Search Results (6,542)

Fifth-generation (5G) networks are facing critical security challenges in device authentication for massive Internet of Things deployments while preserving privacy. Traditional federated learning approaches depend on the computationally expensive homomorphic encryption to protect model gradients, resulting in substantial latency and communication overhead, leading to impractical energy consumption for resource-constrained 5G devices. This paper proposes Zero-Knowledge Federated Learning (ZK-FL), eliminating homomorphic encryption by enabling devices to prove model correctness without revealing gradients. Our approach integrates zero-knowledge proofs with FL updates, where each device generates a proof ${\mathrm{Proof}}_i=\mathrm{ZK}({\mathrm{Gradient}}_i,{\mathrm{Hash}}_i)$, demonstrating computational integrity. The experimental results from 10,000 authentication attempts demonstrate ZK-FL achieves 78.4 ms average authentication latency versus 342.5 ms for homomorphic encryption-based FL (77% reduction), proof sizes of 0.128 kB versus 512 kB (99.97% reduction), and energy consumption of 284.5 mJ versus 6525 mJ (95% reduction), while maintaining 99.3% authentication success rate with formal privacy guarantees. These results demonstrate ZK-FL enables practical privacy-preserving authentication for massive-scale 5G deployment. Full article

Media-based cultural diversity education is approached here as an analytical synthesis that brings together established research traditions in media and communication studies, including mediatization, representation, and framing. It refers to the process through which media are understood to function as informal educational environments that shape how audiences learn about and interpret cultural differences. In contemporary mediatized societies, media institutions, including television and digital platforms, are understood to shape public understandings of diversity through the selection, framing, and visual representation of minority groups. Television is widely regarded as a particularly influential medium because of its wide reach and its institutional role in producing authoritative narratives about social reality. Through news reporting, documentaries, and other factual programming, television has been shown to circulate meanings about cultural diversity and provide audiences with interpretive frameworks through which minority groups are publicly understood. These communicative practices have been shown to influence how audiences perceive cultural difference, interpret social issues, and negotiate questions of belonging within society. By organizing narratives, frames, and visual repertoires through which cultural groups are portrayed, television has been shown to contribute to the formation of shared social knowledge about diversity and about relationships between majority and minority communities. In this sense, television can be understood not only as a channel of information but also as a cultural institution that shapes symbolic boundaries between social groups and influences perceptions of inclusion and exclusion. As an illustrative context, this entry also refers to representations of Roma communities in Central European media environments, where antigypsyism may be understood as a mediated cultural process embedded in everyday media communication. Full article

Advances in regenerative medicine increasingly rely on human-relevant in vitro systems to model the multistage process of wound healing. However, the translation of research into effective therapies remains limited by the inability of traditional 2D cultures and animal models to faithfully replicate the structural and biochemical complexity of human skin. While existing reviews often focus on the structural composition of static skin equivalents, this review addresses a critical knowledge gap: the need for dynamic, time-dependent methodologies that can capture the spatiotemporal evolution of healing, from inflammation to remodeling, in both physiological and pathological conditions. To this end, we critically evaluate next-generation platforms, including 3D bioprinting, organ-on-chip systems, organoids, and iPSC-based models, highlighting their comparative advantages and technical hurdles like vascularization and scalability. The unique contribution of this work lies in providing a forward-looking framework that advocates for the convergence of bioengineering and computational modeling to move beyond “steady-state” snapshots toward predictive, high-resolution dynamic models. We conclude that the future of wound healing research depends on integrating vascular and immune components within these platforms to achieve truly human-relevant, personalized diagnostic and therapeutic tools. Full article

