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The theory of SOC, and related avalanche dynamics, is proposed as the origin of the ubiquitous nonexponential relaxation observed in complex systems. Introducing some scaling laws and relations we have obtained that the normalized relaxation function follows an stretched exponential decay and that the frequency spectrum follows a "1/f" noise. Moreover, in the MF aproach the relaxation is found to be exponential.

We test statistically the hypothesis that radio pulsar glitches result from an avalanche process, in which angular momentum is transferred erratically from the flywheel-like superfluid in the star to the slowly decelerating, solid crust via spatially connected chains of local, impulsive, threshold-activated events, so that the system fluctuates around a self-organised critical state. Analysis of the glitch population (currently 285 events from 101 pulsars) demonstrates that the size distribution in individual pulsars is consistent with being scale invariant, as expected for an avalanche process. The waiting-time distribution is consistent with being exponential in seven out of nine pulsars where it can be measured reliably, after adjusting for observational limits on the minimum waiting time, as for a constant-rate Poisson process. PSR J0537$-$6910 and PSR J0835$-$4510 are the exceptions; their waiting-time distributions show evidence of quasiperiodicity. In each object, stationarity requires that the rate $\lambda$ equals $- \epsilon \dot{\nu} / $, where $\dot{\nu}$ is the angular acceleration of the crust, $ $ is the mean glitch size, and $\epsilon\dot{\nu}$ is the relative angular acceleration of the crust and superfluid. There is no evidence that $\lambda$ changes monotonically with spin-down age. The rate distribution itself is fitted reasonably well by an exponential for $\lambda \geq 0.25 {\rm yr^{-1}}$. For $\lambda < 0.25 {\rm yr^{-1}}$, its exact form is unknown; the exponential overestimates the number of glitching pulsars observed at low $\lambda$, where the limited total observation time exercises a selection bias.

We have studied the dynamics of avalanching wet granular media in a rotating drum apparatus. Quantitative measurements of the flow velocity and the granular flux during avalanches allow us to characterize novel avalanche types unique to wet media. We also explore the details of viscoplastic flow (observed at the highest liquid contents) in which there are lasting contacts during flow, leading to coherence across the entire sample. This coherence leads to a velocity independent flow depth at high rotation rates and novel robust pattern formation in the granular surface.

This article is from Molecular Pain , volume 8 . Abstract Background: Traditional electroencephalography provides a critical assessment of pain responses. The perception of pain, however, may involve a series of signal transmission pathways in higher cortical function. Recent studies have shown that a mathematical method, the neuronal avalanche model, may be applied to evaluate higher-order network dynamics. The neuronal avalanche is a cascade of neuronal activity, the size distribution of which can be approximated by a power law relationship manifested by the slope of a straight line (i.e., the α value). We investigated whether the neuronal avalanche could be a useful index for nociceptive assessment. Findings: Neuronal activity was recorded with a 4 × 8 multichannel electrode array in the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC). Under light anesthesia, peripheral pinch stimulation increased the slope of the α value in both the ACC and S1, whereas brush stimulation increased the α value only in the S1. The increase in α values was blocked in both regions under deep anesthesia. The increase in α values in the ACC induced by peripheral pinch stimulation was blocked by medial thalamic lesion, but the increase in α values in the S1 induced by brush and pinch stimulation was not affected. Conclusions: The neuronal avalanche model shows a critical state in the cortical network for noxious-related signal processing. The α value may provide an index of brain network activity that distinguishes the responses to somatic stimuli from the control state. These network dynamics may be valuable for the evaluation of acute nociceptive processes and may be applied to chronic pathological pain conditions.

Using numerical simulations, we perform an extensive finite-size analysis of the transverse diffusion coefficient in a sheared 2D amorphous solid, over a broad range of strain rates, at temperatures up to the supercooled liquid regime. We thus obtain direct qualitative evidence for the persistence of correlations between elementary plastic events up to the vicinity of the glass transition temperature $T_g$. A quantitative analysis of the data, combined with a previous study of the $T$- and $\dot\gamma$-dependence of the macroscopic stress \cite{ChattorajCaroliLemaitre2010}, leads us to conclude that the average avalanche size remains essentially unaffected by temperature up to $T\sim0.75 T_g$.

