Pub Date : 2025-06-25DOI: 10.1016/j.jnlssr.2025.100224
A.I. Filkov , M. Mohamed , S. Carracher , J. Simkin
The 2019–2020 Black Summer fire season highlighted the vulnerability of water monitoring sites, causing damage, data loss, and negatively affecting water management during massive wildfires. It becomes crucial to reduce the impact of wildfires on them. The current study aims to improve the fire resistance of monitoring equipment. Internal thermal insulation was designed for the instrumentation cabinets and tested under different wildfire conditions to evaluate the performance of various materials and equipment designs. The results demonstrated that the new design of the instrumentation cabinet managed to significantly reduce the effect of thermal exposure and kept the temperatures inside the cabinet below the critical threshold of 70 °C for electronics components. Recommendations of insulation and alternative designs are provided for construction in wildland and wildland-urban interface areas.
{"title":"Improving fire resistance of data monitoring equipment in wildland and wildland-urban interface fires","authors":"A.I. Filkov , M. Mohamed , S. Carracher , J. Simkin","doi":"10.1016/j.jnlssr.2025.100224","DOIUrl":"10.1016/j.jnlssr.2025.100224","url":null,"abstract":"<div><div>The 2019–2020 Black Summer fire season highlighted the vulnerability of water monitoring sites, causing damage, data loss, and negatively affecting water management during massive wildfires. It becomes crucial to reduce the impact of wildfires on them. The current study aims to improve the fire resistance of monitoring equipment. Internal thermal insulation was designed for the instrumentation cabinets and tested under different wildfire conditions to evaluate the performance of various materials and equipment designs. The results demonstrated that the new design of the instrumentation cabinet managed to significantly reduce the effect of thermal exposure and kept the temperatures inside the cabinet below the critical threshold of 70 °C for electronics components. Recommendations of insulation and alternative designs are provided for construction in wildland and wildland-urban interface areas.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 1","pages":"Article 100224"},"PeriodicalIF":3.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048449","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}
Pub Date : 2025-06-22DOI: 10.1016/j.jnlssr.2025.100222
Fabio Lozano , Morgan Johansson , Joosef Leppänen , Mario Plos
The accidental release of a flammable gas on a road can result in a vapour cloud explosion (VCE). Such VCEs generate a blast wave that propagates away from the explosion, potentially damaging nearby structures. The TNO Multi-Energy Method is commonly used for a simplified estimate of the blast load resulting from a VCE. The method characterises the severity and duration of the blast wave using a case-specific strength class and combustion energy (which the method relates to the gas volume of the equivalent blast source). However, no specific guidelines for estimating the strength class in urban roads or related settings (such as carparks) are currently available in the literature. This makes implementing the method in such scenarios challenging and imprecise. The authors’ work used computational fluid dynamics (CFD) to evaluate multiple gas explosion scenarios and proposed recommendations for determining the strength class and gas volume at the blast source. These scenarios comprised a group of vehicles engulfed by a stoichiometric propane-air cloud. It was concluded that the strength class could be reasonably estimated based on the number of vehicles in the transverse direction. Furthermore, the guidance for estimating the gas volume at the equivalent blast source was based on the critical gas volume, after which no further enhancement of overpressure was obtained. The recommendations were implemented in several scenarios and compared with corresponding CFD analyses. The results showed very good agreement for predicting impulse. Predicting overpressure was affected by the inherent asymmetry of the scenarios, although it was possible to achieve acceptable and conservative results.
