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Recently, frequent flood disasters in China have seriously threatened economic development and public safety. This paper addresses the need for a dynamic urban flood emergency knowledge management system in emergency management departments and the lack of systematic knowledge among emergency managers regarding urban flood control. A multimodal knowledge graph-based urban flood emergency knowledge management model was constructed to enhance the decision-making capabilities of emergency management departments, improve the efficiency of public emergency evacuation, and reduce losses from urban flood disasters by analyzing the shortcomings of the existing emergency management system. An intelligent and dynamic flood emergency knowledge management model was built. This paper integrates multimodal knowledge graph technology to establish a multimodal emergency knowledge management framework for urban flood control. It develops and simulates the proposed model’s application scenarios for urban flood emergency evacuation using the Flocking algorithm on the NetLogo platform. Through simulation experiments, the practicality and effectiveness of the model in real flood disaster situations were examined, particularly in simulating crowd evacuation behavior. The study found that the model significantly improves the accuracy of information and decision-making speed during emergency responses and supports emergency management departments in conducting targeted and personalized emergency decisions. This research provides a scientific basis for emergency management departments to optimize their emergency response strategies to flood disasters and serves as a reference and example for the application of multimodal knowledge graph technology in emergency management.

In order to ensure the safety of the Beijing–Shanghai high-speed railway during the construction and operation of the shield tunnel on Line R1 of the Jinan Metro, a numerical simulation of geological disturbance during the underpass of the small-radius stacked tunnel was carried out. We analyzed the mechanism of geological deformation and the construction factors affecting settlement and found that bridge settlement far exceeded safety standards without taking safety control measures. In this regard, safety control technologies such as setting isolation pile sleeve valve pipes, controlling shield tunneling parameters, grouting control technology, and trolley support technology have been proposed. Monitoring was conducted on the uneven settlement of the bridge surface, the internal and external stresses of the pipe segments, and the soil pressure they were subjected to. The results showed that the surface settlement of the bridge after safety control measures was controlled within the safety standard range, and the stress and soil pressure changes of the pipe reinforcement were within the allowable range, further verifying the effectiveness of the safety control measures.

Hospital-based and community-based ‘high-risk cohort’ studies investigating humans at risk of zoonotic infection due to occupational or residential exposure to animals were conducted in Vietnam, with diverse viruses identified from faecal samples collected from humans, domestic and wild animals. In this study, we focus on the positive-sense RNA virus family Picornaviridae, investigating the prevalence, diversity, and potential for cross-species transmission. Through metagenomic sequencing, we found picornavirus contigs in 23% of samples, belonging to 15 picornavirus genera. Prevalence was highest in bats (67%) while diversity was highest in rats (nine genera). In addition, 22% of the contigs were derived from novel viruses: Twelve phylogenetically distinct clusters were observed in rats of which seven belong to novel species or types in the genera Hunnivirus, Parechovirus, Cardiovirus, Mosavirus and Mupivirus; four distinct clusters were found in bats, belonging to one novel parechovirus species and one related to an unclassified picornavirus. There was no evidence for zoonotic transmission in our data. Our study provides an improved knowledge of the diversity and prevalence of picornaviruses, including a variety of novel picornaviruses in rats and bats. We highlight the importance of monitoring the human–animal interface for possible spill-over events.