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We studied the distribution of the annual PM2.5 concentrations of countries and territories as reported by the World Health Organization (WHO) for the last decade using self-reported data from governments and other contributing institutions. The distributions follow various regimes corresponding to the low, intermediate, and high PM2.5 concentrations. The intermediate regime is fitted with a power-law with scaling exponent close to 2.44 and explained from a complexity perspective using a truncated exponential growth model with random truncation growth rates. The self-organization resulting from the interplay between natural and human-induced factors in the atmosphere is deemed to persist despite the gradual evolution of the actual statistical manifestations. [ABSTRACT FROM AUTHOR]

Interpretation plays a central role in using scientific models to explain natural phenomena: Meaning must be bestowed upon a model in terms of what it is and what it represents to be used for model explanations. However, it remains unclear how capacious and complex interpretation in models can be, particularly when conducted by the same group of scientists in the context of one explanatory project. This paper sheds light upon this question by examining modelling and explanatory practices related to the Olami-Feder-Christensen model of earthquakes. This case study shows that various interpretations are intricately connected in the overall meaning of a model used for model explanations. This leads to a manifold picture of interpretation, according to which scientific models are construed as networks of interconnected meanings. As scientists ponder and integrate these various interpretations, guided by locally attended epistemic interests, they achieve model explanations with layers of content, both in their explanantia and explananda. [ABSTRACT FROM AUTHOR]

Groundwater-level and rainfall measurements from 37 borewells in the Visakhapatnam district, Andhra Pradesh, India, from 2002 to 2021 were analyzed using Bayesian Neural Networks (BNNs) to comprehend the predictability of groundwater levels. We found chaotic dynamics in the groundwater and rainfall data, but a dominant trend component was seen in the groundwater-level data from phase plots. Dynamics suggest the presence of self-organized criticality/chaos in the groundwater dynamics over decadal time scales. We used BNN prediction models, (i) nonlinear autoregressive (NAR), (ii) nonlinear input–output (NIO) and (iii) nonlinear autoregressive exogenic Input (NARX), to predict the groundwater levels with rainfall and temperature as exogenic inputs. We noticed ~ 94 to 95% prediction accuracy with the NAR model with optimal inputs and ~ 1% improvement with added exogenic input. Interestingly, the study indicates that (i) the dynamics of the groundwater differ significantly from rainfall and temperature in the region, (ii) the nonlinear autoregressive model based on the self-organized dynamics of groundwater-level changes is robust in providing prediction accuracy up to ~ 95%, and (iii) the dynamics of remaining ~ 5% groundwater-level changes may be due to the presence of randomly varying extreme weather events and man-made/anthropogenic changes. [ABSTRACT FROM AUTHOR]

The natural gas pipeline system (NGPS) is complex with a diverse network topology. This study elucidates the network characteristics of the NGPS, revealing that it shares commonalities with small-world networks, but with differences caused by its structural and operational characteristics. The combination of the low aggregation effect caused by branching structure and the offsetting effect caused by economic and geographical constraints results in a network with small world characteristics. NGPS has longer mean path than common small world network, but the aggregation factor is close to that of a standard small world network. With respect to its small-world characteristics and correlation with self-organized criticality (SOC), an in-depth study of the SOC characteristics of the NGPS was conducted utilizing a combination of traditional logarithm analysis and R/S analysis. The findings establish that there are atypical SOC characteristics, characterized by a weakened power law within a specific functional range, thereby encouraging the advancement of SCADA and other safety technologies. Because of the line-pack, which induces time lags in the NGPS, NGPS can prevent the cascade failures. The reasons for the periodicity and centrality of NGPS data are analyzed by the affecting components, with the R/S analysis verifying a weak positive correlation of the function data across different time series. The interlinking of the SOC and R/S analysis illustrates the connection between critical status, accident type, and operation index. The results prove the reasonability and importance of insurance measures, such as regular internal detection, higher grade steel pipes change and staff training et al., in a novel view of SOC. The future works in building accidents database and human factors study are proposed.

We investigate similarities and differences between natural and simulated slow earthquakes using nonlinear dynamical system tools. We use spatio‐temporal slip potency rate data derived from Global Navigation Satellite System (GNSS) position time series in the Cascadia subduction zone and numerical simulations intended to reproduce their pulse‐like behavior and scaling laws. We provide metrics to evaluate the accuracy of simulations in mimicking slow earthquake dynamics. We investigate the influence of spatio‐temporal coarsening as well as observational noise. Despite the use of many degrees of freedom, numerical simulations display a surprisingly low average dimension, akin to natural slow earthquakes. Instantaneous dynamical indices can reach large values (>10) instead, and differences persist between numerical simulations and natural observations. We propose to use the suggested metrics as an additional tool to narrow the divergence between slow earthquake observations and dynamical simulations. Plain Language Summary: Earthquakes are natural phenomena resulting from the Earth's crust cyclically loading and unloading. The unpredictability of seismic events, combined with the large energy they release during the co‐seismic phase, poses not only scientific challenges but also significant threats to numerous populated regions at risk. Numerical simulations of the seismic cycle are widely used to better understand the dynamics of this natural phenomenon. Nonetheless, a direct comparison of earthquake observations and numerical simulations of the seismic cycle is currently prevented by the lengthy recurrence time of large seismic events rupturing the same fault segment and the short observational record at our disposal. Slow earthquakes, exhibiting lower recurrence times, serve as a viable alternative for validating models against real‐world observations. We investigate similarities and differences between natural and simulated slow earthquakes through the lens of nonlinear dynamical system theory. We study the effects of observational noise and spatio‐temporal coarsening putting the simulations in conditions like real‐world observations. We find that observational noise does not suffice to explain the higher complexity retrieved for natural observations. By refining our understanding of these dynamical systems, this study contributes to advancements in seismic research, offering a picture of the complexities involved on active faults. Key Points: Natural observations and numerical simulations of slow earthquakes share common average dynamical propertiesNatural observations show higher complexity than numerical simulationsMatching instantaneous dynamical properties can help reducing the discrepancies between natural and simulated slow earthquakes [ABSTRACT FROM AUTHOR]

