Effect of normalizing cooling process on microstructure and precipitates in low-temperature silicon steel

Microstructure, precipitate and magnetic characteristic of fmal products with different normalizing cooling processes for Fe-3.2%Si low-temperature hot-rolled grain-oriented silicon steel were analyzed and compared with the hot-rolled plate by optical microscopy （OM）, transmission electron microscopy （TEM）, and energy dispersive spectrometry （EDS）. The results show that, the surface microstructure is uniform, the proportion of recrystallization in matrix increases, and the banding textures are narrowed; the precipitates, whose quantity in normalized plate is more than that in hot-rolled plate greatly, are mainly A1N, MnS, composite precipitates （Cu,Mn）S and so on. Normalizing technology with a temperature of 1120 ℃, holding for 3 min, and a two-stage cooling is a most advantaged method to obtain oriented silicon steel with sharper Goss texture and higher magnetic properties, owing to the uniform surface microstructures and the obvious inhomogeneity of microstructures along the thickness. The normalizing technology with the two-stage cooling is the optimum process, which can generate more fine precipitates dispersed over the matrix, and be beneficial for finished products to get higher magnetic properties.

Composite quantile regression estimation for P-GARCH processes

We consider the periodic generalized autoregressive conditional heteroskedasticity(P-GARCH) process and propose a robust estimator by composite quantile regression. We study some useful properties about the P-GARCH model. Under some mild conditions, we establish the asymptotic results of proposed estimator.The Monte Carlo simulation is presented to assess the performance of proposed estimator. Numerical study results show that our proposed estimation outperforms other existing methods for heavy tailed distributions.The proposed methodology is also illustrated by Va R on stock price data.

A Multiple Feature Approach for Disorder Normalization in Clinical Notes

In this paper we propose a multiple feature approach for the normalization task which can map each disorder mention in the text to a unique unified medical language system（UMLS）concept unique identifier（CUI）. We develop a two-step method to acquire a list of candidate CUIs and their associated preferred names using UMLS API and to choose the closest CUI by calculating the similarity between the input disorder mention and each candidate. The similarity calculation step is formulated as a classification problem and multiple features（string features,ranking features,similarity features,and contextual features） are used to normalize the disorder mentions. The results show that the multiple feature approach improves the accuracy of the normalization task from 32.99% to 67.08% compared with the Meta Map baseline.