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Identifying the current status of the heavy metal(loid) input of agricultural soils is vital for the soil ecological environment of agricultural-producing areas. Most previous studies have typically carried been out in small regions with limited sampling sites, which is insufficient to reveal the overall status of China. This study reviewed publications from over the past 20 years and calculated the input fluxes of heavy metal(loid)s in agricultural soil via atmospheric deposition, fertilizer, manure, and irrigation in different regions of China based on spatiotemporal heterogeneity using a meta-analysis, providing more accurate and reliable results. It was found that the heavy metal(loid) input flux of atmospheric deposition in China is large, while that of fertilizer and manure is relatively low compared to Europe. The major sources of As, Cd, Cr, Ni, and Pb entering the soil was atmospheric deposition, which accounted for 12% to 92% of the total input. Manure was responsible for 19% to 75% of the Cu and Zn input. Cd is the element presenting the most significant risk to the environment of agricultural soils in China and its safety limit will be reached within 100 years for most regions. The region we need to be concerned about is Huang-Huai-Hai due to its comprehensive pollution.

Information propagation prediction aims to analyze the patterns of information spreading in social networks and social media, thus understand and predict the information diffusion process. Recent researches have shown that information propagation is influenced by both group and individual relations. Existing works have mainly concentrated on group relations, including social relation and dynamic structural relation. They ignore a core group relation, i.e., the co-occurrence user relation, and an important individual relation, i.e., the user-preference relation, leading to incomplete modeling of the information propagation process. To address this issue, this paper comprehensively considers both group and individual relations, and proposes an information propagation prediction model that integrates user relation representations and information diffusion topology features. For the group relation, this paper constructs a user co-occurrence graph to learn user similarity relation representations, which are then fused with information diffusion topology features to capture group relations. For the individual relation, this paper fuses user representation and influencing factors to capture internal factors on stimulating users to share information. Experimental results show that the performance of the proposed model on two public datasets is improved, and the MAP@k and hits@k evaluation indicators on the Memetracker dataset are improved by an average of 6.54% and 2.75%, respectively.