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The geometric model presented has a no-undercut shape in the die opening direction and easily represents the smooth ending at ends of drawbeads.
The model presented has a considerably higher correlation coefficient (0.963) and lower root mean square (0.375), mean absolute (0.328), and mean bias errors (0.010) than other models presented in the literature which, at least when applied to the present data set, tend to under-predict the combustion enthalpy.
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The predicted values agree reasonably well with the available experimental data, and the relationships in the model presented have been verified.
The reduced model presented had a PA of 69.3%, 66.4%, and 65.5% at 2, 5, and 7 years, respectively.
The models presented have validity only for particular vessel operational conditions (VOCs), and a large number of work has already been dedicated to system identification of these models.
Despite these limitations, the models presented had generally good discrimination (via the c statistic) and calibration.
Therefore, the exact influence of the different predictors in the models can not be precisely stated and the models presented have the primary function of identifying significant influences as a starting point for further analysis.
Given baseline values of serum C-reactive protein (CRP) and patient characteristics, the models presented have the ability to predict future levels of CRP, across diagnostic categories and patient characteristics.
It is hoped that the model herein presented has heuristic value for a rapprochement of psychoanalysis and neurobiology.
Some uncertainties related to these models are presented having engineering applications in focus.
The mathematical model presented here has been derived from our own experimental data [ 9].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com