{"title":"Guidance for estimating the blast load from vapour cloud explosions in traffic environments using the multi-energy method","authors":"Fabio Lozano , Morgan Johansson , Joosef Leppänen , Mario Plos","doi":"10.1016/j.jnlssr.2025.100222","DOIUrl":"10.1016/j.jnlssr.2025.100222","url":null,"abstract":"<div><div>The accidental release of a flammable gas on a road can result in a vapour cloud explosion (VCE). Such VCEs generate a blast wave that propagates away from the explosion, potentially damaging nearby structures. The TNO Multi-Energy Method is commonly used for a simplified estimate of the blast load resulting from a VCE. The method characterises the severity and duration of the blast wave using a case-specific strength class and combustion energy (which the method relates to the gas volume of the equivalent blast source). However, no specific guidelines for estimating the strength class in urban roads or related settings (such as carparks) are currently available in the literature. This makes implementing the method in such scenarios challenging and imprecise. The authors’ work used computational fluid dynamics (CFD) to evaluate multiple gas explosion scenarios and proposed recommendations for determining the strength class and gas volume at the blast source. These scenarios comprised a group of vehicles engulfed by a stoichiometric propane-air cloud. It was concluded that the strength class could be reasonably estimated based on the number of vehicles in the transverse direction. Furthermore, the guidance for estimating the gas volume at the equivalent blast source was based on the critical gas volume, after which no further enhancement of overpressure was obtained. The recommendations were implemented in several scenarios and compared with corresponding CFD analyses. The results showed very good agreement for predicting impulse. Predicting overpressure was affected by the inherent asymmetry of the scenarios, although it was possible to achieve acceptable and conservative results.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 1","pages":"Article 100222"},"PeriodicalIF":3.4,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926183","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}
Pub Date : 2025-06-17DOI: 10.1016/j.jnlssr.2025.04.002
Jiayi Lu , Bin Sun , Boao Zhang, Zhaowen Pang, Zhaoxia Peng, Shichun Yang, Yaoguang Cao
With the continuous advancement in vehicle intelligence, enhancing safety has emerged as a key priority in intelligent vehicle research and development. Intelligent vehicles are currently limited in supporting autonomous or human-driven modes. This limitation becomes apparent in complex driving scenarios, where vehicle risk response capabilities are inadequate. This paper suggests that shifting from a single decision maker to a human–machine collaboration approach is a potential solution. However, current research on human–machine collaboration in intelligent vehicles primarily focuses on intelligent systems that assist the driver, rather than treating the driver and the system as equals. This approach overlooks the role of the driver in helping the system, lacks effective communication, and diminishes the sense of collaborative cooperation, all of which hinder the promotion of efficient, safe, and stable driving. Inspired by the aviation approach to risk management through Crew Resource Management (CRM), this study introduces the Collaborative Operation Mode (CO-Mode) for intelligent vehicles. Based on CO-Mode’s requirements for human–machine collaborative perception, decision making, and control, the Autonomous Vehicle Collaborative Resource Management (AV-CRM) is proposed. In addition, challenges and future perspectives are explored by analyzing the abilities and limitations of relevant technologies. The proposed AV-CRM redefines the relationship between drivers and intelligent systems, offering new insights into the safety of intelligent vehicles and their technological evolution.
{"title":"CO-Mode with AV-CRM: A novel paradigm towards human–machine collaboration in intelligent vehicle safety","authors":"Jiayi Lu , Bin Sun , Boao Zhang, Zhaowen Pang, Zhaoxia Peng, Shichun Yang, Yaoguang Cao","doi":"10.1016/j.jnlssr.2025.04.002","DOIUrl":"10.1016/j.jnlssr.2025.04.002","url":null,"abstract":"<div><div>With the continuous advancement in vehicle intelligence, enhancing safety has emerged as a key priority in intelligent vehicle research and development. Intelligent vehicles are currently limited in supporting autonomous or human-driven modes. This limitation becomes apparent in complex driving scenarios, where vehicle risk response capabilities are inadequate. This paper suggests that shifting from a single decision maker to a human–machine collaboration approach is a potential solution. However, current research on human–machine collaboration in intelligent vehicles primarily focuses on intelligent systems that assist the driver, rather than treating the driver and the system as equals. This approach overlooks the role of the driver in helping the system, lacks effective communication, and diminishes the sense of collaborative cooperation, all of which hinder the promotion of efficient, safe, and stable driving. Inspired by the aviation approach to risk management through Crew Resource Management (CRM), this study introduces the Collaborative Operation Mode (CO-Mode) for intelligent vehicles. Based on CO-Mode’s requirements for human–machine collaborative perception, decision making, and control, the Autonomous Vehicle Collaborative Resource Management (AV-CRM) is proposed. In addition, challenges and future perspectives are explored by analyzing the abilities and limitations of relevant technologies. The proposed AV-CRM redefines the relationship between drivers and intelligent systems, offering new insights into the safety of intelligent vehicles and their technological evolution.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 1","pages":"Article 100209"},"PeriodicalIF":3.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890529","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}
Cyber–physical systems (CPSs) are becoming increasingly complex, integrating physical entities with diverse computing and communication resources, multiple processors, networks, and devices. One example is the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) system, where interactions among components can lead to UAS collisions and harm to people and property. System Theoretic Process Analysis (STPA) is a systems theory-based technique for conducting early-stage safety analyses of complex systems. The Model the Control Structure step in STPA involves identifying each controller component, its process models, and its control actions. However, conventional STPA process models use only variables and states, which may be insufficient for systems involving entities that transition through multiple state flows. This study introduces a novel extension by integrating Finite State Machine (FSM) modeling into the Model the Control Structure step. The FSM-based approach captures detailed behaviors of entities requiring control by explicitly modeling their states and transitions in an iterative process. This extended STPA was applied to the UTM to control the delivery of UAV packages. The results demonstrate that the FSM extension enhances identifying control actions, feedback loops, process model variables, and unsafe control actions. The study concludes that the extended STPA provides a systematic approach for analyzing CPSs with entities that undergo complex state transitions, contributing to improved systematization and consistency of safety analyses.