Gaze estimation, a critical facet of understanding user intent and enhancing human–computer interaction, has seen substantial advancements with the integration of deep learning technologies. Despite the progress, the application of deep learning in gaze estimation presents unique challenges, notably in the adaptation and optimization of these models for precise gaze tracking. This paper conducts a thorough review of recent developments in deep learning-based gaze estimation, with a particular focus on the evolution from traditional methods to sophisticated appearance-based techniques. We examine the key components of successful gaze estimation systems, including input feature processing, neural network architectures, and the importance of data preprocessing in achieving high accuracy. Our analysis extends to a comprehensive comparison of existing methods, shedding light on their effectiveness and limitations within various implementation contexts. Through this systematic review, we aim to consolidate existing knowledge in the field, identify gaps in current research, and suggest directions for future investigation. By providing a clear overview of the state-of-the-art in gaze estimation and discussing ongoing challenges and potential solutions, our work seeks to inspire further innovation and progress in developing more accurate and efficient gaze estimation systems. Full article

Traditional markets play an important role in the exchange of plant resources and the preservation of traditional food knowledge. This study documents the diversity of food plants traded in Renmin Market, located in Youjiang District, Baise City, Guangxi, China, and evaluates their cultural importance using the Cultural Food Significance Index (CFSI). Field surveys were conducted through market observations and interviews with vendors and local informants. All edible plant species were recorded, including their scientific names, vernacular names, used parts, and modes of consumption. A total of 104 food plant taxa were documented, representing a wide range of plant families and growth forms. The recorded plants were used in four main utilization categories: vegetables, spices, fruits, and beverages. Frequently used plant parts included fruits, leaves, shoots, and underground organs such as roots, rhizomes, and tubers. The CFSI values showed considerable variation in cultural importance among species, ranging from 21.6 to 1764. The highest CFSI values were recorded for Cucurbita pepo, Allium cepa, Cucurbita maxima, and Houttuynia cordata, reflecting their frequent consumption and versatility in local cuisine. Comparative analysis with previous studies in Baise City indicated that 38 species were shared among three markets, while 30 species were recorded exclusively in Renmin Market. These findings highlight the diversity of food plants available in local markets and their importance in maintaining regional culinary traditions and plant-based dietary diversity. Full article

Maritime Autonomous Surface Ships (MASS) are progressing from proof-of-concept to engineering test and initial application phases due to advancements in intelligent sensing, automatic control, and communication technologies. However, numerous studies have shown that the improvement of automation level does not linearly reduce human factor risks. Instead, it exhibits more complex evolutionary characteristics at the medium automation level. In particular, MASS Level 2 (MASS L2) features a “system-dominated, human-supervised” operational mode, and its human factor risks have become one of the key factors restricting the safe operation, large-scale application and sustainable long-term deployment of autonomous ships. This study employs a systematic literature review to analyze 89 core articles (2020–2025) and summarizes the theoretical basis, risk characteristics, and evolutionary trends of human factor risk research in MASS L2. The review results indicate that the current research consensus has gradually shifted from the traditional “human error”-centered explanatory paradigm to a systematic understanding of “information mismatches, opacity, and coupling failures in the human-machine-shore collaborative system”. Typical human factor risks in MASS L2 are mainly manifested as the degradation of supervisory cognition and situation awareness, imbalance in trust in automation, vulnerability in mode switching and takeover, skill degradation, and structural risks in ship-shore collaboration. Based on these findings, this study constructs a classification system and a comprehensive analysis framework for human factor risks in MASS L2, reveals the interaction relationships and dynamic evolution mechanisms among different risk types from a system-level perspective, and further discusses the limitations of existing research in terms of methods, data, and engineering applicability. Finally, considering the development trends of autonomous ship technology, this study proposes future research directions in human factor theoretical modeling, dynamic risk assessment, system design, and operation management. This study aims to provide a systematic knowledge framework for human factor risk research in MASS L2 and offer references for the safety design, safety management, and development of higher-level automation of autonomous ships, while supporting the sustainable and safe advancement of the global intelligent shipping industry. Full article

