安全科学与韧性(英文)最新文献

{"title":"Machine learning for occupational accident analysis: Applications, challenges, and future directions","authors":"Izuchukwu Chukwuma Obasi,&nbsp;Pericles Cheng,&nbsp;Cleo Varianou-Mikellidou,&nbsp;Christos Dimopoulos,&nbsp;Georgios Boustras","doi":"10.1016/j.jnlssr.2025.100250","DOIUrl":"10.1016/j.jnlssr.2025.100250","url":null,"abstract":"<div><div>Machine learning (ML) drives progress in occupational accident prevention across diverse sectors. However, significant challenges persist in aligning these tools with practical safety needs, including accurate risk assessment, incident prediction, and targeted prevention strategies. While prior reviews focused narrowly on specific industries or data types, this study presents a comprehensive analysis of ML models in accident analysis, categorizing them by accident type, industry application, and modeling methodology. This study addresses critical challenges in ML model development—such as data quality, hyperparameter tuning, and managing class imbalances—and examines less-discussed topics, including explanatory variable selection and strategies for mitigating overfitting. This review thoroughly assesses the current state of ML-based accident prediction, highlighting critical gaps, methodological limitations, and potential research directions. By analyzing 504 studies across three perspectives—Accident Type, Industry Application, and Modeling Methodology—this review identifies pressing challenges, including (1) limitations in data quality and availability, especially for real-time sources; (2) inadequate model interpretability across applications; (3) difficulties in handling imbalanced accident datasets; and (4) the lack of an integrated framework for incorporating proactive data and industry-specific risk factors. The findings outline a roadmap for advancing ML in occupational safety by enhancing model robustness, improving interpretability, and expanding data sources. This review aims to better align ML applications with safety objectives, promoting data-driven approaches for effective accident analysis and prevention across industries.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 1","pages":"Article 100250"},"PeriodicalIF":3.4,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolution of human factors research in aviation safety: A systematic review and bibliometric analysis of the intellectual structure","authors":"Elizabeth Amorkor Okine ,&nbsp;Esmaeil Zarei ,&nbsp;Brian J. Roggow ,&nbsp;Naser Dehghan","doi":"10.1016/j.jnlssr.2025.100249","DOIUrl":"10.1016/j.jnlssr.2025.100249","url":null,"abstract":"<div><div>Despite the multitude of research endeavors dedicated to Human Factors (HF) in aviation safety, a comprehensive review remains conspicuously scarce. Accordingly, this study presents the first in-depth systematic review and bibliometric analysis of the vital role played by HF in enhancing the safety and reliability of air transportation. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, we scrutinized the Scopus dataset spanning from 1937 to late 2023. A rigorous screening process was applied to identify relevant documents, ultimately subjecting critical analyses of 1663 documents to address four foundational research questions within HF associated with aviation safety. First, our analysis delves into the identification of key areas of emphasis that have characterized HF in the aviation industry since 1937. By tracing the trajectory of research over time, the study aims to discern the evolution of HF within the aviation context. Furthermore, an exploration of primary challenges and knowledge gaps crucial to research is highlighted, with proposed pathways for future investigations to maximize their impact on air transportation safety. Finally, the study extends its inquiry to compare the existing landscape of human reliability research within the aviation sector with that of Nuclear Power Plants (NPPs) and the Chemical Process Industry (CPI). This holistic approach to understanding HF not only contributes valuable insights into aviation safety but also contextualizes these findings within broader industrial frameworks, revealing the key gaps that exist in human reliability within the aviation industry. The outcomes of this study underscore the indispensable role of HF in establishing and advancing safer and more resilient air transportation systems.