović

Intelligent automation is a core component of Industry 4.0, enabling artificial intelligence (AI) systems to support or execute operational and managerial decisions in real time. In high-risk industrial environments such as mining and metallurgy, real-time decision-making improves efficiency but also raises critical challenges related to trust, explainability, human oversight, and institutional accountability. This study proposes PRIME–INSPECT, a two-layer socio-technical framework designed to support trustworthy AI-driven real-time decision-making. The PRIME (predict, regulate, interpret, mitigate, execute) layer formalizes the operational decision flow, embedding control mechanisms, uncertainty quantification, and explainability into the automation pipeline. The INSPECT (integrity, navigability, supervisory control, policy maturity, ethical compliance, collaboration, trust calibration) layer defines the organizational and governance conditions required for safe deployment. The framework is conceptually developed through a structured literature synthesis and supported by exploratory empirical grounding through stakeholder perceptions from IT and top management participants, alongside an illustrative industrial use case intended to demonstrate conceptual applicability rather than engineering performance validation. The findings highlight the importance of aligning operational AI processes with institutional safeguards to support calibrated trust and responsible automation. The empirical component is intended to provide conceptual and organizational grounding of framework dimensions rather than quantitative validation of predictive performance. PRIME–INSPECT provides a structured architecture for designing and governing AI-enabled real-time decision systems in high-risk industrial contexts.

Applied Sciences doi: 10.3390/app16104824

Authors: Inelva M. Baez-Ortiz Joel Felix-Aispuro Aaron Gutierrez-Lopez Magnolia Soto-Felix J. Ramon Gaxiola-Camacho J. Guadalupe Monjardin-Quevedo

Seismic excitations induce floor accelerations that can damage non-structural components and, in extreme cases, contribute to global structural failure. Although floor acceleration demands have been widely studied, their integration into probabilistic seismic performance and reliability frameworks remains limited within Performance-Based Seismic Design (PBSD). This study addresses this gap by proposing a reliability-based framework that incorporates the stochastic nature of floor accelerations through their probability density functions. Five-story steel and reinforced concrete (RC) buildings, designed according to Mexican codes, were analyzed using nonlinear dynamic simulations in PERFORM 3D under 33 ground motions corresponding to immediate occupancy (IO), life safety (LS), and collapse prevention (CP) levels. Structural reliability was quantified using the probability of failure (pf) and the reliability index (β). Results show that peak accelerations occur at the roof level, with higher demands in the steel structure. For the IO level, β ranged from approximately 2.29 to values above 4.0 in steel buildings, while RC structures reached up to β ≈ 4.97. At LS and CP levels, RC buildings maintained β values generally above 3.0, whereas steel structures showed values as low as β ≈ 1.32. The Kernel distribution best captured response variability, reflecting high dispersion (C.V. > 30%). The proposed framework enhances PBSD by linking acceleration demands with reliability-based decision-making.

Applied Sciences doi: 10.3390/app16104823

Authors: Meshack Magaji Ishaya Moein Jazayeri

Shadows falling on photovoltaic (PV) modules result in partial shading conditions (PSCs). These conditions affect the power generation of a PV system because of their varying nature. As a result of PSCs, multiple peaks are created; therefore, it is important to identify the global maximum power point (GMPP) for optimal output power. Several maximum power point tracking (MPPT) techniques have been proposed in the literature; however, they face challenges such as oscillation at steady state, long convergence time, high complexity, and low accuracy. In this study, an improved musical chairs algorithm with local adaptive exploration is proposed for MPPT of PV systems under partial shading conditions. The proposed method combines the population-based exploration capability of the musical chairs algorithm with a localized duty-cycle adjustment mechanism around the best operating point. Unlike an offline exhaustive scan, the proposed local exploration stage uses only a small set of neighboring duty-cycle candidates, making the method more suitable for online MPPT implementation. The results are analyzed using the MATLAB/Simulink tool for a 4 × 4 PV array under PSCs. The IMCA-LAE algorithm is compared against the perturb and observe (P&O) algorithm, the incremental conductance (INC) algorithm, the musical chairs algorithm (MCA), and the gray wolf and whale optimization algorithm (GWWA) to illustrate the effectiveness of the suggested hybrid MPPT approach. The efficacy is further examined regarding five performance criteria: generated output power, convergence time, mismatch power loss, efficiency, and fill factor. The proposed IMCA-LAE outperformed the other algorithms.

Applied Sciences doi: 10.3390/app16104822

Authors: Boming Wu Wenxue Wang Ming Zhang Peifeng Li Jiayu Chen Yinchuan Guo Xiao Mi

To address the large variability in existing pavement distress in expressway reconstruction and expansion projects in Zhejiang Province, China, a differentiated overlay design and decision-making method based on multi-index evaluation was proposed using the Ningbo section of the Yongtaiwen Expressway as a case study. Based on 3D ground-penetrating radar (GPR), falling weight deflectometer (FWD), and field coring tests, the existing pavement was classified into five conditions: intact pavement, slight and severe surface-layer distress, and slight and severe base-layer distress. For pavements with surface-layer distress, two alternative overlay schemes were designed. Scheme I was defined as a performance-oriented scheme using high-performance SMA/Superpave asphalt layers and an ATB-25 transition layer where necessary to improve fatigue resistance and coordinated structural performance. Scheme II was defined as an economy-oriented scheme using conventional AC layers and crack-resistant or bonding measures to reduce construction cost while maintaining adequate structural capacity. An ABAQUS-based temperature–load coupled finite element model considering the temperature-sensitive viscoelastic characteristics of asphalt layers was established to analyze the mechanical responses and service lives of the overlay schemes, and the entropy weight–TOPSIS method was used for multi-objective comprehensive decision-making. The results showed that temperature–load coupling markedly increased the tensile strain at the bottom of the asphalt overlay and was a key controlling factor in design. All schemes satisfied the 15-year design requirement, while the base-layer fatigue life of the performance-oriented scheme (Scheme I) was generally no lower than that of the cost-oriented scheme (Scheme II), indicating better long-term service reliability. In addition, the relative closeness coefficients of Scheme I under slight and severe surface-layer distress were 0.586 and 0.546, respectively, both higher than those of the cost-oriented scheme. The proposed method can effectively balance technical performance and life-cycle cost and provides a useful reference for differentiated overlay design in similar expressway reconstruction and expansion projects in hot–humid regions.

Applied Sciences doi: 10.3390/app16104821

Authors: Fukun Xiao Zongchao Qu Pan Wu Qingshou Hou

To address the alternating high- and low-stress cycles observed during the analysis of stress evolution and energy field distribution in the folded structural zone of Working Face No. 2 at a certain mine, a three-dimensional geological numerical model was established using Rhino+HyperMesh, incorporating the geological characteristics of the working face. Additionally, a dual-yield model for the goaf was incorporated into the analysis to accurately capture rock behavior. The analysis reveals that in the folded structural zone, the stress at the advance supports reaches its maximum at each inflection point, when the waste rock in the goaf also exhibits significant hardening behavior. Specifically, during the synclinal upward mining stage, the abutment stress reaches 7.6 MPa. In contrast, stress values reach their minimum at the ridge and trough points. In these inflection points, concentrated stresses are also observed on both sides of the coal face in the goaf. Notably, the stress in the haulage gate, due to its greater curvature, is higher than that in the return air drift. Furthermore, the strain energy peaks at the hinge point between the drift and the axis of the anticline. This concentration of strain energy occurs in areas highly prone to roof collapse, and notably, it is maximized where these three factors intersect.

Applied Sciences doi: 10.3390/app16104820

Authors: Yue Qi Zizhao Zhang Yang Hu Peizhi Liu Min Gao Gaoyang Zhai

Mining areas in high-altitude cold and arid mountains exhibit heterogeneous land-cover types, large spatial extent, and fragmented boundaries, which makes large-area monitoring difficult with manual interpretation. This study proposes CS-DeepLabV3+, an enhanced semantic segmentation framework built upon DeepLabV3+ for 1-m optical imagery in the Kunlun Mountains. A contextual modeling block is inserted between the encoder output and the atrous spatial pyramid pooling module to strengthen long-range dependency modeling under complex backgrounds. In the decoder, a channel attention block is applied to fused features to suppress redundant responses and improve separability among confusing categories. Experiments on a self-built dataset (Kunlun-Set) demonstrate improved boundary delineation and region consistency for typical mining-related classes (e.g., tailings ponds, stockpiles, and industrial yards). CS-DeepLabV3+ achieved an 81.83% mean intersection-over-union on the test set, outperforming the DeepLabV3+ baseline by 3.52 percentage points. Ablation studies verify that contextual modeling and channel recalibration provide complementary gains.

Applied Sciences doi: 10.3390/app16104818

Authors: Shang-En Tsai Pei-Ching Yang Wei-Cheng Sun

Adverse-weather image restoration is increasingly needed in edge vision systems, yet many recent methods are developed primarily for accuracy on server-class hardware rather than efficient deployment on resource-constrained platforms. This gap is particularly important for unified-memory edge GPUs, where memory traffic, activation movement, and latency variability can become major bottlenecks during inference. To address this issue, this paper presents an efficient adverse-weather restoration framework for unified-memory edge GPUs based on a memory-traffic-aware fusion strategy. Instead of relying on heavy multi-branch interaction or traffic-intensive feature aggregation, the proposed design emphasizes compact feature exchange, activation-aware computation, and hardware-friendly luminance modulation under constrained memory bandwidth. The framework is developed to preserve restoration quality while reducing unnecessary intermediate data movement, thereby improving runtime efficiency and practical deployability on edge devices. Experiments on ACDC show that the proposed MW-DSNet improves downstream semantic segmentation robustness to 49.8% mIoU under a fixed segmentation head, outperforming the no-restoration input by +6.9 points and TransWeather by +0.8 points. On the NVIDIA Jetson Orin Nano (NVIDIA Corporation, Santa Clara, CA, USA) under the 15 W mode, the FP16 TensorRT engine sustains 30.0 FPS at 720p with 35.1 ms p95 latency, 36.8 ms p99 latency, and 650 MB/frame DRAM traffic. INT8 deployment with night heavy calibration further improves throughput to 42.5 FPS and reduces DRAM traffic to 380 MB/frame while limiting the mIoU drop to 1.7 points. These measured results indicate that memory-traffic-aware fusion and luminance-conditioned modulation provide a practical accuracy–efficiency trade-off for unified-memory edge GPUs.

Applied Sciences doi: 10.3390/app16104817

Authors: Yasin Furkan Gorgulu Pat H. Winfield

This study numerically investigates the aerodynamic and thermal interactions between a full-scale metro train and the surrounding airflow within a confined tunnel environment using steady-state Reynolds-averaged Navier–Stokes (RANS) simulations. The six-car train, with a total length of 108 m and a cross-sectional area of 5.97 m2, operates in a tunnel with a 9.83 square meter cross-section, resulting in a high blockage ratio of approximately 60 percent. The Shear Stress Transport (SST) k–ω turbulence model and a high-resolution finite-volume mesh comprising over 8.5 million elements were employed to capture detailed near-wall phenomena. Six representative motion scenarios were analyzed, including early acceleration, peak cruising, and deceleration phases, with realistic thermal boundary conditions applied by assigning the tunnel air temperature as 29.2 °C and the train surface temperature as 35.0 °C. Velocity, pressure, temperature, and turbulence kinetic energy distributions were extracted from both longitudinal and cross-sectional planes. In addition to visual contour assessments, pointwise and spatially averaged field data were examined to quantify the development of airflow structures, pressure distribution, and thermal behavior. The results reveal speed-dependent aerodynamic resistance, pronounced recirculation and stagnation zones around the train nose and tail, and variations in convective heat transfer rates that evolve with train velocity. These findings provide insights into tunnel ventilation design and thermal management for underground metro operations, representing a novel integration of full-scale computational fluid dynamics (CFD) with thermal characterization under realistic conditions.

Applied Sciences doi: 10.3390/app16104819

Authors: Qi He Zhan Su Pengfei Qian Liquan Tian Xiaoying He Peichen Chu Zhaoming Zhang Tingwei Gu

Aiming at the problems of low separation efficiency and high impurity content in the cleaning process of moist rice threshed materials, this study systematically explored the influence mechanism of moisture on the segregation behavior of rice threshed products by combining physical experiments and CFD-DEM coupling simulation. Physical test results show that moist conditions significantly change the properties of threshed materials: the impurity mass fraction increases from 3.1% to 6.8%, the straw breakage rate rises from 5.97% to 6.99%, and the working load and unbalanced dynamic load of the threshing unit increase noticeably. On this basis, a gas–solid coupling simulation model of the cleaning device embedded with the Johnson–Kendall–Roberts (JKR) cohesive contact model is established. It is found that moist threshed materials exhibit an obvious stratified movement on the sieve surface, in which the bottom straw layer moves slowly while the upper material layer flows rapidly, resulting in a 50% increase in the sieve surface load. This phenomenon directly accounts for the rising impurity content observed in experiments. Through integrated experimental and simulation analysis, this research clarifies the macroscopic laws of moisture effects and reveals three key pathways for moisture to deteriorate cleaning performance: changing physical characteristics of threshed materials, enhancing inter-particle adhesion, and forming stratified movement. The findings provide an innovative research scheme and reliable theoretical support for the design and optimization of high-efficiency cleaning devices for rice combine harvesters.

Applied Sciences doi: 10.3390/app16104816

Authors: Yu-Bin Kwon Jong-Hyun Kim

This paper presents a geometry-driven framework for temporally stable 2D isoline reconstruction from particle-based simulation data. Unlike conventional Marching Squares methods, which assume grid-aligned scalar fields and often suffer from boundary jitter and flickering when applied to unstructured particle distributions, the proposed method constructs a continuous scalar field using an SPH kernel and estimates stabilized normals from level-set gradients at cell-level representative positions. Instead of relying on explicit Quadratic Error Function (QEF) optimization, we introduce a virtual-plane projection strategy that determines isoline vertices using a local geometric constraint. This projection can be interpreted as a first-order geometric approximation of QEF minimization, enabling QEF-free vertex positioning while reducing sensitivity to noisy particle-derived normals. As a result, the proposed method improves robustness in sparse particle regions while preserving important geometric features. To further enhance computational efficiency, we integrate a boundary-aware greedy meshing scheme that merges redundant interior geometry while preserving isoline boundaries. Experimental results demonstrate that the proposed method improves boundary stability and area consistency, reduces temporal variation, and decreases triangle counts by up to 70–75% compared with Marching Squares (MS) and Extended Marching Cube (EMC)-based reconstruction. These results indicate that the proposed framework is suitable for efficient real-time visualization of dynamic particle-based simulations.

Applied Sciences doi: 10.3390/app16104815

Authors: Divya Sree Govada Biplov Oli Daisy Pineda Patrick Ben Emoi Otema Maruthi Sridhar Balaji Bhaskar

Soil degradation, nutrient depletion, and persistent weed pressure represent critical challenges in the adoption of sustainable agriculture practices in subtropical organic farming systems. Reliance on conventional inputs threatens long-term soil health and ecosystem resilience, highlighting the need for regenerative alternatives. Cover crops are widely recognized as multifunctional agroecological tools with the capacity to enhance nutrient cycling, perform weed suppression, and improve soil organic matter. To evaluate their effectiveness in South Florida's subtropical climate and organic raised bed systems, a field experiment was conducted as a Randomized Block Design (RBD) at the Florida International University Organic Garden during the 2024 summer season. The six cover crops species that were tested include green gram (Vigna radiata), hibiscus (Hibiscus sabdariffa), sorghum (Sorghum bicolor), soybean (Glycine max), sunn hemp (Crotalaria juncea), and pearl millet (Pennisetum glaucum). Data collected includes plant establishment, biomass accumulation, weed suppression, soil physiochemical properties, and plant nutrient composition. Sorghum and sunn hemp produced the highest fresh and dry biomass, with sorghum achieving the most effective weed suppression with the lowest weed biomass and weed population. Sunn hemp contributed to enhanced nitrogen content in plant tissues, while hibiscus promoted the highest soil P and N concentrations. Pearl millet exhibited the highest total carbon and organic matter content, indicating potential for enhancing soil carbon content and soil fertility. Results show that each cover crop species can provide a specialized or generalized ecosystem service depending on management goals.

Applied Sciences doi: 10.3390/app16104807

Authors: Hui Guo Xiong Zhao Jingyong Han Zhipei Wu

To improve the machining ability of serial robots, an optimized SSI method is proposed to realize online modal identification. Firstly, the method employs the NExT technology, modal confidence factor, and modal assurance criterion to improve the parameters’ identification accuracy. Next, the machining vibration data at key points in the path of the machining robot are used to identify the modal frequency and damping ratio. Finally, the experimental results show that the optimized SSI method can achieve estimation errors within 7%, and the method of optimized SSI is more accurate than the traditional SSI method. Therefore, if only the modal frequency and the damping ratio are concerned, the method can realize the online recognition of modal parameters in the cutting path of the machining robot, which can provide key input parameters for the process planning and optimization.

