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Table 1 gives the values of the above mentioned model parameters, estimated on the basis of [11] and the report by Mayer and Hurst [9] on their experiments with calves which are hopefully not much different from humans.
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Accordingly, it is important to find a mathematical model to correlate the milling parameters with the synthesis time of metal/ceramic nanocomposites and then optimize the mentioned model.
It is clear that the objective functions and constraints in these two models can be converted into previously mentioned models by changing the relevant parameters.
The results indicated that the PCA_SOFM model had more ability, precision and flexibility in forecasting the pull-off adhesion between concrete layers parameter than the two mentioned models.
Finally, all model parameters mentioned above are quantified using a Ca2+-independent stimulus, which to a large extent excludes differences in Ca2+ signalling or Ca2+ sensitivity as confounding factors.
As mentioned perviously, the model parameter and model structure of LCC prediction are marred by uncertainty.
As mentioned previously, uncertainty in the model parameters may result in a range of possible concentrations.
As already mentioned, likelihood scores may be slightly different if model parameters are optimized using the standard approach because of numerical deviations.
Note, the obtained second PV and PVT model parameters are summarized in Table 6 in which the best parameters are mentioned in bold.
The previously mentioned maximum-likelihood learning procedure is also applicable to estimate the model parameters of DBM.
The benefit of a Gaussian random walk model is that it has very few model parameters rather than a parameterized process model as mentioned in Section 2.3.1.
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