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In order to improve the performance of a micro-electro-mechanical system (MEMS) accelerometer, three algorithms for compensating its temperature drift are proposed in this paper, including deep long short-term memory recurrent neural network (DLSTM-RNN, short DLSTM), DLSTM based on sparrow search algorithm (SSA), and DLSTM based on improved SSA (ISSA). Moreover, the piecewise linear approximation (PLA) method is employed in this paper as a comparison to evaluate the impact of the proposed algorithm. First, a temperature experiment is performed to obtain the MEMS accelerometer’s temperature drift output (TDO). Then, we propose a real-time compensation model and a linear approximation model for neural network methods compensation and PLA method compensation, respectively. The real-time compensation model is a recursive method based on the TDO at the last moment. The linear approximation model considers the MEMS accelerometer’s temperature and TDO as input and output, respectively. Next, the TDO is analyzed and optimized by the real-time compensation model and the three algorithms mentioned before. Moreover, the TDO is also compensated by the linear approximation model and PLA method as a comparison. The compensation results show that the three neural network methods and the PLA method effectively compensate for the temperature drift of the MEMS accelerometer, and the DLSTM + ISSA method achieves the best compensation effect. After compensation by DLSTM + ISSA, the three Allen variance coefficients of the MEMS accelerometer that bias instability, rate random walk, and rate ramp are improved from 5.43×10−4mg, 4.33×10−5mg/s12, 1.18×10−6mg/s to 2.77×10−5mg, 1.14×10−6mg/s12, 2.63×10−8mg/s, respectively, with an increase of 96.68% on average.

Nitrogen pollution in water bodies is becoming increasingly serious, and how to remove nitrogen from water bodies economically and effectively has become a research hotspot. Especially in recent years, with the gradual expansion of aquaculture in China, the content of nitrite and other nitrogen-containing substances in water bodies has been increasing, which inhibits the growth of farm animals and is one of the causes of eutrophication in water bodies. In this study, a strain of bacteria was isolated from the sludge of an aquaculture fishpond and identified as Pseudomonas stutzeri, which can efficiently degrade nitrite. After continuous domestication in nitrite mixed solution, the nitrite nitrogen reduction capacity of P. stutzeri was significantly improved. Univariate experiments aiming to optimize the degradation conditions indicate that the optimal culture conditions for strain F2 are: medium with a carbon source of sodium succinate; C/N of 18; pH of 8; culture temperature of 28 °C; and shaking speed of 210 rpm in the shaker. Under the optimal culture conditions, the NO2−-N concentration of the culture solution was 300 mg/L, and the nitrite removal rate reached 98.67%. Meanwhile, the results of the nitrogen balance test showed that the strain converted 6.1% of the initial nitrogen into cellular organic nitrogen and 62.3% into gaseous nitrogen.