Search Results (14,084)

To unveil the underlying risk structures in complex industrial systems, this paper proposes a hybrid analytical framework that integrates BERTopic modeling, a large language model (LLM), and social network analysis (SNA). This framework aims to extract systemic safety intelligence from unstructured accident reports. It first employs BERTopic to identify latent causal topics based on 745 Chinese accident investigation reports and utilizes DeepSeek-V3.1 (LLM) for semantic refinement and causal mapping of these topics. Subsequently, a semantic network of causal keywords based on positive pointwise mutual information (PPMI) is constructed, and its topological structure is analyzed using SNA methods. The study identifies and analyzes five major risk communities: confined spaces, fire, mining, construction, and road traffic. It reveals that accident causation exhibits the small-world characteristics of multi-factor coupling and non-linearity, with core risk nodes concentrated in systemic inducements such as organizational management and compliance deficiencies. The results demonstrate that this framework effectively identifies the latent systemic risk patterns embedded within the texts, providing methodological support for developing sustainable safety management mechanisms based on design for safety. Full article

Generative Artificial Intelligence (GenAI) is transforming higher education in terms of pedagogy, student involvement, and academic management. This systematic literature review examines 30 peer-reviewed articles published from 2019 to 2025, adhering to PRISMA 2020 and Kitchenham’s methodologies. Descriptive and thematic analyses highlight five opportunities: (a) tailored and adaptive education; (b) deliberate fostering of critical thinking; (c) enhanced accessibility for varied learners; (d) teaching innovation via multimodal content development and feedback; and (e) collaborative methods that regard AI as a co-teacher. Four ongoing challenge categories also surface: (a) risks to academic integrity; (b) excessive dependence on GenAI that may hinder learner independence; (c) inconsistent faculty preparedness and change-management abilities; and (d) differences in infrastructure and policy both regionally and globally. Intersecting ethical issues, such as data privacy, algorithmic bias, transparency, and accountability, highlight the necessity for governance that aligns with institutional risk and reflects societal values. Analyzing the recent literature, this systematic review offers four contributions: (a) a recommendation model for responsible GenAI implementation in higher education institutions; (b) a framework for sustainable integration of GenAI; (c) a highlight of the future research recommendations; and (d) an integrated policy and pedagogical recommendations roadmap. These models emphasize the integration of AI literacy, ethical considerations, and critical thinking goals into educational programs. The review advocates for a strategic, stakeholder-focused approach to implementation that enhances rather than replaces human instruction, thus connecting GenAI’s educational potential with ethical, context-aware avenues for institutional transformation. Full article

The accurate and timely identification of urban building footprints is critical for sustainable urban planning and disaster management. Traditional remote sensing methods for this task often face limitations in scalability, accuracy, and adaptability to complex urban morphologies. This paper addresses these challenges by developing and evaluating a novel data-centric framework that synergistically integrates Graph Neural Networks (GNNs) with zero-shot superpixel segmentation derived from the Segment Anything Model (SAM) applied to Sentinel-2 imagery. A cornerstone of our methodology is a rigorous assessment of OpenStreetMap (OSM) data, refined through temporal NDVI stability analysis to generate high-quality ground truth. We propose an optimized UrbanGraphSAGE model, enhanced with spectral data augmentation and trained using a robust loss function with label smoothing to mitigate label noise. In the complex urban landscape of Algiers, Algeria, our approach achieves a Test F1-Score of 0.7131, demonstrating highly competitive performance with standard pixel-based baselines like U-Net while offering significant topological and computational advantages. Specifically, our model operates with merely 19,585 parameters—orders of magnitude fewer than pixel-based CNNs. A rigorous Gold Standard evaluation against manually labeled imagery confirms the model’s high recall (0.8484) and reliability for automated urban monitoring. Full article

Ionizing radiation affects enzymes, which are essential for most cellular functions, by inducing chemical alterations in their molecular structures, often resulting in the inhibition of their activities. Unraveling the molecular and kinetic mechanisms driving these effects requires irradiation protocols that ensure accurate dose delivery, spatial homogeneity, and reproducibility. In this study, we established a systematic experimental framework that adapts a medical linear accelerator (LINAC) as a precision source for biochemical irradiation experiments. A rigorous protocol was developed that allows enzyme solutions to be irradiated under strictly defined and verifiable dosimetric conditions. Using this approach, we quantified the radiation-induced modulation of enzyme activity in two representative enzymes: invertase (β-fructofuranosidase) and collagenase. For invertase, a pronounced nonlinear decrease in enzyme activity was observed, with the enzyme retaining approximately only 2.2% of its initial activity at 50 Gy. Conversely, collagenase activity exhibited an exponential dose–response behavior over the dose range 0.1–200 Gy, yielding a global inactivation constant of $K = 0.015 {\mathrm{G}\mathrm{y}}^{-1}$. Complementary SDS–PAGE analysis revealed no detectable radiation-induced protein fragmentation or aggregation under the investigated conditions. These results confirm enzyme-specific radiation sensitivity and demonstrate the efficacy of this LINAC-based methodology for quantitative dose–effect studies. Overall, this work provides a versatile experimental tool for applied radiation research, bridging the gap between clinical medical physics and fundamental biochemistry. Full article

