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Freezing transition and correlated motion in a quasi-two-dimensional colloid suspension.

Recent experiments have demonstrated that the deviation of the single-particle displacement distribution from Gaussian form in a dense quasi-two-dimensional colloid suspension is a result of heterogenous dynamics that involves cooperative motions of neighboring colloid particles [J. Chem. Phys. 47, 9142 (2001)]. In this paper, we report the results of molecular dynamics (MD) simulations of a quasi-two-dimensional assembly of nearly hard-sphere colloid particles. The colloid-colloid interaction we use is short ranged and everywhere repulsive; it is related to the Marcus-Rice (MR) and modified MR interactions used in a previous study [Phys. Rev. E 58, 7529 (1998)]. As is the case for those systems, the one we study supports liquid, hexatic, and solid phases. Our calculations show that the deviation of the single-particle displacement distribution from Gaussian form is present in the liquid phase, and that a sharp increase in its magnitude occurs at the liquidus density and extends into the crystalline phase. For densities greater than the liquidus density we find three dynamical relaxation processes that include, at intermediate times, a slowing down in the rate of growth of the diffusive displacement of a particle due to the cage effect. As the density increases toward the solidus density, the dependence of the mean squared displacement on time, at intermediate times, changes from sublinear to zero. The onset of the long-time relaxation mode corresponds to the time at which the deviation of the particle displacement distribution from Gaussian form is a maximum. At this time, which increases exponentially with the density, the self-part of the van Hove function exhibits multiple maxima with respect to r while the distinct part of the van Hove function is a maximum at the origin, thereby signaling jump dynamics. At long times the particle mean square displacement has diffusive character at all densities including solid phase densities. A remarkable feature of our findings is the continuity of character of the particle displacement from the liquid phase through the hexatic phase and into the solid phase. Cooperative jumps that lead to diffusive process in crystals can be explained by a mechanism that involves many such correlated hops in random locations and random directions (but along the crystallographic axes) thereby generating effective random walk behavior. We argue that the collective motion we have found is generated by superpositions of instantaneous normal mode vibrations along diffusive paths. The diffusive paths are along the directions with strong bond orientation correlation, and start to grow in amplitude rapidly on entry into the hexatic phase.

Biophysical Phenomena↗

Scaling properties of self-expanding surfaces.

Scaling properties of self-expanding surfaces are studied with a comparison to those of self-flattening surfaces [Phys. Rev. E 66, 040602(R) (2002)]. The evolution of self-expanding surfaces is described by a restricted solid-on-solid type monomer deposition-evaporation model in which both deposition at the globally lowest site and evaporation at the globally highest site are suppressed. We find numerically that equilibrium surface fluctuation has a scaling behavior with a roughness exponent alpha approximately 1 in one dimension (1D). In contrast, 2D equilibrium surfaces show the same dynamical scaling behavior with alpha=0 (log) and dynamic exponent z approximately 5/2 as 2D self-flattening surfaces. Stationary roughness can be understood analytically by relating the self-expanding growth model to self-repelling random walks. In the case of nonequilibrium growing/eroding surfaces, self-expanding dynamics cause the fluctuation of surfaces to be characterized by alpha approximately 1 in both 1D and 2D.

Models, Statistical↗

Self-diffusion in dense granular shear flows.

Diffusivity is a key quantity in describing velocity fluctuations in granular materials. These fluctuations are the basis of many thermodynamic and hydrodynamic models which aim to provide a statistical description of granular systems. We present experimental results on diffusivity in dense, granular shear flows in a two-dimensional Couette geometry. We find that self-diffusivities D are proportional to the local shear rate gamma; with diffusivities along the direction of the mean flow approximately twice as large as those in the perpendicular direction. The magnitude of the diffusivity is D approximately gamma;a(2), where a is the particle radius. However, the gradient in shear rate, coupling to the mean flow, and strong drag at the moving boundary lead to particle displacements that can appear subdiffusive or superdiffusive. In particular, diffusion appears to be superdiffusive along the mean flow direction due to Taylor dispersion effects and subdiffusive along the perpendicular direction due to the gradient in shear rate. The anisotropic force network leads to an additional anisotropy in the diffusivity that is a property of dense systems and has no obvious analog in rapid flows. Specifically, the diffusivity is suppressed along the direction of the strong force network. A simple random walk simulation reproduces the key features of the data, such as the apparent superdiffusive and subdiffusive behavior arising from the mean velocity field, confirming the underlying diffusive motion. The additional anisotropy is not observed in the simulation since the strong force network is not included. Examples of correlated motion, such as transient vortices, and Lévy flights are also observed. Although correlated motion creates velocity fields which are qualitatively different from collisional Brownian motion and can introduce nondiffusive effects, on average the system appears simply diffusive.

