scholarly journals Self-Organized Criticality: Emergent Complex Behavior in PM10 Pollution

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Shi Kai ◽  
Liu Chun-Qiong ◽  
Li Si-Chuan
Keyword(s):  
Time Series ◽  
Complex Dynamics ◽  
Distribution Functions ◽  
Fluctuation Analysis ◽  
Good Prediction ◽  
Scale Invariant ◽  
Invariant Structure ◽  
Dynamical Characteristics ◽  
Self Organized ◽  
Self Organized Criticality

We analyze long-term time series of daily average PM10 concentrations in Chengdu city. Detrended fluctuation analysis of the time series shows long range correlation at one-year temporal scale. Spectral analysis of the time series indicates 1/f noise behavior. The probability distribution functions of PM10 concentrations fluctuation have a scale-invariant structure. Why do the complex structures of PM10 concentrations evolution exhibit scale-invariant? We consider that these complex dynamical characteristics can be recognized as the footprint of self-organized criticality (SOC). Based on the theory of self-organized criticality, a simplified sandpile model for PM10 pollution with a nondimensional formalism is put forward. Our model can give a good prediction of scale-invariant in PM10 evolution. A qualitative explanation of the complex dynamics observed in PM10 evolution is suggested. The work supports the proposal that PM10 evolution acts as a SOC process on calm weather. New theory suggests one way to understand the origin of complex dynamical characteristics in PM10 pollution.

scholarly journals Self-Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Andrey Dmitriev ◽  
Victor Dmitriev ◽  
Stepan Balybin
Keyword(s):  
Time Series ◽  
Critical State ◽  
Empirical Investigation ◽  
Phenomenological Theory ◽  
Self Organization ◽  
Protest Movements ◽  
Self Organized ◽  
Self Organized Criticality ◽  
Stochastic Sources ◽  
Adiabatic Mode

Recently, there has been an increasing number of empirical evidence supporting the hypothesis that spread of avalanches of microposts on social networks, such as Twitter, is associated with some sociopolitical events. Typical examples of such events are political elections and protest movements. Inspired by this phenomenon, we built a phenomenological model that describes Twitter’s self-organization in a critical state. An external manifestation of this condition is the spread of avalanches of microposts on the network. The model is based on a fractional three-parameter self-organization scheme with stochastic sources. It is shown that the adiabatic mode of self-organization in a critical state is determined by the intensive coordinated action of a relatively small number of network users. To identify the critical states of the network and to verify the model, we have proposed a spectrum of three scaling indicators of the observed time series of microposts.

THE FIXED SCALE TRANSFORMATION: STATUS AND PERSPECTIVES

Fractals ◽  
1993 ◽  
Vol 01 (03) ◽  
pp. 650-662 ◽  
Author(s):  
L. PIETRONERO
Keyword(s):  
Growth Models ◽  
Path Integrals ◽  
Coarse Grained ◽  
Scale Transformation ◽  
Scale Invariant ◽  
Simplified Models ◽  
Self Organized ◽  
Self Organized Criticality ◽  
New Type ◽  
Pile Model

Irreversible fractal growth models like DLA and DBM have confronted us with theoretical problems of a new type that cannot be described in terms of the standard concepts like field theory and the renormalization group. The Fixed Scale Transformation is a theoretical scheme of a new type that is able to treat these problems in a reasonably systematic way. The idea is to focus on the dynamics at a given scale and to compute accurately the correlations at this scale by suitable lattice path integrals. The use of scale invariant growth rules then allows the generalization of these correlations to coarse-grained cells of any size and therefore to obtain the fractal dimension. We summarize the present status of the FST approach by focusing on the most recent results about the scale invariant dynamics of DLA/DBM. The possible extensions to other problems like the sand pile model (self-organized-criticality) and simplified models of turbulence will also be considered.

SELF-ORGANIZED CRITICALITY IN AN EARTHQUAKE MODEL BASED ON ASSORTATIVE SCALE-FREE NETWORKS

2011 ◽  
Vol 22 (05) ◽  
pp. 483-493 ◽  
Author(s):  
MIN LIN ◽  
GANG WANG
Keyword(s):  
Stress Evolution ◽  
Scale Invariant ◽  
Avalanche Size ◽  
Scale Free ◽  
Self Organized ◽  
Dynamical Behaviors ◽  
Period Distribution ◽  
Scale Free Networks ◽  
Earthquake Model ◽  
Self Organized Criticality

A modified Olami–Feder–Christensen (OFC) earthquake model on scale-free networks with assortative mixing is introduced. In this model, the distributions of avalanche sizes and areas display power-law behaviors. It is found that the period distribution of avalanches displays a scale-invariant law with the increment of range parameter d. More importantly, different assortative topologies lead to different dynamical behaviors, such as the distribution of avalanche size, the stress evolution process, and period distribution.

