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Very good correspondence is found between F.E. results and microscopic cross-sections, showing the usefulness and applicability of the method to random lay-ups and optical fiber dimensions.
The method, which is illustrated with some examples, represents a dramatic reduction in the number of focal element evaluations performed when applying the Monte Carlo method to random set theory.
Then we apply the method to random graphs and lattice embedded random regular graphs, and derive the expressions of their characteristic curves.
Our use of whole genome sequence data allowed for targeted identification of phylogenetically informative markers (i.e., SNPs) to distinguish between B. pseudomallei and B. mallei, a preferred method to random identification of SNPs in conserved genes, as was done previously [20].
This algorithm determines "consensus" clusters by measuring the stability of clustering results from the application of a given clustering method to random subsets of the data.
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We saw a broad range of approaches, from generalised boosted methods to random forests, single value decomposition to matrix factorisation and collaborative filtering, with no one class of model outperforming all the others.
Over a 10-day period, we compared PDF file acquisition based on AL methods to random selection based on the performance of the detection model.
One method to impose random imperfections is node shaking or a random deviation in material properties (Kilchert 2013; Heimbs 2008).
We introduced a novel method to clone random DNA fragments independent of ligation reaction.
Factor analysis is used as an objective method to avoid random subjective values in node importance calculation.
We utilize CE optimization method to design random phase information to a given set of pilot symbols to reduce the PAPR of the time domain pilot symbols.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com