Applied Sciences doi: 10.3390/app16104814

Authors: Yuxiong Xia Satoshi Yamane Weixi Wang Hiroki Ihara Jidong Lu Yuxi Luo

Real-time management of short-term ozone peaks during arc welding remains challenging because ventilation- and enclosure-defined transport boundaries can create strong position-dependent peak behavior, even under fixed process settings. This study establishes a coordinate-referenced, event-level monitoring and analysis framework to quantify ozone–fume peak coupling under a controlled local exhaust ventilation (LEV) suction boundary during CO2 arc welding. A controlled process–environment testbed with a defined suction condition was implemented, and synchronized ozone and fume signals were acquired at three sampling points referenced to the arc position and the LEV inlet direction. The particulate channel was anchored to a PM4 gravimetric reference, yielding a condition-specific traceable CPM-to-mass conversion factor K1 of 1.76 × 10−2 mg/(m3·CPM) and enabling standardized peak-fume endpoints on a mass-concentration scale. The primary inferential analysis used the curtain-on dataset, comprising 21 sessions and 42 event-level records balanced across three sampling points. Under the same suction boundary, peak coupling was strongly monitoring-coordinate dependent: LEV-aligned locations showed statistically supported ln–ln scaling between peak ozone and peak fume, whereas the opposite-side location did not exhibit statistically supported scaling; a pooled point-parameterized ln–ln model achieved an adjusted R2 of 0.777. As a descriptive control-relevant contrast, adding a curtain enclosure under continuous LEV produced strong event-level ozone peak suppression at LEV-aligned locations, with a maximum reduction of 87.8%, while attenuation at the opposite-side location remained limited. Overall, the results provide a ventilation-boundary-consistent, coordinate-specific basis for monitoring placement and control evaluation, identifying where peak translation is supported and where direct ozone monitoring remains necessary.

Applied Sciences doi: 10.3390/app16104812

Authors: Jaehyeok Lee Jaeseung Lee Jehyeok Rew

Accurate state-of-charge (SOC) prediction is critical for estimating driving range and ensuring the reliability of electric vehicle (EV) battery management systems. Although machine learning-based SOC prediction models achieve high accuracy, their complex nonlinear structures limit interpretability and hinder practical deployment. This study proposes an automated interpretation framework that integrates a multimodal large language model (MLLM) with Shapley interaction quantification (SHAP-IQ) to explain SOC prediction results. An XGBoost-based SOC prediction model is developed, and SHAP-IQ is employed to analyze both main effects of individual input variables (order 1) and pairwise feature interactions (order 2). SHAP-IQ visualizations and attribution values are provided as inputs to MLLM, which generates instance-level natural language explanations, while cross-validation and aggregation procedures ensure consistency. Experiments using real-world driving data collected from a BMW i3 show that XGBoost outperforms benchmark models in SOC prediction accuracy. The results indicate that, for the analyzed instances, SOC predictions are primarily governed by electrical variables such as battery voltage and current, whereas driving and environmental variables mainly affect the prediction through interaction effects. The proposed framework demonstrates the potential to improve the interpretability of SOC prediction models and can be extended to other energy systems in EVs employing complex machine learning models.

Applied Sciences doi: 10.3390/app16104813

Authors: Walaa Al-Joofi Alaa Sagheer Hala Hamdoun

Effective scientific literature retrieval requires moving beyond surface-level term matching toward structured semantic reasoning. This paper presents a controlled empirical study of multi-stage retrieval for scientific literature, integrating lexical matching, dense semantic modeling, hybrid fusion, and cross-encoder re-ranking within a unified evaluation framework. The study is designed to analyze the interactions, trade-offs, and failure modes of these components in claim-based scientific search. Experiments on the SciFact benchmark demonstrate that dense models capture semantic similarity but remain insufficient when used in isolation. Hybrid fusion broadens the candidate pool but does not consistently outperform the best standalone dense retriever, as RRF-based fusion can dilute strong dense rankings when lexical and semantic signals diverge. Cross-encoder re-ranking proves to be the primary driver of final performance gains, with the best configuration, Hybrid (SciNCL + BM25) + Cross-Encoder, reaching NDCG@10 of 0.523, MAP@10 of 0.479, Recall@10 of 0.642, and MRR@10 of 0.497. Ablation analysis shows that lexical pseudo-relevance feedback (RM3) introduces query drift in claim-focused retrieval, and that passage-level max pooling weakens effectiveness by fragmenting document-level evidence. Cross-domain evaluation on SciFact, PubMedQA, and SciDocs demonstrates that the relative ranking of retrieval paradigms remains stable across datasets with varying difficulty levels, while also revealing that the RRF dilution effect intensifies on harder retrieval tasks. These findings suggest that effective scientific retrieval benefits from integrated multi-stage pipelines, and that understanding component-level interactions is essential for designing robust retrieval systems.

Applied Sciences doi: 10.3390/app16104811

Authors: Zuyang Liu Yanhua Shen Xiaodong Yuan Ruibin Cao

To address the high sensing cost, uneven material distribution, and safety–efficiency trade-off in close-range wheel loader–dump truck collaborative unloading, this study proposes a perception–task–control framework for autonomous unloading. A complementary front–rear vision configuration is used to perceive the dump-truck bed under varying relative viewpoints, and the estimated bed pose is further transformed into executable unloading targets. To improve load distribution, a partition-aware task-generation strategy is developed, by which the unloading objective is extended from a single target point to sequential zone-level targets. An event-triggered two-stage reinforcement learning controller is then designed to organize the unloading process. The first stage guides the loader toward a perception-enabled region, while the second stage performs vision-guided precision alignment and coordinated lifting according to the current zone-level target. A closed-loop co-simulation environment is constructed using MATLAB/Simscape R2025b and Unreal Engine, and field-test data are used for simulation–field response comparison. The simulation results under representative operating conditions show that the proposed framework can complete sequential zone-level unloading without collision under the tested conditions. The quantitative results support the effectiveness of the method in terms of target completion, completion time, terminal positioning accuracy, lifting completion, and collision avoidance. The field-test comparison further indicates that the developed simulation model can reproduce the main trajectory, articulation-angle, and lifting-cylinder displacement responses of the wheel loader during unloading. These results demonstrate the feasibility of integrating low-cost visual perception, partition-aware task generation, and two-stage learning-based control for autonomous wheel-loader unloading.

Applied Sciences doi: 10.3390/app16104808

Authors: Qiang Liu Jiahui Fu Huiyuan Huang Taochuan Zhang Jingliang Lin

As a core component of rotating machinery, bearing health status is directly related to the operational stability and safety of the equipment. In the field of bearing fault diagnosis, although vibration signals are commonly used as monitoring data sources, their contact-based acquisition method is susceptible to constraints imposed by installation conditions and hardware parameters. To address this issue, this paper proposes a non-contact diagnostic method based on acoustic signals and constructs the 1dCMPR-BiSEnet model. In this study, six bearing operating conditions are defined, covering typical failure modes such as inner-race, outer-race, and cage faults. On this basis, a bearing acoustic dataset is constructed. Relying on the collected acoustic data, fault diagnosis experiments are conducted with the proposed method. The experimental results reveal that the diagnostic accuracy of this method reaches 99.97% under different rotational speeds, with an average AUC of 1.0 and an average AP of 0.9996. It delivers better overall performance than comparative algorithms and presents satisfactory robustness under high-noise environments. Furthermore, verified by the University of Ottawa Bearing Dataset, the method achieves a generalization accuracy of 98.06% in vibration signal scenarios. The high accuracy of the proposed method demonstrates that this study can provide valuable insights for relevant research in the field.

Applied Sciences doi: 10.3390/app16104809

Authors: Coston Hezel Plotnizky Nojoumian

This paper is the empirical validation companion to our prior methodology paper introducing the Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D) methodology, conducted through multi-vector adversarial testing on physical NVIDIA Jetson Orin Nano hardware. AZTRM-D unifies DevSecOps automation, the NIST Risk Management Framework, and Zero Trust architecture with AI orchestration via Cybectr Sentinel, featuring six AI subsystems with formal specifications. Testing spanned three progressive hardening stages across seven attack categories under a blind three-tester protocol with inter-rater agreement analysis. Factory-default devices were fully compromised in under five minutes. After full hardening, zero successful breaches were recorded across any tested vector. The CI/CD pipeline achieved a vulnerability detection rate of 96.8% (Wilson 95% CI: [0.891, 0.991]). Sentinel delivered 94.1% precision, 91.8% recall, and 4.2 min average detection time within 12−18% CPU overhead on edge hardware. A 14-capability comparative analysis against five established frameworks found seven capabilities unique to AZTRM-D. The 93.7% adversarial detection rate is reported against DiCE-generated counterfactual inputs and is bounded by the black-box threat model used in evaluation; gradient-based white-box attack evaluation is documented as a scoped Stage 4 future-work item. All three testers are affiliated with Cybectr LLC, the developer of AZTRM-D and Cybectr Sentinel; this conflict of interest is the most significant limitation of the present work, and independent third-party laboratory validation is the highest-priority Stage 4 deliverable.

Applied Sciences doi: 10.3390/app16104810

Authors: Yina Jeong Surak Son

This study proposes the Hydroponic Plant Growth Analysis System (HPGAS), a public-data-based preliminary framework for multimodal plant growth state analysis toward future filter-free aquaponic validation. The HPGAS integrates plant images, water quality signals, and environmental signals to estimate an image-centered growth index, growth stage, and proxy abnormal state probability. Because no public dataset jointly provides plant images, direct growth labels, fish metabolic variables, suspended solids, and nitrification-related measurements from a real filter-free aquaponic system, this study is not a direct operational validation. A two-stage evaluation was conducted using the Autonomous Greenhouse Challenge (AGC), HydroGrowNet, and two aquaponic Internet of Things (IoT) water quality datasets. Stage 1 implemented dataset loaders, image–sensor alignment, proxy label generation, and unimodal and fusion baselines. Stage 2 expanded handcrafted image and sensor-context features and adopted month-wise hold-out evaluation. The image-only model achieved the best growth index regression performance, with a root mean square error (RMSE) of 0.0492 ± 0.0187, whereas the fusion model showed a RMSE of 0.0837 ± 0.0196. Conversely, the fusion model achieved the best proxy abnormal state classification performance, with a F1 score of 0.9695 ± 0.0057 under the clean condition, decreasing to 0.9232 ± 0.0263 under sensor dropout and 0.9132 ± 0.0169 under image noise. Under sensor dropout, the fusion model was more stable than the sensor-only model, whereas under image noise it degraded more than the image-only model. These results indicate that multimodal fusion is most useful for proxy abnormal state classification and robust state interpretation, rather than universally superior scalar growth regression. The HPGAS provides a reproducible baseline for future real filter-free aquaponic experiments, while its operational validity remains to be tested using real filter-free aquaponic data.

Applied Sciences doi: 10.3390/app16104805

Authors: Henda Lopes Elisabete Gonçalves Maria Morais Ana Coimbra João Sousa Paula Oliveira Henrique Trindade Marta Roboredo

Sewage sludge (SS) is a by-product of wastewater treatment processes (WWTPs) and is rich in organic matter and essential nutrients like nitrogen, phosphorus, and potassium, making it a potential fertilizer for agricultural use. However, its application is often limited due to the presence of pathogenic bacteria, viruses, metals, and organic contaminants that can accumulate in soils and crops, raising concerns about food safety. Sewage sludge is additionally challenging to handle due to its high moisture content, low density, and odor emission. To mitigate environmental risks and enhance its usability as a soil fertilizer, SS must be stabilized. Various techniques, including chemical, physical, and biological, can be used to stabilize SS. The addition of lime and composting has received particular attention among these techniques owing to the benefits they offer. Both methods effectively control and eliminate pathogens and reduce metal bioavailability, thus improving their agricultural utility. This review emphasizes the importance of using SS for agricultural purposes, placing particular focus on the procedures of composting and liming to stabilize and enhance the quality of SS, hence promoting its safety.

Applied Sciences doi: 10.3390/app16104806

Authors: Rafał Celiński Janusz Kocki Anna Grzywa-Celińska Katarzyna Dos Santos Szewczyk Anna Berecka-Rycerz

Heart failure (HF) and myocardial infarction (MI) are interconnected syndromes with overlapping pathogenic pathways, including ischemia, neurohormonal activation, and maladaptive remodeling. Hypoxia-response genes, lipid-handling genes, and extracellular matrix (ECM) genes each influence these processes. Understanding their integrated roles can uncover biomarkers and targets. A systematic literature search was conducted (PubMed, Web of Science, and Scopus; 2000–2026; English-only, following PRISMA guidelines) to identify studies on key genes in hypoxia signaling, lipid metabolism, and ECM remodeling in MI/HF. Acute hypoxia (via HIFs) orchestrates metabolic adaptation and inflammation, but chronic HIF activation drives fibrosis and dysfunction. In parallel, genes controlling triglyceride and cholesterol handling (e.g., LPL, APOC3) influence energy supply and vascular risk. Variants in these genes modulate plasma lipids and MI/HF risk. For example, genetic loss-of-function in APOC3 lowers triglycerides and reduces coronary risk. ECM-related genes (e.g., COL4A1, LRP1) govern fibrosis and vascular integrity. Mutations in COL4A1 cause cardiomyocyte hypertrophy and severe fibrosis, while LRP1 regulates matrix remodeling and is upregulated in ischemic myocardium. Throughout, gene functions span acute repair versus chronic maladaptation. Findings derive from mixed sources: rodent models and cell studies demonstrate mechanistic links, while human genetics and cohorts link gene variants to HF/MI outcomes. Many promising biomarkers (e.g., circulating ITGA1) are preliminary, lacking large prospective validation. Not all cited therapeutic ideas have been tested in the treatment of human cardiac disease. The literature mix of species, models, and patient cohorts introduces heterogeneity.

Applied Sciences doi: 10.3390/app16104802

Authors: Jiyuan Li Shengfei Qin Fenghua Zhao Hanqian Ou Zheng Zhou

Helium-rich unconventional natural gas resources have attracted increasing attention from both academia and industry. A pronounced local enrichment of helium has recently been identified in coal-derived unconventional natural gas in the Sanjiaobei block on the eastern margin of the Ordos Basin. To clarify the main controls on helium enrichment in unconventional natural gas in this area and to guide the exploration of helium-rich resources, this study systematically examines the source of helium, its transport carrier, multiphase fractionation processes, and enrichment and accumulation pattern in natural gas. The analysis is based on conventional gas composition, helium volumetric content, carbon isotopes, and noble gas isotopes (He, Ne, and Ar) measured from wellhead gas samples collected from 11 production wells in the block, together with the regional deep structural evolution and hydrogeological conditions. The results show that: (1) the helium volumetric content of natural gas in the study area ranges from 0.0175% to 0.214%, with an average of 0.108%, and most wells fall within the high-helium grade category; (2) the helium isotope ratios 3He/4He (R/Ra) of the samples range from 0.0148 to 0.0824, indicating a typical crustal helium source; the good positive correlation between helium and nitrogen volumetric contents suggests that the two components share a highly consistent source affinity or common migration and accumulation behavior during fluid evolution; and the extremely high He/Ne ratios, on the order of 104, together with excess Ar isotopes, indicate that the gases experienced little dilution by shallow atmospheric water or modern atmospheric fluids during migration and accumulation. The formation of helium-rich unconventional gas reservoirs on the eastern margin of the Ordos Basin is interpreted to be characterized by basement-derived helium supply, activation by tectonothermal events, groundwater transport, efficient fault-controlled migration, reservoir capture along migration pathways, and sealing by stagnant groundwater and lithologic barriers. On this basis, a helium enrichment model is established. This model depicts the geochemical evolution pathway of trace noble gases in a natural gas system and may provide a useful reference for helium resource evaluation in analogous areas.

Applied Sciences doi: 10.3390/app16104804

Authors: Zijie Ye Fuerhaiti Ainiwaer Dongchen Han Xinjun Song Fulin Qu Yuxi Wang Xiaomin Dai Shengqiang Ma

Although deep learning has been successfully used to detect landslide hazards in recent years, existing methods still face challenges due to the variety of landslide characteristics in different terrains and topographies. This study proposes a new framework for landslide detection by comparing various YOLO models. It employs deformable convolutional modules combined with GhostConv modules to enhance feature extraction for landslide targets. The framework uses a structured IoU loss function to optimize the alignment of actual and predicted frames in a directional sense. Additionally, it introduces the CoordAtt attention mechanism to accelerate model convergence and improve training efficiency. The experimental results demonstrate that the enhanced YOLO model (CDCS-YOLO), incorporating four key enhancement modules (Coordinate Attention, Deformable Convolutional Networks, the C3 Module/CSP Architecture and SIoU Loss), achieved a maximum mAP of 96.6%, an accuracy of 96.1%, and a frame rate of 142.6 FPS. Notably, it performed exceptionally well in soil landslide detection, achieving an average detection accuracy surpassing 90%. Based on the experimental results, we explored a morphological landslide classification method further as well as a multi-source differential monitoring strategy integrating UAV imagery, field surveys, ground-based LiDAR data, rainfall information and deformation indicators. The proposed method outperforms the baseline approach and is a promising solution for detecting landslides and geological hazards in Xinjiang.

Applied Sciences doi: 10.3390/app16104803

Authors: Tongyan Zheng Meng Huang Chong Xu Shuai Liu Haoran Dong Xiudan Ma Keifeng Wang

Faced with the challenges of cross-cultural communication of public opinion in emergency events, traditional sentiment recognition methods struggle to accurately capture the complex semantics under multi-lingual and multi-symbol systems. This paper takes the powerful 7.7-magnitude earthquake that struck Myanmar in 2025 as a case study. It constructs a multi-dimensional public opinion annotation framework that integrates four types of semantic information—time, space, subject, and sentiment—by extracting data from multi-source textual materials, including social media, news reports, and government announcements. Building on this foundation, we design an improved Model-Agnostic Meta-Learning (MAML) model that incorporates cultural features to enhance sentiment recognition performance in low-resource cross-linguistic scenarios. Experimental results show that the model outperforms traditional methods in terms of sentiment classification accuracy, cultural semantic deviation rate and metaphor recognition ability. Furthermore, the research reveals the coupling mechanism of public opinion communication of “cultural modulation–agenda game”, and clarifies the influence paths and weight distributions among multiple subjects. The research results provide theoretical support and practical paths for improving the governance capacity of cross-border public opinion in emergency events and the construction of multilingual monitoring models.

