安全科学与韧性(英文)最新文献

China's natural disaster situation presents a complex and severe scenario, resulting in substantial human and material losses as a result of large-scale emergencies. Recognizing the significance of aviation emergency rescue, the state provides strong support for its development. However, China's current aviation emergency rescue system is still under construction and encounters various challenges; one such challenge is to match the dynamically changing multi-point rescue demands with the limited availability of aircraft dispatch. We propose a dynamic task assignment model and a trainable model framework for aviation emergency rescue based on multi-agent reinforcement learning. Combined with a targeted design, the scheduling matching problem is transformed into a stochastic game process from the rescue location perspective. Subsequently, an optimized strategy model with high robustness can be obtained by solving the training framework. Comparative experiments demonstrate that the proposed model is able to achieve higher assignment benefits by considering the dynamic nature of rescue demands and the limited availability of rescue helicopter crews. Additionally, the model is able to achieve higher task assignment rates and average time satisfaction by assigning tasks in a more efficient and timely manner. The results suggest that the proposed dynamic task assignment model is a promising approach for improving the efficiency of aviation emergency rescue.

Fire-detection technology plays a critical role in ensuring public safety and facilitating the development of smart cities. Early fire detection is imperative to mitigate potential hazards and minimize associated losses. However, existing vision-based fire-detection methods exhibit limited generalizability and fail to adequately consider the effect of fire object size on detection accuracy. To address this issue, in this study a decoder-free fully transformer-based (DFFT) detector is used to achieve early smoke and flame detection, improving the detection performance for fires of different sizes. This method effectively captures multi-level and multi-scale fire features with rich semantic information while using two powerful encoders to maintain the accuracy of the single-feature map prediction. First, data augmentation is performed to enhance the generalizability of the model. Second, the detection-oriented transformer (DOT) backbone network is treated as a single-layer fire-feature extractor to obtain fire-related features on four scales, which are then fed into an encoder-only single-layer dense prediction module. Finally, the prediction module aggregates the multi-scale fire features into a single feature map using a scale-aggregated encoder (SAE). The prediction module then aligns the classification and regression features using a task-aligned encoder (TAE) to ensure the semantic interaction of the classification and regression predictions. Experimental results on one private dataset and one public dataset demonstrate that the adopted DFFT possesses high detection accuracy and a strong generalizability for fires of different sizes, particularly early small fires. The DFFT achieved mean average precision (mAP) values of 87.40% and 81.12% for the two datasets, outperforming other baseline models. It exhibits a better detection performance on flame objects than on smoke objects because of the prominence of flame features.

This study assessed the influence of occupational stress, individual resilience, and organizational resilience on the safety performance of healthcare providers during the COVID-19 pandemic. Demographic variables including age, work experience, and gender were explored. Data were collected from 344 healthcare providers employed at a teaching hospital. The entropy method and the multi-criteria decision-making (MCDM) method were used to examine the influence of occupational stress, individual resilience, and organizational resilience on the safe performance of healthcare providers. The results of the entropy method showed that organizational resilience was the most influential factor in the safe performance of older healthcare providers. In contrast, individual resilience was the most significant factor in enhancing the safety performance of younger healthcare providers. Analyses of work experience indicated that individual resilience was the most influential factor in the safe performance of less experienced healthcare providers. Gender-based analysis revealed that individual resilience had a major effect on the safety performance of both women and men. The findings of this study could assist managers in improving the performance of the healthcare sector during pandemics by using and implementing resilience concepts at both the individual and organizational levels.

Crisis information dissemination plays a key role in the development of emergency responses to epidemic-level public health events. Therefore, clarifying the causes of crisis information dissemination and making accurate predictions to effectively control such situations have attracted extensive attention. Based on media richness theory and persuasion theory, this study constructs an index system of crisis information dissemination impact factors from two aspects: the crisis information publisher and the published crisis information content. A multi-layer perceptron is used to analyze the weight of the index system, and the prediction is transformed into a pattern classification problem to test crisis information dissemination. In this study, COVID-19 is considered a representative event. An experiment is conducted to predict the crisis information dissemination of COVID-19 in two megacities. Data related to COVID-19 from these two megacities are acquired from the well-known Chinese social media platform Weibo. The experimental results show that not only the identity but also the social influence of the information publisher has a significant impact on crisis information dissemination in epidemic-level public health events. Furthermore, the proposed model achieves more than 95% test accuracy, precision rate, recall value and f1-score in the prediction task. The study provides decision-making support for government departments and a guide for correctly disseminating crisis information and public opinion during future epidemic-level public health events.

