Testing for multifractality of Islamic stock markets

Science.gov (United States)

Saâdaoui, Foued

2018-04-01

Studying the power-law scaling of financial time series is a promising area of econophysics, which has often contributed to the understanding of the intricate features of the global markets. In this article, we examine the multifractality of some financial processes and the underlying formation mechanisms in the context of Islamic equity markets. The well-known Multifractal Detrended Fluctuation Analysis (MF-DFA) is used to investigate the self-similar properties of two Dow Jones Islamic Market Indexes (DJIM). The results prove that both indexes exhibit multifractal properties. By discussing the sources of multifractality, we find that they are related to the occurrence of extreme events, long-range dependency of autocorrelations and fat-tailed distribution of returns. These results have several important implications for analysts and decision makers in modeling the dynamics of Islamic markets, thus recommending efficient asset allocation plans to investors dealing with Islamic equity markets.

Analysis of heat release dynamics in an internal combustion engine using multifractals and wavelets

International Nuclear Information System (INIS)

Sen, A.K.; Litak, G.; Finney, C.E.A.; Daw, C.S.; Wagner, R.M.

2010-01-01

In this paper we analyze data from previously reported experimental measurements of cycle-to-cycle combustion variations in a lean-fueled, multi-cylinder spark-ignition (SI) engine. We characterize the changes in the observed combustion dynamics with as-fed fuel-air ratio using conventional histograms and statistical moments, and we further characterize the shifts in combustion complexity in terms of multifractals and wavelet decomposition. Changes in the conventional statistics and multifractal structure indicate trends with fuel-air ratio that parallel earlier reported observations. Wavelet decompositions reveal persistent, non-stochastic oscillation modes at higher fuel-air ratios that were not obvious in previous analyses. Recognition of these long-time-scale, non-stochastic oscillations is expected to be useful for improving modelling and control of engine combustion variations and multi-cylinder balancing.

Multifractal Cross Wavelet Analysis

Science.gov (United States)

Jiang, Zhi-Qiang; Gao, Xing-Lu; Zhou, Wei-Xing; Stanley, H. Eugene

Complex systems are composed of mutually interacting components and the output values of these components usually exhibit long-range cross-correlations. Using wavelet analysis, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call multifractal cross wavelet analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, we find the empirical joint multifractality of MFXWT to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indices, and in pairs of index returns and volatilities we find an intriguing joint multifractal behavior. The tests on surrogate series also reveal that the cross correlation behavior, particularly the cross correlation with zero lag, is the main origin of cross multifractality.

Determination of key parameters of vector multifractal vector fields

Science.gov (United States)

Schertzer, D. J. M.; Tchiguirinskaia, I.

2017-12-01

For too long time, multifractal analyses and simulations have been restricted to scalar-valued fields (Schertzer and Tchiguirinskaia, 2017a,b). For instance, the wind velocity multifractality has been mostly analysed in terms of scalar structure functions and with the scalar energy flux. This restriction has had the unfortunate consequences that multifractals were applicable to their full extent in geophysics, whereas it has inspired them. Indeed a key question in geophysics is the complexity of the interactions between various fields or they components. Nevertheless, sophisticated methods have been developed to determine the key parameters of scalar valued fields. In this communication, we first present the vector extensions of the universal multifractal analysis techniques to multifractals whose generator belong to a Levy-Clifford algebra (Schertzer and Tchiguirinskaia, 2015). We point out further extensions noting the increased complexity. For instance, the (scalar) index of multifractality becomes a matrice. Schertzer, D. and Tchiguirinskaia, I. (2015) `Multifractal vector fields and stochastic Clifford algebra', Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(12), p. 123127. doi: 10.1063/1.4937364. Schertzer, D. and Tchiguirinskaia, I. (2017) `An Introduction to Multifractals and Scale Symmetry Groups', in Ghanbarian, B. and Hunt, A. (eds) Fractals: Concepts and Applications in Geosciences. CRC Press, p. (in press). Schertzer, D. and Tchiguirinskaia, I. (2017b) `Pandora Box of Multifractals: Barely Open ?', in Tsonis, A. A. (ed.) 30 Years of Nonlinear Dynamics in Geophysics. Berlin: Springer, p. (in press).

Thermodynamic and multifractal formalism and the Bowen-series map

International Nuclear Information System (INIS)

Rudolph, O.

