Issue 4 (October)

In the context of globalization and rapid development of e-commerce, cross-border air logistics still becomes an important factor restricting cross-border e-commerce. On the basis of analyzing the constituent elements and functions of cross-border air logistics system as well as the multiple structures of cross-border air logistics system, this paper constructs a four-dimensional conceptual model based on Hall's three-dimensional model, and discusses in detail the dimensions of the model as well as the role modes between the dimensions, which provides theoretical foundations and practical guidance for the construction and optimization of cross-border air logistics system in China.

As a key link in the global supply chain, the aviation logistics supply chain is of great significance in protecting the security and development of China's supply chain, and in promoting the economy of air cargo and civil aviation. In recent years, the aviation logistics industry has been driven by both technological innovation and market demand, and has both opportunities and challenges. In order to make the aviation logistics supply chain more resilient when encountering risks and shocks, this paper constructs a set of factors influencing the resilience of aviation logistics supply chain, adopts the DEMATEL-ISM model, analyzes the causality and hierarchical structure between the resilience influencing factors, and puts forward the strategic suggestions to maintain the resilience of aviation logistics supply chain with respect to the important influencing factors, so as to enrich the related research in the field of resilience of aviation logistics supply chain.

With the rapid development and increased complexity of global financial markets, the effectiveness of risk management becomes more and more important. In this paper, a Bayesian network-based financial market risk prediction and decision support system (DSS) is proposed to enhance the accuracy and efficiency of financial decision making. The system architecture includes an input data module, an inference engine, and a user interaction interface, emphasizing data integration and real-time processing capabilities. The input module is responsible for collecting real-time and historical financial data, and the inference engine utilizes Bayesian inference algorithms to assess risks and generate decision recommendations. It is shown that the system is able to effectively capture the complex dependencies of variables in the financial market, provide accurate risk prediction, and thus help decision makers to make scientific judgments in a dynamic environment.

In addition, the user interaction interface is designed to make the system more user-friendly for quick understanding and application of risk assessment results. The integration and maintenance strategy of the system ensures the stability and scalability of the DSS and lays the foundation for future functionality enhancements. Despite the positive results of this study in financial market risk management, there are still some challenges, such as the dependence on the quality of input data and the increase in computational complexity. Future research could explore the integration with other machine learning techniques to further enhance the prediction capabilities