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In this work, some important factors such as ceramic shell strength, heat preservation temperature, standing time and withdrawal rate, which influence the formability of directionally solidified large-size blades of heavy-duty gas turbine with the liquid metal cooling (LMC) process, were studied through the method of microstructure analysis combining. The results show that the ceramic shell with medium strength (the high temperature flexural strength is 8 MPa, the flexural strength after thermal shock resistance is 12 MPa and the residual flexural strength is 20 MPa) can prevent the rupture and runout of the blade. The appropriate temperature (1,520 °C for upper region and 1,500 °C for lower region) of the heating furnace can eliminate the wide-angle grain boundary, the deviation of grain and the run-out caused by the shell crack. The holding time after pouring (3-5 min) can promote the growth of competitive grains and avoid a great deviation of columnar grains along the crystal orientation , resulting in a straight and uniform grain structure. In addition, to avoid the formation of wrinkles and to ensure a smooth blade surface, the withdrawal rate should be no greater than the growth rate of grain. It is also found that the dendritic space of the blade decreases with the rise of solidification rate, and increases with the enlarging distance between the solidification position and the chill plate.

Aiming at the operation safety of city gas polyethylene pipeline, this paper proposes a semi-quantitative risk evaluation technique to determine the possibility of risk failure and severity of consequence of PE gas pipeline. According to the basic procedure of semi-quantitative risk assessment, the failure probability and failure consequence of buried pipeline are analysed, and the safety risk level of each location of pipeline is evaluated comprehensively. The application of semi-quantitative risk assessment technology to gas polyethylene pipeline in a city in China shows that this technology can realize scientific management of pipeline according to risk level and further reduce the possibility of failure, so as to ensure safe, economical and reasonable operation of pipeline.

The applicability of wind speed measurement uncertainty evaluation with the pitot tube for the Guide to the expression of uncertainty in measurement (GUM) method is yet to be validated. Firstly, the uncertainty of the measured value of wind speed by the pitot tube is evaluated by GUM and Monte Carlo method (MCM). Then, the applicability of GUM method is verified by the method given in JCGM 101:2008 GUM supplement1, and it is concluded that the GUM method is still applicable to the evaluation of uncertainty in the wind speed measurement of pitot tube. Finally, comparing the GUM method with MCM, the influence degree of the GUM method on the volatility and dispersion of the probability distribution of wind speed measurement result is -0.31%~0.33% and -0.48%~0.26%, respectively. Since the degree of influence is small to the measured value of wind speed relatively, it is possible to determine whether to correct it according to the actual situation when applying.

This paper presents an approach of solfeggio audio machine assessment based on multiple audio spectrum features. In this machine assessment system, we analyze 13 spectrum audio features of solfeggio audios, and build a machine assessment model based on the scoring results of music experts, aiming to score the solfeggio audio system at the professional level based on machine learning techniques. An effective way is designed to implement the system with high generalization ability in this paper, with deploying 150 practice audio files marked with expert scores for training and testing the model. Various features are extracted to train predictors, which access the distances between practice audio files and their target audio files. In this way, these predictors can mark unknown audio files reliably. The training and testing process are described in detail, and the test results testify that our system can provide expert level scoring.

Leaf nitrogen content is an important index of crop growth and plays an important role in crop growth and development. In this paper, the hyperspectral data of winter wheat and the leaf nitrogen content is used to study winter wheat on flagging stage, flowering stage and grain filling stage. The estimation model of nitrogen content in winter wheat leaves at different growth stages is constructed by using partial least squares method and verified by using a cross-validation method. The results showed that R2 and the RMSE of the three growth stages were 0.53, 0.68, 0.64 and 0.331%, 0.246% and 0.406% respectively, and R2 and RMSE of model validation were 0.44, 0.71, 0.64 and 0.369%, 0.235% and 0.410%. Both the prediction model and the verification model had high reliability. Therefore, it is feasible for UAV to carry hyperspectral monitoring system for retrieving nitrogen content of winter wheat leaves.