Bedform evolution and preserved cross strata are known to respond to floods. However, it is unclear if autogenic dynamics mask the flood signal in bedform evolution and cross strata. To address this, we characterize the temporal structure of autogenic noise in steady‐state bedform evolution in a physical experiment. Results reveal the existence of bedform groups—quasi‐stable collections of bedforms—that migrate at a similar speed as bedforms. We find that bedform and bedform‐group turnover timescales are the key autogenic timescales of bed evolution that set the transition time‐periods between different noise regimes in bedform evolution. Results suggest that bedform‐group turnover timescale sets the lower limit for detecting flood signals in bedform evolution, and floods with duration shorter than bedform turnover timescale can be severely degraded in bedform evolution and cross strata. Our work provides a new framework for interrogating fluvial cross strata for reconstruction of past floods. Plain Language Summary: Bedforms are wavy features found regularly on the beds of rivers. Bedform deposits are the building blocks of the rock record on Earth and Mars. Bedforms and their deposits respond to floods; however, it is unclear if all floods are similarly represented in bedforms and their deposits. To address this, we identified the timescales over which bed elevation and sediment discharge are variable in a steady‐state experiment of bedform evolution using high‐resolution data. We investigated the time series of bed elevation to document the existence of bedform groups, which represent a collection of bedforms that have deep scours at their upstream and downstream end. We find that the turnover timescales (time required to move an entire land feature) of bedforms and bedform groups are the key controls on noise in bedform evolution. Results suggest that the signal of floods with duration less than bedform turnover timescale will not be found in bedform data and their deposits. However, floods with duration greater than the bedform‐group turnover timescale are likely to be expressed in bedform data and their deposits. These results provide a new theory for how floods are represented in river deposits. Key Points: We show the existence of bedform groups, which are quasi‐stable collections of bedforms, previously found in aeolian dune evolution modelsBedform and bedform group turnover timescales are key autogenic timescales that describe the temporal structure of noise in bed elevationFloods of duration shorter than bedform turnover timescale are expected to be unrecognizable in bed elevation and preserved cross strata [ABSTRACT FROM AUTHOR]

Self-organized criticality is a universal theory for dynamical systems that has recently been applied to the cardiovascular system. Precise methodological approaches are essential for understanding the dynamics of cardiovascular self-organized criticality. This study examines how the duration and quality of data recording affect the analysis of cardiovascular self-organized criticality, with a focus on the beat-by-beat heart rate variability time series obtained from seven healthy subjects in a standing position. Drawing a Zipf diagram, we evaluated the distribution of cardiovascular events of bradycardia and tachycardia. We identified tipping points for the distribution of both bradycardia and tachycardia events. By varying the recording durations (1, 2, 5, 10, 20, 30, and 40 min) and sampling frequencies (500, 250, and 100 Hz), we investigated their influence on the observed distributions. While shorter recordings can effectively capture cardiovascular events, they may underestimate the variables describing their distribution. Additionally, the tipping point of the Zipf distribution differs between bradycardia and tachycardia events. Comparisons of the distribution of bradycardia and tachycardia events should be conducted using long data recordings. Utilizing devices with lower sampling frequencies may compromise data fidelity. These insights contribute to refining experimental protocols and advancing our understanding of the complex dynamics underlying cardiovascular regulation. [ABSTRACT FROM AUTHOR]

A new cellular automaton model is developed to examine the nature of temporal sequences of earthquakes. The model takes the space dependence of fault strength into account and assumes that an earthquake produces a continuous area with the stress dropped to zero. In the model, viscous slip is also introduced on the fault and earthquakes are realizable only when the stress accumulates faster than the viscous relaxation. The analysis reveals that the sequences of earthquakes generally satisfy the power law relation between the intensity and frequency of earthquakes so that earthquakes may be in the state of self-organized criticality. On the other hand, periodicity appears in some sequences that consist of the groups of high seismic activity repeated between calm intervals with an almost constant period. Therefore, self-organized criticality and periodicity coexist in these sequences and the claim that earthquakes are unpredictable because of self-organized criticality may be inadequate. [ABSTRACT FROM AUTHOR]

The index of success of the researchers is now mostly measured using the Hirsch index (h). Our recent precise demonstration, that statistically h ∼ N c ∼ N p , where N p and N c denote, respectively, the total number of publications and total citations for the researcher, suggests that average number of citations per paper ( N c / N p ), and hence h, are statistical numbers (Dunbar numbers) depending on the community or network to which the researcher belongs. We show here, extending our earlier observations, that the indications of success are not reflected by the total citations N c , rather by the inequalities among citations from publications to publications. Specifically, we show that for highly successful authors, the yearly variations in the Gini index (g, giving the average inequality of citations for the publications) and the Kolkata index (k, giving the fraction of total citations received by the top (1 − k) fraction of publications; k = 0.80 corresponds to Pareto's 80/20 law) approach each other to g = k ≃ 0.82 , signaling a precursor for the arrival of (or departure from) the self-organized critical (SOC) state of his/her publication statistics. Analyzing the citation statistics (from Google Scholar) of thirty successful scientists throughout their recorded publication history, we find that the g and k for the highly successful among them (mostly Nobel laureates, highest rank Stanford cite-scorers, and a few others) reach and hover just above (and then) below that g = k ≃ 0.82 mark, while for others they remain below that mark. We also find that all the lower (than the SOC mark 0.82) values of k and g fit a linear relationship, k = 1 / 2 + c g , with c = 0.39 , as suggested by an approximate Landau-type expansion of the Lorenz function, and this also indicates k = g ≃ 0.82 for the (extrapolated) SOC precursor mark. [ABSTRACT FROM AUTHOR]

The long-term, large-scale behavior in a problem of stochastic nonlinear dynamics corresponding to the Abelian sandpile model is studied with the use of the quantum-field theory renormalization group approach. We prove the multiplicative renormalization of the model including an infinite number of coupling parameters, calculate an infinite number of renormalization constants, identify a plane of fixed points in the infinite dimensional space of coupling parameters, discuss their stability and critical scaling in the model, and formulate a simple law relating the asymptotic size of an avalanche to a model exponent quantifying the time-scale separation between the slow energy injection and fast avalanche relaxation processes. [ABSTRACT FROM AUTHOR]

