Search Results (6,644)

Wide-bandgap power devices, particularly silicon carbide (SiC) MOSFETs, have seen widespread adoption in electric vehicle (EV) motor drive systems due to their superior switching characteristics, including high switching speeds and high switching frequencies. However, these advantages exacerbate motor terminal overvoltage, with peaks reaching twice the inverter output voltage, causing insulation breakdown in windings and bearing electro-corrosion, which shorten motor lifespan. Traditional overvoltage prediction methods, such as distributed parameter models or detailed ladder network approaches, require extensive system parameters and involve high computational loads, while simplified models lack generality. To address these issues, this paper proposes a simplified prediction method based on a lumped ladder network model combined with frequency scanning. The approach uses impedance analysis to identify anti-resonance frequencies, enabling direct estimation of overvoltage amplitudes without prior knowledge of cable or motor specifics. Experimental validation on a SiC-based drive system demonstrates prediction errors below 10% and a reduction in computational time compared to conventional methods. Full article

This study systematically examines the intersection of emerging digital technologies and organizational accountability within the sustainability domain using the TCCM framework. Guided by the SPAR-4-SLR protocol, a final corpus of 67 high-impact peer-reviewed articles was analyzed to synthesize current knowledge and identify structural gaps in governance architectures. Findings indicate that traditional human-led narrative disclosures are increasingly supplemented or replaced by technology-embedded verification systems offering real-time data granularity. The analysis shows that while the field is largely grounded in Stakeholder Theory and the Resource-Based View, mid-range theorizing is needed to address algorithmic bias and the gap between technological capabilities and accountability practices. Empirical evidence is concentrated in Europe and East Asia, exposing a digital divide that limits the applicability of findings to resource-constrained enterprises. The study provides a conceptual synthesis of how AI, blockchain, and IoT reshape transparency, highlighting the need for governance approaches that prioritize ethical oversight, decentralized validation, and substantive rather than symbolic compliance. Full article

Background/Objectives: Multidrug-resistant (MDR) Enterobacter spp. are critical pathogens within the ESKAPE group, frequently exhibiting resistance to carbapenems. Antimicrobial photodynamic therapy (aPDT) represents a promising non-antibiotic strategy to circumvent these resistance mechanisms. This scoping review aims to map the current evidence regarding the efficacy of aPDT in inactivating Enterobacter spp., identifying the most effective photosensitizers (PS), light parameters, and existing research gaps. Methods: A systematic search was performed across PubMed, Scopus, and Google Scholar (2013–2025) following PRISMA-ScR guidelines and registered on OSF. Studies were included if they evaluated aPDT against Enterobacter spp. (in vitro or in vivo) and provided quantitative data on microbial reduction. Data was extracted using a standardized charting form covering bacterial strains, PS type, light source, and viability reduction. The results from the eligible sources of evidence were synthesized narratively to address the review objectives. Results: Despite the clinical priority of Enterobacter, only seven studies met the eligibility criteria. Methylene Blue remains the most frequently studied PS, achieving reductions of 3–8 log₁₀. Emerging evidence highlights the synergistic efficacy of monocationic chlorins and graphene-based nanomaterials in enhancing the bactericidal effect of light-based treatments. Notably, aPDT demonstrated the ability to inactivate carbapenemases, the bacterial enzymes responsible for carbapenem resistance. However, only two studies evaluated in vivo applications, primarily within dental settings. Conclusions: aPDT is a promising method against MDR Enterobacter spp. and bypasses traditional resistance mechanisms. However, the limited number of studies indicates a significant knowledge gap. Future research should focus on standardized in vivo protocols and the synergy between aPDT and conventional antibiotics to support clinical translation. Full article

