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Search Results: 1 - 10 of 24027 matches for " Zhengyi Jiang "
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Evolution of Internal Crack in BCC Fe under Compressive Loading  [PDF]
Dongbin Wei, Zhengyi Jiang, Jingtao Han
Journal of Modern Physics (JMP) , 2012, DOI: 10.4236/jmp.2012.310197
Abstract: A molecular dynamics model has been developed to investigate the evolution of the internal crack of nano scale during heating or compressive loading in BCC Fe. The initial configuration does not contain any pre-existing dislocations. In the case of heating, temperature shows a significant effect on crack evolution and the critical temperature at which the crack healing becomes possible is 673 K. In the case of compressive loading, the crack can be healed at 40 K at a loading rate 0.025 × 1018 Pa·m1/2/s in 6 × 10-12 s. The diffusion of Fe atoms into the crack area results in the healing process. However, dislocations and voids appear during healing and their positions change continuously.
Xiaoqiang Sun,Liang Chen,Yan Jiang,Zhengyi Li
Acta Crystallographica Section E , 2010, DOI: 10.1107/s1600536810043795
Abstract: In the title compound, C31H28O4, the asymmetric unit contains two crystallographically independent molecules. In these two molecules, the four dihedral angles between each pair of phenyl rings on the same C atoms are 75.4 (1), 83.0 (1), 85.0 (1) and 80.4 (2)°. All of the nonplanar six-membered heterocycles adopt chair conformations. Intermolecular C—H...π and weak C—H...O interactions link the molecules and are effective in the stabilization of the crystal structure.
Simulation of Fluid Flow, Heat Transfer and Micro-Segregation in Twin-roll Strip Casting of Stainless Steel
Xiaoming ZHANG,Zhengyi JIANG,Xianghua LIU,Guodong WANG,

