Search Results (1,866)

Containerized mobile power supplies (CMPS), a critical energy replenishment carrier for all-electric ships, have caused severe economic losses via frequent fire and explosion accidents during temporary port storage in recent years. Existing literature focuses on battery thermal runaway under laboratory conditions and maritime transport risk analysis, but its conclusions are not directly applicable to port temporary storage. Port storage, featuring full-charge quiescent placement and high turnover, differs significantly from maritime transport, while its high-temperature and humid environment is distinct from laboratory settings. Furthermore, no system safety-based risk assessment framework exists, failing to deliver targeted mitigation strategies for practical operations. To address these issues, fault tree analysis (FTA), Bayesian network (BN), and attack–defense game theory were combined to build a systematic safety risk assessment framework. FTA clarified the hazard factors’ correlation mechanism; based on FTA, BN conducted a quantitative evaluation. Extended from BN results, attack–defense game theory identified key risk evolution paths and formulated targeted prevention and control measures. The main conclusions are as follows: Combined with similar accident features and port storage scenario attributes, internal correlations between hazard-inducing factors were clarified via FTA. Based on expert evaluations and BN calculation, the target port’s fire accident occurrence probability was determined as 2.41%, with two core root nodes identified via sensitivity analysis. Two critical risk evolution paths corresponding to IE1 (thermal runaway initiation) and IE2 (failure of protection and emergency response systems) were identified via game theory and traversal method, with occurrence probabilities of 1.50% and 1.77%, respectively. Targeted prevention and control measures adapted to the port storage scenario were proposed based on path triggering mechanisms. These findings provide theoretical support for port enterprises to improve CMPS fire prevention and emergency response capabilities, elevate port safety management levels, and promote the safe development of the all-electric vessel shipping industry. Full article

Seatbelt use is well established as a life-saving measure. Nevertheless, many drivers and passengers continue to neglect seatbelt use. This study examines the risks associated with unrestrained occupants involved in motor vehicle crashes. Using data from the Fatality Analysis Reporting System from 2000 to 2018, the relative risk of fatal traffic accidents for unrestrained vehicle occupants in the United States was estimated using the maximum likelihood estimation method. The findings indicate that unrestrained passengers make up about 12% of all passengers on the road and face a roughly 4.3 times greater likelihood of fatality in severe crashes. Additionally, unrestrained drivers, whose higher risk profiles are linked not only to their lack of restraint but also to broader patterns of hazardous driving behavior, account for over 8% of all drivers and exhibit a risk approximately 5.4 times higher in causing fatal crashes compared to restrained drivers. The findings of this study reveal the prevalence and consequences of unrestrained vehicle occupants and supports ongoing efforts to promote seatbelt utilization and bolster road safety protocols. By doing so, we can alleviate the burden of preventable injuries and fatalities on individuals, families, and society at large, thus fostering a safer and more secure transportation environment for all. Full article

This study aimed to investigate how human, vehicle, and environment (HVE)-related factors and their interactions contribute to fall accidents related to electric scooters (e-scooters). Falls are the most common type of e-scooter accidents, and developing a thorough understanding of the factors that contribute to these accidents is critical for effective accident prevention. Unlike collisions, falls frequently result from the complex interaction among the rider, the vehicle, and the environment. To this end, this study conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and uses the Haddon Matrix framework to identify and classify factors related to e-scooter fall accidents from HVE perspectives, spanning the pre-fall and fall phases. The findings suggest that e-scooter fall accidents are multifactorial, resulting from the interaction of HVE-related factors across accident phases rather than from a single cause. Human-related factors, vehicle attributes, and environmental conditions were all found to contribute to fall risk, with notable interactions identified across all three dimensions. This study contributes to a better understanding of the mechanisms underlying e-scooter fall accidents by systematically identifying these factors and examining their interactions, highlighting the need for further investigation into HVE interactions across diverse accident contexts. Full article

Industrial sites pose numerous hazards where unexpected accidents can occur at any time, and personal protective equipment (PPE) is a primary safeguard for worker safety. In this study, PPE specifically refers to safety helmets, safety shoes, and safety gloves. Manual verification of PPE usage is infeasible in environments with many workers, motivating automated detection. This study proposes a method that integrates the Convolutional Block Attention Module (CBAM) exclusively into the training-only auxiliary reversible branch of YOLOv9’s Programmable Gradient Information (PGI) architecture. The proposed CBAMLinear module enhances gradient information during training while introducing zero additional computational overhead at inference, as the entire auxiliary branch is removed. The proposed YOLOv9 with CBAMLinear achieved consistent mAP@0.5:0.95 gains of 0.005–0.007 over the baseline for the three larger model variants, while maintaining identical inference-time parameters and FLOPs. In industrial safety, even modest performance gains can directly contribute to accident prevention by reducing false positives and false negatives, making this approach well suited for real-time safety management systems in industrial settings. Full article

