Search Results (1,109)

Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This study constructs an integrated full-cycle ecological damage assessment framework for sudden water pollution accidents, integrating three core modules: multi-model pollutant migration prediction, multi-scale aquatic biological damage diagnosis, and three-dimensional ecological-economic loss accounting. The framework adopts a modular design that can potentially accommodate heavy metals (Cd, Cr, As, Pb) and organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), with standardized molecular, individual, and population-level biological endpoints and corresponding pollutant dose–response templates reserved as reference calculation modules. However, applicability beyond this case has not been validated and requires case-specific calibration. To verify the operability and accuracy of the proposed integrated system, a typical tailings dam leakage incident dominated by hexavalent chromium (Cr(VI)) and arsenic (As) pollution was selected as the practical validation case; all field monitoring, pollutant simulation, and final economic loss quantification in this case exclusively rely on on-site measured Cr(VI) and As data, while Cd and PAH-related biological response curves and remediation cost formulas retained in the manuscript only serve as illustrative universal template components of the framework rather than case-measured results. For the Cr(VI)/As pollution case, the advection–diffusion model simulation revealed that the Cr(VI) contamination plume horizontally spread 250 m within 48 h and extended to 560 m after seven days, and anaerobic groundwater environments drove the transformation of toxic mobile trivalent arsenic (As(III)) from primary pentavalent arsenic. The calibrated SWAT model achieved Nash–Sutcliffe efficiency (NSE) coefficients of 0.75 for dissolved Cr(VI) and 0.68 for particulate As. The graph theory-based rapid prediction model cut computation duration down to minutes; when validated against independent field monitoring data, it yielded an average relative error of 14.2%, and its consistency with the SWAT model reached 10.5% relative deviation, satisfying the accuracy requirement for emergency early warning. Field biological monitoring demonstrated substantial ecological impairment: metallothionein (MT) expression in fish tissues was markedly elevated (the reported 6.2-fold induction value derives from standard Cd exposure template tests within the framework, with analogous MT upregulation also observed for field Cr(VI)/As co-stress), and benthic community Shannon diversity declined by over 50% in polluted river reaches. The standardized Ecological Damage Index (EDI) of the case was calculated as 480.2, indicating severe aquatic ecosystem damage, with total comprehensive ecological and economic losses reaching 17.25 million CNY. This study innovatively couples high-precision physical transport models with fast emergency prediction algorithms and establishes a complete multi-tier biological indicator chain linking molecular biomarkers to community integrity metrics; the three-dimensional loss accounting system integrating ecosystem service impairment, restoration expenditure, and post-pollution recovery loss realizes closed-loop full-cycle damage evaluation. The proposed framework, demonstrated for Cr(VI) and As pollution, has a modular design that may potentially be extended to other pollutants such as Cd and PAHs by adjusting model parameters, providing a quantitative reference for emergency disposal, pollution remediation, and ecological compensation of water contamination accidents, although further validation across different pollutants and hydrological settings is required. Full article

Occupational safety management in combined cycle power plant (CCPP) construction requires methods that can both prioritize multiple tasks and explain why critical scenarios arise. This article presents a hybrid case-study methodology integrating questionnaire-based semi-quantitative 5 × 5 risk matrices with task-focused HAZOP. An anonymous structured questionnaire was completed by 105 workers at a CCPP construction site (response rate 75.0%), covering eight major construction activities and four occupational groups. Likelihood and severity ratings were processed in RStudio 2025.09.2 to produce 5 × 5 risk matrices, distribution plots, and “High + Critical” prioritization indices. The highest-ranked tasks were then subjected to HAZOP in PHAWorks RA Edition (Version 1.0.9834). “Work at height” emerged as the highest-ranked perceived-risk activity across all groups, followed by lifting/load handling, chemicals/flammables, and electrical works. Safety technicians tended to assign more conservative ratings than labor personnel, indicating role-dependent differences in risk perception. The HAZOP analysis suggested that the plausible HAZOP-informed accident scenarios and control weaknesses were not only technical but also organizational, including failures in permit-to-work, lockout/tagout, exclusion-zone control, fall protection, and chemical handling discipline. The integrated hybrid workflow bridges rapid semi-quantitative prioritization and structured causal analysis, supporting the translation of field-based perceptions into targeted and actionable safety controls for dynamic energy-project worksites. The findings highlight the critical role of organizational discipline alongside technical measures and support the integration of structured risk assessment within occupational health and safety management systems in dynamic construction environments. Full article

