The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the corporate distribution image, and further reduce corporate operating costs and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identified the locations of primary and secondary logistics nodes but also provided a new perspective for constructing the urban last-mile logistics system, enriching the academic research related to logistics node construction. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

With frequent interactions between social media platforms, the dissemination of information and the interaction of opinions on the internet have become increasingly complex and diverse. This increase in information complexity not only affects the formation of public opinion but may also exacerbate the spread of diseases. Based on multilayer complex networks and combined with the Deffuant-I model, this paper explores the dual impact of information complexity and individual characteristics on both information and disease propagation. Through systematic simulation experiments, this paper analyzes the mechanisms of information complexity, individual compromise, and cognitive ability in the evolution of propagation. This study shows that the interactive effects of individual characteristics and information complexity have a significant impact on disease spread. This research not only provides a new theoretical perspective for understanding complex information dissemination but also offers valuable insights for public policymakers in promoting social harmony and addressing public health emergencies.

The rapid expansion of economic globalization and trade has led to a sharp increase in the shipping investment risks currently faced by cities around the world. This study aims to explore the risk warning mechanism of shipping input under public health events to establish an effective risk warning method. This would enable the rapid identification of potential risk inputs and the implementation of targeted prevention and control measures to ensure public health and safety. This study investigates the mechanisms of both intra-regional and cross-border risk transmission within shipping networks. It establishes a transmission dynamics model (termed the SEIR-SEI model) incorporating climatic, economic, and health factors to analyze the potential inherent risks of regional shipping nodes. It uses the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model, modified by the entropy weight method, to calculate the importance of nodes in the shipping network. This comprehensive approach considers the network’s clustering coefficient, betweenness centrality, closeness centrality, degree centrality, and eigenvector centrality. To validate the practicality of the model, this study selects shipping data with Shanghai, China, as the destination node to conduct simulation computations of different risk propagation chains. The findings demonstrate that overall risk transmission is determined by the joint influence of a node’s inherent risks and propagation probabilities. This study not only clarifies the process of cross-border transmission of public health events through the shipping network between cities of different countries, but also provides insights for the application of shipping input risk assessment systems, enriching the academic research on logistics network propagation.

At present, quantitative data is mainly used as the basis for service-oriented enterprise decision-making. However, a single dimension and type of analysis cannot accurately reflect the uncertainty of enterprise operations. To solve this problem, a hybrid decision method based on fuzzy analytic hierarchy process (F-AHP) and two-dimensional cloud model is constructed, aiming to help service-oriented enterprises represented by online ride-hailing (ORH) optimize their strategies to adapt to the changing market. Firstly, a multi-layer index system is constructed from the two key dimensions of enterprise management and user service, and F-AHP is introduced to calculate the weight of each index, which weakens the fuzziness of human subjective judgment based on comprehensive analysis from the inside out. Secondly, four ORH enterprises are taken as examples; survey data and standard score intervals are qualitatively expressed through the two-dimensional backward cloud generator. Finally, the two-dimensional forward cloud generator is used to quantitatively evaluate the comprehensive situation of each ORH enterprise. The proposed method realizes effective conversion between quantitative description and qualitative concepts, and its effectiveness is verified by comparing it with existing methods. The results show that gaps among ORH enterprises are mainly concentrated in management rather than in service. Market performance largely determines an enterprise’s basic operation scale, while user service quality determines its future development space.