Applied Sciences doi: 10.3390/app16104801

Authors: Vincenzo Barrile Emanuela Genovese Clemente Maesano Davide Borrello Fatma Ben Brahim

Soil Organic Matter (SOM) and Soil Organic Carbon (SOC) are essential for regulating ecosystem functions, soil fertility, and influencing climate change processes, especially in semi-arid regions. The recent improvements in remote sensing instruments and the development of artificial intelligence methodologies, such as machine learning, enable an improved understanding of carbon dynamics, facilitate the estimation of SOC content, and support predictive modeling. This study presents an integrated framework to analyze past and future carbon dynamics in the Sfax Governorate (Tunisia). Land-use and land-cover (LULC) maps for the years 2019, 2020, 2022, and 2024 were generated using a Random Forest algorithm applied to multispectral satellite data in the Google Earth Engine platform, achieving high classification accuracy (overall accuracy up to 0.90). Carbon stocks and their temporal variations were estimated using the InVEST Carbon Storage and Sequestration model, while carbon emissions and the Net Ecosystem Carbon Balance (NECB) were derived by integrating land-use-specific emission factors. Future LULC scenarios for 2030 were simulated through a Cellular Automata model under three alternative development pathways: conservation-oriented (CONS), business-as-usual (BAU), and urban expansion (URB+). The study demonstrates how the integration of machine learning, remote sensing, and ecosystem modeling supports spatially explicit assessment of SOC-related carbon dynamics and provides useful insights for land management and climate mitigation strategies.

Applied Sciences doi: 10.3390/app16104799

Authors: Zhuoran Zhang Qian Wang Xinying Li Yixuan Zhao Lei Zhang Rixin Su

Optical remote sensing imagery plays a crucial role in a wide range of applications, including environmental monitoring, disaster assessment, and urban planning. However, the widespread presence of clouds severely degrades image quality and limits the reliability of downstream analysis. Therefore, accurately recovering cloud-contaminated regions has become a critical problem in remote sensing. Despite recent progress, remote sensing cloud removal methods still face two practical difficulties in complex real-world scenarios: preserving large-scale structural consistency and recovering high-frequency details that are uncertain. To address this problem, we propose SCReD (Structure-Consistent Residual Distribution), a two-stage, structure-guided residual refinement framework for single-image remote sensing cloud removal. Specifically, the first stage introduces a structure-enhanced coarse reconstruction module to improve spatial consistency and provide a more reliable structural condition. The second stage performs conditional latent diffusion in the residual space, where probabilistic modeling is restricted to high-frequency residual refinement rather than full-image regeneration. In this way, SCReD explicitly separates coarse structural recovery from uncertain detail refinement, thereby helping to balance structural stability and texture restoration under severe cloud occlusion. Extensive experiments on representative cloud removal datasets show that SCReD achieves competitive quantitative and visual performance. This is especially true in more challenging real-world scenarios. Additional analyses further show that the two stages play complementary roles within the proposed task-specific framework.

Applied Sciences doi: 10.3390/app16104800

Authors: Xiaoyang Wang Xiao Yu Zhengchun Xu Xiaoyou Yu Zhaohan Zhang Qian Ma Zengjie Shao

In this paper, we propose an enhanced preamble scheme for the Physical Random Access Channel (PRACH) applied to low-altitude integrated sensing and communication (ISAC) systems, aiming to expand the sensing capability of traditional mobile networks with PRACH frames based on ZC sequences. To enable the network to possess target-sensing capability before successful terminal access, we transform PRACH from a mere initial access channel into an ISAC system capable of supporting high-speed terminal access and user equipment sensing by introducing a time–frequency orthogonal block structure and Orthogonal Cover Codes (OCCs). Specifically, we first derive the Cramér–Rao lower bound (CRLB) for estimating the distance and velocity of user equipment using OCC-ZC sequences, and we establish the evaluation metric for communications and named detection probabilities. Then, the ISAC problem is formulated as a multi-objective optimization function. Since the multi-objective optimization problem is non-convex, we propose the NSAG-II algorithm to solve it, simultaneously improving the estimation accuracy of distance and velocity in the sensing aspect and the detection probability in the communication aspect.

Applied Sciences doi: 10.3390/app16104798

Authors: Greta Yordanova Emanuel Emiliyanov Mirela Georgieva

Background/Objectives: Multiple impacted teeth are defined as the sequential impaction of more than two teeth in the alveolar bone, whether unilateral or multilateral. Multiple impactions are an uncommon and rare phenomenon demanding thorough treatment planning and careful execution, but data on the prevalence of multiple impactions is scarce in the literature. In cases of multiple impactions, clinicians generally perform a 3D assessment using CBCT to determine tooth positions, establish a sequence of surgical exposures, implement suitable traction, and utilise appropriate biomechanics. A multidisciplinary approach between orthodontists and oral surgeons is essential to achieve optimal results. Methods: This case report presents non-syndromic multiple impactions of three upper left permanent anterior teeth—21, 22, and 23—along with a retained supernumerary tooth preventing their eruption and a fused primary tooth. The primary teeth and the impacted supernumerary tooth were surgically removed. A digitally designed transpalatal arch was used to preserve the space and to act as anchorage for the orthodontic traction. After an 8-month observational period without spontaneous eruption, surgical exposure was carried out using the closed exposure technique. Subsequently, elastic traction was performed, guiding the impacted teeth into the dental arch. Results: The multiple impacted teeth were successfully aligned in the dental arch, achieving symmetry in the frontal segment while preserving periodontal health. In order to ensure stability during the retention period, thermoformed retainers were used. Conclusions: Each complex and rare clinical case poses a challenge to orthodontists and is important for the scientific literature as it provides valuable clinical experience.

Applied Sciences doi: 10.3390/app16104797

Authors: Giovanni Balboni Stefano Di Paolo Domenico Alesi Amit Meena Simone Bignozzi Margherita Bonaiuti Margherita Mendicino Giulio Maria Marcheggiani Muccioli Stefano Zaffagnini

The available literature provides limited and inadequate data regarding the association between intraoperative knee kinematics, long-term clinical outcomes and survivorship after total knee arthroplasty (TKA). This study aimed to examine the potential relationship between specific intraoperative kinematics laxity assessment, acquired with a computer navigation system, and the long-term clinical outcomes and survivorship in patients undergoing TKA. This study consists of a retrospective cohort analysis of consecutive TKA procedures, in which a surgical navigation system was utilized to intra-operatively assess bone resections, implant positioning and gap balancing. The intraoperative kinematic parameters included varus-valgus laxity at 0° (VV 0) and 30° of flexion (VV 30), anterior–posterior displacement at 90° of flexion (AP 90), and passive range of motion (ROM). Different prosthesis designs were used, with a predominance of the posterior stabilized (PS)-type implant. The Knee Injury and Osteo-arthritis Outcome Score (KOOS) was used to investigate patients’ clinical and functional status. Survival was analyzed with the Kaplan–Meier method. Between-group comparisons were performed using the Mann–Whitney U test. A univariate logistic regression analysis was conducted to identify factors associated with clinical failure. Of 165 eligible patients, 120 were included in the final analysis, with a mean follow-up of 7.7 ± 2.8 years. Revision surgery was required in seven cases, representing surgical failure and an overall survival rate of 94.2%, with survival probabilities of 98.8%, 97.4%, and 93.6% at 6, 8, and 10 years, respectively. Clinical failure (KOOS < 70 in three domains) occurred in 23 patients. No intra-operative surgical parameters, including Hip-Knee-Ankle angle, Preoperative KL grade, prostheses design, VV 0, VV 30, AP 90 and ROM, or demographic variables, were found to be statistically correlated with clinical failure at follow-up. Although, in this navigated TKA cohort, survivorship was acceptable and consistent with previously reported benchmarks, it was not possible to reliably predict survival probability based solely on the intra-operative laxity parameters measured. Nevertheless, the use of surgical navigation can help surgeons accurately assess bone resections and the balance of prosthetic components.

Applied Sciences doi: 10.3390/app16104795

Authors: Sheng Li Yiteng Wang Baijun Li Rui Xu Fengyi Sun Xiaolong Ma

The accurate and efficient acquisition of rock mechanical properties is critical for ensuring the safety and efficiency of underground engineering construction. Traditional laboratory tests are characterized by long cycles, high costs, and an inability to reflect in situ mechanical properties, while existing deep learning models based on while-drilling data suffer from poor noise robustness, insufficient deep feature extraction, and low accuracy in synchronous multi-parameter prediction. To address these limitations, this paper proposes a hybrid deep learning model (CNN-LSTM-MoE) combining a convolutional neural network (CNN), a long short-term memory network (LSTM), and a mixture of experts (MoE) system. The model enables intelligent prediction of elastic modulus, Poisson’s ratio, and yield stress from while-drilling parameters. The proposed model integrates CNN’s local feature extraction capability, LSTM’s temporal dependency modeling capability, and the multi-expert dynamic fusion mechanism of MoE. Furthermore, it incorporates physical constraints from rock fragmentation mechanics and an adaptive multi-objective loss weight optimization strategy to comprehensively enhance the multi-parameter synchronous prediction performance. Experimental results demonstrate that the proposed model achieves coefficients of determination (R2) of 0.8965 for elastic modulus, 0.9193 for Poisson’s ratio, and 0.9813 for yield stress on the laboratory validation dataset, with a mean squared error (mse) of 4.0720. Its prediction performance significantly outperforms benchmark models such as TCN and Transformer time-series architectures. Ablation studies further validate the critical role of the integrated LSTM and MoE modules in improving model accuracy, with the MoE module contributing an average R2 improvement of approximately 24%. This study not only provides an effective method for high-precision acquisition of rock mechanical parameters while drilling, but also offers a feasible solution based on numerical simulation for data augmentation to address the common issue of scarce labeled data in deep learning applications within engineering fields.

Applied Sciences doi: 10.3390/app16104796

Authors: Barry Neary Daniela Butan Ronan MacLoughlin Philip Griffin

Piezo-driven active vibrating mesh devices are increasingly being used across a variety of applications. These include respiratory drug delivery and inhaled vaccine delivery, as well as multiple industrial processes such as coating, improving the efficiency of chemical reactions through mixing and 3D printing in low gravity. The adoption of this technology shall continue to rise as its reliability, the scalability of manufacturing, and the functionalisation of active vibrating mesh assemblies advance. Early-stage design and development of these complex electromechanical devices can be a costly and time-consuming process. Finite element analysis (FEA) allows us to simulate these devices and analyse their input parameter interactions and design optimisation without the expense of costly prototyping, while also reducing time to market. A review of the state of the art in FEA techniques has identified piezoelectric coupling, modal analysis, harmonic response, fluid–structure interaction, acoustic–structural coupling, and thermal analysis as the recommended simulation tools for dry (no liquid present) and wet (with liquid present) state simulations. Theoretical and empirical validation techniques have given us confidence in these tools for vibrating mesh device design iterations and optimisation. This review summarises the current state of the art for the application of these techniques in the development of active vibrating mesh devices intended for use in respiratory drug delivery.

Applied Sciences doi: 10.3390/app16104794

Authors: Valeria Nori Martin Nielsen

This study presents an integrated catalytic system enabling the quantitative production of 2,5-diethoxymethylfuran from HMF through a hybrid sequence that combines Ru-PNP-catalyzed hydrogenation with heterogeneous acid-catalyzed etherification in ethanol. The approach provides complete selectivity under mild conditions and demonstrates the compatibility of homogeneous hydrogenation catalysts with solid acid co-catalysts in a single process environment. In addition, we report the first example of homogeneously catalyzed hydrogenative valorization of HMF employing a co-catalytic, potentially recyclable acid additive. This strategy expands the scope of HMF upgrading pathways and highlights the potential of hybrid catalytic systems for the efficient synthesis of stable, energy-dense furan derivatives relevant to biofuel and biobased chemical applications.

Applied Sciences doi: 10.3390/app16104793

Authors: Socratis Thomaidis Maria Dimitriadi Georgios Chrysochoou Valantis Stefanidakis Maria Antoniadou

This observational study evaluated changes in selected performance parameters of 15 new high-speed dental handpieces after eight months of routine clinical use in a routine educational undergraduate environment (two 4 h daily clinical shifts, five days per week, with repeated sterilization cycles). All handpieces underwent routine cleaning, lubrication, and autoclave sterilization as instructed. The turbine components from the handpieces were disassembled and examined by stereomicroscopy before and after use, while free-running speed and friction grip force were assessed at the same intervals. Two handpieces were no longer operational at follow-up due to ball bearing failure. Paired t-test was performed for free-running speed and friction grip force. Among the remaining handpieces, statistically significant reductions were observed in both free-running speed and friction grip force (p < 0.01). Microscopic examination of the rotors revealed surface alterations consistent with corrosion and wear. Within the limitations of this study, routine clinical use over an eight-month period was associated with measurable changes in key performance characteristics of high-speed dental handpieces in educational clinical settings.

Applied Sciences doi: 10.3390/app16104792

Authors: Sorur Yazdanpanah Silvia Romano Rita Paola Debri Raffaele Conte Gianfranco Peluso

Botanical extracts represent a rich and sustainable source of polyphenolic compounds with significant potential in anticancer research. Among these, hesperidin, naringenin, hydroxytyrosol, oleuropein, and quercetin have attracted considerable attention due to their abundance in widely consumed plants such as citrus fruits, olive derivatives, and various fruits and vegetables. However, their clinical translation is hindered by intrinsic limitations including poor solubility, low stability, and limited bioavailability. In this context, nanotechnology-based drug delivery systems have emerged as a promising strategy to enhance the therapeutic performance of these bioactive compounds. This review provides an overview of polyphenol-rich botanical matrices and focuses on recent advances in their nanoformulation. Various nanocarriers, including polymeric nanoparticles, liposomes, solid lipid nanoparticles, and nanoemulsions, are discussed in terms of their ability to improve physicochemical properties, protect against degradation, and enhance delivery efficiency. Special attention is given to the challenges associated with the encapsulation of complex botanical extracts and the need to preserve their compositional integrity and synergistic effects. Overall, nanoformulation represents a powerful approach to overcome current limitations and unlock the full potential of plant-derived polyphenols in anticancer applications.

Applied Sciences doi: 10.3390/app16104791

Authors: Siyao Liu Fengqi Zhang Zhenyu Zhao Xin Qiao Jiahao Wang Jianrong Gao Yuze Ji Zongru Lei

In Table 4 of the original publication [...]

Applied Sciences doi: 10.3390/app16104790

Authors: Wen Liu Liuyuan Qin Wen Zhang Yanan Yang Zhengang Ding Rui Ma Xianli Zou

Oil and gas reservoirs have been discovered in various strata within the Kekeya structural belt of the Tarim Basin, primarily consisting of pure gas reservoirs, condensate gas fields, and oil reservoirs. There are significant differences in the physicochemical properties of the oil and gas. 17α(H)-rearranged hopanes are commonly found in crude oil and may be key indicators for determining oil and gas migration pathways and correlating oil sources in the study area. Through chromatographic and mass spectrometric analyses of crude oil samples from five different strata in the Kekeya structural belt, the distribution patterns of 17α(H)-rearranged hopanes and 18α(H)-neohopanes were investigated. A comparative analysis of source rocks and crude oil confirmed that 17α(H)-rearranged hopanes originate from the Permian Pusige Formation source rocks. Further analysis of crude oil maturity and oil and gas migration revealed that low to moderate levels of 17α(H)-rearranged hopanes are influenced by maturity, whereas abnormally high levels are associated with oil and gas migration. Therefore, detecting the distribution characteristics of 17α(H)-rearranged hopanes in crude oil provides crucial evidence for determining the source and migration pathways of oil and gas in the Kekeya area.

Applied Sciences doi: 10.3390/app16104789

Authors: Xuan Zhao Jingxuan Xia Chulin Wang Huang Xu Pingan Du Baolin Nie

The intense low-frequency magnetic field generated by the Electromagnetic Aircraft Launch System (EMALS) during operation poses a serious EMI threat to electronic equipment within carrier-based aircraft nacelles. To address this, a three-dimensional transient finite element model of a long-primary double-sided linear induction motor is established. Using a quasi-static equivalent method, the 118 Hz magnetic field distribution inside and outside a typical engine nacelle is characterized. Results indicate that due to the skin depth significantly exceeding material thickness, the eddy-current shielding of the aluminum alloy nacelle is inadequate, producing internal field intensities that far exceed standard limits and directly threaten sensitive onboard electronics. Based on the magnetic shunting principle, a composite shielding strategy is proposed: applying a flexible high-permeability coating on the nacelle surface to attenuate the overall field, supplemented by local permalloy shields for core equipment. Simulation verification demonstrates that this approach reduces the internal field to safe levels. It achieves effective shielding performance while balancing engineering feasibility with lightweight requirements, providing a viable pathway for ensuring the reliable protection of carrier-based aircraft in intense electromagnetic environments.

