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The advantage of this procedure resides in the rearrangement of active potential terms at the end of the first stage using marginal posterior probabilities of different candidate models.
A binary particle swarm optimization (BPSO) algorithm is employed in the first stage using the approximate model for fitness evaluation and selects a subset of good enough solutions.
Single-event kinetic rate constants were estimated in a first stage, using only the ideal-hydrocracking data and a single-event-ideal-hydrocracking model.
We obtain the initial distances between the moving object and the anchors in the first stage using a BeepBeep ranging system, in which they both send and receive sound signals.
In the first stage, using TGA and PSD, the capacity of the tailings to undergo chemical transformations through a thermal treatment of up to 1000 °C and physical transformation through low-energy milling for up to 180 min, were explored.
In Stage 2, we refine our partitions selection by sampling from the preliminary set of partitions created in the first stage using information from all the sensors.
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The first stage uses the spectral method to obtain an initial estimate of parameters.
The first stage uses a binary logistic model to determine the overall crash severity level.
The first stage uses information embedded in the dimensions or units in which the data is represented.
The first stage uses a reflective configuration where a signal is amplified twice in the same EDF.
The first stage uses a reordering scheme that brings as many of the largest matrix elements as possible closest to the main diagonal.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com