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Jian-Wei Zhang,Wen-Bin Zheng,Wan-Lin Cao,Hong-Ying Dong,Wan-Di Li 대한토목학회 2019 KSCE JOURNAL OF CIVIL ENGINEERING Vol.23 No.3
In order to study the restoring force model of low-rise concrete shear wall with single layer of web reinforcement and inclined rebars, a series of specimens were investigated by quasi-static tests. Based on the fitting of experimental data and theoretical analysis, the restoring force model considering four characteristic points (crack point, yield point, peak point and failure point) and degradation of unloading stiffness was established. The hysteretic rule of restoring force model was determined by analyzing characteristic of hysteresis curve for cyclic loading tests. The results show that skeleton curves and hysteresis curves calculated by the restoring force model are in good agreement with the test curves, which can provide reference for the elastic-plastic dynamic analysis of low-rise concrete shear walls with single layer of web reinforcements and inclined rebars. Shaking table tests of two low-rise concrete shear walls were also conducted to investigate dynamic performance and the seismic damage mechanism of low-rise concrete shear wall with single layer of web reinforcement and inclined rebars. Research on dynamic nonlinear simplified model of SAP2000 shows thatthe proposed restoring force model can be used to analyze the elastic and elastic-plastic dynamic response for the low-rise concrete shear wall with single layer of web reinforcement and inclined rebars.
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Mast Cells Tryptase Promotes Intestinal Fibrosis in Natural Decellularized Intestinal Scaffolds
Wan Jian,Wu Tianqi,Liu Ying,Yang Muqing,Fichna Jakub,Guo Yibing,Yin Lu,Chen Chunqiu 한국조직공학과 재생의학회 2022 조직공학과 재생의학 Vol.19 No.4
BACKGROUND: Standard two-dimensional (2D) culture has confirmed the mechanism of mast cells (MCs) in the pathogenesis of inflammatory bowel disease (IBD), but the regulation of signaling responses of MCs may well differ in three-dimensional (3D) microenvironments. The aim of the study was to develop a 3D culture model based on decellularized intestinal scaffolds (DIS) and verify how MCs influenced fibroblasts phenotype in the 3D model. METHODS: DIS were achieved using the detergent technique and extracellular matrix (ECM) components were verified by histologic analysis, quantification and scanning electron microscope. After human colon fibroblasts recellularized into the scaffolds and activated by MCs tryptase and TGFb1, the changes in genes and signaling pathways during fibroblasts activation in 3D were studied and compared with the changes in 2D cell culture on plastic plates. RESULTS: Decellularization process effectively removed native cell debris while retaining natural ECM components and structure. The engrafted fibroblasts could penetrate into the scaffolds and maintain its phenotype. No matter whether fibroblasts were cultured in 2D or 3D, MCs tryptase and transforming growth factor b1 (TGF-b1) could promote the differentiation of fibroblasts into fibrotic-phenotype myofibroblasts through Akt and Smad2/3 signaling pathways. Furthermore, the pro-collagen1a1 and fibronectin synthesis of myofibroblasts in 3D was higher than in 2D culture. CONCLUSION: Our results demonstrated that the DIS can be used as a bioactive microenvironment for the study of intestinal fibrosis, providing an innovative platform for future intestinal disease modeling and screening of genes and signaling pathways.
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Multi-mode Radar Signal Sorting by Means of Spatial Data Mining
Wan, Jian,Nan, Pulong,Guo, Qiang,Wang, Qiangbo The Korea Institute of Information and Commucation 2016 Journal of communications and networks Vol.18 No.5
For multi-mode radar signals in complex electromagnetic environment, different modes of one emitter tend to be deinterleaved into several emitters, called as "extension", when processing received signals by use of existing sorting methods. The "extension" problem inevitably deteriorates the sorting performance of multi-mode radar signals. In this paper, a novel method based on spatial data mining is presented to address above challenge. Based on theories of data field, we describe the distribution information of feature parameters using potential field, and makes partition clustering of parameter samples according to revealed distribution features. Additionally, an evaluation criterion based on cloud model membership is established to measure the relevance between different cluster-classes, which provides important spatial knowledge for the solution of the "extension" problem. It is shown through numerical simulations that the proposed method is effective on solving the "extension" problem in multi-mode radar signal sorting, and can achieve higher correct sorting rate.
