Search Results (209,568)

Effective scheduling and charging management of electric buses is essential for minimizing investment and operational costs while improving transit efficiency. The paper presents an optimization framework which provides a 3D Pareto frontier of fleet size, deadhead distance, and charging cost, while accounting for heterogeneous battery energy, charger power, charging spot capacities, integrated daily and night charging, and a charge sustaining condition. Two optimization approaches are developed: Mixed-Integer Linear Programming (MILP), which finds globally optimal solutions, and an Insertion Heuristic (IH), which generates feasible schedules in a computationally efficient way. The framework operates iteratively, starting with MILP to determine the minimum number of buses for feasible operation. Then, additional buses are incrementally incorporated, and for each fixed fleet size, a multi-objective optimization of scheduling and charging management is applied to minimize deadhead distance and charging costs using both approaches. A case study on a synthetic transport network demonstrates that the proposed IH algorithm achieves nearly optimal performance at a fraction of the computational time and memory requirements of the MILP approach. A Pareto analysis shows that increasing fleet size reduces deadhead distance and charging costs up to a saturation point, beyond which further additions yield minimal benefits. Full article

Multi-channel common-aperture optical systems, which excel at simultaneous multi-spectral information acquisition, are widely used for image fusion. However, complex systems for long-distance multi-band detection suffer from difficulties in assembly and adjustment and light vignetting. To resolve this, the paper proposes a modular design method that splits the optical path into independent modules: the common-aperture optical path adopts an off-axis reflective beam-shrinking structure to extend the focal length and ensure 100% light input, compared with coaxial multi-channel common-aperture systems. The relay optical path of each spectral channel uses a continuous zoom design for smooth detection–recognition switching. Based on the method, a three-channel common-aperture system is developed integrating visible light (VIS), short-wave infrared (SWIR), and mid-wave infrared (MWIR). The modulation transfer function (MTF) and wavefront distribution of the common-aperture optical path approach the diffraction limit. After integration with the relay optical paths, the system, without global optimization, can achieve the following performance: the root mean square (RMS) across the full field of view (FOV) at different focal lengths for each channel is smaller than the detector pixel size (3.45 μm for VIS, 15 μm for SWIR/MWIR); the MTF exceeds 0.2 at the cutoff frequency. Subsequently, the results of the tolerance analysis verify the feasibility of the design for each module and the advantage of the modular layout in the assembly and adjustment of the system. Finally, the paper discusses the influence of parallel plates on the wavefront distortion of the system and proposes optimization thinking using freeform surfaces. The design results of the study validate the feasibility of the modular layout in simplifying the design and assembly of multi-channel common-aperture optical systems. Full article

Generative AI (GenAI) systems are increasingly deployed across high-impact sectors, introducing security risks that fundamentally differ from those of traditional software. Their probabilistic behavior, emergent failure modes, and expanded attack surface, particularly through retrieval and tool integration, complicate threat modeling and control assurance. This paper presents a threat-centric analysis that maps adversarial techniques to the core architectural layers of generative AI systems, including training pipelines, model behavior, retrieval mechanisms, orchestration, and runtime interaction. Using established taxonomies such as the OWASP LLM Top 10 and MITRE ATLAS alongside empirical research, we show that many GenAI security risks are structural rather than configurable, limiting the effectiveness of perimeter-based and policy-only controls. We additionally analyze the impact of regulatory divergence on GenAI security architecture and find that EU frameworks serve in practice as the highest common technical baseline for transatlantic deployments. Full article

