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The results show that the method can be used to identify the elastoplastic parameters with precision even when errors are present in the data.
In this way the discrete differential operators can be represented exactly and metric and all approximation errors are present in the constitutive relations.
It is clear that the traditional ℓ 1-LS method for CS fails at estimating the sparse signal when gross errors are present in the compressed measurements.
It is well known that genotyping errors are present in most microsatellite datasets [53] and the theoretical 6.6% genotyping error rate we report here is not higher than what is expected, or observed, for 'modern' data [53] [55].
Furthermore, annotation errors are present in Genbank [ 25], and the error rate might be clade dependent.
Measurement errors are present in all of the data because of the biological variations and methodological limitations described above.
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Signal gains from disturbance inputs to tracking errors are presented in the input-to-output-practical-stability and integral-input-to-output-practical-stability frameworks.
The errors are presented in Table 1.
Working conditions and absolute errors are presented in Table 4.
The RMS errors are presented in Table 3.
The absolute values of the errors are presented in Table 2 and Figure 2.
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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