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We can see that our method yields very accurate results and converges superlinearly.
This proposed hybrid method yields very good results than simple morphological approach.
The method yields very accurate results even for surfaces with singularities, concavities, complicated shapes or nonzero genus.
It has been shown that this method yields very good results as it is robust against noise, which is very important for our purposes.
The proposed method yields very good result for noisy image also, and it proved that the proposed method is insensitive for noise.
Performance measures of the proposed work are compared with other methods, and this comparison proves that the proposed method yields very good results than other methods.
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As an example, the deterministic threshold accepting method yields very-near-to-optimum tours for the famous 442-cities traveling salesman problem of Grötschel within 1 to 2 s of CPU time.
Within reasonable computation time, the method yielded very accurate values for variances of AAFs.
The first method yielded very high consistency between estimates of mRNA degradation rate (r = 0.925), indicating biological reproducibility of our database (Fig. 1C).
When the causal gene set consists of the genes with 36~49 SNPs, on the other hand, the Z-statistic method yielded very high type 1 error and power.
While Urchuk/Plewes and our CV method yielded very similar estimates, the utility of CV analysis is much greater than just this particular application; it was selected because it provided a direct comparison to established method.
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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