安全科学与韧性(英文)最新文献

Recent years have seen increasing academic interest in exploring the correlation between temperature and crime. However, it is uncertain whether similar long-term trends or seasonality (rather than causal effect) of temperature and crime is the major reason for the observed correlation between them. To explore whether there is still a correlation between temperature and crime when long-term trends and seasonal cycles are filtered out, we use the Kalman filter to decompose the time series of temperature and crimes, and then the fast Fourier transform is used to calculate the exact circle of their seasonality separately. Based on that, the box-plot method and linear regression are used to explore the correlation between temperature residuals and crime residuals. The results show that more than half of the crime types have similar seasonal cycles (approximately 1 year) to that of temperature. Moreover, the daily residual analyses show that temperature residuals have a positive correlation with assault and robbery residuals in all cities, whose average slopes are more than 0.1. The other four types of crimes vary greatly from case to case. The temperature residuals show a weak correlation with the residuals of some crime types.

In response to local sudden disasters, e.g., high-rise office or residential building fire disasters, road occupation can cause conflicts, and traffic directions may be opposite between evacuation vehicles and rescue vehicles; moreover, lane contraflow can be adopted to meet these surge traffic demands. However, lane contraflow that provides more roads for rescue vehicles reduces the traffic supply in the evacuation direction. It is unclear how to control the number of contraflow roads used by rescue vehicles to coordinate evacuation and rescue traffic operations. Here, we adjust the critical rescue traffic volume of reversing the normal road traffic direction to control rescue contraflow. Additionally, we propose a multiobjective mixed integer linear programming formulation for evacuation and rescue traffic optimization. Additionally, considering that the upper limit of the critical rescue traffic volume is unknown and that the proposed formulation includes multiple objectives and multi-priority vehicle classes, a three-stage solving algorithm is developed. Next, a large-scale evacuation and rescue traffic optimization result dataset is obtained for the Nguyen–Dupuis road network, and the impact of different rescue contraflow control plans on evacuation and rescue traffic is studied based on data-driven statistical analysis. The results show that by adjusting the optimal rescue traffic route, the critical rescue traffic volume for reversing the normal road traffic direction can reduce the interference of rescue traffic to evacuation traffic operation performance without reducing rescue traffic operation performance, and can be used to coordinate evacuation and rescue traffic operation under rescue contraflow.

The COVID-19 pandemic has had a profound impact on public mental health, leading to a surge in loneliness, depression, and anxiety. And these public psychological issues increasingly become a factor affecting social order. As researchers explore ways to address these issues, artificial intelligence (AI) has emerged as a powerful tool for understanding and supporting mental health. In this paper, we provide a thorough literature review on the emotions(EMO) of loneliness, depression, and anxiety (EMO-LDA) before and during the COVID-19 pandemic. Additionally, we evaluate the application of AI in EMO-LDA research from 2018 to 2023(AI-LDA) using Latent Dirichlet Allocation (LDA) topic modeling. Our analysis reveals a significant increase in the proportion of literature on EMO-LDA and AI-LDA before and during the COVID-19 pandemic. We also observe changes in research hotspots and trends in both field. Moreover, our results suggest that the collaborative research of EMO-LDA and AI-LDA is a promising direction for future research. In conclusion, our review highlights the urgent need for effective interventions to address the mental health challenges posed by the COVID-19 pandemic. Our findings suggest that the integration of AI in EMO-LDA research has the potential to provide new insights and solutions to support individuals facing loneliness, depression, and anxiety. And we hope that our study will inspire further research in this vital and revelant domin.

As an emerging technology, blockchain provides a range of advantages, such as decentralized and transparent data storage, secure access control, and enhanced data traceability. However, it is rarely applied in the field of public safety. This paper presents an in-depth survey of blockchain technology, focusing on its potential applications and implications within the field of public safety research. We explore the practical needs of multi-party data collaboration in emergency management and discusses the applicability and value of blockchain technology in this context. Additionally, this paper introduces and compares several popular blockchain platforms. By providing a comprehensive examination of blockchain technology and its potential benefits for public safety, this paper seeks to enhance understanding of the technology's capabilities, encourage further research, and inspire innovation in this domain.

Urbanization and the increasing frequency of extreme climates affect the sustainability of urban public transportation systems, and improving resilience is one of the primary directions for sustainable development. To scientifically assess the resilience of urban public transportation systems, a resilience assessment model based on structure and function is established in this study. This model mathematically quantifies and simulates the structural and functional changes in public transportation systems under disruption scenarios and provides a comprehensive assessment of six abilities: 1) structural resistance, 2) structural recoverability, 3) functional resistance, 4) functional recoverability, 5) passenger adaptability, and 6) management adaptability. Depending on the initial failure stations, this model can simulate the resilience of a public transportation system under various scenarios. This model is applied to assess the resilience of public transportation systems in a provincial capital city under an equipment failure scenario. The results show that the impact of equipment failure on resilience varies according to the metro lines, and improvement strategies for functional recoverability and management adaptability are proposed. The weaknesses in the resilience of urban public transportation systems can be identified using the proposed model, which helps provide strategies for improving the capacity to face perturbations.

