Search Results (9,109)

Employees were required to adopt new working methods within a very short time frame during the COVID-19 period through digitalization. While digitalization has been largely perceived as an enabler during the pandemic, its impact on employee health and well-being remains complex and underexplored, particularly in the public sector, where employees have less discretion to adapt digital tools. This study examines how rapid workplace digitalization during COVID-19 affected employee health and well-being in the public sector. Drawing on the job demands–resources (JD-R) framework, we focus on three specific forms of digital work—digital meetings, digital clearance, and digital training—selected because they represent distinct theoretical pathways through which digitalization affects well-being, such as digital meetings and digital training can increase job demands that can deplete employee energy and increase stress, whereas digital clearance operates as a job resource that reduces bureaucratic hurdles and enhances autonomy. To test these ideas, this study uses data from the 2020 Australian Public Service Commission Census (n = 108,085), and applies ordinal and multinomial generalized structural equation modeling (GSEM) to assess the effects of three new ways of working—digital meetings, digital clearance, and digital training—on employees’ health and well-being, as well as the mediating roles of organizational support. The results demonstrate that while digital clearance is positively associated with employee health and well-being, digital meetings and digital training are negatively associated. Organizational support mediates these relationships, underscoring its importance in mitigating adverse effects. These findings highlight the mixed consequences of digitalization for public employees’ health and well-being and point to the need for supportive organizational strategies in times of crisis. As a practical implication, this study suggests that public sector organizations should prioritize employee mental health in teleworking policies, adopt employee-centered digital transformation strategies that provide adequate resources and training support, and implement digital clearance processes that enhance employee well-being, particularly during a crisis. Full article

Model Predictive Control (MPC) has emerged as a promising approach for energy management in hybrid electric vehicles, enabling predictive optimization of powertrain operation. The energy management problem in parallel hybrid powertrains constitutes a Mixed-Integer Nonlinear Programming (MINLP) problem, combining continuous decision variables such as torque distribution with discrete decisions including engine on/off states and clutch engagement. This problem structure presents distinct challenges for different optimization approaches. Gradient-based methods such as Sequential Quadratic Programming (SQP) solve continuous, differentiable optimization problems and require auxiliary methods to handle integer variables, while metaheuristic approaches such as Genetic Algorithms (GA) can handle the mixed-integer structure directly at the cost of increased computational effort. This study presents a systematic comparison between GA and SQP as optimization solvers within an MPC framework for a P1P3 parallel hybrid powertrain. A multi-objective cost function is formulated to simultaneously optimize system efficiency, battery state of charge management, and noise emissions. Both approaches are evaluated across the WLTC as well as a real-world RDE scenario. On the WLTC, both MPC approaches reduce fuel consumption by 0.5–1.0% and improve system efficiency by 3.7–4.6% compared to a state-of-the-art deterministic reference strategy optimized for fuel consumption. At the same time, both approaches additionally achieve substantial reductions in noise emissions compared to the deterministic reference, which was not optimized for acoustic behavior. On both cycles, the GA-based MPC achieves favorable performance compared to SQP, with the performance gap widening from the WLTC to the RDE cycle. Both methods achieve real-time capability, yet SQP reduces computational time by a factor of four compared to GA. As long as computational resources in automotive ECUs remain constrained, this efficiency advantage positions gradient-based optimization for series production applications, whereas metaheuristic methods offer greater flexibility for concept development stages with relaxed real-time requirements. The findings contribute to the understanding of optimization algorithm selection for MINLP energy management problems in hybrid electric vehicles. Full article

This paper presents an original positional pneumatic actuator for long-stroke coordinate mechanisms. The design integrates a rodless pneumatic cylinder, a jet control system, and an external braking device. It achieves a positioning accuracy of 200 microns, a discrete step of 2 mm, and an average speed of 0.15 m/s over a maximum stroke of 6 m. This solution offers a two-fold improvement in technical, economic, and operational performance compared to electromechanical drives. A mathematical model of the drive was developed using SimInTech software and validated with a custom-built experimental stand. The discrepancy between calculated and experimental data does not exceed 18%. The study established the dependence of positioning accuracy on the load and kinematic characteristics of the drive, which helps reduce design time for coordinate mechanisms. As a result of the research, a new scheme of a positional pneumatic actuator has been developed and experimentally confirmed, which allows for a two-fold improvement in technical and economic indicators compared to electromechanical analogs due to the original combination of a rodless cylinder, a jet control system, and an external braking device. Full article

