The literature seems to be sparse on analytical approaches involving the currency fluctuation uncertainty in the supplier selection process.
In the presence of sparse sampling, the analytical models often do not provide satisfactory results when there are so many unknown parameters.
Event (i.e., accident) data for certain mitigation options are sparse, making alternative analytical approaches, such as estimating expected damage, difficult (e.g., Ha-Duong and Loisel 2010).
Moreover, there are a number of metabolites of potential toxicologic interest, such as chloral, dichloroacetic acid, and those derived from glutathione conjugation, for which reliable pharmacokinetic data is sparse because of analytical difficulties or low concentrations in systemic circulation.
However, there are a number of metabolites of potential toxicologic interest, such as chloral, dichloroacetic acid (DCA), or those derived from glutathione conjugation, for which reliable data are sparse because of analytical difficulties or low concentrations in systemic circulation.
For the n-heptane model, with 544 species and 2446 reactions, a factor of 10-speed improvement over the original solution procedure was found using analytical derivatives and sparse linear algebra.
Even though coned disk springs are widely used given their highly customizable design features, the literature on both analytical and experimental characterizations is sparse.
The fine analytical transform or sparsifying basis for sparse representation regularization is critical for recovering the signal.
In this study, a sparse regularization approach is proposed for TLM based on analytical model and redundant dictionary.