Urban radio spectrum monitoring is becoming increasingly complex due to the rapid growth of wireless devices, unauthorized emissions, and dynamic electromagnetic environments in smart cities. Traditional spectrum analysis approaches, based on manual operation or static detection techniques, are no longer sufficient to ensure scalable, autonomous, and secure monitoring. The convergence of two emergent technologies—Large Language Models (LLMs) and the Model Context Protocol (MCP)—facilitates a fundamental shift in radio monitoring. We define this as the AICEBERG paradigm: a novel, stratified architecture where a high-level, intelligent agentic interface (the peak) abstracts the underlying complexity of SCPI-driven hardware integration and radio governance protocols (the foundational base). This autonomous framework provides the necessary objective rigor to audit the stochastic ‘ocean of electromagnetic waves’ characteristic of modern smart cities, ensuring a stable platform for regulatory enforcement amidst high-density signal interference. The proposed system implements a three-layer processing flow, enabling high-level natural language commands to be translated into validated and secure hardware actions on RF spectrum analyzers. A dual-server design separates operational execution from safety validation, ensuring controlled SCPI command handling, parameter verification, and instrument health monitoring. Experimental validation demonstrates the feasibility of autonomous measurement execution. The results show that the proposed architecture reduces human dependency, enhances reproducibility and lowers the expertise barrier required for RF spectrum surveillance. To the best of our knowledge, AICEBERG represents one of the first integrated frameworks to bridge LLMs with SCPI-compliant hardware through the MCP for autonomous radio governance. Full article

Accurately mapping agricultural land use in fragmented hilly and mountainous areas is crucial for resource management but is severely challenged by spatial heterogeneity. While high-resolution (HR) images excel at delineating fine parcel boundaries, their limited spectral and temporal information often leads to spectral confusion among diverse agricultural types. To address this limitation, this study proposes a novel spatiotemporal feature-driven geographical zoning method integrating vegetation phenology, topography, and human activity. This zoning strategy decouples the complex global classification task into relatively simple local problems, providing explicit geoscientific constraints for subsequent classification. The proposed method was validated by classifying plain open-field croplands, sloping croplands, terraces, and greenhouses in the hilly and mountainous areas of Beijing using 2 m resolution satellite images. Compared to traditional global classification methods, the proposed zoning-based method increased the overall accuracy from 84.81% to 90.81%, the Kappa coefficient from 0.74 to 0.85, and the Intersection over Union (IoU) from 77.85% to 90.85%. The advantages of geographic zoning were particularly evident in mitigating spatial heterogeneity and enhancing boundary precision. These findings indicate that integrating dynamic geographical zoning as a priori knowledge successfully bridges the gap between HR spatial details and environmental contexts, offering a robust solution for mapping fragmented agricultural landscapes. Full article

Aneurysmal subarachnoid hemorrhage (SAH) remains a devastating cerebrovascular disorder with high morbidity and mortality, despite advances in aneurysm securing and neurocritical care. Clinical outcomes are determined by early brain injury (EBI), delayed cerebral ischemia (DCI), hydrocephalus, and long-term cognitive impairment, extending beyond the traditional focus on large-vessel vasospasm alone. Emerging evidence identifies the dysfunction of the glymphatic system and meningeal lymphatic pathway, the brain’s primary clearance pathways, as a central and unifying mechanism linking acute hemorrhagic injury to delayed and chronic neurological sequelae. Following SAH, acute intracranial pressure elevation, subarachnoid blood clot burden, loss of arterial pulsatility, venous congestion, astrocytic aquaporin-4 perivascular depolarization, and neuroinflammation converge to suppress cerebrospinal fluid–interstitial fluid exchange and outflow in glymphatic system and subsequent meningeal lymphatic drainage. Persistent clearance failure promotes the retention of blood breakdown products, inflammatory mediators, and metabolic waste, amplifying microvascular dysfunction, cortical spreading depolarizations, blood–brain barrier disruption, and secondary ischemic injury. Importantly, accumulating data highlight venous pathology and meningeal lymphatic impairment as critical, yet underappreciated, contributors to delayed injury and post-SAH hydrocephalus. In this review, we synthesize the current knowledge of the physiological organization of glymphatic and meningeal lymphatic systems, delineate the mechanistic and molecular drivers of their dysfunction after SAH, and discuss clinical implications for EBI, DCI, hydrocephalus, and long-term cognitive outcomes. We further outline future directions, including translational imaging, biomarker development, and therapeutic strategies targeting clearance pathways, to advance disease-modifying approaches in SAH. Full article