Applied Sciences doi: 10.3390/app16104788

Authors: Zhenhan Chen Lizheng Lin Xiaoyu Lin YingXin Chen Lijin Wang

Artificial intelligence (AI) coding systems now range from inline completion to repository-level agents and platform-supported application builders; yet, software engineering still lacks a code-generation-centered operational taxonomy for describing how much work is delegated, under what conditions, and with what responsibility structure. This study proposes Levels of Automated Code Generation (LACG), a six-level taxonomy (L0–L5) for classifying automation in AI-augmented software construction. LACG is organized around four responsibility-aware concepts—The Software Development Task (SDT), Operational Capability Domain (OCD), fallback responsibility, and minimal risk condition—and is assigned to a declared configuration–SDT–OCD tuple rather than to a vendor brand or model family in the abstract. To reduce the risk that public vendor documentation reproduces marketing bias, the method separates declared affordance evidence from routine capability evidence and adopts an evidence-triangulation design. Public documentation is used only to identify configuration boundaries and declared affordances; independent software engineering benchmarks, agent studies, productivity studies, and taxonomy-evaluation literature are used to calibrate the level boundaries and constrain the claims. LACG is then applied to 30 representative current AI coding tool configurations using time-stamped public-documentation records, with boundary logic cross-checked against independent evidence on repository-level issue solving, agent tool use, and context-dependent productivity outcomes. Three anonymized human raters, selected for software engineering or AI-coding-tool expertise and independent of the authors and evaluated vendors, then classified the same prepared, blinded public-documentation records using the LACG coding manual. Exact three-rater agreement was 28/30 (93.3%); adjacent-level and majority agreement were both 30/30 (100.0%); mean pairwise quadratic-weighted Cohen’s kappa was 0.963; and Krippendorff’s alpha for ordinal ratings was 0.963. These agreement statistics test classification consistency over a structured documentary evidence base; they do not test actual tool behavior, direct execution, product performance, safety, productivity, or deployment outcomes. After adjudication, the final sample contains six L1 configurations, nine L2 configurations, and fifteen L3 configurations; no public configuration is classified as L4 or L5 under the fallback-responsibility criterion. The study supports preliminary, documentation-bound classification applicability, boundary calibration, and discriminative vocabulary development, not predictive validation or product-level performance claims. LACG provides an operational vocabulary for future empirical work on AI-augmented software construction, benchmark design, tool comparison, and responsibility allocation, while leaving outcome validation for governance, security, productivity, and procurement to subsequent empirical studies.

Applied Sciences doi: 10.3390/app16104787

Authors: Mahdi Shahsavar Amin Moniri-Morad Javad Sattarvand

Early-stage landslides and rockfalls are often characterized by very small internal accelerations associated with creep and progressive deformation, which are difficult to capture using conventional surface-based displacement monitoring techniques. To address this, the study presents the design and laboratory validation of a prototype in-mass inertial monitoring device, referred to as a Smart Rock, intended for embedded monitoring of rock mass motion. The developed device integrates low-noise inertial measurements with on-board processing to enable real-time characterization of motion signatures within a moving mass. Two sensing configurations, including a low-noise accelerometer-only configuration and a full inertial measurement unit (IMU) configuration, were implemented to evaluate their relative performance for in-mass motion monitoring. Embedded signal processing approaches suitable for landslide motions were developed to identify quasi-static, step-change, and impact-related motion regimes. Laboratory experiments using a controlled robotic testbed generated repeatable motion scenarios representative of creep-like movement, abrupt displacement changes, and impact events. Results showed that Smart Rock resolved very low-magnitude acceleration signatures on the order of 10−5 g and distinguished these from higher-energy motion and impact events, with improved signal stability observed for IMU-based configurations. These findings demonstrated the feasibility of in-mass inertial devices for characterizing landslide and rockfall motion in geotechnical applications. These results should be interpreted as proof-of-concept laboratory validation under controlled conditions.

Applied Sciences doi: 10.3390/app16104786

Authors: Cozmin Adrian Cristoiu Marius-Valentin Drăgoi Roxana-Mariana Nechita Bogdan-Marian Verdete Cristina Luciana Dudici Claudiu Nicușor Cusma

This paper presents a software application developed in Python (v3.9) for obstacle avoidance trajectory planning in the RoboDK (v6.0) virtual environment. The proposed method automatically scans the virtual station, identifies obstacles, discretizes the workspace into a three-dimensional free-space-graph (FSG) and searches for candidate routes between start and finish points. Each route is then verified at the robot level by inverse kinematics and collision control, and the validated solutions can be transformed into preview curves, intermediate points and motion programs executable by the robot. The study includes an initial test scenario performed with the ABB IRB 6650-125/3.2 robot and randomly generated obstacles, followed by a series of benchmark tests performed in different virtual scenarios and with four different robot models. In the comparative tests, the proposed method was evaluated together with a rapidly exploring random tree (RRT) reference planner and the native probabilistic roadmap (PRM) planner, embedded RoboDK. The final scenario included a robotic cell with realistic objects. The results show that the application can identify valid executable routes and, in some cases, several alternative variants for the same pair of target points. Overall, the benchmark suggests that the analyzed methods have different strengths and should be viewed as complementary solutions. In the tested scenarios, RRT was the method with the lowest computational times, while the proposed method offered the possibility of generating several alternative routes. At the current stage, the application can thus be used as an offline programming tool but also as a research and analysis tool for planning robotic trajectories in the presence of static obstacles.

Applied Sciences doi: 10.3390/app16104761

Authors: Halina Tkaczenko Renata Kołodziejska Oleksandr Lukash Oleksandr Yakovenko Lyudmyla Buyun Ivan Kirvel Piotr Kamiński Natalia Kurhaluk

Plastic-derived chemical additives, including bisphenols, phthalates, perfluoroalkyl substances (PFAS) and microplastic-associated contaminants, are now recognised as widespread environmental toxins that measurably affect endocrine signalling, oxidative balance, inflammation and metabolic homeostasis. Continuous exposure through food contact materials, consumer products, and environmental media raises concerns about long-term health effects. An increasing number of epidemiological and experimental studies are linking these exposures to metabolic disorders, reproductive dysfunction, neurodevelopmental alterations, and increased disease susceptibility throughout the lifespan. This narrative review summarises the latest evidence on the toxicological mechanisms of these compounds, with a focus on endocrine disruption, redox imbalance, reproductive impairment, thyroid hormone dysregulation and epigenetic modifications induced by plastic-derived chemicals. Literature was identified through searches of major scientific databases, including PubMed, Scopus, and Web of Science. Reference screening was also employed to complement these searches and ensure comprehensive coverage of vertebrate and invertebrate models. The inclusion criteria encompassed studies published within the last 10 years, focusing on experimental, experimental, and translational research. The review evaluates phytochemicals such as polyphenols, flavonoids, isoflavones, catechins, sulforaphane, and chlorogenic acid as natural agents that can mitigate the biological effects of plastic-derived toxicants. These compounds exhibit antioxidant, anti-inflammatory, and receptor-modulating properties that counteract pathways disrupted by BPA, phthalates, and PFAS. Experimental studies have demonstrated that phytochemicals can modulate oestrogen receptor activity, enhance detoxification systems, reduce oxidative biomarkers and mitigate epigenetic and metabolic alterations induced by micro- and nanoplastics. Emerging nutritional evidence suggests that diets high in polyphenols may reduce the biological impact of plastic-derived contaminants within the body, rather than reducing exposure itself. This effect appears to be especially relevant during sensitive developmental periods, such as the prenatal, early postnatal and adolescent stages.

Applied Sciences doi: 10.3390/app16104785

Authors: Yuki Nakai Yasufumi Takeshita Anna Tanaka Maiki Masuyama

Assessment of trunk function in sports settings remains challenging, as conventional strength measurements may not reflect integrated trunk stabilization. Abdominal pressure (AP), measured non-invasively using an abdominal cuff device, has been proposed as an indicator of coordinated trunk muscle activity; however, its association with sport-specific performance remains unclear. This study examined the within-session reliability of AP and its task-specific associations with performance measures in adolescent female volleyball players. Twenty-six athletes participated in this cross-sectional study. AP was measured twice within a single session, and reliability was evaluated using intraclass correlation coefficients (ICCs), standard error of measurement, minimal detectable change, and Bland–Altman analysis. Associations between AP and 20 m sprint time, T-test performance, and countermovement jump (CMJ) height were assessed using Pearson’s and partial correlations controlling for normalized trunk and hip flexion strength (N/kg). AP showed high reliability (ICC(3,1) = 0.941; 95% CI: 0.873–0.973). AP was significantly correlated with 20 m sprint time and T-test performance, but not with CMJ height. The association with sprint performance remained after adjustment, whereas that with T-test performance was attenuated. These findings suggest that AP is associated with sprint performance and may reflect task-specific associations, rather than representing a generalized or mechanistic indicator of trunk stabilization. Further longitudinal and interventional studies are needed to clarify causal relationships.

Applied Sciences doi: 10.3390/app16104784

Authors: Kapil Hande Manoj Chandak

Modern company activities depend greatly on inventory management, which covers demand forecasting and inventory optimization to guarantee operational effectiveness and customer happiness. This paper presents a new method fusing blockchain technology with cutting-edge deep learning to overcome these restrictions for better inventory management. Initially, the data are preprocessed using Zmin–max normalization (ZMM), and then feature extraction follows. To extract the spatiotemporal features and capture long-term temporal dependencies in demand data, a hybrid deep learning architecture is presented, built on a Deep Convolutional Koopman Network (CKN) integrated with a Coordinate Attention-Based Gated Recurrent Unit (CKN-CGRU).Genetic Secretary Bird Optimization (GSBO) is used to further tune the model automatically. While the CKN captures complex spatial temporal correlations, the GRU effectively models sequential dependencies. Blockchain architecture with smart contracts and improved Proof-of-Stake consensus is integrated to guarantee data integrity and transparency in stock transactions. This makes it possible to securely, automatically, and in a tamper-proof way record inventory projections, orders, and stock updates. The suggested system improves the stakeholder trust in decentralized inventory management by ensuring complete traceability and real-time auditability throughout the process. Experimental outcomes show the efficiency of the proposed model strategy, with an accuracy of 99.94% and precision of 99.93%.

Applied Sciences doi: 10.3390/app16104782

Authors: Yamei Lan Haoran Li Wulang Yi

Nine sets of fin parameter combinations, including a plain tube control group, were modeled. Simulations were performed under steady-state conditions using the EWT Realizable k-ε turbulence model, with benzene and water as working fluids, while accounting for temperature-dependent thermophysical properties. Flow field distribution, temperature profile, Nusselt number, and pressure drop in the shell side of the heat exchanger were analyzed. Response surface methodology was employed to systematically evaluate the coupled effects of fin height and fin spacing on thermal performance. The results indicate that annular fins significantly enhance heat transfer by inducing secondary flow and disrupting the thermal boundary layer. Compared to the smooth tube, the finned tubes increased the Nusselt number (Nu) by up to 28.6% and the total heat transfer rate by 13.55%, while the pressure drop (ΔP) increased by approximately 9.81% to 16.5%. The analysis revealed that fin height is the dominant factor affecting performance, whereas fin spacing plays a regulatory role. As the fins became taller or denser, the temperature field evolved from stable stratification to intense mixing and eventually to local disorder. The study identified an optimal parameter range for engineering applications. A fin height of 2–3 mm combined with a spacing of 10–15 mm achieves the best balance between heat transfer enhancement and flow resistance. Specifically, the combination of h = 3 mm and s = 10 mm yielded the highest Energy Efficiency Coefficient (EEC) of 1.567. This configuration is recommended for large-flow, pressure-drop-sensitive systems, such as those found in petrochemical plants or long-distance heat transmission applications.

Applied Sciences doi: 10.3390/app16104781

Authors: Burak Ergunes Mustafa Kemal Apalak

Reliable determination of the in-plane biaxial mechanical behavior of particle-reinforced composite adhesives under multiaxial stress conditions requires cruciform specimen geometries that achieve high stress uniformity in the measurement zone. In this study, the elastic response obtained from uniaxial tensile tests was verified through representative volume element (RVE)-based micromechanical analyses by systematically examining mesh sensitivity and RVE edge size convergence across multiple random microparticle distributions under periodic boundary conditions. The probability density characterization of the effective elastic constants indicated that the remaining scatter is mainly governed by microstructural randomness and decreases as the RVE edge size increases, supporting a nearly direction-independent effective stiffness associated with the random microparticle distribution. The RVE-predicted mean tensile modulus remained in close agreement with experiments, with relative deviations of approximately −2% to +2% across the investigated reinforcement levels. The validated material parameters were based on a dynamic XGBoost (eXtreme Gradient Boosting) surrogate model driven by the geometric design variables, fillet radius and center thickness, combined with an adapted version of the LIPOTR (Lipschitz Optimization with Trust Region) algorithm. The initial and optimized geometries were then compared using both experimentally determined elastic properties and selected RVE-predicted engineering constants for the 2, 6, and 10 wt% materials. The significant reductions in the equivalent Seqv, normal S11 and S22, and shear S12 stress variations within the gauge zone of the optimized candidate geometry resulted in improved stress homogeneity.

Applied Sciences doi: 10.3390/app16104783

Authors: Fatma Lajmi Achraf Jabeur Telmoudi Nadhira Khezami Bilel Neji

This paper presents a novel Adaptive Sliding Mode Control (ASMC) strategy for nonlinear multivariable systems subjected to parameter uncertainties and external disturbances. The proposed control scheme guarantees robust and smooth state convergence via an adaptive mechanism that dynamically adjusts the switching gain. Unlike conventional SMC techniques, this adaptive formulation effectively mitigates the chattering phenomenon through a continuously updated boundary layer and eliminates the need for prior knowledge of the uncertainty bounds. The effectiveness of the synthesized controller is validated on an autonomous electric vehicle (AEV) platform, a system characterized by strong dynamic coupling. MATLAB/Simulink (version 2022b) simulations are conducted under various operational scenarios, including load variations and strict trajectory tracking. Comparative results with a traditional SMC demonstrate superior convergence, significant chattering reduction, and an optimized energy consumption profile, leading to a 22% reduction in equivalent CO2 emissions. This approach provides a viable and energy-efficient control framework for modern autonomous EVs.

Applied Sciences doi: 10.3390/app16104779

Authors: Fangcai Zhu Zhigang Li Xuebin Xie

This study systematically optimized the geometric parameters of three typical cross-sections for curved-wall highway tunnel inner contours. Aiming to minimize the net excavation area, a unified genetic algorithm-based optimization framework was established for systematic comparison of four typical curved-wall section types and implemented on the Matlab(R2023b) platform, incorporating encoding, selection, crossover, and mutation operations for global optimization of geometric parameters across different section types. The optimized sections were further validated for structural performance using Midas GTS NX. Results show that the proposed multi-type optimization framework effectively reduced tunnel excavation areas across all section types, with mean optimization rates of 2.60% ± 0.21%, 2.11% ± 0.03%, 4.70% ± 0.02%, and 2.54% ± 0.02% (95% CI) achieved for single-circle, triple-circle Model-1, triple-circle Model-2, and five-circle sections, respectively, providing quantitative evidence for section-type selection in highway tunnel design. In terms of structural performance, the optimized sections demonstrated favorable axial force and bending moment characteristics. The findings provide a quantitative basis combining economic efficiency and structural rationality for tunnel section design, offering significant engineering application value and technical support for standardized and refined highway tunnel design in China.

Applied Sciences doi: 10.3390/app16104780

Authors: Chong Jiang Yunfei Zhang Ziqian Ding Fanhuan Zeng

Seismic loading can significantly affect the safety and serviceability of structures supported by piles, making seismic performance a key consideration in pile foundation design. The coupling between slope effect and dynamic loading can significantly alter pile–soil interaction and consequently influence the response of laterally loaded piles. In the present study, a dynamic extension of the static p-y curve model for piles near clay slopes is developed for analyzing the response of laterally loaded piles under dynamic loading, based on adjustment of the real stiffness component, and the spring and dashpot model. A computational program based on the Beam on Dynamic Winkler Foundation (BDWF) model is developed for analyzing the dynamic response of piles near a slope. Comparison with finite element simulation results shows that the complex stiffness scheme provides accurate response predictions, thereby validating the effectiveness of the proposed model. Finally, parametric analyses are carried out to investigate the effects of loading parameters (excitation frequency and load amplitude), pile parameters (pile diameter, pile length, and adhesion coefficient), boundary conditions (pile-head and pile-tip constraints), and slope parameter (slope angle). The pile–soil system exhibits a characteristic frequency governed by the soil shear-wave velocity and pile diameter, while being essentially independent of slope angle and pile length. Near this frequency, the pile-head stiffness and damping ratio change significantly. The proposed method provides a practical tool for steady-state dynamic analysis of laterally loaded piles near clay slopes.

Applied Sciences doi: 10.3390/app16104778

Authors: Marcin Mikulewicz Edward Kijak Katarzyna Skośkiewicz-Malinowska Katarzyna Chojnacka

Background: Corrosion of orthodontic archwires raises biocompatibility concerns; yet, comparative multi-element data across manufacturers remain scarce. Methods: Ni, Cr, Fe, and Ti release was quantified by ICP-OES from SS and NiTi rectangular archwires (0.43 × 0.64 mm) from four manufacturers (Ormco, 3M Unitek, Dentaurum, and American Orthodontics) and immersed in artificial saliva (pH~7.0) and fluoride-containing saliva (+0.05% NaF) at six time points (days 1–35). Release was normalised to wire mass (mg g−1). Non-parametric tests were applied. Results: NiTi wires released significantly more Ni than SS wires in +NaF at all time points (p = 0.029). An exploratory manufacturer effect on Ni release from NiTi was detected (Kruskal–Wallis H = 12.99, p = 0.005); American Orthodontics exceeded Dentaurum and Ormco. Ormco SS released ~3-fold more Fe than other SS wires (H = 13.68, p = 0.003). Ti was detectable exclusively in NiTi wires in +NaF; all specimens were below LOQ in pH~7.0. Cr release was uniformly low (0.017–0.023 mg g−1). Conclusions: Manufacturer identity influences Ni and Fe release independently of alloy type. Fluoride selectively disrupts the NiTi passive film. These exploratory findings, derived from a single-specimen pilot design, may inform clinical material selection in nickel-sensitive patients pending replication.