{"title":"Extending the STPA to model the control structure with Finite State Machine","authors":"Tiago Aroeira Marliere, Cecilia de Azevedo Castro Cesar, Celso Massaki Hirata","doi":"10.1016/j.jnlssr.2025.04.004","DOIUrl":"10.1016/j.jnlssr.2025.04.004","url":null,"abstract":"<div><div>Cyber–physical systems (CPSs) are becoming increasingly complex, integrating physical entities with diverse computing and communication resources, multiple processors, networks, and devices. One example is the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) system, where interactions among components can lead to UAS collisions and harm to people and property. System Theoretic Process Analysis (STPA) is a systems theory-based technique for conducting early-stage safety analyses of complex systems. The <em>Model the Control Structure</em> step in STPA involves identifying each controller component, its process models, and its control actions. However, conventional STPA process models use only variables and states, which may be insufficient for systems involving entities that transition through multiple state flows. This study introduces a novel extension by integrating Finite State Machine (FSM) modeling into the <em>Model the Control Structure</em> step. The FSM-based approach captures detailed behaviors of entities requiring control by explicitly modeling their states and transitions in an iterative process. This extended STPA was applied to the UTM to control the delivery of UAV packages. The results demonstrate that the FSM extension enhances identifying control actions, feedback loops, process model variables, and unsafe control actions. The study concludes that the extended STPA provides a systematic approach for analyzing CPSs with entities that undergo complex state transitions, contributing to improved systematization and consistency of safety analyses.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 4","pages":"Article 100214"},"PeriodicalIF":3.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863743","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}
Pub Date : 2025-06-11DOI: 10.1016/j.jnlssr.2025.100218
Mingze Ma , Xiaofeng Hu
This study proposes a model integrating YOLOv11 and STGCN for accurate and real-time detection of fall behaviors within buildings. A specialized video dataset comprising fall behaviors performed by six volunteers was developed and used to validate the model’s effectiveness in cloud computing and edge computing environments. The results obtained in the cloud computing environment were characterized by ample computational resources and the absence of real-time constraints. The model achieved precision, recall, and F1-score for fall behaviors exceeding 0.98. The model was integrated into edge computing devices in an actual test environment to directly process real-time video stream data. A missed detection rate of 18 % was observed on the Jetson ORIN NX 16GB device, while the Jetson AGX Orin 64GB recorded a lower missed detection rate of 15 %. Similarly, a false alarm rate of 16 % was observed on the Jetson ORIN NX 16GB device and 12 % on the Jetson AGX Orin 64GB device. These performance differences between the high-performance cloud computing cluster and edge computing devices, as well as among different edge computing devices, may be attributed to variations in computational resources, data quality, and device parameters. The results demonstrate the potential of the proposed model for real-time fall detection in resource-constrained environments.