In various biological systems, information from many noisy molecular receptors must be integrated into a collective response. A striking example is the thermal imaging organ of pit vipers. Single nerve fibers in the organ reliably respond to milli-Kelvin (mK) temperature increases, a thousand times more sensitive than their molecular sensors, thermo-transient receptor potential (TRP) ion channels. Here, we propose a mechanism for the integration of this molecular information. In our model, amplification arises due to proximity to a dynamical bifurcation, separating a regime with frequent and regular action potentials (APs), from a regime where APs are irregular and infrequent. Near the transition, AP frequency can have an extremely sharp dependence on temperature, naturally accounting for the thousandfold amplification. Furthermore, close to the bifurcation, most of the information about temperature available in the TRP channels’ kinetics can be read out from the times between consecutive APs even in the presence of readout noise. A key model prediction is that the coefficient of variation in the distribution of interspike times decreases with AP frequency, and quantitative comparison with experiments indeed suggests that nerve fibers of snakes are located very close to the bifurcation. While proximity to such bifurcation points typically requires fine-tuning of parameters, we propose that having feedback act from the order parameter (AP frequency) onto the control parameter robustly maintains the system in the vicinity of the bifurcation. This robustness suggests that similar feedback mechanisms might be found in other sensory systems which also need to detect tiny signals in a varying environment. [ABSTRACT FROM AUTHOR]

One way to resolve the actuation problem of metaphorical language change is to provide a statistical profile of metaphorical constructions and generative rules with antecedent conditions. Based on arguments from the view of language as complex systems and the dynamic view of metaphor, this paper argues that metaphorical language change qualifies as a Self-Organized Criticality state and the linguistic expressions of a metaphor can be profiled as a fractal with spatio-temporal correlations. Synchronously, these metaphorical expressions self-organize into a self-similar, scale-invariant fractal that follows a power-law distribution; temporally, long range interdependence constrains the self-organization process by the way of transformation rules that are intrinsic of a language system. This argument is verified in the paper with statistical analyses of twelve randomly selected Chinese verb metaphors in a large-scale diachronic corpus. [ABSTRACT FROM AUTHOR]

Abstract We investigate similarities and differences between natural and simulated slow earthquakes using nonlinear dynamical system tools. We use spatio‐temporal slip potency rate data derived from Global Navigation Satellite System (GNSS) position time series in the Cascadia subduction zone and numerical simulations intended to reproduce their pulse‐like behavior and scaling laws. We provide metrics to evaluate the accuracy of simulations in mimicking slow earthquake dynamics. We investigate the influence of spatio‐temporal coarsening as well as observational noise. Despite the use of many degrees of freedom, numerical simulations display a surprisingly low average dimension, akin to natural slow earthquakes. Instantaneous dynamical indices can reach large values (>10) instead, and differences persist between numerical simulations and natural observations. We propose to use the suggested metrics as an additional tool to narrow the divergence between slow earthquake observations and dynamical simulations.

Abstract Bedform evolution and preserved cross strata are known to respond to floods. However, it is unclear if autogenic dynamics mask the flood signal in bedform evolution and cross strata. To address this, we characterize the temporal structure of autogenic noise in steady‐state bedform evolution in a physical experiment. Results reveal the existence of bedform groups—quasi‐stable collections of bedforms—that migrate at a similar speed as bedforms. We find that bedform and bedform‐group turnover timescales are the key autogenic timescales of bed evolution that set the transition time‐periods between different noise regimes in bedform evolution. Results suggest that bedform‐group turnover timescale sets the lower limit for detecting flood signals in bedform evolution, and floods with duration shorter than bedform turnover timescale can be severely degraded in bedform evolution and cross strata. Our work provides a new framework for interrogating fluvial cross strata for reconstruction of past floods.

If managers assume a normal or near-normal distribution of Information Technology (IT) project cost overruns, as is common, and cost overruns can be shown to follow a power-law distribution, managers may be unwittingly exposing their organizations to extreme risk by severely underestimating the probability of large cost overruns. In this research, we collect and analyze a large sample comprised of 5,392 IT projects to empirically examine the probability distribution of IT project cost overruns. Further, we propose and examine a mechanism that can explain such a distribution. Our results reveal that IT projects are far riskier in terms of cost than normally assumed by decision makers and scholars. Specifically, we found that IT project cost overruns follow a power-law distribution in which there are a large number of projects with relatively small overruns and a fat tail that includes a smaller number of projects with extreme overruns. A possible generative mechanism for the identified power-law distribution is found in interdependencies among technological components in IT systems. We propose and demonstrate, through computer simulation, that a problem in a single technological component can lead to chain reactions in which other interdependent components are affected, causing substantial overruns. What the power law tells us is that extreme IT project cost overruns will occur and that the prevalence of these will be grossly underestimated if managers assume that overruns follow a normal or near-normal distribution. This underscores the importance of realistically assessing and mitigating the cost risk of new IT projects up front. [ABSTRACT FROM AUTHOR]

The long-term, large-scale behavior in a problem of stochastic nonlinear dynamics corresponding to the Abelian sandpile model is studied with the use of the quantum-field theory renormalization group approach. We prove the multiplicative renormalization of the model including an infinite number of coupling parameters, calculate an infinite number of renormalization constants, identify a plane of fixed points in the infinite dimensional space of coupling parameters, discuss their stability and critical scaling in the model, and formulate a simple law relating the asymptotic size of an avalanche to a model exponent quantifying the time-scale separation between the slow energy injection and fast avalanche relaxation processes.

Unmanned aerial vehicle (UAV) swarms have gained significant attention for their potential applications in various fields. The effective coordination and control of UAV swarms require the development of robust mathematical models that can capture their complex dynamics. The paper introduces mathematical models and relevant paradigms based on the design and analysis of self-organizing swarms of UAVs. The logical and technological construction of the model relies on the theorems developed by authors for obtaining full information exchange during the swarm quasi-random walk. The suggested rotor-router model interprets the discrete-time walk accompanied by the deterministic evolution of configurations of rotors randomly placed on the vertices of the swarm graph. The recommended optimal and fault-tolerant gossip/broadcast schemes support the resilience of swarm to internal failures and external attacks, and cryptographic protocols approve the security. The proposed cloud network topology serves as the implementation framework for the model, encompassing various connectivity options to ensure the expected behavior of the UAV swarms.

