Search Results (578)

Background/Objectives: Health-related quality of life perception (HRQoL) reflects the impact of individuals’ health conditions on their physical, psychological, and social well-being, and can be compromised after an accident The general aim of this study was to analyze the effect of occupational accident (OA) outcomes on injured workers’ HRQoL. Methods: We conducted a cross-sectional study, using a convenience sample of 101 participants at the end of their recovery and professional reintegration (PR) process. They were submitted to a personal injury assessment (PIA) conducted by medico-legal specialists, and data related to injury severity (IS), permanent professional disability (PD), and PR were collected from the respective forensic reports. Subsequently, they underwent a psychological interview and filled out self-report questionnaires to measure HRQoL (SF-36) and resilience (RSA). For each variable, two groups were defined. Analyses included descriptive statistics, correlations, group comparisons, and multiple linear regression analyses. Results: Injured workers reported lower HRQoL than Portuguese norms across most domains. HRQoL scores were positively associated with resilience and PR, and negatively associated with IS and PD. In multivariable models, IS, and RSA emerged as significant independent associated variables of the physical–social HRQoL component. Conclusions: These findings highlight the importance of a biopsychosocial and multidisciplinary approach to OA victims’ professional reintegration, integrating physical treatment and psychological support with resilience-building and work rehabilitation, before medical discharge and PIA. Full article

Background: Falls among older adults are a major public health concern associated with injury, disability, reduced mobility, and loss of independence. Functional impairment, chronic diseases, and unsafe home environments may increase the risk of falls. This study examined environmental, functional, and health-related factors linked to falls among community-dwelling older adults in Iran. Methods: A comparative cross-sectional study was conducted among 329 community-dwelling older adults. Data were collected using standardized assessments of functional ability, home safety, health status, and fall history. Conventional regression and Elastic Net analyses were applied to identify significant predictors of falls. Results: Overall, 28.6% of participants reported at least one fall during the previous 12 months. Falls were significantly more common among females, adults aged ≥85 years, individuals without a spouse, and those with lower educational levels. Fallers showed poorer mobility, balance, and functional independence, greater fear of falling, and a higher risk of home accidents (all p < 0.001). Elastic Net analysis identified use of movement aids as the strongest risk factor, whereas better Performance-Oriented Mobility Assessment (POMA) scores were the main protective factor. Conclusions: Falls among community-dwelling older adults appear to result from the interaction of physical, medical, socioeconomic, and environmental factors. These findings highlight the need for multidimensional fall-prevention strategies in primary care settings. Full article

Background: With increasing life expectancy, older adult populations worldwide are growing rapidly. Falls are among the most prominent problems that older adults face. This study aimed to assess the educational components of the Stopping Elderly Accidents, Deaths, & Injuries (STEADI) program for improving knowledge, skills, and behavioral intentions for fall prevention among older adults. Methods: A quasi-experimental study was conducted with a non-equivalent control group pretest–posttest design, involving 128 older women (≥60 years) in a community center in Riyadh. Data were collected using a structured questionnaire. Descriptive statistics were used to summarize the data. Pearson’s chi-square test was performed to compare demographic and physical characteristics between the groups. Independent-sample t-tests, effect size calculation (Cohen’s d), and ANCOVA-adjusted analyses were used to compare post-intervention outcomes between groups. Within-group changes were compared using a paired t-test. Additionally, one-way analysis of variance (ANOVA) was performed to compare the demographic, health, and physical characteristics of the participants. Statistical significance was set at p ≤ 0.05. Results: The intervention group showed improved knowledge (t = 11.654), skills (t = 7.961), and intention to perform preventive behaviors (t = 3.785), with a significant p-value of <0.0001. Large intervention effects were observed for knowledge (Cohen’s d = 2.30) and skills (Cohen’s d = 1.57). ANCOVA-adjusted analyses confirmed significant intervention effects for knowledge (adjusted mean difference = 5.06, 95% CI 4.46–5.66, p < 0.001) and skills (adjusted mean difference = 1.87, 95% CI 1.56–2.18, p < 0.001). Conclusions: The results indicate that the STEADI program produces significant short-term improvements in knowledge, skills, and behavioral intentions related to fall prevention. The findings emphasize the importance of integrating prevention programs into community settings and activating the role of families in supporting preventive practices. Full article

