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All traits, except for 1 000-seed weight, were significant for b1 mean squares and exhibited directional dominance.
Raw signals were filtered using adaptive filters (least mean squares), and processed online to account for missing data resulting from occlusions (interpolation from neighboring markers).
However, for the assessment evaluation based on the German sample, the weighted mean squares and t-values fit less well.
For the assessment evaluation based on the Swiss sample, the weighted mean squares and t-values were mostly acceptable.
For each of the simulated values, the PDANOVA package of PDAP (Garland Jr et al., 1993) was used to compute within- and between-group sums of squares, mean squares, and corresponding F ratios, as in conventional ANOVA.
Mean squares and heritability estimates for MCB pest resistance traits evaluated in an association panel in three years.
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(root mean square) and peak displacements, respectively.
The mean square and mean absolute errors were 10.52 and 2.60 ppm for the testing set, respectively.
Emphasis is put on the linear separability issue, the perceptron algorithm, and the mean square and least squares solutions.
It is shown that the closed loop tracking control system is stochastically stable in mean square and the estimation errors converge to zero in mean square as well.
Root mean square and mean power frequency were calculated over 2-s intervals and compared to corresponding Fx and Xp values.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com