149 research outputs found
Analysis of Transient Processes in a Radiophysical Flow System
Transient processes in a third-order radiophysical flow system are studied
and a map of the transient process duration versus initial conditions is
constructed and analyzed. The results are compared to the arrangement of
submanifolds of the stable and unstable cycles in the Poincare section of the
system studied.Comment: 3 pages, 2 figure
A Method for Determining the Transient Process Duration in Dynamic Systems in the Regime of Chaotic Oscillations
We describe a method for determining the transient process duration in a
standard two-dimensionaldynamic system with discrete time (Henon map),
occurring in the regime of chaotic oscillationsComment: 4 pages, 2 figure
Influence of subunit structure on the oligomerization state of light harvesting complexes: a free energy calculation study
Light harvesting complexes 2 (LH2) from Rhodospirillum (Rs.) molischianum and
Rhodopseudomonas (Rps.) acidophila form ring complexes out of eight or nine
identical subunits, respectively. Here, we investigate computationally what
factors govern the different ring sizes. Starting from the crystal structure
geometries, we embed two subunits of each species into their native
lipid-bilayer/water environment. Using molecular dynamics simulations with
umbrella sampling and steered molecular dynamics, we probe the free energy
profiles along two reaction coordinates, the angle and the distance between two
subunits. We find that two subunits prefer to arrange at distinctly different
angles, depending on the species, at about 42.5 deg for Rs. molischianum and at
about 38.5 deg for Rps. acidophila, which is likely to be an important factor
contributing to the assembly into different ring sizes. Our calculations
suggest a key role of surface contacts within the transmembrane domain in
constraining these angles, whereas the strongest interactions stabilizing the
subunit dimers are found in the C-, and to a lesser extent, N-terminal domains.
The presented computational approach provides a promising starting point to
investigate the factors contributing to the assembly of protein complexes, in
particular if combined with modeling of genetic variants.Comment: 28 pages, 7 figures, LaTeX2e - requires elsart.cls (included),
submitted to Chemical Physic
Statistics of extremal intensities for Gaussian interfaces
The extremal Fourier intensities are studied for stationary
Edwards-Wilkinson-type, Gaussian, interfaces with power-law dispersion. We
calculate the probability distribution of the maximal intensity and find that,
generically, it does not coincide with the distribution of the integrated power
spectrum (i.e. roughness of the surface), nor does it obey any of the known
extreme statistics limit distributions. The Fisher-Tippett-Gumbel limit
distribution is, however, recovered in three cases: (i) in the non-dispersive
(white noise) limit, (ii) for high dimensions, and (iii) when only
short-wavelength modes are kept. In the last two cases the limit distribution
emerges in novel scenarios.Comment: 15 pages, including 7 ps figure
Symmetries and Fixed Point Stability of Stochastic Differential Equations Modeling Self-Organized Criticality
A stochastic nonlinear partial differential equation is built for two
different models exhibiting self-organized criticality, the Bak, Tang, and
Wiesenfeld (BTW) sandpile model and the Zhang's model. The dynamic
renormalization group (DRG) enables to compute the critical exponents. However,
the nontrivial stable fixed point of the DRG transformation is unreachable for
the original parameters of the models. We introduce an alternative
regularization of the step function involved in the threshold condition, which
breaks the symmetry of the BTW model. Although the symmetry properties of the
two models are different, it is shown that they both belong to the same
universality class. In this case the DRG procedure leads to a symmetric
behavior for both models, restoring the broken symmetry, and makes accessible
the nontrivial fixed point. This technique could also be applied to other
problems with threshold dynamics.Comment: 19 pages, RevTex, includes 6 PostScript figures, Phys. Rev. E (March
97?
Effect of Trends on Detrended Fluctuation Analysis
Detrended fluctuation analysis (DFA) is a scaling analysis method used to
estimate long-range power-law correlation exponents in noisy signals. Many
noisy signals in real systems display trends, so that the scaling results
obtained from the DFA method become difficult to analyze. We systematically
study the effects of three types of trends -- linear, periodic, and power-law
trends, and offer examples where these trends are likely to occur in real data.
We compare the difference between the scaling results for artificially
generated correlated noise and correlated noise with a trend, and study how
trends lead to the appearance of crossovers in the scaling behavior. We find
that crossovers result from the competition between the scaling of the noise
and the ``apparent'' scaling of the trend. We study how the characteristics of
these crossovers depend on (i) the slope of the linear trend; (ii) the
amplitude and period of the periodic trend; (iii) the amplitude and power of
the power-law trend and (iv) the length as well as the correlation properties
of the noise. Surprisingly, we find that the crossovers in the scaling of noisy
signals with trends also follow scaling laws -- i.e. long-range power-law
dependence of the position of the crossover on the parameters of the trends. We
show that the DFA result of noise with a trend can be exactly determined by the
superposition of the separate results of the DFA on the noise and on the trend,
assuming that the noise and the trend are not correlated. If this superposition
rule is not followed, this is an indication that the noise and the superimposed
trend are not independent, so that removing the trend could lead to changes in
the correlation properties of the noise.Comment: 20 pages, 16 figure
Effect of nonstationarities on detrended fluctuation analysis
Detrended fluctuation analysis (DFA) is a scaling analysis method used to
quantify long-range power-law correlations in signals. Many physical and
biological signals are ``noisy'', heterogeneous and exhibit different types of
nonstationarities, which can affect the correlation properties of these
signals. We systematically study the effects of three types of
nonstationarities often encountered in real data. Specifically, we consider
nonstationary sequences formed in three ways: (i) stitching together segments
of data obtained from discontinuous experimental recordings, or removing some
noisy and unreliable parts from continuous recordings and stitching together
the remaining parts -- a ``cutting'' procedure commonly used in preparing data
prior to signal analysis; (ii) adding to a signal with known correlations a
tunable concentration of random outliers or spikes with different amplitude,
and (iii) generating a signal comprised of segments with different properties
-- e.g. different standard deviations or different correlation exponents. We
compare the difference between the scaling results obtained for stationary
correlated signals and correlated signals with these three types of
nonstationarities.Comment: 17 pages, 10 figures, corrected some typos, added one referenc
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