Side-scan sonar (SSS) serves as the primary perceptual instrument for Autonomous Underwater Vehicles (AUVs) in large-scale marine search and rescue (SAR) operations. However, the detection of critical targets is frequently hindered by severe hydro-acoustic noise, the spatial discontinuity of wreckage, and the weak visual signatures of small targets. To surmount these challenges, this paper presents WPG-DetNet. First, we introduce a Wavelet-Embedded Residual Backbone (WERB) to reconstruct the conventional downsampling paradigm. By substituting standard pooling with the Discrete Wavelet Transform (DWT), this architecture explicitly disentangles high-frequency noise from structural information in the frequency domain, thereby achieving the adaptive preservation of edge fidelity for large human-made targets while filtering out speckle interference. Then, addressing the distinct challenge of discontinuous aircraft wreckage, the framework further incorporates a Debris Graph Reasoning Module (D-GRM). This module models scattered fragments as nodes in a topological graph to capture long-range semantic dependencies, transforming isolated instance recognition into context-aware scene understanding. Finally, to bridge the gap between AI and underwater physics, we design a Shadow-Aided Decoupling Head (SADH) equipped with a physics-informed geometric loss. By enforcing mathematical consistency between target height and acoustic shadow length, this mechanism establishes a rigorous discriminative criterion capable of distinguishing weak-echo human bodies from seabed rocks based on shadow geometry. Experiments on the SCTD dataset demonstrate that WPG-DetNet achieves a mean Average Precision ($mA{P}_{50}$) of 97.5% and a Recall of 96.9%. Quantitative analysis reveals that our framework outperforms the classic Faster R-CNN by a margin of 12.8% in $mA{P}_{50}$ and surpasses the Transformer-based RT-DETR-R18 by 5.6% in high-precision localization metrics ($mA{P}_{50:95}$). Simultaneously, WPG-DetNet maintains superior efficiency with an inference speed of 62.5 FPS and a lightweight parameter count of 16.8 M, striking an optimal balance between robust perception and the real-time constraints of AUV operations. Full article

Reliable autofocus is a fundamental prerequisite for precise positioning in micro-assembly systems, where complex reflections, scale variations, and narrow depth-of-field often degrade the robustness of traditional sharpness metrics. To address these challenges, we propose an efficient two-stage autofocus method for a dual-camera micro-vision system based on a spatial-frequency image quality assessment (IQA) model. First, we design WaveMamba-IQA for image sharpness estimation, synergistically combining the Discrete Wavelet Transform with Vision Transformers to capture high-frequency details and semantic features, further enhanced by Multi-Linear Transposed Attention and Vision Mamba for global context modeling. Moreover, we implement a coarse-to-fine autofocus workflow, employing the Covariance Matrix Adaptation Evolution Strategy for global optimization on the horizontal camera, followed by geometric prior-based precise adjustment for the oblique camera. Experimental results on a custom microsphere dataset demonstrate that WaveMamba-IQA achieves a Spearman correlation coefficient of 0.9786. Furthermore, the integrated system achieves a 98.33% autofocus success rate across varying lighting conditions. This method significantly improves the robustness and automation level of micro-assembly systems, effectively overcoming the limitations of manual and traditional focusing techniques. Full article