The National Transport Safety Board found that Wheels-Up Landing was the second highest defining event for aircraft accidents from 2008 to 2022. The increasing push for sustainable propulsion and accompanying novel airframe architectures present new integration and safety challenges for aircraft design and development, among which is the structural integrity and crashworthiness of the aircraft under such extreme events. This paper examines the regulations and design requirements governing aircraft emergency Wheels-Up Landing scenarios, emphasising their implications for aircraft safety and structural integrity. It consolidates standards from aviation authorities, such as the FAA and EASA, which identify and define key requirements relating to occupant safety and fire prevention and protection during such events. The paper then considers the Wheels-Up Landing scenario and its design requirements within the context of future novel aircraft employing sustainable propulsion systems, from higher bypass turbofan to electric- and hydrogen-based technologies. The unique characteristics and challenges of these emerging propulsion technologies are described, highlighting how alternative structural configurations, weight distributions and powerplant architectures may influence the aircraft response under a Wheels-Up Landing event. Finally, an exploration of predictive modelling strategies and methods currently used in Wheels-Up Landing analysis was conducted. While reviewing the breadth of accurate, high-fidelity modelling methods targeting fuselage impact, it also highlighted the gap in both considering the increasingly relevant and frequent powerplant impact scenarios, and the provision of lightweight modelling approaches necessary to rapidly and adequately address the emergency Wheels-Up Landing response early in the aircraft design process. Full article

Liquefaction and dynamic separation of moisture-sensitive solid bulk cargoes may remain latent for much of a voyage and then manifest as a sustained heel, leaving a comparatively short interval for emergency action. This study develops an exploratory reliability-based analysis of accident chronology using a source-traceable registry of 35 casualties and incidents. Eighteen cases provided post-heel information suitable for the principal emergency time reserve analysis; the observations comprised exact, approximate, reconstructed, interval-censored, and right-censored times. Descriptive statistics calculated from the selected central values and censoring bounds yielded a mean emergency time reserve TR of 200.99 min, a median of 192.20 min, and a range of 67.50–335.10 min. In likelihood-based fitting that retained censoring, the Weibull model achieved the lowest AIC (212.51) and BIC (215.18), with Kolmogorov–Smirnov D = 0.097 (p = 0.989). The fitted lower-tail quantiles were Q10 = 105.40 min and Q25 = 147.99 min, substantially shorter than the descriptive mean. Robustness was examined using nonparametric estimators, Akaike-weighted model averaging, source-confidence weighting, leave-one-out analysis, and alternative interval assumptions. The contribution is a reproducible framework for converting heterogeneous casualty narratives into uncertainty-qualified lower-tail time-reserve evidence and non-prescriptive bridge–team decision support. The framework is not a physical stability model and cannot replace ship-specific GM/GZ calculations, approved loading and stability information, or the master’s judgement. Full article

