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The shear strength of the DFN model showed an apparent principal direction.
One model showed an error of less than 5% for 90% of the time.
Diffusion coefficients, estimated using the Higuchi model, showed an increase from 2.28 to 9.60 × 10−7 cm2/h as the particle size decreased.
The other ANN model showed an improved predictive capability that was approximately twice as good as that provided from the DoE model.
The model showed an influence of geometrical design characteristics of the graphical objects (icons) and their groupings (icon structures) on the observed task efficiency.
The model showed an accurate predictability of the diffusion and microstructural evolution in real superalloy-coating diffusion couples studied at high-temperature exposure.
Overall, the decision tree model showed an accuracy of ~ 60%, while the linear equation model has a correlation coefficient of about 0.65 compared to the measured Bd values.
The developed combined lifetime model showed an error of less than 5% RMS compared to the measurement results after a Worldwide harmonized Light vehicles Test (WLTC) was applied for 18 months.
The model showed an overall performance of root mean square error (RMSE) = 1.2, with coefficient of determination (R2) = 0.86, mean bias (MB) = 0.0, and mean ratio (MR) = 1.0 for SSS ranging between ~ 1 and ~ 37 (N = 3640).
The results of the queries after the application of the model showed an average decrease of 21% in returned tuples, which was evaluated as a significant reduce in tuple results.
This metastatic tumor model showed an increased incidence of tumor formation, an accelerated tumorigenesis and a significant hepatic metastasis, therefore offering scientists a proven platform to study chemotherapeutic drugs.
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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