Applied Sciences doi: 10.3390/app16104777

Authors: Samantha Fernandez Martinez Yassine Jaouhari Lorella Giovannelli Matteo Bordiga

The environmental burden from cosmetic production has intensified interest in sustainable and scientifically robust raw materials. Among the emerging alternatives, agro-industrial residues are gaining attention as chemically rich sources of bioactive compounds with potential for dermocosmetic applications. However, research on their molecular activity, formulation performance, and industrial feasibility remains fragmented across the fields of sustainability, dermatology, and engineering. This narrative review synthesizes current knowledge on the phytochemical composition of extracts from agro-residues. It also critically examines their effects on key skin-related pathways, including oxidative stress modulation, extracellular matrix regulation, inflammation, senescence, and barrier function. Compounds such as polyphenols, carotenoids, peptides, and polysaccharides have been reported to influence signaling networks, including Nrf2/ARE, NF-κB, TGF-β/Smad, and PI3K/AKT/mTOR. Importantly, most of this evidence originates from in vitro and ex vivo studies on animal models, while controlled human and clinical studies remain limited; thus, mechanistic findings should not be equated with proven dermocosmetic efficacy. Nevertheless, challenges remain, such as compositional variability, safety-validation requirements, limited skin bioavailability and stability of bioactives in finished formulations, and limitations in scalable green extraction. Economic modeling and life-cycle assessment also highlight the need to verify both financial and environmental viability. Advancing agro-residue-derived bioactives toward mainstream cosmetic use will require strategies that integrate molecular characterization, regulatory alignment, rigorous claims substantiation and sustainable process optimization.

Applied Sciences doi: 10.3390/app16104774

Authors: Chunyu Zhang Xuechao Sun Zhenyu Zhao

Building upon our previous aerodynamic characterizations of skewed jets, this study extends the investigation to systematically evaluate their thermal performance. Turbulent air jets are produced by unilaterally supplying coolant and forcing it through a series of concave perforated blockages having varying relative inner diameters (Din/Dj = 3.0, 4.0 and 5.0) or relative thicknesses (t/Dj = 0.5, 2.0, 4.0, 6.0 and 8.0), with the jet diameter and Reynolds number fixed at Dj = 21 mm and Rej = 20,000, respectively. The results demonstrate that the skewed jets exhibit pronounced asymmetric velocity profiles in both the x–z and y–z planes. Unlike the Gaussian distributions characteristic of conventional axisymmetric jets, these profiles manifest as distinctly skewed or saddle-shaped topologies. This topological distortion is exacerbated by reducing either Din/Dj or t/Dj, albeit through fundamentally different mechanisms: the former only leads to jet deflection from the geometric axis, with the deflection angle increasing non-linearly from α = 4°, 5° to 12°; whilst the latter induces asymmetric internal flow development and exit momentum redistribution. The thermal performance of these jets on an iso-flux target flat plate, characterized by Nusselt number distributions at different jet-to-target spacings (H/Dj = 0 to 8.0), is shown to significantly differ from conventional axisymmetric jets.

Applied Sciences doi: 10.3390/app16104776

Authors: Wang Guo Chunjiang Zhao

Scientific and accurate agronomic knowledge is key to ensuring efficient wheat production. China’s vast agricultural land spans a wide range of longitudes and latitudes, and agronomic practices are closely tied to temporal factors such as wheat growth stages. So agronomic knowledge exhibits significant spatiotemporal variability. Constructing a spatiotemporal knowledge graph of wheat production can offer multi-dimensional data support and enabling deeper knowledge services. Wheat agronomic knowledge is often fragmented and unstructured and efficiently extracting text segments of agronomic knowledge and agronomic knowledge triples are two key challenges. Because of the high proportion and significant production service value of attribute values in agronomic knowledge, an attribute-rich agronomic knowledge graph schema was created. According to the characteristics of agronomic texts, a keyword attention mechanism (KAM) was proposed and integrated with an improved BERT model for sentence-level feature extraction to create an extraction model AgronomicCorpusExtraction for agronomic knowledge text corpora. The agronomic knowledge of wheat production is characterized by non-standard syntax, complex multi-layer structures, diverse entity expression methods, and a wide span of scope, and existing extraction methods cannot achieve satisfactory results. To address the issue, a joint extraction model AgronomicTripleExtraction was proposed to extract entities, attributes, and relations in different phrases, firstly the BERT and BiGRU were used jointly to extract the long and short distance features, and the CRF was used by global normalization joint modeling to extract attributes, then intermediate features between the same type of attributes extracted by average pooling to segment different entities. At last, a relation-aware relation feature enhancement (RAFE) method was created and a MLP was used to extract relations based on the relation matrix constructed from the knowledge graph schema. Ablation experiments were conducted to evaluate the performance for AgronomicCorpusExtraction with and without KAM and that for AgronomicTripleExtraction under four conditions, the model with BiGRU, RAFE, and entity segment, without BiGRU, without RAFF, and without entity segment. The results indicate that the use of KAM improves F1-score by 0.128 and AgronomicTripleExtraction achieves F1 of 0.897, 0.875, 0.871 for attribute, entity and relation extraction when using the three modules simultaneously, and removing any single module leads to a certain degree of performance degradation. Comparative experiments were conducted between AgronomicTripleExtraction and some related state-of-the-art models published recently.

Applied Sciences doi: 10.3390/app16104775

Authors: Xiaoqing Xing Wenjing Wang Jiaqing Shen Zhigang Yao

In this paper, a novel anti-windup prescribed performance terminal sliding mode control method is proposed for the fixed-time tracking problem of a nonholonomic constrained mobile deicing manipulator system with model uncertainty and external disturbance. Firstly, a fixed-time preset performance function related to the initial error is proposed to constrain and transform the tracking error, so as to ensure that the tracking error of the system converges in a fixed time and has good transient and steady-state performance. Secondly, in order to accelerate the convergence to the equilibrium state, a fast terminal sliding mode surface with preset performance tracking error and a new fixed time reaching rate are constructed. By using Lyapunov analysis, the global fixed-time convergence of the scheme is theoretically verified. The control method is compared with the FTSMC method through simulation experiments, and the effectiveness of the designed control method is further verified.

Applied Sciences doi: 10.3390/app16104772

Authors: Lijana Maskeliūnaitė Henrikas Sivilevičius

Passengers choose a mode of public transport from the available options based on characteristics that are important to them. The importance of these characteristics has received little research attention and varies. This study presents 10 characteristics of passenger transport, the significance of which was examined using four MCDM (multi-criteria decision-making) methods. The questionnaire was conducted and 27 specialists (experts) in road, rail and air transport rated the importance of various characteristics (criteria) using rankings, percentage weights and intensity of importance values derived from pairwise criterion comparisons using the Analytic Hierarchy Process (AHP). The results of the study show that the opinions of the expert panel, expressed as ratings, were consistent, as the Kendall’s coefficient of concordance (0.64) was 9.2 times greater than its minimum threshold value of 0.07. The ranks of the criteria were used to calculate their relative weights using the ARTIW-L (average rank transformation into weight–linear) and ARTIW-N (non-linear) methods. The relative weights were calculated from the criteria percentage weights using the DPW (direct percentage weight) method. The consistency ratios of all 27 matrices calculated using the AHP method were less than 0.1. This demonstrates their consistency. The average calculated for each criterion using the four MCDM methods is the final measure of the significance of the passenger transport characteristic. For experts, the most important factors were safety (0.2234), travel costs (0.1488), travel time (0.1465), and comfort (0.1181). Factors of moderate importance included door-to-door delivery (0.0847), environmental friendliness (0.0685), and vehicle capacity (0.0597). The following factors were deemed the least important: service quality (0.0571), the impact of weather conditions (0.0532) and the risk of contracting COVID-19 (0.0400). The most important criterion was 5.6 times more significant than the least important criterion. This data will be used to carry out a thorough evaluation of the different transport options and select the most suitable one for intercity passenger transport.

Applied Sciences doi: 10.3390/app16104773

Authors: Liguo Wu Xiangquan Meng Yueyuan Ren Yucheng Ding Liping Sun Sanping Li Haogang Feng

During the drying of Fritillaria ussuriensis, complex nonlinear interactions occur between process parameters and quality attributes. Conventional approaches rely on empirical trial-and-error, limiting precise control and inverse optimization. This study proposes a hybrid optimization framework combining the whale optimization algorithm (WOA) and particle swarm optimization (PSO) to establish a bidirectional mapping between process variables and quality indicators. The WOA is applied for global optimization of the random forest (RF) hyperparameters, followed by PSO for local refinement. The resulting model enables both forward prediction (from temperature, heating air velocity, dehumidification air velocity, and infrared power to quality indicators) and inverse optimization (from target quality to process parameters). The model achieves high predictive performance, with mean R2 values of 0.9739 (forward) and 0.9736 (inverse), outperforming WOA-RF, PSO-RF, and conventional RF models in accuracy, stability, and generalization. Industrial validation shows prediction errors below 10%, meeting engineering requirements. These results provide an effective approach for drying optimization and support intelligent modeling of rhizome-based medicinal materials.

Applied Sciences doi: 10.3390/app16104771

Authors: Murat Sarıkaya Şener Karabulut Osman Bodur Ayşe Şirin Okyayuz Nagihan Boztunç Öztürk Salih Dağlı

Engineering graphics and technical drawing represent the primary formal communication medium of manufacturing systems, conveying dimensional specifications, tolerances, surface conditions, and geometric intent across the full production chain. Despite their central role in industrial communication, persistent deficiencies in graduate CAD literacy and standards compliance are associated with reported production consequences, yet empirical, multi-stakeholder evidence documenting these associations remains scarce. This study investigates the engineering graphics competency gaps observed in recent engineering graduates and documents their perceived consequences for industrial performance across three national manufacturing contexts. A dual-phase methodology was employed: first, a structured review synthesizing peer-reviewed sources from Scopus, Web of Science, and ERIC; and second, a cross-national mixed-methods case study conducted in Türkiye, Austria, and Hungary, engaging 632 participants, 444 students, 100 educators, and 88 manufacturing and design professionals through validated multi-group questionnaires analyzed via descriptive statistics, chi-square tests, and thematic content analysis (Cohen’s κ = 0.82). Among the 88 industry respondents surveyed, 79.55% reported having experienced production delays attributable to drawing-related errors, 73.86% reported scrap generation, 78.41% reported rework costs, and 72.73% reported material wastage as perceived consequences of graduates’ technical drawing deficiencies. Graduate proficiency in dimensional tolerancing was rated adequate by only 26.14% of industry respondents, and standards/symbol compliance by merely 14.77%. Spatial visualization, ISO/GD&T literacy, and production-ready documentation were consistently identified as the most critically underdeveloped competencies. These findings establish a multi-stakeholder, cross-national evidence base documenting reported associations between engineering graphics instruction quality and manufacturing performance and provide actionable criteria for standards-aligned curriculum co-design between academia and industry.

Applied Sciences doi: 10.3390/app16104760

Authors: David Beltrán-Vargas Fernando García-Páez Manuel Martínez-Morales Cuauhtémoc Franco-Ochoa

Coastal construction projects often require excavation below the water table, where tidal variability and urban infrastructure generate complex groundwater conditions. This study presents a numerical simulation of water table dynamics in a coastal urban area of Mazatlán, México, influenced by tidal forcing, a lake, and an impermeable seawall. Six critical scenarios were modeled using MODFLOW 6 and ModelMuse interface, covering the period from November 2023 to April 2024. The scenarios correspond to astronomical tide events during the new moon phase, when maximum and minimum tide levels occurred within 24 h. These conditions are related to the highest piezometric levels observed in field. Model calibration was based on 18 field observations collected at 09:00, 12:00, and 15:00 across the selected dates. Model outputs closely matched the field observations, with a root mean square error (RMSE) of 0.056 m, and a mean absolute error (MAE) of 0.049 m. Although the differences are minimal, they reflect short-term variability and limited fluctuation during calibration. However, the full monitoring period showed groundwater levels ranging from −0.10 to 0.53 m above mean sea level (masl), emphasizing the importance of understanding short-term dynamics. This modeling approach supports construction planning, helping to anticipate risks and promote better and informed construction practices.

Applied Sciences doi: 10.3390/app16104770

Authors: Ze Zhu Yujin Han Feng Zhu Jie Sun Keming Chen Yushuang Yan Yadong Zhao

Accurate assessment of cumulative fatigue damage in container gantry cranes under long-term cyclic loading is hindered by the inability of traditional single-model methods to capture real-time structural conditions. This paper proposes a digital twin-driven framework that fuses multi-source data for dynamic fatigue life prediction. The framework’s core is an improved Extended Hyper-Heuristic Neural Network (EHH-NN), which incorporates regularization optimization, a split node structure, and ANOVA-based function decomposition to model complex stress responses under limited training data. The improved model achieves a goodness of fit (R2) of 0.942 and a mean relative error of 4.4%, outperforming standard BP and LSTM models while maintaining a prediction time of 42 ms. A closed-loop correction mechanism driven by measured stress feedback is designed to dynamically adjust model outputs, and a prototype system integrating PLC-based data acquisition with Unity3D 2022.3 visualization is developed to demonstrate engineering applicability.

Applied Sciences doi: 10.3390/app16104768

Authors: Chankyu Lee Sangyun Shin Raja R. A. Issa

While the demand for free-form architecture (FFA) has increased with advancements in computer-aided design (CAD) technology, the rationalization of complex surfaces into fabricable panels remains a significant challenge due to high production costs and technical complexity. Practical pain points, such as the prohibitive cost of unique molds and the inefficiency of manual data processing during design iterations, pose substantial economic risks. This study proposes an intelligent surface rationalization framework that integrates parametric design with machine learning algorithms in AutodeskTM Dynamo Studio, a plug-in to Revit. A data-driven classification workflow was developed using four key geometric parameters—planarity, principal curvature (PC), Gaussian curvature (GC), and mean curvature (MC). Two unsupervised learning algorithms, a Gaussian mixture model and K-means clustering, were compared for their classification performance. As a result of two case studies, free-form surface classification by a Gaussian mixture model (CGMM) demonstrated flexibility in modeling complex surface data by probabilistically managing the uncertainty of the curvature distribution, and free-form surface classification by K-means clustering (CKC) was confirmed to be effective for the rapid classification of large-scale panel data. Optimizing the proportion of flat and single-curved panels through the proposed workflow contributes to deriving a reasonable balance between design intent and construction costs/constructability at the early design stage, and strengthening risk management capabilities for FFA.

Applied Sciences doi: 10.3390/app16104769

Authors: Haining Sun Keping Li Yuanxi Xu Yan Liang

A reasonable urban transport structure is necessary to develop low-carbon transport and establish a cleaner and more efficient urban transport system. Urban rail transit plays a significant role in the development of low-carbon transport due to its advantages of efficiency, punctuality, safety, and environmental protection. In this paper, we construct a multi-objective model driven by urban rail transit to optimize the urban passenger transport structure from a systemic perspective. The objective functions of this model include minimizing transport CO2 emissions and travel costs while maximizing travel quality and the utilization rate of public transport operation lines. The non-dominated sorting genetic algorithm II (NSGA–II) is a classic multi-objective optimization algorithm used to optimize conflicting objectives simultaneously. In this paper, the multi-objective optimization model is solved using an improved NSGA–II, extending the local search mechanism into the NSGA–II. To evaluate the validity of the model, this paper takes Beijing, China, as the case area. Based on the development plans of urban rail transit we analyze from a specific year and multiple years. The results illustrate a structural transformation in urban passenger transport and embody a sustainable urban passenger transport structure driven by urban rail transit. This paper proposes a valid method, providing guidance for optimizing the urban transport structure.

Applied Sciences doi: 10.3390/app16104767

Authors: Jixin Xu Xinghua Huang

The solar collector–evaporator is a pivotal component in a direct-expansion solar-assisted heat pump (DX-SAHP) system, and its structural design directly affects the heat-transfer performance and overall coefficient of performance (COP). To investigate the effects of collector–evaporator geometric parameters, a system-level DX-SAHP simulation model was developed in MATLAB (R2016b). Specifically, a roll-bond plate solar collector–evaporator was represented using a distributed-parameter model. The effects of three key geometric parameters—namely, the channel-number allocation ratio between the two flow passes, channel pitch, and equivalent channel diameter—on the system COP were examined. The results show that each parameter exhibits an optimal value that maximizes COP. Furthermore, a coupled parametric analysis was conducted to account for their interactive effects, revealing that the DX-SAHP system achieves a global optimum COP when the channel-number ratio is 1:1, the channel diameter is 2.11 mm, and the channel pitch is 43 mm. Using the climatic conditions of Shanghai as a case study, the performance improvement achieved with the optimized collector–evaporator design was evaluated. The results demonstrate that, under representative meteorological conditions in spring, summer, autumn, and winter, the optimized collector–evaporator increases the system COP by approximately 4.13%, 4.72%, 4.50%, and 3.04%, respectively. These findings provide practical guidance for the structural design and optimization of collector–evaporators in DX-SAHP systems.