Industry Trends

2012 ◽  
Vol 54 (1) ◽  
pp. 6-9 ◽  
Author(s):  
James Houran ◽  
Rense Lange ◽  
Keith Kefgen
Keyword(s):  
Time Series ◽  
Executive Coaching ◽  
Neural Nets ◽  
Noise Distribution ◽  
Pink Noise ◽  
Self Organized ◽  
Self Organized Criticality ◽  
Traditional Organization ◽  
Career Moves ◽  
Industry Trends

We explored the idea that the timing of executives’ career moves was consistent with Bak’s notion of self-organized criticality. Consistent with predictions, time series analysis of job changes for 43 hospitality executives obeyed a power law and revealed a mixture of predictable and unpredictable patterns with a musical nature (pink noise distribution). The data showed better fit for traditional ‘organization men’ versus opportunistic ‘trailblazers.’ These differences in career patterns (rhythms) could be used to reliably distinguish between these two executive-types using neural nets. Potential implications for executive coaching and development are discussed.

Subtle nonlinearity in popular album charts

1999 ◽  
Vol 02 (03) ◽  
pp. 197-208 ◽  
Author(s):  
R. Alexander Bentley ◽  
Herbert D. G. Maschner
Keyword(s):  
Time Series ◽  
Popular Music ◽  
Power Law ◽  
Large Scale ◽  
Selective Sampling ◽  
Self Organized ◽  
Self Organized Criticality ◽  
Critical Model ◽  
Power Law Distributions ◽  
Multiplicative Processes

Large-scale patterns of culture change may be explained by models of self organized criticality, or alternatively, by multiplicative processes. We speculate that popular album activity may be similar to critical models of extinction in that interconnected agents compete to survive within a limited space. Here we investigate whether popular music albums as listed on popular album charts display evidence of self-organized criticality, including a self-affine time series of activity and power-law distributions of lifetimes and exit activity in the chart. We find it difficult to distinguish between multiplicative growth and critical model hypotheses for these data. However, aspects of criticality may be masked by the selective sampling that a "Top 200" listing necessarily implies.

Correlation between Barkhausen noise and mechanical sensitivity in FINEMET-type materials

2009 ◽  
Vol 24 (1) ◽  
pp. 130-134 ◽  
Author(s):  
G. Eszenyi ◽  
S. Szabó ◽  
L. Harasztosi ◽  
F. Zámborszky ◽  
J. Nyéki ◽  
...  
Keyword(s):  
Power Laws ◽  
Distribution Functions ◽  
Barkhausen Noise ◽  
Mechanical Sensitivity ◽  
Composite State ◽  
Noise Parameters ◽  
Self Organized ◽  
Heat Treated ◽  
Self Organized Criticality ◽  
Nanocrystalline Composite

FINEMET-type (Fe75Si15NbBCu) ribbons were heat treated, and their magnetic properties were analyzed. Permeability, thermal, and mechanical sensitivities were measured by commonly used industrial methods, and these properties were correlated with measured magnetic Barkhausen noise parameters. Distributions of peak area, A, and peak noise energy, E, were evaluated. Distribution functions of noise parameters, P(x), were in good agreement with the theory of self-organized criticality (SOC), satisfying power laws in the form P(x)∼x−α. It is found that the noise did not considerably depend on the temperature sensitivity parameter and on the permeability of ribbons. However, a useful correlation between the noise parameters and mechanical sensitivity has been observed. Minimal noise was detected for samples with negligible mechanical sensitivity in an amorphous-nanocrystalline composite state obtained by a heat treatment at 853 K.

scholarly journals Self-Organized Criticality in the Brain

2021 ◽  
Vol 9 ◽  
Author(s):  
Dietmar Plenz ◽  
Tiago L. Ribeiro ◽  
Stephanie R. Miller ◽  
Patrick A. Kells ◽  
Ali Vakili ◽  
...  
Keyword(s):  
Phase Transition ◽  
Order Phase Transition ◽  
Field Potential ◽  
Quiet Time ◽  
Scale Invariant ◽  
Self Organized ◽  
Up States ◽  
Self Organized Criticality ◽  
Neuronal Avalanches ◽  
The Brain

Self-organized criticality (SOC) refers to the ability of complex systems to evolve toward a second-order phase transition at which interactions between system components lead to scale-invariant events that are beneficial for system performance. For the last two decades, considerable experimental evidence has accumulated that the mammalian cortex with its diversity in cell types, interconnectivity, and plasticity might exhibit SOC. Here, we review the experimental findings of isolated, layered cortex preparations to self-organize toward four dynamical motifs presently identified in the intact cortex in vivo: up-states, oscillations, neuronal avalanches, and coherence potentials. During up-states, the synchronization observed for nested theta/gamma oscillations embeds scale-invariant neuronal avalanches, which can be identified by robust power law scaling in avalanche sizes with a slope of −3/2 and a critical branching parameter of 1. This precise dynamical coordination, tracked in the negative transients of the local field potential (nLFP) and spiking activity of pyramidal neurons using two-photon imaging, emerges autonomously in superficial layers of organotypic cortex cultures and acute cortex slices, is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies. A subclass of avalanches, the coherence potentials, exhibits precise maintenance of the time course in propagated local synchrony. Avalanches emerge in superficial layers of the cortex under conditions of strong external drive. The balance of excitation and inhibition (E/I), as well as neuromodulators such as dopamine, establishes powerful control parameters for avalanche dynamics. This rich dynamical repertoire is not observed in dissociated cortex cultures, which lack the differentiation into cortical layers and exhibit a dynamical phenotype expected for a first-order phase transition. The precise interactions between up-states, nested oscillations, and avalanches in superficial layers of the cortex provide compelling evidence for SOC in the brain.

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