Plunger pumps serve as core power equipment in oilfield water injection systems, where their reliable operation directly affects crude oil recovery efficiency and production safety. Failures such as mechanical wear and seal leakage can cause injection pressure fluctuations, increased energy consumption, and even pipeline burst accidents. This study addresses the challenges in plunger pump fault diagnosis, including the difficulty in capturing multi-scale fault features, interference from redundant information in high-dimensional feature spaces, and high model computational complexity. We propose a lightweight fault diagnosis approach called Multi-scale Attention Neural Network (MSLAN), which combines multi-scale convolution and attention mechanisms. In this model, a Separable Multi-scale Fusion Module (SMSF) employs parallel multi-branch convolutional kernels to acquire fault signatures across multiple scales, while computational overhead is reduced through depthwise separable convolution and shared pointwise convolution. Additionally, a Multi-Branch Parallel Attention Module (MBPA) is introduced to finely model complex inter-channel dependencies through a four-branch parallel structure, enhancing the perception of key features and suppressing redundant information. Experimental results on a self-constructed plunger pump dataset, the Case Western Reserve University bearing dataset, and the Southeast University gearbox dataset demonstrate that MSLAN achieves F1-scores of 88.95%, 98.89%, and 99.90%, respectively. While maintaining high diagnostic accuracy, the model exhibits significantly lower parameter count and computational cost compared to baseline models, effectively balancing diagnostic precision and computational efficiency. Ablation studies and visualization analyses further validate the effectiveness of each module. This study establishes an accurate and efficient intelligent fault diagnosis solution for plunger pumps, which is also readily applicable to a broader range of rotating machinery. Full article

Accident diagnosis in nuclear power plants (NPPs) should provide mechanism-aligned evidence that can be reviewed by operators and safety engineers, rather than only a high-confidence accident label. Existing data-driven methods achieve strong classification performance but often express explanations as attention maps, anomalous nodes, prototypes, or causal links separately, making it difficult to obtain a unified diagnostic evidence chain. To address this limitation, we propose a Concept-Constrained Physical Graph (CCPG) framework that formulates accident diagnosis as structured evidence generation. CCPG groups multivariate transient signals into operator-readable physical nodes, extracts node-wise temporal features, and propagates them over a mechanism-guided graph. It then couples a concept bottleneck with implicit latent features, prototype learning, and edge/stage supervision to predict the accident class and a reviewable evidence package. On the evaluated NPPAD five-class simulated benchmark, CCPG achieved saturated clean-set classification (1.000 accuracy) and high paired-challenge accuracy (0.996) while providing concept, affected-node, edge-template, stage-order, and prototype evidence. Additional analyses, including Transformer baselines, feature-restricted and early-window stress protocols, calibration, statistical testing, open-set detection, and scalability profiling, further characterize the robustness, reliability, and applicability of the proposed framework. Full article

The mining industry faces complex operational hazards that necessitate systematic risk assessments to enable proactive accident prevention. While Large Language Models (LLMs) offer significant potential for the automated generation of safety measures, the limited availability of domain-specific terminology and high-quality labelled safety data (in low-resource environments) hinders their direct application. This study investigates and optimises data augmentation strategies to fine-tune LLMs to generate accurate, context-sensitive safety measures from structured coal mine risk records. The study systematically explored four experimental configurations, leveraging the Mistral-7B-Instruct model in conjunction with Quantised Low-Rank Adaptation (QLoRA) for efficient fine-tuning. These configurations comprised: (i) a baseline without augmentation, (ii) input-side lexical augmentation, (iii) output-side multi-reference augmentation, and (iv) a combined strategy. Performance was measured using BLEU, ROUGE, METEOR, and BERTScore metrics, along with statistical significance testing and qualitative analyses. The results show that, compared to other strategies, the input-side data augmentation strategy performs better. The findings indicate that input-side data augmentation yields significant improvements; this strategy increased the BERTScore (F1) from 0.360 to 0.530 and the BLEU score from 16.02 to 29.50 compared to the baseline model. In contrast, output-side multi-reference augmentation contributed to greater learning uncertainty and a consequent decline in performance. Statistical and qualitative analyses confirm that increasing input variety minimises model overfitting and enables the model to generate consistent, applicable, domain-specific safety measures. The proposed methodology provides a highly scalable solution for automated risk management in high-risk industrial environments, such as mining, offering a reliable, data-driven decision-support mechanism that minimises the limitations of manual review. Full article