The article describes the theoretical basis for taking into account the complexity in solving problems of transport demand, the analysis of the concept of “self-organized criticality” is carried out. The problems of describing complex conduct in transport systems are solved, the prerequisites or causes of complex behavior are indicated, the traditional definitions of a “complex transport system” are formalized. Examples of some models demonstrating complex behavior in transport systems are given. Objective: formulation of research directions in the theory of transport systems, which should be developed with the help of conceptual and mathematical devices of individual sciences, such as complexity theory, nonlinear dynamics, to solve problems of transport demand, accompanied by flows of various events and elements in transport macrosystems, to take into account the complexity of problems in the theory of transport systems in modeling the dynamics of transport demand. Approaches used: development of mathematical models of nonlinear transport systems demonstrating complex behavior, including models with power-law distribution of system characteristics within the framework of self-organized criticality, as well as models demonstrating bifurcations. Methods and (or) methodological apparatus of research: methods of the theory of self-organized criticality and synergetics, allowing to take into account the properties of a complex system. The scientific novelty lies in the use of methods and approaches of the theory of self-organized criticality and complexity theory for a wide class of models of transport systems used in problems of transport demand and based on traffic flow models. Directions for further research, recommendations: it is required to analyze real statistics of the behavior of transport systems that correspond to various states of the transport system, including complex behavior, as well as the formulation and solution of problems of the theory of transport macrosystems.

Brain as a dynamic system responds to stimulations with specific patterns affected by its inherent ongoing dynamics. The patterns are manifested across different levels of organization—from spiking activity of neurons to collective oscillations in local field potential (LFP) and electroencephalogram (EEG). The multilevel and multifaceted response activities show patterns seemingly distinct and non-comparable from each other, but they should be coherently related because they are generated from the same underlying neural dynamic system. A coherent understanding of the interrelationships between different levels/aspects of activity features is important for understanding the complex brain functions. Here, based on analysis of data from human EEG, monkey LFP and neuronal spiking, we demonstrated that the brain response activities from different levels of neural system are highly coherent: the external stimulus simultaneously generated event-related potentials, event-related desynchronization, and variation in neuronal spiking activities that precisely match with each other in the temporal unfolding. Based on a biologically plausible but generic network of conductance-based integrate-and-fire excitatory and inhibitory neurons with dense connections, we showed that the multiple key features can be simultaneously produced at critical dynamical regimes supported by excitation–inhibition (E–I) balance. The elucidation of the inherent coherency of various neural response activities and demonstration of a simple dynamical neural circuit system having the ability to simultaneously produce multiple features suggest the plausibility of understanding high-level brain function and cognition from elementary and generic neuronal dynamics. [ABSTRACT FROM AUTHOR]

Self-organized criticality is a universal theory for dynamical systems that has recently been applied to the cardiovascular system. Precise methodological approaches are essential for understanding the dynamics of cardiovascular self-organized criticality. This study examines how the duration and quality of data recording affect the analysis of cardiovascular self-organized criticality, with a focus on the beat-by-beat heart rate variability time series obtained from seven healthy subjects in a standing position. Drawing a Zipf diagram, we evaluated the distribution of cardiovascular events of bradycardia and tachycardia. We identified tipping points for the distribution of both bradycardia and tachycardia events. By varying the recording durations (1, 2, 5, 10, 20, 30, and 40 min) and sampling frequencies (500, 250, and 100 Hz), we investigated their influence on the observed distributions. While shorter recordings can effectively capture cardiovascular events, they may underestimate the variables describing their distribution. Additionally, the tipping point of the Zipf distribution differs between bradycardia and tachycardia events. Comparisons of the distribution of bradycardia and tachycardia events should be conducted using long data recordings. Utilizing devices with lower sampling frequencies may compromise data fidelity. These insights contribute to refining experimental protocols and advancing our understanding of the complex dynamics underlying cardiovascular regulation.

Abstract The secular increase of load causes the evolving power system to operate near the critical margins. In such attitudes, the probability of cascading line outages, as the major reason for recent blackouts, is much more than expected. As a result, an innovative approach for optimal AC transmission expansion planning (TEP) to minimize the risk of cascading blackouts is presented in this paper. The proposed multi‐objective AC‐TEP problem minimizes the expected energy not supplied due to the total load shedding imposed on the system to mitigate the risk of cascading line outages along with the total investment and operational costs of the new transmission lines planned to be optimally added to the system. This probabilistic procedure is based on self‐organized criticality and considers the actual aspects of the power system such as reactive power and voltage concerns in the planning stage. The obtained Pareto front set provides the opportunity for the planners to choose the most preferred solution. The proposed method is implemented and verified in the IEEE 118‐bus test system and compared with two types of TEP approaches. The first one is a previously reported version of this work which was a simplified DC model of the problem and the second one is the conventional AC‐TEP model. The obtained results show the benefits of the proposed AC model on the mitigation of cascading line outages in a more realistic framework, considering the impact of reactive power and voltage stability concerns.

Here we contrast several carcinogenesis models. The somatic-mutation-theory posits mutations as main causes of malignancy. However, inconsistencies led to alternative explanations. For example, the tissue-organization-field-theory considers disrupted tissue-architecture as main cause. Both models can be reconciled using systems-biology-approaches, according to which tumors hover in states of self-organized criticality between order and chaos, are emergent results of multiple deviations and are subject to general laws of nature: inevitable variation(mutation) explainable by increased entropy(second-law-of-thermodynamics) or indeterminate decoherence upon measurement of superposed quantum systems(quantum mechanics), followed by Darwinian-selection. Genomic expression is regulated by epigenetics. Both systems cooperate. So cancer is neither just a mutational nor an epigenetic problem. Rather, epigenetics links environmental cues to endogenous genetics engendering a regulatory machinery that encompasses specific cancer-metabolic-networks. Interestingly, mutations occur at all levels of this machinery (oncogenes/tumor-suppressors, epigenetic-modifiers, structure-genes, metabolic-genes). Therefore, in most cases, DNA mutations may be the initial and crucial cancer-promoting triggers. [ABSTRACT FROM AUTHOR]