1995-01-01

In the theory of quantum chaos one studies the semiclassical behaviour of quantum mechanical systems whose corresponding classical counterparts exhibit chaos. The geodesic motion of a free classical particle on closed Riemann surfaces with constant negative curvature is strongly chaotic. Selberg's theory relates the classical and the quantum mechanical systems. These systems are sometimes considered as model systems in the theory of quantum chaos since they are well understood from a mathematical point of view. In this work we study the multifractal formalism for the geodesic flow on surfaces with constant negative curvature. The multifractal analysis of measures has been developed in order to characterize the scaling behaviour of measures on attractors of classical chaotic dynamical systems globally. In order to relate the multifractal formalism with quantities usually considered in the study of the geodesic flow on Riemann surfaces with constant negative curvature, it is necessary to establish the assertions of the multifractal formalism in a mathematically rigorous way. This is achieved with the help of the thermodynamic formalism for hyperbolic dynamical systems developed by Ruelle, Bowen and others. (orig.)

Multifractal analysis of Moroccan family business stock returns

Science.gov (United States)

Lahmiri, Salim

2017-11-01

In this paper, long-range temporal correlations at different scales in Moroccan family business stock returns are investigated. For comparison purpose, presence of multifractality is also investigated in Casablanca Stock Exchange (CSE) major indices: MASI which is the all shares index and MADEX which is the index of most liquid shares. It is found that return series of both family business companies and major stock market indices show strong evidence of multifractality. In particular, empirical results reveal that short (long) fluctuations in family business stock returns are less (more) persistent (anti-persistent) than short fluctuations in market indices. In addition, both serial correlation and distribution characteristics significantly influence the strength of the multifractal spectrums of CSE and family business stocks returns. Furthermore, results from multifractal spectrum analysis suggest that family business stocks are less risky. Thus, such differences in price dynamics could be exploited by investors and forecasters in active portfolio management.

Clustering Multiple Sclerosis Subgroups with Multifractal Methods and Self-Organizing Map Algorithm

Science.gov (United States)

Karaca, Yeliz; Cattani, Carlo

Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.

Multifractal Desynchronization of the Cardiac Excitable Cell Network During Atrial Fibrillation. I. Multifractal Analysis of Clinical Data

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Guillaume Attuel

2018-03-01

Full Text Available Atrial fibrillation (AF is a cardiac arrhythmia characterized by rapid and irregular atrial electrical activity with a high clinical impact on stroke incidence. Best available therapeutic strategies combine pharmacological and surgical means. But when successful, they do not always prevent long-term relapses. Initial success becomes all the more tricky to achieve as the arrhythmia maintains itself and the pathology evolves into sustained or chronic AF. This raises the open crucial issue of deciphering the mechanisms that govern the onset of AF as well as its perpetuation. In this study, we develop a wavelet-based multi-scale strategy to analyze the electrical activity of human hearts recorded by catheter electrodes, positioned in the coronary sinus (CS, during episodes of AF. We compute the so-called multifractal spectra using two variants of the wavelet transform modulus maxima method, the moment (partition function method and the magnitude cumulant method. Application of these methods to long time series recorded in a patient with chronic AF provides quantitative evidence of the multifractal intermittent nature of the electric energy of passing cardiac impulses at low frequencies, i.e., for times (≳0.5 s longer than the mean interbeat (≃ 10−1 s. We also report the results of a two-point magnitude correlation analysis which infers the absence of a multiplicative time-scale structure underlying multifractal scaling. The electric energy dynamics looks like a “multifractal white noise” with quadratic (log-normal multifractal spectra. These observations challenge concepts of functional reentrant circuits in mechanistic theories of AF, still leaving open the role of the autonomic nervous system (ANS. A transition is indeed observed in the computed multifractal spectra which group according to two distinct areas, consistently with the anatomical substrate binding to the CS, namely the left atrial posterior wall, and the ligament of Marshall

Multifractal Desynchronization of the Cardiac Excitable Cell Network During Atrial Fibrillation. I. Multifractal Analysis of Clinical Data

Science.gov (United States)

Attuel, Guillaume; Gerasimova-Chechkina, Evgeniya; Argoul, Francoise; Yahia, Hussein; Arneodo, Alain