Journal Article↗

Aggregation process on complex networks.

We study the dynamics of the aggregation of particles and the evolution of the mass distribution, on a complex network which is built following the Watts-Strogatz model. The particles perform random walks following the links on the network, and aggregate when they meet other particles. On disordered networks the density of particles decays as t(-1), while on regular networks it decays as t(-1/2). For intermediate levels of network disorder the dynamics follows that of regular networks at intermediate density, and for low density the disorder of the network becomes relevant and the density decays as t(-1). The crossover time between these two regimes scales with network disorder as t approximately p(-2). We study also an annealed model for the aggregation process, in which the quenched disorder of the network is replaced by stochastic long range jumps in the particle dynamics. The annealed model is found to obey a different scaling with network disorder, with a crossover time t approximately p(-1).

Biophysics↗

Diffusion in correlated random potentials, with applications to DNA.

Many biological processes involve one-dimensional diffusion over a correlated inhomogeneous energy landscape with a correlation length xi(c). Typical examples are specific protein target location on DNA, nucleosome repositioning, or DNA translocation through a nanopore, in all cases with xi(c) approximately 10 nm. We investigate such transport processes by the mean first passage time (MFPT) formalism, and find diffusion times which exhibit strong sample to sample fluctuations. For a displacement N, the average MFPT is diffusive, while its standard deviation over the ensemble of energy profiles scales as N(3/2) with a large prefactor. Fluctuations are thus dominant for displacements smaller than a characteristic N(c) >> xi(c) : typical values are much less than the mean, and governed by an anomalous diffusion rule. Potential biological consequences of such random walks, composed of rapid scans in the vicinity of favorable energy valleys and occasional jumps to further valleys, is discussed.

Binding Sites↗

Stability of vacuum in coupled directed percolation processes.

We study the absorbing phase transitions in coupled directed percolation (DP) processes with N -species particles in one dimension. The interspecies coupling is linear, bidirectional, and excitatory. We find that the presence of a spontaneous annihilation process A-->0 is essential in stabilizing the absorbing phase (vacuum). In the coupled contact processes, the vacuum is stable and the system exhibits DP type transitions, regardless of the coupling strength, for all N. However, in the coupled branching annihilation random walks with one offspring (BAW), where particle annihilations occur only through binary diffusion processes A+A-->0, the vacuum becomes unstable with respect to an arbitrarily small branching rate in a sufficiently strong coupling regime for N> or =3. The N=2 BAW exhibits the DP type transition for any coupling strength, but the inclusion of interspecies hard core (HC) interaction makes the vacuum unstable again and the system is always active in a strong coupling regime. Critical behavior near the zero branching point is characterized by the mean-field scaling exponents, beta= nu(radially) = 1/2 and nu(axially) =1, regardless of the presence of HC interaction. We also discuss the effects of the asymmetric coupling.

Journal Article↗

Building reliable lattice Monte Carlo models for real drift and diffusion problems.

We revisit the well-known issue of representing an overdamped drift-and-diffusion system by an equivalent lattice random-walk model. We demonstrate that commonly used Monte Carlo algorithms do not conserve the diffusion coefficient when a driving field of arbitrary amplitude is present, and that such algorithms would actually require fluctuating jumping times and one clock per Cartesian direction to work properly. Although it is in principle possible to construct valid algorithms with fixed time steps, we show that no such algorithm can be used in more than two dimensions if the jumps are made along only one axis at each time step.

