Self-organized Criticality Model for Ocean Internal Waves

In this paper, we present a simple spring-block model for ocean internal waves based on the self-organized criticality （SOC）. The oscillations of the water blocks in the model display power-law behavior with an exponent of -2 in the frequency domain, which is similar to the current and sea water temperature spectra in the actual ocean and the universal Garrett and Munk deep ocean internal wave model [Geophysical Fluid Dynamics 2 （1972） 225; J. Geophys. Res. 80 （1975） 291]. The influence of the ratio of the driving force to the spring coefficient to SOC behaviors in the model is also discussed.     

Olami-Feder-Christensen model on different networks

We investigate numerically the Self Organized Criticality (SOC) properties of the dissipative Olami-Feder-Christensen model on small-world and scale-free networks. We find that the small-world OFC model exhibits self-organized criticality. Indeed, in this case we observe power law behavior of earthquakes size distribution with finite size scaling for the cut-off region. In the scale-free OFC model, instead, the strength of disorder hinders synchronization and does not allow to reach a critical state.     

Effects of Interactive Function Forms and Refractoryperiod in a Self-Organized Critical Model Based on Neural Networks

Based on the standard self-organizing map neural network model and an integrate-and-tire mechanism, we investigate the effect of the nonlinear interactive function on the self-organized criticality in our model. Based on these we also investigate the effect of the refractoryperiod on the self-organized criticality of the system.     

Effects of Interactive Function Forms and Refractoryperiod in a Self-Organized Critical Model Based on Neural Networks

Based on the standard self-organizing map neural network model and an integrate-and-fire mechanism, we investigate the effect of the nonlinear interactive function on the self-organized criticality in our model. Based on the sewe also investigate the effect of the refractoryperiod on the self-organized criticality of the system.     

含崩塌概率的一维沙堆模型的自组织临界性

提出了一个含崩塌概率的一维沙堆模型，并用元胞自动机方法对该模型进行计算机模拟. 结果表明在崩塌概率p从0到1的变化过程中存在两个临界点p₁和p₂. 当p₁2时模型具有自组织临界行为，并且系统在从平凡行为到自组织临界行为之间有一个快速的转变. 当模型具有自组织临界性时，这种自组织临界行为具有普适性，两个临界指数分别是1.50±0.02和1.58±0.15. 该模型能够较好地解释一维米粒堆实验中出现的自组织临界现象 关键词： 自组织临界性 BTW模型 崩塌概率     

SIMULATION ON LOCAL FLUX CREEP IN TYPE-Ⅱ SUPERCONDUCTORS

Using C programming language, we have simulated the flux creep process in nonideal type-Ⅱ superconductors. Global and local magnetization curves are calculated and the logarithmic time dependence of local magnetic induction B under a constant external field is examined. The effects of nonuniform pinning potential and self-organized criticality (SOC) model on the simulations are discussed. The results show that the main feature of flux creep is the relaxation effect. The form of hysteresis loops is dependent on the magnetic field sweep rate. SOC can account for the occurrence of fluctuation to a certain extent and nonuniform pinning potential can enhance the fluctuation.     

Do earthquakes exhibit self-organized criticality?

If earthquakes are phenomena of self-organized criticality (SOC), statistical characteristics of the earthquake time series should be invariant after the sequence of events in an earthquake catalog are randomly rearranged. In this Letter we argue that earthquakes are unlikely phenomena of SOC because our analysis of the Southern California Earthquake Catalog shows that the first-return-time probability PM(T) is apparently changed after the time series is rearranged. This suggests that the SOC theory should not be used to oppose the efforts of earthquake prediction.     

Self-organized critical pinball machine

The nature of self-organized criticality (SOC) is pin-pointed with a simple mechanical model: a pinball machine. Its phase space is fully parameterized by two integer variables, one describing the state of an on-going game, the other describing the state of the machine. This is the simplest possible SOC system, having only two degrees of freedom and no spatial correlations, yet is not solvable by analytical means.     

Frequency–rank correlations of rhodopsin mutations with tuned hydropathic roughness based on self-organized criticality

The behavior of disease-linked mutations of membrane proteins is especially simple in rhodopsin, where they are well-studied, as they are responsible for retinitis pigmentosa, RP (retinal degeneration). Here we show that the frequency of occurrence of single RP mutations is strongly influenced by their transportational survival rates, and that this survival correlates well (82%) with a long-range, non-local hydropathic measure of the roughness of the water interfaces of ex-membrane rhodopsin based on self-organized criticality (SOC). It is speculated that this concept may be generally useful in studying survival rates of many mutated proteins.     

Self-organized criticality in a nutshell

In order to gain insight into the nature of self-organized criticality (SOC), we present a minimal model exhibiting this phenomenon. In this analytically solvable model, the state of the system is fully described by a single-integer variable. The system organizes in its critical state without external tuning. We derive analytically the probability distribution of durations of disturbances propagating through the system. As required by SOC, this distribution is scale invariant and follows a power law over several orders of magnitude. Our solution also reproduces the exponential tail of the distribution due to finite size effects. Moreover, we show that large avalanches are suppressed when stabilizing the system in its critical state. Interestingly, avalanches are affected in a similar way when driving the system away from the critical state. With this model, we have reduced SOC dynamics to a leveling process as described by Ehrenfest's famous flea model.     