Very large-scale biogas (biomethane) projects are important infrastructure systems for integrating organic waste treatment, renewable energy substitution, and carbon mitigation under China’s carbon peaking and carbon neutrality goals. However, their long process chains, concentrated hazardous media, and frequent confined-space operations create coupled safety risks that may undermine sustainable operation. To identify the dominant risk drivers and safety governance priorities, this study investigated five operating very large-scale biogas projects in Shanxi Province, China. On-site inspections, semi-structured interviews, and document reviews were used to identify ten safety-risk causative factors. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was combined with Interpretive Structural Modeling (ISM) to quantify causal relationships and reveal the hierarchical transmission structure among the factors. The results show a structural imbalance between document-based compliance and operational implementation. Although basic safety documents were generally established, only 20% of the projects had scenario-specific emergency response plans for major accident scenarios; the compliance rate of explosion-proof electrical equipment, the configuration rate of fixed monitoring and alarm systems for combustible and toxic gases, and the effective operation rate of forced ventilation facilities were 40%, 60%, and 40%, respectively. Insufficient enterprise safety investment (M1) and unclear external regulatory responsibilities (M2) were the dominant root causes of system-level risk propagation, while inadequate control of high-risk operations (M9) and unsafe worker behavior (M10) were the final manifestations. A four-pillar governance pathway is proposed, including policy and standard improvement, technological support and equipment upgrading, personnel capacity enhancement, and sustainable funding mechanisms. The findings provide empirical evidence for risk-based supervision and indicate how operational safety governance can serve as an enabling condition for the long-term sustainability of the biomethane industry, rather than as a direct measurement of carbon-mitigation or energy-performance outcomes. Full article

Safe and resilient operation of liquefied petroleum gas (LPG) storage and distribution stations is important to the sustainability of fuel infrastructure, yet boiling liquid expanding vapor explosion (BLEVE) prevention requires time-dependent barrier analysis and a clear separation between prevention priorities and post-rupture exposure. This study develops a dynamic–spatial screening framework that integrates evidence-graded basic events, a priority-AND dynamic fault tree, a continuous-time Markov chain (CTMC), memory-efficient Monte Carlo trajectory simulation, modeled-area prevention prioritization, and tank-origin relative-exposure screening. A one-million-trajectory accelerated numerical baseline produced 112,693 BLEVE-conditioned trajectories. Weld cracking and fire-pump failure had the highest dynamic criticality because they act in later barrier-degradation stages, whereas high-wind spread had the greatest conditional involvement. Tank structure had the highest modeled-area prevention priority. Receiver rankings depended on the assumed layout: P5 ranked first under the baseline schematic coordinates, whereas P2 ranked first under the illustrative metric-coordinate scenario across the tested distance-attenuation kernels. Convergence, acceleration-factor, evidence-grade, weight, recovery, alternative-path, static-importance, coordinate, and kernel sensitivities were evaluated. Prevention-priority scores and normalized exposure indices are reported separately and are not interpreted as calibrated accident frequency, physical dose, expected loss, or quantitative risk. By supporting targeted inspection, maintenance, monitoring, and emergency-resource allocation, the framework can contribute to safer, more resilient, and more sustainable operation of LPG infrastructure. Full article

Background/Objectives: Road traffic accidents represent the leading cause of death in children and adolescents in Brazil. In the state of Piauí, non-specialized facilities frequently manage critically injured pediatric patients. Targeted training programs are considered a key strategy for improving outcomes. This study assessed the feasibility of adapting a pediatric trauma course developed in a high-income setting for medical first responders in both pre-hospital and in-hospital settings in a resource-limited environment in northeastern Brazil. Methods: This prospective non-randomized mixed-methods feasibility-oriented implementation and educational evaluation study involved the design of a simulation-based course based on a literature review by a German interprofessional and interdisciplinary team. Local adaptation was achieved through a needs assessment, field visits, and stakeholder collaboration. Implementation included the training of Brazilian instructor candidates, who subsequently delivered the course under supervision. Data were collected using study-specific, non-validated questionnaires at two timepoints and analyzed descriptively and exploratively. Results: A total of 98 healthcare professionals participated in the needs assessment (mean experience 9.7 ± 6.3 years; 62.2% nursing staff); 66 completed the pediatric trauma management section. Although familiarity with the ABCDE approach was high (86%), confidence in pediatric trauma management was significantly lower than management in adults (47% vs. 68%, p = 0.0018), particularly for invasive procedures. During implementation, 38 participants completed the course, with a 100% recommendation rate; approximately 80% felt prepared to teach independently. However, participants and instructors highlighted the need for more practical training, longer course duration, and follow-up instructor training. Conclusions: A context-adapted, simulation-based pediatric trauma training program was feasibly implemented and well accepted in a resource-limited region of Brazil. The train-the-trainer approach shows promise for strengthening local pediatric trauma capacity, but sustained implementation requires continued instructor development, supervised teaching, and long-term evaluation of educational and clinical outcomes. Full article

