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For a first order linear model with autocorrelated error, we have developed a set of efficient RFORDs with efficiency around ninety percent and the developed designs are very close to D-ORFODs.
We were unable to identify a set of efficient technologies that correlated directly to low EUIs.
The proposed protocol implements an efficient node selection algorithm which determines a set of efficient nodes called as E-Nodes.
In this paper we present a set of efficient algorithms for detection and identification of discontinuities in high dimensional space.
To solve this problem effectively, it is very important to put forward a set of efficient solutions.
Seemingly then, a set of efficient linear solvers could be solving important scientific problems for years to come.
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The models are enhanced via a set of effective valid inequalities and an efficient heuristic is developed.
Since Parragh et al. [4] proposed a set of highly efficient and effective neighborhood structures for the static DARP we decided to use the same.
The OpenCV package [12] adds a set of computationally efficient, non-linear machine learning algorithms.
The particular structure of this linear system allows us to construct a set of highly efficient schemes optimized for different precision requirements and time intervals.
Another advantage of DEA is its ability to identify a set of corresponding efficient DMUs that can be referred as benchmarks for every inefficient DMU.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com