This paper is concerned with intriguing possibilities for non-conventional critical behavior that arise when a nearly critical strongly non-equilibrium system is subjected to chaotic or turbulent motion of the environment. We briefly explain the connection between the critical behavior theory and the quantum field theory that allows the application of the powerful methods of the latter to the study of stochastic systems. Then, we use the results of our recent research to illustrate several interesting effects of turbulent environment on the non-equilibrium critical behavior. Specifically, we couple the Kazantsev–Kraichnan "rapid-change" velocity ensemble that describes the environment to the three different stochastic models: the Kardar–Parisi–Zhang equation with time-independent random noise for randomly growing surface, the Hwa–Kardar model of a "running sandpile" and the generalized Pavlik model of non-linear diffusion with infinite number of coupling constants. Using field-theoretic renormalization group analysis, we show that the effect can be quite significant leading to the emergence of induced non-linearity or making the original anisotropic scaling appear only through certain "dimensional transmutation". [ABSTRACT FROM AUTHOR]

The determination of seismic risk in urban settlements has received increasing attention in the scientific community during the last decades since it allows to identify the most vulnerable portions of urban areas and therefore to plan appropriate strategies for seismic risk reduction. In order to accurately evaluate the seismic risk of urban settlements it should be necessary to estimate in detail the seismic vulnerability of all the existing buildings in the considered area. This task could be very cumbersome due to both the great number of information needed to accurately characterize each building and the huge related computational effort. Several simplified methods for the assessment of the seismic vulnerability of existing buildings have been therefore presented in the literature. In order to estimate the occurrence of damage in buildings due to possible seismic phenomena, the published studies usually refer to response spectra evaluated according to seismic events expected in the territory with assumed probabilities. In the present paper seismic events are instead simulated using a modified Olami–Feder–Christensen (OFC) model, within the framework of self-organized criticality. The proposed methodology takes into account some geological parameters in the evaluation of the seismic intensities perceived by each single building, extending the approach presented in a previous study of some of the authors. Here, a large territory in the Sicilian oriental coast, the metropolitan area of Catania, which includes several urbanized zones with different features, has been considered as a new case study. Applications of the procedure are presented first with reference to seismic sequences of variable intensity, whose occurrence is rather frequent in seismic territories, showing that the damage can be progressively accumulated in the buildings and may lead to their collapse even when the intensities of each single event are moderate. Moreover, statistically significant simulations of single major seismic events, equivalent to a given sequence in terms of produced damages on buildings, are also performed. The latter match well with a novel a-priori risk index, introduced with the aim of characterizing the seismic risk of each single municipality in the considered metropolitan area. The proposed procedure can be applied to any large urbanized territory and, allowing to identify the most vulnerable areas, can represent a useful tool to prioritize the allocation of funds. This could be a novelty for risk policies in many countries in which public subsidies are currently assigned on a case-by-case basis, taking into account only hazard and vulnerability. The use of an a-priori risk index in the allocation process will allow to take into due account the relevant role of exposure. [ABSTRACT FROM AUTHOR]

This paper uses a simulation to build new theory about complexity and phase change in processes that are supported by digital technologies. We know that digitized processes can drift (change incrementally over time). We simulate this phenomenon by incrementally adding and removing edges from a network that represents the process. The simulation demonstrates that incremental change can lead to a state of self-organized criticality. As the process approaches this state, further incremental change can precipitate nonlinear bursts in process complexity and significant changes in process structure. Digital technology can be designed and used to influence the likelihood and severity of these transformative phase changes. For example, the simulation predicts that systems with adaptive programming are prone to phase changes, while systems with deterministic programming are not. We use the simulation to generate a set of theoretical propositions about the effects of digitization that will be testable in empirical research. [ABSTRACT FROM AUTHOR]

Many different precursors are known, but not all of which are effective, i.e., giving enough time to take preventive measures and with a minimum number of false early warning signals. The study aims to select and study effective early warning measures from a set of measures directly related to critical slowing down as well as to the change in the structure of the reconstructed phase space in the neighborhood of the critical transition point of sand cellular automata. We obtained a dynamical series of the number of unstable nodes in automata with stochastic and deterministic vertex collapse rules, with different topological graph structure and probabilistic distribution law for pumping of automata. For these dynamical series we computed windowed early warning measures. We formulated the notion of an effective measure as the measure that has the smallest number of false signals and the longest early warning time among the set of early warning measures. We found that regardless of the rules, topological structure of graphs, and probabilistic distribution law for pumping of automata, the effective early warning measures are the embedding dimension, correlation dimension, and approximation entropy estimated using the false nearest neighbors algorithm. The variance has the smallest early warning time, and the largest Lyapunov exponent has the greatest number of false early warning signals. Autocorrelation at lag-1 and Welch’s estimate for the scaling exponent of power spectral density cannot be used as early warning measures for critical transitions in the automata. The efficiency definition we introduced can be used to search for and investigate new early warning measures. Embedding dimension, correlation dimension and approximation entropy can be used as effective real-time early warning measures for critical transitions in real-world systems isomorphic to sand cellular automata such as microblogging social network and stock exchange.

