Search Results (1,066)

Predicting the severity of traffic accidents remains challenging due to the limited ability of existing methods to extract deep semantic information from unstructured accident narratives, as traditional approaches typically depend on structured data alone. This study proposes a severity prediction approach enhanced by semantic risk reasoning derived from large language models (LLMs). A prompt-engineering template is designed to guide LLMs in extracting proxy semantic features from accident descriptions, forming an enriched feature set that incorporates causal logic. These semantic features are fused with traditional structured features through three integration strategies—direct feature concatenation, optimized feature selection, and model-level fusion. Experiments based on 4013 accident records from expressways in Yunnan Province, China, demonstrate that models using LLM-derived semantic features significantly outperform those relying solely on structured features. Notably, the LightGBM model utilizing semantic features within a balanced learning framework achieves a severe accident recall of 77.8%. While model-level fusion proves optimal for XGBoost (improving Macro-F1 to 0.6356), we identify a “feature dilution” effect in other classifiers, where high-quality semantic reasoning is compromised by low-quality structured noise. These findings indicate that the proposed approach effectively enhances the identification of high-risk accidents and offers a novel semantic-aware solution for traffic safety management. Furthermore, the obtained results provide actionable insights for traffic management agencies to optimize emergency response resource allocation and formulate targeted accident prevention strategies. Full article

The emerging urbanization and the extensive increase of the transportation sector are responsible for the significant increase in carbon dioxide emissions. Therefore, replacing traditional cars with Electric Vehicles (EVs) is a promising solution, offering a clearer alternative. EVs are becoming more and more well-known and are being quickly used worldwide. However, the exponential rise in EV sales has also raised a number of issues, which are becoming important and demanding. These challenges include the need of driving security, the battery degradation, the inadequate infrastructure for charging EVs, and the uneven energy distribution. In order for EVs to reach their full potential, intelligent systems and innovative technologies need to be introduced in the field of EVs. This is where business intelligence (BI) can be employed, along with artificial intelligence (AI), data analytics, and machine learning. In this paper, we provide a comprehensive survey on the use of BI strategies in the EV transportation sector. We first introduce the EVs and charging station technologies. Then, research works on the application of BI and data analysis techniques in EV technology are reviewed to further understand the challenges and open issues for the research and industry community. Moreover, related works on accident analysis, battery health prediction, charging station analysis, intelligent infrastructure, locating charging stations analysis, and autonomous driving are investigated. This survey systematically reviews 75 peer-reviewed studies published between 2020 and 2025. Finally, we discuss the fundamental limitations and the future open challenges in the aforementioned topics. Full article

Research into Remotely Piloted Aircraft Systems (RPASs) has expanded rapidly, yet the competencies, knowledge, skills, and other attributes (KSaOs) required of RPAS pilots remain comparatively underexamined. This review consolidates existing studies addressing human performance, subject matter expertise, training practices, and accident causation to provide a comprehensive account of the KSaOs underpinning safe civilian and commercial drone operations. Prior research demonstrates that early work drew heavily on military contexts, which may not generalize to contemporary civilian operations characterized by smaller platforms, single-pilot tasks, and diverse industry applications. Studies employing subject matter experts highlight cognitive demands in areas such as situational awareness, workload management, planning, fatigue recognition, perceptual acuity, and decision-making. Accident analyses, predominantly using the human factors accident classification system and related taxonomies, show that skill errors and preconditions for unsafe acts are the most frequent contributors to RPAS occurrences, with limited evidence of higher-level latent organizational factors in civilian contexts. Emerging research emphasizes that RPAS pilots increasingly perform data-collection tasks integral to professional workflows, requiring competencies beyond aircraft handling alone. The review identifies significant gaps in training specificity, selection processes, and taxonomy suitability, indicating opportunities for future research to refine RPAS competency frameworks and support improved operational safety. Full article

Converter gas is highly susceptible to leakage during the recovery and utilization process, which threatens personnel security and sustainable industrial development. To address this issue, a numerical model was established based on an actual converter fan room, and the accuracy of the simulation was verified through comparison with actual measurement data. In this study, the gas leakage flow field, diffusion trajectories, and hazard zone gradations were analyzed. Results showed that the gas contamination was significantly influenced by the leakage direction, leakage location, and structural boundary. The jet dominated the gas dispersion near the leakage source, with similar initial diffusion characteristics across different scenarios. Then, the diffusion velocity decayed rapidly within a distance of 0.6 m. Obstacles can significantly promote vortex formation, restrict the gas dispersion path, and reduce the extent of the hazardous area. In addition, it can be found that the far-field velocity under downward leakage was the highest, presenting the greatest risk of poisoning. At a height of 1.6 m, a lethal zone with a radius of 0.8 m was formed directly beneath the leakage hole. This work can guide the optimization of the monitoring program and emergency planning for converter gas leakage accidents. Full article

