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This paper treats the behavior of solids which circulate between two environments, both in mixed flow.
Here a stochastic simulation was used to study the impact of population structure on bias and precision of genetic correlation estimates between two environments.
The second way, discussed in section 3, rests on a thought experiment about "slow switching" between two environments.
On-site analysis was done by mounting the system on a partition wall between two environments, which were air conditioned in order to create constant thermal difference (Fig. 6).
Acute angles between two environments represented high positive correlations, whereas wide obtuse angles (>90°) between two environments indicated their dissimilarity.
Slope QTL were mapped by single QTL mapping using the slope between two environments as a phenotype.
Similar(49)
A feedforward neural network architecture was designed to simulate a neuroanatomical differentiation between two environment-behavior relations X-R1 and X-R2, where R1 and R2 denote two different emitted responses (not unconditionally elicited by the reward).
Genetic correlations between two environment states were calculated as the ratio of the genetic covariance between these two environments to the product of their genetic standard deviations.
Concerning usability, significant differences were verified between the two environments.
Differences in protein structural and dynamical behavior between the two environments have been found.
Further, the results show differences in both phenotypic performance and genetic parameters between the two environments.
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