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Modern information and telecommunication, transportation and logistic, economic and financial systems are represented by complex networks exhibiting traffic flows with spatio-temporal long-term persistence. Conventional queuing theory relies largely upon stationary models where traffic flows are assumed independent and are typically characterized by the first two moments of inter-arrival and service time distributions, leading to drastic underestimations of traffic flow delays. Here we extend a recent superstatistical approach focusing on traffic models with variable arrival rates by accounting for interdependent activity patterns on multiple network nodes. We suggest an analytical correction to the conventional stationary queue model given by the Kingman's formula based on the calculation of aggregated inter-arrival times variability from the variabilities of arrival rates at individual nodes and cross-correlations between them. We confirm our analytical approximations by comparing with computer simulation results and large-batch empirical traffic analysis from the backbone of a major academic network. We believe that our results, in combination with recent data on the effects of long-term temporal persistence in network traffic flow, are applicable to various complex networks not limited to information and telecommunication, transportation, and logistics but also to economics and finance, rainfall and river flow dynamics, water accumulation in reservoirs, and many other research domains exhibiting spatio-temporal interdependence patterns. • Abrupt bursts in traffic dynamics are governed by simultaneous multiple nodes access. • Inter-arrival times distribution is derived from cross-correlations between nodes. • Analytical correction for an approximate evaluation of waiting times is proposed. • Results are supported by computer simulations and large-batch traffic data analysis. • Corrections are applicable to complex networks governed by autonomous agents dynamics. [ABSTRACT FROM AUTHOR]

Anthropogenic interventions lead to various direct and indirect impacts on natural ecosystems that are often hindered by natural long-term variability, and thus their detection and evaluation remain challenging. Ecological systems are strongly affected by climate variations that typically exhibit long-term correlations capable of imitating or hindering external trends in finite-time observations, thus complicating their detection and correct attribution to either anthropogenic interventions or natural variability. Here we focus on the quantitative assessment of the alterations in the tree-ring width (TRW) of four tree species in response to the changes in the soil water regime following a drainage experiment in a dwarf-shrub type peatland forest. We consider the long-term effects of the intervention, focusing on two characteristic quantities: the durations of clusters when significant discrepancies with relevant controls could be observed in every single consecutive year, and relative trends in the data reflecting long-term, gradual changes in the ecosystem. By extrapolating pre-drainage TRW dynamics and adjusting for recent climate variations using a multivariate model, we simulate surrogate data series that act as additional controls for the post-drainage time period. By comparing the long-term dynamics of the observational TRW data series against both natural and surrogate controls over several decades following the drainage experiment, we evaluate long-term alterations and gradual trends in the tree growth dynamics and reassess the statistical significance of these effects, taking into account long-term correlations in the natural TRW variations. Our results also indicate pronounced alterations in the drought stress response characterized by significant negative trends in the tree growth dynamics following the 2010 heatwave and associated flash drought in the drained area, while no similar effect could be observed in the undrained area, indicating that the increased productivity of the forest ecosystem following the drainage likely comes at the cost of its reduced drought stress resilience. • Long-term correlations in tree-ring and climate records can imitate or hinder trends. • Extrapolated pre-experimental tree-ring data series can be used as surrogate control. • Significance of multi-year discrepancies can be re-evaluated from cluster durations. • Drainage increases productivity of forests at the cost of drought stress resilience. [ABSTRACT FROM AUTHOR]

We consider waiting times in queuing systems with variable arrival rates in the presence of long-term correlations and periodic trends. We focus on a simplified model where the contributions of periodic and stochastic components could be analyzed separately, leading to queue lengths exhibiting periodic and stochastic resetting, respectively, with their effects summarized additively. We provide an approximate analytical solution that is based on the universal scaling of return interval statistics between level crossing events in long-term correlated data series. The accuracy of our results is validated explicitly by computer modeling, using both simulated data series and empirical traffic data from a network cluster hosting the World Cup '98 web services characterized by extremely variable traffic intensity. We believe that the proposed approach could be useful to characterize the impact of long-term correlations and periodic trends in various complex systems, with prominent examples ranging from information, communication, logistic, transportation networks to climate, hydrological, as well as other natural, social and engineering systems. • Non-stationary traffic is represented by periodic and long-term correlated components. • Suggested traffic decomposition leads to consecutive queues with additive waiting times. • Long-term correlations lead to prolonged arrivals accumulation in the queuing system. • Extra waiting times due to long-term correlations depend on level crossing statistics. • Similar behavior can be observed in complex systems exhibiting long-term correlations. [ABSTRACT FROM AUTHOR]

