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The influence of aerosol types on Solar radiation prediction was studied in this paper. According to Chinese population density, China administration districts were divided into areas with urban aerosol type and rural aerosol type based on Arcgis. The Solar radiation all over China was simulated by the Mesoscale numerical Weather Prediction Model (WRF). The forecasting was compared with the data of “Clouds and the Earth’s Radiant Energy System” released by NASA. The results showed that there was a big improvement when the different aerosol types in different areas were taken into consideration.

This article applies CMS model and selects the data about exports of agricultural products to all the other members of Shanghai Cooperation Organization (SCO) except for China from 1998 to 2018 to decompose the influencing factors of exports of agricultural products from China to all the other members of SCO. The results show that the competitiveness effects plays an important role in promoting the growth of exports while structural change effects is the inhibition effect.

Based on density functional theory (DFT), the adsorption properties of arsenic (As) via the five most abundant oxides in fly ash were investigated. It has been proved that the iron oxide (Fe2O3) in fly ash is the effective active component during the adsorption process. This paper verifies the feasibility of using fly ash as the adsorbent to control As pollution in power plants, and also provides some reference for the subsequent experimental research.

In view of the batch implementations of standard support vector machine must be retrained from scratch every time when the training set is incremental modified, an incremental learning algorithm based on least squares twin multi-class classification support vector machine (ILST-KSVC) is proposed by solving two inverse matrix. The method will be applied on online environment to update initial data, which avoided cumbersome double counting. ILST-KSVC inherited the advantages of the basic algorithm and has some merits of Least square twin support vector machine for excellent performance on training speed and support vector classification regression for K-class’s well classification accuracy. The result will be confirmed no matter in low dimension or in high dimension in UCI datasets.