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In both setups the data was split in ten parts.
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In the first two setups, the data-inferred gene ordering was compared with the known pathways.
After setup, the data is stored in the Windows Azure cloud, and is associated with a SQL database inside Azure.
For finding the linear elastic SIF (i.e., KI) for this experimental setup, the data from elastic analysis are essential.
All of these setups handled the data sparsity issue with a classification accuracy of 82.97 %.
We also want to thank George Swenson for planning the antenna setup and Rolf Weinzierl for help with the setup of the data collection.
Results have been compared with data obtained from CFD simulation of experimental setup and the data available in the literature.
For the ease of demonstration and data privacy, we changed the setup of the data and assumed node 83 located at the bottom right corner to be both the depot (or origin) and the destination node.
The Monte Carlo analysis demonstrated that the presence of the patches did not arise from random fluctuations in the experimental setup or the data (c.f. [5] and [14]).
The large size of our experimental setup and the data analysis presented above allow us to contribute to the two questions we wanted to address.
Samples were quantified by direct flow injection analysis using the analytical setup and the data analysis algorithms described by Liebisch et al. [42].
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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