Frequent accidents in the construction sector arise from the dynamic coupling of multiple risk factors, while conventional single-factor approaches fail to capture the underlying complexity. Drawing on 702 accident investigation reports, this study develops an intelligent, data-driven framework that integrates large language model–based risk identification with association rule mining to systematically uncover risk factors and their coupling patterns. A DeepSeek-based model is employed to extract risk factors from unstructured text, followed by cosine similarity–based optimization to refine factor representations. The FP-Growth algorithm is then applied to identify strong association rules among risk factors. The results reveal that deficiencies in the management dimension account for 68.30% of all identified risks, with inadequate safety education and training emerging as the central hub in the risk coupling network, which is further corroborated by complex network analysis. Moreover, a cascading transmission pathway is identified, whereby environmental deficiencies induce weakened safety awareness, which in turn leads to unsafe behaviors. These findings further demonstrate the nonlinear amplification effects arising from concurrent management failures. By establishing a transformation pathway from unstructured textual data to structured risk knowledge, this study provides a robust, data-driven foundation for precise risk identification and systematic prevention in construction safety management. Full article

The number of patients with lower-limb dysfunction is increasing each year due to aging, illness, accidents, and other factors. Without timely rehabilitation and rapid recovery of walking function, further physical and mental deterioration may be accelerated, potentially leading to long-term bedriddenness. This study discusses gait training in rehabilitation therapy from the perspectives of kinesiology, cognitive science, walking function, and safety, and an omnidirectional gait training robot was developed. This study proposed a control system construction method for an omnidirectional gait training robot based on both prescription-based training and autonomous training. In the prescription-based training system, the target values are derived from the training prescription, and the control objective is to guide the patient to walk along the robot’s prescribed path and speed. In the autonomous training system, the target values are automatically generated based on the patient’s walking intentions, and the control objective is for the robot to safely follow the patient’s movement. A necessary condition for robot-assisted autonomous gait training is effective fall prevention. A fall prediction strategy based on foot position information and handrail pressure data was developed. Using this strategy, the robot can predict falls immediately before they occur, similar to a physical therapist, thereby reducing the risk of falls during gait training. Experimental results demonstrate the feasibility of implementing this strategy. Full article

To identify the key causative factors and their characteristics across different types of chemical accidents, text mining techniques were first applied to extract causative factors from accident investigation reports. The extracted factors were then classified according to an improved Human–Machine–Environment–Management (HMEM) framework, which incorporates an additional government influence layer. To address data imbalance, a random undersampling method was employed. Specifically, sampling was repeated 30 times using different random seeds, and association rule mining was conducted for each sampled dataset. On this basis, a hybrid analytical framework integrating the Apriori algorithm and complex network theory was developed to examine the topological characteristics of the causation network. The results indicate that the network exhibits both small-world and scale-free properties, with strong interconnections among causative factors and a limited number of key nodes playing important bridging roles. PageRank centrality analysis further reveals that nodes associated with all accident types are located in the core region of the network, although differences exist in the associated causative factors across different accident types. In addition, the comprehensive importance analysis indicates that D6 (illegal production organization), B5 (pipeline rupture or blockage), and D12 (unsafe work practices) are the top three most important causative factors. These findings provide a theoretical foundation and practical insights for chemical accident prevention and the improvement of safety management. Full article

Recent advancements in autonomous driving technology are transforming the automotive industry, with advanced driver assistance systems (ADAS) recognized as a crucial transitional technology toward fully autonomous driving. ADAS enhances driver safety and comfort through features such as emergency braking, lane-keeping, and adaptive cruise control, ultimately aiding in traffic accident prevention and reduction in driver fatigue. However, commercial ADAS implementations show substantial variability due to differences in sensor configurations, operational design domain (ODD) definitions, and operational criteria across automakers. To address this gap, this study provides a structured comparative review of commercialized ADAS technologies across 11 major Western and Asian automakers. By encompassing both Western and Asian OEMs, this study compares manufacturer-declared sensor configurations, ODD settings, activation conditions, driver-monitoring requirements, takeover and fallback logic, and update-related characteristics. The review identifies implementation-level differences that affect comparability, user understanding, validation requirements, and standardization needs. Rather than ranking OEM systems by safety performance, this study clarifies the trade-offs among redundancy-oriented, camera-centric, HD-map-dependent, geofenced, and OTA-driven ADAS strategies. The findings support future work on standardized ODD communication, user-centered HMI design, independent validation, and update-aware review frameworks for commercial ADAS. Full article