Cutaneous Radiation Injuries (CRIs) and wounds within an area of radiation exposure (combined injury, CI) are a significant concern for nuclear accidents and radiation combat/terrorist events. CRIs and CI present unique clinical challenges, and effective countermeasures are urgently needed. Here we describe a murine model of CRI and CI in C57BL/6 mice using 16.9 Gy thoracic X-ray irradiation (5.3 Gy/min, 160 kV) ± experimental wound administered immediately. Wound repair and radiation-induced dermatitis were assessed after irradiation. Our previous studies showed that genistein (200 mg/kg, s.c.), administered 24 h prior to irradiation prevented radiation injuries in two murine models. We investigated the effects of genistein in the CI model. Macroscopic and histological analyses showed that radiation significantly delayed wound closure, although wounds did not significantly alter the progression of radiation dermatitis. Genistein improved the early rate of wound closure and significantly reduced dermatitis in mice. Histological analysis showed that genistein improved skin structure and reduced inflammation and fibrosis. Immunohistochemistry showed that genistein attenuated radiation-induced cyclin-dependent kinase inhibitor 1 (p21/waf1) and α-smooth muscle actin and preserved K15 positive skin adult stem cells. These findings suggest that genistein may be an effective prophylactic against CRIs and CI. Full article

Road Traffic Accidents (RTAs) represent a significant public safety issue in rapidly urbanising nations, resulting in considerable fatalities, injuries, and economic losses. This research investigates the spatio-temporal distribution and hotspot dynamics of RTAs in Siliguri City, India, a principal transnational transport corridor connecting northeastern India with adjacent countries. A geocoded dataset comprising RTA incidents from 2021 to 2023 was analysed using integrated GIS-based machine learning and statistical methods. Temporal clusters were identified through Kulldorff’s purely temporal scan statistics, while Kernel Density Estimation (KDE) quantified accident density during morning peak, midday/off-peak, evening peak, and lean/night-time intervals. Spatial clustering was further assessed using LISA-Moran’s I, purely spatial scan statistics, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Emerging Hotspot Analysis (EHA) was employed to detect evolving hotspot patterns over time. The findings indicate that major accident hotspots are concentrated at key intersections and transport corridors, such as Hill Cart Road, Darjeeling More, Sevoke Road, Eastern Bypass, and Burdwan Road. Moran’s I (0.157; p = 0.007) demonstrates significant but moderate spatial autocorrelation, and spatial scan statistics identified three principal high-risk zones. HDBSCAN classified 81.90% of incidents within clustered areas. Lean/night-time periods exhibited the highest accident densities, reaching 14.21 accidents/km² at critical intersections. These results underscore the utility of integrating GIS and machine learning techniques for urban traffic safety planning and hotspot-focused intervention strategies. Full article

