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Finally, the procedure generated a stimulus discrimination accuracy value for each task, in each voxel and subject.
The procedure generated a Pt-rich surface with electrochemical properties different from that presented by the as-received (untreated) sample.
The procedure generated a sample of 629 households.
Each analysis progressively increased the window size and tolerance angle in each repetition, until the procedure generated a single change-point for each biomarker.
The procedure generated simulated final values that can be compared with the empirical ones, in order to analyze the empirical representativeness of the model.
Three executions of the procedure generated motifs that successfully passed the GOT test and were correlated with gene functions (Table 1).
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The procedure generates a large amount of information and even in computerised form presents difficulties for decision-making except for the simplest of products.
In the vicinity of the boundary, the procedure generates irregular Voronoi cells that conform to the domain shape and merge seamlessly with regular control volumes in the domain interior.
The procedure generates new columns until the matrix is rotated back to its original order, resulting into a high column separation.
(iii) The Procedure generates in the feasible allocation space similar trajectories as the MDP Procedure with uniform distribution of the instantaneous surplus occurred at each iteration, which leaves no influence of the planning center on the final plan.
The Procedure generates in the feasible allocation space similar trajectories as the MDP Procedure with uniform distribution of the instantaneous surplus occurred at each iteration, which leaves no influence of the planning center on the final plan.
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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