Rapid advancement in computer vision technologies provides increasing opportunities for the quantitative characterization of animal behavior, although reduction of their analysis to several scalar metrics appears a common limitation for the representation of complex behavioral patterns. Here we suggest an alternative approach to the quantitative assessment of animal behavioral patterns by parameterization of a generalized scalable model based on fractional Brownian motion using detrended fluctuation analysis of the observational movement trajectories and validate it using novel tank test data. In a zebrafish model representative movement patterns are characterized by two asymptotic regimes, with persistent increments at small scales and antipersistent increments at large scales. A single crossover between these asymptotic regimes that appears a single free parameter of the animal movement model acts as a complementary behavioral indicator leading to a more explicit characterization of both stimulative and sedative effects. We show explicitly that the model can be also used for a robust estimation of interpretable scalar metrics commonly used in behavioral analysis leading to the emphasized differences between experimental groups. We believe that this approach, due to its universality, robustness and clear physical interpretation, is a perspective tool for the analysis of animal behavior complexity under various experimental and natural conditions. [Display omitted] • Animal movements in confined space are represented by two asymptotic scaling regimes. • One-parameter model based on fractional Brownian motion represents animal movements. • Crossover position between scaling regimes reflects stimulative and sedative effects. • The model based approach leads to a more robust estimates of animal movement metrics. [ABSTRACT FROM AUTHOR]

We suggest an effective approach for the semi-automated segmentation of biomedical images according to their patchiness based on local edge density estimation. Our approach does not require any preliminary learning or tuning, although a couple of free parameters directly controllable by the end user adjust the analysis resolution and sensitivity, respectively. We show explicitly that the local edge density exhibits excellent correlations with the cell monolayer density obtained by manual domain-expert based assessment, characterized by correlation coefficients ρ > 0. 97. Our results indicate that the proposed algorithm is capable of an efficient segmentation and quantification of patchy areas in various biomedical microscopic images. In particular, the proposed algorithm achieves 95 to 99% median accuracy in the segmentation of image areas covered by the cell monolayer in an in vitro scratch assay. Moreover, the proposed algorithm effectively distinguishes between the native and regenerated tissue fragments in microscopic images of histological sections, indicated by nearly three-fold discrepancy between the local edge densities in the corresponding image areas. We believe that the local edge density estimate could be further applicable as a surrogate image channel characterizing its patchiness either as a substitute or as a complementary source to the conventional cell- or tissue-specific fluorescent staining, in some cases either avoiding or limiting the use of complex experimental protocols. We implemented a simple open-source software tool with for on-the-fly visualization allowing for a straightforward feedback by a domain expert without any specific expertise in image analysis techniques. Our tool is freely available online at https://gitlab.com/digiratory/biomedimaging/bcanalyzer. [Display omitted] • Patchy areas in biomedical images can be detected from local edge densities. • Edge densities and cell monolayer densities exhibit strong correlations ρ > 0.97. • Segmentation of scratch assay monolayer with 95 to 99% accuracy is achieved. • Native and regenerated tissues in histological images are effectively distinguished. • Surrogate edge density channel could substitute tissue-specific fluorescent staining. [ABSTRACT FROM AUTHOR]

Abstract: We study the performance of multifractal detrended fluctuation analysis (MF-DFA) applied to long-term correlated and multifractal data records in the presence of additive white noise, short-term memory and periodicities. Such additions and disturbances that can be typically found in the observational records of various complex systems ranging from climate dynamics to physiology, network traffic, and finance. In monofractal records, we find that (i) additive white noise hardly results in spurious multifractality, but causes underestimated generalized Hurst exponents for all values; (ii) short-range correlations lead to pronounced crossovers in the generalized fluctuation functions at positions that decrease with increasing moment , thus causing significantly overestimated for small and spurious multifractality; (iii) periodicities like seasonal trends (with standard deviations comparable with the one of the studied process) result in spurious “reversed” multifractality where increases with increasing (except for very short time windows). We also show that in multifractal cascades moderate additions of noise, short-range memory, or periodic trends cause flawed results for with , while with remains nearly unchanged. [Copyright &y& Elsevier]

Abstract: We study the predictability of extreme events in records with linear and nonlinear long-range memory in the presence of additive white noise using two different approaches: (i) the precursory pattern recognition technique (PRT) that exploits solely the information about short-term precursors, and (ii) the return interval approach (RIA) that exploits long-range memory incorporated in the elapsed time after the last extreme event. We find that the PRT always performs better when only linear memory is present. In the presence of nonlinear memory, both methods demonstrate comparable efficiency in the absence of white noise. When additional white noise is present in the record (which is the case in most observational records), the efficiency of the PRT decreases monotonously with increasing noise level. In contrast, the RIA shows an abrupt transition between a phase of low level noise where the prediction is as good as in the absence of noise, and a phase of high level noise where the prediction becomes poor. In the phase of low and intermediate noise the RIA predicts considerably better than the PRT, which explains our recent findings in physiological and financial records. [Copyright &y& Elsevier]

