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22 results on '"Zhang, Peisong"'

3. An Oriented Attention Model for Infectious Disease Cases Prediction

Author

Zhang, Peisong, Wang, Zhijin, Chao, Guoqing, Huang, Yaohui, Yan, Jingwen, 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, Fujita, Hamido, editor, Fournier-Viger, Philippe, editor, Ali, Moonis, editor, and Wang, Yinglin, editor

Published
2022
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9. HFMD Cases Prediction Using Transfer One-Step-Ahead Learning.

Author

Huang, Yaohui, Zhang, Peisong, Wang, Ziyang, Lu, Zhenkun, and Wang, Zhijin

Subjects
FOOT & mouth disease, TRANSFER of training, COXSACKIEVIRUS diseases, INFANT diseases, VIRUS diseases, FORECASTING
Abstract

Hand, foot and mouth disease (HFMD) is a susceptible viral infectious disease to infants and children, which led to millions of cases and hundreds of deaths annually in China. Existing predictive methods commonly learn the development patterns from historical observations. However, almost all these methods are neglect the immediate impact of exogenous factors on HFMD transmission. To solve the limitation, we consider the approximately unidirectional influences from temperature to confirmed cases and then propose a transfer one-step-ahead learning (Tr-OSH) method to establish their association. The Tr-OSH method first extract the unidirectional representation from temperature observations, and subsequently transfer the obtained representation for HFMD cases prediction. Moreover, we notice the independent correlation of each time step and period, and generate the independent representation by the relevance to upcoming values. Intensive experiments on real-world HFMD datasets demonstrate that our Tr-OSH method much efficaciously improves prediction accuracy. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Response of bacterial communities in rubber plantations to different fertilizer treatments

Author

Zhengzao Cha, Qinghuo Lin, Zhang Peisong, Wei Luo, and Zhiyang Zhang

Subjects
biology, Soil organic matter, Environmental Science (miscellaneous), engineering.material, biology.organism_classification, complex mixtures, Agricultural and Biological Sciences (miscellaneous), Bacterial communities, Actinobacteria, Agronomy, Hevea brasiliensis Muell. Arg, engineering, Organic fertilizer, Gemmatimonadetes, Original Article, Natural rubber yield, Fertilizer, Soil fertility, Proteobacteria, Biotechnology, Acidobacteria, Chemical fertilizer
Abstract

In the present study, the effects of chemical fertilizer (CF) and organic fertilizer plus chemical fertilizer application (OF–CF) on natural rubber yield, soil properties, and soil bacterial community were systematically investigated in rubber plantations. The rubber dry yield was 26.3% more in the OF treatment group than in the CF treatment group. The contents of total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), and available potassium (AK) as well as soil organic matter (SOM) and pH value were higher following OF–CF treatment. Using Illumina sequencing, a total of 927 operational taxonomic units (OTUs) were obtained following CF treatment, while 955 OTUs were obtained after OF–CF treatment. Relative abundance analysis showed the relative abundances of four phyla (Acidobacteria, Proteobacteria, Actinobacteria, Gemmatimonadetes) were different between the two treatment groups. Correlation analysis revealed Acidobacteria, Bacteroidetes, Thaumarchaeota, Elusimicrobia, Verrucomicrobia were the key taxa that determined the soil properties. Additionally, five OTUs (OTU_506, OTU_391, OTU_189, OTU_278, OTU_1057) were thought to be related to the biodegradation of natural rubber. Taken together, these results improve our understanding of the OF-mediated improvement in soil fertility and contribute to the identification of rubber-degrading bacteria in rubber plantations. Electronic supplementary material The online version of this article (10.1007/s13205-019-1821-6) contains supplementary material, which is available to authorized users.

Published
2019

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