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10.3390/ijms19030649 http://scihub22266oqcxt.onion/10.3390/ijms19030649 C5877510!5877510 !29495342     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\29495342 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Int+J+Mol+Sci 2018 ; 19 (3 ): ä Nephropedia Template TP {{tp|p=29495342 |t=2018. Hazard Screening Methods for Nanomaterials: A Comparative Study |pdf=|usr=}}{{29495342 }} gab.com Text Hazard Screening Methods for Nanomaterials: A Comparative Study JO: Int J Mol Sci http://www.kidney.de/pget.php?&tw=1&pmid=29495342 #FOAvip Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs-TiO?, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework. Twit Text FOAVip Hazard Screening Methods for Nanomaterials: A Comparative Study ????Int J Mol Sci http://www.kidney.de/pget.php?&tw=1&pmid=29495342 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29495342 #FOAvip English Wikipedia <ref>{{Cite pmid|29495342 }}</ref> Hazard Screening Methods for Nanomaterials: A Comparative Study #MMPMID29495342 Sheehan B ; Murphy F ; Mullins M ; Furxhi I ; Costa AL ; Simeone FC ; Mantecca P Int J Mol Sci 2018[Feb]; 19 (3 ): ä PMID29495342 show ga Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs-TiO?, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework. |Algorithms [MESH] |Bayes Theorem [MESH] |Chemical Phenomena [MESH] |Hazardous Substances/*analysis/*chemistry [MESH] |Models, Theoretical [MESH] |Monte Carlo Method [MESH] |Nanostructures/*chemistry [MESH] |Reproducibility of Results [MESH] |Risk Assessment/*methods [MESH]Hazard Screening Methods for Nanomaterials: A Comparative Study (epmc).pdf DeepDyve Pubget Overpricing