This paper proposes an impulsive SIRQ model for the analysis of computer network resilience against malware propagation and distributed denial-of-service (DDoS) attacks. The model extends classical epidemic frameworks by combining the continuous-time dynamics of malicious object spreading with discrete control actions corresponding to mass updates, node isolation, and access control policies. A qualitative analysis of the resulting system of impulsive differential equations is performed. The basic reproduction number ${\mathcal{R}}_0$, identified as a threshold parameter characterizing the intensity of attack propagation, and sufficient conditions for the global asymptotic stability of the infection-free state are established. It is shown that, under periodic impulsive control, the infection-free state can be stabilized with respect to the target population coordinates even when ${\mathcal{R}}_0>1$. An exponential decay estimate for the total active threat is derived, guaranteeing the asymptotic extinction of the infected and quarantined node populations. The proposed approach provides quantitative criteria for the effectiveness of impulsive cyber defense strategies and offers a theoretical foundation for the design of adaptive multi-layer protection systems for critical information infrastructures. Practical interpretation of the results illustrates the dependence of the critical impulsive control period on the model parameters and demonstrates the applicability of the approach to cybersecurity strategy design. Full article

Parkinson’s disease (PD) is associated with progressive gait deterioration; however, widely used clinical scales such as the Hoehn & Yahr (H&Y) stage are limited in capturing continuous severity changes due to subjectivity and discrete grading. This study proposes a two-stage explainable AI framework using vertical ground reaction force (VGRF) signals to achieve reproducible PD detection and continuous severity estimation. In the first stage, three deep learning models, temporal convolutional network (TCN), BiGRU with attention, and FCNN-Transformer, were trained using windowed VGRF signals under repeated subject-wise data segmentation. All models achieved high discrimination performance (AUC ≥ 0.93), with FCNN-Transformer showing the highest mean AUC (0.940) and statistically superior performance (paired Wilcoxon test, p < 0.05). Stability-based explainable AI using Integrated Gradients consistently identified variability-related VGRF features as the most informative, which were also significantly different between groups at the data level (p < 0.001, FDR-corrected). In the second stage, XGBoost regression was applied to PD subjects to predict continuous H&Y severity, achieving strong correlation with clinical grades (Spearman ρ = 0.921, p < 0.001), low error (MAE = 0.158, RMSE = 0.241), and high determination (R² = 0.953). This shows that gait-based features are a sensitive enough signal to continuously quantify disease progression. In addition, in the TREND prospective longitudinal cohort (n = 696), wearable walking indicators differed significantly from those of non-patients prior to diagnosis, and a decline in walking pace was observed approximately four years before Parkinson’s disease diagnosis, providing the basis for early screening and monitoring using gait-based digital biomarkers. These results demonstrate that gait-based digital biomarkers can objectively quantify both PD presence and disease progression. The proposed framework provides a reproducible, explainable, and clinically interpretable AI-based decision support approach for PD assessment. Full article

This article presents a critical analysis of the evolution of geomatics data acquisition technologies in architectural heritage. The review traces this progression from 15th-century scientific drawings through discrete surveying methods and early analogue photogrammetry, culminating in modern massive capture systems such as digital photogrammetry and laser scanning. These advanced techniques have fundamentally transformed traditional approaches to 3D modelling. Based on the recent literature, we explore how this data is increasingly integrated into heritage building information modelling (HBIM), digital twins, and AI-based tools, driving a paradigm shift in the development of sustainable conservation strategies for historic buildings. Finally, while digital tools can actively reduce the environmental impact of buildings, we critically weigh these benefits against the direct energy and carbon costs of the digitisation process itself. Full article

In modern production, Wire Arc Additive Manufacturing (WAAM) is becoming an essential technology for manufacturing complex components. However, the complexity of planning such processes constrains their widespread use in production cycles. Using various numerical simulation approaches allows for the investigation of resulting geometries with respect to process parameters, reducing the need for experiment-based process planning. Similar to various subtractive processes, there is increased interest in integrating simulation approaches into digital twin applications for planning and optimization of WAAM processes. This requires dynamic geometry mapping and simulation time comparable to the process duration. In this paper, a numerical simulation employing a Dexel-based geometry representation and a model for single-bead geometry parameter prediction is investigated as a vital alternative to Finite Element Method (FEM)-based simulations. The focus lies on the accuracy of the simulated components with respect to the simulation settings, the time needed for it to complete, and the degree of compliance between the simulated and produced multi-layer structures. Using optimized simulation settings achieves an accuracy loss of under 7% due to geometry discretization, with a simulation time that is approximately 37% faster than the process duration. The simulated components closely correspond to the experimental ones in terms of width and height, with a volumetric similarity ranging from 63.3% to 88.8%. Full article