本研究提出了一个整合YOLOv11和STGCN的模型，用于准确实时地检测建筑物内的坠落行为。开发了包含六名志愿者摔倒行为的专门视频数据集，并用于验证该模型在云计算和边缘计算环境中的有效性。在云计算环境中获得的结果具有计算资源充足和没有实时约束的特点。该模型对跌倒行为的查全率、查全率和f1得分均超过0.98。在实际测试环境中，将该模型集成到边缘计算设备中，直接处理实时视频流数据。Jetson ORIN NX 16GB设备的漏检率为18%，而Jetson AGX ORIN 64GB设备的漏检率较低，为15%。同样，在Jetson ORIN NX 16GB设备上观察到的误报率为16%，在Jetson AGX ORIN 64GB设备上观察到的误报率为12%。高性能云计算集群和边缘计算设备之间以及不同边缘计算设备之间的这些性能差异可能归因于计算资源、数据质量和设备参数的差异。结果证明了该模型在资源受限环境下进行实时跌倒检测的潜力。
{"title":"A deep learning and edge computing integrated approach for fall behavior detection in buildings","authors":"Mingze Ma , Xiaofeng Hu","doi":"10.1016/j.jnlssr.2025.100218","DOIUrl":"10.1016/j.jnlssr.2025.100218","url":null,"abstract":"<div><div>This study proposes a model integrating YOLOv11 and STGCN for accurate and real-time detection of fall behaviors within buildings. A specialized video dataset comprising fall behaviors performed by six volunteers was developed and used to validate the model’s effectiveness in cloud computing and edge computing environments. The results obtained in the cloud computing environment were characterized by ample computational resources and the absence of real-time constraints. The model achieved precision, recall, and F1-score for fall behaviors exceeding 0.98. The model was integrated into edge computing devices in an actual test environment to directly process real-time video stream data. A missed detection rate of 18 % was observed on the Jetson ORIN NX 16GB device, while the Jetson AGX Orin 64GB recorded a lower missed detection rate of 15 %. Similarly, a false alarm rate of 16 % was observed on the Jetson ORIN NX 16GB device and 12 % on the Jetson AGX Orin 64GB device. These performance differences between the high-performance cloud computing cluster and edge computing devices, as well as among different edge computing devices, may be attributed to variations in computational resources, data quality, and device parameters. The results demonstrate the potential of the proposed model for real-time fall detection in resource-constrained environments.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 4","pages":"Article 100218"},"PeriodicalIF":3.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722051","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}
Pub Date : 2025-06-06DOI: 10.1016/j.jnlssr.2025.100215
Nelson Chambi Quiroz , David Mauricio , Jorge Inche Mitma , Celso Sanga
Musculoskeletal disorders (MSDs) in the mining sector are frequently associated with exposure to ergonomic risk factors, leading to decreased productivity and increased absenteeism. This study presents a systematic literature review of ergonomic evaluation methods, identified risk factors, and affected body regions, based on journal articles indexed in ScienceDirect, Emerald, Taylor & Francis, Wiley, Scopus, Sage, IEEE Xplore, and Web of Science. From an initial pool of 213 studies, 49 primary studies were selected, 82 % of which were published in Q1 or Q2 journals. The review identifies six individual ergonomic assessment methods (Nordic Questionnaire, ISO/IEC 2631–1, REBA, OWAS, NIOSH, and Risk Score) and two hybrid methods (Bayesian Network + REBA, and RULA + Nordic Questionnaire). Additionally, it categorizes 13 affected body parts (e.g., back, shoulders, neck, waist) and 11 ergonomic factors (e.g., heavy loads, repetitive tasks, vibration, static postures, and work schedules). The findings reveal that a majority of studies focus on underground mining operations, with the Nordic Questionnaire and ISO/IEC 2631–1 being the most frequently used methods. The back and shoulders are the most commonly studied body parts, while vibration and working hours emerge as the most prevalent risk factors. Finally, six challenges are proposed to address current research gaps, including the integration of deep learning techniques and the evaluation of less-studied joints such as the wrist and elbow. This review provides a valuable foundation for researchers and mine safety professionals seeking to advance ergonomic assessment in mining environments.
