A dynamic Monte Carlo model for predicting radiant exposure distribution in dental composites: model development and verifications

2005 ◽  
Author(s):  
Yin-Chu Chen ◽  
Jack L. Ferracane ◽  
Scott A. Prahl
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Model ◽  
Model Development ◽  
Dental Composites ◽  
Radiant Exposure ◽  
Dynamic Monte Carlo ◽  
Exposure Distribution

Model Development and the Assessment of Competing Models

1974 ◽  
Vol 18 (4) ◽  
pp. 425-428
Author(s):  
Mark G. Pfeiffer ◽  
Arthur I. Siegel
Keyword(s):  
Monte Carlo ◽  
Real World ◽  
Utility Theory ◽  
Practical Problem ◽  
Monte Carlo Model ◽  
Model Development ◽  
Multiattribute Utility ◽  
Levels Of Abstraction ◽  
Multiattribute Utility Theory ◽  
Competing Models

The paper describes the process of model development and applies multiattribute utility theory to the practical problem of optimizing the selection among competing models designed for the same purpose. As an example, two models (Human Interactive and Monte Carlo) are compared which differ on the basis of their existing levels of abstraction, or their degree of remoteness from the real world. The slight superiority of the Monte Carlo Model resulted largely because it had higher utilities for the more important attributes of models such as repeatability of output, degree of error/low variability, and feasibility of use and application.

scholarly journals Including operational data in QMRA model: development and impact of model inputs

2008 ◽  
Vol 7 (1) ◽  
pp. 77-95 ◽  
Author(s):  
Kenza Jaidi ◽  
Benoit Barbeau ◽  
Annie Carrière ◽  
Raymond Desjardins ◽  
Michèle Prévost
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Model ◽  
Model Development ◽  
Microbial Risk ◽  
Relative Risks ◽  
The Mean ◽  
Log Normal ◽  
Simplified Calculation ◽  
The Impact ◽  
Selection Of

A Monte Carlo model, based on the Quantitative Microbial Risk Analysis approach (QMRA), has been developed to assess the relative risks of infection associated with the presence of Cryptosporidium and Giardia in drinking water. The impact of various approaches for modelling the initial parameters of the model on the final risk assessments is evaluated. The Monte Carlo simulations that we performed showed that the occurrence of parasites in raw water was best described by a mixed distribution: log-Normal for concentrations > detection limit (DL), and a uniform distribution for concentrations < DL. The selection of process performance distributions for modelling the performance of treatment (filtration and ozonation) influences the estimated risks significantly. The mean annual risks for conventional treatment are: 1.97E−03 (removal credit adjusted by log parasite = log spores), 1.58E−05 (log parasite = 1.7 × log spores) or 9.33E−03 (regulatory credits based on the turbidity measurement in filtered water). Using full scale validated SCADA data, the simplified calculation of CT performed at the plant was shown to largely underestimate the risk relative to a more detailed CT calculation, which takes into consideration the downtime and system failure events identified at the plant (1.46E−03 vs. 3.93E−02 for the mean risk).

Monte Carlo Model Development for Elemental Analysis Using a Natural Gamma Ray Spectroscopy Tool

2015 ◽  
Author(s):  
Qiong Zhang ◽  
Freddy Mendez ◽  
John Longo ◽  
Sandeep Gade ◽  
Steve Bliven
Keyword(s):  
Monte Carlo ◽  
Elemental Analysis ◽  
Gamma Ray ◽  
Monte Carlo Model ◽  
Model Development ◽  
Natural Gamma ◽  
Gamma Ray Spectroscopy

Dynamic Monte Carlo Simulation of thel,l-Lactide Ring-Opening Polymerization

2016 ◽  
Vol 11 (1) ◽  
pp. 1600039 ◽  
Author(s):  
Dimitrios Meimaroglou ◽  
Prokopios Pladis ◽  
Costas Kiparissides
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ring Opening ◽  
Ring Opening Polymerization ◽  
Dynamic Monte Carlo

Monte Carlo model for describing charge transfer in irradiated CCDs

1998 ◽  
Author(s):  
Dennis J. Gallagher ◽  
Raymond Demara ◽  
Gary Emerson ◽  
Wayne W. Frame ◽  
Alan W. Delamere
Keyword(s):  
Monte Carlo ◽  
Charge Transfer ◽  
Monte Carlo Model
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