The development of cross-border e-commerce is generally faced with problems such as high freight, long transportation time, and low service level. However, overseas warehouses can effectively solve the above problems to a certain extent, and they can improve consumer satisfaction. Therefore, this paper proposed a method combined with the entropy technique for order of preference by similarity to ideal solution (E-TOPSIS) model and complex network analysis theory to make a comprehensive determination of overseas warehouse locations for China’s e-commerce exports in the context of the Belt and Road Initiative (B&R). We selected 62 countries along the B&R as pre-candidates for overseas warehouse locations and then evaluated the significance of each node in cross-border e-commerce for Chinese export products. Finally, 15 countries were identified as the optimal overseas warehouse locations for Chinese export products along the B&R. The results can provide reference for overseas warehouse deployment of Chinese cross-border e-commerce enterprises as well as the development and the construction of the B&R.

The utilization of gene selection techniques is crucial when dealing with extensive datasets containing limited cases and numerous genes, as they enhance the learning processes and improve overall outcomes. In this research, we introduce a hybrid method that combines the binary reptile search algorithm (BRSA) with the LASSO regression method to effectively filter and reduce the dimensionality of a gene expression dataset. Our primary objective was to pinpoint genes associated with COVID-19 by examining the GSE149273 dataset, which focuses on respiratory viral (RV) infections in individuals with asthma. This dataset suggested a potential increase in ACE2 expression, a critical receptor for the SARS-CoV-2 virus, along with the activation of cytokine pathways linked to COVID-19. Our proposed BRSA method successfully identified six significant genes, including ACE2, IFIT5, and TRIM14, that are closely related to COVID-19, achieving an impressive maximum classification accuracy of 87.22%. By conducting a comparative analysis against four existing binary feature selection algorithms, we demonstrated the effectiveness of our hybrid approach in reducing the dimensionality of features, while maintaining a high classification accuracy. As a result, our hybrid approach shows great promise for identifying COVID-19-related genes and could be an invaluable tool for other studies dealing with very large gene expression datasets.

The prediction of ship traffic flow is an important fundamental preparation for layout and design of ports as well as management of ship navigation. However, until now, the temporal characteristics and accurate prediction of ship flow sequence in port are rarely studied. Therefore, in this study, we investigated the presence of long-range dependence in container ship flow sequences using the Multifractal Detrended Fluctuation Analysis (MF-DFA). We considered three representative container ports in the world—including Shanghai, Singapore, and Rotterdam container ports—as the study sample, from 1 January 2013 to 31 December 2017. Empirical results suggested that the ship flow sequences are deviated from normal distribution, and the sequences with different time scales exhibited varying degrees of long-range dependence. Furthermore, the ship flow sequences possessed a multifractal nature, where the larger the time scale of ship flow time series, the stronger the multifractal characteristics are. The weekly ship flow sequence in the port of Singapore owned the highest degree of multifractality. Furthermore, the multifractality presented in the ship flow sequences of container ports are due to the correlation properties as well as the probability density function of the ship flow sequences. The study outlines the importance of adopting these features for an accurate modeling and prediction for maritime ship flow series.