安全科学与韧性(英文)最新文献

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.

Offshore oil and gas drilling operations are going to remote and harsh arctic environments with demands for heightened safety and resilience of operational facilities. The remote and harsh environment is characterized by extreme waves, wind, storms, currents, ice, and fog that hinder drilling operations and cause structural failures of critical offshore infrastructures. The risk, safety, reliability, and integrity challenges in harsh environment operations are critically high, and a comprehensive understanding of these factors will aid operations and protect the investment. The dynamics, environmental constraints, and the associated risk of the critical offshore infrastructures for safe design, installation, and operations are reviewed to identify the current state of knowledge. This paper introduces a systematic review of harsh environment characterization by exploring the metocean phenomena prevalent in harsh environments and their effects on the floating offshore structures performance and supporting systems. The dynamics of the floating systems are described by their six degrees of freedom and their associated risk scenarios. The systematic methodology further explores the qualitative, quantitative, and consequences modeling techniques for risk analysis of floating offshore systems in a harsh environment. While presenting the current state of knowledge, the study also emphasizes a way forward for sustainable offshore operations. The study shows that the current state of knowledge is inexhaustive and will require further research to develop a design that minimizes interruption during remote harsh offshore operations. Resilient innovation, IoT and digitalization provide opportunities to fill some of the challenges of remote Arctic offshore operations.

There seems to be a paucity in extant literature that assesses the relationship between Safety Management Systems (SMS) and High Reliability Theory (HRT) behavior process of mindful organizing (MO) among aerospace organizations. There could be benefits for organizational safety by exploring this relationship in high-reliability organizations (HROs) like the aerospace industry. Using a modified Safety Organizing Scale (SOS) by Vogus and Sutcliffe (2007) and a validated SMS scale, the relationship between SMS and MO was measured. The perceptions of a cross-section of respondents from commercial airlines with SMS and commercial space licensees without SMS in the United States (U.S.) was assessed. A four-factor model of MO had acceptable fit. A model showing the relationship between SMS and MO had good fit and showed a high significant strength of relationship (r = 0.82, p = 0.000) with a big effect size. There were also significant differences in mean responses among management personnel and non-management personnel on the MO factor “sensitivity to operations” and the result suggests managers were better at identifying personnel with skills and knowledge to ensure safer task accomplishment than non-management personnel. The study results suggest that the SMS requirements for commercial airlines in the U.S. can enrich the identification and understanding of MO factors and it may be beneficial for the commercial space industry to formally adopt SMS. Future research studies may include direct comparisons in multiple aerospace organizations using a larger sample size to determine the overall understanding of MO factors and how it affects SMS.

To investigate the effect of tunnel slope on hot gas movement and smoke distribution in a slopping tunnel fire, a series of tunnel fire models are built by fire dynamics simulator (FDS), with a slope varies from 0 to 10%. Parameters such as ceiling temperature and airflow velocity are measured. The results indicate that the relationship between smoke back-layering length and tunnel slope can be described as an exponential function. The smoke temperature at the downstream exit first increased and then decreased with a higher slope. The airflow velocity at downstream outlet increased nonlinearity when tunnel slope was less than 8%. In the slope tunnel, the fire smoke spread process can be divided into three stages. Fire smoke spreads upstream to the peak distance, subsequently, the upstream smoke layer decreases gradually, the tunnel fire reaches a quasi-steady state. The backflow characteristics of smoke in sloped tunnels are coupled with the downstream length and outlet smoke temperature. In the initial stage of a slope tunnel fire, smoke spreads upstream for a long distance, endangering human health.

This study aimed to address the problem of post-disaster emergency material dispatching from multiple supply points to multiple demand points. In large-scale natural disasters, it is very important for multiple emergency material supply points to serve as sources of materials for multiple disaster sites and to determine emergency material scheduling solutions accurately. Furthermore, the quantity of emergency materials required at each disaster site is uncertain. To address this issue, in this study, we developed an emergency material scheduling model with multiple logistics supply points for multiple demand points based on the grey interval numbers. To optimize the proposed multi-supply-point and multi-demand-point emergency material scheduling mode, a multi-objective optimization algorithm based on a genetic algorithm was used. Experimental results demonstrate that the multi-objective optimization method can solve the emergency logistics scheduling problem better than the particle swarm optimization multi-objective solution algorithm. Additionally, the multi-supply point and multi-demand point emergency material dispatch model and optimization algorithm provides robust support for emergency management system decision-makers when they need to respond quickly to disaster relief activities.

