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The influence of several modelling parameters was investigated, including geometric nonlinearity, element properties, concrete model, reinforcement model and boundary condition.
The second series was used to check the accuracy of the modification suggested into minimum reinforcement model.
These data were used to calibrate a weighted log-likelihood root distribution model and a root reinforcement model.
The reinforcement model proposed predicts a learning process that takes the risk of the customer portfolio into account.
The negative reinforcement model of addiction would suggest that smokers may consume alcohol to relieve negative affective symptoms, such as those associated with MDD and withdrawal from nicotine.
This negative reinforcement model of smoking cessation and relapse has demonstrated the importance of one's ability to tolerate nicotine withdrawal symptoms, particularly negative affect states, in remaining abstinent from smoking.
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Implications of the results for conditioned reinforcement models are discussed.
The experimentally obtained storage modulus values have been compared with theoretical prediction, using different composite reinforcement models.
Other reinforcement is modeled through discrete reinforcement modeling method (concrete and steel nodes merged into single entities).
Particulate filler composite reinforcement models were used to understand the reinforcement mechanism of these nanogels.
Guth and Kerner particulate reinforcement models have been used to understand the reinforcement behavior of these gels.
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