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In practical, data analysis order value p may range from 1 to at most 10 or so [40].
In practical data acquisition, it is very likely that pseudorange errors at particular epochs suddenly have significantly larger values than the expected ones.
The modeling results and performance data are used to analyze specific dynamic performance of heat exchangers used in practical data center cooling applications.
However, considering that in practical data the errors come from many sources like measurement errors and approximate modeling, the optimal discretization on simulated data is of less importance and interest.
Nevertheless in practical data processing, the fitting result ( left[{Delta mathrm{N}}_1^{mathrm{p}}{left(mathrm{k}right)}_{mathrm{p}mathrm{oly}},{Delta mathrm{N}}_2^{mathrm{p}}{left(mathrm{k}right)}_{mathrm{p}mathrm{oly}}right] ) might not be perfect.
Graphical representations of LD for multiple-SNP genotypes have been developed to assess the presence of LD in practical data sets.
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The insufficiency of labeled training data for representing the distribution of entire dataset is a major obstacle in various practical data mining applications.
The aforementioned enormous values of big data are embodied in practical big data applications which bring a large number of big data-related challenging research issues.
In particular, the increased interest in clinical applications of metabolic phenotyping makes practical data visualization in a biological/medical framework of great importance.
The performance of POCS evaluated through practical data in [18, 19] confirms these theoretical claims.
Moreover, obtaining the practical data in the emergency situations or experimenting the panic situations is troublesome and the data obtained from these two methods may not be accurate and reliable.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com