The accelerated aging of the population in vulnerable urban contexts poses significant challenges for architecture, particularly with regard to the quality of life of older adults. Within this framework, the present study aimed to analyze the association between sensory architecture and the quality of life of older adults and to translate this empirical evidence into context-informed design criteria for the development of a comprehensive center for older adults. The study adopted a quantitative approach with a non-experimental, cross-sectional, and correlational design. A structured questionnaire on sensory architecture and quality of life was administered to family members and caregivers acting as proxy respondents, demonstrating high internal consistency (Cronbach’s $\mathit{\alpha }>0.90$). Given the ordinal nature of the data, inferential analysis was conducted using Spearman’s rho coefficient. Within the analyzed dataset, the results revealed a statistically significant and strong association between sensory architecture and the quality of life of older adults ($\rho$ > 0.80). At the dimensional level, visual and tactile stimuli exhibited the highest associations, followed by the social relationships dimension, while therapeutic environments showed a moderate association, allowing the identification of an empirical hierarchy among the analyzed dimensions within this dataset. These findings support the interpretation of sensory architecture as a construct statistically associated with indicators of quality of life, from a non-causal perspective. Based on this hierarchy, the results were articulated into an evidence-based architectural structure, serving as analytical input to inform context-specific criteria for spatial organization, materiality, comfort, orientation, and social interaction derived from the observed statistical associations. The study contributes a methodological approach that systematically connects correlational quantitative findings with architectural design considerations, particularly in urban contexts characterized by limited specialized infrastructure. However, a key limitation is the use of proxy respondents (family members and caregivers), which should be considered when interpreting the results. Full article

This study presents two independently optimised Extreme Gradient Boosting (XGBoost) regression models, one for predicting corrosion current density ($i_{corr}$) and one for predicting corrosion potential ($E_{corr}$) parameters of carbon steel rebar embedded in mortar and immersed in simulated pore solution. An experimental dataset consisting of 216 measurements was curated from a systematic potentiodynamic scan study covering six chloride contamination levels, two carbonation states (non-carbonated and carbonated), four moisture conditions for mortar (65%, 85%, 95% relative humidity, and submerged), and three conditioning durations for simulated pore solution (36 h, 72 h and 20 days). Hyperparameters of the XGBoost models were optimised using a Bayesian optimisation framework with the Tree-structured Parzen Estimator (TPE) sampler over 300 trials. Model performance was assessed using 5-fold cross-validation and a random 80:20 train–test split. The optimised models achieved cross-validation $R^2$ scores of 0.936 and 0.953 for $i_{corr}$ and $E_{corr}$, respectively. On the hold-out test set, $R^2$ values of 0.933 and 0.945 were obtained with test RMSE values of 0.2 log₁₀(µA/cm²) and 41.9 mV, respectively. The contribution of each input feature to model predictions was quantified and visualised using the SHapley Additive exPlanations (SHAP) methodology. SHAP analysis reveals that chloride content has the highest impact on $i_{corr}$, followed by carbonation state and the low-humidity condition, while for $E_{corr}$, chloride content and the Submerged condition have the greatest impact. An interactive web application was developed using Streamlit, enabling researchers and practitioners to obtain corrosion parameter predictions. The findings provide data-driven insights into the relative importance of environmental factors governing rebar corrosion, with direct implications for the development of accurate corrosion prediction models for reinforced concrete service life assessment. Full article

Background: Ecuador is a genetically diverse population setting shaped by long-term interactions among Native American, European, and African populations across distinct ecological regions. Although multiple studies have examined ancestry patterns in Ecuadorian populations, the available evidence remains fragmented and methodologically heterogeneous. Objective: To systematically identify, critically appraise, and synthesize published studies on genetic ancestry and population structure in Ecuador. Methods: A systematic review was conducted in accordance with PRISMA 2020. Searches were performed in PubMed/MEDLINE, Scopus, Web of Science Core Collection, SciELO, and Google Scholar through 31 January 2026. Eligible studies reported extractable ancestry-related data from Ecuadorian populations using autosomal, mitochondrial DNA, Y-chromosomal, or other ancestry-relevant genetic markers. Methodological quality was assessed using an adapted Joanna Briggs Institute framework. Owing to substantial heterogeneity across marker systems, sampling strategies, and ancestry inference methods, findings were synthesized qualitatively rather than by meta-analysis. Results: Of 1243 records identified, 12 studies met the inclusion criteria. Across marker systems, the evidence consistently supported a three-way admixture framework involving Native American, European, and African ancestry components, together with substantial regional and population-specific heterogeneity. Autosomal studies generally showed higher Native American ancestry in Highland and Native American populations, whereas African ancestry was more prominent in Afro-Ecuadorian and some Coastal populations. Uniparental markers further supported persistent sex-biased admixture, with predominant Native American maternal lineages and comparatively greater European or African paternal contributions depending on region and population history. Conclusions: Ecuadorian populations share a broad three-way admixture framework, but with marked internal heterogeneity across regions and population groups. These findings highlight the importance of geographic and demographic context in ancestry interpretation and the need for larger, more balanced, and methodologically standardized genomic studies in Ecuador. Full article