The discovery of power laws in conflict intensities has spurred numerous explanation attempts. Two different interpretations have persisted: the notion that power laws are spurious results of random processes and the opposing view that power-law distributions attest to endogenous dynamics linked to self-organized criticality (SOC). We substantiate the SOC forest-fire model for intrastate conflicts, conceptualizing conflict potential as social pressure, measured by horizontal inequality. This potential is triggered by infinitesimal events. Their occurrence depends on the interaction density between conflict actors, operationalized as the conjunction of state capacity and non-state governance. In a global analysis of 143 conflict dyads, we find that 40 conform to a power law and 33 to a stretched exponential distribution, the two outcomes predicted by the model. We find evidence that the forest-fire model is a plausible approximation of the dynamics of intrastate conflicts, accounting for both the conformity and the non-conformity to power laws. [ABSTRACT FROM AUTHOR]

In this study, self-organized criticality theory was applied to analyze the unscheduled downtime (USDT) characteristic of a complex manufacturing system. The long-range correlation of the USDT series was verified through the Hurst exponent value. Then, we developed a model to depict the relationship between the frequency and USDT duration with the extreme value theory. The parameters of the model were estimated with the maximum likelihood method and updated with Bayesian inference when new data was available. Finally, the effectiveness of the proposed method was validated by the USDT data of the bottleneck system from a semiconductor assembly and test factory. [ABSTRACT FROM AUTHOR]

The long time behaviour of solutions to generalized stochastic porous media equations on bounded intervals with zero Dirichlet boundary conditions is studied. We focus on a degenerate form of nonlinearity arising in self-organized criticality. Based on the so-called lower bound technique, the existence and uniqueness of an invariant measure is proved. [ABSTRACT FROM AUTHOR]

Background: This study aimed at answering the following research questions: (1) Does the self-reported level of sensory-processing sensitivity (SPS) correlate with complexity, or criticality features of the electroencephalogram (EEG)? (2) Are there significant EEG differences comparing individuals with high and low levels of SPS? Methods: One hundred fifteen participants were measured with 64-channel EEG during a task-free resting state. The data were analyzed using criticality theory tools (detrended fluctuation analysis, neuronal avalanche analysis) and complexity measures (sample entropy, Higuchi's fractal dimension). Correlations with the 'Highly Sensitive Person Scale' (HSPS-G) scores were determined. Then, the cohort's lowest and the highest 30% were contrasted as opposites. EEG features were compared between the two groups by applying a Wilcoxon signed-rank test. Results: During resting with eyes open, HSPS-G scores correlated significantly positively with the sample entropy and Higuchi's fractal dimension (Spearman's ρ = 0.22, p < 0.05). The highly sensitive group revealed higher sample entropy values (1.83 ± 0.10 vs. 1.77 ± 0.13, p = 0.031). The increased sample entropy in the highly sensitive group was most pronounced in the central, temporal, and parietal regions. Conclusion: For the first time, neurophysiological complexity features associated with SPS during a task-free resting state were demonstrated. Evidence is provided that neural processes differ between low- and highly-sensitive persons, whereby the latter displayed increased neural entropy. The findings support the central theoretical assumption of enhanced information processing and could be important for developing biomarkers for clinical diagnostics. [ABSTRACT FROM AUTHOR]

Cardiovascular self-organized criticality has recently been demonstrated. We studied a model of autonomic nervous system changes to better characterize heart rate variability self-organized criticality. The model included short and long-term autonomic changes associated with body position and physical training, respectively. Twelve professional soccer players took part in a 5-week training session divided into "Warm-up", "Intensive", and "Tapering" periods. A stand test was carried out at the beginning and end of each period. Heart rate variability was recorded beat by beat (Polar Team 2). Bradycardias, defined as successive heart rates with a decreasing value, were counted according to their length in number of heartbeat intervals. We checked whether bradycardias were distributed according to Zipf's law, a feature of self-organized criticality. Zipf's law draws a straight line when the rank of occurrence is plotted against the frequency of occurrence in a log–log graph. Bradycardias were distributed according to Zipf's law, regardless of body position or training. Bradycardias were much longer in the standing position than the supine position and Zipf's law was broken after a delay of four heartbeat intervals. Zipf's law could also be broken in some subjects with curved long bradycardia distributions by training. Zipf's law confirms the self-organized nature of heart rate variability and is strongly linked to autonomic standing adjustment. However, Zipf's law could be broken, the significance of which remains unclear. [ABSTRACT FROM AUTHOR]

We study generalizations of the forest fire model introduced in [4] and [10] by allowing the rates at which the trees grow to depend on their location, introducing long-range burning, as well as a continuous-space generalization of the model. We establish that in all the models in consideration the expected time required to reach a site at distance x from the origin is of order $(\!\log x)^{(\!\log 2)^{-1}+\delta}$ for any $\delta>0$. [ABSTRACT FROM AUTHOR]

The secular increase of load causes the evolving power system to operate near the critical margins. In such attitudes, the probability of cascading line outages, as the major reason for recent blackouts, is much more than expected. As a result, an innovative approach for optimal AC transmission expansion planning (TEP) to minimize the risk of cascading blackouts is presented in this paper. The proposed multi‐objective AC‐TEP problem minimizes the expected energy not supplied due to the total load shedding imposed on the system to mitigate the risk of cascading line outages along with the total investment and operational costs of the new transmission lines planned to be optimally added to the system. This probabilistic procedure is based on self‐organized criticality and considers the actual aspects of the power system such as reactive power and voltage concerns in the planning stage. The obtained Pareto front set provides the opportunity for the planners to choose the most preferred solution. The proposed method is implemented and verified in the IEEE 118‐bus test system and compared with two types of TEP approaches. The first one is a previously reported version of this work which was a simplified DC model of the problem and the second one is the conventional AC‐TEP model. The obtained results show the benefits of the proposed AC model on the mitigation of cascading line outages in a more realistic framework, considering the impact of reactive power and voltage stability concerns. An innovative approach for optimal AC transmission expansion planning (AC‐TEP) to minimize the risk of cascading blackouts is presented in this paper. The proposed multi‐objective AC‐TEP problem minimizes the expected energy not supplied due to the total load shedding imposed on the system to mitigate the risk of cascading line outages along with the total investment and operational costs of the new transmission lines planned to be optimally added to the system. This probabilistic procedure is based on self‐organized criticality and considers the actual aspects of the power system such as reactive power and voltage concerns in the planning stage. [ABSTRACT FROM AUTHOR]

