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Statistical continuum theory based approaches are commonly used for the computation of the effective properties of heterogeneous materials.
Whilst t-test based approaches are commonly used for analysing microarray data it is apparent that when there is large variance between biological replicates (as is the case with Streptomyces cultures) and/or crucial single time-point changes (in this analysis), the t-test has shortcomings [[ 43, 44], this study].
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However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data.
Blind source separation (BSS -based approaches are commonly implemented via independent component analysis (ICA), and are widely used in multi-microphone speech separation.
However, antibody-based approaches are commonly restricted to the few existing antibodies.
Classifier-based approaches are commonly used in microbial ecology, where they have been designed for the analysis of ribosomal amplicon libraries (Wang et al., 2007).
A regression-model based approach is a commonly used, efficient method for estimating streamwater constituent load when there is a relationship between streamwater constituent concentration and continuous variables such as streamwater discharge, season and time.
A family based approach is imperative.
Two major optimization approaches are commonly adopted; the gradient-based nonlinear optimization method and the evolutionary based method.
Currently, two approaches are commonly used: empirical approaches and physical-based model.
Fourth, multiscalar approaches are commonly desirable.
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