Augmented Reality (AR) is transforming education by integrating digital and real-world elements to create immersive and practical learning experiences. AR offers unique benefits in education, such as enhancing student engagement, facilitating understanding of complex concepts through visualizations, and promoting collaborative learning. However, it also faces significant barriers, including high costs, technological limitations, and a lack of standardized evaluation frameworks. Drawing on examples across STEM (Science, Technology, Engineering, and Mathematics), humanities, and arts education, this article highlights how AR can effectively enhance learning outcomes. This narrative review synthesizes recent research on AR in education, drawing on empirical and conceptual studies across different educational levels and domains. Additionally, this paper examines the relationship between AR and major learning theories, presenting relevant case studies and the application of AR across various educational domains and target audiences. The review offers practical recommendations for educators, instructional designers, and researchers aiming to integrate AR into formal and informal learning environments, and introduces the ARCADE framework (Align–Rationale–Configure–Activate–Document–Evolve) as an actionable cycle to guide the design, implementation, and reporting of AR-based educational interventions. Full article

Background: University students are increasingly vulnerable to both depressive symptoms and hazardous alcohol use, particularly in the aftermath of the COVID-19 pandemic. Disruptions in circadian rhythms, hormonal dysregulation, and changing social dynamics may heighten susceptibility to maladaptive coping behaviors such as alcohol consumption. While this relationship has been widely studied in Western populations, limited data exist for Eastern European contexts. This study investigated the association between alcohol consumption and depressive symptoms among Romanian university students and explored potential gender differences in this post-pandemic cohort. Methods: A cross-sectional study was conducted among 103 Romanian university students at the University of Medicine and Pharmacy of Craiova, Romania. Participants anonymously completed a combined survey integrating the Alcohol Use Disorders Identification Test (AUDIT) and the Depression subscale of the Depression, Anxiety and Stress Scale (DASS-21). Statistical analyses included Pearson correlation, linear regression, and subgroup comparisons to evaluate associations between alcohol use and depression severity. Results: The mean AUDIT score was 5.4 ± 5.8, while the mean DASS-21 Depression score was 13.8 ± 9.5. A strong positive correlation was observed between AUDIT and depression scores (r = 0.72, 95% CI [0.62, 0.80], p < 1 × 10⁻¹⁷). Linear regression revealed that AUDIT scores significantly predicted depression severity (R² = 0.496, p < 0.001), with each one-point increase in AUDIT score associated with a 1.31-point rise in depression score. Male students reported significantly higher alcohol use than females (p = 0.005), while depression scores did not differ significantly by gender (p = 0.110). The alcohol–depression association was similarly strong across genders. Conclusions: Hazardous alcohol use was highly prevalent and strongly associated with increased depressive symptoms among university students. These findings highlight the need for integrated mental health and substance use screening programs in university settings to support early identification and intervention. Full article

Migraine is a complex neurovascular disorder in which immune signaling intersects with vascular and neural circuits, yet the tissue and cell-type context of common genetic risk remains incompletely defined. We integrated large-scale migraine genome-wide association study (GWAS) summary statistics with Genotype-Tissue Expression (GTEx) v8 expression and splicing quantitative trait loci (eQTLs and sQTLs), Bayesian co-localization, single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from migraine cases and controls, a healthy single-cell multi-omics atlas (assay for transposase-accessible chromatin (ATAC) plus RNA), high-dimensional weighted gene co-expression network analysis (hdWGCNA), and embryo-level spatial transcriptomics. Genetic signals were enriched in peripheral arteries, heart, and blood, and gene-level enrichment highlighted mucosal–smooth muscle organs including the bladder and the cervix endocervix. Cell-type prioritization consistently implicated endothelial and vascular smooth muscle lineages, with additional support for inhibitory interneurons and bladder epithelium. In PBMC T cells, co-expression modules capturing cytotoxic/activation and T-cell receptor signaling programs contained migraine-prioritized genes, including PTK2B, nominating immune activation circuitry as a component of genetic susceptibility. Spatial projection further localized risk concordance to craniofacial/meningeal interfaces and visceral smooth muscle–mucosal structures. Together, these analyses delineate a systemic neuroimmune–vascular architecture for migraine and provide genetically anchored candidate pathways and targets for mechanistic and therapeutic follow-up. Full article