The intermittency of fog occurrence (the switching between fog and no-fog) is a key stochastic feature that plays a role in its duration and the amount of moisture available. Here, fog intermittency is studied by using the visibility time series collected during the month of July 2022 on Sable Island, Canada. In addition to the visibility, time series of air relative humidity and turbulent kinetic energy, putative variables akin to the formation and breakup conditions of fog, respectively, are also analyzed in the same framework to establish links between fog intermittency and the underlying atmospheric variables. Intermittency in the time series is quantified with their binary telegraph approximations to isolate clustering behavior from amplitude variations. It is shown that relative humidity and turbulent kinetic energy bound many stochastic features of visibility, including its spectral exponent, clustering exponent, and the growth of its block entropy slope. Although not diagnostic, the visibility time series displays features consistent with Pomeau–Manneville Type-III intermittency in its quiescent phase duration PDF scaling ( − 3 / 2 ), power spectrum scaling ( − 1 / 2 ), and signal amplitude PDF scaling ( − 2 ). The binary fog time series exhibits properties of self-organized criticality in the relation between its power spectrum scaling and quiescent phase duration distribution. [ABSTRACT FROM AUTHOR]

The Olami, Feder and Christensen (OFC) spring-block model has proven to be a powerful tool for analyzing and comparing synthetic and real earthquakes. This work proposes the possible reproduction of Utsu's law for earthquakes in the OFC model. Based on our previous works, several simulations characterizing real seismic regions were performed. We located the maximum earthquake in these regions and applied Utsu's formulae to identify a possible aftershock area and made comparisons between synthetic and real earthquakes. The research compares several equations to calculate the aftershock area and proposes a new one with the available data. Subsequently, the team performed new simulations and chose a mainshock to analyze the behavior of the surrounding events, so as to identify whether they could be catalogued as aftershocks and relate them to the aftershock area previously determined using the formula proposed. Additionally, the spatial location of those events was considered in order to classify them as aftershocks. Finally, we plot the epicenters of the mainshock, and the possible aftershocks comprised in the calculated area resembling the original work of Utsu. Having analyzed the results, it is likely to say that Utsu's law is reproducible using a spring-block model with a self-organized criticality (SOC) model. [ABSTRACT FROM AUTHOR]

With the spring-block model proposed by Olami, Feder, and Christensen (OFC), we obtained a time series of synthetic earthquakes with different values of the conservation level ( β ), which measures the fraction of the energy that a relaxing block passes to its neighbors. The time series have multifractal characteristics, and we analyzed them with the Chhabra and Jensen method. We calculated the width, symmetry, and curvature parameters for each spectrum. As the value of conservation level increases, the spectra widen, the symmetric parameter increases, and the curvature around the maximum of the spectra decreases. In a long series of synthetic seismicity, we located earthquakes of the greatest magnitude and built overlapping windows before and after them. For the time series in each window, we performed multifractal analysis to obtain multifractal spectra. We also calculated the width, symmetry, and curvature around the maximum of the multifractal spectrum. We followed the evolution of these parameters before and after large earthquakes. We found that the multifractal spectra had greater widths, were less skewed to the left, and were very pointed around the maximum before rather than after large earthquakes. We studied and calculated the same parameters and found the same results in the analysis of the Southern California seismicity catalog. This suggests that there seems to be a process of preparation for a great earthquake and that its dynamics are different from the one that occurs after this mainshock based on the behavior of the parameters mentioned before. [ABSTRACT FROM AUTHOR]

Social inequalities are ubiquitous and evolve towards a universal limit. Herein, we extensively review the values of inequality measures, namely the Gini (g) index and the Kolkata (k) index, two standard measures of inequality used in the analysis of various social sectors through data analysis. The Kolkata index, denoted as k, indicates the proportion of the 'wealth' owned by (1 − k) fraction of the 'people'. Our findings suggest that both the Gini index and the Kolkata index tend to converge to similar values (around g = k ≈ 0.87 , starting from the point of perfect equality, where g = 0 and k = 0.5 ) as competition increases in different social institutions, such as markets, movies, elections, universities, prize winning, battle fields, sports (Olympics), etc., under conditions of unrestricted competition (no social welfare or support mechanism). In this review, we present the concept of a generalized form of Pareto's 80/20 law ( k = 0.80 ), where the coincidence of inequality indices is observed. The observation of this coincidence is consistent with the precursor values of the g and k indices for the self-organized critical (SOC) state in self-tuned physical systems such as sand piles. These results provide quantitative support for the view that interacting socioeconomic systems can be understood within the framework of SOC, which has been hypothesized for many years. These findings suggest that the SOC model can be extended to capture the dynamics of complex socioeconomic systems and help us better understand their behavior. [ABSTRACT FROM AUTHOR]

This study investigates accelerated physical–chemical processes in a complex adaptive surface-engineered system represented by a nano-multilayer TiAlCrSiYN/TiAlCrN PVD coating under the extreme tribological conditions of ultra-high-performance dry machining of hardened H 13 tool steel. These processes are similar to the different catalyzing phenomena. Experimental results of tool life vs. wear rate, SEM/TEM data of the worn surfaces, XPS and EDS data of tribo-films formed on the friction surfaces, and chip surface morphology are presented in this study. The corresponding relationships between self-organization, self-organized criticality, and various catalyzing phenomena were evaluated on the basis of the accrued data. A method of enhancing these processes through the variation of machining conditions is also outlined, which resulted in the improvement of coated tool life by 35%. [ABSTRACT FROM AUTHOR]

Mineralization in the Earth's crust can be regarded as a singular process resulting in large amounts of mass accumulation and element enrichment over short time or space scales. The elemental concentrations modeled by fractals and multifractals show self-similarity and scale-invariant properties. We take the view that fluid-pressure variations in response to earthquakes or fault rupture are primarily responsible for changes in solubility and trigger transient physical and chemical variations in ore-forming fluids that enhance the mineralization process. Based on this general concept, we investigated mineral precipitation processes driven by rapid fluid pressure reductions by coupling mineralization to a cellular automaton model to reveal the nonlinear mechanism of the orogenic gold mineralization process using simulation. In the model, fluid pressure can increase to the rock failure condition, which was set as lithostatic pressure at a depth of 10 km (270 MPa), due to either porosity reduction or dehydration reactions. Rapid drops in pressure resulting from fault rupture or local hydrofracture may induce repeated gold precipitation. The geochemical patterns generated by the model evolve from depletion to enrichment patterns, and from spatially random to spatially clustered structures quantified by multifractal models and geostatistics. Results show how metal elements self-organize to form high metal concentration patterns displaying self-similarity and scale-invariance. These transitions are attributed to the growth and coalescence of sub-networks with different fluid pressures up to the percolation threshold, resulting in a wide range of fluid pressure reductions and gold precipitation in the form of clusters. The results suggest that cyclic evolution of fluid pressure and its effects on gold precipitation systems can effectively mimic the repeated mineralization superposition process, and generate complex geochemical patterns characterized by a multifractal model. The nonlinear behavior exhibits scale-invariance and self-organized critical threshold, where mineral phase separations result from fluid pressure reductions associated with fault failure.

