Mobile Internet refers to a new mode of information interaction through mobile terminals and the use of wireless communication to access information services. The mobile Internet has provided a new model for the traditional information circulation mode, rural economic structure, and social transformation. With an association between the mobile Internet and farmers’ adoption behavior of new green technology, this paper investigated the impact of mobile Internet application on farmers’ adoption of green technology through a mediating effect model. From the perspective of new agricultural technology diffusion and based on the perspective of factor substitution, we decompose the mechanism that mobile Internet applications affect the adoption of new wheat varieties. Along the path of information transmission, evaluation, and adoption of new wheat varieties, we deeply explored the impact of the mobile Internet on the adoption of new wheat varieties by different types of wheat growers. The results show that there is a significant mediating effect of information acquisition ability, risk attitude, and expected return on the influence of the mobile Internet on green technology adoption. For the adoption mechanism of new green varieties, there was a distinct difference among various farmers.

Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.

In view of the lack of consideration of uncertainty factors in vegetable price prediction research and that short-term vegetable price prediction is crucial for stabilizing the industry's development, this paper focuses on the Chinese vegetable market. From the standpoint of perfecting feature variables, we add the Baidu index with extreme weather as the keyword and the Chinese economic policy uncertainty index as the new feature variables, while analyzing whether using the CNN-LSTM deep learning model with attention mechanism can increase the accuracy of short-term vegetable price predictions. Additionally, determine whether using China's economic policy uncertainty index and the Baidu index with extreme weather as the keyword can increase the accuracy of short-term vegetable price predictions. Firstly, the study uses Spearman correlation to rank the correlations of the variables. Secondly, it uses the TVP-VAR model to analyze the time-varying effects of the Baidu and China Economic Policy Uncertainty indices on the vegetable price index. Lastly, it uses the control variable method and fuses the CNN-LSTM-Attention model to test the effectiveness of the additional feature variables. According to the experimental results, RMSE, MAE, MAPE and R² are 2.37, 1.78, 1.00% and 0.75, respectively. Benchmark research results indicate that the model's short-term prediction ability can be considerably enhanced by using the Baidu index and the China economic policy uncertainty index with extreme weather as the keywords.