安全科学与韧性(英文)最新文献

{"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":"DLW-CI: A Dynamic Likelihood-Weighted Cooperative Infotaxis approach for multi-drone cooperative multi-source search","authors":"Bin Chen ,&nbsp;Xiaoran Zhang ,&nbsp;Yatai Ji ,&nbsp;Yong Zhao ,&nbsp;Zhengqiu Zhu","doi":"10.1016/j.jnlssr.2025.04.001","DOIUrl":"10.1016/j.jnlssr.2025.04.001","url":null,"abstract":"<div><div>Drones have gradually been employed to search for unknown sources during leakage accidents. However, current studies have mainly focused on the single-source search problem, while in practical situations, the location and quantity of the sources are commonly unknown. Existing multi-source search methods fail to accurately estimate the source term, primarily due to the inefficient utilization of concentration information. This limitation results in sub-optimal drone movement strategies. To address these issues, we propose a Dynamic Likelihood-Weighted Cooperative Infotaxis (DLW-CI) approach. The approach integrates the Infotaxis cognitive search strategy with multi-drone cooperation by optimizing both source term estimation and the cooperative mechanism. Specifically, we devise a novel source term estimation method that leverages multiple parallel particle filters, with each filter estimating the parameters of a potentially unknown source in scenarios. Subsequently, we introduce a cooperative mechanism based on dynamic likelihood weight to prevent multiple drones from concurrently estimating and searching for the same source. The results show that the success rate for the localization of 2–4 diffusion sources reaches 90%, 78%, and 42% respectively when employing the DLW-CI approach, achieving a 37% average improvement over baseline methods. Our findings indicate that the proposed DLW-CI approach significantly improves estimation accuracy and search efficiency for multi-drone cooperative multi-source search, making a valuable contribution to environmental safety monitoring applications.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100206"},"PeriodicalIF":3.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313631","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":"Urban road collapse risk assessment based on the extended xLSTM Network","authors":"Jiahao Zhou ,&nbsp;Juncai Jiang ,&nbsp;Yizhao Wang ,&nbsp;Wenfeng Bai ,&nbsp;Fei Wang ,&nbsp;Long Chen ,&nbsp;Qinglun He","doi":"10.1016/j.jnlssr.2024.10.005","DOIUrl":"10.1016/j.jnlssr.2024.10.005","url":null,"abstract":"<div><div>Rapid urbanization has substantially increased the complexity of urban underground spaces. This complexity leads to frequent road collapse incidents that pose significant threats to the safety and property of urban residents. Therefore, accurate methods of performing early road collapse risk assessments are crucial for preventing these incidents and emergency preparedness. In this study, road collapse incident data for 2016–2021 were collected for Foshan, Guangdong Province, a city in southern China. Utilizing InSAR time-series data from Sentinel-1 satellites, ground subsidence maps were generated, and the publicly accessible Ground Subsidence Trend-Based Urban Road Collapse Risk Dataset (GSTURCRD) was constructed. A novel risk assessment method for urban road collapse based on an extended long short-term memory (xLSTM) network was proposed. This method introduces two new LSTM variants, the scalar LSTM (sLSTM) and the matrix LSTM (mLSTM), incorporating exponential gating and an innovative matrix memory structure. These variants are integrated using residual connections to form a comprehensive network architecture that enables effective learning and representation of the temporal features. The experimental results from the dataset demonstrate that the proposed method significantly outperforms the original LSTM network and traditional machine learning methods regarding assessment capability (its accuracy was 0.886, and its recall was 0.857). Furthermore, the method's effectiveness was validated by an analysis of actual incidents that occurred in Foshan; thus, its ability to generate accurate and timely detections and provide early warnings for high-risk road sections in urban areas was confirmed.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 4","pages":"Article 100205"},"PeriodicalIF":3.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665613","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":"Quantitative resilience assessment on critical infrastructures – A systematic literature review of the last decade (2014-2024)","authors":"Andrea De Marco ,&nbsp;Davide Berardi ,&nbsp;Marta Galuppi ,&nbsp;Mara Lombardi","doi":"10.1016/j.jnlssr.2025.02.002","DOIUrl":"10.1016/j.jnlssr.2025.02.002","url":null,"abstract":"<div><div>The everyday functioning of infrastructures and the capacity of their owners to provide high-quality services are critical to the well-being of communities and societies. When disruptions endanger society's well-being, security, and economy, some of these infrastructures are relevant and, as a result, are designated as critical for an entire nation. Therefore, research has focused on a peculiar infrastructure ability, or capacity, that summarizes these concepts in one word: resilience; its quantification, however, is not straightforward. Many authors have attempted to assess resilience using different subjects, infrastructures, resilience phases, and models. Therefore, through a systematic literature review based on the PRISMA protocol, this work presents an overview of quantitative methods and techniques developed over the past decade to numerically assess the resilience of critical infrastructures (CIs). A total of 111 papers were included in the study, which focused on studies published between April 2014 and April 2024. This article focuses on a model-based classification, highlighting strengths and weaknesses in the proposed methodologies, and performs a SWOT analysis on the modeling approaches identified within the retrieved papers. Future works could be useful to extend this research to those papers that describe and quantify single domains, stages, and features of CIs resilience, exploring the possibility of searching protocols to identify and correctly address them.