2018-01-01

Atrial fibrillation (AF) is a cardiac arrhythmia characterized by rapid and irregular atrial electrical activity with a high clinical impact on stroke incidence. Best available therapeutic strategies combine pharmacological and surgical means. But when successful, they do not always prevent long-term relapses. Initial success becomes all the more tricky to achieve as the arrhythmia maintains itself and the pathology evolves into sustained or chronic AF. This raises the open crucial issue of deciphering the mechanisms that govern the onset of AF as well as its perpetuation. In this study, we develop a wavelet-based multi-scale strategy to analyze the electrical activity of human hearts recorded by catheter electrodes, positioned in the coronary sinus (CS), during episodes of AF. We compute the so-called multifractal spectra using two variants of the wavelet transform modulus maxima method, the moment (partition function) method and the magnitude cumulant method. Application of these methods to long time series recorded in a patient with chronic AF provides quantitative evidence of the multifractal intermittent nature of the electric energy of passing cardiac impulses at low frequencies, i.e., for times (≳0.5 s) longer than the mean interbeat (≃ 10−1 s). We also report the results of a two-point magnitude correlation analysis which infers the absence of a multiplicative time-scale structure underlying multifractal scaling. The electric energy dynamics looks like a “multifractal white noise” with quadratic (log-normal) multifractal spectra. These observations challenge concepts of functional reentrant circuits in mechanistic theories of AF, still leaving open the role of the autonomic nervous system (ANS). A transition is indeed observed in the computed multifractal spectra which group according to two distinct areas, consistently with the anatomical substrate binding to the CS, namely the left atrial posterior wall, and the ligament of Marshall which is

Multi-scale interactions of geological processes during mineralization: cascade dynamics model and multifractal simulation

Directory of Open Access Journals (Sweden)

L. Yao

2011-03-01

Full Text Available Relations between mineralization and certain geological processes are established mostly by geologist's knowledge of field observations. However, these relations are descriptive and a quantitative model of how certain geological processes strengthen or hinder mineralization is not clear, that is to say, the mechanism of the interactions between mineralization and the geological framework has not been thoroughly studied. The dynamics behind these interactions are key in the understanding of fractal or multifractal formations caused by mineralization, among which singularities arise due to anomalous concentration of metals in narrow space. From a statistical point of view, we think that cascade dynamics play an important role in mineralization and studying them can reveal the nature of the various interactions throughout the process. We have constructed a multiplicative cascade model to simulate these dynamics. The probabilities of mineral deposit occurrences are used to represent direct results of mineralization. Multifractal simulation of probabilities of mineral potential based on our model is exemplified by a case study dealing with hydrothermal gold deposits in southern Nova Scotia, Canada. The extent of the impacts of certain geological processes on gold mineralization is related to the scale of the cascade process, especially to the maximum cascade division number n_max. Our research helps to understand how the singularity occurs during mineralization, which remains unanswered up to now, and the simulation may provide a more accurate distribution of mineral deposit occurrences that can be used to improve the results of the weights of evidence model in mapping mineral potential.

Nonlinear temperature effects on multifractal complexity of metabolic rate of mice

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Fabio A. Labra

2016-10-01

Full Text Available Complex physiological dynamics have been argued to be a signature of healthy physiological function. Here we test whether the complexity of metabolic rate fluctuations in small endotherms decreases with lower environmental temperatures. To do so, we examine the multifractal temporal scaling properties of the rate of change in oxygen consumption r(VO2, in the laboratory mouse Mus musculus, assessing their long range correlation properties across seven different environmental temperatures, ranging from 0 °C to 30 °C. To do so, we applied multifractal detrended fluctuation analysis (MF-DFA, finding that r(VO2 fluctuations show two scaling regimes. For small time scales below the crossover time (approximately 102 s, either monofractal or weak multifractal dynamics are observed depending on whether Ta  15 °C respectively. For larger time scales, r(VO2 fluctuations are characterized by an asymptotic scaling exponent that indicates multifractal anti-persistent or uncorrelated dynamics. For both scaling regimes, a generalization of the multiplicative cascade model provides very good fits for the Renyi exponents τ(q, showing that the infinite number of exponents h(q can be described by only two independent parameters, a and b. We also show that the long-range correlation structure of r(VO2 time series differs from randomly shuffled series, and may not be explained as an artifact of stochastic sampling of a linear frequency spectrum. These results show that metabolic rate dynamics in a well studied micro-endotherm are consistent with a highly non-linear feedback control system.

Intermittency and multifractional Brownian character of geomagnetic time series

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G. Consolini

2013-07-01

Full Text Available The Earth's magnetosphere exhibits a complex behavior in response to the solar wind conditions. This behavior, which is described in terms of mutifractional Brownian motions, could be the consequence of the occurrence of dynamical phase transitions. On the other hand, it has been shown that the dynamics of the geomagnetic signals is also characterized by intermittency at the smallest temporal scales. Here, we focus on the existence of a possible relationship in the geomagnetic time series between the multifractional Brownian motion character and the occurrence of intermittency. In detail, we investigate the multifractional nature of two long time series of the horizontal intensity of the Earth's magnetic field as measured at L'Aquila Geomagnetic Observatory during two years (2001 and 2008, which correspond to different conditions of solar activity. We propose a possible double origin of the intermittent character of the small-scale magnetic field fluctuations, which is related to both the multifractional nature of the geomagnetic field and the intermittent character of the disturbance level. Our results suggest a more complex nature of the geomagnetic response to solar wind changes than previously thought.

