In recent years, urban resilience has attracted increasing attention from researchers and managers from the international community at the national, regional, and urban levels. Numerous multi-dimensional and cross-disciplinary investigations, campaigns, and outlines have significantly promoted the development goal of resilience in cities worldwide. However, the existing definitions and interpretations of urban resilience still call for a more comprehensive, systematic, and exhaustive analysis as urbanization accelerates and the complex risks of various safety events increase. To this end, we rethink the extension and connotation of urban resilience based on a review and analysis of critical hotspots, realistic demand, and development trends. A conceptual classification with three aspects and three typical tiers of urban resilience is proposed, which further promotes a new definition and interpretation by incorporating the resilience extension of urban systems. In addition, the six-dimensional characteristics are extracted to furnish the urban resilience connotation, and four-stage improvement measures are introduced accordingly. In addition, the newly developed urban resilience is applied to a case analysis of a large-scale disaster, which demonstrates the necessity and significance of this study. The new extension and connotation investigation will be helpful for the improvement and implementation of urban resilience, thereby guiding the construction of resilient cities.
Mathematical and computational models are useful tools for virtual policy experiments on infectious disease control. Most models fail to provide flexible and rapid simulation of various epidemic scenarios for policy assessment. This paper establishes a multi-scale agent-based model to investigate the infectious disease propagation between cities and within a city using the knowledge from person-to-person transmission. In the model, the contact and infection of individuals at the micro scale where an agent represents a person provide insights for the interactions of agents at the meso scale where an agent refers to hundreds of individuals. Four cities with frequent population movements in China are taken as an example and actual data on traffic patterns and demographic parameters are adopted. The scenarios for dynamic propagation of infectious disease with no external measures are compared versus the scenarios with vaccination and non-pharmaceutical interventions. The model predicts that the peak of infections will decline by 67.37% with 80% vaccination rate, compared to a drop of 89.56% when isolation and quarantine measures are also in place. The results highlight the importance of controlling the source of infection by isolation and quarantine throughout the epidemic. We also study the effect when cities implement inconsistent public health interventions, which is common in practical situations. Based on our results, the model can be applied to COVID-19 and other infectious diseases according to the various needs of government agencies.
Abstract Numerical models provide detailed information of situation in buildings during a fire. But the models can have unsatisfactory practical performance for emergency response, due to model defects and deviations of pre-set input parameters. Data assimilation methods for error mitigation have shown great performance improvement by combining real-time measurements with fire models. However, these methods cannot revise the simulation effectively when there are obvious systematic errors between fire model and sensor readings. Ensemble Kalman filter (EnKF) is one of classical data assimilation methods. In this paper, a decentralized EnKF-based fire prediction method is proposed for the dynamic situation awareness in building fires. Individual EnKF-based fire model is established for each substructure of a building. The proposed method is less affected by systematic error of the fire model. Based on the EnKF predictions, important information of smoke and fire hazard are extracted and a fire hazard mapping is created for first responders. The multi-compartment case study validates the effectiveness of the EnKF-based fire prediction and dynamic situation awareness for emergency response.
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