Sentence examples for computational samples from inspiring English sources

One of the important differences between simulation and experiments in grain boundary (GB -dominated metallic structures is the lack of impurities such as oxyGB -dominatedational sametallic

An effective and flexible method for the generation of computational samples for mesoscopic modeling of anisotropic networks of carbon nanotube (CNT) bundles with various degrees of CNT alignment is developed and applied for investigation of structural self-organization of nanotubes into vertically aligned CNT forests and fibers.

Structural characteristics of the computational samples, such as bundle size distribution, average and maximum bundle sizes, magnitude of the Herman orientation factor, average tilt of CNT segments with respect to the direction of alignment, and average tortuosity of the nanotubes, are calculated and related to parameters of the sample preparation procedure.

We apply a previously developed design optimization framework to the problem of finding good bandit problem experiments, and develop computational sampling schemes for implementing the approach.

The lack of correlation between the fraction of satisfied MOHCA-seq pairwise constraints and the accuracy of the models (Table 2) suggests that unsatisfied constraints are due to conformational fluctuations in the RNA or radical-source linkers around the dominant global structures of these molecules, rather than systematic structural shifts or incomplete computational sampling.

Computational conformational sampling underpins much of molecular modeling and design in pharmaceutical work.

Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions.

For each flux in our data set, we compared the coefficient of variation (CV) derived from computational Monte Carlo sampling to the CV from published measurements.

A formulation based on a block reduction of the system Jacobian proves powerful to allow various numerical algorithms to be exploited, including frequency domain solvers, reconstruction of a term describing the fluid structure interaction from the sparse data which incurs the main computational cost, and sampling to place the expensive samples where they are most needed.

Due to the limitation of labeled training samples, computational complexity, and other difficulties, active learning (AL) algorithms aiming at finding the most informative training samples have been an active topic of research in remote sensing image classification in the last few years.

By applying the same computational pipeline to samples from different tissues and cancer types, our work facilitates biologically meaningful cross-cancer comparisons.