Mineralization in the Earth's crust can be regarded as a singular process resulting in large amounts of mass accumulation and element enrichment over short time or space scales. The elemental concentrations modeled by fractals and multifractals show self-similarity and scale-invariant properties. We take the view that fluid-pressure variations in response to earthquakes or fault rupture are primarily responsible for changes in solubility and trigger transient physical and chemical variations in ore-forming fluids that enhance the mineralization process. Based on this general concept, we investigated mineral precipitation processes driven by rapid fluid pressure reductions by coupling mineralization to a cellular automaton model to reveal the nonlinear mechanism of the orogenic gold mineralization process using simulation. In the model, fluid pressure can increase to the rock failure condition, which was set as lithostatic pressure at a depth of 10 km (270 MPa), due to either porosity reduction or dehydration reactions. Rapid drops in pressure resulting from fault rupture or local hydrofracture may induce repeated gold precipitation. The geochemical patterns generated by the model evolve from depletion to enrichment patterns, and from spatially random to spatially clustered structures quantified by multifractal models and geostatistics. Results show how metal elements self-organize to form high metal concentration patterns displaying self-similarity and scale-invariance. These transitions are attributed to the growth and coalescence of sub-networks with different fluid pressures up to the percolation threshold, resulting in a wide range of fluid pressure reductions and gold precipitation in the form of clusters. The results suggest that cyclic evolution of fluid pressure and its effects on gold precipitation systems can effectively mimic the repeated mineralization superposition process, and generate complex geochemical patterns characterized by a multifractal model. The nonlinear behavior exhibits scale-invariance and self-organized critical threshold, where mineral phase separations result from fluid pressure reductions associated with fault failure.

Numerous studies suggest that biological neuronal networks self-organize toward a critical state with stable recruitment dynamics. Individual neurons would then statistically activate exactly one further neuron during activity cascades termed neuronal avalanches. Yet, it is unclear if and how this can be reconciled with the explosive recruitment dynamics within neocortical minicolumns in vivo and within neuronal clusters in vitro, which indicates that neurons form supercritical local circuits. Theoretical studies propose that modular networks with a mix of regionally subcritical and supercritical dynamics would create apparently critical dynamics, resolving this inconsistency. Here, we provide experimental support by manipulating the structural self-organization process of networks of cultured rat cortical neurons (either sex). Consistent with the prediction, we show that increasing clustering in neuronal networks developing in vitro strongly correlates with avalanche size distributions transitioning from supercritical to subcritical activity dynamics. Avalanche size distributions approximated a power law in moderately clustered networks, indicating overall critical recruitment. We propose that activity-dependent selforganization can tune inherently supercritical networks toward mesoscale criticality by creating a modular structure in neuronal networks. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of self-organized criticality (SOC) to research on, and debates about, preventing the occurrence of human-mediated failures in aviation security. It aims to persuade readers (policymakers, security professionals, and academics) from different fields that this is a good topic for further research and one that is worth pursuing. This paper views aviation security as a complex system that produces risky behaviors because of chance interactions between many forms of human error. By rethinking the Bak-Tang-Wiesenfeld (BTW) sandpile model, the paper uses the explanatory nature of SOC to explore whether criticality may be viewed as a crucial component of aviation security. It elaborates on the effects of arbitrary interactions between various types of error using the BTW sandpile model’s illustrative characteristics. The paper concludes by speculating that SOC can be used to generate new ways of thinking about interventions to mitigate failure and that academics and security professionals from various fields could collaborate to develop fresh ideas and integrated concepts to further our understanding of a crucial subject for the aviation industry. [ABSTRACT FROM AUTHOR]

Charged particles exhibit self‐organized behaviour during the discharge state transitions inside an electrostatic cavity, along with the energy redistribution in different coherent modes with progressive anode potential. Multiple sheath formations around the spherical metallic ball anode placed inside the cavity transit to intensely glowing multiple anode spot formations (fireballs) on the anode surface with progressive anode potential. The intensely glowing anode spots reorganize over the anode surface and multiply quickly on increasing the anode potential. The nonlinear analysis of the fluctuating discharge current reveals the presence of self‐organized criticality (SOC) during different states of discharge along with a strong memory effect within (10–104) time lags in every state of discharge. The multiple anode spots exhibit long‐range correlation in the absence of a magnetic field, indicating SOC behaviour. The present study investigates energy distribution among the coherent modes of discharge through the application of the Empirical Mode Decomposition technique. The analysis indicates that the introduction of a magnetic field results in the interchange of coherent modes carrying most of the energy during multiple spot formations. The present experimental configuration produces stable multiple coherent modes along with the evolution of anodic sheath layers in the absence and then in the presence of the magnetic field. Relating SOC with such discharge transitions may help to understand the relation between complex structure formations in plasma with nonlinear aspects of glow discharge, which is of vital importance in the investigation of complexities in space plasma. [ABSTRACT FROM AUTHOR]

We revisit the sandpile model and examine the effect of introducing site-dependent thresholds that increase over time based on the generated avalanche size. This is inspired by the simplest means of introducing stability into a self-organized system: the locations of collapse are repaired and reinforced. Statistically, for the case of finite driving times, we observe that the site-dependent reinforcements decrease the occurrence of very large avalanches, leading to an effective global stabilization. Interestingly, however, long simulation runs indicate that the system will persist in a state of self-organized criticality (SOC), recovering the power-law distributions with a different exponent as the original sandpile. These results suggest that tipping the heavy-tailed power-laws into more equitable and normal statistics may require unrealistic scales of intervention for real-world systems, and that, in the long run, SOC mechanisms still emerge. This may help explain the robustness of power-law statistics for many complex systems.