In multivariate models, non-pregnant women were more likely to have menstrual irregularity with higher levels of stress (p < 0.01).
These spectral characteristics suggest that these daytime F-region irregularities resemble the so-called daytime ESF-like irregularities with high magnetic aspect sensitivity discussed by Chau and Woodman (2001).
The meridional slices of geographical sectors characterized by a high density of the GPS and GLONASS measurements can represent spatio-temporal dynamics of the intense ionospheric plasma density irregularities with high resolution and they can be used for detailed studies of the space weather drivers on the processes of the ionospheric irregularities generation, their evolution and lifetimes.
Extensive numerical examples are also presented to demonstrate that the NNR algorithm can deliver satisfactory convergence for the SA method on data sets with high irregularity in their point distributions.
No correlation conclusion is also drawn for the East Pacific sector where a negative regression is noticed and the OLR occurrences are low to compare with high irregularity occurrences from June to October.
Recent studies have suggested that menstrual irregularity is associated with higher levels of androgens in a population-based sample of premenopausal women [ 12, 13], while in female elite athletes oligomenorrhea was associated with higher bone mass and PCOS [ 14].
Standard GPS observations, with a 30 s sampling, provide measurements of the irregularities at high latitudes with a scale size of the order of >30 km.
Indeed, the proportion of cells with high nuclear irregularity increased from 6 to 26% in 48 h after cotreatment.
The occurrence of ionospheric irregularities at high latitudes, with dimensions of several kms down to decameter scale size shows strong correlation with geomagnetic disturbance, season and solar activity.
Especially Multifractal Analysis, based on Generalized Fractal Dimensions (GFD), is a powerful mathematical tool for modeling many physical and biological time signals with high complexity and irregularity.
Empirical results demonstrate that the novel hybrid ensemble learning paradigm can outperform some other popular forecasting models in both level prediction and directional forecasting, indicating that it is a promising tool to predict complex time series with high volatility and irregularity.
