scholarly journals Evaluation of drug-drug interactions for oncology therapies:in vitro-in vivoextrapolation model-based risk assessment

2015 ◽  
Vol 79 (6) ◽  
pp. 946-958 ◽  
Author(s):  
Nigel J. Waters
Keyword(s):  
Risk Assessment ◽  
Drug Interactions ◽  
Model Based

Biomarker‐informed model‐based risk assessment of organic anion transporting polypeptide 1B mediated drug‐drug interactions

2021 ◽  
Author(s):  
Emi Kimoto ◽  
Chester Costales ◽  
Mark A. West ◽  
Yi‐an Bi ◽  
Manoli Vourvahis ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Drug Interactions ◽  
Organic Anion ◽  
Organic Anion Transporting Polypeptide ◽  
Model Based

scholarly journals A Study on the Probability of Failure Model Based on the Safety Factor for Risk Assessment in a Water Supply Network

2015 ◽  
Vol 119 ◽  
pp. 206-215 ◽  
Author(s):  
Jeewon Seo ◽  
Moonsoo Koo ◽  
Kibum Kim ◽  
Jayong Koo
Keyword(s):  
Risk Assessment ◽  
Water Supply ◽  
Safety Factor ◽  
Probability Of Failure ◽  
Supply Network ◽  
Failure Model ◽  
Water Supply Network ◽  
Model Based

Model-based risk assessment of concrete spalling in tunnel linings under fire loading

2014 ◽  
Vol 77 ◽  
pp. 207-215 ◽  
Author(s):  
Yiming Zhang ◽  
Matthias Zeiml ◽  
Christian Pichler ◽  
Roman Lackner
Keyword(s):  
Risk Assessment ◽  
Model Based ◽  
Fire Loading

Developing a cloud model based risk assessment methodology for tunnel-induced damage to existing pipelines

2014 ◽  
Vol 29 (2) ◽  
pp. 513-526 ◽  
Author(s):  
Limao Zhang ◽  
Xianguo Wu ◽  
Queqing Chen ◽  
Miroslaw J. Skibniewski ◽  
Jingbing Zhong
Keyword(s):  
Risk Assessment ◽  
Cloud Model ◽  
Assessment Methodology ◽  
Model Based ◽  
Risk Assessment Methodology

scholarly journals Safety Risk Assessment of Tourism Management System Based on PSO-BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenru Guo
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Management System ◽  
Evaluation Model ◽  
Risk Index ◽  
Security Risk ◽  
Bp Network ◽  
Model Based ◽  
Tourism Management ◽  
Better Than

With the development of science and technology, system management is gradually applied to tourism management. How to correctly assess the security risks of the tourism management system has become an important means to maintain passenger information. The security risk index of the travel management system is input into the PSO-BP network as a sample, and the corresponding risk value of the index is used as the network output. The results show that the error results, accuracy (96.53%), training time (216 s), number of iterations (275 times), and convergence speed are all better than traditional BP network. The relative error of PSO-BP network (0.32%) is better than that of BP network, with 300 iterations, and the error is close to 10–5. The average evaluation accuracy of S based on PSO-BP network is 99.72%, and the average time consumed is 2.512 s. It is superior to the evaluation model based on fuzzy set and entropy weight theory and the evaluation model based on gray correlation analysis and radial basis function neural network. In conclusion, the security risk assessment of the tourism management system based on PSO-BP network can effectively assess the security risk of the tourism management system.

scholarly journals Development of a machine learning model to estimate biotic ligand model-based predicted no‐effect concentrations for copper in freshwater

2021 ◽  
Author(s):  
Jiwoong Chung ◽  
Geonwoo Yoo ◽  
Jinhee Choi ◽  
Jong-Hyeon Lee
Keyword(s):  
Risk Assessment ◽  
Electrical Conductivity ◽  
Environmental Risk ◽  
Biotic Ligand Model ◽  
Data Availability ◽  
Outcome Variable ◽  
Model Based ◽  
Biotic Ligand ◽  
Input Variables ◽  
Ligand Model

The copper biotic ligand model (BLM) has been used for environmental risk assessment by taking into account the bioavailability of copper in freshwater. However, the BLM-based environmental risk of copper has been assessed only in Europe and North America, with monitoring datasets containing all of the BLM input variables. For other areas, it is necessary to apply surrogate tools with reduced data requirements to estimate the BLM-based predicted no-effect concentration (PNEC) from commonly available monitoring datasets. To develop an optimized PNEC estimation model based on an available monitoring dataset, an initial model that considers all BLM variables, a second model that requires variables excluding alkalinity, and a third model using electrical conductivity as a surrogate of the major cations and alkalinity have been proposed. Furthermore, deep neural network (DNN) models have been used to predict the nonlinear relationships between the PNEC (outcome variable) and the required input variables (explanatory variables). The predictive capacity of DNN models in this study was compared with the results of other existing PNEC estimation tools using a look-up table and multiple linear and multivariate polynomial regression methods. Three DNN models, using different input variables, provided better predictions of the copper PNECs compared with the existing tools for four test datasets, i.e., Korean, United States, Swedish, and Belgian freshwaters. The adjusted r2 values in all DNN models were higher than 0.95 in the test datasets, except for the Swedish dataset (adjusted r2 > 0.87). Consequently, the most applicable model among the three DNN models could be selected according to the data availability in the collected monitoring database. Because the most simplified DNN model required only three water quality variables (pH, dissolved organic carbon, and electrical conductivity) as input variables, it is expected that the copper BLM-based risk assessment can be applied to monitoring datasets worldwide.

Quantitative risk assessment of a hydrogen refueling station by using a dynamic physical model based on multi-physics system-level modeling

2021 ◽  
Author(s):  
Tomoya Suzuki ◽  
Kaname Kawatsu ◽  
Kento Shiota ◽  
Yu-ichiro Izato ◽  
Masahiro Komori ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Physical Model ◽  
Quantitative Risk Assessment ◽  
System Level ◽  
Model Based ◽  
System Level Modeling
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