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The process of grain boundary engineering, involving cycles of straining and annealing, is found to substantially reduce the mass and size of random boundary clusters.
We found that the power and sample size estimates depend on the within-subject correlation and the size of random effects.
Furthermore, an easy to implement and repeatable method is presented to select the optimum size of random sets of ignition points, implicitly fine-tuned to the spatial variation of the input data.
The different measurement principles used in these designs tended to lead to systematic differences (biases) when measuring the same quantity, and to differences in the typical size of random errors (imprecision) introduced by each type of sampler.
where M is the ensemble size of random numbers {R1, R1,...., R M } and N is the number of ensembles.
Due to the large sample size of random censoring data, this method is used to estimate the distribution parameters.
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We have tested different sizes of random subsets in the model matching experiment, smaller size than the assigned number generally cannot get satisfied precision and even not convergent.
In this case, the LD is maintained and the sizes of random gene sets correspond to the original size of gene sets.
In the latter case, the LD is maintained and the sizes of random gene sets correspond to the original size of gene sets.
To create these tools for rare variant/common disease studies, much interest is being generated towards investing in re-sequencing either large sample sizes of random chromosomes or smaller sample sizes of patients with extreme phenotypes.
For quantitation of metastatic nodule size, photos of random fields were obtained and then the sizes of at least 20 nodules were determined using NIH Image software and averaged.
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