Soil salinity is a major constraint on global agricultural productivity. This study evaluated the efficacy of a cell-free extract from Trichoderma hamatum (designated BEYF) in enhancing salt stress tolerance in lettuce (Lactuca sativa). Lettuce plants under normal and salt-stressed conditions exposed to 200 mM NaCl were treated with either water or YF (the working solution of BEYF) at concentrations of 0.05, 0.10, and 0.25 mg/L. Compared to the control, YF application significantly improved plant growth under salt stress, as indicated by increased plant height, biomass, leaf area, and other agronomic traits. Physiologically, YF mitigated oxidative membrane damage, as indicated by reduced electrolyte leakage and malondialdehyde (MDA) content, while promoting the accumulation of the osmoprotectant proline. Histochemical staining further confirmed that YF effectively suppressed hydrogen peroxide (H₂O₂) accumulation and preserved cell viability under salt stress. At the molecular level, YF significantly up-regulated the expression of key stress-responsive genes, including those involved in abscisic acid biosynthesis (NCED1, NCED2), signaling (WRKY58), and proline synthesis (P5CSs). Collectively, our findings demonstrate that BEYF enhances lettuce salt tolerance through integrated physiological, cellular, and transcriptional adaptations, supporting its potential as a sustainable biostimulant for improving crop cultivation in saline soils. Full article

This paper presents a physics-consistent hybrid surrogate framework for simulating the mechanical behavior of reinforced concrete beams that utilize waste fired clay (WFC) as a partial substitute for cement. The main contribution is the integration of empirically observed deformation behavior with physics-informed learning to produce an interpretable, mechanically valid surrogate model. Full-field surface deformation fields were measured using Digital Image Correlation (DIC) under monotonic loading and processed through a convolutional neural network (CNN) to extract deformation- and crack-sensitive features. These features were integrated with experimentally measured stress–strain data within a Physics-Informed Neural Network (PINN) in which equilibrium and conditional constitutive monotonicity constraints were enforced through the loss function. A Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was utilized as a downstream parametric exploration tool to examine trade-offs among maximum load capacity, material cost, and embodied CO₂ inside a constrained mixture-design space. Model interpretability was assessed by SHapley Additive exPlanations (SHAP), indicating that deformation-driven kinematic factors predominantly influence stress prediction, whereas WFC content and reinforcement parameters have a secondary, mixture-level impact. The resulting framework achieves enhanced predictive accuracy (R² = 0.969) relative to its individual components and operates as an offline, physics-calibrated surrogate rather than a real-time digital twin, providing a reliable and interpretable basis for structural assessment and sustainability-oriented design evaluation of WFC-modified reinforced concrete beams. Full article

Rapid assessment of existing reinforced concrete (RC) buildings is essential for effective seismic risk mitigation, particularly in highly active regions such as Bingol, Turkiye. This study evaluates the local performance of three Rapid Visual Screening (RVS) methods—RBTY-2019, FEMA-P154, and IITK-GSDMA—using verified post-earthquake damage data from the 2003 Bingol Earthquake (SERU-2003). To overcome the limitations of traditional RVS approaches, an Artificial Neural Network (ANN) model was developed and trained with the same dataset to predict building damage levels based on structural deficiency parameters. The ANN achieved a regression coefficient above 0.90 and 100% consistency in test predictions, demonstrating superior accuracy and adaptability to local construction characteristics. A Local Scaling Function (LSF) was also proposed to translate RBTY-2019 performance scores into empirical damage states, achieving 100% consistency with observed data. The findings highlight the reliability of locally trained AI models and the importance of adapting national screening regulations to regional seismic experiences. This integrated ANN–RVS framework provides a practical, data-driven tool for local authorities to prioritize urban building stock and strengthen disaster risk management strategies. Full article

