1,699 research outputs found
DNA viewed as an out-of-equilibrium structure
The complexity of the primary structure of human DNA is explored using
methods from nonequilibrium statistical mechanics, dynamical systems theory and
information theory. The use of chi-square tests shows that DNA cannot be
described as a low order Markov chain of order up to . Although detailed
balance seems to hold at the level of purine-pyrimidine notation it fails when
all four basepairs are considered, suggesting spatial asymmetry and
irreversibility. Furthermore, the block entropy does not increase linearly with
the block size, reflecting the long range nature of the correlations in the
human genomic sequences. To probe locally the spatial structure of the chain we
study the exit distances from a specific symbol, the distribution of recurrence
distances and the Hurst exponent, all of which show power law tails and long
range characteristics. These results suggest that human DNA can be viewed as a
non-equilibrium structure maintained in its state through interactions with a
constantly changing environment. Based solely on the exit distance distribution
accounting for the nonequilibrium statistics and using the Monte Carlo
rejection sampling method we construct a model DNA sequence. This method allows
to keep all long range and short range statistical characteristics of the
original sequence. The model sequence presents the same characteristic
exponents as the natural DNA but fails to capture point-to-point details
Nonlinear dynamic systems in the geosciences
Geophysical phenomena are often characterized by complex, random-looking deviations of the relevant variables from their average values. Typical examples of such aperiodicity are the intermittent succession of Quaternary glaciations as revealed by the oxygen isotope record of deep-sea cores of the last 106 years or the pronounced spatial disorder characterizing geologic materials. A major task of the geoscientist is to reconstitute from this type of record the principal mechanisms responsible for the observed behavior. Traditional approaches attribute the complexity encountered in the record of a natural variable to external uncontrollable factors and to poorly known parameters whose presence tends to blur fundamental underlying regularities. Here, we consider that complexity might be an intrinsic property generated by the nonlinear character of the system's dynamics. We review bifurcations, chaos, and fractals, three important mechanisms leading to complex behavior in nonlinear dynamic systems, and stress the role of the theory of nonlinear dynamic systems as a major tool of interdisciplinary research in the geosciences. The general ideas are illustrated on the dynamics of Quaternary glaciations and the dynamics of tracer transport in a sediment
Nonlinear dynamic systems in the geosciences
Geophysical phenomena are often characterized by complex, random-looking deviations of the relevant variables from their average values. Typical examples of such aperiodicity are the intermittent succession of Quaternary glaciations as revealed by the oxygen isotope record of deep-sea cores of the last 106 years or the pronounced spatial disorder characterizing geologic materials. A major task of the geoscientist is to reconstitute from this type of record the principal mechanisms responsible for the observed behavior. Traditional approaches attribute the complexity encountered in the record of a natural variable to external uncontrollable factors and to poorly known parameters whose presence tends to blur fundamental underlying regularities. Here, we consider that complexity might be an intrinsic property generated by the nonlinear character of the system's dynamics. We review bifurcations, chaos, and fractals, three important mechanisms leading to complex behavior in nonlinear dynamic systems, and stress the role of the theory of nonlinear dynamic systems as a major tool of interdisciplinary research in the geosciences. The general ideas are illustrated on the dynamics of Quaternary glaciations and the dynamics of tracer transport in a sediment
Thermostating by Deterministic Scattering: Heat and Shear Flow
We apply a recently proposed novel thermostating mechanism to an interacting
many-particle system where the bulk particles are moving according to
Hamiltonian dynamics. At the boundaries the system is thermalized by
deterministic and time-reversible scattering. We show how this scattering
mechanism can be related to stochastic boundary conditions. We subsequently
simulate nonequilibrium steady states associated to thermal conduction and
shear flow for a hard disk fluid. The bulk behavior of the model is studied by
comparing the transport coefficients obtained from computer simulations to
theoretical results. Furthermore, thermodynamic entropy production and
exponential phase-space contraction rates in the stationary nonequilibrium
states are calculated showing that in general these quantities do not agree.Comment: 16 pages (revtex) with 9 figures (postscript