Prolonged power outages debilitate the economy and threaten public health. Existing research is generally limited in its scope to a single event, an outage cause, or a region. Here, we provide one of the most comprehensive analyses of large-scale power outages in the U.S. from 2002 to 2019. This analysis is based on the outage data collected under U.S. federal mandates that concern large blackouts, typically of transmission systems and exclude much more common but smaller blackouts, typically, of distribution systems. We categorized the data into four outage causes and computed reliability metrics, which are commonly used for distribution-level small outages only but useful for analyzing large blackouts. Our spatiotemporal analysis reveals six of the most resilient U.S. states since 2010, improvement of power resilience against natural hazards in the south and northeast regions, and a disproportionately large number of human attacks for its population in the Western Electricity Coordinating Council region. Our regression analysis identifies several statistically significant predictors and hypotheses for U.S. resilience to large blackouts. Furthermore, we propose a novel framework for analyzing outage data using differential weighting and influential points to better understand power resilience. We share curated data and code as Supplementary Materials.

In the context of frequent occurrences of disasters worldwide, disaster-coping capability is imperative for risk reduction and contemporary emergency management. The global COVID-19 pandemic since 2020 has further highlighted the significance of resilience construction at different geographical scales. Overall, the conceptual cognition of resilience in disaster management covers multiple elements and has diverse yielding on regional assessment. This study assesses the local resilience to the public health disaster in the prefecture-level cities, focusing on two dimensions consisting of vulnerability and capability in the targeted provincial region of Jiangsu in China. To this end, based on the vulnerability-capability framework, the Rough Analytic Hierarchy Process (Rough AHP) method was applied to the resilience assessment. Drawing upon the criteria derived from literature, the criteria weights were determined with the RAHP method and we assessed urban resilience with census data. In addition, the hierarchical factors contributing to urban resilience were determined using robustness analysis. This research provides constructive ideas for regional disaster reduction and contributes to the government's capability to improve urban resilience.

Embraced within the framework of crime opportunities integrated with Social Disorganization theory and Broken Windows theory, this paper intends to explore the patterns of four types of acquisitive crimes, using social media data, i.e., Twitter, Foursquare and cross-sectional data acquired through text analysis technique. With Greater London as the study area, models like negative binominal regression (NBR) and geographically weighted regression (GWR) are performed to illustrate the aggregated relationships between acquisitive crimes and crime opportunities at London-wide and sub-regional MSOAs levels respectively. The results work towards to hypotheses that: the tweets sentiment could reflect property-related crime rates positively in light of Broken Windows Theory; more tweets with negative sentiment may incur increases in acquisitive crimes. It contributed to existing studies in (1) providing empirical evidence for integrating these three theories; (2) complementing current research on local discrepancies of acquisitive crimes by utilising both GWR and NBR models; (3) challenging the traditional stereotypes about racial disparities with the finding that ethnic heterogeneity and instrumental crimes have counterintuitive association, especially taking education factor into consideration; (4) implicating some localised acquisitive crime prevention strategies to policy makers in light of the reality that the relationship between local variations and different crime types may vary by place.

In recent years, undesirable disasters attacked the cities frequently, leaving heavy casualties and serious economic losses. Meanwhile, disaster detection based on the Internet of Things(IoT) has become a hot spot that benefited from the established development of smart city construction. And the IoT is visibly sensitive to the management and monitoring of disasters, but massive amounts of monitoring data have brought huge challenges to data storage and data analysis. This article develops a new and much more general framework for disaster emergency management under the IoT environment. The framework is a bottom-up integration of highly scalable Raw Data Storages(RD-Stores) technology, hybrid indexing and queries technology, and machine learning technology for emergency disasters. Experimental results show that hybrid index and query technology have better performance under the condition of supporting multi-modal retrieval, and providing a better solution to offer real-time retrieval for the massive sensor sampling data in the IoT. In addition, further works to evaluate the top-level sub-application system in this framework were performed based on the GPS trajectory data of 35,000 Beijing taxis and the volumetric ground truth data of 7,500 images. The results show that the framework has desirable scalability and higher utility.

Social media analytics have played an important role in disaster identification. Recent advances in deep learning (DL) technologies have been applied to design disaster classification models. However, the DL-based models are hindered by insufficient training samples, because data collection and labeling are very expensive and time-consuming. To solve this issue, a privacy-preserving federated transfer learning approach for disaster classification (FedTL) is proposed, which can allow distributed social computing nodes to collaboratively train a comprehensive model. In the FedTL, Paillier homomorphic encryption method is used to protect the social computing nodes’ data privacy. In particular, the transfer learning technology is adopted as a novel application to reduce the computation and communication costs in the federated learning system. The FedTL is verified by a real disaster image dataset collected from social networks. Theoretical analyses and experiment results show that the FedTL is effective, secure, efficient. In addition, the FedTL is highly extensible and can be easily applied in other transfer learning models.