Energy scarcity has evolved into one of the most pressing challenges confronting the global community today. Fuel-driven loaders suffer from drawbacks such as high fuel consumption, low energy conversion efficiency, and heavy pollution, which not only aggravate atmospheric environmental pollution but also exacerbate the global energy crisis, directly undermining sustainable development goals. In contrast, permanent magnet synchronous motors (PMSMs) have become the preferred choice for the electrification of loaders owing to their exceptional torque density, strong overload capacity, and high reliability. However, during the optimal design of high-power interior permanent magnet synchronous motors (IPMSMs), traditional methods encounter issues with inadequate optimization efficiency and excessive computational expenses, thus hindering the large-scale deployment of power systems for eco-friendly loaders. Therefore, this paper takes a 125 kW, 3000 rpm IPMSM as the research object and proposes a multi-objective optimization strategy integrating a high-precision surrogate model with modern intelligent algorithms. This approach not only enhances motor performance but also cuts down computational overhead, which holds considerable significance for reducing industrial carbon emissions and driving the sustainable development of the manufacturing industry. Taking the key performance of IPMSM as the optimization objective and the related structural parameters as the optimization variables, the multi-performance characteristic index, interaction effect and comprehensive sensitivity of the variables are calculated and analyzed by fuzzy Taguchi experiment, and the hierarchical dimension reduction in the variables is completed. The Multicriteria Optimal-Latin Hypercube Sampling (MO-LHS) method is adopted to construct the sample data space, and a back-propagation neural network (BPNN) surrogate model is used to predict and fit the motor performance. The second-generation non-dominated sorting genetic algorithm (NSGA-II) is employed for iterative optimization, and the optimized motor dimension parameters are obtained through the Pareto optimal solution. Finally, through finite element analysis (FEA) and experiments, the rated torques obtained are 417.6 N·m and 425.1 N·m, respectively, with an error not exceeding 1.8%. This verifies the correctness and effectiveness of the proposed multi-objective optimization method based on the surrogate model. Full article

This paper addresses the motion control for an x-rudder underwater vehicle, which features a bow rudder and four independent x-shaped stern rudders. To achieve coordinated operation of bow and stern rudders of the x-rudder underwater vehicle, the motion controller is divided into two parts: dynamic controller and control distributor. A model-free sliding mode parameter optimization control algorithm for underwater vehicles based on reinforcement learning (RL) is proposed. The proposed algorithm integrates a fast terminal sliding mode controller based on prior model knowledge with a model-free, data-driven input derived from reinforcement learning, ensuring both efficiency and adaptability. The control allocator employs an improved sequential quadratic programming approach to tackle the mixed minimization problem, considering various evaluation criteria and constraints. The effectiveness of the proposed control method is validated through numerical simulations across different conditions, and its performance is compared in terms of accuracy, convergence, and computational complexity. Full article

The rapid integration of renewable energy into power systems has made voltage oscillations caused by the intermittency of wind and solar power a critical operational challenge. To mitigate these issues, this paper proposes a multi-mode coordinated reactive power control strategy to enhance voltage stability in renewable energy clusters. The approach integrates two key indicators: voltage sensitivity for steady-state regulation and an improved multi-renewable energy station short circuit ratio (MRSCR) that accounts for dynamic power interactions. Validation is conducted using a hardware-in-the-loop (HIL) platform combining real-time RMS-based simulation with physical controllers. Case studies on an offshore wind cluster demonstrate that the proposed method reduces voltage fluctuation amplitude more effectively than conventional automatic voltage control (AVC), successfully suppressing oscillations. The results confirm that the strategy exhibits stronger adaptability to varying grid conditions and offers a scalable solution for oscillation mitigation in large-scale renewable energy integration. Full article