Journal Article↗

Stochastic dynamical model for stock-stock correlations.

We propose a model of coupled random walks for stock-stock correlations. The walks in the model are coupled via a mechanism that the displacement (price change) of each walk (stock) is activated by the price gradients over some underlying network. We assume that the network has two underlying structures, describing the correlations among the stocks of the whole market and among those within individual groups, respectively, each with a coupling parameter controlling the degree of correlation. The model provides the interpretation of the features displayed in the distribution of the eigenvalues for the correlation matrix of real market on the level of time sequences. We verify that such modeling indeed gives good fitting for the market data of US stocks.

Journal Article↗

Monte Carlo simulations in generalized isobaric-isothermal ensembles.

We present three generalized isobaric-isothermal ensemble Monte Carlo algorithms, which we refer to as the multibaric-multithermal, multibaric-isothermal, and isobaric-multithermal algorithms. These Monte Carlo simulations perform random walks widely in volume space and/or in potential energy space. From only one simulation run, one can calculate isobaric-isothermal-ensemble averages in wide ranges of pressure and temperature. We demonstrate the effectiveness of these algorithms by applying them to the Lennard-Jones 12-6 potential system with 500 particles.

Journal Article↗

Statistical analysis of the extreme values of stress time series from the Portevin-Le Châtelier effect.

In an effort to understand the deterministic vs stochastic character of the Portevin-Le Châtelier (PLC) phenomenon, we investigate the structure of the underlying mechanism that generates the stick-slip patterns of stress over time. The stress time series is reduced to a series of successive pairs of minimum and maximum values representing the stick-slip patterns and a statistical analysis by means of hypothesis testing is applied to it. The null hypothesis of least deterministic structure is that the time series of extreme values is a bounded random walk of alternating direction (BRWAD); that is, besides the constraint of succession of minima to maxima bounded at a predefined range there are no other correlations in the data. To implement the test we use surrogate data generated by a model consistent with a BRWAD type process, which also uses the statistics of the original data to best mimic them. The proposed hypothesis testing is found to perform properly on simulated data from stochastic and deterministic systems. For the PLC time series, the null hypothesis is rejected at a high level of confidence giving evidence for some deterministic structure in the succession of the extreme stress values. This result allows for further statistical analysis including also the time aspect of the stick-slip patterns.

Journal Article↗

Microscopic models for dielectric relaxation in disordered systems.

It is shown how the Debye rotational diffusion model of dielectric relaxation of polar molecules (which may be described in microscopic fashion as the diffusion limit of a discrete time random walk on the surface of the unit sphere) may be extended to yield the empirical Havriliak-Negami (HN) equation of anomalous dielectric relaxation from a microscopic model based on a kinetic equation just as in the Debye model. This kinetic equation is obtained by means of a generalization of the noninertial Fokker-Planck equation of conventional Brownian motion (generally known as the Smoluchowski equation) to fractional kinetics governed by the HN relaxation mechanism. For the simple case of noninteracting dipoles it may be solved by Fourier transform techniques to yield the Green function and the complex dielectric susceptibility corresponding to the HN anomalous relaxation mechanism.

Journal Article↗

Base-sequence-dependent sliding of proteins on DNA.

The possibility that the sliding motion of proteins on DNA is influenced by the base sequence through a base pair reading interaction, is considered. Referring to the case of the T7 RNA-polymerase, we show that the protein should follow a noise-influenced sequence-dependent motion which deviate from the standard random walk usually assumed. The general validity and the implications of the results are discussed.

Base Pairing↗

Fitness threshold accepting over extremal optimization ranks.

We treat the problem of selecting the next degree of freedom for update in an extremal optimization algorithm designed to find the ground state of a system with a complex energy landscape. We show that there exists a best distribution for selecting the next degree of freedom in order to optimize any linear function of the state probabilities, e.g., the expected number of visits to the ground state. We dub the class of algorithms using this best distribution in conjunction with extremal optimization fitness threshold accepting. In addition, we construct an extended random walk and use it to show that fitness threshold accepting is optimal also for several other measures of algorithm performance, such as maximizing the expected probability of seeing the ground state and minimizing the expected value of the lowest energy seen.

