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An analysis of the existing batch process supported by material flow modelling generated a reference point for the developed production process.
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The existing models generated a large amount of scattered data, which predicted an under or overestimation of shear capacity.
The ASR-induced expansions of fly ash or lithium bearing mortar bars by the proposed models generated a good correlation with those obtained by the experimental procedures.
The ANOVA model generated a significance level for each probe set, along with the fold change, and imputed gene annotations.
The OLS model generated a mean closest to the observed mean and had the smallest MAE and MSE.
The regression model generated a predicted probability of surgery for each patient.
Furthermore, these models generated a negative covariance between additive and maternal genetic effects.
This learning model generated a hazard score for each endometrial carcinoma patient based on combinations of marker stainings.
A likelihood-ratio test was used to determine whether the additive-dominance model generated a significantly better fit than the simple additive model.
The results of the multivariable model generated a R2 of 0.7473, showing that our model accounted for 74.73% of the variance in log inpatient charges.
All four models generated a double map for the Isl2-EphA3ki/ki muthere there were, however, subtle differences between the model results and experimental data.
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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