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In an earlier study we already presented the comparable performance of the heuristic in comparison to model selection criterions [23].
As shown in Table 3 (model 3 in comparison to model 2) the fit of the model is declining after constraining the model.
According to the obtained above results, it can be expected that link beam shear force in Model D has lower value in comparison to Model B at the top quarter level of building.
In comparison to Model [2a(b)], Models [7a(b)] to [9a(b)] result in absolute differences significantly above 10 6) and, therefore, show a very strong (strong) preference for explanatory models using the adjusted collateralization ratio.
One striking result of the RF analysis is the small number of associated markers that are found for all the metabolites in comparison to model (1).
Nevertheless, in comparison to model fish species with a fully sequenced genome, e.g., zebrafish, the in silico identification of conserved miRNAs families in the Asian seabass was not possible due to the absence of whole genomic information for the species.
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In comparison to models 1 and 2, we see that the hierarchy of educational levels is less obvious.
In an earlier study, our modified EM algorithm generated improved QSAR/QSPR models in comparison to models based on equally weighted GMMs [27].
This model considers demand-side and supply-side uncertainties simultaneously, which makes it more realistic in comparison to models in the existing literature.
Merlo et al. (2014) observed that, in comparison to models without wood density as a predictor, models which included density had R 2 values that were about 4 9%% higher.
In this paper, it is shown that modeling using the vector-based HMM gives a better fit for the empirical data in comparison to modeling with Cauchy, Bessel-K form (BKF) and generalized Gaussian (GG) distributions.
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