Human factors remain the predominant contributors to aviation accidents, yet the cognitive foundations of pilot performance have not been systematically defined. Existing cognitive frameworks inadequately represent the complex, high-demand environment of flight operations. This systematic review and meta-analysis aimed to identify a core cognition set for piloting, defined as the minimal group of cognitive modules most consistently related to flight performance, and to examine how these modules are affected by aviation-specific risk factors. A total of 93 studies were included in this review. Of these, 31 reported quantitative associations between cognitive performance and flight outcomes. A three-level mixed-effects meta-regression model was applied to estimate pooled effect sizes for each cognitive module. Meanwhile, 74 studies examined the influence of aviation factors such as fatigue, hypoxia, gravitational load, and aging. Meta-analytic findings indicated four cognitive modules (Perception, Working Memory, Multitasking Flexibility, and Psychomotor) as showing the strongest and most reliable associations with flight performance (Fisher’s z = 0.356–0.440, p < 0.001). The narrative synthesis corroborated that these four modules were most sensitive to operational and physiological risks such as fatigue, sleep deprivation, hypoxia, and aging. Working memory and cognitive flexibility consistently emerged as the earliest indicators of cognitive deterioration. Full article

To address the challenges in risk assessment of complex emergency systems, such as difficulties in closed-loop structure modeling, insufficient quantification of dynamic evolution, and poor adaptability to multiple scenarios, this study proposes a dynamic risk assessment method that integrates the System-Theoretic Accident Model and Processes (STAMP) and the Cascading Failure Propagation Model (CFPM). The novelty of this coupling lies in a bidirectional “qualitative diagnosis → quantitative prediction” logic: STAMP’s identification of Unsafe Control Actions (UCAs) provides a theory-grounded blueprint for configuring the CFPM network topology and propagation parameters, while CFPM’s dynamic simulation translates these qualitative control flaws into computable risk evolution trajectories. The proposed framework adopts a two-layer structure of “qualitative modeling–quantitative analysis”. STAMP is used to construct a hierarchical control structure, identify Unsafe Control Actions (UCAs), and analyze the nonlinear interaction mechanisms among “human–organization–technology” factors. For typical scenarios of “fault not processed” and “online fault processing”, CFPM is employed to abstract the system into a node network, quantify the time-step propagation process of node failure probability, calculate the system residual performance index, and generate real-time risk evolution curves. A case study of the Tianjin Port ‘8·12’ explosion accident demonstrates that this method effectively captures the closed-loop interaction characteristics and dynamic risk evolution patterns of emergency systems. Quantitative results reveal a distinct contrast between the two handling scenarios: in the absence of maintenance intervention, system residual performance deteriorates exponentially and rapidly approaches a critical threshold; in contrast, effective online maintenance significantly retards risk accumulation and facilitates gradual system recovery, thereby preventing further escalation of consequences. Compared to traditional methods like Bayesian Networks, it shows stronger applicability by explicitly modeling closed-loop feedback structures and enabling discrete time-step quantification of risk accumulation, and can accurately identify control flaws and quantify risk accumulation effects, thereby providing support for optimizing emergency strategies. Future research should focus on enhancing the method’s adaptability to data uncertainty and cybersecurity threats. Full article

