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From these, liquid chromatography mass spectrometry (LC MS) [329], and nuclear magnetic resonance spectroscopy (NMR) based approach are generally preferred analytic methods because they are based on the physical properties of MNPs, which are not influenced by other external factors and easily reproducible [330].
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A family based approach is imperative.
The uncertainties involved in this approach are generally <5%.
From a scaled-model test, it is suggested that the CFD simulation based on the porous medium approach is generally acceptable.
GWAS approach is generally based on LD patterns in experimental populations, and the population structure is an important factor influencing the extent and range of LD [ 32].
Automated feature generation approaches are generally based on a "trial and attempt" type of methodology, meaning that stochastic searching and optimization algorithms are commonly applied.
Besides, it can be argued that results based on low-informative approaches are generally easier to communicate to a diverse or uninitiated audience, since, as mentioned by Box and Tiao [13], they represent "what someone who a priori knew very little about an unknown parameter should believe in light of the data" (p. 22).
Detect-before-track approaches are generally based on a segmentation process.
Offloading approaches are generally based on using other connectivity capabilities of mobile terminals, whenever it is possible, instead of using further expensive cellular bands.
MI approaches are generally based on an imputation model from which plausible values for the missing data are obtained.
This approach is generally accepted for STXM stack based spectroscopic analysis and requires the identification the background areas reliably in order to access the background intensity data.
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