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So if you as the user were able to say, "I want dogs barking" then it can actually then match the concept of dog to the concept of poodle and it is matching barking to barking and it is then doing this sort of semantic match for you which uses words you are not even using in your query and matching those against the document.
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Any further negotiating is then done over private channels.
An eigenvalue analysis is then done to determine the modal response of impacted laminates.
The selection of the predicted category is then done through a plurality voting procedure.
The question is then: do you decide to help or not?
Tissue biopsy is then done under direct vision.
Finalization of the document is then done by the MoH.
Analysis of heterogeneity is then done in 2D for each image subset.
The same is then done with d i, j representing shortest route.
In Kissplice, the enumeration is then done in each BCC independently.
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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