Effects of Some Neurobiological Factors in a Self-organized Critical Model Based on Neural Networks

Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse,the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.     

Effects of Some Neurobiological Factors in a Self-organized Critical Model Based on Neural Networks

Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse,the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.     

Bursty events and incremental diffusion in a local diffusion and multi—scale convection system

A one-dimensional cellular automaton is defined without the critical gradient rule (Δh>Δh_c) which is essential to the existence of avalanches in self-organized criticality (SOC) models. Instead, only the local diffusion rule is used, however, the characteristics of SOC, such as the bursty behaviour, power-law decay in fluctuation spectra, self-similarity over a broad range of scales and long-time correlations, are still observed in these numerical experiments. This numerical model is established to suggest that the bursty events and the incremental diffusion observed universally in fusion experiments do not necessarily imply the submarginal dynamics.     

Search of self-organized criticality processes in magnetically confined plasmas: hints from the reversed field pinch configuration

In order to test the self-organized criticality (SOC) paradigm in transport processes, a novel technique has been applied for the first time to plasmas confined in reversed field pinch configuration. This technique consists of an analysis of the probability distribution function of the times between bursts in density fluctuations measured by microwave reflectometry and electrostatic probes. The same analysis has also been applied to intermittent events sorted out from the Gaussian background. In both cases, the experimental results disagree with the predictions for a SOC system.     

Self-organized criticality in MHD driven plasma edge turbulence

We analyze long-range time correlations and self-similar characteristics of the electrostatic turbulence at the plasma edge and scrape-off layer in the Tokamak Chauffage Alfvén Brésillien (TCABR), with low and high Magnetohydrodynamics (MHD) activity. We find evidence of self-organized criticality (SOC), mainly in the region near the tokamak limiter. Comparative analyses of data before and during the MHD activity reveals that during the high MHD activity the Hurst parameter decreases. Finally, we present a cellular automaton whose parameters are adjusted to simulate the analyzed turbulence SOC change with the MHD activity variation.     

Delayed self-organized criticality and earthquake modeling

Delayed self-organized criticality is defined. It is shown to preserve the power-law behavior of self-organized criticality with a significant change in the exponents. A delayed version of the Ito—Matsuzaki model for earthquakes is constructed and studied. This model explains some fractal features of earthquakes as well as the Gutenberg-Richter and Omori laws. Furthermore theb value obtained from the delayed model is closer to observations than theb value of the undelayed model.     

Self-organized criticality occurs in non-conservative neuronal networks during Up states

During sleep, under anesthesia and in vitro, cortical neurons in sensory, motor, association and executive areas fluctuate between Up and Down states (UDS) characterized by distinct membrane potentials and spike rates [1, 2, 3, 4, 5]. Another phenomenon observed in preparations similar to those that exhibit UDS, such as anesthetized rats [6], brain slices and cultures devoid of sensory input [7], as well as awake monkey cortex [8] is self-organized criticality (SOC). This is characterized by activity "avalanches" whose size distributions obey a power law with critical exponent of about [Formula: see text] and branching parameter near unity. Recent work has demonstrated SOC in conservative neuronal network models [9, 10], however critical behavior breaks down when biologically realistic non-conservatism is introduced [9]. We here report robust SOC behavior in networks of non-conservative leaky integrate-and-fire neurons with short-term synaptic depression. We show analytically and numerically that these networks typically have 2 stable activity levels corresponding to Up and Down states, that the networks switch spontaneously between them, and that Up states are critical and Down states are subcritical.     

Self-organization of the critical state in granular superconductors

The critical state in granular superconductors is studied using two mathematical models: systems of differential equations for the gauge-invariant phase difference and a simplified model that is described by a system of coupled mappings and in many cases is equivalent to the standard models used for studying self-organized criticality. It is shown that the critical state of granular superconductors is self-organized in all cases studied. In addition, it is shown that the models employed are essentially equivalent, i.e., they demonstrate not only the same critical behavior, but they also lead to the same noncritical phenomena. The first demonstration of the existence of self-organized criticality in a system of nonlinear differential equations and its equivalence to self-organized criticality in standard models is given in this paper.     

Black swans,power laws,and dragon-kings: Earthquakes,volcanic eruptions,landslides, wildfires,floods, and SOC models

Extreme events that change global society have been characterized as black swans. The frequency-size distributions of many natural phenomena are often well approximated by power-law (fractal) distributions. An important question is whether the probability of extreme events can be estimated by extrapolating the power-law distributions. Events that exceed these extrapolations have been characterized as dragon-kings. In this paper we consider extreme events for earthquakes, volcanic eruptions, wildfires, landslides and floods. We also consider the extreme event behavior of three models that exhibit self-organized criticality (SOC): the slider-block, forest-fire, and sand-pile models. Since extrapolations using power-laws are widely used in probabilistic hazard assessment, the occurrence of dragon-king events have important practical implications.     

What Can One Learn About Self-Organized Criticality from Dynamical Systems Theory?

We develop a dynamical system approach for the Zhang model of self-organized criticality, for which the dynamics can be described either in terms of iterated function systems or as a piecewise hyperbolic dynamical system of skew-product type. In this setting we describe the SOC attractor, and discuss its fractal structure. We show how the Lyapunov exponents, the Haussdorf dimensions, and the system size are related to the probability distribution of the avalanche size via the Ledrappier–Young formula.