In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes.

The smile effect, which happens when various light-emitting units convert vertical offsets into angular offsets following fast-axis alignment, is an unavoidable part of the packaging process for diode laser arrays and has a detrimental influence on the feedback locking of external cavity diode lasers. The external cavity spectral beam combining system without output coupler, which employs the 0th order diffracted light with low grating energy to enable feedback locking of the light-emitting unit, is particularly severely impacted by the grin effect. The external cavity spectral beam combining system without output coupler uses three different types of corrective structures to simulate and explain the grin effect in various ways. The results show that while also ensuring adequate feedback and better combined beam quality, the double - separated fast-axis collimator can significantly correct the smile effect-induced beam shift. However, some light-emitting units' output beams cannot be corrected for the S-shaped smile effect with complex position offset changes. The fast-axis telescope system can effectively offset the inadequate feedback brought on by the various types of grin effect, but it is unable to enhance the beam quality of the combined beam spot, which degrades as the degree of smile effect increases. The combination of double-separated fast-axis collimator and fast-axis telescope system can complement each other to ensure sufficient feedback for different forms of smile effect to achieve stable wavelength locking and better beam quality of the combined spot.

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