材料科学技术学报 , 2006,
Abstract: In twin-roll strip casting process, metal flow and temperature distribution in the molten pool directly affect the stability of the process and the quality of products. In this paper, a 3D coupled thermal-flow fenite element modeling (FEM) simulation for twin-roll strip casting of stainless steel was performed. Influences of the pouring temperature and casting speed on the temperature fields were obtained from the numerical simulation. The micro-segregation of the solutes during the strip casting process of stainless steel was also simulated. A developed micro-segregation model was used to calculate the micro-segregation of solutes in twin-roll casting of stainless steel. The relationship between the solidus fraction in solidification and temperature was given,which was used to determine the LIT (liquid impermeable temperature), ZST (zero strength temperature) and ZDT (zero ductility temperature) in the period of non-equilibrium solidification. The effect of temperature on the micro-segregation was discussed. According to the computational results, the solidification completion temperature in the twin-roll strip casting of stainless steel was then determined, which can provide a basis for controlling the location of solidification completion temperature and analysing the crack of the casting strip.
A Type 2C Protein Phosphatase FgPtc3 Is Involved in Cell Wall Integrity, Lipid Metabolism, and Virulence in Fusarium graminearum
Jinhua Jiang,Yingzi Yun,Qianqian Yang,Won-Bo Shim,Zhengyi Wang,Zhonghua Ma
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0025311
Abstract: Type 2C protein phosphatases (PP2Cs) play important roles in regulating many biological processes in eukaryotes. Currently, little is known about functions of PP2Cs in filamentous fungi. The causal agent of wheat head blight, Fusarium graminearum, contains seven putative PP2C genes, FgPTC1, -3, -5, -5R, -6, -7 and -7R. In order to investigate roles of these PP2Cs, we constructed deletion mutants for all seven PP2C genes in this study. The FgPTC3 deletion mutant (ΔFgPtc3-8) exhibited reduced aerial hyphae formation and deoxynivalenol (DON) production, but increased production of conidia. The mutant showed increased resistance to osmotic stress and cell wall-damaging agents on potato dextrose agar plates. Pathogencity assays showed that ΔFgPtc3-8 is unable to infect flowering wheat head. All of the defects were restored when ΔFgPtc3-8 was complemented with the wild-type FgPTC3 gene. Additionally, the FgPTC3 partially rescued growth defect of a yeast PTC1 deletion mutant under various stress conditions. Ultrastructural and histochemical analyses showed that conidia of ΔFgPtc3-8 contained an unusually high number of large lipid droplets. Furthermore, the mutant accumulated a higher basal level of glycerol than the wild-type progenitor. Quantitative real-time PCR assays showed that basal expression of FgOS2, FgSLT2 and FgMKK1 in the mutant was significantly higher than that in the wild-type strain. Serial analysis of gene expression in ΔFgPtc3-8 revealed that FgPTC3 is associated with various metabolic pathways. In contrast to the FgPTC3 mutant, the deletion mutants of FgPTC1, FgPTC5, FgPTC5R, FgPTC6, FgPTC7 or FgPTC7R did not show aberrant phenotypic features when grown on PDA medium or inoculated on wheat head. These results indicate FgPtc3 is the key PP2C that plays a critical role in a variety of cellular and biological functions, including cell wall integrity, lipid and secondary metabolisms, and virulence in F. graminearum.
The Yellow Sea green tides were dominated by one species, Ulva (Enteromorpha) prolifera, from 2007 to 2011
Jin Zhao,Peng Jiang,ZhengYi Liu,Wei Wei,HanZhi Lin,FuChao Li,JinFeng Wang,Song Qin
Chinese Science Bulletin , 2013, DOI: 10.1007/s11434-012-5441-3
Abstract: Green tides have occurred in the Yellow Sea successively from 2007 to 2011. Genetic analysis of the 5-year green-tide-forming algae needs to be performed to determine the source of the biomass and understand the mechanism of the green tide blooms. In this study, free-floating green algae were collected at different sites in the Yellow Sea in 2010 and 2011. Data on 182 free-floating samples and 155 attached Ulva samples from previous studies on the Yellow Sea green tides from 2007 to 2009 were also taken into consideration. Morphology observation and molecular phylogenetic analyses indicated that the Yellow Sea green tides were dominated by a single species, Ulva prolifera, from 2007 to 2011. Considering that at least five Ulva species inhabit the north coast of China, the unialgal composition of the green tides implied that (1) there may be some special physiology and propagation pathways of U. prolifera for its rapid expansion, (2) the mechanisms of the Yellow Sea green tide formation were similar for the last five years, and (3) the intra-species genetic variation and population structure of U. prolifera need to be studied to determine the exact origin of the bloom-forming biomass.
Analysis of genetic variation within and among Ulva pertusa (Ulvaceae, Chlorophyta) populations using ISSR markers
Jin Zhao,Peng Jiang,Nan Li,JinFeng Wang,ZhengYi Liu,Song Qin
Chinese Science Bulletin , 2010, DOI: 10.1007/s11434-009-0715-0
Abstract: Ulva pertusa is a native species to Asia along the western coast of Pacific Ocean, with new occurrence records in the eastern coast of Pacific, the northwest coast of Atlantic and the Mediterranean Sea. However, little is known about its population genetic structure. In this study, twelve U. pertusa populations from 3 coastal areas of China: Qingdao, Yantai and Dalian, were applied to ISSR analysis. The selected 4 ISSR primers amplified 120 polymorphic bands totally. Nei’s gene diversity (H) ranged from 0.0729 to 0.1496, and Shannon’s information index (I) ranged from 0.1072 to 0.2196. Genetic diversity was greater within Qingdao populations (H = 0.2069, I = 0.3232). Analysis of molecular variance (AMOVA) showed the greatest variance within populations (68.57%), much less variance among populations (22.63%) and among areas (8.79%). Unweighted pair-group mean analysis (UPGMA) indicated that clustering of U. pertusa individuals mainly relates to their populations and geographic distances separating those populations. Genetic differentiation and limited gene flow among U. pertusa populations were indicated by ISSR analysis.