Maritime Domain Awareness (MDA) relies heavily on data acquired from high-resolution optical spaceborne sensors; however, processing this massive quantity of sensor data via traditional supervised deep learning is severely bottlenecked by its dependency on exhaustively annotated datasets. Under extreme data scarcity, conventional architectures suffer severe performance degradation, rendering them impractical for time-critical, zero-day deployments. To overcome this barrier, we propose a training-free inference paradigm that leverages the extensive pre-trained knowledge of Large Vision-Language Models (VLMs). Specifically, we introduce a Domain Knowledge-Enhanced In-Context Learning (DK-ICL) framework coupled with a Macro-Topological Chain-of-Thought (MT-CoT) strategy. This approach bridges the perspective gap between natural images and top–down optical sensor imagery by translating expert remote sensing heuristics into a strict, step-by-step reasoning pipeline. Extensive evaluations demonstrate the substantial efficacy of this framework. Armed with merely 4 visual exemplars per category as in-context triggers, our MT-CoT augmented VLMs outperform traditional models trained under identical scarcity by over 38% in F1-score. Crucially, real-world case studies confirm that this zero-gradient approach maintains robust generalization on unannotated, out-of-distribution coastal clutters, achieving performance parity with data-heavy networks trained on 50 times the data volume. By substituting massive human annotation and GPU optimization with scalable logical deduction, this paradigm establishes a resource-efficient foundation for next-generation intelligent maritime sensing networks. Full article

Indoor speech propagation causes minute vibrations in surrounding objects, enabling remote speech recovery through passive eavesdropping. Unlike traditional methods that rely on acoustic waves, passive eavesdropping uses object vibrations, making it difficult to defend against, even in soundproof environments. However, weak vibration signals and noise interference make speech recovery challenging. Existing studies mainly focus on deep learning for signal reconstruction, requiring large datasets and high computational power, which complicates real-time, on-device deployment. To address this, we propose a lightweight passive speech recovery system based on millimeter-wave radar. Without prior knowledge of object locations or numbers, the system can adaptively fuse multi-source signals for real-time speech reconstruction. To counteract the noise characteristics of millimeter-wave radar and the weak amplitude of vibration signals, we designed a set of low-complexity noise suppression and signal enhancement algorithms, ensuring efficient operation on edge devices. Experimental results demonstrate that in single-target scenarios, the proposed system achieved a Mel Cepstral Distortion (MCD) of 3.923 and a Word Error Rate (WER) of 12.9%. In multi-target scenarios, the SNR improved by 3.65 dB, MCD decreased by an average of 1.52, and WER decreased by an average of 15.83%, making the method effective and practical in complex acoustic environments. Full article

Task scheduling in cloud computing environments is a complex NP-hard problem that requires maximizing resource utilization while satisfying quality-of-service (QoS) constraints. Traditional meta-heuristic algorithms often become stuck in local optima, while single deep reinforcement learning (DRL) models exhibit instability when exploring large-scale solution spaces. To address this, this paper proposes a hybrid scheduling algorithm based on multi-objective sand cat colony optimization (MoSCO). This algorithm utilizes a D3QN network to extract task features and guide population initialization, followed by a multi-objective Sand Cat Swarm Optimization (SCSO) algorithm for refined local search. Results from 50 independent replicate experiments conducted in a simulated cloud environment, coupled with an analysis of the dynamic convergence process, demonstrate that MoSCO exhibits significant superiority and robustness. Scatter plot convergence analysis further confirms that MoSCO’s knowledge injection mechanism effectively overcomes the blind exploration phase of traditional algorithms and successfully breaks through the local optimum bottleneck in the late iteration stages of single reinforcement learning, achieving higher-quality, denser, and more stable convergence. Furthermore, 3D and 2D Pareto front analyses show that MoSCO generates highly competitive, well-distributed non-dominated solutions, offering flexible trade-off options for conflicting objectives. Compared to PureD3QN, H-SCSO, and NSGA-II, MoSCO exhibits the smallest performance fluctuations in box plots. Specifically, MoSCO elevates the average resource utilization of clusters to 92.20%, while reducing the average maximum Makespan and Tardiness to 528 and 4187, respectively. Experimental data confirm that MoSCO effectively balances global exploration with local exploitation, delivering stable, high-quality solutions for dynamic cloud task scheduling. Full article

