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Physically-Based Constitutive Modelling of As-Cast CL70 Steel for Hot Deformation
Fei Chen,Xiaodong Zhao,Jinyu Ren,Huiqin Chen,Xiaofeng Zhang 대한금속·재료학회 2021 METALS AND MATERIALS International Vol.27 No.6
In order to conduct numerical simulation of plasticity forming and confirm the processing parameters of heat deformationfor as-cast CL70 steel, the hot deformation behaviors of as-cast CL70 steel were studied by isothermal compression testswhich used a Gleeble-1500D thermal mechanical simulation tester for the deformation temperatures ranging from 1173 to1523 K and the strain rates ranging from 0.001 to 1 s−1. Flow stress curves of the steel were obtained under high temperature. The flow stress constitutive models of the work hardening-dynamical recovery period and dynamical recrystallizationperiod were established for as-cast CL70 steel. In work hardening-dynamic recovery period, the flow stress was predicted byemploying the evolution rule of dislocation density in the constitutive model. In dynamic recrystallization period, the flowstress after the critical strain was predicted by employing the dynamic recrystallization kinetics in the constitutive model. To improve the prediction accuracy of the model, the dynamic recovery coefficient is modified in the traditional physicallybasedconstitutive model. The results indicate that the proposed physically-based constitutive model has high accuracy inpredicting the flow stress under hot deformation for as-cast CL70 steel.
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Chen Qin,Yu Yilin,Zhang Xiang,Zhao Ren,Zhang Jinyu,Liu Diqiu,Cui Xiuming,Ge Feng 한국식물생명공학회 2021 Plant biotechnology reports Vol.15 No.5
The ERF-type transcription factors (TFs) play vital roles in plant secondary metabolism. ERF TFs simultaneously regulate the expression levels of key enzyme genes involved in the biosynthesis of secondary metabolites due to its “multi-point control” function. In this study, one gene of ERF TFs from Panax japonicus (PjERF1) was cloned. The open reading frame of PjERF1 was 801 bp and encoded 266 amino acids. Phylogenetic analysis showed that PjERF1 belonged to ERF subfamily with a typical conserved domain. Subcellular localization found that PjERF1 protein might be located in eukaryotic cell nucleus. Yeast one-hybrid assay demonstrated that PjERF1 could bind to the promoters of PjβAS, PjCAS, and PjSE specifically and regulate the expression levels of such key enzyme genes involved in the triterpene saponins biosynthesis. Therefore, in the PjERF1 overexpression cell lines, the expression levels of some key enzyme genes involved in the triterpenoid saponins biosynthesis were significantly increased compared with those in non-transgenic cell line. As a result of it, the biosynthesis of chikusetsusaponin IV and IVa, and other ginsenosides (Rd, Rb1, Re, and R0) were also promoted in the PjERF1 over- expression cell lines. This study indicated that PjERF1 could regulate the biosynthesis of saponins in P. japonicus through controlling the expression levels of key enzyme genes related to the biosynthesis of triterpenoid saponins.
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Cellular and Molecular Mechanisms of Intestinal Fibrosis
Wu Xiaomin,Lin Xiaoxuan,Tan Jinyu,Liu Zishan,He Jinshen,Hu Fan,Wang Yu,Chen Minhu,Liu Fen,Mao Ren 거트앤리버 소화기연관학회협의회 2023 Gut and Liver Vol.17 No.3
Intestinal fibrosis associated stricture is a common complication of inflammatory bowel disease usually requiring endoscopic or surgical intervention. Effective anti-fibrotic agents aiming to control or reverse intestinal fibrosis are still unavailable. Thus, clarifying the mechanism underpinning intestinal fibrosis is imperative. Fibrosis is characterized by an excessive accumulation of extracellular matrix (ECM) proteins at the injured sites. Multiple cellular types are implicated in fibrosis development. Among these cells, mesenchymal cells are major compartments that are activated and then enhance the production of ECM. Additionally, immune cells contribute to the persistent activation of mesenchymal cells and perpetuation of inflammation. Molecules are messengers of crosstalk between these cellular compartments. Although inflammation is necessary for fibrosis development, purely controlling intestinal inflammation cannot halt the development of fibrosis, suggesting that chronic inflammation is not the unique contributor to fibrogenesis. Several inflammation-independent mechanisms including gut microbiota, creeping fat, ECM interaction, and metabolic reprogramming are involved in the pathogenesis of fibrosis. In the past decades, substantial progress has been made in elucidating the cellular and molecular mechanisms of intestinal fibrosis. Here, we summarized new discoveries and advances of cellular components and major molecular mediators that are associated with intestinal fibrosis, aiming to provide a basis for exploring effective anti-fibrotic therapies in this field.
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An Integrated Artificial Neural Network-based Precipitation Revision Model
( Tao Li ),( Wenduo Xu ),( Li Na Wang ),( Ningpeng Li ),( Yongjun Ren ),( Jinyue Xia ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.5
Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today’s operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.
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