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Any disagreement in the process of selecting, assessing and collecting data was solved by a third researcher (GJ).
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The problem of extracting pore size distributions from characterization data is solved here with particular reference to adsorption.
A test example with all the needed data is solved to shows the features of the presented approach.
Disagreements between the two observers related to the data were solved by a third researcher, unaware of the answers given by each observer.
Further, the data association problem between the tracker and the data is solved at each node separately and the association with maximum likelihood is transmitted along with data to other nodes.
The efficient allocation problem for the multi-stage process for a given historical data is solved by the general linear model approach, and then the predicted responses are ordered to choose the subsequently optimal equipment paths.
To maximize data quality, all observed random or potentially systematic inconsistencies within resulting data were solved.
Even if the data are solved in the correct space group and unit cell, the data quality will be poor.
The reported data were solved through intrinsic phasing (SHELXTL XT-2014) and refined by least-squares refinement on F SHELXL-20144) (Sheldrick, 2008 ▶).
Conway weighed in: "Big data is solving big problems and we need a home to foster the next generation of this unique breed of scientist.
Disagreement in data extraction between reviewers was solved by consensus.
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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