The escalating antimicrobial resistance (AMR) crisis necessitates the development of innovative anti-infectives with novel mechanisms of action. Nevertheless, research on natural products remains constrained by low-throughput screening and limited mechanistic insights. Artificial intelligence (AI) is catalyzing a pivotal paradigm shift—from the mere isolation of active compounds to precisely deciphering their modes of action. This review highlights AI’s transformative role in bridging ethnopharmacological knowledge and modern pharmacology to decode the mechanisms of plant-derived anti-infectives. Case studies on berberine, baicalein, danshensu derivatives, and rosmarinic acid derivatives from Coleus amboinicus illustrate AI’s capacity to map traditional therapeutic concepts to specific pathways (e.g., biofilm inhibition, inflammasome modulation) and to predict precise binding interactions and pharmacophores with high precision. Leveraging statistical correlations between ethnobotanical usage patterns and chemical similarity, we propose a “Knowledge–Data–Mechanism” three-layer framework centered on deep mechanistic insight. Integrating Chinese initiatives, such as the CNDR (China’s National Drug Repository) database and the TCM-AI platform, with global traditional medicine wisdom, this strategy provides an actionable roadmap for modernizing anti-infective discovery. Validated applications of this paradigm have demonstrated order-of-magnitude acceleration in mechanistic characterization, rapidly yielding structurally novel agents with well-defined, target-specific actions—a critical advancement in addressing the urgent global threat of antimicrobial resistance. Full article

Traditional palliative care education may limit the development of clinical competencies and attitudes required to alleviate suffering and improve quality of life. Gamification has been proposed as an alternative educational strategy in this field. Background/Objectives: This study aimed to assess the association between gamification-based intervention and palliative care knowledge among nursing students at a public university. Methods: This single-group, pre–post-intervention study was conducted in the Nursing Program of the Universidad Técnica del Norte, Ecuador, including 136 students from the accessible population. Palliative care knowledge was assessed before and after the intervention using the validated Palliative Care Quiz for Nursing (PCQN-SV). Student satisfaction and Moodle usability were assessed using a 10-item Likert-type questionnaire. The gamified educational intervention was delivered online over 60 h. Data were analyzed using descriptive statistics and Wilcoxon signed-rank tests for paired comparisons, and exploratory logistic regression analyses were conducted to evaluate contextual differences across hospitals. Statistical significance was set at α = 0.05. Results: The mean age was 22.9 years (SD = 1.89), and 73.5% were female. Knowledge scores increased significantly after the intervention (Wilcoxon signed-rank test, p < 0.001; r = 0.35). The proportion of students achieving sufficient knowledge (≥13 correct responses) increased from 27.2% (37/136) at baseline to 49.3% (67/136) post-intervention. Contextual analysis indicated variability across clinical training sites, with Lago Agrio showing higher odds of sufficient knowledge (aOR = 3.25; 95% CI [1.26–8.41]; p = 0.015). Conclusions: The gamified intervention was associated with increased palliative care knowledge among nursing students. Heterogeneity across hospitals suggests that contextual factors may influence the magnitude of change. Full article

Knowledge Tracing, which estimates how students’ knowledge evolves during interactions with educational content, is a cornerstone of Intelligent Tutoring Systems. While deep learning models achieve superior predictive performance in this task, they lack interpretability, a limitation that is particularly critical in educational contexts. We introduce gTransformer, a new type of grounded Transformer model bridging deep learning performance with intrinsic interpretability through representational grounding. It adds theory-based parameters to input interaction sequences and uses attention mechanisms to transform them into latent representations. These are projected into enriched parameters that incorporate historical learning context while preserving semantics. Validation demonstrates: (1) structural encoding around theoretical concepts (probing selectivity $\Delta R^2>0.5$); (2) semantic alignment; and (3) functional alignment with quantified confidence. Results show that gTransformer achieves predictive performance competitive with state-of-the-art architectures while offering intrinsically interpretable predictions. The trade-off is characterised by a significant Area Under the Curve (AUC) gain over traditional theory-based models (+19.9%), with a minimal cost (3.9%) relative to non-interpretable configurations. Critically, gTransformer enables context-aware personalisation by differentiating students based on longitudinal learning trajectories rather than immediate responses, capturing patterns that traditional models cannot represent. This offers a practical path toward adaptive instruction driven by artificial intelligence that is both accurate and interpretable. Full article