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100201"},"PeriodicalIF":3.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633751","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":"Spatio-temporal prediction of terrorist attacks based on GCN-LSTM","authors":"Yingjie Du ,&nbsp;Ning Ding ,&nbsp;Hongyu Lv","doi":"10.1016/j.jnlssr.2025.02.005","DOIUrl":"10.1016/j.jnlssr.2025.02.005","url":null,"abstract":"<div><div>Terrorist attacks represent a significant threat to national order, social stability, and economic security. Accurate prediction of such attacks is a critical task for casualty reduction, enhanced decision-making, and optimal resource distribution in counter-terrorism efforts. This paper introduces an innovative spatio-temporal fusion framework that combines graph convolutional network (GCN) with long short-term memory (LSTM) models. By capturing and merging spatio-temporal features from relevant events, the proposed GCN-LSTM model achieves remarkable accuracy in predicting terrorist attacks. The experimental results demonstrate outstanding performance, with the model attaining minimal RMSE and MAE values of 0.037 and 0.031, respectively, surpassing all baseline models (LSTM, GCN, and CNN-LSTM-Transformer). Through its effective interpretation of complex spatio-temporal patterns underlying terrorist attacks, our model substantially enhances the predictive accuracy across diverse time horizons. These findings carry crucial implications for enhancing counter-terrorism strategies.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 2","pages":"Pages 186-195"},"PeriodicalIF":3.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821369","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":"Evaluation of the underground space safety resilience of Chinese urban agglomerations based on the “Pressure-State-Response”: A case study of underground rail transit in 26 cities","authors":"Chong Li,&nbsp;Yibao Wang","doi":"10.1016/j.jnlssr.2025.02.001","DOIUrl":"10.1016/j.jnlssr.2025.02.001","url":null,"abstract":"<div><div>Urban underground space disasters exhibit characteristics including complex causal mechanisms and high risk. They also present unpredictability and risk coupling amplification. Differences in underground space safety resilience (USSR) and key pathways of urban agglomerations are issues that remain under-discussed. This study is based on the perspective of resilience genesis, and it constructs a Pressure-State-Response (PSR) analysis framework. This study employs methods such as the composite index method, Dagum Gini coefficient, and fuzzy set Qualitative Comparative Analysis (fsQCA) to explore the differences in safety resilience levels and key pathways of underground spaces of urban agglomerations. The study offered several findings: (1) The safety resilience index rankings of the five major urban agglomerations, from highest to lowest, are as follows: Chengdu-Chongqing City Group, Pearl River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, Triangle of Central China and Yangtze River Delta urban agglomeration. In addition, the underground space safety resilience systems of cities were clustered and categorized into demonstration and leadership type, striving catch-up type, and stable development type. (2) Overall differences between urban agglomerations are reflected in the differences between individual urban agglomerations. The internal development imbalance is the primary reason for the more significant differences in and between the Pearl River Delta and Yangtze River Delta urban agglomerations. (3) High-level safety resilience of urban underground space is the result of a multi-factor combination in the PSR framework. In addition, there are four high-level safety resilience configuration paths, and these paths are categorized into three modes: \"state-driven response\", \"pressure-triggered response\", and \"autonomous response\". Policy implications and countermeasures for urban underground space development are proposed for each mode of urban underground space safety resilience. These proposals offer theoretical references for optimizing the safety resilience of underground spaces of urban agglomerations.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100200"},"PeriodicalIF":3.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570848","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":"JMoE-FAP: A novel model for telecom network fraud victimization pattern analysis","authors":"Tuo Shi ,&nbsp;Jing Hu ,&nbsp;Danyang Li ,&nbsp;Min Chen","doi":"10.1016/j.jnlssr.2025.01.006","DOIUrl":"10.1016/j.jnlssr.2025.01.006","url":null,"abstract":"<div><div>A comprehensive examination of the victimization process, coupled with the development of effective preventive strategies, represents the most promising approach for mitigating telecom network fraud. However, the limited availability of telecom fraud case text data hinders the advancement of robust data extraction algorithms, thereby complicating the identification of victimization patterns. To address this gap, this study proposes a victimization process analysis model that leverages mixed expert event joint extraction, utilizing real telecom fraud case data. The model integrates LERT-MoE to extract trigger words and arguments related to the victimization process from law enforcement reports, followed by the application of a dot-product attention mechanism for argument role classification. To the best of our knowledge, this represents the first attempt to apply a mixture-of-experts model with a purpose-built dot-product attention mechanism for the in-depth analysis of telecom network fraud victimization patterns, overcoming the limitations of previous methods in managing the complexity and diversity of fraudulent behaviors. Additionally, the Apriori method is employed to uncover prevalent behavioral patterns in the victimization process. Experimental results demonstrate that the proposed model outperforms baseline models in precision, accuracy, and F1-score for event extraction tasks in telecom fraud instances. Furthermore, the model identifies more granular fraud patterns within the victimization process, offering a valuable knowledge base for the development of targeted preventive strategies. The identified patterns can be used to design focused awareness campaigns, enhance fraud detection algorithms, and improve law enforcement training, thereby significantly increasing the effectiveness of anti-fraud initiatives.