Journal Article↗

1/f scaling in heart rate requires antagonistic autonomic control.

We present systematic evidence for the origins of 1/f -type temporal scaling in human heart rate. The heart rate is regulated by the activity of two branches of the autonomic nervous system: the parasympathetic (PNS) and the sympathetic (SNS) nervous systems. We examine alterations in the scaling property when the balance between PNS and SNS activity is modified, and find that the relative PNS suppression by congestive heart failure results in a substantial increase in the Hurst exponent H towards random-walk scaling 1/ f(2) and a similar breakdown is observed with relative SNS suppression by primary autonomic failure. These results suggest that 1/f scaling in heart rate requires the intricate balance between the antagonistic activity of PNS and SNS.

Adaptation, Physiological↗

Waves in a reaction-transport system with memory, long-range interactions, and transmutations.

We develop a theory of wave propagation into an unstable state for a system of integral equations with memory, long-range interactions, and transmutations. In particular we use continuous-time random walk theory to describe the transport and transmutation processes. We use a hyperbolic scaling and Hamilton-Jacobi formalism to derive formulas for the speed of propagation of the traveling wave generated by the system in the long-time large-distance limit. Our theory is valid for arbitrary waiting-time, jump-length and, transmutation probability density functions and the propagation speed can generally be found numerically. However, we illustrate our theory by considering an example where analytic results are possible--that is, for a system of Markovian reaction-transport equations. We derive formulas to determine the propagation speed in both the so-called weakly coupled and strongly coupled cases.

Journal Article↗

Autocorrelation functions for phase separation in ternary mixtures.

We present numerical and analytical results for the autocorrelation functions which characterize domain growth in ternary mixtures. The numerical results are obtained from Monte Carlo simulations of the spin-1 Blume-Emery-Griffiths model with spin-exchange kinetics. Further, we model the autocorrelation functions using an approach based on the continuous-time random walk formalism. The aging property of these functions is related to the time dependence of the domain-size distribution. Our analytical results are found to be in good agreement with the numerical data.

Journal Article↗

Fractional diffusion modeling of ion channel gating.

An anomalous diffusion model for ion channel gating is put forward. This scheme is able to describe nonexponential, power-law-like distributions of residence time intervals in several types of ion channels. Our method presents a generalization of the discrete diffusion model by Millhauser, Salpeter, and Oswald [Proc. Natl. Acad. Sci. U.S.A. 85, 1503 (1988)] to the case of a continuous, anomalous slow conformational diffusion. The corresponding generalization is derived from a continuous-time random walk composed of nearest-neighbor jumps which in the scaling limit results in a fractional diffusion equation. The studied model contains three parameters only: the mean residence time, a characteristic time of conformational diffusion, and the index of subdiffusion. A tractable analytical expression for the characteristic function of the residence time distribution is obtained. In the limiting case of normal diffusion, our prior findings [Proc. Natl. Acad. Sci. U.S.A. 99, 3552 (2002)] are reproduced. Depending on the chosen parameters, the fractional diffusion model exhibits a very rich behavior of the residence time distribution with different characteristic time regimes. Moreover, the corresponding autocorrelation function of conductance fluctuations displays nontrivial power law features. Our theoretical model is in good agreement with experimental data for large conductance potassium ion channels.

Animals↗

Increment definitions for scale-dependent analysis of stochastic data.

It is common for scale-dependent analysis of stochastic data to use the increment Delta(t,r) =xi(t+r)-xi(t) of a data set xi(t) as a stochastic measure, where r denotes the scale. For joint statistics of Delta(t,r) and Delta(t, r') the question of how to nest the increments on different scales r, r' is investigated. Here we show that in some cases spurious correlations between scales can be introduced by the common left-justified definition. The consequences for a Markov process are discussed. These spurious correlations can be avoided by an appropriate nesting of increments. We demonstrate this effect for different data sets and show how it can be detected and quantified. The problem allows to propose a unique method to distinguish between experimental data generated by a noiselike or a Langevin-like random-walk process, respectively.

Journal Article↗