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This single inspection run allows a direct comparison of the two data evaluation methods because external influence to the ultrasonic measurement (handling, temperature, etc).
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In order to assess the reliability of genome-assisted evaluation, two data sets (training and testing) were constructed.
These concepts are the key to flexibly assemble and apply the three universal stages of data evaluation: (1) archiving of acquired data, (2) processing the data using signal processing algorithms and (3) visualizing the results in appropriate graphical formats.
Full factorial experimental design with three center points and data evaluation were carried out using MODDE (Umetrics, Sweden).
After DQB1 amplicon generation, NGS (MiSeq, Illumina), data evaluation with two different HLA software tools (NGSengine, GenDx and Omixon Twin, Omixon) we ended up with allele-specific contigs computed relying on a phasing analysis of the individual single nucleotide variants (SNVs) pattern present.
When the testing and training data are recorded in two separate days, the unpredictable data evaluation may happen due to slight shift of electrode positions and impedances and possible changes in the subject's motivation level [25].
Thanks are also due to Manuela Ablasser, Bettina Germann, Lisbeth Postl and Jutta Vollmann for help with data evaluation, and two anonymous reviewers for helpful comments.
Here, we provide a comprehensive evaluation of two data driven approaches to estimating waves near-shore (linear and ANN), and also contrast these with a more traditional spectral wave simulation model (SWAN).
For data evaluation, average values from three to six experiments were calculated based on average values of duplicates or triplicates.
This provided two data points per system for our evaluation.
These automatically derived variables will be a replica of the original variables and the two data sets will be compared for evaluation.
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