Applied Sciences doi: 10.3390/app16104766

Authors: Guanghui Li Yue Qian Yong Cheng You Huang Lingbin Zeng Shixin Yao Xingkong Ma

Existing multi-view clustering approaches based on matrix factorization often fail to jointly capture global high-order correlations and local view-specific characteristics, and they typically suffer from instability in generating final clustering labels. To overcome these limitations, this paper presents a multi-view subspace clustering method termed dual-tensor constrained multi-view subspace clustering (DTCMVSC). Specifically, for each view, we learn an independent latent representation matrix, a projection matrix, and a basis matrix. The latent representations and projection matrices are stacked into third-order tensors, upon which tensor nuclear norm regularization is imposed to simultaneously exploit consensus structures and complementary information across views. Additionally, a consensus regularization term and adaptive view weights are introduced to align the latent representations of different views toward a unified consensus subspace. The resulting optimization problem is efficiently solved under the ADMM framework, after which a similarity matrix is constructed from the consensus representation and spectral clustering is performed to obtain the final labels. Experimental evaluations on six benchmark datasets demonstrate the superiority of DTCMVSC. Specifically, it achieves an ACC of 86.10% on CMU and an NMI of 94.17% on ORL, surpassing even the lowest-performing state-of-the-art baselines by 63.08 and 18.53 percentage points, respectively.

Applied Sciences doi: 10.3390/app16104765

Authors: Guoliang Zhao Dingtian Lin Yaxin Tan Xitong Zhang Shence Zhang Baoquan Yang Junteng Wang Xinyi Tang

Regular inspection of defects like sprayed concrete cracking and water seepage is crucial for the long-term safety of reservoir slopes in hydraulic engineering. Traditional manual inspections suffer from low efficiency and high cost. This paper presents a weighted Traveling Salesman Problem (TSP) model established by a Genetic Algorithm (GA) to optimize Unmanned Aerial Vehicle (UAV) inspection paths for these slopes. The model integrates UAV acceleration and deceleration physics. It weights the flight distance, converting it into flight time, and uses 3D-coordinate data to form the objective function. We calibrated key parameters, including acceleration and speed thresholds, by fitting displacement-time quadratic functions to field data from a DJI Matrice 350 RTK UAV. Tests on multiple slope models show the weighted GA optimizes the planned path by 46.2%, improves average inspection efficiency by 7.90% over an algorithm simulating human decision-making, and by 7.66% over a standard (non-weighted) GA. This work provides a reference for intelligent path planning on reservoir slopes and is applicable to similar scenarios like highway and railway slopes.

Applied Sciences doi: 10.3390/app16104764

Authors: Yerzhan Sapatayev Kuanysh Samarkhanov Vitaliy Yakovlev Irina Prozorova Vadim Bochkov Vitaliy Pospelov

During the decommissioning of the BN-350 reactor, the spent nuclear fuel (SNF) was transferred to long-term dry storage in TUK-123 transport and storage cask systems designed for transportation and long-term storage with a design service life of approximately 50 years. The TUK-123 system consists of a UKKh-123 storage package, which is a sealed metal-concrete cask (MCC), and a protective-damping cover (PDC) used only during transportation. Radiation characteristics are a key quantitative criterion for assessing the safety of long-term storage in the absence of direct access to fuel and cask components. This paper presents the results of a computational study of the radiation characteristics of BN-350 SNF under dry storage conditions as of 1 January 2025. Spatial distributions of the ambient dose equivalent rate were determined for normal storage conditions and for accident scenarios involving partial failure of fuel assembly (FA) canisters and fuel redistribution. It was established that in the near-field region, the dose fields are formed predominantly by long-lived fission products and activation nuclides, whereas the neutron contribution is determined mainly by the spontaneous fission of actinides and (α, n) reactions. The results obtained provide a quantitative basis for assessing the radiation safety of long-term BN-350 SNF dry storage.

Applied Sciences doi: 10.3390/app16104763

Authors: Long Huu Nguyen Minh Nhat Phung Hung Thanh Nguyen Cuc Thi Kim Nguyen Hai Hong Hoang

Cranial reconstruction is a critical task in computer-assisted surgery, requiring both high geometric accuracy and computational efficiency for patient-specific implant design. While recent deep learning approaches, particularly 3D UNet-based models, have demonstrated promising performance, most studies primarily focus on architectural modifications, with limited attention to the systematic impact of data preparation and training strategies on reconstruction quality. In this study, we present a comprehensive data-centric investigation of key factors influencing the performance of a baseline 3D UNet for cranial defect reconstruction. Specifically, we analyze the effects of data preprocessing (denoising), dataset organization (ordered versus randomized training), defect morphology diversity, convolutional kernel size, and loss function design under controlled experimental conditions. Experiments were conducted on 250 complete skulls (NRRD format) from the MUG500+ dataset, with synthetically generated defects across multiple anatomical regions. From these volumes, a total of 3750 training samples were generated, including: (i) 1250 noisy samples with diverse defect morphologies, (ii) 1250 denoised samples with ellipsoidal defects, and (iii) 1250 denoised samples with multiple defect types. The results demonstrate that data-centric and training-related factors have a substantial impact on model performance, in several cases exceeding the influence of architectural design. In particular, denoising significantly improves boundary stability and reduces geometric error, while incorporating diverse defect morphologies enhances generalization to unseen shapes. Additionally, ordered training contributes to more stable convergence, and an optimal kernel size of (3 × 3 × 3) achieves the best trade-off between accuracy and computational efficiency. A hybrid Dice and boundary loss further improves boundary precision compared to conventional Dice loss. The optimized configuration achieves a Dice Similarity Coefficient of 0.94 and a Hausdorff Distance of 3.8 mm, with an average inference time of 0.004 s per case. These results demonstrate that data-centric optimization can be as influential as, or even more impactful than, architectural design in cranial defect reconstruction. The findings provide practical and reproducible guidelines for developing efficient, robust, and clinically applicable deep learning-based systems for patient-specific cranial implant design.

Applied Sciences doi: 10.3390/app16104749

Authors: Yuhan Wei Zhengjie Yuan Guorui Feng Yingjing Wei Yin Li Kai Hou

To address underground shotcrete support scenarios with potential radiation-protection requirements, a bismuth oxide/clay functional filler was incorporated into a baseline shotcrete formulation. Functional filler dosage, calcium formate dosage, and PCE dosage were selected as variables, and Box–Behnken response surface methodology was used to establish quadratic regression models for 28 d compressive strength, fluidity, and bond strength. Representative optimized mixtures were further evaluated by MCNP5 simulation, gamma-ray air-kerma attenuation tests, and SEM. The models showed good fitting and predictive performance within the investigated design space. Functional filler dosage mainly controlled compressive strength and bond strength, whereas PCE dosage dominated fluidity. Under the constraints of compressive strength ≥ 25 MPa, fluidity of 160–170 mm, and bond strength ≥ 0.8 MPa, three representative mixtures were selected for shielding-, strength-, and interface-priority strategies. Simulated and measured results showed consistent shielding-performance rankings, and the optimized mixtures exhibited higher gamma-ray attenuation than the blank mixture. BBD26 achieved the highest shielding performance, with measured shielding rates of 65.51% and 51.54% at 661.7 keV and 1.25 MeV, respectively. Thickness-gradient tests indicated exponential attenuation, while SEM revealed differences in Bi-bearing particle distribution and matrix continuity.

Applied Sciences doi: 10.3390/app16104762

Authors: Emanuela Genovese Davide Borrello Clemente Maesano Vincenzo Barrile

The use of satellite products for the identification of landslide-prone areas and zones affected by subsidence represents a research field in continuous evolution, thanks to the possibility of integrating radar data in multiple ways. Such information can be used as a static feature, as a criterion for the selection of landslide-absence samples, or as a true dynamic input. This work adopts the latter perspective, proposing an integrated framework of backscatter analysis and SBAS-InSAR analysis for the identification and characterization of landslide-affected areas. GRD images were preprocessed and analyzed through Google Earth Engine, from which temporal backscatter descriptors useful for highlighting instability signals were extracted. These were then combined with the results of the SBAS-InSAR technique. The integration of the two components allows the synergistic combination of different information derived from satellite products together with data characterizing the territory, improving the ability to identify areas subject to instability. The results, obtained over a portion of territory in Southern Italy, show that the inclusion of dynamic Sentinel-1 data significantly improves the identification of susceptibility areas. The synergistic use of dynamic SAR information allows the model to move beyond static or single-source susceptibility mapping, providing an updatable framework that supports near-real-time monitoring. The outputs are integrated into a 3D/4D WebGIS with Decision Support System (DSS) connotation, which further enhances the practical applicability of the methodology by enabling the real-time visualization and interpretation of the results.

Applied Sciences doi: 10.3390/app16104758

Authors: Guidan Zhong Binbin Zhao Yuan Xue

The carbon allowance price series exhibits nonlinearity, non-stationarity, and high noise due to multiple factors. Accurate forecasting is crucial to the stability of the carbon market and to resource allocation. This paper proposes a forecasting framework using multi-scale decomposition and a TCN–LSTM hybrid model. First, the original carbon allowance price series is decomposed using CEEMDAN optimized by PSO. Then, VMD performs secondary decomposition of complex components based on sample entropy. Next, transfer entropy identifies causal relationships between each component and the original series, enabling reconstruction based on causality. Finally, a TCN–LSTM model uses reconstructed sequences to forecast carbon prices. The method achieves high-precision short-term forecasts using only the carbon allowance price series, avoiding reliance on external variables. Empirical results on the Hubei carbon market show an optimal lag of 3, with R2 = 0.8873, outperforming the single LSTM and TCN models and achieving a lower RMSE. The forecast using January–March 2026 data shows stable carbon prices with slight fluctuations. This study provides a reliable method for data-constrained short-term carbon price forecasting, supporting decision-making and policy assessment.

Applied Sciences doi: 10.3390/app16104759

Authors: Agata Paneth Aleksandra Szopa Karolina Wojtunik-Kulesza Joanna Lachowicz-Radulska Anna Serefko Izabela Korona-Głowniak Anna Oniszczuk Katarzyna Dzitko Nazar Trotsko

The increasing prevalence of fungal infections caused by Candida species, together with rising antifungal resistance, highlights the urgent need for novel therapeutic agents with improved efficacy and safety. In this study, a series of 3-substituted rhodanine derivatives (3–6) were synthesized and evaluated as potential multifunctional compounds combining antifungal activity and ferric reducing capacity in the FRAP assay. The compounds were characterized using FT-IR and NMR spectroscopy and assessed for their physicochemical and pharmacokinetic profiles through in silico ADME analysis. Biological evaluation revealed that compounds 3 and 5 exhibited the most promising antifungal activity against a panel of clinically relevant Candida strains, with compound 5 demonstrating broad-spectrum, predominantly fungicidal effects. In contrast, compounds bearing a bulky 4-chlorobenzoyl substituent (4 and 6) showed reduced activity, indicating the importance of structural features for antifungal efficacy. Ferric reducing capacity assessment using the FRAP assay confirmed that all compounds possess reducing activity, with compounds 3 and 6 showing the highest potential. Safety evaluation using zebrafish (Danio rerio) embryos and larvae revealed concentration-dependent toxicity for all compounds. Notably, compounds 5 and 6 exhibited significant embryotoxicity and neurobehavioral effects at low micromolar concentrations, whereas compound 3 demonstrated a more favorable safety profile, with minimal impact on development and locomotor activity. Taken together, these results indicate that compound 3 provides a balanced combination of antifungal activity and reduced toxicity, while compound 5 represents a highly active but more toxic derivative. The observed structure–activity relationships emphasize the importance of carefully tuning substituent-dependent properties to optimize both biological activity and safety, supporting the continued investigation of rhodanine-based multifunctional antifungal agents targeting fungal proliferation and ferric reducing properties.

Applied Sciences doi: 10.3390/app16104752

Authors: Yu Zhu Zhengziyan Li Dejun Gao Yong Liu

To investigate the slope stability of the north bank anchorage of the Yellow River Three Gorges Bridge during foundation pit excavation and operational stages, a true three-dimensional geological model was established using Rhino6 and numerical simulations were performed using FLAC3D7.0, supplemented by stereographic projection kinematic analysis and the shear strength reduction (SSR) method. Systematic simulations were conducted for foundation pit excavation, main cable load application, heavy rainfall, and two seismic loading conditions, and the deformation characteristics and plastic zone evolution patterns of the slope under different conditions were analyzed. The stereographic projection kinematic analysis indicates that the dominant discontinuity sets do not constitute kinematically admissible planar sliding, wedge sliding, or toppling failure modes, confirming the validity of adopting a continuum model. The numerical simulation results show that the maximum slope displacement after foundation pit excavation is 13.13 mm, with the plastic zone exhibiting a discontinuous scattered distribution, and the slope is overall stable. After the application of the main cable load, the maximum displacement decreases to 7.86 mm; the counterweight effect of the anchorage self-weight significantly improves the deep stability, while the horizontal cable force generates a wedge-shaped shear plastic zone at the slope toe. Under heavy rainfall conditions, rock mass saturation leads to an increase in the maximum displacement to 11.76 mm with expanded plastic zone volume, where the deterioration of strength parameters and the increase in pore water pressure are the primary causes of reduced stability. Under seismic conditions, the maximum displacements under the natural and artificial seismic waves are 15.83 mm and 17.29 mm, respectively, exhibiting a significant elevation amplification effect with extensive plastic zone development in the shallow surface layer. The shear strength reduction analysis yields factors of safety of 2.4 and 2.27 for the heavy rainfall and seismic conditions, respectively, both significantly exceeding the code requirements, demonstrating that the slope possesses an adequate safety margin under extreme loading conditions.

Applied Sciences doi: 10.3390/app16104757

Authors: Jiajun Yuan Ruohua Zhou Cunhang Fan

Speech enhancement (SE) aims to recover clean speech from noisy speech while preserving intelligibility, speaker identity, and runtime efficiency. Existing language-model (LM)-based methods may lose fine acoustic details due to discretization, whereas diffusion models often require many iterative denoising steps. This study proposes an efficient speech enhancement framework based on flow matching and a gated bidirectional Mamba2 backbone. The model predicts a continuous velocity field in the Mel-spectrogram domain and introduces a DiMamba block that captures past and future context through shared-weight bidirectional state-space modeling with adaptive gating. Experiments on the DNS Challenge test set and additional VoiceBank test data show that the proposed method achieves strong perceptual quality and speaker preservation while substantially reducing inference cost. The model reaches a real-time factor of 0.31, more than five times faster than diffusion baselines, and achieves a word error rate of 4.7% and a quality mean opinion score of 3.58. These results indicate that flow matching combined with gated bidirectional Mamba2 provides an effective quality–efficiency trade-off for offline speech enhancement.

Applied Sciences doi: 10.3390/app16104756

Authors: Xusheng Zhang Gianluca Di Flumeri Alessia Vozzi Andrea Giorgi Patrizia Cherubino Arianna Trettel Stefano Menicocci Gianluca Borghini Fabio Babiloni Pietro Aricò Vincenzo Ronca

Background: Digital learning platforms increasingly leverage semantic web technologies to support interoperable and adaptive e-learning. However, the usability and cognitive impact of web-based authoring tools are still mainly assessed through subjective questionnaires and interaction logs, which provide limited time resolution and weak diagnostic power for identifying specific interface bottlenecks. Methods: We propose a multimodal evaluation of SOULSS, a semantic web-oriented platform for creating and optimizing digital learning contents. Eighteen participants completed an authoring workflow organized into three macro-segments (tutorial, initialization, module creation) while wearable electroencephalography, electrodermal activity, photoplethysmography, and eye tracking were recorded; objective metrics were analyzed both across macro-segments and within predefined micro-activities, whereas subjective engagement was collected after each macro-segment using the UES-SF. Results: Objective measures indicated increased EEG-derived mental workload and stress, higher tonic sympathetic arousal, and greater visual search and interaction effort during initialization and module creation, while UES-SF scores were lower during initialization. Fine-grained analyses localized critical elements to tutorial navigation options, the new course entry point, and spoiler-related controls. Repeated-measures correlations linked subjective scores with objective markers and supported an association between stress-related activation and delayed visual discovery. Conclusions: Integrating neurophysiological and eye tracking measures enables a more diagnostic assessment of semantic web-based authoring platforms than questionnaires alone, providing actionable evidence for iterative UX optimization and supporting a more user-centred design of digital educational tools.

Applied Sciences doi: 10.3390/app16104755

Authors: Shaohui Li Weijia Huang Kun Xie Chenglin Cai

To address tracking accuracy degradation caused by noise in sensor observations, a maneuvering target tracking algorithm based on an improved Received Signal Strength Indicator (RSSI) ranging model is proposed for Wireless Sensor Networks (WSNs). The traditional deterministic ranging model is replaced by a backpropagation neural network optimized via the Osprey Optimization Algorithm (OOA-BP), which directly maps noisy RSSI measurements to precise physical distances. Filtering and tracking are executed using an Extended Kalman Filter (EKF) combined with a uniform circular motion model, demonstrating the robustness of the observation model across dynamic predictions. Simulation results validate the efficacy of the proposed framework. In the distance estimation phase, the OOA-BP model reduces the average ranging error to 0.04 m. During dynamic tracking, the integrated OOA-BP-EKF architecture demonstrates superior tracking performance compared to standard frameworks, reducing the Root Mean Square Error (RMSE) by 15.33% and 59.89% compared to GA-BP and standard BP algorithms, respectively.

