118 research outputs found
Towards the timely detection of toxicants
We address the problem of enhancing the sensitivity of biosensors to the
influence of toxicants, with an entropy method of analysis, denoted as
CASSANDRA, recently invented for the specific purpose of studying
non-stationary time series. We study the specific case where the toxicant is
tetrodotoxin. This is a very poisonous substance that yields an abrupt drop of
the rate of spike production at t approximatively 170 minutes when the
concentration of toxicant is 4 nanomoles. The CASSANDRA algorithm reveals the
influence of toxicants thirty minutes prior to the drop in rate at a
concentration of toxicant equal to 2 nanomoles. We argue that the success of
this method of analysis rests on the adoption of a new perspective of
complexity, interpreted as a condition intermediate between the dynamic and the
thermodynamic state.Comment: 6 pages and 3 figures. Accepted for publication in the special issue
of Chaos Solitons and Fractal dedicated to the conference "Non-stationary
Time Series: A Theoretical, Computational and Practical Challenge", Center
for Nonlinear Science at University of North Texas, from October 13 to
October 19, 2002, Denton, TX (USA
Probability flux as a method for detecting scaling
We introduce a new method for detecting scaling in time series. The method
uses the properties of the probability flux for stochastic self-affine
processes and is called the probability flux analysis (PFA). The advantages of
this method are: 1) it is independent of the finiteness of the moments of the
self-affine process; 2) it does not require a binning procedure for numerical
evaluation of the the probability density function. These properties make the
method particularly efficient for heavy tailed distributions in which the
variance is not finite, for example, in Levy alpha-stable processes. This
utility is established using a comparison with the diffusion entropy (DE)
method
The random growth of interfaces as a subordinated process
We study the random growth of surfaces from within the perspective of a
single column, namely, the fluctuation of the column height around the mean
value, y(t)= h(t)-, which is depicted as being subordinated to a
standard fluctuation-dissipation process with friction gamma. We argue that the
main properties of Kardar-Parisi-Zhang theory, in one dimension, are derived by
identifying the distribution of return times to y(0) = 0, which is a truncated
inverse power law, with the distribution of subordination times. The agreement
of the theoretical prediction with the numerical treatment of the 1 + 1
dimensional model of ballistic deposition is remarkably good, in spite of the
finite size effects affecting this model.Comment: LaTeX, 4 pages, 3 figure
The Dynamics of EEG Entropy
EEG time series are analyzed using the diffusion entropy method. The
resulting EEG entropy manifests short-time scaling, asymptotic saturation and
an attenuated alpha-rhythm modulation. These properties are faithfully modeled
by a phenomenological Langevin equation interpreted within a neural network
context
Skewness as measure of the invariance of instantaneous renormalized drop diameter distributions – Part 2: Orographic precipitation
Abstract. Here we use the skewness parameter, and the procedure developed in the companion paper (Ignaccolo and De Michele, 2012), to investigate the variability of instantaneous renormalized spectra of rain drop diameter in presence of orographic precipitation. Disdrometer data, available at Bodega Bay and Cazadero, California, are analyzed either as a whole, or as divided (using the bright band echo) in precipitation intervals weakly and strongly influenced by orography, and compared to results obtained at Darwin, Australia. We find that also at Bodega Bay and Cazadero exists a most common distribution of the skewness values of instantaneous spectra of drop diameter, but peaked at values greater than 0.64, found at Darwin. No appreciable differences are found in the skewness distributions of precipitation weakly and strongly influenced by orography. However the renormalized drop diameter spectra of precipitation with strong orographic component have fatter right tail than precipitation with a weaker orographic component. The differences between orographic and non-orographic precipitation are investigated within the parametric space represented by number of drops, mean value and standard deviation of drop diameter. A filter is developed which is able to identify 1 min time intervals during which precipitation is mostly of orographic origin
Skewness as measure of the invariance of instantaneous renormalized drop diameter distributions – Part 1: Convective vs. stratiform precipitation
Abstract. We investigate the variability of the shape of the renormalized drop diameter instantaneous distribution using of the third order central moment: the skewness. Disdrometer data, collected at Darwin Australia, are considered either as whole or as divided in convective and stratiform precipitation intervals. We show that in all cases the distribution of the skewness is strongly peaked around 0.64. This allows to identify a most common distribution of renormalized drop diameters and two main variations, one with larger and one with smaller skewness. The distributions shapes are independent from the stratiform vs. convective classification
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