Aggressive riding behavior is a key contributing factor to road accidents, particularly in motorcycling, where rider dynamics directly influence vehicle stability and control. Despite growing interest in objective behavioral assessment, validated classification frameworks specific to motorcycles remain scarce in the literature. This pilot study investigated the feasibility of a standard deviation-based method for classifying aggressive riding behavior in a single experienced motorcyclist navigating two distinct environments: an urban route (UR) and a suburban national route (SNR). The participant completed two 20 min rides under real-world conditions. The UR was characterized by frequent accelerations, braking, speed bumps, and traffic lights, whereas the SNR features low traffic density and minimal interruptions. Longitudinal acceleration data were continuously recorded using a Vicon Blue Trident measurement unit mounted on the motorcycle seat. Drawing on the threshold principles established in automotive research, an environment-specific classification framework was developed to categorize riding events into normal, aggressive, and dangerous levels for both acceleration and deceleration maneuvers. The derived thresholds revealed pronounced environmental differences: UR thresholds (acceleration: 2.122 m/s²; deceleration: −2.134 m/s²) were approximately three times lower than those observed in the SNR (acceleration: 6.16 m/s²; deceleration: −7.09 m/s²). From more than four million recorded data points, approximately 88% of the riding behavior was classified as normal in both routes. In the UR, 9.27% of events were identified as aggressive and 4.37% as dangerous, compared with 7.27% aggressive and 5.35% dangerous events in the SNR. These preliminary findings suggest that environment-specific thresholds may be essential for accurately characterizing motorcycle riding behavior, and caution against the direct application of fixed automotive criteria to motorcycle safety analyses. All findings are specific to one rider on two routes and must not be extrapolated to other motorcyclists, vehicle types, or road contexts without replication. Full article

In response to the frequent safety incidents associated with the core electrical systems (i.e., traction battery, charging system, and drive motor) of new energy vehicles (NEVs) and the lack of forward-looking quantitative risk assessment methods in existing detection and diagnostic technologies, this study introduces the Layer of Protection Analysis (LOPA) methodology into the field of NEV safety. Unlike qualitative methods (e.g., FMEA, FTA) or purely data-driven diagnosis, this work establishes a tailored semi-quantitative LOPA framework that defines scenario-specific independent protection layer (IPL) identification criteria and probability of failure on demand (PFD) assignment rules for NEV applications. Typical risk scenarios, including battery thermal runaway, electrical faults in charging systems, overheating of drive motors, and battery internal short circuits caused by mechanical abuse, are systematically analyzed in terms of their failure mechanisms and evolution processes. A tailored quantitative risk assessment framework is established and applied to conduct full-process risk evaluations for the four scenarios. The results indicate that, under the synergistic effect of multiple protection layers—including inherently safe design, basic process control systems, safety instrumented systems, and physical protection measures—the accident consequence frequencies of all scenarios are significantly lower than the tolerable risk thresholds. This verifies the applicability and effectiveness of the LOPA method in NEV safety analysis. The proposed quantitative framework provides a scientific basis for safety design optimization, identification of critical protective elements, and operation and maintenance strategy formulation throughout the lifecycle of NEVs. Furthermore, the limitations of data portability from process industries are discussed, and sensitivity analyses are conducted to confirm the robustness of the conclusions. Full article

When accidents occur, official investigations are carried out, and reports are generated, which are usually reviewed for safety improvements. The retrieval of information is typically performed manually, which can lead to biases, errors, and poor judgement. Moreover, manual reviews can be tedious and highly time-consuming tasks. For these reasons, the implementation of LLMs has been analysed in this context. However, previous studies have been limited, and no proper justification for the implemented LLMs has been provided. Consequently, this work proposes a comparative framework to assess LLM candidates across two main dimensions: cybersecurity and performance. Specifically, a total of 9 LLMs from different providers were analysed, and 18 prompt injection techniques were implemented across 7 categories based on OWASP LLM01:2025 and previous academic studies. Additionally, a RAG system based on these results is introduced to validate the potential of these models in supporting experts in the retrieval of information from maritime accident reports. For validation purposes, a case study on the Marine Accident Investigation Branch (MAIB) reports was conducted. Results show that a comparative framework is required, as model selection may vary depending on the task being performed, which is critical from both a performance and cybersecurity perspective. Full article

