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Accurate prediction and further dissection of neonicotinoid elimination in the water treatment by CTS@AgBC using multihead attention-based convolutional neural network combined with the time-dependent Cox regression model.
Zhang, Chao; Li, Xiaoyong; Li, Feng; Li, Gugong; Niu, Guoqiang; Chen, Hongyu; Ying, Guang-Guo; Huang, Mingzhi.
Afiliación
  • Zhang C; School of Civil Engineering & Transportation, South China University of Technology, Guangzhou 510640, PR China.
  • Li X; SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, PR China.
  • Li F; School of Civil Engineering & Transportation, South China University of Technology, Guangzhou 510640, PR China. Electronic address: hjlifeng@scut.edu.cn.
  • Li G; School of Civil Engineering & Transportation, South China University of Technology, Guangzhou 510640, PR China.
  • Niu G; SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, PR China.
  • Chen H; School of Civil Engineering & Transportation, South China University of Technology, Guangzhou 510640, PR China.
  • Ying GG; SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, PR China.
  • Huang M; SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, PR China; School of Resources and Enviro
J Hazard Mater ; 423(Pt A): 127029, 2022 02 05.
Article en En | MEDLINE | ID: mdl-34479086
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Contaminantes Químicos del Agua / Purificación del Agua / Quitosano / Nanopartículas del Metal Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Hazard Mater Asunto de la revista: SAUDE AMBIENTAL Año: 2022 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Contaminantes Químicos del Agua / Purificación del Agua / Quitosano / Nanopartículas del Metal Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Hazard Mater Asunto de la revista: SAUDE AMBIENTAL Año: 2022 Tipo del documento: Article Pais de publicación: Países Bajos