[Display omitted] • The proposed gradient-based transformation improves online detection of bursts. • The gradient based method is robust against variations of particular signal shapes. • It outperforms Pan-Tompkins algorithm in the exact positioning of single ECG cycles. • The gradient based method is effective for the electrophoretic gel image analysis. Despite recent success in advanced signal analysis technologies, simple and universal methods are still of interest in a variety of applications. Wearable devices including biomedical monitoring and diagnostic systems suitable for long-term operation are prominent examples, where simple online signal analysis and early event detection algorithms are required. Here we suggest a simple and universal approach to the online detection of events represented by abrupt bursts in long-term observational data series. We show that simple gradient-based transformations obtained as a product of the signal and its derivative lead to the improved accuracy of the online detection of any significant bursts in the observational data series irrespective of their particular shapes. We provide explicit analytical expressions characterizing the performance of the suggested approach in comparison with the conventional solutions optimized for particular theoretical scenarios and widely utilized in various signal analysis applications. Moreover, we estimate the accuracy of the gradient-based approach in the exact positioning of single ECG cycles, where it outperforms the conventional Pan-Tompkins algorithm in its original formulation, while exhibiting comparable detection effectiveness. Finally, we show that our approach is also applicable to the comparative analysis of lanes in electrophoretic gel images widely used in life sciences and molecular diagnostics like restriction fragment length polymorphism (RFLP) and variable number tandem repeats (VNTR) methods. A simple software tool for the semi-automated electrophoretic gel image analysis based on the proposed gradient based methodology is freely available online at https://bitbucket.org/rogex/sds-page-image-analyzer/downloads/. [ABSTRACT FROM AUTHOR]

Recently we have suggested a universal superstatistical model of user access patterns and aggregated network traffic. The model takes into account the irregular character of end user access patterns on the web via the non-exponential distributions of the local access rates, but neglects the long-term correlations between these rates. While the model is accurate for quasi-stationary traffic records, its performance under highly variable and especially non-stationary access dynamics remains questionable. In this paper, using an example of the traffic patterns from a highly loaded network cluster hosting the website of the 1998 FIFA World Cup, we suggest a generalization of the previously suggested superstatistical model by introducing long-term correlations between access rates. Using queueing system simulations, we show explicitly that this generalization is essential for modeling network nodes with highly non-stationary access patterns, where neglecting long-term correlations leads to the underestimation of the empirical average sojourn time by several decades under high throughput utilization. [ABSTRACT FROM AUTHOR]

Chaotic return maps are widely used to model various dynamical systems such as charged particle movement, laser beam dynamic, celestial body orbiting and many others. Return maps are commonly obtained by discretization of continuous equations using the Euler–Cromer operator with the only motivation that it is the simplest symplectic operator. Recent progress in geometric integration raised considerable interest to symmetric operators due to their ability to preserve some geometric properties of continuous flows this way yielding better agreement between discrete and continuous dynamical systems. Here we compare symmetric and asymmetric discretization approaches applied to several examples of Hamiltonian systems. In particular, we suggest symmetric modifications of Chirikov and Hénon maps and show explicitly that the implied symmetric integration procedure yields reflectional symmetry in the phase space. For verification, we show that a smooth even perturbation function used instead of a discontinuous delta pulse provides asymptotically similar results. Numerical experiments using several statistical methods show that symmetric and asymmetric maps, while yielding similar asymptotic behavior, often exhibit considerably different statistical properties for intermediate regimes providing smoother transitions that are more reminiscent to those observed in various natural phenomena. We believe that the proposed approach may be useful for modeling empirical systems by preserving their keynote physical properties. [ABSTRACT FROM AUTHOR]

Abstract Quantitative assessment of cooperativity effects is essential for a better understanding of the interactions between system components that is an important step on the way from black-box to structural models of various complex dynamical systems. In this paper, we consider five widely used mutual information metrics and test their performance using simulated stochastic data series with introduced phase- or amplitude randomization as well as data series generated by chaotic maps. We show the performance of all studied methods in both stationary mode and during phase transitions, indicating specific coupling patterns they can reveal from the system dynamics, as well as certain properties they appear invariant to. Finally, we demonstrate how a combination of several metrics can be used for a more detailed analysis of dynamical systems exhibiting characteristic phase transitions, including examples of both simulated chaotic maps and observational data series from physiological and geophysical complex systems. Highlights • Cooperativity metrics in complex systems are affected by both cross- and autocorrelations. • Time delay stability is unaffected by additive while affected by multiplicative white noise. • Coherence is invariant to short-term, while cross-conditional entropy to long-term correlations. • Combination of several metrics provide a better characterization of phase transitions. • Phase transition in geomagnetic network reveals a characteristic pattern during a solar flare. [ABSTRACT FROM AUTHOR]