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By studying the asymptotic property of the relative drift estimation error of CBTS algorithms, we discover (almost sure) divergence conditions and mean-square divergence conditions of CBTS algorithms.
These structures superimpose (Figure 4) with 0.8 Å root mean square deviation (RMSD) value indicating structural similarity despite high divergence in the amino acid sequences.
Structural similarity or divergence was evaluated by a pairwise root mean square deviation (RMSD) value upon superposition of the backbone Cα trace from the two groups of structurally equivalent atoms in MHC class I α1 and α2 domains.
root mean square deviation.
Normalized root mean square deviation.
Percent root mean square difference.
Root mean square deviation normalized.
RMSD: Root mean square deviation; PDB: Protein Data Bank.
The residual Root Mean Squares (RMS) divergence of GNSS single differential pseudorange and range in the prediction arc relative to the orbit determination arc are reduced by 65%and90%0%, respectively, using GNSS data to estimate the orbit compared to using ground-based data.
Using these conditions we clearly point out that the DCTS algorithm is divergent, the ATS algorithm is divergent almost surely, the WMTS algorithm is mean-square divergent, but the LSTS overcomes all of these divergence conditions.
The root-mean-square deviation.
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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