In order to capture the evolving landscape of research on emergency-synergy in public health emergencies and systematically synthesize key studies and their characteristics in this domain, this study employed the scientific knowledge mapping tool CiteSpace to conduct a visual analysis of relevant literature from the past decade. Data were retrieved from core collections of China National Knowledge Infrastructure (CNKI) and Web of Science (WOS). Concurrently, existing research findings were systematically examined across five dimensions: synergistic models, mechanisms, subjects, decision-making processes, and model construction. The results indicate that research output on emergency-synergy in public health emergencies grew steadily alongside the progression of Corona Virus Disease 2019(COVID-19) pandemic, with a subsequent decline as the pandemic subsided. Overall publication volume in this field remains modest: the retrieval yielded 121 academic papers, 306 master's and doctoral theses, and 54 foreign-language documents. Research focus is predominantly concentrated in areas such as emergency management, collaboration mechanisms, and multi-stakeholder synergy. The analysis further reveals that evolutionary game theory and dynamic disease transmission theory have gained significant traction among researchers in modeling emergency-synergy. The synergistic mechanism featuring "government leadership, multi-party participation, and clear division of responsibilities" has garnered widespread recognition within the academic community. Nevertheless, challenges persist regarding the practical implementation of multi-subject emergency synergy under government leadership, warranting further in-depth investigation. It is projected that future research will advance toward theoretical refinement of emergency-synergy governance models and the diversification of synergistic subjects.

To enhance operational efficiency and ensure flight safety, a multi-objective flight slot optimization model was developed by comprehensively considering the requirements of airlines, air traffic control authorities, slot coordination authorities, and airports, subject to airport capacity, terminal waypoint capacity, and safety requirements. The model aimed to achieve balanced and efficient slot allocation within airport clusters, and its effectiveness was validated using actual flight data from Beijing Capital International Airport, Beijing Daxing International Airport, and Tianjin Binhai International Airport in the Beijing-Tianjin-Hebei airport cluster. The results show that the optimized slot distribution becomes more balanced, with peak-hour flight numbers reduced by 7%, the average adjustment per airline decreased by 18.2%, and the total adjustment across the airport cluster reduced by 10.4%. At Beijing Capital Airport, capacity utilization efficiency during peak hours is improved, with redundancy increasing from 2.9% to 8.3%. At Beijing Daxing Airport, slot distribution becomes more balanced, with redundancy increasing from 1.6% to 6.5%. At Tianjin Binhai Airport, the optimized flight volume is maintained within capacity limits, improving safety margins, with redundancy rising from -13.9% to 8.3%. The model effectively enables the rational allocation of flight schedules, significantly enhancing operational safety and resource utilization efficiency.

In order to increase the systematicity and flexibility of disaster response behavior at the grassroots level, a triangle model of county disaster resilience was constructed. Taking the rainstorm in Jianchang, Liaoning Province in 2024 as an example, social network analysis method was used to establish an association network among rainstorm disaster events, county disaster bearing bodies and emergency management plans. The system dynamics model reflecting the evolution process of county disaster resilience was designed by introducing the time series dimension, and the individual attributes of network nodes were used as the basis for model evaluation. The results show that the county disaster resilience shows an evolutionary trend of slowly decreasing first and then steadily increasing, and the growth rate gradually accelerates. During the pre-disaster warning stage, disaster events, disaster-bearing bodies, and emergency management plans respectively possess the characteristics of suddenness, exposure, and absorption. Organizational preparations, communication preparations, material preparations, and publicity preparations have the greatest room for optimization. During the disaster response stage, disaster events, disaster-bearing bodies, and emergency management plans respectively possess coupling and uncertainty, sensitivity, and adaptability characteristics. There is still room for improvement in the capacity to evacuate the masses, communication guarantee mechanisms, and the resilience of power facilities. During the post-disaster recovery stage, disaster-bearing bodies and emergency management plans respectively possess adaptability and adjustment characteristics. At this time, it is necessary to optimize the planting structure of crops and enhance the flood resistance capacity of roads. Emergency management plans always have the characteristic of learning throughout the entire cycle of disaster response.

