Simulation of Subrapid Solidification and Secondary Cooling for the Strip Casting of IF Steel

A combination of droplet solidification tester and confocal laser scanning microscope was used to simulate subrapid solidification and secondary cooling process pertinent to the strip casting. The IF steel droplet had a delamination structure and the bottom part went through sub-rapid solidification. During secondary cooling, γ/α transformation mechanism belonged to interface-controlled massive transformation and the ferrite grains grew quickly. With the increase of cooling rate, the γ/α transformation temperature decreased and the incubation period and phase transformation duration reduced. The hardness showed a slight increase due to fine-grain strengthening. With coiling temperature increasing from 600 °C to 800 °C, the grain size became larger, precipitates became coarse, and defects in grain were recovered. Consequently, the hardness decreased

Numerical Simulation of Bubble Free Rise after Sudden Contraction Using the Front-Tracking Method

Based on the front-tracking method (FTM), the movement of a single bubble that rose freely in a transverse ridged tube was simulated to analyze the influence of a contractive channel on the movement of bubbles. The influence of a symmetric contractive channel on the shape, speed, and trajectory of the bubbles was analyzed by contrasting the movement with bubbles in a noncontractive channel. As the research indicates, the bubbles became more flat when they move close to the contractive section of the channel, and the bubbles become less flat when passing through the contractive section. This effect becomes more obvious with an increase in the contractive degree of the channel. The symmetric contractive channel can make the bubbles first decelerate and later accelerate, and this effect is deeply affected by Reynolds number (Re) and Eötvös number (Eo)

NMRDSP: An Accurate Prediction of Protein Shape Strings from NMR Chemical Shifts and Sequence Data

<div><p>Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at <a href="http://cal.tongji.edu.cn/NMRDSP/index.jsp" target="_blank">http://cal.tongji.edu.cn/NMRDSP/index.jsp</a>.</p></div