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

While general network dynamics have been extensively modeled using stochastic methods, the emergence of dense Internet of Things (IoT) ecosystems demands a more specialized analytical framework. IoT environments are characterized by extreme non-linearity and sensitivity to initial conditions, where traditional models often fail to account for chaotic latency and packet loss. This paper introduces a specialized approach that integrates Chaos Theory with the innovative paradigm of Vibe Coding—an AI-assisted development and analysis methodology that allows for the `encoding’ and interpretation of the dynamic `vibe’ or signature of network fluctuations in real-time. By categorizing network behavior into four distinct scenarios (quiescent, perturbed, attacked, and perturbed–Attacked), the proposed framework utilizes deep learning to transform chaotic signals into actionable intelligence. Our findings demonstrate that this specialized synergy between chaos analysis and Vibe Coding provides superior classification of adversarial threats, such as DoS and injection attacks, fostering intelligent native security for next-generation IoT infrastructures. Full article

Sleep plays a fundamental role in human health and cognitive functioning, motivating the development of reliable and scalable methodologies for sleep stage classification (SSC). Recent advances in non-invasive and economically sustainable sensing technologies enable continuous sleep monitoring beyond laboratory settings. However, SSC remains a challenging data analytics task due to the intrinsic class imbalance among sleep stages. This study investigates the effectiveness of different imbalanced data management strategies within a machine learning framework for non-invasive SSC. The proposed approach relies exclusively on heart rate and motion signals, which can be acquired through wearable devices or contactless under-mattress sensors, making it suitable for longitudinal monitoring scenarios. Using the PhysioNet DREAMT dataset, 32 experimental scenarios are defined by combining data-level techniques (ADASYN oversampling with different balancing weights), algorithm-level strategies (cost-sensitive learning), and hybrid solutions. Four model families are evaluated—Decision Tree, k-Nearest Neighbors, Ensemble Classifiers, and Artificial Neural Networks—across classification tasks involving 2, 3, 4, and 5 sleep stages. The experimental results show that ensemble-based models provide robust and consistent performance under severe class imbalance, achieving macro accuracies of 82% for sleep–wake detection, 73% for 3-stage classification, 72% for 4-stage classification, and 64% for 5-stage classification. These findings confirm the relevance of imbalance-aware analytics and demonstrate the feasibility of accurate, minimally invasive SSC within big data and cognitive computing paradigms. Full article

This study develops a reduced-order predictive model of the Sit-To-Stand (STS) task to examine whether a simplified biomechanical representation can reproduce key STS patterns reported in the literature and to investigate the role played in movement by a flexible trunk. The model represents the human body as a planar multibody system and formulates STS as an optimization problem within a discrete mechanics framework. This formulation combines reduced model complexity, explicit torso flexibility, and a structure-preserving numerical approach for trajectory generation. Simulations were used to evaluate the effects of movement duration, reduced joint strength, and seat height on joint torques, kinematics, trunk motion, and ground reaction forces (GRFs). The results reproduced several qualitative trends reported in previous experimental studies, including increased peak joint torques and GRFs with shorter movement duration, lower joint strength, and reduced seat height, as well as greater compensatory trunk motion under more demanding conditions. These findings suggest that the proposed framework captures key adaptive features of STS mechanics and may provide useful insights for rehabilitation analysis and the design of assistive technologies such as lower-limb exoskeletons and rehabilitation devices. At the same time, the present work should be regarded as an initial methodological study, since validation is currently qualitative and further experimental calibration, quantitative validation, and sensitivity analysis remain part of ongoing work. Full article