We introduce a toy model displaying the avalanche dynamics of failure in scale-free networks. In the model, the network growth is based on the Barab\'asi and Albert model and each node is assigned a capacity or tolerance, which is constant irrespective of node index. The degree of each node increases over time. When the degree of a node exceeds its capacity, it fails and each link connected to it is is rewired to other unconnected nodes by following the preferential attachment rule. Such a rewiring edge may trigger another failure. This dynamic process can occur successively, and it exhibits a self-organized critical behavior in which the avalanche size distribution follows a power law. The associated exponent is $\tau \approx 2.6(1)$. The entire system breaks down when any rewired edges cannot locate target nodes: the time at which this occurs is referred to as the breaking time. We obtain the breaking time as a function of the capacity. Moreover, using extreme value statistics, we determine the distribution function of the breaking time.

Depicts several types of avalanches of different sizes. Sequences illustrate dry snow avalanches in motion, ranging in size from minor snow movements to large and destructive avalanches accompanied by extensive dust cloud formation and wind blast. Several examples of slab avalanche release are depicted which demonstrate the unstable character and dangerous nature of this type of avalanche. Scenes of medium-sized avalanches show the difference in size and character of motion which is determined by the stability of the snow in the middle of the avalanche path.  Colors in original film had faded- basic color corrected version included.

The aim of this study is to investigate a wave dynamics and size scaling of avalanches which were created by the mathematical model {[}J. \v{C}ern\'ak Phys. Rev. E \textbf{65}, 046141 (2002)]. Numerical simulations were carried out on a two dimensional lattice $L\times L$ in which two constant thresholds $E_{c}^{I}=4$ and $E_{c}^{II}>E_{c}^{I}$ were randomly distributed. A density of sites $c$ with the threshold $E_{c}^{II}$ and threshold $E_{c}^{II}$ are parameters of the model. I have determined autocorrelations of avalanche size waves, Hurst exponents, avalanche structures and avalanche size moments for several densities $c$ and thresholds $E_{c}^{II}$. I found correlated avalanche size waves and multifractal scaling of avalanche sizes not only for specific conditions, densities $c=0.0$, 1.0 and thresholds $8\leq E_{c}^{II}\leq32$, in which relaxation rules were precisely balanced, but also for more general conditions, densities $0.0

We probe the dynamics of intermittent avalanches caused by steady addition of grains to a quasi-two dimensional heap. To characterize the time-dependent average avalanche flow speed v(t), we image the top free surface. To characterize the grain fluctuation speed dv(t), we use Speckle-Visibility Spectroscopy. During an avalanche, we find that the fluctuation speed is approximately one-tenth the average flow speed, and that these speeds are largest near the beginning of an event. We also find that the distribution of event durations is peaked, and that event sizes are correlated with the time interval since the end of the previous event. At high rates of grain addition, where successive avalanches merge into smooth continuous flow, the relationship between average and fluctuation speeds changes to dv Sqrt[v].

Slowly driven elastic interfaces, such as domain walls in dirty magnets, contact lines, or cracks proceed via intermittent motion, called avalanches. We develop a field-theoretic treatment to calculate, from first principles, the space-time statistics of instantaneous velocities within an avalanche. For elastic interfaces at (or above) their (internal) upper critical dimension d >= d_uc (d_uc = 2, 4 respectively for long-ranged and short-ranged elasticity) we show that the field theory for the center of mass reduces to the motion of a point particle in a random-force landscape, which is itself a random walk (ABBM model). Furthermore, the full spatial dependence of the velocity correlations is described by the Brownian-force model (BFM) where each point of the interface sees an independent Brownian-force landscape. Both ABBM and BFM can be solved exactly in any dimension d (for monotonous driving) by summing tree graphs, equivalent to solving a (non-linear) instanton equation. This tree approximation is the mean-field theory (MFT) for realistic interfaces in short-ranged disorder. Both for the center of mass, and for a given Fourier mode q, we obtain probability distribution functions (PDF's) of the velocity, as well as the avalanche shape and its fluctuations (second shape). Within MFT we find that velocity correlations at non-zero q are asymmetric under time reversal. Next we calculate, beyond MFT, i.e. including loop corrections, the 1-time PDF of the center-of-mass velocity du/dt for dimension d < d_uc. The singularity at small velocity P(du/dt) ~ 1/(du/dt)^a is substantially reduced from a=1 (MFT) to a = 1 - 2/9 (4-d) + ... (short-ranged elasticity) and a = 1 - 4/9 (2-d) + ... (long-ranged elasticity). We show how the dynamical theory recovers the avalanche-size distribution, and how the instanton relates to the response to an infinitesimal step in the force.