To enhance urban resilience against disaster chains, this study proposed an analytical framework and methodology for identifying influencing factors from a disaster chain perspective. Firstly, a comprehensive list of urban resilience influencing factors was established by focusing on the core capacities of resilient cities within disaster chain contexts. Secondly, an improved fuzzy DEMATEL-ISM integrated method was introduced to identify top-level factors, clarify hierarchical structures and interaction pathways among influencing factors, and analyze resilience enhancement approaches from economic and demographic perspectives. Finally, taking the resilience construction practice of Chengdu in the context of disaster chain as an example, the rationality of this theoretical analysis was verified. Results indicate that the top-level design factors, aimed at addressing disaster chains, primarily focus on prevention, preparedness, and adaptation. Fundamental factors include insurance protection, emergency command in the disaster chain scenario and government departments' ability to jointly handle chain disruptions to reduce disasters, adequacy of emergency resource preparation, and ecological environment restoration and governance. Direct influencing factors encompass housing quality, medical assistance capacity, emergency evacuation capacity, and basic living security. The most effective approaches to enhance economic resilience in disaster chains are strengthening insurance protection and accelerating infrastructure repair, both promoting economic recovery. Enhancing population resilience relies primarily on measures such as housing security, life support, emergency evacuation support, and basic living security. Chengdu maintains relatively sound resilience construction under disaster chain scenarios, though certain aspects require further optimization.

In order to realize scientific and efficient post-disaster transfer of the population after the occurrence of major natural disasters, the emergency bus evacuation planning considering the transfer urgency was studied according to the different severity of the disaster. Firstly, a continuous emergency bus evacuation model was established with the goal of minimizing the evacuation completion time and evacuation delay satisfaction degree. Secondly, a HS-GA based on transfer urgency coding was designed and applied for problem-solving, and the effectiveness of the algorithm was verified using examples of different scales. Finally, the evacuation planning considering the transfer urgency was compared with those without considering the transfer urgency based on the case. The results show that the HS-GA is better than Genetic Algorithm (GA) and Neighborhood Search GA (NS-GA). According to the case solved by HS-GA, a comparison is made with evacuation planning that does not consider transfer urgency. When transfer urgency is considered, the evacuation delay dissatisfaction degree is reduced by 51.4%. Additionally, shorter evacuation completion times are observed at both Class I and Class II disaster sites. The average time taken to arrive at the shelter is also found to be shorter. At the same time, the dissatisfaction degrees of evacuation delay considering the urgency of transfer under different emergency bus quantity are all lower. The evacuation model considers the evacuation priority of the hard-hit areas under different severity of the disaster, and can make reasonable dispatch arrangements under the circumstance of limited rescue vehicles, which is more objective and in line with the actual situation.

To optimize vehicle design and improve road safety, the effect of the NBLEH on pedestrians head injuries during ground contact was investigated. The impact with the ground AIS 1+ pedestrian landing injury cases was selected, and data analysis along with multicollinearity tests was conducted. The results show that as the NBLEH increases, the severity of pedestrian head injuries exhibits a corresponding upward trend. Through Logistic regression analysis, it is further revealed that for AIS 2+ head injuries in adult pedestrians can be jointly predicted by vehicle speed, age, and NBLEH. Specifically, the risk of injuries is increased with higher values of NBLEH, faster speeds, and older ages. Analysis of real accident videos demonstrates that a smaller NBLEH results in increased full-body rotation angles of pedestrians. However, once NBLEH surpasses 0.9, the rotation angle notably diminishes. The collision force transmission and the pedestrian's landing posture are altered by different values of NBLEH. Additionally, cadaver crash tests were conducted using two types of vehicles with different front-end heights to establish test conditions with different NBLEH values. The results indicate that NBLEH not only influences the timing of head contact with the ground, but also that there is an interaction between head landing posture and collision speed, further complicating head injuries. Parametric simulation studies reinforced that NBLEH significantly influences factors such as posture, angle, timing, and speed during pedestrian head-to-ground contact, thereby increasing the complexity and uncertainty of surrounding injury mechanisms. The simulation results show that both vehicle speed and NBLEH influence the pedestrian flip angle, with NBLEH values less than 1 producing larger pedestrian flip angles than values greater than 1.

