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Exact(48)
The positive predictive value for these predictions ranged from 0.80 to 0.85.
Although the positive predictive values for these predictions are low (<11%), mainly due to the rapid growth of the propagated networks, they are significant (P ≤ 0.001, Supplementary Table 1) even after randomizations based on networks of the same sizes and degree distributions as those of the test networks (i.e. the PIE approach).
For this reason, FEA (finite element analysis) based fatigue analysis is increasingly popular and the input loads used for these predictions are derived from test or CAE-predicted virtual prototype.
Reliable methods for predicting delta during study design and better reporting of the basis for these predictions are needed to minimize the risk of trial failure from type II statistical errors and resulting waste of research resources.
For these predictions, the five types of the friction materials were produced and tested.
An acceptable accuracy for these predictions of the computer models is presented.
Similar(12)
Across six experiments, we found essentially no evidence for either of these predictions.
Significant interactions were observed between the HP and FT terms for these prediction models.
The histogram of the error percentage for these prediction models over the three testing periods is plotted and fitted with a normal distribution line in Fig. 11.
Using the numbers of communities generated by each algorithm ( N = 322 for the map equation versus N = 413 for modularity maximisation), we calculated the Z-score for these prediction scores.
Good model fit proved to be stable for these prediction models (see Table 4).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com