Fine particulate matter (PM2.5) has attracted extensive attention because of its baneful influence on human health and the environment. However, the sparse distribution of PM2.5 measuring stations limits its application to public utility and scientific research, which can be remedied by satellite observations. Therefore, we developed a Geo-intelligent long short-term network (Geoi-LSTM) to estimate hourly ground-level PM2.5 concentrations in 2017 in Wuhan Urban Agglomeration (WUA). We conducted contrast experiments to verify the effectiveness of our model and explored the optimal modeling strategy. It turned out that Geoi-LSTM with TOA reflectance, meteorological conditions, and NDVI as inputs performs best. The station-based cross-validation R2, root mean squared error and mean absolute error are 0.82, 15.44 μg/m3, 10.63 μg/m3, respectively. Based on model results, we revealed spatiotemporal characteristics of PM2.5 in WUA. Generally speaking, during the day, PM2.5 concentration remained stable at a relatively high level in the morning and decreased continuously in the afternoon. While during the year, PM2.5 concentrations were highest in winter, lowest in summer, and in-between in spring and autumn. Combined with meteorological conditions, we further analyzed the whole process of a PM2.5 pollution event. Finally, we discussed the loss in removing clouds-covered pixels and compared our model with several popular models. Overall, our results can reflect hourly PM2.5 concentrations seamlessly and accurately with a spatial resolution of 5 km, which benefits PM2.5 exposure evaluations and policy regulations.

通过分析远程桌面协议(RDP)的安全性,提出使用安全套接层扩展RDP通信协议栈的改进方案.为解决通信协议客户端的身份认证强度较低,难以抵御中间人攻击等安全问题,设计一种基于椭圆曲线算法的安全传输协议,实现基于通信协议的双向身份认证机制.结合移动终端对改进协议进行实验分析和安全性比对,验证了其可行性和通信双方的身份有效性,构建了移动虚拟化数据安全传输体系.

A prediction model of deformation resistance for finishing stands during hot continuous rolling process based on ANFIS was proposed. The subtractive clustering algorithm was adopted to build an initial fuzzy inference system, so the initial structure and parameters of fuzzy system were determined adaptively. Then the original prediction model of deformation resistance was established based on ANFIS, the hybrid algorithm was applied to training the parameters of fuzzy rules and the final prediction model was obtained. The modeling experiments were carried out and the results show that the new built model is effective for carbon steel, micro-alloyed steel, alloy steel and other steel grades, and it has better prediction accuracy than other four models including BP neural networks, Zhou Jihua's model, Baosteel 1880HSM model and SMS model.