Nanoparticles of ferrimagnetic magnetite (Fe₃O₄) are cornerstones of modern nanoscience and technology, primarily due to their superparamagnetic behavior. Beyond traditional applications in magnetorheology and magnetic hyperthermia, these materials are increasingly vital in fields like active matter, where precise surface fine-tuning is crucial. While coating isotropic, quasi-spherical magnetite nanoparticles with silica is a well-established and versatile route towards functionalization, transferring this achievement to nanorod systems remains a significant challenge. Successful coating of these high-aspect-ratio geometries would allow to exploit the direction-dependent properties and increased magnetic anisotropies. However, current literature largely focuses on polycrystalline rods composed of small, clustered subunits, which limits their magnetic potential. This work describes a breakthrough in the homogeneous silica coating and stabilization of monocrystalline magnetite nanorods. We demonstrate that the superior magnetic properties of these “naked” monocrystalline rods induce strong dipole-dipole interactions, which trigger aggregation and typically prevent the isolation of individual and homogeneously coated core-shell nanoparticles. By investigating the specific mechanisms of this aggregation, we established a robust coating procedure that yields the desired isolated particles. Critically, we show that the magnetite nanorods retain their monocrystalline integrity within the silica shell, thereby preserving the enhanced magnetic properties of the original nanocrystals. Full article

The integration of digital technologies into early childhood education extends beyond mere technical necessity; it constitutes a fundamental pillar of social sustainability within the teaching profession. Yet, a persistent paradox remains in teacher education: the “Attitude–Competence Gap,” where pre-service teachers’ enthusiasm for technology fails to translate into practical proficiency. This study interrogates this disconnect within a STEAM framework, specifically examining whether digital competence is driven by general technological attitudes or domain-specific pedagogical beliefs. Utilizing an explanatory sequential mixed-methods design, we analyzed data from 200 Child Development students, followed by in-depth semi-structured interviews with 15 participants who exhibited high attitudes but low initial competence. Hierarchical regression analysis yielded a critical insight: while general attitudes toward digital storytelling did not predict competence (p > 0.05), pedagogical beliefs regarding the use of children’s literature in mathematics were a strong predictor of technical proficiency (β = 0.35, p < 0.001). Qualitative evidence corroborated that students overcame technical limitations not through technological affinity but through a motivation to concretize abstract mathematical concepts via storytelling. These findings suggest that to foster sustainable STEAM education, teacher training curricula must prioritize the “why” (pedagogical conviction) over the “how” (technical mechanics), thereby closing the gap between digital intention and action. This study uniquely demonstrates that domain-specific pedagogical convictions, rather than general technological enthusiasm, are the fundamental drivers of digital competence in STEAM, providing an empirical basis for more resilient teacher education models. Full article

High-grade serous ovarian carcinoma (HGSOC) is characterised by profound genomic instability and limited durable responses to standard therapy, leading to poor prognosis. The use of next-generation sequencing technologies has improved understanding of its molecular landscape, revealing consistent Tumour Protein p53 (TP53) mutations, homologous recombination defects, pathway alterations, and epigenetic dysregulation. Such genomic profiling now underpins the classification criteria between the ovarian cancer subtypes described by the Cancer Genome Atlas. Widespread chromosomal instability and pathogenic variants in multiple genes distinguish HGSOC from other subtypes of ovarian cancer and, further, from low-grade serous ovarian cancer. Importantly, the new-found understanding of the genomic landscape of HGSOC guides the use of platinum-based chemotherapies and Poly(ADP-ribose) Polymerase (PARP) inhibitors, with homologous recombination deficiency emerging as a cancer vulnerability that enhances treatment response. A combined multi-omics approach integrates transcriptomics, proteomics, metabolomics, and epigenomics to further the understanding of the characteristics, therapeutic targets and treatment resistance within HGSOC. Despite these advances, major challenges persist, including intratumoural heterogeneity and the poor diversity of genomic datasets. Artificial Intelligence (AI) technology, Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing, neoantigen-guided immunotherapy and ovarian cancer vaccination indicate a promising future for genomics-guided interventions and support the integration of genomics within multi-omic approaches to improve HGSOC outcomes. Full article