安全科学与韧性(英文)最新文献

An emergency platform is an informatization support platform for disaster information perception, disaster situational awareness, and emergency decision command. This is a key tool for ensuring the efficiency and effectiveness of emergency responses. Numerous large countries have conducted in-depth research on the key technologies of emergency platforms. Over the past 20 years, with the reform of emergency management mechanisms, China has developed two generations of emergency platform systems. This paper reviews the two generations of emergency platforms and summarizes their key technologies including multi-source-based monitoring and early warning, multi-hazard risk assessment, “scenario-response”-based decision support, synthetical forecasting based on incident chain, and emergency common operational picture (COP) for command and dispatch. Future research directions for the next generation emergency platforms are also proposed.

Guidance signs are generally considered to be key in improving the efficiency of emergency building evacuations. During a group evacuation, how obedient is the group to the evacuation signs? Would different evacuation signs affect the judgment of evacuated groups? Evacuation experiments were conducted to explore the influence of evacuation signs on the path selection of evacuation groups. Fourteen classes with 514 participants participated in these experiments, and each class participated in one experiment. Six classes conducted the experiments without guidance signs, and the rest conducted the experiments with guidance signs. The influences of the sign's color (red and green), pattern (arrow and running man), and status (whether it flashes) on the route selection were studied. The results show that (1) the evacuation signs significantly affect the route choices of the evacuated groups. (2) The herding effect during the evacuation process does not influence the guiding effect of evacuation signs. (3) Evacuation signs of different colors and patterns have different guiding effects, and the Green running man sign has the best effect. (4) The constant bright, or flashing evacuation sign corresponding to the flashing Red running man has a significant impact on the route selection of the evacuated group. The results may provide a reference for improving the emergency evacuation guidance model of office buildings or other buildings with a large flow of people and optimize ideas for the design and layout of emergency evacuation signs.

This paper focuses on the bus evacuation problem with pedestrians’ short-distance walking ability between bus stations during no-notice disasters in downtown areas. A mixed-integer linear programming model is proposed to solve this problem. The objective function is to minimize the evacuation time and number of casualties. The model obtains the flow of evacuees and buses on each arc to the route of buses in the process of evacuation. Furthermore, a real-time bus evacuation demand estimation method is proposed based on smart card data. Finally, the example of Zhongguancun area in Beijing is used to verify the practicality and validity of the model. The results show that pedestrian short-distance walking can effectively reduce casualties and improve the utilization rate of buses.

Accidents induced by natural disasters at sports sites may cause catastrophic loss of great concern. However, previous studies on risk assessments of sports sites have only focused on operational risk and equipment failure. With the frequent occurrence of extreme disasters, the risk of domino chains caused by natural disasters at large-scale events, such as large-scale winter sports sites, cannot be ignored. In this study, a natural disaster-induced accident-chain evolution analysis model (NAEA model) is proposed. Based on the results of the NAEA model, a fuzzy Bayesian network for domino accidents triggered by an earthquake at large-scale winter sports sites was established. Through sensitivity analysis and scenario analysis, it was found that fire and explosion accidents and crowded stampede accidents are the main causes of serious loss in domino disaster chains in large-scale sports sites. Simultaneously, improving the early warning capability, reliability of electrical equipment, and automatic sprinkler systems are the most effective ways to prevent and control major accidents. In addition, an optimal safety strategy improvement analysis was performed to facilitate the decision-making of safety managers to prevent serious accidents and reduce accident loss.

Safety management systems (SMSs) are widely applied across many industrial sectors, and a large body of literature has been published addressing their design, implementation, effectiveness, and associated challenges. This article presents a high-level analysis of the SMS research domain, guided by a set of questions addressing the contents, structure, and evolution the research domain, its dominant themes and focus topics, the key scientific domains and journals contributing to its development, and the key publications serving as an intellectual basis for SMS related research. The results show a rapidly increasing volume of research outputs and a shift from research based in North America and Europe to Asia and Australia. There is only a limited number of institutions enduringly contributing to the field, and there are relatively few stable research collaborations, with the number of Chinese institutions publishing SMS related research fast expanding in recent years. The domain is strongly interdisciplinary and embedded in applied domains of science, with industrial engineering the most contributing category, as well as categories focusing on the industrial application domains. A temporal evolution of the research activity in different application domains is apparent, with an initial focus on occupational health and safety, followed by process safety, patient safety, food safety, and construction safety. SMS research has a strong relation to safety culture and safety climate research, and while safety and risk management concepts and theories form an important knowledge base for most application domains, the dominant views on accident causation differ between these. Research on SMS in the food industry is relatively separated from the other application domains. Based on the findings, various future research directions are discussed.

