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No, that's not it, I mean complexity is good.
Information content measures (metric entropy and mean information gain) and complexity measures (effective measure complexity and fluctuation complexity) were computed based on the binary encoding of 5-year streamflow and precipitation time series data.
The distribution of complexity factor values is lognormal, and using an interval of 1 Mbpd, the mean is empirically computed as 4.02 with a standard deviation of 1.71 (Fig. 20).
The mean, standard deviation, and Hjorth parameters (which describe the signal characteristics in terms of activity, mobility, and complexity) were computed.
The initial centers for the rough k-means are computed based on the rough set, which can reduce the time complexity of the rough k-means clustering.
On every iteration, the Bayesian information criterion (BIC), which penalizes model complexity, is computed.
Secondly, the alignment of the huge amount of short reads to multiple references often means a high complexity of computing.
Frequencies and means were computed.
Least squares means were computed.
If that means complexity, so be it.
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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