Search Results (6,639)

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

Plant-derived extracellular vesicles (PDEVs) are nanoscale carriers produced through conserved plant mechanisms, including multivesicular body (MVB) formation and consequent extracellular vesicle release. MVBs are formed through repeated rounds of intracellular vesicles’ fusion, thus leading to the incorporation into PDEVs of lipids, proteins, miRNAs, nucleic acids, and secondary metabolites, derived from different cellular compartments. PDEVs possess a bilayer lipid membrane, which protects their cargo from degradation and facilitates membrane–membrane fusion with target cells. Ayurvedic medicinal plants are renowned for their extensive phytochemical diversity and enduring efficacy in addressing inflammation, infections, metabolic disorders, cancer, and neurodegeneration. However, the clinical translation of traditional herbal preparation is severely bottlenecked by batch-to-batch variability, restricted compound bioavailability, mechanistic uncertainties, and limitations of conventional large-scale extractions. This perspective research study critically proposes PDEVs as an innovative interpretation for Ayurvedic medicinal plants utilization. We identify and evaluate medicinal plants with established therapeutic characteristics that remain unexamined in PDEV research, hence presenting compelling opportunities for future investigation. By establishing a synergistic bridge between ancient Ayurvedic knowledge and modern nanomedicine, this perspective provides a methodological roadmap to guide health-efficient plant selection and accelerate translational research in next-generation therapeutics. Full article

Accurate prediction of corrosion-fatigue crack growth rate in aluminum alloys is critical for the safety assessment of aerospace structures. Conventional empirical fracture-mechanic models often struggle to capture multiphysics coupling effects, whereas purely data-driven machine-learning models may lack physical interpretability and generalize poorly beyond the training distribution. To address this challenge, this study proposes a physics-guided knowledge-based XGBoost (KBXGB) model. Based on a comprehensive dataset comprising 2786 experimental records, Permutation Feature Importance was utilized to identify 11 key features, including the stress intensity factor range, stress ratio, frequency, and environmental parameters. The KBXGB framework learns the residual between physics-based empirical models (e.g., the Paris and Walker laws) and measured experimental data, recasting the complex nonlinear mapping into a correction of the systematic deviations of the physical models, thereby achieving deep integration of domain knowledge and data-driven learning. Test results demonstrate that the KBXGB model achieves a coefficient of determination (R²) of 0.9545 and a reduced Mean Relative Error (MRE) of 1.61% on the test set, outperforming standard XGBoost and traditional regression models. Crucially, in independent extrapolation validation, the standard XGBoost model failed (R² = 0.2858) with non-physical staircase artifacts, whereas the KBXGB model maintained high predictive fidelity (R² = 0.8646) and successfully reproduced physical crack growth trends. The proposed approach effectively mitigates the “black-box” limitations of machine learning in sparse data regions, offering a high-precision and physically robust tool for corrosion fatigue-life prediction under complex service conditions. Full article

Existing scholarship on Sino-Western hybrid architecture (yanglou) has often treated Chinese elements as marginal, overlooking the agency of indigenous spatial logic. This study examines how traditional Chinese feng shui mediated the localization of Western architecture in the late Qing Dynasty through the case of the Tianjin Postal Museum. The research has three objectives: to distinguish Western architectural features from Chinese spatial rationales, to analyze the mediating mechanisms of feng shui, and to interpret the implications of this case for indigenous knowledge systems in the process of modernization. Using spatial semantic analysis based on UAV mapping and field surveys, the study finds that although the museum displays Western structural systems and proportional canons, its underlying spatial organization follows Chinese logic. This organization includes an enclosed courtyard, a north–south axis that structures dynamic and static zones, and re-signified elements such as the octagonal tower and parapet, which were repurposed to regulate qi and mitigate sha. The findings suggest that feng shui functioned as a pragmatic indigenous framework that enabled the creative appropriation of Western forms and challenged passive diffusion models of architectural modernization. Full article

This study explores Serbia’s rich ethnopharmacological heritage by systematically documenting the traditional use of medicinal plants for treating skin diseases during the 19th and 20th centuries. Drawing on key ethnographic sources—including monographs, scholarly articles, and field reports—the review analyzes historical records of folk medicine practices and their cultural contexts. A total of 164 plant species from 63 botanical families, as well as one mushroom species, were identified as being used in the treatment of skin-related conditions classified according to the International Classification of Primary Care. Reported ailments were grouped into three main categories: hair and scalp disorders, bites, and various inflammatory skin conditions such as eczema and psoriasis. Remedies for wound healing were the most frequently documented, both in terms of application and diversity of plant species employed. By preserving and systematizing this historical knowledge, the study provides a valuable foundation for future pharmacological and dermatological research, highlighting the continued relevance of traditional remedies in modern clinical practice. Full article

Almond (Prunus dulcis (Mill.) D.A. Webb) is one of the most economically important nut crops worldwide, valued for its nutritional properties and adaptability to diverse agroecological environments. This review summarizes current knowledge on almond domestication, genetic diversity, production trends, and improvement strategies, with a focus on drought tolerance under climate change. Archaeobotanical and molecular evidence indicate central Asia and the eastern Mediterranean as key centers of origin, where recurrent introgression from wild Prunus species contributed to the high genetic variability of cultivated almond. Global production trends reveal increasing challenges due to prolonged drought, climate variability, and rising water and energy costs, particularly affecting major producers such as the United States. Mediterranean regions are transitioning from traditional low-density orchards to intensive systems, where cultivar and rootstock choice are crucial for sustainability. Self-fertile and late-blooming cultivars improve yield stability, while interspecific hybrid rootstocks enhance water use efficiency and tolerance to drought and poor soils. Drought stress impacts almond physiology and yield, although moderate deficit irrigation can maintain productivity and improve kernel quality. Future improvement relies on germplasm conservation, marker-assisted selection, and genomic tools to develop climate-resilient cultivars integrated with sustainable water management strategies. Full article