Multifractal structure in Latin-American market indices

International Nuclear Information System (INIS)

Zunino, Luciano; Figliola, Alejandra; Tabak, Benjamin M.; Perez, Dario G.; Garavaglia, Mario; Rosso, Osvaldo A.

2009-01-01

We study the multifractal nature of daily price and volatility returns of Latin-American stock markets employing the multifractal detrended fluctuation analysis. Comparing with the results obtained for a developed country (US) we conclude that the multifractality degree is higher for emerging markets. Moreover, we propose a stock market inefficiency ranking by considering the multifractality degree as a measure of inefficiency. Finally, we analyze the sources of multifractality quantifying the contributions of two factors, the long-range correlations of the time series and the broad fat-tail distributions. We find that the multifractal structure of Latin-American market indices can be mainly attributed to the latter.

Multifractal analysis of the long-range correlations in the cardiac dynamics of Drosophila melanogaster

International Nuclear Information System (INIS)

Vitanov, Nikolay K.; Yankulova, Elka D.

2006-01-01

By means of the multifractal detrended fluctuation analysis (MFDFA) we investigate long-range correlations in the interbeat time series of heart activity of Drosophila melanogaster-the classical object of research in genetics. Our main investigation tool are the fractal spectra f(α) and h(q) by means of which we trace the correlation properties of Drosophila heartbeat dynamics for three consequent generations of species. We observe that opposite to the case of humans the time series of the heartbeat activity of healthy Drosophila do not have scaling properties. Time series from species with genetic defects can be long-range correlated. Different kinds of genetic heart defects lead to different shape of the fractal spectra. The fractal heartbeat dynamics of Drosophila is transferred from generation to generation

Weyl and Riemann-Liouville multifractional Ornstein-Uhlenbeck processes

International Nuclear Information System (INIS)

Lim, S C; Teo, L P

2007-01-01

This paper considers two new multifractional stochastic processes, namely the Weyl multifractional Ornstein-Uhlenbeck process and the Riemann-Liouville multifractional Ornstein-Uhlenbeck process. Basic properties of these processes such as locally self-similar property and Hausdorff dimension are studied. The relationship between the multifractional Ornstein-Uhlenbeck processes and the corresponding multifractional Brownian motions is established

Multifractals theory and applications

CERN Document Server

Harte, David

2001-01-01

Although multifractals are rooted in probability, much of the related literature comes from the physics and mathematics arena. Multifractals: Theory and Applications pulls together ideas from both these areas using a language that makes them accessible and useful to statistical scientists. It provides a framework, in particular, for the evaluation of statistical properties of estimates of the Renyi fractal dimensions.The first section provides introductory material and different definitions of a multifractal measure. The author then examines some of the various constructions for describing multifractal measures. Building from the theory of large deviations, he focuses on constructions based on lattice coverings, covering by point-centered spheres, and cascades processes. The final section presents estimators of Renyi dimensions of integer order two and greater and discusses their properties. It also explores various applications of dimension estimation and provides a detailed case study of spatial point patte...

MULTIFRACTAL STRUCTURE OF CENTRAL AND EASTERN EUROPEAN FOREIGN EXCHANGE MARKETS

Directory of Open Access Journals (Sweden)

Cn#259;pun#351;an Rn#259;zvan

2012-07-01

Full Text Available It is well known that empirical data coming from financial markets, like stock market indices, commodities, interest rates, traded volumes and foreign exchange rates have a multifractal structure. Multifractals were introduced in the field of economics to surpass the shortcomings of classical models like the fractional Brownian motion or GARCH processes. In this paper we investigate the multifractal behavior of Central and Eastern European foreign exchange rates, namely the Czech koruna, Croatian kuna, Hungarian forint, Polish zlot, Romanian leu and Russian rouble with respect to euro from January 13, 2000 to February 29, 2012. The dynamics of exchange rates is of interest for investors and traders, monetary and fiscal authorities, economic agents or policy makers. The exchange rate movements affect the international balance of payments, trade flows, and allocation of the resources in national and international economy. The empirical results from the multifractal detrending fluctuation analysis algorithm show that the six exchange rate series analysed display significant multifractality. Moreover, generating shuffled and surrogate time series, we analyze the sources of multifractality, long-range correlations and heavy-tailed distributions, and we find that this multifractal behavior can be mainly attributed to the latter. Finally, we propose a foreign exchange market inefficiency ranking by considering the multifractality degree as a measure of inefficiency. The regulators, through policy instruments, aim to improve the informational inefficiency of the markets, to reduce the associated risks and to ensure economic stabilization. Evaluation of the degree of information efficiency of foreign exchange markets, for Central and Eastern Europe countries, is important to assess to what extent these countries are prepared for the transition towards fully monetary integration. The weak form efficiency implies that the past exchange rates cannot help to

Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion

Science.gov (United States)

Lin, Jinshan; Chen, Qian

2013-07-01

Vibration data of faulty rolling bearings are usually nonstationary and nonlinear, and contain fairly weak fault features. As a result, feature extraction of rolling bearing fault data is always an intractable problem and has attracted considerable attention for a long time. This paper introduces multifractal detrended fluctuation analysis (MF-DFA) to analyze bearing vibration data and proposes a novel method for fault diagnosis of rolling bearings based on MF-DFA and Mahalanobis distance criterion (MDC). MF-DFA, an extension of monofractal DFA, is a powerful tool for uncovering the nonlinear dynamical characteristics buried in nonstationary time series and can capture minor changes of complex system conditions. To begin with, by MF-DFA, multifractality of bearing fault data was quantified with the generalized Hurst exponent, the scaling exponent and the multifractal spectrum. Consequently, controlled by essentially different dynamical mechanisms, the multifractality of four heterogeneous bearing fault data is significantly different; by contrast, controlled by slightly different dynamical mechanisms, the multifractality of homogeneous bearing fault data with different fault diameters is significantly or slightly different depending on different types of bearing faults. Therefore, the multifractal spectrum, as a set of parameters describing multifractality of time series, can be employed to characterize different types and severity of bearing faults. Subsequently, five characteristic parameters sensitive to changes of bearing fault conditions were extracted from the multifractal spectrum and utilized to construct fault features of bearing fault data. Moreover, Hilbert transform based envelope analysis, empirical mode decomposition (EMD) and wavelet transform (WT) were utilized to study the same bearing fault data. Also, the kurtosis and the peak levels of the EMD or the WT component corresponding to the bearing tones in the frequency domain were carefully checked

Multifractality in edge localized modes in Japan Atomic Energy Research Institute Tokamak-60 Upgrade

International Nuclear Information System (INIS)

Bak, P.E.; Asakura, N.; Miura, Y.; Nakano, T.; Yoshino, R.

2001-01-01

The temporal losses of confinement during edge localized modes in the Japan Atomic Energy Research Institute Tokamak-60 Upgrade (JT-60U) show multifractal scaling and the spectra are generally smooth, but in some cases there are signs of discontinuous derivatives. Dynamics of the Sugama-Horton model, interpreted as edge localized modes, also display multifractal scaling. The spectra display singularities in the derivative, which can be interpreted as a phase transition. It is argued that the multifractal spectra of edge localized modes can be used to discriminate between different experimental discharges and validate edge localized mode models

Upper Estimates on the Higher-dimensional Multifractal Spectrum of Local Entropy%局部熵高维重分形谱的上界估计

Institute of Scientific and Technical Information of China (English)

严珍珍; 陈二才

2008-01-01

We discuss the problem of higher-dimensional multifractal spectrum of lo-cal entropy for arbitrary invariant measures. By utilizing characteristics of a dynam-ical system, namely, higher-dimensional entropy capacities and higher-dimensional correlation entropies, we obtain three upper estimates on the higher-dimensional mul-tifractal spectrum of local entropies. We also study the domain of higher-dimensional multifractal spetrum of entropies.

Multifractal features of EUA and CER futures markets by using multifractal detrended fluctuation analysis based on empirical model decomposition

International Nuclear Information System (INIS)

Cao, Guangxi; Xu, Wei

2016-01-01

Basing on daily price data of carbon emission rights in futures markets of Certified Emission Reduction (CER) and European Union Allowances (EUA), we analyze the multiscale characteristics of the markets by using empirical mode decomposition (EMD) and multifractal detrended fluctuation analysis (MFDFA) based on EMD. The complexity of the daily returns of CER and EUA futures markets changes with multiple time scales and multilayered features. The two markets also exhibit clear multifractal characteristics and long-range correlation. We employ shuffle and surrogate approaches to analyze the origins of multifractality. The long-range correlations and fat-tail distributions significantly contribute to multifractality. Furthermore, we analyze the influence of high returns on multifractality by using threshold method. The multifractality of the two futures markets is related to the presence of high values of returns in the price series.

