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Summary: Art painting is a very important major in China's education system. Under the background of the new era, the country vigorously promotes the modernization of education and the development of intelligence. Many subjects of teaching have been more mature and carried out intelligent teaching based on virtual reality technology. But throughout the art painting profession, the research on this aspect is almost blank. The main reason is that there are deep academic barriers between art painting and computer science. Therefore, this paper puts forward the application research of multi‐source information art painting fusion interactive 3D dynamic scene virtual reality technology. This paper deeply analyzes the needs of art painting and its teaching reform and believes that the existing virtual reality technology cannot adapt to the teaching environment of art painting. In view of the possible inaccurate calculation and other problems, this paper puts forward the optimization scheme. The core of the scheme is to use the information integration advantages of multi‐source information fusion to provide more comprehensive and complete object information for 3D modeling of virtual reality technology, so as to improve the imaging quality of virtual reality technology. In order to meet the requirement of high precision, the calculation method is optimized, the calculation steps are simplified, and the sensor selection is optimized again. This paper introduces the design principle and operation steps of the optimization method in detail. In order to further verify the actual effect of this method, comparative analysis method is used to test the comprehensive performance. In order to ensure the test quality, the median filtering algorithm and fuzzy algorithm are compared. Through the analysis and comparison of the data, the optimization method in this paper greatly improves the accuracy of calculation and the comprehensive performance is improved. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of China's economy, the extensive mode of growth supported by coal has caused great damage to resources and the environment. Problems such as gas drainage, coal gangue dumping, water resources destruction, and surface subsidence during the coal mining process have directly led to severe damage to the environment in the mining area to ensure the survival and development of the coal industry, and to achieve sustainable development, we must actively promote the "optimized layout, intensive development, green mining, safe production, and clean use." This paper proposes the multimedia communications model and green low‐carbon development mechanism in coal industry with cloud Internet of Things (IoT) to deal with this challenge. In a distributed computing scenario, the communication bandwidth between different nodes is very limited and heterogeneous, and some different computing nodes support a wide range of computing capabilities. Decentralized computing connects devices with computing power to the network, and the nodes communicate directly or indirectly will deal with this challenge effectively. The multimedia transmission is also applied to guarantee the visualized model. The experiment compared with the other state‐of‐the‐art methodologies has proven that the proposed model has the higher transmission efficiency and robustness. [ABSTRACT FROM AUTHOR]

Summary: Predicting mobile users' future traffic usage plays an important role for mobile operators to optimize their services. Although a variety of prediction methods have been proposed and applied to this issue, none of them could answer whether the users' traffic behaviors can be predicted. By employing the entropy theory, this paper analyzes the predictability of users' behaviors on traffic types and accounts. Moreover, the mobile network traffic data collected from a major city in China is utilized to test the entropy theory and reveal the accuracy levels. Furthermore, by comparing the prediction results between the years 2012 and 2014, a conclusion is drawn that a large portion of users' behaviors can be predicted, but their traffic usage habits become more difficult to predict in 2014 than in 2012. The results also imply that different entropies achieve more similar predictability in 2014 than in 2012, which indicates the consistency of users' behaviors. Overall, this study reveals the predictability and consistency of users' behavior, which is of great significance for mobile operators. [ABSTRACT FROM AUTHOR]