采矿部门的肌肉骨骼疾病（MSDs）往往与接触人体工程学风险因素有关，导致生产力下降和缺勤率增加。本研究对人体工程学评估方法、确定的危险因素和受影响的身体部位进行了系统的文献综述，基于ScienceDirect、Emerald、Taylor &；Francis, Wiley, Scopus, Sage, IEEE explore, and Web of Science。从最初的213项研究中，选择了49项主要研究，其中82%发表在Q1或Q2期刊上。该综述确定了六种单独的人体工程学评估方法（北欧问卷、ISO/IEC 2631-1、REBA、OWAS、NIOSH和风险评分）和两种混合方法（贝叶斯网络+ REBA和RULA +北欧问卷）。此外，它还对13个受影响的身体部位（如背部、肩部、颈部、腰部）和11个人体工程学因素（如重负荷、重复任务、振动、静态姿势和工作时间表）进行了分类。调查结果显示，大多数研究集中于地下采矿作业，北欧调查表和ISO/IEC 2631-1是最常用的方法。背部和肩部是最常被研究的身体部位，而振动和工作时间是最普遍的危险因素。最后，提出了解决当前研究空白的六个挑战，包括深度学习技术的集成和对腕部和肘部等较少研究的关节的评估。这一综述为研究人员和矿山安全专业人员寻求在矿山环境中推进人体工程学评估提供了有价值的基础。
{"title":"Systematic literature review of Ergonomic evaluation methods in the mining sector (2015-2024)","authors":"Nelson Chambi Quiroz , David Mauricio , Jorge Inche Mitma , Celso Sanga","doi":"10.1016/j.jnlssr.2025.100215","DOIUrl":"10.1016/j.jnlssr.2025.100215","url":null,"abstract":"<div><div>Musculoskeletal disorders (MSDs) in the mining sector are frequently associated with exposure to ergonomic risk factors, leading to decreased productivity and increased absenteeism. This study presents a systematic literature review of ergonomic evaluation methods, identified risk factors, and affected body regions, based on journal articles indexed in ScienceDirect, Emerald, Taylor & Francis, Wiley, Scopus, Sage, IEEE Xplore, and Web of Science. From an initial pool of 213 studies, 49 primary studies were selected, 82 % of which were published in Q1 or Q2 journals. The review identifies six individual ergonomic assessment methods (Nordic Questionnaire, ISO/IEC 2631–1, REBA, OWAS, NIOSH, and Risk Score) and two hybrid methods (Bayesian Network + REBA, and RULA + Nordic Questionnaire). Additionally, it categorizes 13 affected body parts (e.g., back, shoulders, neck, waist) and 11 ergonomic factors (e.g., heavy loads, repetitive tasks, vibration, static postures, and work schedules). The findings reveal that a majority of studies focus on underground mining operations, with the Nordic Questionnaire and ISO/IEC 2631–1 being the most frequently used methods. The back and shoulders are the most commonly studied body parts, while vibration and working hours emerge as the most prevalent risk factors. Finally, six challenges are proposed to address current research gaps, including the integration of deep learning techniques and the evaluation of less-studied joints such as the wrist and elbow. This review provides a valuable foundation for researchers and mine safety professionals seeking to advance ergonomic assessment in mining environments.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 4","pages":"Article 100215"},"PeriodicalIF":3.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771361","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}
Pub Date : 2025-06-06DOI: 10.1016/j.jnlssr.2025.100220
Qing Deng , Yanchao Ye , Wei Wang , Hui Zhang
Facing the global pandemic of coronavirus disease 2019 (COVID-19), countries and regions have implemented different policies and non-pharmacological interventions (NPIs) according to their circumstances. These policies and intervention measures provide new insights into assessing local resilience from the perspective of response capacities in the public health system. This study aims to establish a multi-dimensional and dynamic resilience assessment model based on the index system method. The complete assessment model includes building a comprehensive system, executing the system in specific scenarios, and measuring resilience. The comprehensive system, like a guideline, is constructed from six key categories. The system involves the entire society, encompassing various levels, including country, state, province, city, local community, and individual. It considers not only policy formulation but also the actual implementation of the policy. The comprehensive system does not necessarily apply to all scenarios during the system's implementation. The actual case, the prevention & control in England, is introduced to assess the local resilience and verify the proposed assessment model. The results prove that our model can be used to assess local resilience for the public health system and seek capacity improvement when responding to epidemic situations.
