Sentence examples for factors that quantify from inspiring English sources

The simple method is based on the use of dynamic amplification factors that quantify the dynamic response for a given perturbation at the hang-off point relative to the static response.

In a real application, such as the Habanero dataset, it is difficult to determine definitively which algorithm performs better due to the many uncertainties but the number of association points, the number of final fractures and the error are three important factors that quantify the effectiveness of our algorithm.

The existing fate factors that quantify the loss of wetland area due to water consumption are combined with the EFs in order to calculate characterization factors.

To formalise this, we used Bayes factors that quantify how much more evidence there is for a GLM with one response measure relative to another.

The correction factors that quantify the accuracy of the recovery were at least 1.4 times lower when using the model describing the vasculature (except for A-V: at least 1.1-fold).

Here, we replace these assessment factors with extrapolation factors that quantify the variation in the extrapolation of toxicity from one to many species and from lethal to sublethal effects based on available, relevant toxicity data.

Therefore, the direct trust factor that quantifies the behaviour of node x is deduced from the above calculations as: {T_x}^D t)=left({mathbb{E}}^x(d)right)=MBig(delta left|alpha, beta Big) right.

With these two profiles, we propose a Bayesian regression approach called domainRBF (domain Ranking with Bayes Factor) to calculate a Bayes factor that quantifies the strength of association between the query domain and the query phenotype, using the phenotype similarity profile as the response variable and the domain proximity profile as the predictor variable.

We developed effect factors (EF) that quantify the contribution to potential global extinction of species due to a loss in wetland area that is caused by water consumption.

Briefly, the E-Model combines a set of characterization metrics of the transport system and provides as output a rating factor, R, that quantifies the users' satisfaction.

Risk scores using these systems can range from simple additive scores to the products of more complex multivariable models that quantify risk factors according to their association with adverse outcomes [ 7].