LUO Chengping,JIANG Zhouhong,Liu Zhengyi,

材料研究学报 , 1996,
Abstract: 用TEM研究了新型钎钢40MnMoV的正火组织,发现它与Si-Mn-Mo系的另一种钎钢55SiMnMo相似,由无碳化物上,下贝氏体Bu、Bl、以孪晶马氏体为主的“块状组织”和约15%残余奥氏体Ar组成,但40MnMoV的Bu,Bl较多,Ar和L则较少;Bu板条较粗大,其间分布着不连续的A小片条;Bl片中有渗碳体与粤氏体A共存;L的尺寸较小,硬度较低,讨论了该钢正火时贝氏体(B)相变的原因及影响贝氏
Analysis of genetic variation within and among Ulva pertusa (Ulvaceae, Chlorophyta) populations using ISSR markers

Jin Zhao,Peng Jiang,Nan Li,JinFeng Wang,ZhengYi Liu,Song Qin,

科学通报(英文版) , 2010,
Abstract: Ulva pertusa is a native species to Asia along the western coast of Pacific Ocean,with new occurrence records in the eastern coast of Pacific,the northwest coast of Atlantic and the Mediterranean Sea.However,little is known about its population genetic structure.In this study,twelve U.pertusa populations from 3 coastal areas of China:Qingdao,Yantai and Dalian,were applied to ISSR analysis.The selected 4 ISSR primers amplified 120 polymorphic bands totally.Nei's gene diversity (H) ranged from 0.0729 to 0.149...
An ensemble method with convolutional neural network and deep belief network for gait recognition and simulation

HE Zhengyi
, ZENG Xianhua, GUO Jiang

- , 2018, DOI: 10.6040/j.issn.1672-3961.0.2017.427
Abstract: 摘要: 针对高斯过程的条件受限玻尔兹曼机(Gaussian-based conditional restricted Boltzmann machine, GCRBM)时序模型可以对单一种类的步态时序数据进行很好的预测,但对多类步态时序数据难以识别和预测的问题,提出一种集成卷积神经网络(convolutional neural network, CNN)和深信网(deep belief network, DBN)的步态识别与模拟方法。利用所有类步态数据训练多个不同结构的CNNs模型,利用多类数据训练多个DBNs模型学习低维特征,并通过低维特征训练多个GCRBMs模型。在步态识别与模拟时,CNNs分类器通过投票法确定步态数据的类别;通过识别到的类所对应的DBNs模型低维特征作为对应GCRBMs模型的输入预测目标数据的后期时序低维特征;利用DBNs重构阶段将后期时序低维特征模拟出步态图像。在CASIA系列步态数据集上的试验结果表明:与支持向量机(support vector machine, SVM)、集成DBN和CNN等方法相比,本研究方法的识别率有一定的提高,提出的模型能够根据步态时序预测结果模拟出真实的步态序列图像,证实了模型的有效性。
Abstract: The Gaussian-based conditional restricted Boltzmann machine(GCRBM)time series model could efficiently predict for single type of gait time series data, but the model could not make accurate recognition and prediction for multi-category gait time series data. To solve the problem above, an ensemble/integrated method with convolutional neural network(CNN)and deep belief network(DBN)for gait recognition and simulation was proposed. Multiple CNNs models with different structures were trained by all the gait data. Multiple DBNs models corresponding to the multi-category data were trained to study low dimensional features, and corresponding to train multiple GCRBMs models through the low dimensional features. In the step of recognition and simulation, model will identify the class of gait data with all CNNs classifiers by the “minority-obeying” voting strategy, then the low-dimensional feature of the DBNs model corresponding to the identified class was used as the input of the corresponding GCRBMs model to predict the late timing low-dimensional feature of the target data. The gait images could be reconstructed by the corresponding DBNs model. Compared with the method of support vector machine(SVM), integrated DBN and CNN, the proposed method’s gait recognition rate was improved based on CASIA gait datasets. Moreover, the predicting result could be simulated to the true gait sequences by the proposed method, which demonstrated the validity of the model
生物多样性 , 1994,
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