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Modeling was achieved using Stata 15.0.
Successful modeling was achieved for all of the considered experiments, for both non-retained and retained molecules.
Modeling was achieved using Nose thermostat molecular dynamics (MD) simulations (T=353 K, 0.5 fs/step, 6000 steps).
The best-fit equation to predict FFM from linear regression modeling was achieved by adding variables, such as weight, sex, and age to the basic model, bringing adjusted R to 89% (standard error = 0.90, p<0.001) that explained 89% of variance in FFM estimated by a deuterium oxide dilution method (34).
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In EDAs, modeling is achieved by building a probabilistic graphical model that represents a condensed representation of the features shared by the selected solutions.
Close replication of CAD models was achieved.
As can be seen in Figure 9, a good agreement between the various models was achieved.
The best model was achieved using bagging combined with differential costs for misclassification (overall accuracy = 52.0%).
Further validation of the model was achieved by introducing much coarser particles in the feed.
Results were analyzed by Response Surface Methodology and a quadratic polynomial model was achieved.
The pseudo-second-order model was achieved from the adsorption kinetic studies.
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