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Multi-mode Radar Signal Sorting by Means of Spatial Data Mining
Jian Wan,Pulong Nan,Qiang Guo,Qiangbo Wang 한국통신학회 2016 Journal of communications and networks Vol.18 No.5
Formulti-mode radar signals in complex electromagneticenvironment, different modes of one emitter tend to be deinterleavedinto several emitters, called as “extension”, when processingreceived signals by use of existing sorting methods. The “extension”problem inevitably deteriorates the sorting performance ofmulti-mode radar signals. In this paper, a novel method based onspatial data mining is presented to address above challenge. Basedon theories of data field, we describe the distribution information offeature parameters using potential field, and makes partition clusteringof parameter samples according to revealed distribution features. Additionally, an evaluation criterion based on cloud modelmembership is established to measure the relevance between differentcluster-classes, which provides important spatial knowledgefor the solution of the “extension” problem. It is shown through numericalsimulations that the proposed method is effective on solvingthe “extension” problem in multi-mode radar signal sorting,and can achieve higher correct sorting rate.
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Performance Evaluation and Modeling Method Research Based on IaaS Cloud Platform
Jian Wan,Xianghong Yang,Zujie Ren,Zheng Ye 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.10
With the widespread use of cloud platforms, their performance evaluation tools also have become the research hot spot of academic circle. So far, many performance evaluation tools of the cloud platform have been designed in their corresponding application scenarios, which have brought much convenience on the performance evaluation and management of the cloud platform. In order to predict the maximum number of virtual machines that can be opened by the cloud platform, this paper integrates the current tools of performance evaluation and proposes a performance evaluation tool based on IaaS cloud platform. The key of the performance evaluation tool is that it not only can evaluate the performance of the cloud platform, but also can predict the maximum number of virtual machines that can be opened by the cloud platform when the configuration of the virtual machine and the workload of each virtual machine have been known. This special performance evaluation tool has not been put forward now. And, the prediction model has been introduced into this tool in this paper that is the most important and core part. Lastly, to test the effectiveness of cloud platform performance evaluation tool proposed in this paper, some tests have been done on the IaaS cloud platform. According to the contrast results of the forecast error among models, establishing support vector machine and neural network as single forecasting model. The results show combined model can be chosen as the prediction model of cloud platform performance evaluation tool.
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Jianing Wan,Ruirong Yi,Yan Li,Yukiko Kinjo,Aki Sadashima,Takao Terashita,Katsuji Yamanaka,Tadanori Aimi 한국버섯학회 2011 한국버섯학회지 Vol.9 No.2
In order to confirm the presence of putative glucoamylase gene in Tricholoma matsutake genome, the genomic DNA was prepared from T. matsutake NBRC30773 strain and was used as template to clone the glucoamylases gene (TmGlu1). We obtained the nucleotide sequence of TmGlu1 and its franking region. The coding region (from ATG to stop codon) is 2,186 bp. The locations of exons and introns were determined from the nucleotide sequences of 3’- and 5’-RACE PCR and RT- PCR products. On the other hand, to investigate the relationship between composition of medium and glucoamylase expression, we checked the expression level of glucoamylase gene by realtime reverse transcription PCR and measurement of glucoamylase enzyme activity. It was found that enzyme activity of glucoamylase was very low in different medium. Expression of glucoamylases gene appeared to not be affected by different carbon source.
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An Efficient Data Collection Protocol for Maximum Sensor Network Data Persistence
Jian Wan,Li Yang,Wei Zhang,Huayou Si,Jin Feng 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.11
Sensor network has lot applications in the early warning and assistant of disaster environment such as debris flows, floods and forest fires. However, such disaster environment pose an interesting challenge for data collection since sensor nodes may be destroyed unpredictably and centrally, resulting in the decrease of data persistence in the network. Growth Codes Protocol (GCP) first focuses on increase sensor network data persistent in the disaster. However, the completely random data transmission way in GCP may cause a large number of invalid data transmissions and therefore, the efficiency of data collection of the protocol is not ideal in the late stage of data collection. In this paper, we propose an efficient data collection protocol (DGCP) to maximize sensor network data persistence by changing the completely random data transmission way. Packet classification mechanism and a novel dynamic probability model of data transmission in DGCP are proposed to control the effective direction of data flow. Furthermore, we found that the parameter optimization problem of the probabilistic model is a problem of searching the optimal solution in a mathematical view. Based on this property, we propose a genetic algorithm to optimize the dynamic probability model. The performance of the proposed DGCP is shown by a comparative experimental study. When compared with GCP, our DGCP has better performance in a variety of environments
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