17 research outputs found
Scaling-violation phenomena and fractality in the human posture control systems
By analyzing the movements of quiet standing persons by means of wavelet
statistics, we observe multiple scaling regions in the underlying body
dynamics. The use of the wavelet-variance function opens the possibility to
relate scaling violations to different modes of posture control. We show that
scaling behavior becomes close to perfect, when correctional movements are
dominated by the vestibular system.Comment: 12 pages, 4 figures, to appear in Phys. Rev.
Correlation studies of open and closed states fluctuations in an ion channel: Analysis of ion current through a large conductance locust potassium channel
Ion current fluctuations occurring within open and closed states of large
conductance locust potassium channel (BK channel) were investigated for the
existence of correlation. Both time series, extracted from the ion current
signal, were studied by the autocorrelation function (AFA) and the detrended
fluctuation analysis (DFA) methods. The persistent character of the short- and
middle-range correlations of time series is shown by the slow decay of the
autocorrelation function. The DFA exponent is significantly larger
than 0.5. The existence of strongly-persistent long-range correlations was
detected only for closed-states fluctuations, with . The
long-range correlation of the BK channel action is therefore determined by the
character of closed states. The main outcome of this study is that the memory
effect is present not only between successive conducting states of the channel
but also independently within the open and closed states themselves. As the ion
current fluctuations give information about the dynamics of the channel
protein, our results point to the correlated character of the protein movement
regardless whether the channel is in its open or closed state.Comment: 12 pages, 5 figures; to be published in Phys. Rev.
On the continuing relevance of Mandelbrot’s non-ergodic fractional renewal models of 1963 to 1967
The problem of “1∕ƒ” noise has been with us for about a century. Because it is so often framed in Fourier spectral language, the most famous solutions have tended to be the stationary long range dependent (LRD) models such as Mandelbrot’s fractional Gaussian noise. In view of the increasing importance to physics of non-ergodic fractional renewal models, and their links to the CTRW, I present preliminary results of my research into the history of Mandelbrot’s very little known work in that area from 1963 to 1967. I speculate about how the lack of awareness of this work in the physics and statistics communities may have affected the development of complexity science, and I discuss the differences between the Hurst effect, “1∕ƒ” noise and LRD, concepts which are often treated as equivalent
Avalanche Dynamics in Evolution, Growth, and Depinning Models
The dynamics of complex systems in nature often occurs in terms of
punctuations, or avalanches, rather than following a smooth, gradual path. A
comprehensive theory of avalanche dynamics in models of growth, interface
depinning, and evolution is presented. Specifically, we include the Bak-Sneppen
evolution model, the Sneppen interface depinning model, the Zaitsev flux creep
model, invasion percolation, and several other depinning models into a unified
treatment encompassing a large class of far from equilibrium processes. The
formation of fractal structures, the appearance of noise, diffusion with
anomalous Hurst exponents, Levy flights, and punctuated equilibria can all be
related to the same underlying avalanche dynamics. This dynamics can be
represented as a fractal in spatial plus one temporal dimension. We develop
a scaling theory that relates many of the critical exponents in this broad
category of extremal models, representing different universality classes, to
two basic exponents characterizing the fractal attractor. The exact equations
and the derived set of scaling relations are consistent with numerical
simulations of the above mentioned models.Comment: 27 pages in revtex, no figures included. Figures or hard copy of the
manuscript supplied on reques
Application of computational mechanics to the analysis of natural data: an example in geomagnetism
We discuss how the ideal formalism of computational mechanics can be adapted to apply to a noninfinite series of corrupted and correlated data, that is typical of most observed natural time series. Specifically, a simple filter that removes the corruption that creates rare unphysical causal states is demonstrated, and the concept of effective soficity is introduced. We believe that computational mechanics cannot be applied to a noisy and finite data series without invoking an argument based upon effective soficity. A related distinction between noise and unresolved structure is also defined: Noise can only be eliminated by increasing the length of the time series, whereas the resolution of previously unresolved structure only requires the finite memory of the analysis to be increased. The benefits of these concepts are demonstrated in a simulated times series by (a) the effective elimination of white noise corruption from a periodic signal using the expletive filter and (b) the appearance of an effectively sofic region in the statistical complexity of a biased Poisson switch time series that is insensitive to changes in the word length (memory) used in the analysis. The new algorithm is then applied to an analysis of a real geomagnetic time series measured at Halley, Antarctica. Two principal components in the structure are detected that are interpreted as the diurnal variation due to the rotation of the Earth-based station under an electrical current pattern that is fixed with respect to the Sun-Earth axis and the random occurrence of a signature likely to be that of the magnetic substorm. In conclusion, some useful terminology for the discussion of model construction in general is introduced