Search Results (5,243)

This paper presents, for the first time, the structure-dependent parameter trade-off optimization on figure-of-merit (R_onC_off) and power compression of AlGaN/GaN high electron mobility transistors (HEMTs) for radio frequency (RF) switch applications. For GaN HEMTs operating in switching mode, it was demonstrated that R_onC_off can be effectively reduced by increasing the gate foot length (L_{g_foot}), decreasing the gate cap length (L_{g_cap}), reducing the gate bias resistance (r_g), and adopting a high work function metal for the gate electrode (Φ_g). However, these parameter adjustments affect power compression and R_onC_off in opposing manners. This paper also presents supplementary research on the effects of source-drain spacing (L_ds) and gate width (W_g) on switching performance. This research achieves a dynamic balancing method for structural parameters, delivering application-specific design rules for different scenarios ranging from high-frequency to high-power applications. Full article

Credit card fraud detection remains a major challenge due to extreme class imbalance and evolving attack patterns. This paper proposes a practical hybrid pipeline that combines conditional tabular generative adversarial networks (CTGANs) for targeted minority-class synthesis with Light Gradient Boosting Machine (LightGBM) for classification. Inspired by symmetry principles in machine learning, we leverage the adversarial equilibrium of CTGAN to generate realistic fraudulent transactions that maintain distributional symmetry with real fraud patterns, thereby preserving the structural and statistical balance of the original dataset. Synthetic fraud samples are merged with real data to form augmented training sets that restore the symmetry of class representation. We evaluate Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) classifiers, and a LightGBM model on a public dataset using stratified 5-fold validation and an independent hold-out test set. Models are compared using sensitivity, precision, F-measure(F₁), and area under the precision–recall curve (PR-AUC), which reflects symmetry between detection and false-alarm trade-offs. Results show that CTGAN-based augmentation yields large and consistent gains across architectures. The best-performing configuration, CTGAN + LightGBM, attains sensitivity = 0.986, precision = 0.982, F₁ = 0.984, and PR-AUC = 0.918 on the test data, substantially outperforming non-augmented baselines and recent methods. These findings indicate that conditional synthetic augmentation materially improves the detection of rare fraud modes while preserving low false-alarm rates, demonstrating the value of symmetry-aware data synthesis in classification under imbalance. We discuss generation-quality checks, risk of distributional shift, and deployment considerations. Future work will explore alternative generative models with explicit symmetry constraints and time-aware production evaluation. Full article

Among existing preference elicitation methods, the trade-off method offers an advantage over others in mitigating the influence of probability weighting on preferences, as it does not require assuming a specific form for the probability weighting function. However, when accounting for the dependence of probability weighting on the choice-set outcome range (CSOR), the conventional trade-off method may lead to improper elicitation of preferences due to its inability to control the CSOR. In order to concurrently circumvent the impacts of the CSOR and probability weighting on preferences in the elicitation procedure, we introduce the Range-Fixed Trade-off Method (RFTM) and provide its full derivation and concrete implementation steps under the framework of rank-dependent utility theory (RDU). The RFTM not only retains the advantages of the conventional trade-off method but also evades the effects of the CSOR on preferences by fixing the CSOR. The results of empirical investigations into the efficacy of RFTM indicate that, compared to the existing trade-off method, utility functions derived from RFTM exhibit a lower degree of risk aversion. This result is compatible with existing experimental observations and conclusions, thus implying that RFTM can effectively elicit individual preferences, thereby preventing or mitigating the bias in preferences arising from CSOR variations in the conventional trade-off approach. Furthermore, the experimental results demonstrate that the probability weighting function remains nonlinear even within a fixed CSOR. This indicates that, under the premise of preferences depending on the CSOR, non-expected utility theories still hold promising development prospects in the future. In summary, RFTM not only provides a more effective and reliable approach for preference elicitation but also makes it feasible to study the impact of changes in the CSOR on preferences, thereby providing methodological support for the future development of CSOR-dependent non-expected utility theories. Full article

