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574 results on '"Zhang, Zhiqian"'

2. Shear Viscosity Effect on High-Speed Deformation of Copper

Author

Msolli, Sabeur, Wei, Yuefan, Zhang, Zhiqian, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A.M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Maharjan, Niroj, editor, and He, Wei, editor

Published
2024
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4. Research progress on extraction technology, nutritional ingredient and health benefit of grape seed oil

Author

MENG Yuzhu, LEI Changgui, CHEN Jinping, ZHANG Xiaodong, and ZHANG Zhiqian

Subjects
grape seed oil, extraction technology, nutritional component, functional activity, grape pomace, Food processing and manufacture, TP368-456
Abstract

Grape pomace is a major by-product produced during grape processing, including grape skin, pulp and seed. Grape seed oil (GSO) obtained from grape seeds is one of the vegetable oil with high nutritional value. It is rich in nutrients such as polyunsaturated fatty acids, tocopherols, phytosterols and phenols. It has many physiological effects such as antioxidant, anti-inflammatory, hypolipidemic, weight loss and antibacterial. Based on the relevant research reports at home and abroad in recent years, this review systematically summarized the extraction technologies, nutritional compositions and health effects of GSO. The research and development direction of GSO in the future was also prospected, aiming to provide a reference value for the high-value application of grape processing by-products.

Published
2024
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11. A Finite Time Cooperative Control Strategy for Energy Storage Systems in DC Microgrids

Author

Shi, Tianyu, Zhang, Zhiqian, Wang, Qi, Hu, Cungang, Liu, Shiming, Zhang, Zhenbin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Hu, Cungang, editor, and Cao, Wenping, editor

Published
2023
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28. Extreme Multi-label Classification with Hierarchical Multi-task for Product Attribute Identification

Author

Zhang, Jun, Cai, Menqian, Zhao, Chenyu, Zhang, Xiaowei, Zhang, Zhiqian, Chen, Haiheng, Xu, Sulong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gama, João, editor, Li, Tianrui, editor, Yu, Yang, editor, Chen, Enhong, editor, Zheng, Yu, editor, and Teng, Fei, editor

Published
2022
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32. Screening antimicrobial peptides and probiotics using multiple deep learning and directed evolution strategies

Author

Zhang, Yu, primary, Liu, Li-Hua, additional, Xu, Bo, additional, Zhang, Zhiqian, additional, Yang, Min, additional, He, Yiyang, additional, Chen, Jingjing, additional, Zhang, Yang, additional, Hu, Yucheng, additional, Chen, Xipeng, additional, Sun, Zitong, additional, Ge, Qijun, additional, Wu, Song, additional, Lei, Wei, additional, Li, Kaizheng, additional, Cui, Hua, additional, Yang, Gangzhu, additional, Zhao, Xuemei, additional, Wang, Man, additional, Xia, Jiaqi, additional, Cao, Zhen, additional, Jiang, Ao, additional, and Wu, Yi-Rui, additional

Published
2024
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42. Course Knowledge Graph for Computer Sciences

Author

Zhang, Shaohong, Zhong, Qiyu, Liu, Yang, Zhang, Zhiqian, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Meng, Hongying, editor, Lei, Tao, editor, Li, Maozhen, editor, Li, Kenli, editor, Xiong, Ning, editor, and Wang, Lipo, editor

Published
2021
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43. NEXT: A Neural Network Framework for Next POI Recommendation

Author

Zhang, Zhiqian, Li, Chenliang, Wu, Zhiyong, Sun, Aixin, Ye, Dengpan, and Luo, Xiangyang

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

The task of next POI recommendation has been studied extensively in recent years. However, developing an unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging, because of the heterogeneity nature of these information. Further, effective mechanisms to handle cold-start and endow the system with interpretability are also difficult topics. Inspired by the recent success of neural networks in many areas, in this paper, we present a simple but effective neural network framework for next POI recommendation, named NEXT. NEXT is an unified framework to learn the hidden intent regarding user's next move, by incorporating different factors in an unified manner. Specifically, in NEXT, we incorporate meta-data information and two kinds of temporal contexts (i.e., time interval and visit time). To leverage sequential relations and geographical influence, we propose to adopt DeepWalk, a network representation learning technique, to encode such knowledge. We evaluate the effectiveness of NEXT against state-of-the-art alternatives and neural networks based solutions. Experimental results over three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI recommendation. Further experiments demonstrate the superiority of NEXT in handling cold-start. More importantly, we show that NEXT provides meaningful explanation of the dimensions in hidden intent space.

Published
2017
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44. RBBP4: A novel diagnostic and prognostic biomarker for non‐small‐cell lung cancer correlated with autophagic cell death.

Author

Zhan, Yajing, Zhang, Zhiqian, Yin, Ankang, Su, Xiyang, Tang, Nan, Chen, Yi, Zhang, Zebin, Chen, Wei, Wang, Juan, and Wang, Wei

Subjects
*GENE expression, *CELL death, *LUNG cancer, *PROGNOSIS, *CANCER prognosis, *NECROSIS
Abstract

Background: Non‐small‐cell lung cancer (NSCLC) often presents at later stages, typically associated with poor prognosis. Autophagy genes play a role in the progression of tumors. This study investigated the clinical relevance, prognostic value, and biological significance of RBBP4 in NSCLC. Methods: We assessed RBBP4 expression using the GSE30219 and TCGA NSCLC datasets and NSCLC cells, exploring its links with clinical outcomes, tumor immunity, and autophagy genes through bioinformatics analysis after transcriptome sequencing of RBBP4‐knockdown and control PC9 cells. We identified differentially expressed genes (DEGs) and conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and protein–protein interaction network analyses. The significance of autophagy‐related DEGs was evaluated for diagnosis and prognosis using the GSE30219 dataset. Experiments both in vivo and in vitro explored the biological mechanisms behind RBBP4‐mediated autophagic cell death in NSCLC. Results: RBBP4 overexpression in NSCLC correlates with a poorer prognosis. Eighteen types of immune cell were significantly enriched in cultures that had low RBBP4 expression compared high expression. DEGs associated with RBBP4 are enriched in autophagy pathways. Transcriptomic profiling of the PC9 cell line identified autophagy‐related DEGs associated with RBBP4 that exhibited differential expression in NSCLC, suggesting prognostic applications. In vitro experiments demonstrated that RBBP4 knockdown induced autophagy and apoptosis in PC9 cells, promoting cell death, which was inhibited by 3‐MA. In vivo, targeted siRNA against RBBP4 significantly reduced tumor development in PC9 cell‐injected nude mice, elevating autophagy‐related protein levels and inducing apoptosis and necrosis in tumor tissues. Conclusion: In NSCLC, RBBP4 upregulation correlates with poor prognosis and altered immunity. Its knockdown induces autophagic cell death in NSCLC cells. These results indicate RBBP4 as a potential NSCLC diagnostic marker and its autophagy modulation as a prospective therapeutic target. [ABSTRACT FROM AUTHOR]

Published
2024
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