In order to study the scientific and reasonable location selection of regional emergency rescue stations and base-level emergency rescue stations in the planning and construction stage of the subway network, firstly, the topology model of the subway network was constructed based on complex network theory. Relevant indicators were selected to construct the evaluation system of node importance, and GWR model was used to predict passenger flow to supplement the research data. Secondly, aiming at the highest efficiency, optimal coverage and lowest cost, the multi-objective location model of regional emergency rescue stations was established, and NSGA-Ⅱ algorithm was used to solve the optimal scheme. At the same time, a hybrid analysis calculation method was designed to solve the comprehensive importance value parameters of nodes in the model, and based on the site selection results of regional emergency rescue stations, the enumeration method was used to solve the optimal solution of base-level emergency rescue stations. Finally, Xi'an metro planning network was taken as an example to verify the calculation. The results show that compared with the optimal solution in the case of "no consideration of node importance", the regional location solution with "consideration of node importance" saves about 2.19% in cost, improves the rescue efficiency by about 9.43%, and exceeds the optimal solution with about 4.66% in coverage. Therefore, the location scheme of "considering node importance" is more in line with the actual needs of the subway network.

To optimize the emergency management system for plateau disasters and to address the scientific demand for improving governance effectiveness under extreme plateau environments, this study developed a dual-network framework—"risk propagation-emergency response"—using the Ms6.8 Shigatse earthquake in Tibet as a case study. Based on complex network theory, it uncovered the cascading dynamics of seismic risks and multi-actor coordination governance patterns. The analysis demonstrates that the cascading disaster network exhibits a core-periphery structure, where critical nodes trigger multi-level chain reactions via infrastructure fragility and cultural sensitivity. Plateau-specific disaster chains are identified, such as "cold survival crisis → mass livestock deaths → pressure of harmless treatment", alongside culturally embedded risks like "language barriers" and "religious site damage", and their impacts further promote the cultural adaptive reconstruction of the traditional emergency management system. The coordination network shows a decentralized, short-path, high-density topology, indicating efficient collaboration despite persistent hierarchical redundancy.

In order to more accurately capture the evolutionary patterns of disaster risks and facilitate scientific risk assessment, this study innovatively integrated the dual natural and social attributes of disasters. By leveraging the outcomes from event extraction, event relationship identification, and event generalization, a method based on causal association strength was developed to mine event logic. Subsequently, an emergency event evolutionary graph was constructed for assessing chain-reaction disaster risks. Combined with a similarity matching method that incorporates the multi-dimensional attributes of disaster events, an empirical study was conducted using earthquake disasters as a case study. The results demonstrate that the proposed disaster risk assessment framework based on the event evolutionary graph can rapidly identify historical earthquake events with high similarity to the Luding earthquake case. Furthermore, by leveraging the intrinsic interconnections and evolutionary paths among risks revealed by the graph, the framework can predict secondary disasters (e.g., landslide-dammed lakes) and derivative social risks (e.g., rumor propagation).

To achieve high-precision recognition of wall hollowing sound signals and improve multi-category detection accuracy, a multi-feature fusion method for wall hollowing detection based on BO-SVM was proposed. First, the collected knocking sound signals from different wall types were preprocessed by pre-emphasis, framing, and windowing, and both MFCC and MSC were extracted. The two acoustic features were concatenated at the frame level and normalized to construct a fused feature dataset. Then, a BO-SVM classification model was developed, and the kernel function penalty and parameters were optimized using five-fold cross-validation to establish the MFCC+MSC-BO-SVM model. Finally, classification experiments were conducted using hollow and non-hollow from multiple wall types, including cement, coating, marble, and ceramic tile walls. The results show that the fused features outperform single features in terms of accuracy, recall, and F₁-score. The MFCC+MSC-BO-SVM model achieves an overall recognition accuracy of 96.36%, representing improvements of 6.61%, 9.58%, 15.27%, 13.90%, and 5.02% compared with standard SVM, Random Forest, K-Nearest Neighbor, Grid Search-optimized SVM, and Chaos Particle Swarm Optimization SVM respectively. Furthermore, the BO method can obtain the optimal parameter combination with fewer iterations, showing superior convergence and classification stability.