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The problem variables include field and collector parameters.
The Hugoniot enables calculation of all problem variables without resorting to an assumed constitutive model.
They involve finding values for problem variables subject to constraints that specify which combinations of values are consistent.
Sensitivity studies involving different problem variables are performed, helping to identify the appropriate solution method for specific problems.
Range of problem variables were considered in a way that the possibility of bearing capacity failure is low enough.
We suppose here that the budget of uncertainty is given by a function of the problem variables, yielding an uncertainty multifunction.
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To address this problem, variable pathlength (VP) spectroscopy in conjunction with Partial Least Squares regression (PLS) was used to monitor preparative chromatography.
where X (t) is the update of the problem variable (X=left (^{boldsymbol {lambda }}_{boldsymbol {beta }}right)) at time t.
Indeed, analytical computation of Lagrange dual variables and considering normalization parameter η as the optimization problem variable can be considered for future investigations.
Constraints (1g -(1h) impose that in the optimal solution to the problem variable y ij may assume value 1 only if both vertices i and j are considered.
Because of the nonhomogeneity of variable exponent problems, variable exponent problems are more complicated than constant exponent problems, and many results and methods for constant exponent problems are invalid for variable exponent problems.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com