Applied Sciences doi: 10.3390/app16104753

Authors: Baichuan Zhu Guoqiang Liu

Asphalt pavement maintenance is critical for road service life and traffic safety, yet conventional crack detection and Pavement Condition Index (PCI) assessment methods suffer from inefficiency and subjectivity. This paper presents an integrated system for intelligent crack recognition and automated PCI evaluation, aiming to bridge the gap between automated identification and intelligent assessment. The system employs an optimized YOLOv11l-seg model for precise crack segmentation and geometric parameter extraction, and introduces a refined PCI model incorporating geometry-based adjustment factors for differentiated scoring. Using unmanned aerial vehicle (UAV) data, a fully automated workflow is established—from image acquisition and stitching to crack detection, PCI calculation, and result visualization. Experimental results demonstrate the accuracy of extracted crack parameters and the superior discriminative capability and engineering rationality of the proposed PCI model over conventional approaches. The generated panoramic condition maps provide intuitive visual support for maintenance decision-making. This research validates the feasibility of a fully auto-mated closed-loop system from detection to evaluation, offering a practical solution for intelligent pavement maintenance.

Applied Sciences doi: 10.3390/app16104754

Authors: Maria-José Morais Hélder S. Sousa José C. Matos Madalena Araújo

Railway track assets may suffer from different types of degradation due to aging, traffic conditions, environmental conditions, and natural and man-made hazards, which affect their performance in terms of reliability and availability, as well as passenger safety and comfort. By knowing which variables influence the degradation and performance of railway tracks, and the most appropriate maintenance and renewal actions, it is possible to define the most appropriate Performance Indicators. The use of predictive models to forecast these indicators can support the decision-making process during the maintenance management over time. In this work, a proposal including the selection of the most appropriate Performance Indicators is presented, together with a brief overview of predictive models used for railway systems. Based on that, a holistic framework to forecast the railway track performance aiming to support the decision-making process is given and its applicability is discussed. The proposed framework integrates the selection, processing, and aggregation of different types of Performance Indicators within a predictive modelling framework, enabling the analysis even when data availability is limited. The applicability of the framework is demonstrated through an illustrative example based on inspection data. The results illustrate the evolution of track condition states over time within a probabilistic framework.

Applied Sciences doi: 10.3390/app16104750

Authors: Chunkai Yan Tianyu Cheng Bojun Zhou Ling Jiang Jiahao Zhao Juping Gu

Adjacent segment degeneration remains a major biomechanical concern after lumbar fusion, whereas fully dynamic topping-off constructs may provide an alternative strategy by preserving segmental motion and unloading degenerated discs. In this study, a three-dimensional nonlinear finite element model of the L1-L5 lumbar spine with L3-L5 double-segment degeneration was developed to compare transforaminal lumbar interbody fusion (TLIF)-based pedicle screw fixation systems (PSFS) and Bioflex-based pedicle screw dynamic stabilization systems (PSDSS). Three interspinous process spacers, namely DIAM, Wallis, and Coflex-F, were implanted at L3-L4, and three pedicle screw diameters of 6.5, 5.5, and 4.5 mm were evaluated under flexion and extension to quantify screw-rod parameter sensitivity. The results showed that both TLIF- and Bioflex-based topping-off constructs reduced intradiscal pressure (IDP) and restricted excessive range of motion (ROM) at the transition segment, especially during extension, with a maximum L3-L4 IDP reduction of 39.49% compared with the degenerated model. Compared with fusion-based constructs, Bioflex-based PSDSS provided greater surgical-segment unloading, reducing L4-L5 IDP by 55.07% in extension and 25.30% in flexion. However, this motion-preserving effect was accompanied by higher pedicle screw stress sensitivity; in the 4.5 mm Bioflex model, the average L4 screw stress reached 15.62 MPa in flexion, representing a 51.71% increase compared with the 6.5 mm screw. In contrast, PSFS constructs showed greater stress variation in the rigid connecting rods. Overall, under the present modeling assumptions, Bioflex-based fully dynamic topping-off constructs showed more favorable disc unloading and transition-segment motion regulation than fusion-based configurations, but their biomechanical benefit should be balanced against diameter-dependent pedicle screw stability.

Applied Sciences doi: 10.3390/app16104751

Authors: Ying Zhao Wanqian Zhu Lingling Guo Bing Nan Xuying Lan Shui Liu Yongnian Zhou Jian He Chun Hu Huiting Chen Yingfeng Wu Shumin Yang Zhaohong Zhang Chunpeng Wang

A dedicated data acquisition system has been developed and commissioned for the tender-energy spectroscopy beamline BL16U1 at the Shanghai Synchrotron Radiation Facility. The system implements a distributed architecture integrating EPICS-based hardware control with the Bluesky experiment orchestration environment, supporting multiple X-ray absorption spectroscopy modes including transmission, total electron yield, total fluorescence yield, and partial fluorescence yield detection. A key technical feature is the hardware-level synchronization between a multi-channel silicon drift detector and a multichannel scaler, enabling precise timing for fluorescence-XAS measurements. A unified graphical interface based on Control System Studio provides streamlined experiment control and real-time data visualization. System validation using standard reference samples demonstrates successful acquisition of high-quality Cl K-edge XANES spectra in fluorescence mode, high signal-to-noise Co K-edge EXAFS data in transmission mode with extended k-space coverage up to 16 Å−1, and high-sensitivity Ti K-edge fluorescence XAFS on dilute (1–3%) TiO2 polymorphs. These results confirm the system’s capability for reliable, high-precision spectroscopy across the tender-energy range (2–16 keV), supporting both trace-element analysis and detailed local-structure determination. The fully integrated system is now operational at the beamline, providing a robust platform for advanced X-ray absorption studies in environmental, catalytic, and materials science.

Applied Sciences doi: 10.3390/app16104748

Authors: Jianhua Zhang Shuaiqi Pang Xiaohai Ren Yong Zhang Yuxin Du Geng Na

Post-disaster rescue scenarios often involve complex and variable terrains, imposing heterogeneous mobility requirements on different transport modes. Single-type vehicles face challenges in independently completing comprehensive rescue tasks. This study addresses the critical problem of coordinating heterogeneous aerial and ground vehicles to collaboratively plan relay rescue routes. To tackle the NP hard multi-terrain, multi-vehicle, and multi-route path planning problem, we propose a New Adaptive Multi-Strategy Particle Swarm Optimization algorithm (AMS-PSO-NEW). The algorithm features a synergistic integration of differential evolution’s multi-strategy mutation, SHADE-based adaptive parameter control, population diversity monitoring with restart mechanisms, and multi-level local search. A sequential hybrid mechanism is designed in which DE-generated trial vectors serve as reference positions for PSO velocity updates, enabling balanced global exploration and local exploitation. By leveraging adaptive parameter tuning, success history memory, and diverse population maintenance, AMS-PSO-NEW effectively overcomes premature convergence and low accuracy issues typical in discrete combinatorial optimization using traditional PSO, achieving a balanced global exploration and local exploitation. Performance validation is conducted over six rescue scenarios varying in scale and complexity, benchmarking AMS-PSO-NEW against nine algorithms: PSO, GA, NSGA-II, GWO, DE, ABC, CS, Q-learning, and MIP. Results demonstrate superior performance across four metrics (rescue success rate, average rescue time, total cost, and fairness), with significant improvements in high-complexity environments.

Applied Sciences doi: 10.3390/app16104747

Authors: Liling Zhu Zhipeng Wu

To address the accuracy limitations in identifying micro-scale and low-distinguishability defects, we proposes an improved EfficientNet model for wafer defect classification in semiconductor fabrication. In particular, we construct the model using EfficientNetV2 architectures as the backbone and introduce a multi-scale self-attention enhancement module to strengthen the capture capability for critical defect characteristics. This module consists of four parallel self-attention enhancement modules, aiming to obtain spatial context information at different levels and enhance relevant features through a self-attention mechanism. Meanwhile, we merge the manually extracted features of defects with the CNN’s fully connected layer, effectively compensating for the deficiency of automatic features in the differentiated representation of defects. The manual feature extraction module leverages image processing techniques to capture diverse morphological characteristics of defects including geometric features, moment features and texture features. We simulate and generate a lithography SEM image dataset with various types of defects based on the typical line-space structure and the ICCAD2019 mask pattern dataset. The total sample size of the wafer defect dataset is 1500, covering 15 typical defects with an average distribution. The classification performance of models is evaluated on the simulated defect dataset. The results indicate that the overall classification accuracy of the improved model reaches 96.60%, representing an improvement of 8.14% compared to the original EfficientNetV2. This demonstrates the superiority of the proposed model in addressing classification tasks involving micro-scale and low-distinguishability defects.

Applied Sciences doi: 10.3390/app16104746

Authors: Jiyun Woo Dae Kee Min Bong-Jae Lee Eui-Chan Jeon

In the semiconductor industry, fluorinated gases with high global warming potential (GWP) are recognized as significant sources of greenhouse gas emissions. This study presents a measurement-based analysis of a 300 mm wafer etching process using CF4, C4F6, and C4F8 gases. The use rate of gas (Ui), unreacted fraction (1-Ui), and by-product generation rate (Bi) were evaluated under varying plasma intensity conditions. The results show that the unreacted fraction (1-Ui) decreased with increasing plasma intensity for all process gases, indicating enhanced gas dissociation efficiency. In contrast, the by-product generation rate (Bi) exhibited non-linear behavior due to the complex interplay of dissociation and recombination reactions within the plasma. Furthermore, the measured Ui and Bi values showed significant deviations from the default emission factors provided in the 2006 IPCC Guidelines and the 2019 Refinement. Variability analysis based on the coefficient of variation (CV) was conducted using measurements obtained under different plasma conditions (n = 3). The results indicate that Ui exhibited relatively stable behavior with low variability (CV < 0.3), whereas Bi showed higher variability depending on the type of by-product gas, reflecting stronger sensitivity to process conditions. These findings highlight that IPCC default emission factors may not adequately reflect actual process conditions and underscore the importance of incorporating measurement-based, condition-dependent variability into emission estimation.

Applied Sciences doi: 10.3390/app16104745

Authors: Run Liu Ming Shi Wenxiang Zheng

The integrated subway station structure means that there are no construction joints between the station and the accessory structure, and the station structure and the ancillary structure are considered as a whole. This paper focuses on the seismic response of integrated subway stations in liquefied sites. In this paper, the numerical simulation method is used to establish a calculation model under the two working conditions of considering liquefaction and not considering liquefaction. The results are as follows. (1) The liquefaction area is mainly distributed on both sides of the accessory structure, at the junction of the base plate of the accessory structure and the side wall of the station and at the base plate of the station structure, and the liquefaction area at the junction of the base plate of the accessory structure and the side wall of the station gradually disappears with increased in seismic intensity. (2) The amount of uplift of the structure under liquefaction conditions is proportional to the seismic intensity, and at the same time the asymmetry of the seismic wave causes the structure to rotate during the uplift. (3) The inter-story displacement difference, inter-story displacement angle, and swing amplitude of the structure under the liquefaction condition are smaller than those under the non-liquefaction condition. (4) The axial pressure ratio of the station hall level and equipment level under the liquefied condition is smaller than that under the non-liquefied condition, but for the platform level, regardless of the seismic intensity, the axial pressure ratio of its columns under the liquefied condition is larger than that under the non-liquefied condition. It can be seen that soil liquefaction causes uplift of the structure, while the asymmetry of seismic waves causes the structure to rotate. Liquefaction of the soil weakens the strength of the seismic waves and thus reduces the deformation of the structure. The pore pressure caused by soil liquefaction acts directly on the station base plate, causing an increase in the column axial pressure ratio at the platform level, while the station hall level and equipment level, which are farther away from the base plate, are not affected by their column axial pressure ratios.

Applied Sciences doi: 10.3390/app16104744

Authors: Giulia Bernagozzi Rossella Arrigo Alberto Frache

The increasing use of recycled polypropylene (rPP) in technical and outdoor applications requires strategies to limit photo-oxidative degradation while maintaining adequate performance after reprocessing. In this work, the photo-oxidative stability of rPP films was investigated under accelerated weathering conditions, focusing on the effect of a commercially available additive, Nexamite® R201 (NEX), previously shown to partially restore PP molecular weight after reprocessing. Films of rPP and rPP containing 5 wt.% NEX were produced by cast extrusion and exposed to cyclic UVA irradiation and water condensation in a QUV chamber, and the evolution of the functional and structural degradation of the materials was monitored as a function of aging time. Spectroscopical analyses showed progressive oxidation in both systems, with carbonyl growth starting after an induction period of about 200 h. A faster increase in the carbonyl index was observed for rPP containing NEX, indicating that the additive does not improve chemical oxidative resistance under the adopted conditions. However, NEX significantly enhanced the retention of mechanical properties during aging, with higher elongation and stress at break compared with unmodified rPP, thus delaying embrittlement. Overall, the results show that the investigated additive effectively mitigates the loss of mechanical integrity during photo-aging, likely as a consequence of the macromolecular restructuring induced during reprocessing.

Applied Sciences doi: 10.3390/app16104742

Authors: Zoran Zorić Maja Repajić Antonela Ninčević Grassino Melita Mokos Branka Maričić Sanja Dragović

This study evaluated the effects of abiotic factors and extraction conditions on the yield, chemical composition, and antimicrobial activity of essential oil (EO) from Pistacia lentiscus L. leaves collected at four Adriatic locations during three phenological stages. Steam distillation was performed at 0.3, 0.7, and 1 bar. EO yield increased significantly with pressure, reaching a maximum at 1 bar, while the flowering stage provided the highest yields overall. Leaves from Vela Luka produced the highest EO yield, whereas Pag samples yielded the least. GC–MS analysis identified 56 components, accounting for 99.19–99.99% of total EO, with α-pinene, limonene, myrcene, and β-pinene as the dominant constituents, confirming a monoterpene-rich chemotype. All EO samples showed low but measurable inhibitory activity against Escherichia coli AB1157 and Erwinia amylovora EaED, as assessed by the disk diffusion method. Pearson correlation and PCA analyses indicated a positive association between monoterpene content and inhibition zone diameter against E. coli, and a positive association between monoterpene alcohol content and inhibition against E. amylovora. As antimicrobial activity was assessed exclusively by the disk diffusion method, the present findings may serve as an indicative basis for future investigations into the relationship between EO chemical composition and antimicrobial potential, and they require validation through quantitative, standardized antimicrobial testing.

Applied Sciences doi: 10.3390/app16104743

Authors: Qingyu Zhang Yuefeng Li Xudong Pan Xiaolei Wang Linlin Chen Zengle Li

Due to the complex geological conditions and the low ability to drill into deep strata, the current traditional mechanical methods in deep well drilling have the problems of low production efficiency and high working costs. Aiming at the problem of modularization of industrial applications of pulse generators in high-voltage electric pulse rock breaking technology, a high-voltage electric pulse rock breaking system based on the compound booster method is proposed in this study, and the miniaturization of high-voltage electric pulse rock breaking equipment is realized. A small pulse generator based on the compound booster method is designed. A set of high-voltage electric pulse rock-breaking experimental platforms is set up with a multistage BK booster circuit and a voltage-doubling circuit. Under the condition that the output discharge frequency is 5 Hz and the peak voltage is not less than 500 kV, the platform is used to carry out the fracturing experiment of ~3000 m Yingcheng formation rhyolite. COMSOL Multiphysics software was used to build a rock breakdown simulation model, and the rock-crushing process was revealed under the action of the high-voltage electrical pulse through simulation. The results show that the experimental platform can meet the requirements of the high-voltage electric pulse rock-breaking experiment, and the combined pressure booster method expands the design idea for the industrial application of high-voltage electric pulse drilling technology.

Applied Sciences doi: 10.3390/app16104741

Authors: Leah A. Constantinou Robin N. Kaur Gary S. Caldwell

Zeolites are naturally abundant, low-cost aluminosilicate minerals commonly found in sedimentary rock. The surface chemistry of zeolite can be modified via cationic surfactant loading, termed a surfactant-modified zeolite (SMZ), that can be used as an antimicrobial technology in water treatment processes. This raises the possibility that SMZs could be utilised to treat blooms of harmful algae and cyanobacteria; however, there is a lack of understanding of the toxicity of SMZs to non-target microorganisms, including non-problematic algae and cyanobacteria. To address this knowledge gap, this research investigates whether hexadecyltrimethylammonium-bromide (HDTMA-Br) SMZs are toxic to the cyanobacterium Synechococcus elongatus and the microalgae Chlorella vulgaris, Nannochloropsis oculata and Duniallela salina. The cells were exposed to natural zeolite, HDTMA-Br surfactant and HDTMA-Br SMZ for 24 h and analysed 2- and 26 h post-exposure via flow cytometry and imaging pulse amplitude modulated fluorometry. There was an overall trend of reduced cell density in the SMZ and surfactant treatments. The SMZ treatment reduced the effective PSII quantum yield (Y(II)) but increased the quantum yield of regulated energy dissipation for C. vulgaris. When exposed to the surfactant treatment, no Y(II) signals were detected from any species. We conclude that SMZs are toxic to non-target microorganisms, with resilience dependent upon cell wall structure.