We study the avalanche dynamics in the data packet transport on scale-free networks through a simple model. In the model, each vertex is assigned a capacity proportional to the load with a proportionality constant $1+a$. When the system is perturbed by a single vertex removal, the load of each vertex is redistributed, followed by subsequent failures of overloaded vertices. The avalanche size depends on the parameter $a$ as well as which vertex triggers it. We find that there exists a critical value $a_c$ at which the avalanche size distribution follows a power law. The critical exponent associated with it appears to be robust as long as the degree exponent is between 2 and 3, and is close in value to that of the distribution of the diameter changes by single vertex removal.

Bibliography: p. 43-44

See the updated version arXiv:nlin/0111028.

Avalanche dynamics is an indispensable feature of complex systems. Here we study the self-organized critical dynamics of avalanches on scale-free networks with degree exponent $\gamma$ through the Bak-Tang-Wiesenfeld (BTW) sandpile model. The threshold height of a node $i$ is set as $k_i^{1-\eta}$ with $0\leq\eta

Avalanche behavior of gravitationally-forced granular layers on a rough inclined plane are investigated experimentally for different materials and for a variety of grain shapes ranging from spherical beads to highly anisotropic particles with dendritic shape. We measure the front velocity, area and the height of many avalanches and correlate the motion with the area and height. We also measure the avalanche profiles for several example cases. As the shape irregularity of the grains is increased, there is a dramatic qualitative change in avalanche properties. For rough non-spherical grains, avalanches are faster, bigger and overturning in the sense that individual particles have down-slope speeds $u_p$ that exceed the front speed $u_f$ as compared with avalanches of spherical glass beads that are quantitatively slower, smaller and where particles always travel slower than the front speed. There is a linear increase of three quantities i) dimensionless avalanche height ii) ratio of particle to front speed and iii) the growth rate of avalanche speed with increasing avalanche size with increasing $\tan\theta_r$ where $\theta_r$ is the bulk angle of repose, or with increasing $\beta_P$, the slope of the depth averaged flow rule, where both $\theta_r$ and $\beta_P$ reflect the grain shape irregularity. These relations provide a tool for predicting important dynamical properties of avalanches as a function of grain shape irregularity. A relatively simple depth-averaged theoretical description captures some important elements of the avalanche motion, notably the existence of two regimes of this motion.

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 $1/f$ 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 $d$ 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.

We introduce the standard distribution width of fitness to characterize the global and individual features of a ecosystem in the Bak-Sneppen evolution model. Through tracking this quantity in evolution, a different hierarchy of avalanche dynamics, $w_{0}$ avalanche is observed. The corresponding gap equation and the self-organized threshold $w_{c}$ are obtained. The critical exponents $\tau ,$ $\gamma $and $\rho $, which describe the behavior of the avalanche size distribution, the average avalanche size and the relaxation to attractor, respectively, are calculated with numerical simulation. The exact master equation and $\gamma $ equation are derived. And the scaling relations are established among the critical exponents of this new avalanche.

We present a two-dimensional system which exhibits features of self-organized criticality. The avalanches which occur on the surface of a pile of rice are found to exhibit finite size scaling in their probability distribution. The critical exponents are $\tau$ = 1.21(2) for the avalanche size distribution and $D$ = 1.99(2) for the cut-off size. Furthermore the geometry of the avalanches is studied leading to a fractal dimension of the active sites of $d_B$ = 1.58(2). Using a set of scaling relations, we can calculate the roughness exponent $\alpha = D - d_B$ = 0.41(3) and the dynamic exponent $z = D(2 - \tau)$ = 1.56(8). This result is compared with that obtained from a power spectrum analysis of the surface roughness, which yields $\alpha$ = 0.42(3) and $z$ = 1.5(1) in excellent agreement with those obtained from the scaling relations.

