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At least 1,441 records · Page 80Linked to original sources
PubMed2000

Spinodal decomposition of two-dimensional fluid mixtures: A spectral analysis of droplet growth

The spinodal decomposition of two-dimensional fluid mixture is studied by numerical simulation. For the high viscous fluid mixture it has not been evident whether the interfacial tension is relevant to the droplet growth or not. A length scale R defined by the structure function extracting the effect of the long wavelength mode justifies a rapid growth close to R approximately t, but the length scale energetically defined reveals a much slower growth R approximately t(0.5), where t is time. This discrepancy represents the violation of the dynamical scaling with single length scale. The slow gowth of the length scale is attributed to the accumulation of the number of isolated droplets in phase separating state, whereas the rapid growth represents the relevance of the surface tension as the driving force in two dimensions. For a low viscous fluid mixture the dynamical scaling is a good assumption with the growth law R approximately t(2/3) up to a very large Reynolds number Re approximately 1500, which is the limit in the present simulation.

Journal Article↗
PubMed2003

Interpretation of scaling properties of electroencephalographic fluctuations via spectral analysis and underlying physiology.

Detrended fluctuation analysis has recently demonstrated the existence of two approximate temporal scaling regimes in locally detrended human electroencephalographic (EEG) fluctuations, and has suggested a connection between the location of the breakpoint between regimes and the alpha resonance near 10 Hz. It is shown here that these scalings can be explained in terms of the filtering of the underlying power spectrum implied by the detrending process. Using a recent physiologically based model of EEG generation, the main features of the scalings, and deviations from them, are related to the underlying physiology of dendritic propagation and muscle electrical activity, and it is concluded that the effects of such physiological features are usually clearer in spectra.

Electroencephalography↗
PubMed1990

Sampling frequency of the electrocardiogram for spectral analysis of the heart rate variability.

The R-R interval measurement from digitized electrocardiograms (ECG) contains an error due to the finite sampling frequency which may jeopardize the beat-to-beat analysis of the heart rate. In this paper, we develop a model to describe and quantitate this error. The "measured" R-R interval is modeled as the sum of the "true" R-R interval and of the error of measurement. The first and second order statistics of the error are computed in order to investigate its influence on the heart rate variability (HRV) power spectrum. They are found to be only functions of the ECG sampling frequency and, in particular, the power spectrum of the error contributes an additive high-pass filter-like term (colored noise) to the power spectrum of the HRV. The accuracy of the model is tested via a simulation procedure. The model indicates that the relative balance between the HRV and the error power spectra is important and should be checked before any variability analysis on the heart rate. This balance may be favorable to the error when 1) the sampling frequency of the ECG is too low, and/or 2) the variability of the heart rate is too little. In these cases, the HRV spectrum analysis may not give reliable results. Two tests are proposed in order to evaluate the error influence either in specific frequency bands or in the total frequency range.

Computer Simulation↗