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If any parameter with regard to a set of ingredients changes, a new input data is to be generated again to use the phenomenological model to obtain the corresponding fluid-to-binder ratio and in turn to arrive at the appropriate mix proportions to meet the specific strength requirement.
To close a gap completely, new primer pairs have to be generated again and again since the maximum read length for Sanger sequencing is restricted.
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Otherwise, new populations are generated again by GA and optimized afresh by LM algorithm until the optimal solutions meet the accuracy requirements.
The strata were uplifted by the Dongying Movement, and organic acid was generated again.
After that, an annotated KDM file and logs are generated again.
Otherwise, the conflict and nonconflict graphs are generated again for an updated value of Tstage.
The other markers are generated (again depending on the image and encoding parameters) by the Init_JPEG.
If a feature with the same id is generated again, the count for the feature is incremented.
If any values are outside the bound, random numbers were generated again so that the set of random distortion parameters conforms with the bound.
With dichotomized variables using respective optimal cutoff values for 30-day mortality, ROC curves of SOFA score, combined biomarkers, and combination of SOFA score and biomarkers were generated again, and their AUC were compared for the prognostic utility of multi-marker approach.
Random sets of deletions were generated (again with the same lengths and on the same chromosomes as the original set), and each deletion was scored for the presence of Alus intersecting/between the breakpoints.
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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