Communication traffic prediction is of great guiding significance for communication planning management and improvement of communication service quality. However, due to the complex spatiotemporal... Show More

Communication traffic prediction is of great guiding significance for communication planning management and improvement of communication service quality. However, due to the complex spatiotemporal correlation and uncertainty caused by the spatial topology and dynamic time characteristics of mobile communication networks, traffic prediction is facing enormous challenges. We propose a mobile traffic prediction method using dynamic spatiotemporal synchronous graph convolutional network (DSSGCN). DSSGCN has designed multiple components, which can effectively capture the heterogeneity in the local space-time map. More specifically, the network not only models the dynamic characteristics of nodes in the spatiotemporal graph of network traffic, but also captures the dynamic spatiotemporal characteristics of the edges of mobile service data with different time stamps. The outputs of these two components are fused by collaborative convolution to obtain the prediction results. Experiments on two ground truth mobile traffic datasets show that our DSSGCN model has good prediction performance.

This paper uses advanced analytics to explore emerging trends in sustainable human resource management (HRM). Analyzing publications from 1991 to 2023 via the Scopus database identifies key trends... Show More

This paper uses advanced analytics to explore emerging trends in sustainable human resource management (HRM). Analyzing publications from 1991 to 2023 via the Scopus database identifies key trends and contributions in sustainable HRM research, including sustainability, green HRM, and corporate social responsibility. The findings highlight an increasing research volume, significant global contributions, and a growing academic interest in integrating sustainability into HR practices. Despite its reliance on the Scopus database, this review provides valuable insights for HR practitioners and organizations aiming to enhance sustainability through HR strategies. It also maps future research directions in sustainable HRM.