Sentence examples for modes we estimated from inspiring English sources

For quadrupole modes, we estimated an FOM of 3.9 for the nanorod of type B and 7.4 for the nanobipyramid of type D, both much larger than the FOM values [3, 6 9] reported for dipole modes in the both structures, suggesting the great promise of using quadruple resonances in single-particle RI sensing.

Because we have empirically observed that the 40th percentile in this case coincides with the mode, we estimate the variance based on the distance between the 20th and 40th percentiles.

Furthermore, using Normal Mode Analysis we estimated the effect of ligand binding on overall protein flexibility, a measure which has been used in combination with structural conservation to predict the location of allosteric sites [ 13].

By examining the fracture modes and load displacement curves, we estimated the interface strength of ZCS to be ∼0.6 ± 0.1 GPa, with fracture occurring exactly along the ZCS interface.

In order to evaluate the variations in the percent modulations (i.e., POM, PVM or PKM) within and across different control modes and training sessions, we estimated their values for each neuron in 2-minute non-overlapping intervals.

Having calculated graphene-substrate total interaction energy taking into account substrate-induced strain, we estimated E2g mode energy changes due to anharmonic phonon-phonon interaction as the temperature decreases from room temperature to a value close to absolute zero for both substrates.

We estimated the mode and magnitude of selection acting on tadpole trophic morphology in each pond using body size as a fitness proxy [ln SVL; see 21, 62].

We estimate the mode by maximization over the curve estimator, which is given as a weighted mean of the observations, including most of the common kernel estimators, such as Gasser Müller, Priestley Chao and Nadaraya Watson.

To show that the dominant modes are actually these attractors we estimate their basin of attraction by applying the aforementioned template matching algorithm in the following way: we first calculate the correlation coefficient of the dominant patterns with each snapshot (see Materials and Methods).

Based on responses from a revealed preferences survey, we estimate a mode choice model that treats travel time as a latent variable and incorporates different sources of data along with information on travel activities.

Given an observed sequence O, we estimate the mode of the posterior distribution as follows: ϕ = argmax z ∏ c = 1 C ∏ t = 1 N c N o t | ℳ c + D c z c, Φ c N z c | 0, I. (6).