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100199"},"PeriodicalIF":3.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597571","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":"Evaluation of the cognitive performance of nuclear power plant operators using heart rate variability under hot-humid exposure","authors":"Lingzhong Du ,&nbsp;Ming Jia ,&nbsp;Tao Chen ,&nbsp;Yan Zhang ,&nbsp;Yicheng Zhang ,&nbsp;Zhihui Xu ,&nbsp;Yanchao Su ,&nbsp;Xiangbiao Xing ,&nbsp;Tingdong Chen ,&nbsp;Cheng Yang","doi":"10.1016/j.jnlssr.2025.01.004","DOIUrl":"10.1016/j.jnlssr.2025.01.004","url":null,"abstract":"<div><div>When sudden disasters such as earthquakes and tsunamis culminate in the outage of control systems in nuclear power plants, the operators may be exposed to extremely hot-humid environmental conditions while still facing demanding cognitive tasks. To investigate how their cognitive performance is related to their heart rate variability (HRV), we exposed nuclear power plant operators to four high temperature and humidity environments and measured their HRV during seven different cognitive tasks for 30 min. Generalized linear mixed-effects models (GLMMs) are used to analyze the relationship between HRV and operators' cognitive performance. Studies have shown that all indicators of HRV have a significant impact on the cognitive ability of operators. In both the low frequency (LF) and high frequency (HF), especially in the very low frequency (VLF) domain, HRV significantly correlates with the operator's cognition. Although the cognitive performance aggravates slightly within 30 min, increasing the humidity under fixed high temperatures does not exhibit an overall effect on cognitive performance. Henceforth, this study provides a guidance for safety limits of the operator's working duration. Moreover, VLF, LF, and HF domains might be potential physiological indicators to monitor the cognitive ability of operators.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100197"},"PeriodicalIF":3.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570846","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":"An improved multi-velocity cellular automaton model that considers psychological impatience","authors":"Hongpeng Qiu ,&nbsp;Wenke Zhang ,&nbsp;Meng Shi ,&nbsp;Eric Wai Ming Lee","doi":"10.1016/j.jnlssr.2024.11.006","DOIUrl":"10.1016/j.jnlssr.2024.11.006","url":null,"abstract":"<div><div>The cellular automaton (CA) model is an essential tool for studying pedestrian evacuation dynamics; moreover, improving simulations of actual evacuation environments and increasing the reliability of evacuation data are important problems for researchers. The complex psychological dynamics of pedestrians during evacuation and the evacuation velocities are essential components of such models. This study proposes and verifies a synchronously updated multi-velocity evacuation impatient CA model with a corresponding time correction formula. Our model that considers pedestrians’ psychological impatience can simulate complex scenarios in which different pedestrians simultaneously evacuate at different speeds. In addition, our model accurately simulates and reproduces the phenomena found in actual experiments: as the self-growth parameter of impatience increases, the evacuation efficiency first increases and then decreases, and as the contagion parameter of impatience increases, the evacuation efficiency decreases. The time correction method and formula are critical for obtaining reliable results from simulations in which pedestrians evacuate at various speeds, and they are expected to be indispensable parts of multi-velocity CA models for predicting complex evacuation scenes.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100196"},"PeriodicalIF":3.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563950","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":"Resilience in Cyber-Physical Infrastructures: R-KPI prioritization, framework development, and case study insights","authors":"Ali Aghazadeh Ardebili ,&nbsp;Marco Boscolo ,&nbsp;Antonella Longo ,&nbsp;Mahdad Pourmadadkar ,&nbsp;Antonio Ficarella ,&nbsp;Elio Padoano","doi":"10.1016/j.jnlssr.2024.12.005","DOIUrl":"10.1016/j.jnlssr.2024.12.005","url":null,"abstract":"<div><div>Critical infrastructures (CIs) embody cyber-physical-social systems (CPSSs) where physical entities are integrated with cyber components, shaping service delivery through end-user behavior. The seamless operation of CIs is vital for society, and the CPSS resilience relies on interdependencies with AI-integrated technologies. The complexity of the system, and the interconnections with other infrastructures, along with the socio-technical transition towards digitization raised the necessity of implementing Resilience Engineering. This motivates exploration of the scientific literature on resilience key performance indicators (R-KPIs) which support strategies for ensuring service continuity. Therefore, this article aims to identify R-KPIs for AI-integrated CIs and prioritize the extracted R-KPIs using a hybrid Multi-Criteria Decision-Making (MCDM) approach. The results show the importance of employing R-KPIs that measure risk probability, energy self-sufficiency level of the system under study, and performance indicators including functionality loss, recovery time, and minimum performance level after disturbance as the most effective R-KPIs in the domain of this study. After identifying and prioritizing the R-KPIs, a general framework is proposed to employ these R-KPIs in modeling the resilience of a CPS. Finally, a case study demonstrates the implementation of the framework and KPIs in a real-life scenario.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100194"},"PeriodicalIF":3.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491290","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}