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 1","pages":"Article 100249"},"PeriodicalIF":3.4,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing resilience potentials in management of occupational safety and health in hospitals: Development and validation of a tool","authors":"J. Afonso-Fernandes ,&nbsp;J. Barbosa ,&nbsp;P. Arezes ,&nbsp;C. Pardo-Ferreira ,&nbsp;J.C. Rubio-Romero ,&nbsp;M.A. Rodrigues","doi":"10.1016/j.jnlssr.2025.100247","DOIUrl":"10.1016/j.jnlssr.2025.100247","url":null,"abstract":"<div><div>A resilient Occupational Safety and Health (OSH) management system is crucial for effectively addressing potential future public emergencies, ensuring the continuous protection of workers' safety and health. Therefore, it is essential for organizations, particularly hospitals, to assess their resilient performance and employ tools that are appropriate and tailored to their specific context. This study aims to enhance the understanding of resilience potentials in OSH management within hospital settings. To this end, an assessment tool was developed based on the Resilience Assessment Grid (RAG). A Delphi study involving subject matter experts was conducted to refine the tailored RAG tool. Following this, a pilot test was administered to 404 healthcare professionals across three public hospitals, with subsequent psychometric analysis. Exploratory Factor Analysis (EFA) identified a four-dimensional structure. Goodness-of-fit indices demonstrated acceptable values, confirming the adequacy of the measurement model. Reliability testing indicated that the 29 item assessment tool is both valid and reliable. The tailored RAG tool was successfully validated, enabling the identification of strengths and weaknesses in OSH management.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 1","pages":"Article 100247"},"PeriodicalIF":3.4,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing urban infrastructure resilience: Analyzing cascading failures and critical node dependencies through multilayer network models","authors":"Cong Lu ,&nbsp;Jianjun She ,&nbsp;Hezhi Pan ,&nbsp;Zihao Guo ,&nbsp;Xuanling Zhou ,&nbsp;Zhijian Li","doi":"10.1016/j.jnlssr.2025.100245","DOIUrl":"10.1016/j.jnlssr.2025.100245","url":null,"abstract":"<div><div>As urbanization and industrialization progress, urban infrastructure systems grow increasingly complex, heightening their vulnerability to cascading failures from natural disasters and human-induced disruptions. Strengthening the resilience of these systems is critical for sustainable urban development and sustaining residents’ quality of life. This study introduces a novel framework to analyze cascading failure propagation within infrastructure networks. Utilizing the implicit interdependency model, we construct a multilayer network that delineates interconnections and dependencies across infrastructure sectors. The PageRank algorithm is used to identify critical nodes by evaluating their network centrality, thereby highlighting key components within the system. Through simulations of random, PageRank-based, and betweenness-based attack scenarios, we explore failure dynamics and their propagation patterns. Additionally, we evaluate mitigation strategies, with the community periphery augmentation strategy proving most effective, enhancing resilience by linking peripheral nodes between communities. This research systematically connects the significance of key nodes to cascading effects, uncovering vulnerabilities and providing actionable insights for disaster response and recovery planning.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 1","pages":"Article 100245"},"PeriodicalIF":3.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Studying the dynamics of crowd panic propagation during emergency evacuation","authors":"Yushan Li ,&nbsp;Changchun Liu ,&nbsp;Yi Yang","doi":"10.1016/j.jnlssr.2025.03.001","DOIUrl":"10.1016/j.jnlssr.2025.03.001","url":null,"abstract":"<div><div>Casualties during emergency evacuations are often attributed to people’s panic-driven extreme behaviors rather than the accidents themselves. The propagation of panic is influenced by various factors. Based on the susceptible–infectious–recovered–susceptible (SIRS) model, a system dynamics (SD) model was developed using AnyLogic software to investigate the spread of panic emotions within a population. A case study focused on hospital emergency evacuations was conducted, wherein factors influencing panic propagation were divided into individual and group levels. The population was classified into three categories—staff, caregivers, and patients—and the effect of the ratio of these categories on evacuation efficiency was examined. Based on these classifications, an evacuation simulation experiment was conducted to examine the effects of panic emotions on evacuation efficiency. Results indicate that optimal hospital evacuation efficiency is achieved with a staff:caregiver:patient ratio of 2:2:1. The overall evacuation process is significantly impacted by panic, resulting in a 64 % increase in evacuation times when panic propagation is considered compared to scenarios where it is not. Furthermore, the initial 10 s following a disaster were identified as crucial for managing severe panic. Valuable insights for improving emergency evacuation management are provided by this study.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 4","pages":"Article 100207"},"PeriodicalIF":3.