We study a simple model for a neuron function in a collective brain system. The neural network is composed of uncorrelated random scale-free network for eliminating the degree correlation of dynamical processes. The interaction of neurons is supposed to be isotropic and idealized. This neuron dynamics is similar to biological evolution in extremal dynamics with isotropic locally interaction but has different time scale. The evolution of neuron spike takes place according to punctuated patterns similar to the avalanche dynamics. We find that the evolutionary dynamics of this neuron function exhibit self-organized criticality which shows power-law behavior of the avalanche sizes. For a given network, the avalanche dynamic behavior is not changed with different degree exponents of networks, $\gamma \geq 2.4$ and refractory period correspondent to the memory effect, $T_r$. In addition, the avalanche size distributions exhibit the power-law behavior in a single scaling region in contrast to other networks. However, the return time distributions displaying spatiotemporal complexity have three characteristic time scaling regimes.

We study avalanche dynamics and local activity of forced-flow imbibition fronts in disordered media. We focus on the front dynamics as the mean velocity $\bar{v}$ of the interface is decreased and the pinning state is approached. Scaling arguments allow us to obtain the statistics of avalanche sizes and durations, which become power-law distributed due to the existence of a critical point at $\bar{v}= 0$. Results are compared with phase-field numerical simulations.

A detailed characterization of avalanche dynamics of wet granular media in a rotating drum apparatus is presented. The results confirm the existence of the three wetness regimes observed previously: the granular, the correlated and the viscoplastic regime. These regimes show qualitatively different dynamic behaviors which are reflected in all the investigated quantities. We discuss the effect of interstitial liquid on the characteristic angles of the material and on the avalanche size distribution. These data also reveal logarithmic aging and allow us to map out the phase diagram of the dynamical behavior as a function of liquid content and flow rate. Via quantitative measurements of the flow velocity and the granular flux during avalanches, we characterize novel avalanche types unique to wet media. We also explore the details of viscoplastic flow (observed at the highest liquid contents) in which there are lasting contacts during flow, leading to coherence across the entire sample. This coherence leads to a velocity independent flow depth at high rotation rates and novel robust pattern formation in the granular surface.

We report experimental measurements of avalanche behavior of thin granular layers on an inclined plane for low volume flow rate. The dynamical properties of avalanches were quantitatively and qualitatively different for smooth glass beads compared to irregular granular materials such as sand. Two scenarios for granular avalanches on an incline are identified and a theoretical explanation for these different scenarios is developed based on a depth-averaged approach that takes into account the differing rheologies of the granular materials.

A model for crack propagation and contact line depinning is studied. Although the model contains nonlocal interactions, it obeys general scaling relations for depinning via localized bursts or avalanches. Our numerical result for the roughness exponent in one dimension, $\chi=0.49 \pm 0.05$, agrees with recent experiments on cracks measuring the in-plane roughness $\chi \simeq 0.5 - 0.6$, as well as mean field arguments giving $\chi=1/2$, but is significantly higher than the functional renormalization group prediction $\chi = 1/3$.

We describe a directed avalanche model; a slowly unloading sandbox driven by lowering a retaining wall. The directness of the dynamics allows us to interpret the stable sand surfaces as world sheets of fluctuating interfaces in one lower dimension. In our specific case, the interface growth dynamics belongs to the Kardar-Parisi-Zhang (KPZ) universality class. We formulate relations between the critical exponents of the various avalanche distributions and those of the roughness of the growing interface. The nonlinear nature of the underlying KPZ dynamics provides a nontrivial test of such generic exponent relations. The numerical values of the avalanche exponents are close to the conventional KPZ values, but differ sufficiently to warrant a detailed study of whether avalanche correlated Monte Carlo sampling changes the scaling exponents of KPZ interfaces. We demonstrate that the exponents remain unchanged, but that the traces left on the surface by previous avalanches give rise to unusually strong finite-size corrections to scaling. This type of slow convergence seems intrinsic to avalanche dynamics.