Applied Sciences doi: 10.3390/app16104740

Authors: Mostafa Taghizade Firouzjaee Ghazal Naderi Ross Gore Neda Moghim

The spread of fake news on online social networks is driven by imitation-based user behavior and network topology, often leading to persistent misinformation clusters and echo chambers. In this study, we develop a spatial evolutionary game-theoretic framework in which agents update their latent opinions through payoff-biased imitation, while external fact-checkers act as non-imitative intervention nodes. Building on this formulation, we propose an adaptive, boundary-aware intervention mechanism that dynamically regulates both the density and spatial allocation of fact-checkers according to real-time system conditions. Competing information clusters are identified through local neighborhood composition, enabling boundary nodes, i.e., interfaces between fake-news and non-fake-news regions, to be detected and targeted where strategic shifts are most likely to occur. Importantly, fact-checking is modeled as an external intervention that may induce a probabilistic lasting correction on agents’ latent opinions after removal, capturing more realistic post-intervention behavior. Unlike static strategies that assume fixed fact-checker distributions, the proposed approach continuously reallocates interventions toward structurally critical regions, while adaptively adjusting resource intensity based on misinformation prevalence. Extensive simulations on small-world, scale-free, and random networks show that the adaptive model consistently outperforms static baselines, reducing the final fake-news prevalence by over 90%, accelerating suppression, and improving overall system efficiency. Statistical tests confirm the significance of these improvements (p<0.001), while sensitivity analyses demonstrate robustness across parameter settings and intervention assumptions.

Applied Sciences doi: 10.3390/app16104739

Authors: Gengquan Li Yingkang Yao Yingguo Hu Rui Nie Congcong Hou

The long-term safety of rockfill dams is critically dependent on the quality of the filling material. Therefore, it is essential to predict the blasting grading distribution scientifically and accurately. This paper proposes an engineering-scale numerical simulation method to predict the grading distribution of hydraulic-grade rockfill material and verify its effectiveness through major hydropower engineering applications. Firstly, based on the mechanical mechanism of rock fragmentation and relevant experimental data, the critical blasting damage threshold for rock detachment is determined. Then, the threshold is used to identify the boundaries of blasted rock blocks, enabling the quantitative derivation of grading curves. The method is applied to simulate the blasting mining of fill materials at the Lianghekou Hydropower Station. A comparison with field screening data demonstrates that the method effectively simulates the spatial distribution of blast-induced block sizes. Furthermore, numerical simulations investigate the influence of hole arrangements and delay times on fragmentation. The results indicate a significant difference in the grading distribution characteristics between rectangular and staggered hole arrangements, with the rectangular arrangement conducive to producing a continuously non-uniform gradation. Delay time also markedly impacts fragmentation, primarily affecting the distribution of medium and large-sized blocks. The mean fragment size initially decreases and then gradually increases with longer delay times.

Applied Sciences doi: 10.3390/app16104738

Authors: Huang Zhang Guo Wu Wang

To address the issues of severe goaf collapse, difficulties in secondary extraction, and insufficient pillar stability encountered during the mining of high stopes north of Line 60 at the Dongguashan Copper Mine, this paper takes these high stopes as the research object. Based on an analysis of the engineering geological conditions, goaf failure characteristics, and current mining status in this area, a study on pillar stability and the mechanical behavior of combined mining is conducted. Given the susceptibility of pillars with high aspect ratios to bending instability, the secondary extraction pillar is simplified as a rod with fixed ends. A mechanical model for the triangular pillar’s stability is established, the critical instability equation is derived, and the influence of the reserved width on the pillar’s critical stress and safety factor is analyzed. Subsequently, based on the critical instability equation, the relationship between the reserved pillar width and critical stress is obtained to optimize the pillar dimensions. Simultaneously, to mitigate the adverse effects of primary stope collapse on secondary extraction, optimized schemes such as three-stope combined mining and two-stope combined mining are proposed. A mechanical model for combined mining is established based on the Protodyakonov’s arch theory to analyze the stress distribution characteristics of the surrounding rock in the goaf under different mining schemes. The calculated stress of the original rectangular pillar is 29.01 MPa. When the reserved width exceeds 4 m, the pillar safety factor becomes greater than 1.6, satisfying the stability requirement. In addition, three combined mining schemes were compared using Protodyakonov’s arch theory. The goaf spans of the three schemes are 40 m, 26.6 m, and 36 m, respectively. The results indicate that the two-stope combined mining scheme transfers the main roof load to the adjacent ore body and backfill, reducing the load borne by the barrier pillar and providing a better balance between safety and production efficiency. The proposed framework, integrating field goaf detection, pillar buckling analysis, reserved-width optimization, and combined mining comparison, provides a practical method for the stability control and secondary recovery of deep high stopes.

Applied Sciences doi: 10.3390/app16104737

Authors: Jiajie Lu Viviana Desantis Marco Brando Mario Paracchini Marco Marcon

Low-light enhancement technologies are of great significance for visual driver assistance applications and autonomous driving systems. Infrared vision can improve nighttime visibility but also faces challenges of low resolution and lack of color information. This paper presents a unified framework for RGB-guided infrared super-resolution and infrared-visible fusion that achieves high-resolution output under limited computational resources. Our approach employs a U-Net architecture with novel triple-grouped window attention (TGWA) encoding that captures global dependencies through grouped attention while reducing computational overhead, and adaptive multi-dilated convolutional (AMDC) decoding that adaptively selects optimal dilation rates using mixture-of-experts-inspired routing. Experiments on multiple datasets achieve competitive super-resolution and fusion results with minimal computational complexity, while real-world downstream object detection validation confirms robust performance in challenging nighttime scenarios. Quantitatively, the proposed method achieves 28.744 dB/0.872 SSIM on PBVS24 and 31.424 dB/0.882 SSIM on HDRT-Night for 8× infrared super-resolution, reaches competitive fusion quality on both MSRS and HDRT-Night, and attains 69.4% mAP@0.5 in downstream object detection on FLIR_aligned, while requiring only 1.12 M parameters and 85.44 G FLOPs. This work provides new possibilities for seeing clearly in the dark.

Applied Sciences doi: 10.3390/app16104736

Authors: Lanbo Lai Xiaolin Wang Gholamreza Kefayati Eric Hu Kim Choon Ng

This paper proposed a novel desiccant evaporative cooling system integrated with shallow geothermal energy with three different configurations. The first two configurations (I and II) employed shallow geothermal energy for precooling and post-cooling, respectively, while Configuration III utilised geothermal energy for both precooling and post-cooling. The performance of these systems was examined and compared to a benchmark system, a conventional solid desiccant M-cycle cooling system, under various operating conditions. Furthermore, a case study was conducted to evaluate the viability of these schemes under a hot and humid climate in Darwin, Australia. The results indicated that all three configurations outperformed the benchmark system regarding supply air conditions and required a lower regeneration temperature to achieve similar cooling performance. Configurations I and III could maintain the supply air humidity rate below 15 g/kg and contribute up to 30.46% of dehumidification performance through the condensation effect in humid conditions. Configuration III exhibited the highest energy efficiency, with a thermal COP up to 0.82 under different humidity levels, and this system also consumed 37.27% less water than the benchmark system.

Applied Sciences doi: 10.3390/app16104733

Authors: Marceli do N. da Conceição Javier M. Anaya-Mancipe Henrique M. da Fonseca Roberto C. C. Ribeiro Rossana M. S. M. Thiré

The utilization of waste, defined as commercially worthless or discarded material, is becoming an increasingly important topic in the context of environmental material overload. Thus, the development of new products should integrate waste, adding commercial value to materials that would have otherwise been discarded. In this context, Material Extrusion (ME), the most widely used technique in Additive Manufacturing (AM), has introduced a new manufacturing model, opening opportunities for developing innovative products. On the other hand, during the beneficiation process of ornamental rocks, tons of mineral waste are generated. This study aims to develop a polylactic acid (PLA) filament using Beige Bahia marble waste as a raw material source via the ME technique. Compositional mapping through Energy Dispersive Spectroscopy (EDS) indicated that, as the mass fraction increased, particle clustering within the PLA matrix decreased. Mass compositions ranging from 0–30% mineral waste to PLA were evaluated. Gel Permeation Chromatography (GPC) showed that the PLA molar mass in the PLA00 and PLA30 compositions was 109,103 and 120,103 g.mol−1, respectively, indicating that the mineral waste helped preserve the polymer’s molar mass during material processing. An increase was observed in the elastic modulus. The total roughness profile demonstrated higher values for pure PLA, while the partial roughness profile showed higher noise due to the greater presence of particles on the surface. The final product exhibited characteristics like the original rock and could serve as an alternative when such features are desired.

Applied Sciences doi: 10.3390/app16104734

Authors: Xiaohui Yang Jiaxu Jin Lei Tan Fei Liu Yang Yang Ze Li

Accurate segmentation of tunnel lining joints is essential for intelligent tunnel inspection, yet it remains challenging because of texture interference, occlusion-induced discontinuity, and the slender topology of joints in complex in situ environments. This study proposes DASMambaNet, a dedicated segmentation network that integrates Dynamic Adaptive Inhibition Convolution for direction-aware feature extraction and background suppression, a Multi-scale Context-aware Mamba Fusion module for long-range continuity reasoning, and a Lightweight Alignment and Confidence-Modulated Directional Fusion module for boundary-sensitive decoding. Experiments were conducted on the self-constructed TJSD dataset containing 4997 annotated images collected from real tunnel sections. The proposed method achieved 94.12% Dice, 74.63% IoU, 98.86% pixel accuracy, and 88.39% precision, outperforming representative CNN- and Transformer-based methods. Qualitative and ablation results further showed that the network effectively suppresses background noise, restores the continuity of occluded joints, and improves boundary localization. These results indicate that DASMambaNet provides an effective and practical solution for automated tunnel lining joint segmentation and can support subsequent tunnel inspection and structural condition assessment.

Applied Sciences doi: 10.3390/app16104727

Authors: Emmanuel Arriola Jozal Carrido Mark Francis Sedano Ulysses Ante Prince William Lim Arvin Oliver Ng Renzo Wee Roider Pugal Toni Beth Lopez

This study explored the development of lattice-based panels for satellite applications using Direct Metal Laser Sintering and aimed to optimize lightweight, high-strength structures suitable for CubeSat deployment. Three lattice configurations, namely Body-Centered Cubic, Octet, and Gyroid, were evaluated. While Gyroid lattices exhibited the highest compressive strength at 13,825.8 N, the BCC lattice was selected for the final design due to superior manufacturability and weight reduction potential. The final optimized panel weighed 185.7 g, achieving an 11.4% reduction from the initial rib-type design and a 65.2% reduction from a solid panel. Finite Element Analysis and mechanical testing confirmed that the fabricated structures met the necessary mechanical requirements for aerospace launch conditions.

Applied Sciences doi: 10.3390/app16104735

Authors: Melody Jiale Chiam Violet Man-Chi Ko Pui Wah Kong

Topical analgesics are increasingly recognised as an essential part of treatment for painful musculoskeletal conditions, valued for their localised effectiveness and safer profile compared to systemic options. Recent evidence suggests that the effects of topical analgesics go beyond simply alleviating pain from sports injuries; they also enhance sports performance immediately after application. We conducted a local survey of 141 adults engaging in exercise to gain insight into the use patterns and perceptions of topical analgesics; 102 reported using over-the-counter topical analgesics. Fisher–Freeman–Halton analyses confirmed that usage habits were similar between younger (21–30 years) and older (≥31 years) age groups, as well as between those exercising more frequently (≥3 times per week) and less frequently (once or twice per week). Participants typically employed topical analgesics to alleviate muscle aches, pain, or soreness (n = 92, 90.2%), relieve joint pain or discomfort (n = 57, 55.9%), and/or reduce muscle stiffness (n = 39, 38.2%). Most applied them at home as needed (n = 71, 69.6%), with some participants using them after exercise (n = 60, 58.8%). Regarding the frequency of topical analgesic use, most participants used analgesics fewer than five times a year (n = 54, 52.9%). Over half of the participants viewed topical analgesics as effective or very effective. To conclude, our survey revealed that adults engaging in exercise commonly used topical analgesics to relieve musculoskeletal pain or discomfort as needed.

Applied Sciences doi: 10.3390/app16104731

Authors: Ting Pan Xingyu Wang Fei Li Zhipeng Yu Kai Liu Zhenglong Li Zhanghua Yin Siyan Hong Bingyuan Hong

Understanding the law of gas diffusion in soil is essential for pipeline risk assessment. A 3D CFD model of natural gas leakage and diffusion from buried pipelines was developed in Ansys Fluent using the Detached Eddy Simulation turbulence model, with soil as an isotropic porous medium. Six parameters—burial depth, pipe diameter, transportation pressure, leak hole diameter, hole shape, and hole orientation—were systematically investigated under an identical numerical framework. First Danger Time (FDT), Farthest Danger Range (FDR), and Ground Danger Range (GDR) were used as unified hazard metrics for quantitative cross-factor comparison. As burial depth increases, FDT and FDR increase monotonically, whereas GDR peaks at 2.0 m. Increasing pipe diameter reduces FDT, FDR, and GDR. Higher pipeline pressure shortens FDT and expands FDR and GDR. Leak hole diameter dominates: increasing from 5 to 200 mm reduces FDT to nearly zero. For equal cross-sectional area, rectangular slits yield the highest mass flow rate and largest danger range, while triangular holes produce the shortest FDT owing to strong corner-induced turbulence. Upward leaks give the shortest FDT but smallest FDR; downward leaks produce the largest FDR; side leaks cause the longest FDT but largest GDR. Factor influence ranking by FDT and FDR change is: leak hole diameter, burial depth, pipeline pressure, hole orientation, pipe diameter, and hole shape. These results provide a quantitative framework for hazard assessment, sensor layout optimization, and emergency-response prioritization in buried natural gas pipelines.

Applied Sciences doi: 10.3390/app16104730

Authors: Lidija Rihar Elvis Hozdić Mladen Perinić David Ištoković

Insulation-material manufacturers face increasing pressure to improve productivity, cost efficiency, energy performance and worker safety while maintaining stable quality in highly constrained production environments. Existing lean and smart-manufacturing studies often examine isolated tools, individual monitoring technologies or material-level sustainability, but fewer studies provide conservative plant-level validation of an integrated intervention in insulation-material production. This study therefore examines the optimization of insulation-material production in a human-supervised cyber–physical manufacturing system through an industrial before–after intervention. The framework combines bottleneck identification, value stream mapping, SMED, selective automation, preventive maintenance and KPI-based digital monitoring. The baseline system was constrained by manual crusher loading, long changeovers, inefficient pallet transport, repeated breakdowns, scrap and limited real-time visibility. After implementation, productivity increased from 7864 to 9000 kg/day (+14.5%), monthly production costs decreased from EUR 200,000 to EUR 180,000 (−10%), breakdown frequency fell from 5 to 3 events/month (−40%), scrap decreased from 5% to 3% (−40%), crusher loading time fell from 30 to 10 min/pallet (−66%), annual energy use dropped from 500 to 450 MWh (−10%) and reported safety incidents decreased to zero during the 12-month post-implementation observation period. An OEE-based surrogate model yielded pre- and post-state theoretical capacity estimates differing by less than 1%, supporting internal consistency. The results are interpreted as descriptive and practically meaningful before–after differences because the full raw monthly dataset is commercially sensitive and classical inferential testing was not performed. The study contributes by presenting a reproducible, conservative and human-supervised CPS-oriented plant-intervention protocol rather than by claiming a fully autonomous closed-loop CPS.

Applied Sciences doi: 10.3390/app16104732

Authors: Hengnian Qi Hao Wang Quanqing Liao Zijun Han Chu Zhang

Xinyu pear is one of the important pear cultivars in China. Owing to its rich nutritional composition, high quality, and distinctive flavor, it is highly favored in the market. In this study, near-infrared spectroscopy was employed to determine the soluble solid content (SSC) of Xinyu pears. To investigate the influence of spectral preprocessing on SSC prediction, near-infrared spectra of two batches of Xinyu pear samples were collected using the same portable spectrometer under different acquisition parameters, resulting in differences in spectral bands. A linear interpolation method was introduced to the first batch to generate a new dataset to match the dimensionality of the second batch, and a total of three datasets were used. Five preprocessing methods, including moving average smoothing (MA), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative (D1), and second derivative (D2), together with three regression models, namely partial least squares regression (PLSR), support vector regression (SVR), and convolutional neural network (CNN), were systematically evaluated and compared in terms of predictive accuracy. Overall, PLSR achieved the best prediction performance, followed by CNN and SVR. Certain differences in model performance were observed among the three datasets. In general, MA exhibited the best overall performance across different datasets and models. Although SNV and MSC were slightly inferior to MA, they showed relatively stable predictive accuracy. By contrast, prediction models based on derivative spectra generally performed poorly. To further exploit the complementary information contained in differently preprocessed spectra, a four-branch CNN model was constructed using raw spectra, MA-preprocessed spectra, SNV-preprocessed spectra, and MSC-preprocessed spectra as separate inputs. Based on the fused features extracted by the CNN, PLSR and SVR models were subsequently developed. The prediction correlation coefficients of the feature-fusion CNN model on the prediction sets of the three datasets were 0.8811, 0.8259, and 0.7064, respectively. For the original datasets of the first and second batches, the feature-fusion model outperformed all single-preprocessing models. For the dataset generated by linear interpolation, the predictive performance of the feature-fusion strategy was comparable across the three models; specifically, its accuracy in SVR exceeded that of all single-preprocessing models, while its accuracies in CNN and PLSR surpassed those of most preprocessing methods. These results demonstrate that integrating feature information from spectra subjected to different preprocessing methods is a feasible strategy for improving prediction accuracy. This study provides an effective reference for SSC prediction in Xinyu pears based on portable spectrometers.