Industrial accident reporting systems provide the foundation for establishing future prevention strategies by collecting and analyzing accident-related data. While some industrial accidents occur as isolated events, many exhibit a process-oriented nature in which a sequence of temporally connected events accumulates and ultimately leads to a final accident. Nevertheless, a substantial proportion of accident reports are prepared by injured workers or employers who lack specialized safety knowledge. As a result, critical information about the conditions, procedures, and actions involved in accident progression is often insufficiently documented. Such information loss hinders a comprehensive understanding of accident causation and, consequently, reduces the effectiveness of preventive measures. To address this limitation, this study proposes an event-based accident information reporting framework that enables injured workers and employers without professional safety expertise to record accidents in a structured manner following their temporal sequence. The proposed framework defines the observed actions and conditions throughout the accident occurrence process as a series of discrete “events,” each of which is classified by an occurrence type. Furthermore, each occurrence type is linked to a corresponding object that reflects its characteristics, allowing accident components to be described in a standardized and systematic form. The framework is designed to be easily completed through a simple selection-and-entry process centered on occurrence types, thereby facilitating consistent and uniform reporting. When applied to 462 fatal industrial accident cases that occurred in South Korea in 2018, the proposed method indicated that approximately 55% of accidents involved multi-stage event sequences, highlighting the importance of process-related information that is not captured by conventional outcome-centered classification systems. In addition, the distribution of occurrence types differed substantially from patterns observed in existing reporting practices. The structured reporting approach proposed in this study may contribute to the preservation and accumulation of essential information on accident occurrence processes, thereby supporting more effective accident prevention efforts. This study does not propose a new investigation methodologies. Instead, it aims to improve accident reporting quality at the data input stage. Full article

To improve risk control at coal mining faces and reduce accident risks, this study first extracts high–frequency risk factors from 171 valid coal mining face accident cases (2020–2023) and integrates synthesis of the literature to establish a 21–factor risk indicator system covering human–machine–environment–management dimensions, and invites 10 senior experts in coal mine safety–covering mining engineering, safety science and engineering, mine ventilation, geological disaster prevention and coal mine safety management–for evaluation. Secondly, a hierarchical structure of factors is developed based on adversarial interpretive structural modeling (AISM), and the driving force and dependence of each factor are analyzed using the matrix impact cross–reference multiplication applied to a classification (MICMAC). A fuzzy Bayesian network (FBN) model is then constructed with the AISM structure as a topological constraint to clarify factor relationships and quantify the risk propagation uncertainty. Finally, an empirical analysis is conducted using the X Coal Mine. The results indicate that the “illegal and irregular organization of production” is the root control factor. The risk probability of the mining face is 86.1%, with “inadequate specialized prevention and control” having a high occurrence probability, and “illegal operation” and “illegal command” showing the most significant probability changes. Sensitivity analysis identifies “inadequate specialized prevention and control” as the most sensitive factor, which, together with the environmental factors, falls into the Level I unacceptable risk category. This research determines risk control priorities and provides a theoretical basis for coal mine safety management. Full article

Background/Objectives: Cervical spine injuries (CSIs) are life-threatening conditions commonly resulting from road traffic accidents and falls; improper first aid management can significantly worsen neurological outcomes. Public awareness and correct first aid response are critical for preventing secondary injury; despite this, available data from the southern provinces of Saudi Arabia remain insufficient. This study aimed to assess public awareness and first aid preparedness for CSIs across four southern regions of Saudi Arabia. Methods: A cross-sectional design was employed across multiple regions, encompassing 1179 adults from Jazan, Aseer, Al-Baha, and Najran between 2025 and 2026. A validated online questionnaire was employed for data collection to assess CSI awareness, recognition of injury signs, and appropriate first aid responses. Awareness scores of ≥60% were classified as good. Multiple linear regression analysis was performed to identify independent predictors of awareness. Results: The mean awareness score was 16.0 ± 4.8 out of a possible total of 20 points, corresponding to 80% of the total score, with 87% of participants demonstrating good awareness. The majority of respondents recognized the importance of spinal immobilization (89%), maintaining head–neck alignment (95%), and contacting emergency services before intervention (93%). Correct responses to emergency scenarios were recorded in 83% of participants. Notably, only 39% had attended formal medical or trauma training, and merely 3% reported real-life first aid experience. Training attendance (β = 1.39, p < 0.001) and marital status (married; β = 1.37, p = 0.004) were identified as independent predictors of higher awareness scores. Conclusions: Although general public awareness of CSI first aid is high, formal training participation remains critically low, revealing a substantial gap between knowledge and practice. Integrating structured first aid training into community, workplace, and primary healthcare settings is essential to improve trauma outcomes and reduce preventable disability. Full article

