Xuzhou Institute of Technology

About: Xuzhou Institute of Technology is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 1696 authors who have published 1521 publications receiving 13541 citations.

Meso-structure and fracture mechanism of mudstone at high temperature

Abstract: The meso-structure mineral composition and fracture mechanism of uniaxial compressed mudstone samples at high temperature were analyzed by XRD and scanning electron microscopy. The effect of temperature on mudstone composition and fracture mechanism were studied from a meso-structural perspective, and the relationship between meso-structure and macro-mechanical characteristics at high temperature was revealed. The findings demonstrated that the fluctuation in diffraction intensity of kaolinite in the mudstone caused the fluctuation in its mechanical properties. The overall structure underwent a phase change around 600 °C, which led to the sudden change in the mechanical properties of mudstone samples. When the temperature reached 600 °C, the crystalline state worsened and kaolinite disappeared; however, some illite was produced, indicating that the chemical reaction of the structure and sudden drop of bearing capacity of the mudstone. Mudstone fracturing at high temperature involves mainly intergranular and transgranular fractures, which are typical in micro-brittle tensile failure. Considering the macro-fracture characteristics of mudstone, the results suggested that macro-fracture under external force corresponds to the meso-fracture.

IMKPse: Identification of Protein Malonylation Sites by the Key Features Into General PseAAC

Abstract: Currently, lysine malonylation is treated as one of the most key protein post translational modification in the field of biology and lysine plays a significant role for the regulation of several biological processions. Therefore, accurately identification such modification type will make contributions to understanding their biological processions in this field. The experimental approaches to identify such type of modification sites are time-wasting and laborious in some degree. So, it is necessary and urgent to design and propose computational biology approaches to identify these sites. In this paper, we proposed the IMKPse model that utilized general PseAAC as the classification features and employed flexible neural tree as classification model. In order to deal with the overfitting problem, we utilized the independent datasets of each species. More specifically, such algorithm initially employed amino acid properties from the general PseAAC as the candidate features. With the comparison of candidate features, such a method has the ability to finding out the top five features among them. When evaluated on three data sets in testing set, IMKPse obtained MCC value of 0.9185, 0.9097, and 0.9525 in three species, including E.coli, M.musculus, and H.sapiens, respectively. Meanwhile, IMKPse obtained MCC value of 0.9149, 0.9060, and 0.9467, respectively, in the independent sets. In addition, then, we make some combinations among the top five features. The results demonstrate that the proposed algorithm has superior performances than other approaches. A user-friendly web resource of IMKPSE is available at http://121.250.173.184 .