COVID-19 is a constantly challenging global health issue due to its strong intensity, rapid mutation and high infectiousness. The new Delta and Omicron variants have triggered massive outbreaks worldwide. Even China, which has done a good job in outbreak prevention, is still heavily affected by the virus. The long-term fight against multiple COVID-19 outbreaks is ongoing. In this study, we propose an SEIQR model that considers the incubation period and quarantine measurement. We verified our model using actual outbreak data from four Chinese cities. Numerical simulations show that a five-day delay results in a double resurgence scale. Our model can be used as a tool to understand the spread of the virus quantitatively and provide a reference for policymaking accordingly.

Since the rapid spread of the COVID-19 worldwide, the pandemic has led to a huge impact on global sporting events. As a major international event, the 2022 Beijing Winter Olympics has commonalities with the 2008 Beijing Olympics, the 2014 Sochi Winter Olympics, and the 2020 Tokyo Olympics in terms of international public opinion context and epidemiological background. In this study, over 1 million pieces of UGC(User Generated Contents) in Chinese and English languages were obtained from social media platforms such as Twitter, YouTube, as well as traditional mass media in various countries to compare the differences between the two languages in international public opinion. Using sentiment analysis, this study explores the evolution of international public opinion topics and sentiment differences among the above four Olympic Games. The analysis results show that:1) regardless of traditional mass media or online social media, there is a more obvious tendency of general politicization in the topics of the 2008 Beijing Olympics and 2022 Beijing Winter Olympics, and extreme emotional remarks of the 2022 Beijing Winter Olympics are more frequent; 2) in the topic of political opinion involving China, international Chinese public opinion presents more negative sentiment than those in English; 3) Among the topics involving COVID-19, the negative level of public opinion in Chinese and English is opposite for the 2020 Tokyo Olympics and the 2022 Beijing Winter Olympics;4) International public opinion on the topic of sports events is significantly more positive in Chinese than in English;5) YouTube’s Chinese opinion environment is better than English.

This paper explores and extends understanding of the role and significance of whole-of-society resilience programmes that support cities when dealing with complex crises, like the COVID-19 pandemic. Highlighting the complexity of whole-of-society resilience as different actors locally shape it, we ask the question: How can collaboration between formal and informal resilience practices help to enhance resilience across the ‘whole-of-society’? We answer this question by reviewing the importance of whole-of-society resilience and its complexity in a city’s governance of the COVID-19 crisis. We argue that the necessity of renewing approaches to building local resilience capabilities across the whole-of-society requires synchronisation across and between formal and informal approaches – that is, “bottom-up” and governmental initiatives – to meet the diverse needs of communities. Secondly, we detail two recent practice-orientated initiatives that have taken a renewal approach to building resilience through the involvement of whole-of-society in planning recovery from COVID-19 using international standard ISO/TS 22393; and a new initiative called the National Consortium for Societal Resilience [UK+]. Finally, we signpost a set of critical questions for whole-of-society resilience practice.

Natural gas is extensively used as a clean energy source in cities and industries; consequently, there are associated risks of accidental explosions. To reduce the hazards associated with natural gas explosions, it is important to study the inherent laws of natural gas blast loads in unconfined spaces and establish load models. Using experiments on natural gas explosions in unconfined spaces, this study demonstrates the influence of natural gas concentrations, propagation distances, and gas volumes upon explosion loads. A new load model was proposed for the overpressure–time history curves of natural-gas explosions in an unconfined space. A comparison with the empirical model indicated that the predictive effect was superior to that of previous models, such as the TNT equivalent model and the TNO multi-energy model.

Casualty prediction is meaningful to the emergency management of natural hazards and human-induced disasters. In this study, a two-step machine learning method, including classification step and regression step, is proposed to predict the number of casualties under emergencies. In the classification step, whether there are casualties under an incident is firstly predicted, then in the regression step, samples predicted to have casualties are used to further predict the exact number of the casualties. Using an open-source dataset, this two-step method is validated. The results show that the two-step model performs better than the original regression models. Back propagation(BP) neural network combined with Random Forest performs the best in terms of the death toll and the number of injuries. Among all the two-step models, the lowest mean absolute error (MAE) for the death toll is 1.67 while that for the number of injuries is 4.13, which indicates that this method can accurately predict the number of casualties under emergencies. This study's results are expected to provide support for decision-making on rapid resource allocation and other emergency responses.