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Then, its corresponding vector v∈C n.
(beta_{n}^{{prime }}) = a corresponding vector of taste parameters.
Hence, the corresponding vector is in (15,11).
The corresponding vector fields and symmetry reductions are derived.
Here ((cdot)^{T}) denotes the transpose of the corresponding vector.
Thus, the study performance of each student is reflected through the values of the corresponding vector.
Then, X is the vector of control variables and β is the corresponding vector of coefficients.
where is the th decoupled objective function, and is the corresponding vector of auxiliary variables.
Finally, we derive the corresponding vector and scalar subdivision schemes, which lend themselves to a fast implementation.
Along with system (3) we consider the corresponding vector map F = ( f, x n, g, y n ).
Z is a vector that includes demand side variables, τ is a corresponding vector of parameters, and υ i is a random error.
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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