The growth of the cruise industry and rising passenger numbers have led to an increase in cruise-related accidents, presenting challenges for mass rescue operations. It is crucial to understand the evolution of MAss Life-Threatening Incidents at Sea (MALTISs) in order to make effective decisions in such situations. This study, therefore, presents a scenario deduction model for MALTIS, integrating knowledge element theory, Bayesian Networks (BNs), fuzzy set theory, and improved Dempster–Shafer (DS) evidence theory. Based on knowledge element theory, this study identifies the scenario elements in typical maritime accidents. Given the large scale and complex disaster chain characteristics of MALTISs, the BN method is employed to convert the scenario elements into BN nodes, therefore constructing the MALTIS deduction model. To minimize the subjectivity associated with expert assessments, this study combines fuzzy set theory and the improved DS evidence theory to integrate the opinions of multiple experts, thereby enhancing the reliability of the model’s deduction. BN inference is then used to calculate the probabilities of various situational states, and sensitivity analysis is conducted to identify the key nodes. The Costa Concordia grounding incident serves as an empirical case study. The deduction results closely align with the actual accident evolution, and sensitivity analysis reveals five critical nodes in the event’s progression. This validates the effectiveness of the proposed scenario deduction model. These findings demonstrate that the model can effectively support emergency decision-making in MALTISs. Full article

Drowsy driving and sudden medical emergencies are major contributors to traffic accidents, necessitating continuous, non-intrusive driver monitoring. Since current technologies often struggle to balance accuracy with practicality, this study presents the design, fabrication, and validation of a smart steering wheel prototype. The device integrates dry-contact electrocardiogram (ECG), photoplethysmography (PPG), and inertial sensors to facilitate multimodal physiological monitoring. The system underwent a two-stage evaluation involving a single participant: laboratory validation benchmarking acquired signals against medical-grade equipment, followed by real-world testing in a custom electric research vehicle to assess performance under dynamic conditions. Laboratory results demonstrated that the prototype captured high-quality signals suitable for reliable heart rate variability analysis. Furthermore, on-road evaluation confirmed the system’s operational functionality; despite increased noise from motion artifacts, the ECG signal remained sufficiently robust for continuous R-peak detection. These findings confirm that the multimodal smart steering wheel is a feasible solution for unobtrusive driver monitoring. This integrated platform provides a solid foundation for developing sophisticated machine-learning algorithms to enhance road safety by predicting fatigue and detecting adverse health events. Full article

Oil spills remain among the most severe anthropogenic threats to marine ecosystems, with consequences that span ecological, socio-economic, and human health domains. While numerous studies have investigated individual accidents such as Exxon Valdez, Prestige, and Deepwater Horizon, systematic comparative analyses across multiple large-scale incidents remain limited. This review addresses this critical gap by synthesizing findings from fourteen major oil spills worldwide. It examines the roles of oil type and environmental conditions, emphasizing impacts on fish, seabirds, shoreline habitats, and benthic organisms, as well as on long-term ecosystem recovery. Across cases, coastal waters, shorelines, and benthic communities consistently emerged as the most impacted habitats, reflecting both the persistence of oil in nearshore environments and the challenges of long-term restoration. Biologically, all trophic levels were affected: plankton, fish, seabirds, and benthic invertebrates were highly vulnerable, while marine mammals and reptiles suffered population-level effects. By integrating cross-case evidence, this review highlights recurring patterns, key uncertainties, and long-lasting ecosystem disruptions that persist decades after acute events. The Deepwater Horizon spill stands out as the most ecologically severe incident, whereas earlier spills such as Exxon Valdez, Erika, and Prestige remain benchmarks for ecological damage. Thus, this state-of-the-art review provides the most comprehensive comparative assessment of oil spill impacts to date and offers technical recommendations for enhancing preparedness, response, and resilience in the face of future spills. Full article

In this study, we developed a novel water quality model that integrated hydrodynamic, solute transport, and geochemical reactions processes. This model was built upon the open-source ELCIRC hydrodynamic model, the TVD-format solute transport model, and the PhreeqcRM geochemical reaction engine. The accuracy of the model was rigorously validated using a 2D chain decay analytical solution, demonstrating its capability to accurately simulate water flow, solute transport, and chemical reactions. To evaluate the practical applicability of the model, case studies involving the 2012 Huaihe River benzene leakage accident and the acetic acid leakage accident in the Gulei sea area were simulated. Findings indicate that the model effectively captures the diffusion and attenuation dynamics of the benzene contamination plume. Furthermore, it accurately depicts the reaction–diffusion interaction with seawater following acetic acid release. Notably, the versatility and flexibility of the model were further demonstrated by its ability to simulate a wide range of pollutants and their associated biochemical processes. This addresses the limitations of existing water quality models and provides a powerful tool for environmental monitoring and assessment. The results of this study offer valuable insights for improving water quality management and emergency response strategies in the face of environmental pollution incidents. Full article

