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Amongst other things, this lists the miles per gallon (mpg) the model achieves when tested on the Environmental Protection Agency's simulated city and highway driving cycles.
Results obtained through simulation show that the model achieves a high degree of accuracy.
In contrast, when the period is appropriately shorter, as shown in the figure, the model achieves dynamic transition.
The model achieves good agreement with flow stress strain evolution and yield data collected over many studies.
The model achieves 1.84 normalized mean error (NME) on the AFLW database [1], which outperforms 3DDFA [2] by 61.8%.
The model achieves a good level of accuracy by carefully modeling calcine production, crane dispatching, and equipment delays.
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The model achieved mean absolute percentage errors ranging from 3%to6%6% for the day ahead prediction.
The model achieved reasonable prediction (R2 = 0.939; Figure 27), but some inaccuracies occur.
The model achieved a mean square error ranging from 5.7% to 15.33%.
The model achieved a correlation coefficient greater than 98% for sunny days and less than 95% for cloudy days.
The model achieved an adjusted R of.10.
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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