Self-organized criticality has been claimed to play an important role in many natural and social systems. In the present work we empirically investigate the relevance of this theory to stock-market dynamics. Avalanches in stock-market indices are identified using a multi-scale wavelet-filtering analysis designed to remove Gaussian noise from the index. Here new methods are developed to identify the optimal filtering parameters which maximize the noise removal. The filtered time series is reconstructed and compared with the original time series. A statistical analysis of both high-frequency Nasdaq E-mini Futures and daily Dow Jones data is performed. The results of this new analysis confirm earlier results revealing a robust power law behaviour in the probability distribution function of the sizes, duration and laminar times between avalanches. This power law behavior holds the potential to be established as a stylized fact of stock market indices in general. While the memory process, implied by the power law distribution of the laminar times, is not consistent with classical models for self-organized criticality, we note that a power-law distribution of the laminar times cannot be used to rule out self-organized critical behaviour.

We investigate the avalanche dynamics of the abelian sandpile model on arbitrarily large balls of the expanded cactus graph (the Cayley graph of the free product $\mathbb{Z}_3 * \mathbb{Z}_2$). We follow the approach of Dhar and Majumdar (1990) to enumerate the number of recurrent configurations. We also propose the filling method of enumerating all the recurrent configurations in which adding a grain to a designated origin vertex (far enough away from the boundary vertices) causes topplings to occur in a specific cluster (a connected subgraph that is the union of cells, or copies of the 3-cycle) within the first wave of an avalanche. This filling method lends itself to combinatorial evaluation of the number of positions in which a certain number of cells topple in an avalanche starting at the origin, which are amenable to analysis using well-known recurrences and corresponding generating functions. We show that, when counting cells that topple in the avalanche, the cell-wise first-wave critical exponent of the Abelian sandpile model on the expanded cactus is 3/2.

The presented article attempts to characterize the encryption dynamics of delayed dynamics based block ciphers, designed for the encryption of binary data. For such encryption algorithms, the encryption process relies on a coupling dynamics with time delay between different bits in the plaintext (i.e.\ the "initial" message to be encrypted). Here, the principal dynamics of the encryption process is examined and the Hammingdistance is used to quantify the change in ciphertext (i.e.\ the plaintext after encryption) upon changing a single bit in the plaintext message or slightly perturbing the key used during encryption. More precisely, the previously proposed "encryption via delayed dynamics" (in short: EDDy) algorithm as well as its extended version (termed ExEDDy) are analyzed by means of numerical simulations. As a result it is found that while EDDy exhibits a rather poor perfomance, ExEDDy performes considerably better and hence constitutes a significant improvement over EDDy. Consequently, the results are contrasted with those obtained for a block cipher that implements the encryption/decryption dynamics by means of reversible cellular automata.

We give a simple and elementary proof of the identity $$\sum_{r=1}^n\sum_{k_1,...,k_r\ge 1: \sum_{i=1}^r k_i= n} \frac {n!} {k_1!k_2!...k_r!}k_1^{k_2}...k_{r-1}^{k_r}=(n+1)^{n-1}$$ where $n\in \mathbb N$. A first application of this formula shows Cayley's theorem \cite{Caley} on the number of trees with $n+1$ vertices (in fact the formula is equivalent to Cayley's result). A second application gives the distribution of avalanche sizes, which can be deduced for general dynamical systems and also as a bilogically motivated urn model in probability. In particular, the law of avalanche sizes in Eurich et al. \cite{EHE} and Levina \cite{Levina} is closely related to this dynamical representation.

Using a simple lattice model for granular media, we present a scenario of self-organization that we term self-organized structuring where the steady state has several unusual features: (1) large scale space and/or time inhomogeneities and (2) the occurrence of a non-trivial peaked distribution of large events which propagate like ``bubbles'' and have a well-defined frequency of occurrence. We discuss the applicability of such a scenario for other models introduced in the framework of self-organized criticality.