Inadequate vehicle maintenance management is one of the main causes of road accidents and elevated operating costs in light vehicles. This paper addresses this problem through the development and implementation of a low-cost integrated system for preventive maintenance management and alerts. The device, based on an open-hardware architecture (Arduino Mega 2560), integrates Global Positioning System (GPS) and mobile communication (GSM/LTE) modules to monitor distance traveled in real time and notify the user via SMS about the proximity of critical services such as oil changes, brake inspections, and timing-belt replacements. Its technical contribution lies in the integration of non-intrusive virtual ignition, filtered GPS-based odometry, configurable MicroSD-based persistence, and progressive SMS alert logic into a low-cost aftermarket system for conventional vehicles without OBD-II dependence. Experimental validation was conducted in the city of Guayaquil using a 2012 Hyundai Accent. Field tests were carried out in three scenarios: a dense urban route, a peripheral road, and interurban routes. Results showed satisfactory accuracy with a global average percentage error of 3.98% compared to the vehicle’s odometer and 100% effectiveness in sending alerts under the tested conditions (20/20 events; exact 95% binomial confidence interval: 83.2–100.0%). These results provide strong evidence of technical feasibility for the proposed architecture under the tested conditions in a representative single-vehicle proof-of-concept, while broader cross-vehicle validation remains necessary before generalizing the system to the wider diversity of aging fleets. Full article

The construction industry remains among the most hazardous, with workers in horizontal transportation infrastructure facing additional risks from dynamic work zones, live traffic exposure, and variable environmental conditions. Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) offer new approaches to accident analysis and prevention, yet their applications toward improving occupational safety in transportation construction have not been comprehensively reviewed. This paper presents a systematic review of 54 studies published between 2016 and 2025 collected from two online databases (Transportation Research International Documentation and Web of Science). This review synthesizes how immersive technologies contribute to occupational risk assessment, safety training, and real-time hazard monitoring in the construction of roads, bridges, tunnels, and work zones. Each study is classified across two dimensions: the immersive medium (VR, AR, etc.) and the operational context within the construction lifecycle (onsite tools, offsite monitoring and planning, simulation-based analysis, and workforce education). This dual classification is the first to systematically map immersive technology applications for occupational safety, specifically within horizontal transportation infrastructure. The findings of this review demonstrate the unique use cases of each immersive medium, revealing that VR is primarily used for controlled experimentation and full-immersion remote analysis, whereas AR and handheld devices are preferred for field-deployed applications. Despite these promising capabilities, widespread adoption remains limited by hardware constraints, challenging field conditions, and organizational resistance. This suggests that future work should focus on safety systems tested in real-world settings and rigorously evaluated by domain experts to enable their integration into standard workplace risk management practices. Full article

In the complex and high-risk underground environment of coal mining, ensuring occupational health and safety is a fundamental pillar of social sustainability. Traditional safety compliance is insufficient to prevent unpredictable accidents and sustain long-term enterprise resilience. Thus, fostering proactive safety citizenship behavior is essential for enhancing organizational resilience. Drawing on the cognitive appraisal theory of stress, this study constructs a double-edged sword model of felt accountability on miners’ safety citizenship behavior. A three-wave time-lagged survey was conducted among 375 frontline coal miners in China, with data analyzed using SPSS 26.0 and AMOS 24.0. The findings show that felt accountability can increase work engagement and promote employee safety citizenship behavior, while also enhancing psychological strain and inhibiting employee safety citizenship behavior. In addition, safety-specific transformational leadership amplifies the positive impact of felt accountability on work engagement and mitigates its effects on psychological strain. These findings enrich our understanding of the impact of felt accountability, and provide practical insights for coal enterprise managers to improve sustainable safety performance and foster a socially sustainable work environment. Full article

Same-level falls are the most frequent occupational accidents, yet traditional manual analysis of accident reports is labor-intensive and limits large-scale prevention strategies. In this pilot study, we aimed to evaluate the accuracy of using large language models (LLMs) to automate the classification of occupational accident text data without task-specific pretraining. We analyzed data from 2619 same-level-fall-related injury cases, using expert manual classification as the reference standard. Four models—GPT-4o mini, GPT-4.1 mini, GPT-4.1, and o4-mini—were compared using accuracy and Cohen’s kappa. The o4-mini model demonstrated the highest performance, showing statistical superiority in the complex “causal agent” category with 72.8% accuracy. For other classification tasks, the top models achieved accuracies of 82–92%, with Cohen’s kappa coefficients > 0.7, indicating substantial agreement with expert judgments. These findings suggest that LLMs can classify occupational accident text with substantial agreement with the expert-derived reference standard in this dataset. This automated approach enables efficient, high-frequency analysis of large datasets, offering a promising tool for large-scale occupational accident surveillance and screening. Full article