Despite the success of RNA secondary structure prediction for simple, short RNAs, the problem of predicting RNAs with long-range tertiary folds remains.
Beyond purely chronometric measures, this hypothesis establishes a critical prediction for simple decision tasks; If the processing stages involved in a cognitive task are merely rescheduled during dual task performance, the quality of the decision should be unaffected by a concurrent task.
Psychophysical studies have confirmed this prediction for simple curves (Houtkamp et al., 2003; Scholte et al., 2001), but it has yet to be investigated for natural images.
We generate predictions for both simple and complex hypothetical landscapes, to illustrate the effect of including the behavioral rule, and for real landscapes.
In Experiment 1, we compared the local assignment and global assignment hypothesis predictions for a simple case of discarding one of two kinds of objects into one of two unlabeled colored bins.
CTE predictions for the simple case of axisymmetric particles are illustrated by calculations for glass particles in the shape of spheres, fibers and discs in an epoxy resin and are compared to those from the popular Chow theory.
Modifications of the mechanism suggested by Daly et al. are not compatible with DME validation results in the original mechanism development or with flame speed predictions for other simple fuels such as methane.
Including information from relatives into the training set improved within-group prediction substantially for simple traits like anthesis date and anthesis-silking interval, but less so for grain yield.
We present both the absolute increase in AUC between pairs of nested models as well as the increase in AUC for a given model as a percentage of the area above the curve (i.e., (1-AUC) or the fraction of incorrect predictions) for the simpler model.
The selected turbulence models are applied for the prediction of simple as well as complex jet flow applications to underpin knowledge about the accuracy obtained from the two-equation turbulence models.
