The model parameters, evaluated based on the measurements of thicknesses as function of Cu2 + concentration and exposure time, were further validated by comparing the prediction of the model with another set of independent measurement; here, the depletion of Cu2 + ions in the conditioning solution above the reacting gel is measured as function of time.
The most widely used method is the local sensitivity analysis which consists of calculating the partial derivatives of the model state variables to the model parameters evaluated at the normal operating point where all the parameters have their nominal value.
Moreover, in our study, in contrast to Cai et al. [ 48, 49], we combined features including model free and model based parameters evaluated by DCE-MRI data and DW-MRI data.
Other model input parameters evaluated in sensitivity analyses (treatment cessation rates, mortality rates, infectiousness with incomplete viral suppression, testing rates, and the proportion in the high-risk group) demonstrated similar relative effects between the strategies over the range of likely values.
The model-independent parameters evaluated were: (i) IAUGC60, in mM.sec, and (ii) the pre-contrast T1, in seconds.
The DCE-MRI model-dependent parameters evaluated were: (i) Ktrans, in min-1, (ii) kep, in min-1, (iii) ve, and (iv) vp.
The model parameters were evaluated on the basis of more than 7000 experimental data points.
Model parameters are evaluated by a designed algorithm based on Genetic Algorithm (GA).
The probable global optimal values of the proposed thermodynamic model parameters are evaluated by the differential evolution (DE) optimization strategy.
Based on the concrete formulation and the cement chemical composition, the model parameters are evaluated with their associated random distribution.
