Sentence examples for develop weighted from inspiring English sources

We develop weighted essentially non-oscillatory reconstruction techniques based on Hermite interpolation both for semi-Lagrangian and finite difference methods.

Herein, we develop weighted estimators that reflect unequal selection probabilities and differential nonresponse rates, and we derive variance estimators that properly account for the sampling design and the potential relatedness of participants in different sampling units.

The method uses gradient analysis (ordination method of Canonical Correspondence Analysis) for assessing the weights of Ns descriptors' effects, which are further used to develop weighted severity indices; the severity index of WQ (Swq) and Es invasion (Se), respectively.

Instead, we develop weighted estimates that are adapted to the geometry.

Seo et al. [ 15] used the detection p-values to develop weighted Pearson correlations between expression measurements from two different arrays.

Under this study design, we developed weighted estimating procedures for model parameters in marginal multiplicative intensity models and for the cumulative baseline hazard function.

At the BBS route scale, we buffered routes at 100 m, 1 km, and 10 km radii, intersected these buffers with county boundaries, and developed weighted averages for each forest variable within each buffer width.

Therefore, we developed weighted multiple testing procedures.

To examine the association between hospital performance and readmission diagnoses across the range of hospital 30 day risk standardized readmission rates, we developed weighted regression models of the relation between the rates and each of the 10 most common readmission diagnoses across hospitals.

The reconstruction was performed on the basis of the assignment of TT and DR proteins to COGs, together with COG-based phyletic patterns of 62 other sequenced bacterial and archaeal genomes [ 37], using a previously developed weighted parsimony method [ 41] (see Methods and Additional file 2).

In this paper, variational mode decomposition (VMD) and a newly developed weighted online sequential extreme learning machine (WOSELM) are integrated to detect and classify the power quality events (PQEs) in real-time.