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An orthotropic honeycomb material model was identified by comparing the two test models to the experimental data.
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Note, that the total sum of these fractions reflects the total number of peaks where at least one site was found by any of the two tested models.
Table 2 Summary of the estimated parameters, resulting AIC and relative variance of the response logAGR explained by the fixed effects, random intercepts and residuals (unexplained) for the three tested models Model M1 M2 M3 Est.
For the remaining models where the lag length is fixed, the highest-ranked model has the lowest AICc value of the four tested models.
Table 2 shows the statistics for the fitting of performance with the three tested models.
However, none of the three tested models with scale-level data returned an adequate fit in Boschen and Oei's study [ 32].
The model that predicted PEs from key determinants (model 7) is the one that explains the least variability of all the seven tested models, implying that for predicting PEs it is advisable to include microenvironment concentration data into the model.
Of the three tested models with purifying selection, the one in which the distribution of replacement rates is derived from a stochastic population model with additive per-gene fitness yields the best fits to the data.
Although we believed that the relevance of weighting could be crucial in determining the possible scores and helps to distinguish among the most important topics in the prioritization process, our study found no difference between the four tested models.
Depending on the condition, two test models were proposed.
Section 5 describes the (electro- thermal simulation for two telectro- thermalckage model and a Power-MOsimulationodel.
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