Traditional villages embody regional culture and local knowledge, yet culture–tourism integration often suffers from a mismatch between resource value and effective transformation. To address this problem, this study proposes a two-dimensional “benefit–obstacle” diagnostic and strategy-matching framework and tests its case-based applicability in Dayuwan Village. First, a cultural landscape gene (CLG) atlas was constructed for the village based on a geo-information coding scheme, covering both tangible and intangible CLGs. Second, a four-dimensional evaluation system was operationalized through five expert judgments and 106 valid on-site questionnaires collected from tourists (n = 67) and residents (n = 39). Criterion weights were determined using an AHP–entropy combination approach, and the comprehensive benefit closeness coefficient was calculated via TOPSIS. Third, an obstacle degree identification model was employed to pinpoint key constraints and derive composite obstacle degrees. Results within the Dayuwan case show that the TOPSIS closeness coefficients of the 17 genes ranged from 0.653 to 0.782 (mean = 0.714), with 4, 6, and 7 genes classified as excellent, good, and medium, respectively; composite obstacle degrees ranged from 0.0228 to 0.1975. In Dayuwan Village, higher obstacle degrees clustered mainly in intangible CLGs, whereas Ming–Qing architecture and frequently practiced folk-cultural genes showed comparatively lower obstacle degrees. The transformation process is constrained by four mechanisms—landscape character protection, economic transformation, social identity, and market demand—with economic transformation constraints being the most prominent. Based on the benefit–obstacle matrix, 17 CLGs were classified into five activation scenarios and matched with corresponding revitalization strategies. This framework links benefit ranking, obstacle diagnosis, and strategy matching, and provides a case-based diagnostic reference for the conservation and culture–tourism integration of villages with comparable heritage conditions, subject to local recalibration of indicators, weights, and thresholds. Full article

This study maps the global scholarly landscape on digital parenting and children’s digital device use through bibliometric analysis of 628 Scopus articles (2010–2025). Using PRISMA-guided screening and science-mapping visualisations (VOSviewer and CiteSpace), the review identifies publication growth, influential sources, intellectual structures, and thematic clusters shaping the field. The mapped knowledge structure is dominated by health and media-effects traditions, with major research fronts centred on parental mediation, screen-time outcomes, online safety, and digital wellbeing. Crucially, the analysis shows that parenting perspectives remain weakly represented within this global corpus, with limited engagement with faith-based concepts that could shape mediation practices and moral reasoning in households. This underrepresentation contributes to a Western-centric evidence base, indicating a need for Islamically situated digital parenting research that integrates developmental concerns with ethics and culturally grounded mediation strategies. The study concludes by proposing a focused research agenda to strengthen theory building and empirical work in family contexts. Full article

Information security compliance has become critical for organizations worldwide, with the ISO/IEC 27000 family serving as the most widely adopted framework for establishing information security management systems. Despite their global acceptance, these standards present significant interpretation challenges due to their formal language, abstract structure, and extensive cross-referencing across 97 documents. Traditional retrieval-augmented generation (RAG) systems, which rely on independent text chunking and dense vector retrieval, prove inadequate for such highly interconnected regulatory materials, often fragmenting contextual relationships and reducing accuracy. This study introduces a privacy-preserving RAG framework that integrates LightRAG, a knowledge graph-based retrieval system, with locally hosted open-source language models. Unlike chunk-based RAG systems that treat document segments independently, the system in this study constructs a semantic knowledge graph that explicitly models relationships between clauses through typed edges representing cross-references, semantic similarity, and hierarchical dependencies. To enable rigorous evaluation, we developed a curated benchmark dataset of 222 multiple-choice questions with authoritative ground-truth answers, systematically constructed from official ISO standards, certification preparation materials, and academic sources. Through systematic evaluation on this benchmark, we show that knowledge graph-based retrieval achieves higher accuracy than chunk-based RAG and non-retrieval LLM baselines within the evaluated setup. The analysis indicates that embedding model quality is strongly associated with system performance, that hybrid retrieval modes combining local and global graph traversal tend to yield better accuracy, and that mid-sized open-source models paired with strong retrievers can approach the performance of larger proprietary systems. The best configuration achieves 90.54% accuracy, demonstrating the promising effectiveness of graph-structured retrieval for multiple-choice regulatory questions. Full article

