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From the deployment generated by the scheduler, the code generation module generates a generic representation of the code in XML.
The parts-list module generates a parts list using either standard and/or non-standard parts.
Finally, based on UMSPs, StoS similarities, and ItoI similarities, the behavior prediction module generates a list of shopping suggestions for the target user.
Each expert module generates a (possibly empty) candidate list for each clue, and the lists are merged together and placed into the grid by a centralized solver.
In addition, this module generates a file in Json format from the filtered results.
As a result, this module generates a list of 3D patterns detected in each PDB file.
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The highly robust transmission at the PXI module generates an error propagation effect.
The database query module generates an error warning because the user simulator has not provided the mandatory information needed to carry out the query.
Furthermore, this module generated a power of 4.5 nW when a load resistance matched the internal module resistance of 30 kΩ.
The model is composed of two hierarchical levels, a perceptual module generating a perceptual choice and confidence on each trial, and a learning module updating global SPEs across trials from local decision confidence and feedback, which are then used to make task choices at the end of blocks (Supplementary Methods).
One primer set that monitored hybrids from both the YAC and YAC-GALpr module generated a strong DIP signal only in the presence of galactose.
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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