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In the prediction using combined characteristics, with the goals of building a cost-effective model, decreasing the model complexity, and improving the predictive power, wrapper-based variable selection was conducted by logistic regression and greedy search algorithm (backward elimination).
Nevertheless, by defining polarisation as a transient situation where 10%% of the domain holds more than 50%% of the molecules, they were able to show that within their model decreasing the numbers of molecules, which increases the random fluctuations, could trigger polarisation.
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The proposed model decreases the total inefficiency of all DMUs.
The mixed model decreased the maximum values of each single model.
The proposed model decreases the running time in addition to improving the performance and convergence rate of the algorithm.
In comparison to the reference model, the optimal cyclone model decreases the pressure drop by 20.70% and the cut-off size by 75.38%.
Compared to similar technologies, the model decreases the cost of calibration equipment, simplifies the calibration procedure and improves the calculation precision.
The incorporation of weathering into the model decreases the rate of hillslope change when theoretical rates of sediment transport exceed sediment supply.
Several rounds of smoothing and remeshing the model decreased the number of triangles to less than 32,000.
The results clearly indicate that the interlocking model increases the robustness to parameter variation over the simpler Goodwin model, while the parallel model decreases the robustness.
The overall gain in the training model decreased the most when slope was withdrawn from calculations, indicating that this variable provides a substantial amount of useful information that is not already contained in the other variables (Fig. 2).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com