The pattern of international agricultural trade is undergoing profound changes. The influence of country risks on the international agricultural trade pattern is prominent. In this paper, we comprehensively analyze the international agricultural trade patterns and explore the influence of country risks on them. Specifically, we first construct an international agricultural trade network (IATN) based on complex network theory. Second, we analyze each country’s diversity of import sources and the position of countries in the IATN using the Herfindahl–Hirschman Index (HHI) and network indicators, such as in-degree, out-degree, weighted in-degree, weighted out-degree, and betweenness centrality. Third, this paper explores the influence of different types of country risks, including economic risk and political risk, on international agricultural trade patterns using the panel regression method. The results show that countries played different roles and occupied different positions in the international agricultural trade pattern; notably, the United States occupied a core position, while Japan and Mexico had insufficient diversity in import sources. Moreover, based on the panel regression method, we find that political risks have a positive impact on the agricultural trade pattern, while an unstable economic environment could inhibit the agricultural trade pattern in various countries. This study could provide references for countries to implement agricultural trade policies regarding country risks to ensure stable agricultural trade relations and national food security.

Heavy metal pollution in soil is threatening the ecological environment and human health. However, field measurement of heavy metal content in soil entails significant costs. Therefore, this study explores the estimation method of soil heavy metals based on remote sensing images and machine learning. To accurately estimate the heavy metal content, we propose a hybrid artificial intelligence model integrating least absolute shrinkage and selection operator (LASSO), genetic algorithm (GA) and error back propagation neural network (BPNN), namely the LASSO-GA-BPNN model. Meanwhile, this study compares the accuracy of the LASSO-GA-BPNN model, SVR (Support Vector Regression), RF (Random Forest) and spatial interpolation methods with Huanghua city as an example. Furthermore, the study uses the LASSO-GA-BPNN model to estimate the content of eight heavy metals (including Ni, Pb, Cr, Hg, Cd, As, Cu, and Zn) in Huanghua and visualize the results in high resolution. In addition, we calculate the Nemerow index based on the estimation results. The results denote that, the simultaneous optimization of BPNN by LASSO and GA can greatly improve the estimation accuracy and generalization ability. The LASSO-GA-BPNN model is a more accurate model for the estimate heavy metal content in soil compared to SVR, RF and spatial interpolation. Moreover, the comprehensive pollution level in Huanghua is mainly low pollution. The overall spatial distribution law of each heavy metal content is very similar, and the local spatial distribution of each heavy metal is different. The results are of great significance for soil pollution estimation.

Stock volatility is an important measure of financial risk. Due to the complexity and variability of financial markets, time series forecasting in the financial field is extremely challenging. This paper proposes a “model fusion learning algorithm” and a “feature reconstruction neural network” to forecast the future 10 min volatility of 112 stocks from different industries over the past three years. The results show that the model in this paper has higher fitting accuracy and generalization ability than the traditional model (CART, MLR, LightGBM, etc.). This study found that the “model fusion learning algorithm” can be well applied to financial data modeling; the “feature reconstruction neural network” can well-model data sets with fewer features.

No abstract

Cobalt is a key resource for the global energy transition, and the differences in the natural endowment of cobalt have led to frequent cobalt trade among countries. This study aims to reveal the dependence patterns of cobalt trade among countries and the impact of country risks (including political and economic risks) on the patterns. First, a cobalt import dependence network (CIDN) and a cobalt export dependence network (CEDN) are established using the network analysis method. Furthermore, this study uses network indicators to reveal the dependence patterns of cobalt trade among countries, and construct diversification indices of trade relations to further analyze the import source risk and the market concentration of cobalt trade. The results indicate that most cobalt importers have a high import source risk, and most cobalt exporters have a high market concentration. Finally, based on the panel regression methods, we reveal an interesting result showing that the dependence patterns of cobalt trade are significantly influenced by country risks. Specifically, on the one hand, for importers, an increase in political risk or economic risk has a negative impact on their dependence patterns of cobalt trade. On the other hand, for exporters, an increase in political risk or economic risk has different effects on their dependence patterns of cobalt trade. This study suggests that countries should pay more attention to the role of country risks in driving the dependence patterns when making cobalt trade policies.