The paper analyses the economic impact of the reduction in road traffic accidents in Slovakia between 2000 and 2024 and quantifies both direct and indirect costs of road crashes. Over this period, annual crashes declined from more than 50,000 to approximately 11,500 and fatalities from over 600 to 262, demonstrating the effectiveness of national road safety strategies. The methodology is based on the national road accident database, complemented by macroeconomic and demographic indicators, and follows European recommendations for the valuation of external costs of transport. The study applies the value of a statistical life, the value of a statistical life year, the relative severity index and the critical accident rate, with particular emphasis on comparing the value of a statistical life and the relative severity index. The total VSL-based economic costs of road traffic crashes in 2024 are estimated at approximately €1.25 billion, underscoring the scale of the socioeconomic burden. Building on the forecasted values for 2025, the paper further tests and compares these methodologies on a specific road section, illustrating their practical implications for project appraisal and safety management. The results confirm that VSL-based estimates systematically exceed RSI-based estimates by 21–45% per year, reflecting the broader societal costs captured by the VSL concept. The study shows that investments in safety measures are economically worthwhile and reduce the burden on public finances, while also highlighting the need to harmonize methodologies and improve data quality. 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

Capsizing, sinking, and flooding accidents occurring in the coastal waters of the Republic of Korea constitute a persistent marine safety concern, accounting for approximately 17% of total fatalities associated with marine accidents. Previous statistical analyses of accident causation have identified key contributing factors such as adverse weather conditions, improper cargo loading, and deficiencies in vessel maintenance; however, the complex interdependencies among these factors have not been sufficiently quantified. To address this limitation, this study proposes a fuzzy Bayesian network (FBN) model to systematically evaluate and quantify the risk factors associated with capsizing, sinking, and flooding accidents. A total of 164 adjudicated marine accident cases that occurred in Korean coastal waters over a 10-year period (2015–2024) were analyzed (data collection cutoff: 31 December 2024) to estimate prior probabilities for six major causal categories. Conditional probability tables (CPTs) were derived through a structured Delphi survey conducted with marine safety experts possessing more than 10 years of professional experience. To mitigate the subjectivity inherent in expert judgment, triangular fuzzy numbers (TFNs) and centroid-based defuzzification were applied. Sensitivity analysis identified sea state (SI = 0.0155) and cargo loading condition (SI = 0.0125) as the two most influential factors affecting the probability of capsizing. Scenario analysis further revealed that when adverse weather conditions and improper cargo loading occur simultaneously, the probability of capsizing increases to 39.3%, representing a 5.3 percentage point increase compared to the baseline. In addition, the model demonstrated a close agreement with observed accident outcome distributions, with a Kullback–Leibler (KL) divergence of 0.038, indicating differences within 1.3 percentage points across all outcome categories. The findings of this study provide practical implications for targeted marine safety interventions and the prioritization of regulatory measures in the coastal waters of the Republic of Korea. Full article

Tanker cargo operations involve hazardous cargo environments, complex technical systems and stringent operational procedures. These conditions make accident analysis particularly demanding and require analytical approaches that consider the specific operational context of tanker cargo handling. Existing Human Factors Analysis and Classification System (HFACS) adaptations used in maritime safety research provide a useful framework for analysing human and organisational factors, but they do not fully capture the operational characteristics of tanker cargo operations. As a result, some factors specific to tanker cargo handling remain insufficiently represented in existing HFACS-based analyses. Therefore, this study develops and validates a domain-specific HFACS framework for tanker cargo operations (HFACS-TCO) and applies it to the analysis of accident investigation reports. The framework was developed through an iterative process based on accident report analysis, expert evaluation and the development of structured coding guidelines. The reliability of the coding procedure was assessed using Fleiss’s kappa coefficient to evaluate inter-rater agreement. The proposed framework extends existing HFACS adaptations by incorporating cargo operation-specific organisational, operational and environmental factors. A total of 27 accident investigation reports related to tanker cargo operations were analysed. From these reports, 333 causal factors were identified and classified using the HFACS-TCO framework. The results show that tanker cargo accidents rarely arise from a single cause and usually involve multiple interacting organisational, operational and human factors. Most factors were identified at the levels of Preconditions for Unsafe Acts, Organisational Influences and External Factors, indicating that many accident conditions are established before unsafe acts occur at the operational level. The analysis also shows that most accidents involve factors across several HFACS levels, indicating that tanker cargo incidents develop through interactions between different system levels. The proposed HFACS-TCO framework provides a structured, domain-specific approach to analysing tanker cargo accidents and supports a more systematic identification of organisational and human factors in tanker cargo-related operations. Full article