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The predicted and measured responses demonstrate the accuracy of both models with average deviations of only 4.67% and 1.53% for penetration and softening point, respectively.
Statistical error analysis shows that the developed LSSVM model outperforms all existing predictive models with average absolute relative error of 0.19% and correlation coefficient of 0.999.
The experimental thermal conductivity data were correlated with the Wassiljewa mixing rules as modified by Mason and Saxena, and predicted using two new empirical models, with average deviations under 0.4% and 0.6%, respectively.
The results showed that the external GEP and RF models (EGEP and ERF) might be suitable approaches for modeling LAI by average scatter index (SI) values of 0.275 and 0.270 (for cropland) and 0.273 and 0.279 (for grassland) when compared to the local GEP and RF models with average SI values of 0.207 and 0.204 (cropland), and 0.249 and 0.204 (grassland), respectively.
The element numbers for four different models with average element lengths of 4, 5, 6, and 7 mm were 92749, 54634, 35031, and 28087, respectively.
Nonetheless, the convergence of the SA algorithms is slow after the first minute of run time, with both SA-based optimization methods requiring about 50 min to find models with average CC very close to 1.0.
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We also include a simple ionosphere model with average mass M = 28 amu ions that were generated inside the ionosphere.
The displacement is conducted in a homogeneous reservoir model with average permeability, porosity and oil saturation being 2,000 mD, 0.3 and 0.7, respectively.
Deeper, at −3500 m asl (Fig. 6d), temperatures between 20 and 160 °C are predicted by the model, with average temperatures of ~90 °C apart from the thermal anomalies.
The results demonstrate the predictive power of the proposed model with average F-scores of 89.8% for the week prediction model and of 86.7% for the bi-week prediction model.
Finally, the alcohol and methyl ester product distributions from the literature proved to be reasonably described by this new model with average relative deviations of 22.9% and 14.1%, respectively.
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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