In the context of manufacturing logistics, this study sheds light on the difficult task of concurrently optimizing cost, time, influence on sustainability, and spatial efficiency. Specifically, this addresses the integrated challenge of material handling equipment selection and facility space allocation, a crucial decision-making domain where conventional single-objective methodologies frequently overlook vital considerations. While recent research predominantly relies on meta-heuristics and simulation-based solution methodologies, they do not guarantee a global optimum solution space. To effectively address this multifaceted decision environment, a Mixed-Integer Linear Programming (MILP) model is developed and resolved utilizing two distinct scalarization methodologies: the conventional ϵ-constraint method and the augmented ϵ-constraint method (AUGMECON2). The comparative analysis indicates that although both methods effectively identify the Pareto front, the AUGMECON2 approach offers a more robust assurance of solution efficiency by incorporating slack variables. The results illustrate a convex trade-off between capital expenditure and operational flow time, indicating that substantial reductions in time necessitate strategic investments in higher-capacity equipment fleets. Furthermore, the analysis underscores a significant conflict between achieving extreme operational efficiency and adhering to facility design standards, as reducing time or energy consumption beyond a specific point requires deviations from optimal space allocation policies. Ultimately, a “Best Compromise Solution” is determined that harmonizes near-optimal operational efficiency with strict compliance to spatial constraints, providing a resilient framework for sustainable manufacturing logistical planning. Full article

In contemporary semiconductor manufacturing, wafer-handling robots are essential for achieving high-speed and high-precision wafer transportation. However, the demand for rapid motion and lightweight design introduces flexible transmission components that are prone to residual vibrations, which degrade positioning accuracy and system stability. To address this challenge, this paper proposes a vibration-suppression trajectory planning method based on the Gray Goose Optimization (GGO) algorithm. The proposed algorithm integrates grouped global search with local optimization capabilities, making it well suited for solving multi-objective optimization problems. Comparative tests conducted on eight randomly selected multimodal benchmark functions from the CEC2013 test suite verify the effectiveness and robustness of the GGO algorithm. Establishing a multi-objective function that considers both motion time and vibration energy enables the GGO algorithm to determine the switching time points of an S-shaped velocity profile, thereby generating smooth trajectories with continuous velocity and acceleration. By varying different initial conditions, the trade-off between motion time and vibration energy is systematically analyzed with respect to angular displacement, initial acceleration, and time-weighting factors. Simulation results indicate that the planned trajectories exhibit negligible displacement variation under zero-mean disturbances. The velocity error remains within 0.1 deg·s⁻¹, and the acceleration error is confined within 0.2 deg·s⁻². Consequently, Pareto-optimal solutions are successfully obtained with respect to both motion time and residual vibration energy. Full article

Immersive technologies such as Desktop Virtual Reality (DVR), Immersive Rooms (IR), and fully immersive Virtual Reality (VR) are transforming K-12 education by enabling experiential, multisensory, and participatory learning. Yet their pedagogical impact depends not only on hardware fidelity but on the interplay between technological affordances, instructional design, and learner characteristics. Guided by the Cognitive Affective Model of Immersive Learning (CAMIL), this mixed-methods study examined how these factors jointly shape affordances, challenges, students perceived learning, and self-assessment in authentic classroom contexts. Data were collected from 31 teachers and 252 students across 21 schools using teacher interviews, classroom observations, and student questionnaires. Findings revealed that agency and presence emerged as central affordances but also as potential challenges, depending on lesson design and cognitive load. DVR consistently supported higher perceived learning and stronger links between engagement and self-assessment, while IR showed the weakest outcomes and VR displayed trade-offs between immersion and control. The study proposes a revised CAMIL framework that integrates social co-presence, learner characteristics, and perceived learning as essential components for understanding immersive learning in schools. These results highlight that effective immersion arises from pedagogical orchestration, not technological intensity alone. Full article

