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Changes in surface layer were evaluated based on mass material removal Δm [mg/s] and maximal depth of penetration PWJ hmax [mm].
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Variants were called using bcftools and then filtered using vcfutils varFilter (minimal depth of 8, maximal depth of 100, Phred scores of SNP call ≥ 30, and no indel present within a 2 bp window) as previously reported [ 6].
Signal for TUA6-GFP (green) at a maximal depth of 1.5 μm was projected on the curved surface and processed with a modified version of a 2D image analysis algorithm (Boudaoud et al., 2014 ) to compute fiber orientation.
In SoCo, the maximal depth of trees equals the number of contextual variables excluding user and item.
It is based on the shear deformations equality on the layers border in a section with maximal depth of the compression zone.
Here, we define the maximal depth of trees as five in RPMF.
Specially, the maximal depth of trees over Food dataset is two in SoCo.
(2) The maximal depth of each tree is five because there are four intermediate levels and one terminal level.
The parameter d max = 170.94 gives the maximal depth of CS in units of a, equivalent to 20 mm.
Then the maximal depth of trees over Food dataset is four in RPFM. Figure 6 shows that the deeper of trees, the better prediction quality, and RPFM outperforms RPMF and SoCo in terms of MAE and RMSE.
The maximal depth of trees can be inferred from the parameter S. For instance, if the value of S is 'C2 4,C3 6,C1 10,C0 5', the meaning is: (1) at the root node of decision trees, the R can be divided into four groups by using k-means method according to the similarity between factor vectors of context (C_{2}).
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