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According to the R2 values in Table 2, the Langmuir isotherm model shows best fit to the equilibrium MB biosorption data than the other isotherm models at all studied temperatures (Fig. 7).
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The S Ω model showed best agreement with experiments for the hill case and the slope cases.
Based on the error analysis, only the response t95% and recovery t95% model showed best results in validation with a low relative prediction error of 0.003 and 0.001 and low root mean square error for cross validation of 9.5 and 4.68 respectively.
In this study, the SLAB model shows the best data fit (Table 1).
The combined model shows the best prediction, as the wildfire frequency increases with the hazard.
The model shows the best fit to the data under observation.
The mixed agent model shows the best performance among the three when tested under two different shop conditions.
From the results, it is clear that new model shows the best results as compare to other contesting models in application to the control.
On the basis of this assessment, the UC-CN model shows the best performance allowing for small changes in the trend level and asymmetries in the cyclical component.
Among the computations using various revised k ε models compared here, Durbin's revised k ε model shows the best agreement with the experiment.
Kong's model shows the best prediction for global vertical illuminance as compared to those by Perez, Klucher, Hay, Temps Coulson and Muneer.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com