The proliferation of biometric authentication systems in critical security applications has highlighted the urgent need for robust defense mechanisms against sophisticated adversarial attacks. This paper presents ShieldNet, an adversarially resilient Convolutional Neural Network (CNN) framework specifically designed for secure iris biometric authentication. Unlike existing approaches that apply adversarial training or gradient regularization independently, ShieldNet introduces a synergistic dual-layer defense framework featuring three key components: (1) an attack-aware adaptive weighting mechanism that dynamically balances defense priorities across multiple attack types, (2) a smoothness-regularized gradient penalty formulation that maintains differentiable gradients while encouraging locally smooth loss landscapes, and (3) a consistency loss component that enforces prediction stability between clean and adversarial inputs. Through extensive experimental validation across three diverse iris datasets, MMU1, CASIA-Iris-Africa, and UBIRIS.v2, and rigorous evaluation against strong adaptive attacks including AutoAttack, PGD-100 with random restarts, and transfer-based black-box attacks, ShieldNet demonstrated robust performance, achieving 87.3% adversarial accuracy under AutoAttack on MMU1, 85.1% on CASIA-Iris-Africa, and 82.4% on UBIRIS.v2, while maintaining competitive clean data accuracies of 94.7%, 93.9%, and 92.8%, respectively. The proposed framework outperforms existing state-of-the-art defense methods including TRADES, MART, and AWP, achieving an equal error rate (EER) as low as 2.8% and demonstrating consistent robustness across both gradient-based and gradient-free attack scenarios. Comprehensive ablation studies validate the complementary contributions of each defense component, while latent space analysis confirms that ShieldNet learns genuinely robust feature representations rather than relying on gradient obfuscation. These results establish ShieldNet as a practical and reliable solution for deployment in high-security biometric authentication environments. Full article

This study conducts a landslide deformation assessment in Tianshui, Gansu Province, on the Chinese Loess Plateau, utilizing the Small Baseline Subset InSAR (SBAS-InSAR) method integrated with velocity direction conversion and Z-score clustering. The Chinese Loess Plateau is one of the most landslide-prone regions in China due to frequent rains, strong topographical gradients and severe soil erosion. By constructing subsets of interferograms, SBAS-InSAR can mitigate the influence of decorrelation to a certain extent, making it a highly effective technique for monitoring regional surface deformation and identifying landslides. To overcome the limitations of the satellite’s one-dimensional Line-of-Sight (LOS) measurements and the challenge of distinguishing true landslide signals from noise, two optimization strategies were implemented. First, LOS velocities were projected onto the local steepest slope direction, assuming translational movement parallel to the slope. Second, a Z-score clustering algorithm was employed to aggregate measurement points with consistent kinematic signatures, enhancing identification robustness, with a slight trade-off in spatial completeness. Based on 205 Sentinel-1 Single-Look Complex (SLC) images acquired from 2014 to 2024, the integrated workflow identified 69 “active, very slow” and 63 “active, extremely slow” landslides. These results were validated through high-resolution historical optical imagery. Time series analysis reveals that creep deformation in this region is highly sensitive to seasonal rainfall patterns. This study demonstrates that the SBAS-InSAR post-processing framework provides a cost-effective, millimeter-scale solution for updating landslide inventories and supporting regional risk management and early warning systems in loess-covered terrains, with the exception of densely forested areas. Full article

