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Abstract Purpose Microbial resources are abundant in fermented grains of the Chinese Fen-flavor Baijiu, which is closely related to the quality of Baijiu. The purpose of this study was to investigate the microbial community structure and function in Daqu and fermented grains. Methods We systematically compared two technical approaches, amplicon sequencing, and metagenomic sequencing, to analyze the microbial communities during Baijiu fermentation. Result The results showed that lactic acid bacteria (LAB) and yeasts were the main microorganisms in the fermentation process. Firmicutes (Lactobacillus, Pediococcus, and Weissella) were the dominant bacteria, and Ascomycota (Issatchenkia or Pichia) was the dominant fungus in fermented grains. Moreover, Pichia kudriavzevii, Lichtheimia ramosa, and Companilactobacillus paralimentarius were the dominant species at the initial stage of fermentation by metagenomic sequencing. Latilactobacillus curvatus, Loigolactobacillus coryniformis subsp. coryniformis, and Lentilactobacillus parabuchneri became dominant during the middle stage of fermentation. Lentilactobacillus parabuchneri and Lactobacillus acetotolerans were the dominant species in the final stage of fermentation. Spearman correlation analysis showed that LAB inhibited the growth of yeasts. Conclusion Combining the two sequencing methods provided valuable insights into the dynamic succession of microorganisms during the fermentation of Baijiu. It had had a particular significance for mining microbial species resources in fermented grains.

To effectively monitor the reliability of civil aircraft flap deflection angle, combined with the quick access recorder(QAR), the collaborative intelligent moving Kriging(CIMK) method is proposed by absorbing the Kriging model, decomposition and co-ordination strategy, equilibrium optimizer(EO), and moving least square(MLS). Among them, the decomposition coordination strategy is used to deal with the relationship between the flaps left and right deflection angles. MLS is employed to select effective modeling samples and solve the undetermined coefficients of Kriging model. EO method is applied to determine optimizing the local compact support region radius of MLS. Firstly, the fault reason for flap left-right asymmetry is analyzed to clarify the main characteristic parameters in QAR data. Secondly, combined with the QAR data of relevant influencing parameters, the civil aircraft flap deflection model(limit state function) is constructed by using CIMK. Then, the reliability and influence of civil aircraft flap deflection angle are analyzed by Monte Carlo(MC) sampling method. The results show that when the flap deflection angle is 3°, the reliability is 0.450 2, and the important factors affecting the flap deflection angle are Mach number, left angle of attack, right angle of attack, etc. Compared with the response surface method(RSM), Kriging, support vector machine(SVM), and back-propagation-artificial neural network(BP-ANN), the average absolute error accuracy of the proposed method is relative improved by 53.02%, 51.43%, 49.03%, and 44.04%, the average relative error accuracy is relative improved by 68.36%, 66.76%, 64.41%, and 62.64%, and the modeling efficiency is relative improved by 50.62%, 26.35%, and 43.01% respectively compared with Kriging, SVM and BP-ANN. When the number of simulations is 103, the analysis accuracy is relative improved by 8.82%, 7.25%, 6.22%, and 3.98% respectively.

Temperature is one of the important characteristic parameters of civil aircraft hydraulic system performance. To effectively realize the temperature monitoring of hydraulic system, the method of Kriging model based on atomic search (ASOKM) is proposed based on Kriging model and atomic search optimization (ASO) algorithm. Firstly, the temperature fault causes of hydraulic system are analyzed, and the fault logic diagram is established to clarify the characteristic parameters affecting the hydraulic system temperature. Then, combined with the quick access recorder (QAR) data, the modeling principle of ASOKM method is introduced. Finally, the effectiveness of the proposed ASOKM method is verified by the temperature monitoring analysis of domestic civil aircraft hydraulic systems. The results show that, in comparison with response surface method (RSM), Kriging, and back-propagation-artificial neural network (BP-ANN) models, the training and monitoring average absolute errors of the ASOKM method are lower, which is of higher accuracy, efficiency and robustness. The proposed ASOKM method can provide a reference for reliability analysis, fault diagnosis, and predictive maintenance of the hydraulic system.