The core issue here is the difficulty in determining the true cracking mode for the nonlinear crack problem, which requires the most active crack or cracks to be identified among a group of potentially active cracks during each load increment in numerical analysis.
In the context of our method, this makes each feature selection problem much easier as the regulators have to be identified among a much smaller set of genes.
Their phenotypes have to be identified among a group of 10 fly images on a parallel handout (File S2).
In an explanatory setting, at risk gene-phenotype combinations will be identified among a very large number of possible combinations by logic regression-based methods recently proposed [ 106, 107].
By exhaustively testing all combinations of gene sets and datasets, our approach guarantees that the best gene signature will be identified among a pool of pre-defined gene sets.
On the basis of previous performance in AB experiments in our laboratory in which two targets had to be identified among an RSVP stream of distractors, a group of 10 new blinkers (six female, aged 21 20, mean 24.5) and a group of 9 non-blinkers (seven female, aged 18 26, mean 22.7, of whom 7 had participated in the previous two experiments) were formed.
On the basis of previous performance in AB experiments in our laboratory in which two targets had to be identified among an RSVP stream of distractors [12], [20], [21], [22], [24], [28], [29], two groups of volunteers were formed: A blinker group (seven female, aged 21 35, mean 24.5) and a non-blinker group (seven female, aged 21 27, mean 23.6), consisting of 12 participants each.
Conclusions: Areas of common educational need were identified among a variety of learners.
Three radicleless mutants, radicleless 1 (radiclelesscleless 2 (rand), and radicleless 3 (ral3), were identified among a total of 188 rice embryo mutants [35].
A robust 7-factor model was identified among a core of 73 clinical item scores, accounting for 51% of score variance.
Four groups of poor readers were identified among a population of students with learning disabilities attending a special class in secondary school: normal readers; specific poor decoders; specific poor comprehenders, and general poor readers (deficits in both decoding and comprehension).
