安全科学与韧性(英文)最新文献

Staircase choice is one of the most critical factors leading to the difference in pedestrian flow and evacuation routes in buildings with multiple staircases. Neither the shortest path to the building exit nor the locally quickest path to the nearest staircase can represent the natural mode of evacuation path choices for an authentic evacuation simulation. Thus, a prediction-based approach is established to predict and simulate evacuation choices, which helps to address three key issues: (1) extracting evacuation data through a controlled experiment; (2) establishing a Logit model for staircase choice prediction based on experimental data; (3) developing a prediction-based cellular automaton model. The proposed approach has achieved the coupling between choice prediction and evacuation simulation. A comparison with Pathfinder software is conducted to reveal the superiority of the prediction-based CA model for simulating staircase choice.

Safety training is the exercise normally conducted for all the current and future employees of a company to identify and recognize occupational hazards and diseases as well as determine the appropriate controlling methods. Moreover, virtual reality (VR) is a technology developed to virtually simulate the surrounding environment to ensure immersive experience and interaction through artificial three-dimensional (3D) platforms. VR devices have been developed to be more compact, easy to use, and affordable to enable people to enjoy immersive virtual experiences and provide interactive and realistic content. This has made technology one of the most popular forms of media for different kinds of training, such as safety-related ones. Therefore, this study aimed to review the use of VR in safety training through the systematic literature review (SLR) method. The process focused on developing 4 primary questions (PQs) classified into 11 systematic research questions (SRQs) for discussion points concerning current developments in VR technology applications. These were further combined with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagrams in selecting the relevant literature. The questions were also used to investigate the scenarios, methods, objectives, and outcomes of previous studies. The results showed the need for further studies on the application of VR technology in safety training in other fields such as firefighting, chemical industry, maritime, etc. Furthermore, several scenarios such as construction design, disaster response, rescue procedures, and others need to be included. This study also provides information on the gaps for future study, including the exploration of a broader range of industries and VR scenarios.

In recent years, research has focused heavily on the investigation of functionalized ammonium polyphosphate (APP) flame retardants to improve the fire safety of epoxy resins (EP). The reason for this is the dual nature of APP's performance in fire protection of EP. This article provides a comprehensive overview of the advances in the use of functionalized APP flame retardants to improve the fire resistance of EP materials. It then presents the improvement of the modification of the functionalized APP flame retardants in terms of the hydrophobicity, compatibility and catalytic ability of the flame retardants, as well as the effects on the fire resistance, heat resistance, smoke reduction and mechanical properties of the EP composites. After the summary and comparison of the relevant studies, it is clear that the functionalized APP flame retardants can effectively improve the fire safety of EP composites and offset the adverse effects of APP in EP flame retardant applications. In addition, APP flame retardants can obtain various excellent functions through the use of materials with different properties, and the interaction between APP and materials can also lead to more efficient fire protection. However, the current problem is to find ways to streamline the process and minimise the costs associated with functionalized APP flame retardants, as well as to use them effectively in industrial production. We hope that this review can provide valuable hints and insights for the practical application of functionalized APP in EP and perspectives for future research.

Evaluating the resilience of the innovation ecosystem to maintain its performance, in the sense of resistance to disruption and recovery after it, has recently received more attention. Several studies have been conducted to model different ecosystems and evaluate their resilience. However, modeling the innovation ecosystem from a holistic perspective and performing a quantitative assessment of its resilience have received less attention. This paper models the innovation ecosystem holistically and evaluates its resilience index using a quantitative approach through five main steps. In the first step, a case study related to the innovation ecosystem of Iran's Ministry of Energy, called the Power Innovation Ecosystem, is modeled by combining system dynamics and agent-based modeling. Upon validating the model in the second step, the disruption of the loss of experts is investigated in the third step, and all possible actions to recover each actor are analyzed. In the fourth step, the performance of the ecosystem is simulated before and after the disruption using the data gathered in the previous steps. Finally, resilience is calculated in two different ways in the fifth step. Several improvement solutions are also suggested when considering that the resilience index of the innovation ecosystem is at a medium level. This research may assist policymakers in observing the resilience level of the innovation ecosystem based on the proposed model. By applying strategic changes to this model, they can determine the effects of their policies and make the most appropriate decisions to increase the resilience of the innovation ecosystem.

The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety. The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light. This often results in a high false alarm rate in complex air transportation environments. The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information. This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model. Dual-wavelength optical sensors, flue gas analyzers, and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy. The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective, which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN (recurrent neural network) and CNN (convolutional neural network). Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information, respectively, which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism. Finally, the output results of the two models are fused through the gate mechanism. The research results show that, compared with the traditional single-feature detection technology, the multi-technology collaborative fire detection method can better capture fire information. Compared with the traditional deep learning model, the multivariate fire prediction model constructed by the improved Transformer can better detect fires, and the accuracy rate is 0.995.

Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time, leading to significant deep uncertainty. Due to deep uncertainty, decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future, thus making it difficult to specify appropriate models and parameters to describe the system of interest. In this paper, we propose a dynamic exploratory hybrid modeling framework that fits data, models, and computational experiments together to simulate complex systems with deep uncertainty. In the framework, one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experiments to explore the diversity of future scenarios. Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system. This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated. Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions. The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.

The COVID-19 outbreak had a significant negative impact on the world, and the fifth wave of COVID-19 in Hong Kong brought a considerable shock to Chinese society. There is a growing call for more resilient cities. However, empirical evidence and validation of modeling studies of resilience indicators for urban community responses to the COVID-19 pandemic still need to be provided. In this study, a resilience assessment indicator model comprising 4 subsystems, 7 indicators, and 12 variables was developed to assess the resilience of Hong Kong communities in response to COVID-19 (i.e., Resilience Index). Furthermore, this study utilized regression models such as geographically weighted regression (GWR) and multiscale GWR (MGWR) to validate the resilience model proposed in this study at the model and variable levels. In the regression model, the Resilience Index and the individual variables in the resilience model are explanatory variables, and the outcomes of the COVID-19 pandemic (confirmed cases, confirmation rate, discharged cases, discharge rate) are dependent variables. The results showed that: (i) the resilience of Hong Kong communities to the COVID-19 pandemic was not strong in general and showed some clustered spatial distribution characteristics; (ii) the validation results at the model level showed that the Resilience Index did not explain the consequences of the COVID-19 pandemic to a high degree; (iii) the validation results at the variable level showed that the MGWR model was the best at identifying the relationships between explanatory variables and the dependent variable; and (iv) compared with the model-level assessment results, the variable-level assessment explained the consequences of the COVID-19 pandemic better than the model level assessment results. The above analysis and the spatial distribution maps of the resilience variables can provide empirically based and targeted insights for policymakers.

With the rapid increase in urban gas consumption, the frequency of maintenance and repair of gas pipelines has escalated, leading to a rise in safety accidents during these processes. The traditional manual supervision model presents challenges such as inaccurate monitoring results, incomplete risk factor analysis, and a lack of quantitative risk assessment. This research focuses on developing a dynamic risk assessment technology for gas emergency repair operations by integrating the monitoring outcomes of artificial olfactory for gas leakage information and video object recognition for visual safety factor monitoring data. To quantitatively evaluate the risk of the operation process, a three-dimensional risk assessment model combining gas leakage with risk-correlated sensitivity was established as well as a separate three-dimensional risk assessment model integrating visual risk factors with predictable risk disposition. Furthermore, a visual risk quantification expression mode based on the risk matrix-radar map method was introduced. Additionally, a risk quantification model based on the fusion of visual and olfactory results was formulated. The verification results of simulation scenarios based on field data indicate that the visual-olfactory fusion risk assessment method can more accurately reflect the dynamic risk level of the operation process compared to simple visual safety factor monitoring. The outcomes of this research can contribute to the identification of safety status and early warning of risks related to personnel, equipment, and environmental factors in emergency repair operations. Moreover, these results can be extended to other operational scenarios, such as oil and gas production stations and long-distance pipeline operations.

The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases. Traditional epidemiological models, rooted in the early 20th century, have provided foundational insights into disease dynamics. However, the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools. This is where AI for Science (AI4S) comes into play, offering a transformative approach by integrating artificial intelligence (AI) into infectious disease prediction. This paper elucidates the pivotal role of AI4S in enhancing and, in some instances, superseding traditional epidemiological methodologies. By harnessing AI's capabilities, AI4S facilitates real-time monitoring, sophisticated data integration, and predictive modeling with enhanced precision. The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S. In essence, AI4S represents a paradigm shift in infectious disease research. It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks. As we navigate the complexities of global health challenges, AI4S stands as a beacon, signifying the next phase of evolution in disease prediction, characterized by increased accuracy, adaptability, and efficiency.

The study critically examines the principles, mechanisms, and effectiveness of different damage control techniques in dealing with natural disasters, emphasizing their pivotal role in minimizing casualties and economic losses. Each of these damage control techniques is mapped based on their applications and relevance in the key areas of natural disaster management. By utilizing various real-world instances, the present study shows that the effective implementation of various innovative techniques is shaping the space of natural disaster management in a global context. The integration of different innovative techniques into the existing natural disaster management system has improved the survival rate, economic performance, and sustainable development. The study finds that innovative disaster financing models, clear strategies, and creating awareness among communities can improve the overall efficiency of innovative techniques that are currently used for damage control during natural disaster events. Despite the substantial advantages of these creative strategies, the study acknowledges challenges such as financial constraints, unclear policy goals, and community adaptation requirements. The study also indicates that in the future, automatic damage restoration, quick prototyping, and additive engineering will play a vital role in controlling damage from catastrophic events, while it acknowledges limitations in temporal scope, generalizability, and financial constraints.