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The Corona Virus Disease 2019(COVID-19) has a dramatic effect on my country's market and financial system. Although China has controlled the deterioration of the epidemic, this global epidemic will inevitably have an impact on the global economy including China. In order to study the shock effect of the COVID-19 on the market financial system, this paper builds a data model processing system based on the event analysis method, and analyzes the shock effect from three aspects of supply chain finance, financial securities, and corporate financial systems. Moreover, this paper uses crawler technology to obtain valid data from major websites, analyzes model data with mathematical statistics combined with event models, and outputs the results and compares them with the actual situation. Through data analysis, it can be seen that the model constructed in this paper can effectively reflect the shock effect of the COVID-19 on the market financial system. Finally, the comparison method is used to compare the research results with the actual situation. The results show that the two are basically the same. Therefore, it can be seen that the proposed research method has significant effects and has certain reference value for studying the shock effect of the epidemic on the financial system.

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With the development of network science, the coupling between networks has become the focus of complex network research. However, previous studies mainly focused on the coupling between nodes, while ignored the coupling between edges. We propose a novel cascading failure model of two-layer networks. The model considers the different loads and capacities of edges, as well as the elastic and coupling relationship between edges. In addition, a more flexible load-capacity strategy is adopted to verify the model. The simulation results show that the model is feasible. Different networks have different behaviors for the same parameters. By changing the load parameters, capacity parameters, overload parameters, and distribution parameters reasonably, the robustness of the model can be significantly improved.

Ocular diseases are closely related to the physiological changes in the eye sphere and its contents. Using biomechanical methods to explore the relationship between the structure and function of ocular tissue is beneficial to reveal the pathological processes. Studying the pathogenesis of various ocular diseases will be helpful for the diagnosis and treatment of ocular diseases. We provide a critical review of recent biomechanical analysis of ocular diseases including glaucoma, high myopia, and diabetes. And try to summarize the research about the biomechanical changes in ocular tissues (e.g., optic nerve head, sclera, cornea, etc.) associated with those diseases. The methods of ocular biomechanics research in vitro in recent years are also reviewed, including the measurement of biomechanics by ophthalmic equipment, finite element modeling, and biomechanical analysis methods. And the preparation and application of microfluidic eye chips that emerged in recent years were summarized. It provides new inspiration and opportunity for the pathogenesis of eye diseases and personalized and precise treatment.

Due to the lopsided nature of investor investment-related model research under the traditional P2P environment, and in order to improve the research effect, this study proposes an agent-based complex network testing investor trust model. This model is based on interest trust, and combines with the Bayesian method to effectively evaluate the model trust, and builds a multi-steady-state agent system based on this. At the same time, it effectively analyzes the evolutionary mechanism of the system, and validates the model's application in combination with comparative experiments. The research shows that the model can effectively improve the success rate of executing tasks and shorten the distance between cooperative agents, thus ensuring the reliability of the selection of cooperative objects and providing theoretical reference for subsequent related research.

How to identify influential nodes in complex networks is an essential issue in the study of network characteristics. A number of methods have been proposed to address this problem, but most of them focus on only one aspect. Based on the gravity model, a novel method is proposed for identifying influential nodes in terms of the local topology and the global location. This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes, replaces the shortest distance with a probabilistically motivated effective distance, and fully considers the influence of nodes and their neighbors from the aspect of gravity. On eight real-world networks from different fields, the monotonicity index, susceptible-infected-recovered (SIR) model, and Kendall's tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods. The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.