The objective of this study is to develop a label-free fluorescent sensor for the quantitative detection of hypochlorite ions (ClO⁻) and validate its applicability in biological samples, particularly exploring the potential of ClO⁻ as a biomarker for stress-induced hypertension (SIH). Male Sprague-Dawley rats (8 weeks old, 250–300 g) were used to establish the SIH model. A guanine-rich (G-rich) signal DNA sequence (S-DNA) was rationally designed, with a ClO⁻-responsive phosphorothioate (PS) moiety integrated into the probe architecture. In the absence of ClO⁻, the S-DNA folds into a stable G-quadruplex structure, which specifically binds to ThT and triggers a significant enhancement of the dye’s fluorescence intensity. Upon introduction of ClO⁻, the specific hydrolysis reaction between the PS moiety and ClO⁻ induces cleavage of the S-DNA into two discrete fragments, thereby abrogating G-quadruplex formation and resulting in a remarkable quenching of ThT fluorescence. This proposed method exhibits excellent anti-interference capability against other reactive oxygen species (ROS) and achieves a low detection limit of 41.2 nM for ClO⁻. Furthermore, this strategy was successfully applied to the quantitative determination of endogenous ClO⁻ in human cells and the plasma of stress-induced hypertensive (SIH) rats, highlighting its substantial potential for clinical and physiological research. Full article

This study rigorously evaluates 13 Convolutional Neural Network (CNN) architectures for Thai dialect recognition. By treating Automatic Speech Recognition (ASR) as a computer vision texture classification task, we processed an extensive 840-h dataset from the Spoken Language Systems, Chulalongkorn University (SLSCU) corpus. Raw audio from four major dialects—Central, Northern (Khummuang), Northeastern (Korat), and Southern (Pat-tani)—was transformed into 2D Mel-spectrograms using the Short-Time Fourier Transform (STFT). We analyzed a diverse range of architectures, including the VGG, Inception, ResNet, DenseNet, and MobileNet families, to establish the optimal trade-off between mathematical complexity and spectral feature extraction. Our experimental results identify NASNet-Mobile as the most effective model, achieving a macro-average F1-score of 0.9425. The analysis suggests that NASNet’s search-optimized cell structure is uniquely capable of capturing the multiscale texture of phonetic formants. In contrast, we observed a catastrophic mode collapse in VGG16 (32.97% accuracy), likely due to excessive parameter bloat, while Xception and MobileNetV2 maintained robust generalization. Confusion matrix analysis reveals high acoustic distinctiveness for Southern Thai (96.7% recall), whereas Northern Thai exhibits significant spectral overlap with Central Thai. These results support the hypothesis that CNNs interpret spectrograms as textures rather than discrete objects, positioning NASNet-Mobile as a high-performance, low-latency baseline for edge-device deployment in resource-constrained environments. Full article

The physical structure formed during the packing of granular grain constitutes a fundamental ecological factor within the grain bulk ecosystem. Accurate simulations of grain-packing behavior help to deepen our understanding of this ecosystem. In this study, a white hard wheat was selected as the test material, and a high-fidelity multi-sphere discrete element model of wheat kernels was constructed using three-dimensional laser scanning. Physical experiments were conducted to determine the basic physical properties of the kernels, including true density and bulk density. Using the angle of repose as the calibration parameter, the wheat-packing process was investigated with the discrete element method (DEM). The results indicated that the coefficients of static and rolling friction between particles were highly significant factors governing the angle of repose. The optimal parameter combination consisted of a particle–particle coefficient of restitution of 0.500, a coefficient of static friction of 0.388, and a coefficient of rolling friction of 0.054. The mean angle of repose obtained from the DEM packing simulation was 28.46°, corresponding to a relative error of 3.16% compared with the physical experiment. This calibrated parameter set is therefore considered accurate and reliable, and it provides baseline data for DEM simulations of wheat grain bulks. Full article

