A series of new arylpropenamide derivatives containing different aryl groups were synthesized, characterized, and evaluated for their anti-hepatitis B virus (HBV) activities. A new high accuracy QSAR model of arylpropenamide was constructed based on a more completely activities data and calculation parameter. The 2D-QSAR equations, by using DFT and multiple linear regression analysis methods, revealed that higher value of thermal energy (TE) and lower entropy (S(ө) ) increase the anti-HBV activities of the arylpropenamide molecules. Predictive 3D-QSAR models were established by SYBYL multifit molecular alignment rule. The optimum models were all statistically significant with cross-validated and conventional coefficients, indicating that they were reliable enough for activity prediction.

Distance education system plays an important role in promoting the integration of professional characteristics and application practice. In order to solve the problem of network throughput decline caused by communication channel data conflict, a distance teaching system of interior design course is designed based on Internet of Things and education platform. In the hardware part, the audio acquisition circuit and video acquisition circuit are designed to ensure the quality of signal transmission. In the software part, the communication architecture is set based on the Internet of Things technology, and a course data anticollision algorithm is proposed, which uses the spread spectrum code to separate the corresponding data to avoid data collision. Based on the education platform, the function structure of distance education system is designed to complete the course management and maintenance. The experimental results show that the maximum throughput of the distance education system designed in this paper is 3.54 MB/s, which is 0.86 MB/s and 0.97 MB/s more than the system based on big data technology and artificial intelligence, and it can effectively avoid the problem of data conflict.

Combined with the development requirements of current environmental art mining system, the design method of environmental art design element mining system based on deep learning is optimized, and the hardware configuration of environmental art design element mining system is introduced. Combined with the principle of deep learning, the system software operation algorithm and function are improved, so as to improve the effect of environmental art design element mining, Ensure the operation effect of the system to the greatest extent. Finally, the experiment proves that the environment art design element mining system based on deep learning has high effectiveness in the practical application process, which can better guide the design of environment art and fully meet the research requirements.

The large group of left-behind children with the absence of parental accompanying are likely to have serious physical and psychological problems, which may lead to serious public safety and social economic troubles in adulthood. Such unique phenomenon calls us attention on the impact of parents on household educational investment. Based on the data of China Family Panel Studies in 2014, This paper examines the effects of parents’ cognitive ability on household educational investment for their children. The research propositions were tested using multiple regression analysis methods. Results indicate that parents’ cognitive ability can significantly improve the level of monetary and non-monetary investment in education. We also find that compared with their counterparts, the cognitive ability of left-behind children’s parents fails to affect their household educational investment, due to the “parent-child separation effect”. Further analysis shows that improving the regional informatization level of parents of left-behind children can alleviate the “parent-child separation effect”, and finally facilitate cognitive ability’s role in increasing household educational investment. These findings enlighten education policy makers and households a feasible way to alleviate the imbalance and insufficiency of household educational investment among left-behind children families.