Background/Objectives: Federated learning (FL) offers a way for healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data. This systematic review aims to comprehensively synthesize the ethical dimensions of FL in healthcare, integrating privacy preservation, algorithmic fairness, governance, and equitable access into a unified analytical framework. The application of FL in healthcare between January 2020 and December 2024 is examined, with a focus on ethical issues such as algorithmic fairness, privacy preservation, governance, and equitable access. Methods: Following PRISMA guidelines, six databases (PubMed, IEEE Xplore, Web of Science, Scopus, ACM Digital Library, and arXiv) were searched. The PROSPERO registration is CRD420251274110. Studies were selected if they described FL implementations in healthcare settings and explicitly discussed ethical considerations. Key data extracted included FL architectures, privacy-preserving mechanisms, such as differential privacy, secure multiparty computation, and encryption, as well as fairness metrics, governance models, and clinical application domains. Results: Out of 3047 records, 38 met the inclusion criteria. The most popular applications were found in medical imaging and electronic health records, especially in radiology and oncology. Through thematic analysis, four key ethical themes emerged: algorithmic fairness, which addresses differences between clients and attributes; privacy protection through formal guarantees and cryptographic techniques; governance models, which emphasize accountability, transparency, and stakeholder engagement; and equitable distribution of computing resources for institutions with limited resources. Considerable variation was observed in how fairness and privacy trade-offs were evaluated, and only a few studies reported real-world clinical deployment. Conclusions: FL has significant potential to promote ethical AI in healthcare, but advancement will require the development of common fairness standards, workable governance plans, and systems to guarantee fair benefit sharing. Future studies should develop standardized fairness metrics, implement multi-stakeholder governance frameworks, and prioritize real-world clinical validation beyond proof-of-concept implementations. Full article

This paper addresses the limitations of conventional single-stage direct-control maximum power point tracking (MPPT) methods, such as the Perturb and Observe (P&O) algorithm. Fixed-step-size duty-cycle perturbations cause a trade-off between slow tracking with small oscillations and fast tracking with large oscillations, along with poor responsiveness to rapid weather variations and output voltage fluctuations. Two main contributions are presented. First, a fractional-order DC–DC boost converter (FOBC) is introduced, incorporating fractional-order dynamics to enhance system performance beyond improvements in control algorithms alone. Second, a novel indirect-control MPPT strategy based on a two-stage architecture is developed, where the P&O algorithm generates the optimal voltage reference and a fractional-order linear-quadratic-integral (FOLQI) controller—designed using a fractional-order small-signal model—regulates the PV module voltage to generate the FOBC duty cycle. Hardware-in-the-loop simulations confirm substantial performance improvements. The proposed FOLQI-based indirect-control approach with FOBC achieves a maximum MPPT efficiency of 99.26%. An alternative indirect method using a classical linear-quadratic-integral (LQI) controller with an integer-order boost converter reaches 98.38%, while the conventional direct-control P&O method achieves only 94.21%, demonstrating the superiority of the proposed fractional-order framework. Full article

This paper presents a hierarchical model architecture for the analysis and optimization of Fuel Cell Electric Vehicles (FCEVs). The model encompasses the levels of cell, stack, and complete vehicle, which are interconnected through clearly defined transfer parameters. At the cell level, electrochemical and thermodynamic processes are mapped, the results of which are aggregated at the stack level into characteristic maps such as current–voltage curves and efficiency profiles. These maps serve as interfaces to the vehicle level, where the electric powertrain—comprising the fuel cell, energy storage, electric motor, and auxiliary consumers—is integrated. Special attention is given to the trade-off between the lifetime and dynamics of the fuel cell, which is methodically captured through variable parameter vectors. The transfer parameters enable consistent and scalable modelling that considers both detailed cell and stack information as well as vehicle-side requirements. On this basis, various vehicle configurations can be evaluated and optimized with regard to efficiency, lifetime, and drivability. Full article

