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Multi-physics simulations based on multi-component multi-solver modeling approach were performed for high-temperature fuel cells.
By reducing the features in the model using machine learning to identify relevant features, and randomizing the training data using target shuffling (Nisbet et al., 2009), we decreased the risk of overfitting and helped ensure the patterns detected by each modeling approach were unlikely to be due to chance.
Finally the estimated DALYs in Norway based on the modeling approach were very similar to those calculated using observed data from the Registry, serving as further indication of the validity of proportion cured and proportion treated based on the former procedures.
29 Considering that sites included in the current modeling approach were selected using a combination of purposive and random methods, a further improvement to the approach presented here would involve defining a likelihood function more appropriate for preferentially sampled geostatistical data, as described in Diggle and others.
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The system nonlinear mathematical model, derived using Lagrangian modeling approach, is simulated in MATLAB/Simulink® environment.
They show that this joint modeling approach is better than independent modeling in terms of predictive performance.
Such a modeling approach is particularly accurate for modeling oscillatory biological behaviors like those observed in the Drosophila circadian cycles.
Our modeling approach is to couple a ventricular model and a compatible Purkinje model.
The applicability of our modeling approach is not restricted to the modeling of Th17 cell differentiation.
The main goal of our modeling approach was to address two violations of model assumptions and thereby improve model performance.
Model fact sheet −Which kind of modeling approach is used?
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