Sentence examples for augmented prediction from inspiring English sources

In addition, the present work suggests that these Data Assimilation techniques, which are at an embryonic stage of development in CFD, may have the potential to be pushed even further using the augmented prediction as a powerful tool for the optimization of the free parameters in the numerical simulation.

An improved understanding of how natural microbial communities mediate biogeochemical cycles will augment predictions of how these processes respond to disturbances from climate change to anthropogenic chemical deposition.

Furthermore, prediction from individual-level endorsement of aggression and authoritarian attitudes to more child externalizing behaviors was augmented by prediction from cultural-level endorsement of aggression and authoritarian attitudes, respectively.

A general state space model of plant is assumed to be available and the model is augmented for prediction of future output.

The variance in Section II personality disorders could be predicted by their theoretically comprising Section III personality traits, but additional Section III personality traits augmented this prediction.

These related works clearly indicate that admission control augmented with prediction models tends to produce better results.

Again, IP-10 significantly augmented the prediction of SVR among genotype 1 infected for all three IL28B SNPs with the exception of G allele carriage at rs8099917 (Figure 5).

Protein-coding annotations were augmented with predictions of RNA genes based on the Rfam database and INFERNAL software (Gardner et al. 2009) and predictions of transcription factor binding sites based on motifs from RegTransBase (Kazakov et al. 2007).

The study explored differences in the accuracy of the 2 steps of screening information and whether parents' reports of parenting practices augments the prediction of negative outcomes.

Modification of TEM to augment its prediction accuracy was demonstrated by Jarungthammachote and Dutta [15] and Huang and Ramaswamy [16] for air gasification, Loha et al. [17] for steam gasification and Lim and Lee [18] for air steam gasification.

A possible kernel-based approach would be the application of a Gaussian process to learn the model and exploit its possibility to augment a prediction with its estimated standard deviation.