Conventional methods for calculating sortie mission reliability of shipborne vehicle layouts suffer from excessive computational overhead, long runtimes, and large labeled data requirements. To address these limitations, this work proposes a specialized graph neural network architecture tailored for limited-data small-sample scenarios, denoted as the Small-Sample Graph Neural Network (SS-GNN). The proposed SS-GNN integrates multi-relational graph convolutional layers, an adaptive attention weighting mechanism, small-sample regularization techniques, and an uncertainty quantification module to accurately capture the heterogeneous multidimensional dependencies between vehicles. To further improve learning performance under data-scarce conditions, we employ a hybrid training strategy combining meta-learning-based pretraining, contrastive learning for representation enhancement, knowledge distillation, and transfer learning. Experimental results demonstrate that SS-GNN substantially outperforms traditional reliability calculation methods, classical machine learning models, and state-of-the-art GNN baselines across three key dimensions: predictive accuracy, computational efficiency, and generalization robustness, while also providing theoretically grounded uncertainty estimates for all predictions. This work provides both a theoretical foundation and a practical technical framework for shipborne vehicle reliability prediction and offers a generalizable solution for small-sample graph regression tasks in industrial domains. Future work will focus on extending the approach to extremely low-data regimes via specialized few-shot learning algorithms, incorporating dynamic relation modeling for time-varying sortie processes, and integrating domain knowledge graphs to broaden its operational applicability. Full article

The ability to autonomously navigate and explore complex 3D environments in a purposeful manner, while integrating visual perception with natural language interaction in a human-like way, represents a longstanding research objective in Artificial Intelligence (AI) and embodied cognition. Vision-Language Navigation (VLN) has evolved from geometry-driven to semantics-driven and, more recently, knowledge-driven approaches. With the introduction of Large Language Models (LLMs) and Vision-Language Models (VLMs), recent methods have achieved substantial improvements in instruction interpretation, cross-modal alignment, and reasoning-based planning. However, existing surveys primarily focus on traditional VLN settings and offer limited coverage of LLM-based VLN, particularly in relation to Sim2Real transfer and edge-oriented deployment. This paper presents a structured review of LLM-enabled VLN, covering four core components: instruction understanding, environment perception, high-level planning, and low-level control. Edge deployment and implementation requirements, datasets, and evaluation protocols are summarized, along with an analysis of task evolution from path-following to goal-oriented and demand-driven navigation. Key challenges, including reasoning complexity, spatial cognition, real-time efficiency, robustness, and Sim2Real adaptation, are examined. Future research directions, such as knowledge-enhanced navigation, multimodal integration, and world-model-based frameworks, are discussed. Overall, LLM-driven VLN is progressing toward deeper cognitive integration, supporting the development of more explainable, generalizable, and deployable embodied navigation systems. Full article

Mulberry is a plant species of significant economic value and is widely incorporated into various traditional medicinal formulations. Its multiple botanical parts (leaves, branches, fruits, seeds, and roots) possess both nutritional and therapeutic properties. Throughout its growth cycle, mulberry is exposed to a range of abiotic and biotic stresses. In response, the plant has evolved a suite of stress tolerance mechanisms, notably including the synthesis of diverse secondary metabolites. These metabolites, which encompass phenolic acids, flavonoids, and volatile aromatic compounds, exhibit pronounced pharmacological activities. This review systematically elucidates the roles of mulberry-derived phenolic compounds, alkaloids, and polysaccharides, which demonstrate a broad spectrum of biological effects, including antioxidant, antibacterial, antiviral, anticancer, anti-inflammatory, neuroprotective, anti-obesity, antidiabetic, and anti-enteritis activities. By integrating knowledge of mulberry’s adaptive mechanisms to abiotic and biotic stresses with the therapeutic functions of its extracts, this review aims to provide novel insights to guide future molecular breeding strategies and drug development efforts. Full article

