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pro vyhledávání: '"Shima Shamkhali Chenar"'
Autor:
Shima Shamkhali Chenar, Zhiqiang Deng
Publikováno v:
Journal of Water and Health, Vol 19, Iss 2, Pp 254-266 (2021)
This paper presents a hybrid model for predicting oyster norovirus outbreaks by combining the Artificial Neural Networks (ANNs) and Principal Component Analysis (PCA) methods and using the Moderate Resolution Imaging Spectroradiometer (MODIS) satelli
Externí odkaz:
https://doaj.org/article/f8d3c743924440979b5e000fa1efb0a8
Publikováno v:
محیط زیست و مهندسی آب, Vol 5, Iss 3, Pp 174-185 (2019)
Due to its particular physical and chemical conditions, river estuary can affect the structure and concentration of heavy metals present in river water at the time of mixing freshwater and seawater. The mixing of saline and fresh water plays an essen
Externí odkaz:
https://doaj.org/article/cf1684df51264f0f97a6990d156da89f
Autor:
Shima Shamkhali Chenar, Zhiqiang Deng
Publikováno v:
Water Research. 128:20-37
Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide but exact causes of the outbreaks are rarely identified, making it highly unlikely to reduce the risks. This paper presents a genetic programming (GP) bas
Autor:
Shima Shamkhali Chenar, Zhiqiang Deng
Publikováno v:
Marine Environmental Research. 130:275-281
This paper presents an artificial intelligence-based approach to identifying environmental indicators of oyster norovirus outbreaks in coastal waters. It was found that oyster norovirus outbreaks are generally linked to the extreme combination of ant
Autor:
Shima Shamkhali Chenar, Zhiqiang Deng
Publikováno v:
International Journal of Environmental Health Research. 27:40-51
Norovirus is the most common cause of outbreaks of non-bacterial gastroenteritis in human. While the winter seasonality of norovirus outbreaks has been widely reported, the association between norovirus outbreak epidemics and environmental factors re
Autor:
Zhiqiang Deng, Shima Shamkhali Chenar
Publikováno v:
Environment International, Vol 111, Iss, Pp 212-223 (2018)
This paper presents an artificial intelligence-based model, called ANN-2Day model, for forecasting, managing and ultimately eliminating the growing risk of oyster norovirus outbreaks. The ANN-2Day model was developed using Artificial Neural Network (
Publikováno v:
Journal of Coastal Research. 289:847-854
Shamkhali Chenar, S.; Karbassi, A.; Hajizadeh Zaker, N., and Ghazban, F., 2013. Electroflocculation of metals during estuarine mixing (Caspian Sea). Estuaries as a geochemical reactor can change the chemical forms of trace metals during the mixing of
Predicting Incidence of Norovirus Epidemiology in Oyster Harvesting Areas along Louisiana Gulf Coast
Autor:
Zhiqiang Deng, Shima Shamkhali Chenar
Publikováno v:
Annals of Epidemiology. 27:523
Autor:
Shamkhali Chenar, Shima1 (AUTHOR), Deng, Zhiqiang1 (AUTHOR) zdeng@lsu.edu
Publikováno v:
International Journal of Environmental Health Research. Feb2017, Vol. 27 Issue 1, p40-51. 12p. 1 Chart, 2 Graphs, 2 Maps.
Autor:
Chenar, Shima Shamkhali1 sh86.shamkhali@gmail.com, Karbassi, Abdulreza1, Zaker, Nasser Hadjizadeh1, Ghazban, Fereydoun1
Publikováno v:
Journal of Coastal Research. Jul2013, Vol. 29 Issue 4, p847-854. 8p. 4 Black and White Photographs, 3 Charts.