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing is pivotal for the Space–Air–Ground–Sea Integrated Network (SAGSIN) to support heterogeneous task offloading. However, the inherent resource constraints of UAVs limit their ability to support intensive and concurrent task processing in dynamic environments. In such complex scenarios, the dual requirements of discrete model partitioning and continuous bandwidth allocation make it difficult for traditional reinforcement learning algorithms to achieve optimal resource matching. Therefore, in this paper, we design a joint optimization framework based on Asynchronous Advantage Actor-Critic (A3C) and proximal policy optimization (PPO). Specifically, the model partitioning strategy is learned through PPO, which utilizes a clipped objective function to ensure training stability and generalization across complex Deep Neural Network (DNN) structures. Moreover, the framework leverages the asynchronous multi-threaded architecture of A3C to dynamically allocate bandwidth, effectively accommodating rapid fluctuations in terminal access. Finally, to prevent resource monopolization and ensure fairness, a weighted priority scheduling mechanism based on task urgency and computation time is introduced. Extensive simulations show that the proposed algorithm outperforms existing approaches in terms of task completion rate, task processing latency, and resource utilization under dynamic SAGSIN scenarios. Full article

Accurate simulation of reinforced concrete (RC) members under cyclic loading requires hysteresis models that capture degradation and pinching, yet inverse identification of such models remains challenging because the internal-state evolution is strongly coupled and sensitive to incremental consistency. This study develops a physics-constrained, model-based framework to identify the full twelve-parameter Bouc–Wen–Baber–Noori (BWBN) model directly from cyclic force–displacement records and to deploy the calibrated parameters in OpenSees. Parameter estimation is posed as a bound-constrained nonlinear least-squares problem, where each objective evaluation advances the BWBN internal variables through a discrete incremental constitutive update and accumulates the energy-driven deterioration measure using a consistent trapezoidal work integration. Validation on nine RC column tests covering flexural, flexural–shear, and shear failures shows good agreement between simulated and experimental hysteresis loops, with R² ranging from 0.956 to 0.986 and RMSE ranging from 0.06 to 0.09 over the full records. Unlike simpler hysteresis models that omit degradation and pinching, the calibrated BWBN model reproduces mode-dependent deterioration and reloading pinching, and the identified parameters can be used directly in OpenSees for subsequent nonlinear simulations. Full article

The European cherry fruit fly (Rhagoletis cerasi L.) poses a significant pest threat to cherry production due to its rapid reproduction and host specificity, causing substantial economic damage. This study presents a novel, explainable, and biologically inspired data-driven classification model based on fruit characteristics to support targeted and sustainable pest control strategies. In research conducted at four different locations in Elazığ province, three population classes were determined based on the number of adult individuals caught in traps, and 10 different fruit characteristics were measured in fruit samples belonging to each class. The data used in this study are original data obtained by the authors. To examine the relationship between pomological characteristics of cherry fruit and cherry fruit fly density, the Chaotic Rule-based–Strength Pareto Evolutionary Algorithm2 (CRb-SPEA2) method, developed as a multi-objective and chaos-integrated evolutionary rule mining framework, was adapted. The developed algorithm aimed for high performance, interpretability, and transparency. Accuracy, Precision, and Recall metrics, which are conflicting objectives, were optimized with Pareto-optimal solutions, yielding selectable results for domain experts. To increase population diversity and reduce the risk of early convergence and getting stuck in a local optimum, the Tent chaotic mapping mechanism was also integrated into the system. Furthermore, the model was trained without the need for predefined automatic discretization of the continuous value ranges of the attributes. The proposed model achieved superior results across all classes, with the highest accuracy rate of 82.6% recorded in the High class, demonstrating excellent sensitivity and recall values. Full article