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Mountain highways in China are located in various natural geographical areas with intense tectonic activity, steep topography and a high frequency of extreme precipitation events. These conditions make the highways vulnerable to the occurrence of multiple large debris flows simultaneously during heavy rainfall. To manage this hazard risk, a broader understanding of the hazard effects of debris flows and the vulnerability of highways is needed to reduce the losses resulting from these hazardous events. Accordingly, we analysed the effects of debris flow hazards on mountain highways and established an updated systematic indicator system to describe the vulnerability of highway infrastructure and movable hazard-affected objects. Next, we proposed a new integrated model of highway vulnerability based on the environmental sensibility, structural properties and functional effects of the highway infrastructure and on the exposure probability and quantity of movable hazard-affected objects. By analysing the characteristics of elements affected by debris flows, we developed a systematic and quantitative method of vulnerability assessment for mountain highways. Finally, this implemented method was applied to a case study in the Xiqu section of the Sichuan-Tibet Highway, an area seriously affected by debris flows during each rainy season. The hazard characteristics of disasters were analysed, and the affected highway sections were divided into four vulnerability levels. The analysis of the results indicated that the calculated vulnerability coincides with the actual effects of the disaster, which strongly suggests that the vulnerability assessment generated by the proposed method can serve as a pertinent guide for route selection, road rehabilitation and hazard mitigation of highways affected by debris flows in mountainous regions.

We present and analyze a model of Opinion Dynamics and Bounded Confidence on the Flocking movement world. There are two systems for interaction. The theorem of 'Flocking' limits the agent's movement around the world and 'Bounded Confidence' chooses the agents to exchange the opinion. Every time step, agent i looks for the agents in its eyeshot and adjusts their opinion based on the algorithm of Bounded Confidence. When the exchange ends, every agent moves itself in a specifically direction according to Flocking theorem. We simulated the opinion formation process using the proposed model, results show the system is more realistic than the classic BC model.

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