安全科学与韧性(英文)最新文献

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.

In this study, full-scale fire experiments were conducted in a hydropower station to investigate smoke propagation during tunnel construction. The flame height, smoke temperature and stratification, smoke descent and spread velocity were analyzed via measurements and on-site observations. The initial combustion stage was largely affected by ignition source during tunnel construction for diesel pool fire, and the average flame height in the fully developed stage could reach 1.4–2.1 m in experimental fire scenarios. The gradient of the smoke temperature evolution near the fire was the opposite for the upstream and downstream regions. The longitudinal temperature distribution was concentrated in a small range at the heights of the smoke layer, and gradually decreased by air entrainment as the height decreased, while further increasing in the lower half of the tunnel height in the near-fire region under heat radiation from the fire source. Moreover, distinct and stable smoke stratification formed during the fully developed combustion stage, and the smoke layer interface was at approximately half the tunnel height. Smoke descent was aggravated in the decay stage of combustion, and the fire risk remained high after the fully developed period. The smoke front spread velocity was empirically determined for the full-scale tunnel fire scenarios. Conclusions from full-scale experiments can support smoke control design and on-site fire emergency response plans for hydropower stations.

Currently, coal mining faces the uncertainty of the risk of coal and gas outbursts and inaccurate prediction results. Owing to this, an artificial immune algorithm (AIA) was developed for coal and gas outburst prediction based on the Hamming distance (HD) calculation method of antibody and antigen affinity called the Hamming distance artificial intelligence algorithm (HDAIA). The correlation matrix of coal and gas outburst indicators was constructed using the interpolation function in the algorithm. The HD algorithm was used to obtain the affinity between the antibody and antigen, and the minimum HD was screened to obtain the prediction result. The collected dynamic data of the drilling cuttings gas desorption index K₁ and the drilling cuttings weight S during the excavation process of the 11,192-working face of a coal mine in Guizhou Province, China, were used as prediction indices. The results indicate that the prediction result of the HDAIA for the risk of coal and gas outbursts is consistent with the actual risk of outbursts, and it has a good prediction of the risk of coal and gas outbursts. The HDAIA can be used as a novel method for predicting the risk of coal and gas outbursts.

Fire-based emergency management service (EMS) personnel are dispatched to various incidents daily, many of which have unique occupational risks. To fully understand the variability of incident types and how to best prepare and respond, an exploration of the U.S. coding system of incident types is necessary. This study uses potential exposure to SARS-CoV-2 as a case example to understand if and how coding categories for incident call types may be updated to improve data standardization and emergency response decision making. Researchers received emergency response incident data generated by three fire department computer-aided dispatch (CAD) systems between March and September 2020. Each incident was labeled EMS, Fire, or Other. Of the 162,766 incidents, approximately 8.1% (n = 13,144) noted potential SARS-CoV-2 exposure within their narrative descriptions of which 86.3% were coded as EMS, 9.9% as Fire, and 3.9% as Other. To assess coding variability across incident types, researchers used the original 3-incident type variable and a new 5-incident type variable reassigned by researchers into EMS, Fire, Other, Hazmat, and Motor Vehicle. Logit regressions compared differences in potential exposure using the 3- and 5-incident type variables. When evaluating the 3-incident type variable, those responding to a Fire versus an EMS incident were 84% less likely to be associated with potential exposure to SARS-CoV-2. For the 5-incident type variable, those responding to Fire incidents were 77% less likely to be associated with a potential exposure than those responding to EMS incidents. Changes in potential exposure between the 3- and 5-incident type models show the need to understand how incident types are assigned. This demonstrates the need for data standardization to accurately categorize incident types to improve emergency preparedness and response. Results have implications for incident type coding at fire department municipality and national levels.

This study introduces a quantitative scenario-building method for analyzing emergency scenarios based on ontological methods and the EOC (element-object-consequence) model. The ontological structure of disasters concisely describes the knowledge, concepts, attributes, and relationships of the disaster scenario. It reduces the granularity of the data from the document to the data level. Disaster ontologies comprise a set of basic knowledge of a given domain, which is reusable, relatively fixed, and applicable in different areas at different periods. The EOC model is based on the ontology of a disaster and adopts a multiclass structure for the development of a complete process scenario and the adaptation of a disaster scenario by combining objects, elements, environments, and consequences.

