Influence of Phragmites communis and Zizania aquatica on rhizosphere soil enzyme activity and bacterial community structure in a surface flow constructed wetland treating secondary domestic effluent in China.

The present study aims to investigate the effects of Phragmites communis and Zizania aquatica on rhizosphere soil enzyme activity and bacterial community structure in a surface flow constructed wetland (SFCW) for the treatment of domestic sewage from the Shanxi province of China. The basic physical and chemical properties of the soil, the contents of soil urease (UE), alkaline phosphatase (ALP), soil microbial biomass carbon and nitrogen (SMBC, SMBN), and bacterial community structure were measured in the Phragmites communis group (PG), Zizania aquatica group (ZG), and control group (CG), respectively. The results showed that (1) the contents of UE, ALP, SMBC, and SMBN in rhizosphere soil of PG were more than those of ZG; (2) the highest bacterial abundance and α-diversity appeared in PG, in which Gp6 was the most abundant bacterial genus in PG; (3) the main functions of the dominant bacteria Gp6 and Longilinea in PG were involved in metabolizing multiple carbohydrates and participating in the carbon cycle in the soil based on the clusters of orthologous groups pathway analysis data; (4) the bacterial community of PG was mainly affected by the positive correlation with arsenic, nickel, or SMBC via the redundancy analysis. Collectively, Phragmites communis is a recommended species for wastewater wetland treatment system in Shanxi province, and the special enzymes and dominant bacteria in plant rhizosphere soil had obvious functions of removing organic pollutants. Besides, the influences of environmental factors on rhizosphere bacteria and the combined effects of Phragmites communis and dominant bacteria in wetland wastewater treatment system should be taken seriously.

High-G Calibration Denoising Method for High-G MEMS Accelerometer Based on EMD and Wavelet Threshold.

High-G MEMS accelerometers have been widely used in monitoring natural disasters and other fields. In order to improve the performance of High-G MEMS accelerometers, a denoising method based on the combination of empirical mode decomposition (EMD) and wavelet threshold is proposed. Firstly, EMD decomposition is performed on the output of the main accelerometer to obtain the intrinsic mode function (IMF). Then, the continuous mean square error rule is used to find energy cut-off point, and then the corresponding high frequency IMF component is denoised by wavelet threshold. Finally, the processed high-frequency IMF component is superposed with the low-frequency IMF component, and the reconstructed signal is denoised signal. Experimental results show that this method integrates the advantages of EMD and wavelet threshold and can retain useful signals to the maximum extent. The impact peak and vibration characteristics are 0.003% and 0.135% of the original signal, respectively, and it reduces the noise of the original signal by 96%.