Most geotechnical stability research is linked to “active” failures, in which soil instability occurs due to soil self-weight and external surcharge applications. In contrast, research on passive failure is not common, as it is predominately caused by external loads that act against the soil self-weight. An earlier active trapdoor stability investigation using the Terzaghi's three stability factor approach was shown to be a feasible method for evaluating cohesive-frictional soil stability. Therefore, this technical note aims to expand “active” trapdoor research to assess drained circular trapdoor passive stability (blowout condition) in cohesive-frictional soil under axisymmetric conditions. Using numerical finite element limit analysis (FELA) simulations, soil cohesion, surcharge, and soil unit weight effects are considered using three stability factors (F_c, F_s, and F_γ), which are all associated with the cover-depth ratio and soil internal friction angle. Both upper-bound (UB) and lower-bound (LB) results are presented in design charts and tables, and the large dataset is further studied using an artificial neural network (ANN) as a predictive model to produce accurate design equations. The proposed passive trapdoor problem under axisymmetric conditions is significant when considering soil blowout stability owing to faulty underground storage tanks or pipelines with high internal pressures.

Polynomial chaos expansions (PCEs) have been used in many real-world engineering applications to quantify how the uncertainty of an output is propagated from inputs by decomposing the output in terms of polynomials of the inputs. PCEs for models with independent inputs have been extensively explored in the literature. Recently, different approaches have been proposed for models with dependent inputs to expand the use of PCEs to more real-world applications. Typical approaches include building PCEs based on the Gram–Schmidt algorithm or transforming the dependent inputs into independent inputs. However, the two approaches have their limitations regarding computational efficiency and additional assumptions about the input distributions, respectively. In this paper, we propose a data-driven approach to build sparse PCEs for models with dependent inputs without any distributional assumptions. The proposed algorithm recursively constructs orthonormal polynomials using a set of monomials based on their correlations with the output. The proposed algorithm on building sparse PCEs not only reduces the number of minimally required observations but also improves the numerical stability and computational efficiency. Four numerical examples are implemented to validate the proposed algorithm. The source code is made publicly available for reproducibility.

In this study, civil gas energy accidents reported by the China Gas Network and related organizations from 2012 to 2021 were collected, and a comprehensive multidimensional correlation analysis was conducted considering factors such as accident timing, geography, causes, and casualties. The results identified July and August, Mondays and Sundays, and the morning, mid-day, and evening cooking times as the high-incidence months, days, and times for gas accidents, respectively. Gas accidents were found to occur more frequently in eastern coastal areas, provincial capitals, and larger cities, while residential and construction sites were identified as high-risk areas for gas accidents. Explosions were the most prevalent type of gas accident, followed by leaks, fires, and poisoning. Third-party construction and valve issues were identified as the primary factors contributing to gas leakage, whereas cooking was identified as the most common ignition source. An analysis of the Pearson correlation coefficient indicated a significant correlation among the gas accident factors. Moreover, a time-series prediction model was developed to forecast gas accidents in China, with the results demonstrating fluctuating gas accidents. This study proposes targeted preventive measures in terms of publicity, education, equipment, and facilities to provide scientific support to government units to improve civil gas energy security measures.

With the rapid development of China's economy, external dependence on petroleum resources continues to increase, and their security has become an important part of national security. To evaluate the security of China's petroleum resource supply in a scientific and objective manner, this study establishes a corresponding petroleum life-cycle evaluation index system, based on the theory and method of the whole life-cycle security evaluation of mineral resources, and conducts further independence and grey correlation analysis on the indexes for the purpose of evaluating the petroleum risk situation in China, based on relevant public data from the past 10 years. The results show that the overall trend of China's oil risk has a “U”-shaped characteristic of first decreasing and then increasing. Furthermore, the analysis finds that China's mineral resources have been greatly influenced by the domestic production situation and international trade. These results suggest that the security of petroleum supply can be improved by safeguarding international trade in petroleum resources, strengthening the strategic reserves of domestic petroleum resources, and developing new alternative clean energy sources to improve the resilience of petroleum supply security. This study's research methodology is more logical and systematic than traditional methods, and the analysis of the factors is comprehensive and of high application value, providing implications for the establishment of a big data analysis and evaluation index system for oil resource security.

Volunteer teams provide valuable support after large-scale disasters. However, excessive volunteer participation poses challenges for formal operations. Therefore, an appropriate decision-making method is required to quickly determine the number of volunteers required after a disaster. This study proposes a data-driven decision-making (D³M) method for typhoon disaster volunteerism that can effectively predict the number of volunteers required. Disaster data from actual cases were gathered, analyzed, and preprocessed to prepare the model. Feature selection, D³M model training and optimization, and model validation were performed to fine-tune the volunteer participant predictions. Using data from an actual typhoon in the Philippines, the rationality and efficacy of the method were verified through a comparative analysis of the experimental results. The proposed method learns from disaster-event data to quickly predict the number of volunteers needed, such that it not only reasonably allocates volunteers to assist professional teams in rescue but also avoids secondary problems caused by an overwhelming response.