The unique natural environment and climate of tropical island regions present significant challenges to construction. Under these variable natural conditions and complex construction processes, identifying and analyzing potential risks that could lead to vulnerabilities in construction safety systems and clarifying their transmission pathways remains a pressing issue. To fill this research gap, a GV-IB model for vulnerability analysis of construction safety systems in tropical island building projects (CSSTIBPs) was established. This model constructs a vulnerability analysis index system for tropical island construction safety systems based on the Grey Relational Analysis (GRA) and Vulnerability Scoping Diagram (VSD), considering exposure, sensitivity, and adaptability. By combining the artificial fish swarm algorithm with the K2 algorithm and the EM algorithm, an Improved Bayesian Network (IBN) is constructed to analyze and infer the influencing factors and disaster chains of vulnerability in tropical island construction safety systems. The IBN can effectively overcome the dependence on node order and data gaps in traditional Bayesian Network construction methods. The effectiveness of the model is verified by analyzing Hainan Island, China. The research results show that (a) The IBN stability verification showed an Area Under ROC Curve (AUC) of 0.783 > 0.7, indicating high effectiveness in identifying vulnerability factors. (b) Within the vulnerability measurement nodes of the CSSTIBPs, the influence on the system decreases in the following order is exposure (0.41), sensitivity (0.31), and adaptability (0.03). (c) Emergency response time, safety training, hazard identification time, accident response time, and duration of severe weather are key factors affecting the vulnerability of CSSTIBPs. Full article

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

Airborne iodine-131 plays a pivotal role in both nuclear medicine and nuclear safety due to its radiotoxicity, volatility, and affinity for the thyroid gland. Although the total exhaled activity after medical I-131 therapy is minimal, over 95% of this activity appears in volatile organic forms, which evade standard filtration and reflect metabolic pathways of iodine turnover. Our experimental work in patients and mice confirms the metabolic origin of these species, modulated by thyroidal function. In nuclear reactor environments, both under routine operation and during accidents, organic iodides such as [¹³¹I]CH₃I have also been identified as major airborne components, often termed “penetrating iodine” due to their low adsorption to conventional filters. This review compares the molecular speciation, environmental persistence, and dosimetric impact of airborne I-131 across clinical, technical, and accidental release scenarios. While routine reactor emissions yield negligible doses (<0.1 µSv/year), severe nuclear incidents like Chernobyl and Fukushima have resulted in significant thyroid exposures. Doses from these events ranged from tens of millisieverts to several Sieverts, particularly in children. We argue that a deeper understanding of chemical forms is essential for effective risk assessment, filtration technology, and emergency preparedness. Iodine-131 exemplifies the dual nature of radioactive substances: in nuclear medicine its radiotoxicity is therapeutically harnessed, whereas in industrial or reactor contexts it represents an unwanted hazard. The same physicochemical properties that enable therapeutic efficacy also determine, in the event of uncontrolled release, the range, persistence, and the potential for unwanted radiotoxic exposure in the general population. In nuclear medicine, exhaled activity after radioiodine therapy is minute but largely organically bound, reflecting enzymatic and metabolic methylation processes. During normal reactor operation, airborne iodine levels are negligible and dominated by inorganic vapors efficiently captured by filtration systems. In contrast, major accidents released large fractions of volatile iodine, primarily as elemental [¹³¹I]I₂ and organically bound iodine species like [¹³¹I]CH₃I. The chemical nature of these compounds defined their atmospheric lifetime, transport distance, and deposition pattern, thereby governing the thyroid dose to exposed populations. Chemical speciation is the key determinant across all scenarios. Exhaled iodine in medicine is predominantly organic; routine reactor releases are negligible; severe accidents predominantly release elemental and organic iodine that drive environmental transport and exposure. Integrating these domains shows how chemical speciation governs volatility, mobility, and bioavailability. The novelty of this review lies not in introducing new iodine chemistry, but in the systematic comparative synthesis of airborne radioiodine speciation across medical therapy, routine nuclear operation, and severe accident scenarios, identifying chemical form as the unifying determinant of volatility, environmental transport, and dose. Full article