The traditional craft of Buddha statue carving represents an important form of cultural heritage in many Asian societies, yet the transmission of this knowledge is increasingly threatened by modernization and the declining number of skilled artisans. This study explores the use of Virtual Reality (VR) as an innovative tool for preserving and teaching the cultural knowledge associated with Salah Lanna stone Buddha carving. A VR-based learning environment was developed to simulate traditional carving techniques, tools, and cultural narratives related to Lanna Buddhist art. The system was designed using Unity 3D and integrated hand-tracking interaction to enable immersive practice of carving procedures. The prototype was evaluated through expert review involving ten specialists in Buddha carving, art education, and VR technology. The evaluation assessed five dimensions: usability, authenticity, cultural relevance, immersion, and perceived learning potential. Results indicate high levels of expert evaluation results regarding the effectiveness of the system, with average scores of 4.6 for usability, 4.8 for authenticity, 4.7 for cultural relevance, 4.5 for immersion, and 4.9 for perceived learning potential on a five-point scale. The findings suggest that VR technology can provide a promising platform for preserving traditional craftsmanship and supporting immersive cultural learning. By integrating technical training with cultural narratives, the system demonstrates potential for enhancing access to traditional craft education while contributing to the digital preservation of Salah Lanna cultural heritage. Full article

The bovine milk microbiome is a complex and dynamic microbial ecosystem, comprising both commensal and pathogenic bacteria. Its composition is shaped by endogenous factors, including udder physiology, lactation stage, and health status, particularly mastitis, as well as by exogenous factors, such as housing conditions, farm infrastructure, milking practices, and post-milking processing. Mastitis not only alters milk quality but also induces persistent dysbiosis that may persist even after clinical recovery, highlighting the need for continuous microbiome monitoring to ensure milk safety. Advances in molecular and metagenomic techniques have enabled the detection of microbial taxa that are difficult to identify using traditional culture-based methods. However, challenges remain due to low microbial biomass, reagent contamination, and the inability to distinguish live from dead bacteria, all of which complicate accurate characterization. Environmental contamination from skin, air, and equipment, along with microbial shifts during transport, storage, pasteurization, and product separation, further modulate microbial communities. While mastitis-related changes in milk microbiota have been extensively studied, the effects of other bovine diseases and systemic health conditions remain largely unexplored, constituting a critical knowledge gap. Understanding the factors that shape milk microbial communities is essential for ensuring dairy product safety, optimizing herd management, and developing microbiome-based innovations in milk production. Full article

Coronary atherosclerosis in master athletes represents a paradox: despite the well-established cardiovascular benefits of regular exercise, highly trained endurance athletes show a higher prevalence of coronary plaques than their non-athletic peers. The mechanisms behind this finding are multifactorial, involving sustained high shear stress on the vascular wall, exercise-induced inflammatory activation, altered calcium homeostasis, and interactions between genetic predisposition and sport-specific lifestyle factors. Although athletes tend to exhibit predominantly calcified—potentially more stable—plaques, recent studies highlight that mixed and non-calcified lesions are also present, particularly among lifelong endurance athletes, raising questions about their true long-term risk. Clinically, traditional risk scores often underestimate risk in this population, making multimodal assessment with tools such as coronary calcium scoring and coronary CT angiography essential. This review synthesizes the current knowledge on mechanisms, clinical implications, diagnostic strategies, and prevention of coronary atherosclerosis in athletes, while underscoring key gaps that future research must address. Full article