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The results demonstrate that the sphere relaxation based approach generates the deformed mesh with high quality, especially regarding complex boundaries and large deformations.
Numerical results indicate that the presented approach generates the optimum configuration with higher accuracy and a higher success rate when compared with genetic algorithms (GAs) and particle swarm optimization (PSO).
The approach generates the whole set of optimal solutions compared to the one point solution of the deterministic approaches (MINLP) while allowing additional complexity of process level models in the site-wide integration due to the systematic structural decomposition of a system into its basic elements/subsystems.
This approach generates the learner for a particular dataset.
When all variables are integer and feasible region is bounded, their approach generates the whole set of efficient solutions; and it would be expensive using the method for enumerating the whole set of efficient solutions except for small problems.
Thus, in both yeast and human, our integrated approach generates the greatest number of novel predictions, with higher quality compared to the baseline approach of combined clustering with a PPI network.
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This approach generated the lowest porosity (3.2 ± 0.93 area%) for 80Cr20W and led to homogenous particle distributions.
Centralized ECov and ECent make use of the proposed location-based approach generating the indicators based on the samples gathered in the AOIs without distinguishing their serving cell.
To ensure that results provided a sufficient and broad overview of the existing knowledge, we used a deductive approach, generating the paper's headings (direct costs, indirect costs, CEAs) then searching for studies that fit those categories.
Given a matching pair of gait features under different views, generative approaches generate the gait features under the same view for better matching [4 7].
Our approach generates all the combinational triplets of genes, estimates networks of the triplets by BN, and unites the networks into a single network containing all genes.
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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