Evacuation signs are a key factor in the effectiveness of occupants’ urgent escape from buildings. Different sign features may have disparate impacts on individual and crowd behavior. This study aims to quantitatively investigate the effectiveness of building evacuation signs with different features during pedestrian evacuation using eye-tracking devices. Ten experiments were conducted in a building in T-junction scenarios, and four sets of features related to evacuation signs (color, position, graphics, and flashing) were considered. SMI BeGaze was used to analyze the eye movements of the occupants. The fixation duration of the evacuees and the ratio of signs detected and followed were quantitatively derived and compared for each experiment. The results show that it is easier for evacuees to detect signs at a low sightline, and signs posted at that level of sight can provide better guidance. When signs are posted high up, red signs are easier to detect than green signs. Most evacuees prefer to follow the evacuation signs once they detect them; however, there is no significant difference in the effect of what is posted on the evacuation signs, such as a running man or an arrow. Conversely, flashing of signs is highly helpful in influencing evacuees’ behavior, but flashing is not helpful in detecting signs. More importantly, red signs with a running man graphic located in a low position have the best guiding effects on evacuees. The fixation behaviors of evacuees vary according to sign features, particularly in regard to flashing and color. Evacuees stare longer at twinkling signs. Differences in the colors and positions of signs result in significantly different perceptual behaviors among evacuees. Our findings are useful for building designers and provide guidance for developing effective evacuation strategies.

Community security and risk management is essential for urban governance. To identify community risks more accurately, the space-time characteristics of community burglary cases in a community located in C city are analyzed in this study based on data from 2018 to 2020 using the hot police theory. The results are as follows: (1) In terms of time, the occurrence of theft cases is closely related to seasonal changes and daily work and rest and is affected by social activities. (2) In terms of space, the type of community and surrounding environment affect the generation and distribution of household theft hotspots, while urban planning and reconstruction cause the dissipation and transfer of hotspots. Therefore, to prevent and control risk, infrastructure and urban environment renovation should be carried out, community governance efficiency should be improved, and police work mode should be transformed to avoid potential hotspots, except for targeted interventions on existing hotspots.

This article describes the practice of co-evolutionary and transformative resilience through a case study conducted in Turin (Italy). According to a broad definition, resilience includes performing actions of urban design and planning, innovating community-based project procedures, and creating positive financial outcomes that are assessable because of the monitoring process of short- and long-term outcomes and impacts. Through the Turin-based case of the Bottom Up! Architecture Festival, this article observes processes in which resilience is in action in metropolitan areas, feeding urban projects and practices of self-organization of the social and financial actors involved. By applying the definition of community projects, the festival manages to take territorial problems and crises (the pandemic, inequality, etc.) and view them as an opportunity to change the system, recommending integrated action on the natural, cultural, financial, and social capital, innovating practices and holding society and institutions more accountable. The transformation of spaces relies on collaborations between social and institutional actors, operating spatially concentrated transformations in the city of Turin, and using flexible governance tools based on co-planning and crowdfunding for project design and financing.

Existing research models can neither indicate the availability of shared bikes nor detect unusable ones owing to a lack of information on bike maintenance and failure. To improve awareness regarding the availability of shared bikes, we propose an innovative approach for detecting unusable shared bikes based on reinforcement learning and the PageRank algorithm. The proposed method identifies unusable shared bikes depending on the local travel data and provides a ranking of the shared bikes according to their availability levels. Given a sliding time window, the value function for the reinforcement learning model was determined by considering the cumulative number of unavailable shared bikes, the proportion of rental cancelations at the same stations, and the mean time between the cancelations. Reinforcement learning was then used to identify shared bikes with the worst availability. An availability ranking for the shared bikes below the reward threshold was performed using the PageRank algorithm. The proposed detection approach was applied to a trip dataset of a real-world bike-sharing system to illustrate the modeling process and its effectiveness. The detection results of unusable shared bikes in the absence of failure and feedback data can provide essential information to support the maintenance management decisions regarding shared bikes.

During a terrorist attack on a supermarket, the use of emergency exits is essential for effective evacuation and saving lives. However, people tend to ignore emergency situations. This behavior can lengthen evacuation times, endanger individuals, and even prove fatal. In this context, we conducted a series of experiments to explore the links between cognition and the dynamics of human capabilities in a complex and changing environment. In a series of behavioral experiments and computer simulations, we found that active guidance by green flashing lights at emergency exits impacts the behavior of individuals in an emergency evacuation situation in a supermarket; this tested our hypothesis that changing the environment in turn changes the evacuation behavior of individuals. We also show that environmental modification can help in decision-making in an emergency situation. Furthermore, the results of computer simulations support a possible modeling of the influence of affordances on the evacuation behavior of agents in a complex virtual environment.