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Skeleton-based detection of anomalous personal protective equipment doffing behaviors among healthcare workers","authors":"Qiang Zhang,&nbsp;Lixin Yang,&nbsp;Ying Qi,&nbsp;Teng Wan,&nbsp;Qiushi Li,&nbsp;Renwen Miao","doi":"10.1016/j.jnlssr.2025.100229","DOIUrl":"10.1016/j.jnlssr.2025.100229","url":null,"abstract":"<div><div>Identification of doffing behaviors of personal protective equipment (PPE) plays a crucial role in ensuring the safety of healthcare workers. With the continuous emergence of new infectious diseases, accurate detection of anomalous behaviors during PPE doffing procedures has become increasingly critical. In complex medical environments, conventional visual methods have demonstrated limited capability in accurately capturing the subtle movements involved in the multistep PPE doffing process. To address the challenges of low motion heterogeneity and minimal amplitude variations in PPE doffing procedures, this study presents a skeleton keypoint-based anomaly detection model. The proposed model innovatively integrates spatiotemporal embedding modules and adaptive attention mechanisms, allowing the precise detection of subtle changes in localized hand movements. In contrast to the limitations of conventional methods in characterizing fine-grained feature differences, this model demonstrates significantly enhanced capability in identifying anomalous PPE doffing behaviors. Extensive experimental results indicate that the model outperforms existing methods in key metrics, including precision and recall, providing novel technical support for the management of standardized PPE in medical settings.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 1","pages":"Article 100229"},"PeriodicalIF":3.4,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A resilience evolution model of urban lifeline systems during operation based on performance state transitions","authors":"Dongyue Zhao ,&nbsp;Qian Chen ,&nbsp;Xiaolong Zhao ,&nbsp;Yunhe Tong ,&nbsp;Changkun Chen ,&nbsp;Shijie Xia","doi":"10.1016/j.jnlssr.2025.100231","DOIUrl":"10.1016/j.jnlssr.2025.100231","url":null,"abstract":"<div><div>To better understand the resilience evolution dynamics of urban lifeline systems over extended operational periods, this study introduces a model inspired by the susceptible-infected-recovered (SIR) model, which is traditionally used to simulate population health transitions. By analyzing the mechanisms governing the performance state evolution of urban lifeline systems under disaster scenarios, integrating a disaster scenario model with resilience assessment methodologies, and comprehensively considering three key resilience components—resistance, recovery, and adaptability—we develop a system dynamics resilience‒reliability (SDR-R) model. A hypothetical case study is conducted to validate the model’s applicability. The results indicate that the interplay of resistance, recovery, and adaptability influences the dynamic evolution of system performance across three states: disability performance, survivability performance, and recovery performance. The model reveals a cyclical pattern in resilience enhancement, with adaptability emerging as a critical determinant. Moreover, the SDR-R model not only simulates urban lifeline performance state evolution under single disaster scenarios but also captures resilience evolution trends over long-term system operations. The case study findings reveal that resilience decreases as disaster severity intensifies, yet positive feedback from adaptability fosters resilience improvement over time. The process of resilience evolution can be divided into four distinct phases: initial impact, adaptive priming, adaptive enhancement, and threshold effect. Notably, resilience dynamics vary significantly across disaster levels. While systems exhibit high resilience under low-level disasters, resilience gradually stabilizes at a high level in medium- and high-level disaster scenarios. However, extreme disasters introduce greater fluctuations in resilience, underscoring the necessity for targeted resilience-enhancing strategies. The insights derived from this study offer methodological guidance for understanding urban lifeline resilience evolution and developing strategies to enhance system robustness.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 4","pages":"Article 100231"},"PeriodicalIF":3.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A position-aware attention model based on double-level contrastive learning for hyper-relational knowledge graph representation in emergency management","authors":"Xinzhi Wang ,&nbsp;Weijian Zhu ,&nbsp;Jiang Kai ,&nbsp;Xiangfeng Luo ,&nbsp;Jianqiang Huang","doi":"10.1016/j.jnlssr.2025.100223","DOIUrl":"10.1016/j.jnlssr.2025.100223","url":null,"abstract":"<div><div>Effective emergency management relies on timely risk identification and decision-making, wherein natural language processing plays a vital role. Hyper-relational knowledge graph (HKG) representation, which embeds entities and their complex relations into latent space, provides a strong foundation for supporting emergency responses. Existing methods consider either inter-entity or inter-fact dependencies, leading to the loss of interaction information at the unconsidered level (fact level or entity level). To address the above issue, we propose a position-aware