{"title":"Resilience assessment of public health system at multi-levels: An emergency management capacity quantification model for pandemic response","authors":"Qing Deng , Yanchao Ye , Wei Wang , Hui Zhang","doi":"10.1016/j.jnlssr.2025.100220","DOIUrl":"10.1016/j.jnlssr.2025.100220","url":null,"abstract":"<div><div>Facing the global pandemic of coronavirus disease 2019 (COVID-19), countries and regions have implemented different policies and non-pharmacological interventions (NPIs) according to their circumstances. These policies and intervention measures provide new insights into assessing local resilience from the perspective of response capacities in the public health system. This study aims to establish a multi-dimensional and dynamic resilience assessment model based on the index system method. The complete assessment model includes building a comprehensive system, executing the system in specific scenarios, and measuring resilience. The comprehensive system, like a guideline, is constructed from six key categories. The system involves the entire society, encompassing various levels, including country, state, province, city, local community, and individual. It considers not only policy formulation but also the actual implementation of the policy. The comprehensive system does not necessarily apply to all scenarios during the system's implementation. The actual case, the prevention & control in England, is introduced to assess the local resilience and verify the proposed assessment model. The results prove that our model can be used to assess local resilience for the public health system and seek capacity improvement when responding to epidemic situations.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 4","pages":"Article 100220"},"PeriodicalIF":3.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852589","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}
Pub Date : 2025-06-06DOI: 10.1016/j.jnlssr.2025.100216
Ping Zhang , Yujie Cui , Lizhong Yang , Kaixuan Wang , Wenjun Liu , Jinzhong Wu
In recent years, with continued urbanization, subway stations with complex structures have developed rapidly, posing serious challenges to daily crowd management and emergency evacuation safety. A social force-based evacuation guidance model was developed to simulate the process of pedestrian evacuation at the concourse level of a subway station in Shenyang by combining questionnaires and field surveys. The objective was to explore the impact of various factors—such as the viewing distance, guidance number, guidance location, and proportion of pedestrians accepting the guiding information—on evacuation efficiency in subway station halls with multiple exits. The results showed that static leaders reduced the evacuation time, especially in situations with a limited field of view. Compared with the “no guidance” case, the evacuation time was shortened by approximately 20.16 % and 9 % when the viewing distances were 2 and 6 m, respectively. Moreover, a small number of static leaders could effectively guide the crowd to evacuate the subway station hall, demonstrating the positive role of guidance in emergency evacuations. Considering the actual situation and human cost, it was reasonable to have eight static leaders in the subway station hall, which reduced the evacuation time by approximately 40.47 % compared to the situation without guidance. Influenced by the viewing distance, scene layout, and pedestrian density, when static leaders were distributed at the exits, evacuation improved. Moreover, the higher the percentage of pedestrians accepting the guidance information, the better the evacuation performance. This study provides scientific support for guidance arrangements in the daily management and emergency evacuation of subway stations.
{"title":"Study on emergency evacuation guidance in the subway station hall","authors":"Ping Zhang , Yujie Cui , Lizhong Yang , Kaixuan Wang , Wenjun Liu , Jinzhong Wu","doi":"10.1016/j.jnlssr.2025.100216","DOIUrl":"10.1016/j.jnlssr.2025.100216","url":null,"abstract":"<div><div>In recent years, with continued urbanization, subway stations with complex structures have developed rapidly, posing serious challenges to daily crowd management and emergency evacuation safety. A social force-based evacuation guidance model was developed to simulate the process of pedestrian evacuation at the concourse level of a subway station in Shenyang by combining questionnaires and field surveys. The objective was to explore the impact of various factors—such as the viewing distance, guidance number, guidance location, and proportion of pedestrians accepting the guiding information—on evacuation efficiency in subway station halls with multiple exits. The results showed that static leaders reduced the evacuation time, especially in situations with a limited field of view. Compared with the “no guidance” case, the evacuation time was shortened by approximately 20.16 % and 9 % when the viewing distances were 2 and 6 m, respectively. Moreover, a small number of static leaders could effectively guide the crowd to evacuate the subway station hall, demonstrating the positive role of guidance in emergency evacuations. Considering the actual situation and human cost, it was reasonable to have eight static leaders in the subway station hall, which reduced the evacuation time by approximately 40.47 % compared to the situation without guidance. Influenced by the viewing distance, scene layout, and pedestrian density, when static leaders were distributed at the exits, evacuation improved. Moreover, the higher the percentage of pedestrians accepting the guidance information, the better the evacuation performance. This study provides scientific support for guidance arrangements in the daily management and emergency evacuation of subway stations.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 4","pages":"Article 100216"},"PeriodicalIF":3.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829522","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}
Pub Date : 2025-06-06DOI: 10.1016/j.jnlssr.2025.100221
Hanjun Guo , Yuwei Mo , Zixi Wang , Rongxue Kang , Ke Tang , Qiuju Ma
Working at height is widespread across various industries, with frequent and hazardous falls occurring regularly. Such tasks are often linked to multifactorial issues, where the interplay of diverse factors leads to accidents that are challenging to control effectively. This study establishes an index system for the factors influencing falls from height by statistically analyzing 101 incidents, identifying 64 causative elements classified into four categories. These include 17 factors related to operator condition and behavior, 13 concerning equipment and facility conditions, 7 pertaining to site conditions, and 27 associated with production operations management. Utilizing the Apriori algorithm and Gephi software, the study mined the association rules of causal factors in falls from height and constructed their network diagram. By examining association rules with high support, confidence, and lift, the relationships between key causal factors leading to accidents are clarified, identifying critical operational control points and providing a scientific foundation for reducing the incidence of falls from height. Currently, China's standards related to working at height remain fragmented. This study lays the foundation for the development of comprehensive, systematic, generic safety management standards for working at height, satisfying the needs of the field.