The construction industry is a critical pillar of Hong Kong’s economic development and urban growth, yet it continues to face significant safety challenges amid increasing construction activity and workforce demands. While efforts to accelerate housing development and infrastructure projects are essential for addressing societal needs, they must be balanced with effective measures to prevent accidents and manage workplace hazards. This paper reviews the common causes, patterns, and emerging trends of construction-related accidents in Hong Kong and examines the Tai Po Wang Fuk Court fire as a representative case study of systemic safety failure. Using documentary evidence on a fire that caused 168 deaths, 79 injuries, and spread across seven of eight towers, the study identifies five aligned failure layers. Drawing upon archival records, official reports, legislative documents, and public accounts, the study applies James Reason’s Swiss Cheese Model to demonstrate how deficiencies in material selection, worker behaviour, fire protection systems, contractor management, and regulatory oversight aligned to enable the incident. Building on these findings, the paper proposes a 5E framework—Engineering, Education, Enforcement, Engagement, and Evaluation—to provide a structured approach for strengthening construction safety management and fire risk governance. The framework offers practical guidance for policymakers, regulators, contractors, property managers, and other stakeholders seeking to enhance safety culture, improve regulatory compliance, and promote resilience in the construction sector. The findings contribute to the broader discourse on construction safety by demonstrating how systemic failures can be translated into targeted interventions for preventing similar incidents in the future. Full article

The growing adoption of cycling as part of the transition toward sustainable urban mobility, driven by climate change concerns and increasing congestion, has heightened the need to ensure cyclist safety in metropolitan areas. This study proposes an integrated spatio-temporal analytical framework to examine the evolution of reported bicycle–vehicle injury accidents in the Lisbon Metropolitan Area (LMA). The framework combines Geographic Information Systems (GIS)-based spatial statistics with Emerging Hotspot Analysis (EHA) to identify and track changes in accident clustering over time, across pre-, during-, and post-COVID-19 containment periods. This study contributes by applying Emerging Hotspot Analysis to bicycle accident data, an approach still largely unexplored, and by proposing a sequential and integrated framework that links traditional spatial analysis methods with dynamic hotspot detection and machine learning techniques, enabling a shift from static pattern identification to enhanced interpretation of evolving accident occurrence patterns and hotspot dynamics. Results reveal evidence of spatial consolidation and changing hotspot distributions over time, with emerging hotspots increasingly located in suburban transition zones and at the edges of existing cycling infrastructure. These patterns may reflect changes in mobility demand and infrastructure provision, although the absence of exposure data prevents a direct assessment of this relationship. Complementary analysis using forest-based machine learning models identifies key factors associated with hotspot formation and accident severity, including crash type, temporal patterns (e.g., day of the week), and environmental conditions such as slope and lighting. These findings highlight the value of combining spatio-temporal analysis with predictive modelling to support data-driven urban planning and targeted safety interventions. Lisbon provides a relevant case study for cities undergoing similar transitions toward sustainable transport systems. Full article

Background/Objectives: Excess mortality (EM) is a key indicator for assessing the population-level impact of large-scale health crises, particularly when cause-of-death ascertainment is incomplete or delayed. However, EM estimates are sensitive to methodological decisions, highlighting the need for operationally feasible approaches suitable for routine public health surveillance. Methods: We conducted time series analyses of routinely collected mortality data from a subnational setting in northern Mexico. Deaths recorded between 2015 and 2022 were grouped into 28 cause-of-death categories. Expected deaths during the COVID-19 pandemic period (March 2020–July 2022) were estimated using negative binomial regression models fitted to pre-pandemic data (2015–2019), incorporating a linear trend and monthly indicators to account for long-term trends and seasonality. Newey–West standard errors were used to address serial correlation and heteroskedasticity. EM was defined as the difference between observed and expected deaths. Results: An estimated 14,482 excess deaths were observed during the pandemic period, corresponding to a 30.9% increase relative to expected mortality. Time-series models identified four distinct peaks of excess mortality coinciding with major pandemic waves. Although COVID-19 accounted for most excess deaths, among non-COVID causes, only ischemic heart disease and diabetes showed statistically significant excess mortality among major non-communicable diseases after baseline adjustment. However, additional categories—including non-transport-related accidents, ill-defined causes, and other endocrine, metabolic, hematological, and immunological diseases—also exhibited statistically significant excess mortality. For several causes, increases in crude mortality did not translate into statistically significant excess mortality. Conclusions: Negative binomial time series regression provides an implementable framework for estimating EM, underscoring the importance of expected mortality estimation for understanding population-level mortality dynamics during health emergencies. Full article