Background/Objectives: Bacterial outer membrane vesicles (OMVs) play a role in bacterial communication, virulence, antimicrobial resistance, and host–pathogen interaction. OMV isolation is a key step for studying these particles’ functions; nevertheless, isolation procedures can greatly influence the yield, purity, and structural integrity of OMVs, thereby affecting downstream biological analyses and functional interpretation. Methods: In this study, we compared the efficacy of two OMV isolation techniques, differential ultracentrifugation (dUC) and size-exclusion chromatography (SEC), in separating and concentrating vesicles produced by two Escherichia coli strains belonging to uropathogenic (UPEC) and Shiga toxin-producing (STEC) pathotypes. The isolated OMVs were characterized using a multi-analytical approach including transmission and scanning electron microscopy (TEM, SEM), nanoparticle tracking analysis (NTA), dynamic light scattering (DLS), ζ-potential measurement, and protein quantification to assess the purity of the preparations. Results: Samples obtained by dUC exhibited higher total protein content, broader particle size distributions, and more pronounced contamination by non-vesicular material. In contrast, SEC yielded morphologically homogeneous and structurally well-preserved vesicles, higher particle-to-protein ratios, and lower total protein content, reflecting reduced co-isolation of protein aggregates. NTA and DLS analyses revealed polydisperse populations in samples obtained with both isolation methods, with DLS measurements highlighting the contribution of larger or transient aggregates. ζ-potential values were close to neutrality for all samples, consistent with limited electrostatic repulsion and with the aggregation tendencies observed in some preparations. Conclusions: This study describes features of OMV produced by two relevant E. coli strains considering two isolation strategies which exert method- and strain-dependent effects on vesicle properties, including size distribution and surface charge, and emphasizes the trade-offs between yield, purity, and vesicle integrity. Full article

The fall armyworm (FAW) Spodoptera frugiperda is a polyphagous pest that causes significant damage to various crops and rapidly develops resistance to insecticides. Broflanilide, a novel meta-diamide insecticide, has shown effectiveness against lepidopteran pests, but the risk of resistance and associated fitness costs in FAW remain unclear. This study evaluated the development of resistance to broflanilide over nine generations of selection using the diet incorporation method at the 70% lethal concentration (LC₇₀) concentration. Following nine generations of selection, the LC₅₀ value increased from 0.134 mg/kg to 0.232 mg/kg, showing a 1.73-fold increase in resistance ratio (RR). The calculated heritability of resistance (h²) was 0.084, which suggested that resistance of FAW against broflanilide is evolving at a slow rate. Based on the projected rate of resistance progression, a 10-fold increase in LC₅₀ would take between 30.1 and 66.4 generations, assuming selection mortality rates of 90% and 50%, respectively. Fitness costs were evaluated using age-stage, two-sex life table analysis, revealing reduced fecundity and pupal weight in the broflanilide-selected (Brof-SEL) strain compared to the wild-type. The relative fitness of the Brof-SEL strain was 0.38, indicating trade-offs in biological traits. These findings suggested a low risk of rapid resistance development against broflanilide. However, effective integrated pest management strategies against FAW require the judicious use of this insecticide in combination with biological control measures, including the deployment of parasitoids and predators, to promote a more environmentally sustainable approach. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

Ensuring sustained driver attention is critical for intelligent transportation safety systems; however, the performance of data-driven driver distraction detection models is often limited by the high cost of large-scale manual annotation. To address this challenge, this paper proposes an adaptive semi-supervised driver distraction detection framework based on teacher–student learning and deformable pyramid feature fusion. The framework leverages a limited amount of labeled data together with abundant unlabeled samples to achieve robust and scalable distraction detection. An adaptive pseudo-label optimization strategy is introduced, incorporating category-aware pseudo-label thresholding, delayed pseudo-label scheduling, and a confidence-weighted pseudo-label loss to dynamically balance pseudo-label quality and training stability. To enhance fine-grained perception of subtle driver behaviors, a Deformable Pyramid Sparse Transformer (DPST) module is integrated into a lightweight YOLOv11 detector, enabling precise multi-scale feature alignment and efficient cross-scale semantic fusion. Furthermore, a teacher-guided feature consistency distillation mechanism is employed to promote semantic alignment between teacher and student models at the feature level, mitigating the adverse effects of noisy pseudo-labels. Extensive experiments conducted on the Roboflow Distracted Driving Dataset demonstrate that the proposed method outperforms representative fully supervised baselines in terms of mAP@0.5 and mAP@0.5:0.95 while maintaining a balanced trade-off between precision and recall. These results indicate that the proposed framework provides an effective and practical solution for real-world driver monitoring systems under limited annotation conditions. Full article