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The analysis of the experimental data using linear model gave poor R 2 (0.1844).
Both the Burgers and Power model gave poor experimental fit for the recovery domain, mainly for the modified binders.
Data fitting with the binding-dominated recovery model gave poor results (data not shown).
However, the model gave poor predictions for the subgroup of pesticides containing phosphate or thiophosphate group and polychlorinated hydrocarbons (Huuskonen et al., 1998).
This model gave poor specificity and sensitivity results that highlighted the well-known fact that insulin resistance is a heterogeneous disorder that is not only dependent on weight, sex, and age (14).
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The pseudo second-order, intraparticle diffusion models and Bangam model gives poor fitting with low R2 values.
The predicted bed expansion closely follows the measured results while predictions based on the two-phase model give poor agreement with the measurements.
While one gives encouragingly good results, the other, which appears to have better mesh stiffness modelling, gives poor comparisons with experiments.
Unfortunately, these models give poor predictions of total filling time in the case of flexible injection processes such as in Vacuum Assisted Resin Infusion (VARI) or RTM-Light.
Customization improved the statistical qualities of the model but gave poor uniformity of fit.
In those instances, the model gave a poor fit to the data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com