Real-time monitoring of operator fatigue is critical for ensuring operational safety and reliability in dynamic industrial environments, especially under swing conditions such as offshore floating nuclear power platforms. This study proposes a multimodal fatigue monitoring framework based on surface electromyography (sEMG) and heart rate variability (HRV) sensors for real-time fatigue recognition. Experiments were conducted on a six-degree-of-freedom motion platform with three swing levels, involving 23 participants performing simulated emergency operation tasks. Four machine learning models (Naive Bayes, K-Nearest Neighbor, Multilayer Perceptron, and Random Forest) were employed for fatigue state classification. The results show that the Random Forest model achieves the best performance, with an overall accuracy of 98.2%, 100% true positive rate for the normal state and fatigue, and 66.7% true precision for severe fatigue. The proposed multimodal fusion method effectively suppresses motion artifacts and improves recognition robustness under swing interference. Rigorous subject-level stratified cross-validation eliminates sample leakage risks; bootstrap confidence intervals and pairwise significance tests statistically verify model performance differences; class imbalance mitigation strategies are deployed to quantify uncertainty for the scarce severe-fatigue category; literature-supported Borg CR-10 grading thresholds are validated via retrospective cutoff sensitivity analysis to guarantee reliable fatigue labeling. This sensor-based intelligent monitoring system provides a reliable solution for real-time fatigue detection of operators in dynamic digital industrial scenarios, supporting accident prevention and sustainable operation of high-risk industrial systems. Full article

Iron, with the unique stability of its nucleus, occupies an unusual position among the elements: its abundance on Earth is not simply a geological accident but a direct consequence of nuclear reactions that happened inside stars billions of years ago. Formed at the final stages of fusion in stars, iron spread through space by supernova explosions and became part of the material that formed Earth, eventually becoming the dominant component of the planet’s core. At the surface, iron’s redox chemistry shaped the early atmosphere and oceans, and its availability as a soluble ferrous ion in the anaerobic Archean ocean made it a natural cofactor for the first enzymatic reactions. That same redox flexibility and the ability of iron to shuttle between Fe²⁺ and Fe³⁺ across a wide range of electrochemical potentials explain why virtually every major metabolic pathway in biology depends on iron in one form or another. Yet iron is also dangerous: free and chelated iron can catalyze the production of toxic hydroxyl radicals through Fenton chemistry, the reactivity of which depends strongly on the nature of the chelating ligand, and every living system must balance its need for iron against the oxidative damage that uncontrolled iron causes. This tension between catalytic necessity and chemical toxicity has driven much of the regulatory complexity we observe in modern iron metabolism. In this review, we first outline iron’s journey from its formation in stars to its role in shaping Earth’s structure and the emergence of early iron-dependent biology. We then discuss in detail how fundamental physical and chemical factors continue to influence living systems. Full article

Modeling bivariate count data with complex dispersion and dependence structures remains a significant challenge in statistical data analysis. This article introduces two new bivariate count distributions derived from the generalized shifted inverse trinomial distribution. The proposed models, denoted by BGIT-I and BGIT-II, are constructed using convolution and trivariate reduction methods. They provide flexible joint frameworks for modeling correlated count data while accommodating different marginal dispersion patterns. BGIT-I allows negative, near-zero, and positive dependence, whereas BGIT-II induces non-negative dependence through a common component. The proposed models have simple, tractable probability generating functions, which facilitate the derivation of probabilistic properties and motivate a probability-generating-function-based estimation approach alongside maximum-likelihood estimation. The finite-sample performance of the estimators is further examined through a Monte Carlo simulation study. The practical utility of the proposed models is illustrated using two real bivariate count data sets involving shunter accidents and patient counts in critical care and emergency room settings. The results show that the proposed BGIT models provide competitive alternatives for modeling bivariate count data with different dispersion and dependence characteristics. Full article