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Your search keyword '"Shi Danyang"' showing total 13 results
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13 results on '"Shi Danyang"'

2. Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM

Author

Wu, Fanming, Li, Dengao, Zhao, Jinhua, Feng, Ran, Shi, Danyang, Zhang, Xinfang, Zhao, Jumin, 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, Yang, Changfeng, editor, and Xie, Jun, editor

Published
2024
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3. Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model

Author

Li, Dengao, Zhang, Xinfang, Zhao, Jumin, Wu, Fanming, Feng, Ran, Zhao, Jinhua, Shi, Danyang, 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, Yang, Changfeng, editor, and Xie, Jun, editor

Published
2024
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4. Research on Zenith Tropospheric Delay Model Based on TCN Improving HGPT2 Model

Author

Li, Dengao, Shi, Danyang, Zhao, Jumin, Wu, Fanming, Yan, Liangquan, Feng, Ran, Zhang, Xinfang, Zhao, Jinhua, 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, Yang, Changfeng, editor, and Xie, Jun, editor

Published
2024
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11. Graphene-Based Virus Enrichment Protocol Increases the Detection Sensitivity of Human Norovirus in Strawberry and Oyster Samples.

Author

Zhou, Shuqing, Jin, Min, Yin, Jing, Shi, Danyang, Li, Haibei, Gao, Zhixian, Chen, Zhengshan, Yang, Zhongwei, Chen, Tianjiao, Wang, Huaran, Li, Junwen, and Yang, Dong

Subjects
DETECTION limit, ROTAVIRUSES, BACTERIOPHAGES, OYSTERS, RECOMBINASES, NOROVIRUSES
Abstract

Human noroviruses (HuNoVs), the most prevalent viral contaminant in food, account for a substantial proportion of nonbacterial gastroenteritis cases. Extensive work has been focused on the diagnosis of HuNoVs in clinical samples, whereas the availability of sensitive detection methods for their detection in food is lacking. Here, we developed a virus enrichment approach utilizing graphene-based nanocomposites (CTAB-rGO-Fe3O4) that does not rely on large instruments and is suitable for on-site food pretreatment. The recovery efficiency of the developed virus enrichment procedure for serially diluted GII.4 norovirus ranged from 10.06 to 72.67% in strawberries and from 2.66 to 79.65% in oysters. Furthermore, we developed a real-time recombinase polymerase amplification (real-time RPA) assay, which can detect as low as 1.22 genome copies µL−1 of recombinant plasmid standard and has no cross-reactivity with genomes of astrovirus, rotavirus, adenovirus, and MS2 bacteriophage. Notably, the combined virus enrichment and real-time RPA detection assay enhanced the detection limits to 2.84 and 37.5 genome copies g−1 in strawberries and oysters, respectively, compared to those of qPCR. Our strategy, the graphene-based virus enrichment method combined with real-time RPA, presents a promising tool for sensitively detecting HuNoVs in food samples. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Representation Learning on Heterostructures via Heterogeneous Anonymous Walks

Author

Guo, Xuan, Jiao, Pengfei, Zhang, Wang, Pan, Ting, Jia, Mengyu, Shi, Danyang, and Wang, Wenjun

Abstract

Capturing structural similarity has been a hot topic in the field of network embedding (NE) recently due to its great help in understanding node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks, while the related study on heterogeneous networks is still void. In this article, we try to take the first step for representation learning on heterostructures, which is very challenging due to their highly diverse combinations of node types and underlying structures. To effectively distinguish diverse heterostructures, we first propose a theoretically guaranteed technique called heterogeneous anonymous walk (HAW) and give two more applicable variants. Then, we devise the HAW embedding (HAWE) and its variants in a data-driven manner to circumvent using an extremely large number of possible walks and train embeddings by predicting occurring walks in the neighborhood of each node. Finally, we design and apply extensive and illustrative experiments on synthetic and real-world networks to build a benchmark on heterostructure learning and evaluate the effectiveness of our methods. The results demonstrate our methods achieve outstanding performance compared with both homogeneous and heterogeneous classic methods and can be applied on large-scale networks.

Published
2024
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