Multifractional theories: an unconventional review

Energy Technology Data Exchange (ETDEWEB)

Calcagni, Gianluca [Instituto de Estructura de la Materia, CSIC,Serrano 121, 28006 Madrid (Spain)

2017-03-27

We answer to 72 frequently asked questions about theories of multifractional spacetimes. Apart from reviewing and reorganizing what we already know about such theories, we discuss the physical meaning and consequences of the very recent flow-equation theorem on dimensional flow in quantum gravity, in particular its enormous impact on the multifractional paradigm. We will also get new theoretical results about the construction of multifractional derivatives and the symmetries in the yet-unexplored theory T{sub γ}, the resolution of ambiguities in the calculation of the spectral dimension, the relation between the theory T{sub q} with q-derivatives and the theory T{sub γ} with fractional derivatives, the interpretation of complex dimensions in quantum gravity, the frame choice at the quantum level, the physical interpretation of the propagator in T{sub γ} as an infinite superposition of quasiparticle modes, the relation between multifractional theories and quantum gravity, and the issue of renormalization, arguing that power-counting arguments do not capture the exotic properties of extreme UV regimes of multifractional geometry, where T{sub γ} may indeed be renormalizable. A careful discussion of experimental bounds and new constraints are also presented.

Multifractal structures for the Russian stock market

Science.gov (United States)

Ikeda, Taro

2018-02-01

In this paper, we apply the multifractal detrended fluctuation analysis (MFDFA) to the Russian stock price returns. To the best of our knowledge, this paper is the first to reveal the multifractal structures for the Russian stock market by financial crises. The contributions of the paper are twofold. (i) Finding the multifractal structures for the Russian stock market. The generalized Hurst exponents estimated become highly-nonlinear to the order of the fluctuation functions. (ii) Computing the multifractality degree according to Zunino et al. (2008). We find that the multifractality degree of the Russian stock market can be categorized within emerging markets, however, the Russian 1998 crisis and the global financial crisis dampen the degree when we consider the order of the polynomial trends in the MFDFA.

MULTIFRACTAL STRUCTURES DETECTED BY VOYAGER 1 AT THE HELIOSPHERIC BOUNDARIES

International Nuclear Information System (INIS)

Macek, W. M.; Wawrzaszek, A.; Burlaga, L. F.

2014-01-01

To better understand the dynamics of turbulent systems, we have proposed a phenomenological model based on a generalized Cantor set with two rescaling and one weight parameters. In this Letter, using recent Voyager 1 magnetic field data, we extend our two-scale multifractal analysis further in the heliosheath beyond the heliospheric termination shock, and even now near the heliopause, when entering the interstellar medium for the first time in human history. We have identified the scaling inertial region for magnetized heliospheric plasma between the termination shock and the heliopause. We also show that the degree of multifractality decreases with the heliocentric distance and is still modulated by the phases of the solar cycle in the entire heliosphere including the heliosheath. Moreover, we observe the change of scaling toward a nonintermittent (nonmultifractal) behavior in the nearby interstellar medium, just beyond the heliopause. We argue that this loss of multifractal behavior could be a signature of the expected crossing of the heliopause by Voyager 2 in the near future. The results obtained demonstrate that our phenomenological multifractal model exhibits some properties of intermittent turbulence in the solar system plasmas, and we hope that it could shed light on universal characteristics of turbulence

MULTIFRACTAL STRUCTURES DETECTED BY VOYAGER 1 AT THE HELIOSPHERIC BOUNDARIES

Energy Technology Data Exchange (ETDEWEB)

Macek, W. M. [Faculty of Mathematics and Natural Sciences, Cardinal Stefan Wyszyński University, Wóycickiego 1/3, 01-938 Warsaw (Poland); Wawrzaszek, A. [Space Research Centre, Polish Academy of Sciences, Bartycka 18 A, 00-716 Warszawa (Poland); Burlaga, L. F., E-mail: macek@cbk.waw.pl, E-mail: anna.wawrzaszek@cbk.waw.pl, E-mail: lburlagahsp@verizon.net [NASA Goddard Space Flight Center, Code 673, Greenbelt, MD 20771 (United States)

2014-10-01

To better understand the dynamics of turbulent systems, we have proposed a phenomenological model based on a generalized Cantor set with two rescaling and one weight parameters. In this Letter, using recent Voyager 1 magnetic field data, we extend our two-scale multifractal analysis further in the heliosheath beyond the heliospheric termination shock, and even now near the heliopause, when entering the interstellar medium for the first time in human history. We have identified the scaling inertial region for magnetized heliospheric plasma between the termination shock and the heliopause. We also show that the degree of multifractality decreases with the heliocentric distance and is still modulated by the phases of the solar cycle in the entire heliosphere including the heliosheath. Moreover, we observe the change of scaling toward a nonintermittent (nonmultifractal) behavior in the nearby interstellar medium, just beyond the heliopause. We argue that this loss of multifractal behavior could be a signature of the expected crossing of the heliopause by Voyager 2 in the near future. The results obtained demonstrate that our phenomenological multifractal model exhibits some properties of intermittent turbulence in the solar system plasmas, and we hope that it could shed light on universal characteristics of turbulence.