{"title":"Association analysis of causative factors of fall from height accidents","authors":"Hanjun Guo , Yuwei Mo , Zixi Wang , Rongxue Kang , Ke Tang , Qiuju Ma","doi":"10.1016/j.jnlssr.2025.100221","DOIUrl":"10.1016/j.jnlssr.2025.100221","url":null,"abstract":"<div><div>Working at height is widespread across various industries, with frequent and hazardous falls occurring regularly. Such tasks are often linked to multifactorial issues, where the interplay of diverse factors leads to accidents that are challenging to control effectively. This study establishes an index system for the factors influencing falls from height by statistically analyzing 101 incidents, identifying 64 causative elements classified into four categories. These include 17 factors related to operator condition and behavior, 13 concerning equipment and facility conditions, 7 pertaining to site conditions, and 27 associated with production operations management. Utilizing the Apriori algorithm and Gephi software, the study mined the association rules of causal factors in falls from height and constructed their network diagram. By examining association rules with high support, confidence, and lift, the relationships between key causal factors leading to accidents are clarified, identifying critical operational control points and providing a scientific foundation for reducing the incidence of falls from height. Currently, China's standards related to working at height remain fragmented. This study lays the foundation for the development of comprehensive, systematic, generic safety management standards for working at height, satisfying the needs of the field.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 4","pages":"Article 100221"},"PeriodicalIF":3.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892937","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}
Pub Date : 2025-06-06DOI: 10.1016/j.jnlssr.2025.100219
Todd D. Smith, Kiran Mondal
Stone, sand, and gravel mining accounts for 80 % of all mining operations. Maintaining a skilled labor force is essential for this large and growing mining sector. Occupational injuries negatively impact worker health and well-being and are costly. Additionally, injuries may impact workers’ job satisfaction and their desire to seek other employment. The impact of work-related occupational injuries on job satisfaction and turnover intention has not been explored in stone, sand, and gravel mining operations. This study analyzed cross-sectional data from 459 workers employed in the stone, sand, and gravel mining industry in the midwestern United States. Mplus was used to complete path analysis to assess a hypothesized model and its relationships. Analyses determined the model was a good fit for the data, occupational injuries negatively impacted worker job satisfaction, job satisfaction negatively impacted turnover intention, and job satisfaction mediated the relationship between occupational injuries and turnover intention. These findings confirm posited hypotheses and provide evidence that occupational injuries not only harm workers but also result in diminished job satisfaction and ultimately turnover intention, important business outcomes for the stone, sand, and gravel mining industry.
{"title":"An analysis of relationships between occupational injury, job satisfaction and turnover intention among stone, sand, and gravel mine workers","authors":"Todd D. Smith, Kiran Mondal","doi":"10.1016/j.jnlssr.2025.100219","DOIUrl":"10.1016/j.jnlssr.2025.100219","url":null,"abstract":"<div><div>Stone, sand, and gravel mining accounts for 80 % of all mining operations. Maintaining a skilled labor force is essential for this large and growing mining sector. Occupational injuries negatively impact worker health and well-being and are costly. Additionally, injuries may impact workers’ job satisfaction and their desire to seek other employment. The impact of work-related occupational injuries on job satisfaction and turnover intention has not been explored in stone, sand, and gravel mining operations. This study analyzed cross-sectional data from 459 workers employed in the stone, sand, and gravel mining industry in the midwestern United States. Mplus was used to complete path analysis to assess a hypothesized model and its relationships. Analyses determined the model was a good fit for the data, occupational injuries negatively impacted worker job satisfaction, job satisfaction negatively impacted turnover intention, and job satisfaction mediated the relationship between occupational injuries and turnover intention. These findings confirm posited hypotheses and provide evidence that occupational injuries not only harm workers but also result in diminished job satisfaction and ultimately turnover intention, important business outcomes for the stone, sand, and gravel mining industry.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 4","pages":"Article 100219"},"PeriodicalIF":3.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863742","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}
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