This study aimed to evaluate the comparative predictive efficacy of the SARIMA statistical model and the Prophet machine learning model for forecasting monthly traffic accidents across the 24 provinces of Ecuador, addressing a critical research gap in model selection for geographically and socioeconomically heterogeneous regions. By integrating classical time series modeling with algorithmic decomposition techniques, the research sought to determine whether a universally superior model exists or if predictive performance is inherently context-dependent. Monthly accident data from January 2013 to June 2025 were analyzed using a rolling-window evaluation framework. Model accuracy was assessed through Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics to ensure consistency and comparability across provinces. The results revealed a global tie, with 12 provinces favoring SARIMA and 12 favoring Prophet, indicating the absence of a single dominant model. However, regional patterns of superiority emerged: Prophet achieved exceptional precision in coastal and urban provinces with stationary and high-volume time series—such as Guayas, which recorded the lowest MAPE (4.91%)—while SARIMA outperformed Prophet in the Andean highlands, particularly in non-stationary, medium-to-high-volume provinces such as Tungurahua (MAPE 6.07%) and Pichincha (MAPE 13.38%). Computational instability in MAPE was noted for provinces with extremely low accident counts (e.g., Galápagos, Carchi), though RMSE values remained low, indicating a metric rather than model limitation. Overall, the findings invalidate the notion of a universally optimal model and underscore the necessity of adopting adaptive, region-specific modeling frameworks that account for local geographic, demographic, and structural factors in predictive road safety analytics. Full article

The problem of adequate quantitative analysis of anthropogenic film pollution of water areas according to synthetic aperture radar (SAR) satellite imagery is addressed here. A quantitative analysis of anthropogenic film pollution (AFP) in the studied coastal water areas of the north sector of the Black Sea and Avacha Gulf has been conducted. The analysis utilized a method that involved the statistical processing of data related to AFP identified within the cells of a regular spatial grid. Time series of Sentinel-1 SAR satellite imagery were used as initial data. Spatiotemporal distributions of the proposed quantitative criterion (e_AFP, ppm) have been calculated and analyzed. This criterion characterizes the intensity of AFP impact within the selected regions of marine waters based on measuring the relative frequency of an AFP event. Among them, the area of the emergency fuel oil spill that occurred in 2024–2025 near the Kerch Strait was investigated (e_AFP values near the wreckage of tankers reached ~13,000 ppm), as well as the area of the emergency oil spill near the Novorossiysk terminal that occurred in 2021 (e_AFP ≤ 6000 ppm). Accidents led to an approximately 3–6-fold increase in e_AFP values against the background level of 0–2000 ppm. The spatiotemporal variability of e_AFP across various water areas and under different conditions has been demonstrated and discussed. Full article

Emerging evidence highlights a strong association between cerebrovascular accident (CVA) and major depressive disorder (MDD), mediated by immune dysregulation. Elevated levels of proinflammatory cytokines, reduced adaptive immune responses, altered immune cell composition, and increased microglial activation characterize this bidirectional relationship. Microglial activation appears to be a central molecular mechanism linking CVA and MDD, underscoring the immune system’s crucial role in disease pathogenesis. This interplay suggests that immune-driven processes not only exacerbate neurological damage but also contribute to psychiatric manifestations. Based on current literature, the role of proinflammatory processes, particularly microglial activation, in the relationship between CVA and MDD warrants special attention. In this context, the participation of myeloid differentiation factor 88 (MyD88), a cytosolic adaptor protein, appears to play a key role in proinflammatory signaling pathways driving microglial activation. Thus, focusing on MyD88 emerges as a promising complementary strategy for future research and for advancing our understanding of the mechanisms underlying microglial homeostasis dysregulation and its link to the pathophysiology of MDD and CVA. Full article

The research and development of new accident-tolerant fuel cladding materials has emerged as a critical focus in international academic and engineering fields following the Fukushima nuclear accident. Due to the outstanding resistances in corrosion and radiation as well as high-temperature creep properties, oxide dispersion-strengthened (ODS) FeCrAl alloys have been studied extensively during the past decade. Current review articles in this field have primarily focused on the effects of chemical composition on the anti-corrosion performance and species of nano-oxide. However, several key issues have not been given adequate attention, including processing methods and parameters, high-temperature stability mechanisms, post-deformation microstructural evolution and high-temperature mechanical properties. This paper reviews the progress of basic research on ODS FeCrAl alloys, including preparation methods, the effects of preparation parameters, the thermal stability and irradiation stability of oxides, the microstructural deformation, and the mechanical properties at elevated temperatures. The aspects mentioned above not only provide valuable references for understanding the effects of preparation parameters on the microstructure and properties of ODS FeCrAl alloys but also offer a comprehensive framework for the subsequent optimization of ODS FeCrAl alloys for nuclear reactor applications. Full article