Multifractal Detrended Fluctuation Analysis of alpha and theta EEG rhythms with musical stimuli

International Nuclear Information System (INIS)

Maity, Akash Kumar; Pratihar, Ruchira; Mitra, Anubrato; Dey, Subham; Agrawal, Vishal; Sanyal, Shankha; Banerjee, Archi; Sengupta, Ranjan; Ghosh, Dipak

2015-01-01

Highlights: • EEG was done to record the brain electrical activity of 10 subjects in response to simple acoustical tanpura stimuli. • Empirical Mode Decomposition (EMD) technique used to make the EEG signal free from blink and other muscular artifacts. • Multifractal Detrended Fluctuation Analysis (MFDFA) performed to assess the complexity of extracted alpha and theta brain rhythms. • The findings show spectral width i.e. complexity of alpha and theta rhythms increase in all the seven frontal locations studied, under the effect of musical stimuli. - Abstract: Electroencephalography (EEG) was performed on 10 participants using a simple acoustical stimuli i.e. a tanpura drone. The tanpura drone is free from any semantic content and is used with a hypothesis that it provides a specific resting environment for the listeners. The EEG data was extracted for all the frontal electrodes viz. F3, F4, F7, F8, Fp1, Fp2 and Fz. Empirical Mode Decomposition (EMD) was applied on the acquired raw EEG signal to make it free from blink as well as other muscular artifacts. Wavelet Transform (WT) technique was used to segregate alpha and theta waves from the denoised EEG signal. Non-linear analysis in the form of Multifractal Detrended Fluctuation Analysis (MFDFA) was carried out on the extracted alpha and theta time series data to study the variation of their complexity. It was found that in all the frontal electrodes alpha as well as theta complexity increases as is evident from the increase of multifractal spectral width. This study is entirely new and gives interesting data regarding neural activation of the alpha and theta brain rhythms while listening to simple acoustical stimuli. The importance of this study lies in the context of emotion quantification using multifractal spectral width as a parameter as well as in the field of cognitive music therapy. The results are discussed in detail.

Multifractal analysis of complex networks

International Nuclear Information System (INIS)

Wang Dan-Ling; Yu Zu-Guo; Anh V

2012-01-01

Complex networks have recently attracted much attention in diverse areas of science and technology. Many networks such as the WWW and biological networks are known to display spatial heterogeneity which can be characterized by their fractal dimensions. Multifractal analysis is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. In this paper, we introduce a new box-covering algorithm for multifractal analysis of complex networks. This algorithm is used to calculate the generalized fractal dimensions D q of some theoretical networks, namely scale-free networks, small world networks, and random networks, and one kind of real network, namely protein—protein interaction networks of different species. Our numerical results indicate the existence of multifractality in scale-free networks and protein—protein interaction networks, while the multifractal behavior is not clear-cut for small world networks and random networks. The possible variation of D q due to changes in the parameters of the theoretical network models is also discussed. (general)

Multifractal property of Chinese stock market in the CSI 800 index based on MF-DFA approach

Science.gov (United States)

Zhu, Huijian; Zhang, Weiguo

2018-01-01

CSI 800 index consists of CSI 500 index and CSI 300 index, aiming to reflect the performance of stocks with large, mid and small size of China A share market. In this paper we analyze the multifractal structure of Chinese stock market in the CSI 800 index based on the multifractal detrended fluctuation analysis (MF-DFA) method. We find that the fluctuation of the closing logarithmic returns have multifractal properties, the shape and width of multifractal spectrum are depended on the weighing order q. More interestingly, we observe a bigger market crash in June-August 2015 than the one in 2008 based on the local Hurst exponents. The result provides important information for further study on dynamic mechanism of return fluctuation and whether it would trigger a new financial crisis.

A multifractal analysis of Asian foreign exchange markets

Science.gov (United States)

Oh, G.; Eom, C.; Havlin, S.; Jung, W.-S.; Wang, F.; Stanley, H. E.; Kim, S.

2012-06-01

We analyze the multifractal spectra of daily foreign exchange rates for Japan, Hong-Kong, Korea, and Thailand with respect to the United States in the period from 1991 until 2005. We find that the return time series show multifractal spectrum features for all four cases. To observe the effect of the Asian currency crisis, we also estimate the multifractal spectra of limited series before and after the crisis. We find that the Korean and Thai foreign exchange markets experienced a significant increase in multifractality compared to Hong-Kong and Japan. We also show that the multifractality is stronger related to the presence of high values of returns in the series.