Research on hub-and-spoke logistics networks can effectively advance the construction of the Northeast China Sea–Land Grand Corridor. In the context of regional synergistic development, this study investigates the optimization of the logistics network for the Northeast China Land–Sea Grand Corridor. Focusing on 43 prefecture-level cities across Liaoning, Jilin, Heilongjiang, and Inner Mongolia, a hub-and-spoke logistics network optimization model is developed. The model aims to minimize total network costs while satisfying specific network resilience thresholds. It integrates multi-modal transport and incorporates considerations such as economies of scale, node heterogeneity in resilience evaluation, and route redundancy. Based on this, the study employs the entropy weight method to establish a comprehensive evaluation system for regional logistics and economic development levels and applies an improved coupling coordination degree model to assess the synergistic relationship between these two systems. A modified gravity model, with the coupling coordination degree as a moderating coefficient, is constructed to quantify the strength of logistics–economic linkages between cities. Furthermore, social network analysis and a logistics affiliation model are used to identify key hub cities. The results demonstrate that the optimized network significantly enhances transport efficiency, achieves substantial economies of scale and strikes a balance between cost efficiency and system resilience. This research provides a quantitative foundation and practical reference for node layout planning and multi-modal transport organization along the Northeast China Sea–Land Grand Corridor, and its methodological framework can inform logistics network planning in similar regions. Full article

Background: Global water scarcity, intensified by climate change, demands optimization of irrigation systems consuming 70% of freshwater resources. Despite significant investments in modernizing irrigation infrastructure from open channels to pressurized networks, performance often falls below expectations. Objective: This study develops an integrated diagnostic and simulation framework for evaluating and improving large-scale pressurized irrigation systems by adapting the Mapping System and Services for Pressurized Irrigation (MASSPRES) methodology. Methods: The framework integrates three components: (1) demand flow dynamics determination using stochastic modelling; (2) hydraulic performance simulation incorporating multiple flow regimes; and (3) performance analysis using relative pressure deficit and reliability indicators. The methodology combines deterministic soil water balance calculations with stochastic farmer behaviour modelling. Results: Application to the Sinistra Ofanto irrigation scheme revealed localized pressure deficits during peak demand periods. The rehabilitation strategy restored full hydraulic feasibility of the network, increasing the proportion of hydraulically satisfied operating configurations from 62% to 100% under peak demand conditions and ensuring adequate pressure at all 317 hydrants across the system. Conclusions: The methodology provides robust decision support for cost-effective rehabilitation, ensuring reliable water delivery while promoting water-energy efficiency. Full article

Composite materials are extensively used in the aeronautical and aerospace industries for their high strength-to-weight ratios but are vulnerable to barely visible impact damage (BVID), which can severely compromise structural integrity. Digital shearography (DS) provides a non-contact, full-field solution for subsurface inspection; however, low signal-to-noise ratios in raw phase maps often hinder precise damage identification. This study explores a post-processing methodology utilizing a band-pass filtering algorithm and temporal summation to isolate damage-related spatial frequencies. An in-house digital shearography system was used to inspect a carbon-fiber-reinforced polymer (CFRP) plate subjected to 13.5 J and 26.2 J impacts. Twelve phase maps, acquired during the thermal cooling stage, were processed using a multi-pass filters to systematically analyze different frequency ranges. Results demonstrate that summing multiple filtered phase maps significantly enhances the contrast of damage signatures compared to single phase maps or traditional unwrapping techniques. Furthermore, quantitative assessment using image quality metrics, such as the generalized contrast-to-noise ratio (gCNR), confirmed that optimal frequency selection is essential for an accurate damage delineation. This approach provides a robust framework for improving the reliability and sensitivity of non-destructive testing in composite structures. Full article

With the exponential growth of natural gas reserves and utilization scale in China, underground gas storage (UGS) facilities—critical infrastructure within the natural gas production-supply-storage-sales system—have entered a phase of rapid expansion. As the core component connecting subsurface reservoirs with surface systems, wellbore integrity directly influences operational safety and service lifespan of UGS facilities. However, current leakage detection and integrity analysis methodologies for gas storage wellbores remain deficient in effective real-time monitoring capabilities. Traditional methods, however, are constrained by limited spatial coverage and insufficient precision, rendering them inadequate for comprehensive, continuous safety monitoring requirements. To address this industry challenge, this study proposes a real-time wellbore integrity monitoring framework based on distributed fiber optic sensing technology, integrating distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) devices into a synergistic monitoring system. The DTS component enables preliminary localization of potential leakage points through detection of minute temperature anomalies along the wellbore, while the DAS unit accurately identifies acoustic signatures caused by gas leakage within casings via monitoring of acoustic vibration signals propagating along the optical fiber. Through joint analysis of DTS and DAS data streams, real-time diagnosis of wellbore leakage events and integrity status can be achieved. Field trials demonstrated that this hybrid monitoring system achieved leakage localization accuracy within 1.0 m, effectively distinguishing normal operational signals from abnormal leakage characteristics. During actual monitoring operations, no indications of wellbore integrity compromise were detected; only minor noise and interference signals originating from surface construction activities were observed. Full article