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The resulting matrix is normalized to fit to physical constraints.
For weighting the criteria, the best-worst method is applied, and because of having target criterion in the selection problem, the decision matrix is normalized by the target-based technique.
The direct-influential matrix is normalized using Eq. (4) and is shown in Table 4.
To make the performance indices dimensionless and comparable, the decision matrix is normalized.
The matrix is normalized so that, where is the maximum number of pixels that are included in a box of size.
In this model, first of all, the average of six metrics (criteria) in all problems is calculated then this matrix is normalized.
Similar(47)
The resulting data matrix was normalized using an internal standard, Ribitol, in 100% methanol (20 1), followed by normalization with the fresh weight of each sample.
Consequently, this matrix was normalized with the quantile method.
The total dimensions relation matrix was normalized by Eq. (10).
To compare the activity vectors, the columns of the matrix are normalized as shown in Fig. 6.
elements derived from a Gaussian distribution N 0,1), and each row in the matrix was normalized to a unit magnitude.
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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