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The SVM classifier with the best performance is obtained by testing different values of the kernel parameters.
In the first experiment, we illustrate the effect of the action costs on the convergence by testing different values of κ.
The optimization is carried by testing different values for bext and msusp with a starting wave height of 0.25 m and a starting peak wave period of 6.70 s until a reasonably close value to 100%% in power efficiency was achieved (Fig. 13).
In order to derive a simple confidence estimate, we begin by testing different values of θ ranging from 1 to 5. For any given value of θ we proceed systematically deriving separating lines in the zmax ρθ plane.
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In our experiments, we altered the ratio between αP and βP by fixing βP equal to 1 and testing different values of αP.
The tuning is performed by testing different parameter values and choosing the one resulting the best correlation between the estimated and the true genetic values.
The threshold for correlating domains was found by testing different R values (0.80, 0.85, 0.90, and 0.99).
Future studies could expand on this study by testing different amounts of coffee card values and their association with response rates.
These values were chosen for the implementation of our algorithm by testing different combinations to align synthetic point clouds.
In addition to the weight between X-ray and geometry terms, we now optimize the relative weight of B-factor restraints and others by testing seven different values.
In comparison with previous models, testing different (epsilon -values doepsilon -valueshe equilibrium pointedoest by the simulationot
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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