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Second and most importantly, we aim to detect risk factors with the highest predictive value for problem behavior in the target group.
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This is of great value for problems with a large number of basic variables such as in stochastic finite element analysis.
They involve finding values for problem variables subject to constraints that specify which combinations of values are consistent.
Finally, in Table 3, we report the approximately optimal solutions and values for problem L-Mifflin with different starting points.
This section is devoted to an investigation of the Hölder behavior of the perturbed optimal value mappings for problem (2.1).
For analysis purposes, a "no problem" approach was taken where missing values for these data points were replaced with the equivalent value for no problem.
The optimal objective value for the problem (Snyder and Daskin 2006).
We assume that the boundary values and for Problem 1 can be extended to functions defined on such that.
The same is also true for other boundary value problems (for Floquet problems, see e.g. [16 18]).
Body mass index, age, and blood pressure had no predictive value for bleeding problems.
Chun and Hokari [19] proved the coincidence of Nucleolus and Shapley value for queueing problems.
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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