The ongoing impact of the novel coronavirus disease 2019 (COVID-19) on work and daily life persists as we transition from emergency to normal circumstances. The continuous mutation of viral strains has resulted in a shift from a single strain to multiple cross-strains, contributing to the spread of the epidemic. Variations in infection rates of the same strain occur because of the implementation of diverse preventive measures at different times. This study investigated the dynamics of the pandemic in the presence of concurrent strains. Building on the classical Susceptible, Exposed, Infected, and Recovered (SEIR) model, a robust piecewise multi-strain cross-epidemic trend prediction model was proposed that employs the Hodges–Lehmann estimator to handle uncertain and contamination-prone epidemic information. A comparative analysis of epidemic spread trend curves across diverse populations using different robust methods revealed the superiority of the Hodges–Lehmann estimator-based model over the traditional method. The accurate prediction results of the model demonstrate its high reliability in tracking the changing trend of the COVID-19 outbreak, thereby supporting its implementation in subsequent epidemic prevention and control measures.

An outbreak of Ebola virus disease (EVD) (Sudan virus) was first reported in the Republic of Uganda on September 20, 2022. As of November 17, 2022, 151 confirmed cases have been reported, including 55 deaths (with a case fatality rate among confirmed cases of 39%).

During the EVD outbreak in 2013–2016, international travel played a significant role in the spread of the disease across national borders. During that time, several tasks requiring improvement were identified in the World Health Organization (WHO) Southeast Asia Region (SEAR), including inadequate risk communication and risk assessment, data management gaps for surveillance purposes, inadequate capacity in molecular diagnostic techniques, lack of adequate planning for a surge of cases, and inadequate isolation rooms.

It is therefore recommended that all countries of the WHO SEAR revisit their level of Ebola preparedness and address existing gaps. The emergence and rapid global spread of coronavirus disease 2019 (COVID-19) have reiterated that the world has become a global village. This was confirmed by the spread of the monkeypox virus with cases reported in the WHO SEAR. Therefore, given the weak health infrastructure in the region, complacency could wreak havoc on the healthcare system if another epidemic emerges without an adequate level of preparedness.

Artificial intelligence generated content (AIGC) is a production method based on artificial intelligence (AI) technology that finds rules through data and automatically generates content. In contrast to computational intelligence, generative AI, as exemplified by ChatGPT, exhibits characteristics that increasingly resemble human-level comprehension and creation processes. This paper provides a detailed technical framework and history of ChatGPT, followed by an examination of the challenges posed to political security, military security, economic security, cultural security, social security, ethical security, legal security, machine escape problems, and information leakage. Finally, this paper discusses the potential opportunities that AIGC presents in the realms of politics, military, cybersecurity, society, and public safety education.

Telecom network fraud has become the most common and concerning type of crime and is an important public security incident that threatens urban resilience. Therefore, preventing a continuous rise in telecommunications and network fraud will help establish a resilient urban governance system. Undertaking the spatiotemporal forecasting of telecommunications-network fraud trends is of significant importance for aiding public security agencies in proactive crime prevention and implementing targeted anti-fraud campaigns. This study presents a telecommunication network fraudulent crime prediction method called TSE-Tran, which integrates temporal representation and transformer architecture. The time-series data of telecommunication-network fraud occurrences were input into the TimesNet module, which maps the sequence data to a more precise feature representation tensor that accounts for both intra- and inter-cycle features. Subsequently, the data are fed into the transformer-encoder module for further encoding, capturing long-range dependencies in the time-series data. Finally, occurrences of future telecommunication network frauds are predicted by a fully connected layer. The results of the study demonstrate that the proposed TSE-Tran method outperforms benchmark methods in terms of prediction accuracy. The results of this study are expected to aid in the prevention and control of telecommunications and network frauds effectively strengthen the resilience of urban development and the ability to respond to public security incidents.