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This paper presents two new procedures for ranking and selection (R&S) problems where the best system designs are selected from a set of competing ones based on multiple performance measures evaluated through stochastic simulation.
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The procedure for ranking the tasks for scientific workflows is described in section 5.1.
So Andersen and Petersen (1993) developed a procedure for ranking efficient units.
We describe a new landscape-specific procedure for ranking vertebrate forest wildlife species for their relative risk of extirpation, and apply it to a ca. 189,000 ha intensively managed industrial-forest landscape in northwestern New Brunswick, Canada (J.D. Irving, Ltd., Black Brook District).
This is also the case with our procedure for ranking the countries, using 10.000 different sets of weights, but not necessarily so: we can use the weights to get a representative score for a country, cf. footnote 6.
Taking such uncertainties into account, a procedure is proposed for ranking creep propensities exhibited during nanoindentation under specified conditions.
As in Alberton et al. [ 18], the numerical procedure could be adapted to procedures proposed in literature for ranking parameters according to their estimability (e.g., Weijers and Vanrolleghem [ 19]; Sandink et al. [ 20]; Brun et al. [ 24]; Yao et al. [ 25]; Li et al. [ 21]; Secchi et al. [ 14]; Chu and Hahn [ 23]; Sun and Hahn [ 26]; Lund and Foss [ 22]; Chu et al. [ 27]).
If exposure information is not systematically available, empirical Bayes procedures are suggested as a basis for ranking the observed clusters in order of priority for further investigation.
In this work, we introduce a new procedure for conformation ranking.
Consequently we build an efficient training procedure for Rank-LSVM using random block coordinate descent method with a linear convergence rate.
We also developed procedures for affinity ranking of protein ligand interactions from ligand-induced changes in chemical denaturation data, and proved the principle for this by correctly ranking the affinity of previously unreported peptide PDZ domain interactions.
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