attention model based on dual-level contrastive learning (PDCL) for HKG representation. First, the complete and co-occurrence graphs were constructed and encoded using different graph convolutional networks, generating different embedding views for entities and facts. Second, entity-level and fact-level contrastive objectives were designed to enhance information exchange between the two levels in a self-supervised manner. Finally, a linear transformation corresponding to the ordinal information of each element was used to integrate positional constraints into the representation of the HKG. Experimental results for three benchmark datasets showed that the PDCL model outperformed existing state-of-the-art methods. Especially, MRR and Hits@1 values could be improved by up to 1.8% and 3.3%, respectively.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 1","pages":"Article 100223"},"PeriodicalIF":3.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selecting vehicle dispatching plan for typhoon emergency evacuation based on fault-tolerance analysis","authors":"Zhao-ge Liu ,&nbsp;Xiang-yang Li","doi":"10.1016/j.jnlssr.2025.01.005","DOIUrl":"10.1016/j.jnlssr.2025.01.005","url":null,"abstract":"<div><div>Unexpected scenarios often occur during typhoon response, which is likely to cause the failure of evacuation vehicle dispatching and other preparedness plans. To solve this problem, a vehicle dispatching plan selecting method based on fault-tolerance analysis is proposed, which considers the bounded rationality of emergency decision-makers. The method improves the capability of responding to unexpected scenarios by increasing backup resources. First, under the expected scenarios, a bi-level programming model for arranging the quantities of each type of vehicle and their routes is established, with the goal of minimizing the expected total evacuation time. A corresponding solving algorithm is designed. Second, possible unexpected scenarios are preset by integrating local and non-local historical experiences, and the scenario influences on vehicle dispatching constraints are analyzed. Third, under unexpected scenarios, a fault-tolerance plan set is established considering the failure risk of vehicle dispatching and fault-tolerant cost. The optimal plan is selected by calculating and ranking fault-tolerant rates. Finally, a case study in Shenzhen, China is provided to verify the reasonability and effectiveness of the method. The results show that the proposed method can help discover and address the ‘fault’ of vehicle dispatching plans during emergency preparedness and thus improve evacuation capabilities in emergency response. The proposed method can be used to develop evacuation vehicle dispatching planning methods with comprehensive scenario adaptability and a precisely improved capability.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100198"},"PeriodicalIF":3.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"National resilience: Development and validation of a new four-dimensional model for disaster preparedness assessment","authors":"Avi Zigdon ,&nbsp;Osnat Lavenda ,&nbsp;Eyal Lewin","doi":"10.1016/j.jnlssr.2025.100228","DOIUrl":"10.1016/j.jnlssr.2025.100228","url":null,"abstract":"<div><div>The studies described here aim to develop and empirically validate a more accurate and reliable model for assessing national resilience, emphasizing its importance for disaster risk reduction and disaster preparedness. Two large-scale surveys were conducted in Israel—Study 1 in 2019 (<em>N</em> = 748) and Study 2 in 2020 (<em>N</em> = 1198)—during the early phase of the COVID-19 pandemic. Based on exploratory and confirmatory factor analyses, a 13-item scale was developed to assess four dimensions of national resilience: Patriotism (<em>α</em> = 0.860), Political Trust (<em>α</em> = 0.783), Perceived Internal Threats (<em>α</em> = 0.768), and Perceived External Threats (<em>α</em> = 0.787). Together, these four factors explained 61.69 % of the total variance. Confirmatory factor analysis (CFA) confirmed the four-factor structure as the best-fitting model (GFI = 0.916; CFI = 0.867; RMSEA = 0.095), outperforming both the one- and three-factor alternatives. The model proved to be consistent across different demographic groups and in different social contexts. The results provide a validated and scalable tool for assessing the socio-psychological dimensions of national resilience. By capturing citizens’ emotional engagement, trust in institutions, and perceptions of internal and external threats, the model provides an evidence-based framework for assessing a nation's adaptive capacity. It enables policymakers and disaster management experts to monitor the resilience of the population over time, identify weaknesses in social cohesion or institutional confidence, and develop targeted interventions to strengthen national preparedness in the face of complex and evolving crises.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 1","pages":"Article 100228"},"PeriodicalIF":3.4,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}