Model error and sequential data assimilation. A deterministic formulation
Data assimilation schemes are confronted with the presence of model errors
arising from the imperfect description of atmospheric dynamics. These errors
are usually modeled on the basis of simple assumptions such as bias, white
noise, first order Markov process. In the present work, a formulation of the
sequential extended Kalman filter is proposed, based on recent findings on the
universal deterministic behavior of model errors in deep contrast with previous
approaches (Nicolis, 2004). This new scheme is applied in the context of a
spatially distributed system proposed by Lorenz (1996). It is found that (i)
for short times, the estimation error is accurately approximated by an
evolution law in which the variance of the model error (assumed to be a
deterministic process) evolves according to a quadratic law, in agreement with
the theory. Moreover, the correlation with the initial condition error appears
to play a secondary role in the short time dynamics of the estimation error
covariance. (ii) The deterministic description of the model error evolution,
incorporated into the classical extended Kalman filter equations, reveals that
substantial improvements of the filter accuracy can be gained as compared with
the classical white noise assumption. The universal, short time, quadratic law
for the evolution of the model error covariance matrix seems very promising for
modeling estimation error dynamics in sequential data assimilation
Predicting the coherence resonance curve using a semi-analytical treatment
Emergence of noise induced regularity or Coherence Resonance in nonlinear
excitable systems is well known. We explain theoretically why the normalized
variance () of inter spike time intervals, which is a measure of
regularity in such systems, has a unimodal profile. Our semi-analytic treatment
of the associated spiking process produces a general yet simple formula for
, which we show is in very good agreement with numerics in two test
cases, namely the FitzHugh-Nagumo model and the Chemical Oscillator model.Comment: 5 pages, 5 figure
Derrick's theorem beyond a potential
Scalar field theories with derivative interactions are known to possess
solitonic excitations, but such solitons are generally unsatisfactory because
the effective theory fails precisely where nonlinearities responsible for the
solitons are important. A new class of theories possessing (internal) galilean
invariance can in principle bypass this difficulty. Here, we show that these
galileon theories do not possess stable solitonic solutions. As a by-product,
we show that no stable solitons exist for a different class of derivatively
coupled theories, describing for instance the infrared dynamics of superfluids,
fluids, solids and some k-essence models.Comment: 4 page
Quasi Markovian behavior in mixing maps
We consider the time dependent probability distribution of a coarse grained
observable Y whose evolution is governed by a discrete time map. If the map is
mixing, the time dependent one-step transition probabilities converge in the
long time limit to yield an ergodic stochastic matrix. The stationary
distribution of this matrix is identical to the asymptotic distribution of Y
under the exact dynamics. The nth time iterate of the baker map is explicitly
computed and used to compare the time evolution of the occupation probabilities
with those of the approximating Markov chain. The convergence is found to be at
least exponentially fast for all rectangular partitions with Lebesgue measure.
In particular, uniform rectangles form a Markov partition for which we find
exact agreement.Comment: 16 pages, 1 figure, uses elsart.sty, to be published in Physica D
Special Issue on Predictability: Quantifying Uncertainty in Models of Complex
Phenomen
Phase Structure of the 5D Abelian Higgs Model with Anisotropic Couplings
We establish the phase diagram of the five-dimensional anisotropic Abelian
Higgs model by mean field techniques and Monte Carlo simulations. The
anisotropy is encoded in the gauge couplings as well as in the Higgs couplings.
In addition to the usual bulk phases (confining, Coulomb and Higgs) we find
four-dimensional ``layered'' phases (3-branes) at weak gauge coupling, where
the layers may be in either the Coulomb or the Higgs phase, while the
transverse directions are confining.Comment: LaTeX (amssymb.sty and psfig) 21 pages, 17 figure
Stochastic Resonance in Two Dimensional Landau Ginzburg Equation
We study the mechanism of stochastic resonance in a two dimensional Landau
Ginzburg equation perturbed by a white noise. We shortly review how to
renormalize the equation in order to avoid ultraviolet divergences. Next we
show that the renormalization amplifies the effect of the small periodic
perturbation in the system. We finally argue that stochastic resonance can be
used to highlight the effect of renormalization in spatially extended system
with a bistable equilibria
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