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Rock bursts have become one of the most severe risks in underground coal mining and its forecasting is an important component in the safety management. Subsurface microseismic (MS) monitoring is considered potentially as a powerful tool for rock burst forecasting. In this study, a methodology for rock burst forecasting involving the use of a fuzzy comprehensive evaluation model was developed, which allows for a more quantitative evaluation of the likelihood for the occurrence of a rock burst incident. In the fuzzy model, the membership function was built using Gaussian shape combined with the exponential distribution function from the reliability theory. The weight of each index was determined utilising the performance metric F score from the confusion matrix. The comprehensive forecasting result was obtained by integrating the maximum membership degree principle (MMDP) and the variable fuzzy pattern recognition (VFPR). This methodology has been applied to a coal mine in China to forecast rock bursts. To select MS indices for rock burst forecasting using the fuzzy evaluation model, laboratory acoustic emission (AE) measurements of coal samples collected from the mine were performed. The model parameters were first calibrated using historical MS data over a period of four months, during which six rock burst incidents were observed. This calibrated model was able to forecast the occurrence of a subsequent rock burst incident in the mine.

Apple pomace treated by steam explosion (SE-AP) was mixed with wheat flour, the wheat dough characteristics and biscuit quality are deserved to investigate. In this paper, the characteristics of wheat dough blended with SE-AP, including sedimentation values, pasting properties, and farinographic features were measured; the textural properties and sensory evaluation of the blended biscuits were analyzed. The results showed that the sedimentation values of wheat dough gradually decreased when SE-AP was less than 10%, which was almost no influence on the biscuit quality. The more SE-AP was added, the less values of peak viscosity, trough viscosity and final viscosity, which was disadvantage to the processing quality of wheat flour; however, the values of breakdown and setback increased with the addition of SE-AP, which improved the processing quality. Dough development time, stability time, and farinograph quality number decreased with the addition of SE-AP, which was unfavourable to the quality of wheat flour. When the addition of SE-AP was less than 10%, the hardness of biscuits decreased, springiness and resilience increased, and the chewability improved. According to the texture properties and organoleptic evaluation, the sensor score of the biscuits made from weak-gluten wheat with 10% (m/m) SE-AP added was the highest.

3D facial landmark, which offers more expressive and occlusive information than its 2D counterpart, receives more and more attention from researchers in recent years. The top performing algorithms for 3D facial landmark detection are mainly divided into two categories: the two-step approach and the volume representation method. However, the former lacks the relation of depth with plat and the latter leads to large computation. In this paper, we propose the Combined Heatmap and Coordinate Regression (CHCR), which is an end-to-end method for 3D facial landmark detection from a single 2D image. To achieve that, we innovatively present the combined heatmap of three channels, and each channel of heatmap records the likelihoods of landmarks location of any two different axes. Such representation maintains the relation of various view while decreases either the channels for encoding or dimension for decoding. Then we retrieve the 3D coordinate vectors from corresponding combined heatmap by coordinate regression. Hence, an encoder-decoder network with a simple CNN attached to an hourglass module is designed to cope with the whole process. Experiments show our model is extremely light-weighted and runs faster than any other methods while on high performance of accuracy.

The sweet potato &beta, amylase (SPA) was modified by 6 types of methoxy polyethylene glycol to enhance its specific activity and thermal stability. The aims of the study were to select the optimum modifier, optimize the modification parameters, and further investigate the characterization of the modified SPA. The results showed that methoxy polyethylene glycol maleimide (molecular weight 5000, Mal-mPEG5000) was the optimum modifier of SPA, Under the optimal modification conditions, the specific activity of Mal-mPEG5000-SPA was 24.06% higher than that of the untreated SPA. Mal-mPEG5000-SPA was monomeric with a molecular weight of about 67 kDa by SDS-PAGE. The characteristics of Mal-mPEG5000-SPA were significantly improved. The Km value, Vmax and Ea in Mal-mPEG5000-SPA for sweet potato starch showed that Mal-mPEG5000-SPA had greater affinity for sweet potato starch and higher speed of hydrolysis than SPA. There was no significant difference of the metal ions&rsquo, effect on Mal-mPEG5000-SPA and SPA.