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Exact(3)
where σ0 is a constant parameter used in combination with similarity values to control the heterogeneity of the weights as in Equation 20 and w m is a constant ensuring a minimum weight value.
In fact, for both commodities the maximum weight values remain invariant (i.e., equal to 11), while the minimum weight value is much lower for commodity 14 than 13 due to their corresponding weights' distributions.
where (x i,y i ) denotes the location of SU i, σ0 stands for a constant value (equal to 100 for the next simulations) to control the heterogeneity of the associated weights and w m a constant for ensuring a minimum weight value (equal to 10−6 for the examined scenario).
Similar(57)
The results are presented in the form of optimum values of crosssectional dimensions for minimum weight of column.
They defined the influence of a community as the minimum weight of nodes in that community; the top influential community was the one with largest influence value.
However, it is in general observed that minimum weight and maximum efficiency configurations do not necessarily coincide, as both objectives compete at intermediate values of heat recovery.
There is no minimum weight.
The proposed concepts lead to minimum weight solutions.
A direct search method is used for minimum weight optimization.
Thus, the minimum weight design gives suitable results.
The optimal dimensions and the minimum weight have been evaluated.
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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