Virtual reality (VR) is increasingly used as an interactive instructional medium in engineering education, yet evidence on practical implementation and student-reported experience remains limited. This study examined students’ perceived experience and usability across VR and traditional instruction within a crossover design in a UV-C water disinfection lesson. Using a mixed 2 × 2 crossover design, 52 undergraduate engineering students completed both a VR lesson (Meta Quest 3; Unreal Engine 5.4) and a content-aligned traditional session delivered with slides and a physical UV disinfection stand. After each session, participants reported perceived flow (short Flow Index) and engagement (adapted User Engagement Scale); the System Usability Scale (SUS) was completed after the VR session only. A brief knowledge quiz and open-ended feedback were also collected and used descriptively. Students reported higher perceived flow and engagement in the VR condition than in the traditional condition, and VR usability was generally rated acceptable-to-excellent, with higher SUS scores observed in the VR-first sequence than in the traditional-first sequence. Qualitative feedback emphasized clarity and interactivity, and most participants expressed a preference for a blended approach. Overall, the results support the practical feasibility and positive user acceptance of the VR lesson in this instructional context. The findings also suggest that perceived usability may be associated with instructional sequence, although this pattern should be interpreted cautiously within the perception-based scope of the study. Full article

Background/Objectives: Periodontitis represents a significant global health burden, yet preventive health literacy remains critically low among emerging adults—a developmental stage where lifelong health behaviors crystallize. This study evaluated the effectiveness of the GUM (an acronym of Gum Understanding Mission) game, an interactive gamified digital tool incorporating AI-informed or manual feedback, for improving periodontitis literacy among tenth-semester Software Engineering students at the University of Guayaquil. Methods: In a controlled pre-test/post-test experiment, 50 participants were randomly assigned to either the GUM game intervention or a traditional lecture. Both groups completed identical knowledge assessments immediately before and after their respective 50-min instructional sessions. The GUM game featured adaptive questioning, immediate elaborated feedback, and comprehensive performance analytics, while the control group received instructor-led didactic instruction with a subsequent question-and-answer session. Results: The GUM group improved from a baseline of 21% to 94% correct responses, while the lecture group increased from 22% to 67% ($p<0.001$). Error reduction was 74% in the GUM group versus 45% in the control group. However, the study’s scope is currently limited to a single, digitally literate cohort, and knowledge retention over time was not assessed. Conclusions: These findings suggest that a self-directed, feedback-driven serious game can substantially outperform traditional methods in fostering periodontitis literacy within this population. Further research is needed across diverse populations with extended follow-up periods to assess knowledge retention and generalizability. Full article

Generative artificial intelligence (AI) has demonstrated significant potential in education, yet empirical research on its application in “hardware-driven” interdisciplinary design courses remains scarce. This study employed a randomized crossover experimental design in an IoT Hardware and Design Innovation course at Hunan University. Twelve industrial design undergraduates with no prior IoT background alternated between AI-assisted (ChatGPT-4o) and traditional learning resource conditions across six short-cycle tasks. The crossover design enabled each participant to serve as both experimental and control subjects, yielding 72 observation-level data points. Grounded in Cognitive Load Theory, the study examined three dimensions: process efficacy, difficulty structure, and switching adaptation costs. Results indicated that AI significantly improved perceived task completion efficiency, self-reported goal attainment, and learning experience, yet self-assessed knowledge transfer did not differ significantly between conditions. AI reduced the total number of reported difficulties but altered the difficulty-type distribution: resource-retrieval difficulties decreased while information-verification difficulties increased—a phenomenon we term “difficulty restructuring”. Furthermore, switching from AI back to traditional resources incurred significantly higher adaptation costs than the reverse transition, revealing emerging dependency risks. These findings suggest that generative AI may function more as a “difficulty restructurer” than a “difficulty eliminator” in hardware-driven design education, providing exploratory empirical evidence for incorporating verification literacy into future course design and calling for calibrated scaffold fading that may help mitigate emerging dependency risks. Full article