Our results hint at a plausible relationship between the neuropsychiatric symptoms and reorganization of the structural brain network of patients with systemic lupus erythematosus. Brain connectivity analysis may be a potential tool to subtype these patients.

PurposeThe purpose of this paper is to explore the determinants of consumer purchase intention (CPI) of cross-border e-commerce (CBEC) in the countries of the Belt and Road Initiative (BRI).Design/methodology/approachThis study proposes a research model of the antecedents of CPI on CBEC in BRI countries. Study participants were consumers with CBEC shopping experience in BRI-associated countries (n = 278). Structural equation modeling was used to test the research model.FindingsTrust has the greatest effect on CPI, while perceived security has the least effect. In addition, in BRI-associated countries, in contrast to the previous study, product presentation was found to have a significant positive influence on CPI in CBEC. Platform simplicity and logistic service have a significant positive influence on CPI. Practical implicationsThese findings offer important implications for CBEC. Consumers' trust in product providers has the greatest impact on CPI. Simplicity, timely shipment tracking and the fast delivery speed of the platform will increase CPI. The results suggest a highly successful tactic for enhancing consumers' perceptions of product authenticity and interest. Finally, this study provides insights into BRI. Originality/valueThis study contributes to the literature on CBEC. It explores the multilevel (i.e. product presentation, platform simplicity, logistic service, perceived security, and trust) determinants of CPI on CBEC. The study provides insights into the determinants of CPI in BRI countries.

Overlapping community is a response to the real network structure in social networks and in real society in order to solve the problems such as the parameters of the existing overlapping community discovery algorithm being too large, excessive overlap and no guarantee of stability of multiple runs. In this paper, the method of calculating the node degree of membership was proposed, and an overlapping community discovery algorithm based on the local optimal expansion cohesion idea was designed. Firstly, the initial core community was constructed with the highest importance node and its neighbor nodes. Secondly, the core community was extended by node attribution degree until the termination condition of the algorithm was satisfied. Finally, the experimental results were compared with the existing algorithms. The experiments show that the result of the division by the improved algorithm has been significantly improved compared to the other algorithms, and the community structure after the division is more reasonable.

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