Two-dimensional multifractal cross-correlation analysis

International Nuclear Information System (INIS)

Xi, Caiping; Zhang, Shuning; Xiong, Gang; Zhao, Huichang; Yang, Yonghong

2017-01-01

Highlights: • We study the mathematical models of 2D-MFXPF, 2D-MFXDFA and 2D-MFXDMA. • Present the definition of the two-dimensional N 2 -partitioned multiplicative cascading process. • Do the comparative analysis of 2D-MC by 2D-MFXPF, 2D-MFXDFA and 2D-MFXDMA. • Provide a reference on the choice and parameter settings of these methods in practice. - Abstract: There are a number of situations in which several signals are simultaneously recorded in complex systems, which exhibit long-term power-law cross-correlations. This paper presents two-dimensional multifractal cross-correlation analysis based on the partition function (2D-MFXPF), two-dimensional multifractal cross-correlation analysis based on the detrended fluctuation analysis (2D-MFXDFA) and two-dimensional multifractal cross-correlation analysis based on the detrended moving average analysis (2D-MFXDMA). We apply these methods to pairs of two-dimensional multiplicative cascades (2D-MC) to do a comparative study. Then, we apply the two-dimensional multifractal cross-correlation analysis based on the detrended fluctuation analysis (2D-MFXDFA) to real images and unveil intriguing multifractality in the cross correlations of the material structures. At last, we give the main conclusions and provide a valuable reference on how to choose the multifractal algorithms in the potential applications in the field of SAR image classification and detection.

Multifractal detrended cross-correlations between crude oil market and Chinese ten sector stock markets

Science.gov (United States)

Yang, Liansheng; Zhu, Yingming; Wang, Yudong; Wang, Yiqi

2016-11-01

Based on the daily price data of spot prices of West Texas Intermediate (WTI) crude oil and ten CSI300 sector indices in China, we apply multifractal detrended cross-correlation analysis (MF-DCCA) method to investigate the cross-correlations between crude oil and Chinese sector stock markets. We find that the strength of multifractality between WTI crude oil and energy sector stock market is the highest, followed by the strength of multifractality between WTI crude oil and financial sector market, which reflects a close connection between energy and financial market. Then we do vector autoregression (VAR) analysis to capture the interdependencies among the multiple time series. By comparing the strength of multifractality for original data and residual errors of VAR model, we get a conclusion that vector auto-regression (VAR) model could not be used to describe the dynamics of the cross-correlations between WTI crude oil and the ten sector stock markets.

Searching for a multifractal signature of the lake algal proliferation, a multifractal correlation

Science.gov (United States)

Mezemate, Yacine; Tchiguirinskaia, Ioulia; Bonhomme, Celine; Schertzer, Daniel; Lemaire, Bruno Jacques; Vinçon leite, Brigitte; Lovejoy, Shaun

2013-04-01

Green algae proliferations affect water bodies such as the Lake Bourget (France). They are an environmental issue as well as a mater of public health. In the framework of the PROLIPHYC project a system based on temperature and chlorophyll measurements coupled to a lake model was implemented to predict sudden algal blooms. This classical approach relies on the analysis of large scale trends of the measured fields and does not take into account small scale fluctuations. A more innovative approach has been developed by the R2DS PLUMMME project to investigate the correlation between environmental fields across the full range of space-time scales, down to the smallest scale of observations. The first results of the project demonstrate that multi-scaling behaviour of environmental fields, such as temperature and chlorophyll, becomes evident only after the removal of the large-scale data trends that otherwise induce biases to the multifractal parameter estimates. First, a spectral analysis of temperature and chlorophyll data is performed on sub-samples of the time series to investigate the scaling behaviour. The multifractal analysis (Trace Moment, Double Trace Moment) directly applied on each sub-sample shows unsatisfying results on some sub-samples, in particular on those having a strong gradient compared with the amplitude of the fluctuations. Hence, non-stationary and seasonal effects should be first removed from the time series. To put on evidence a good scaling of the analysed data, we choose the Hilbert-Huang transform to de-trend the data. This method has been widely used for different fields (see F.G.Schmitt et al, 2009 for review). After having applied this method, the K(q) function shows that the investigated fields are indeed multifractal and the determination of their multifractal parameters becomes robust. Then, we proceed to a multifractal correlation analysis between the fields. In conclusion, we discuss the prediction of algal blooms based on multifractal

Use of wavelet-packet transforms to develop an engineering model for multifractal characterization of mutation dynamics in pathological and nonpathological gene sequences

Science.gov (United States)

Walker, David Lee

1999-12-01

This study uses dynamical analysis to examine in a quantitative fashion the information coding mechanism in DNA sequences. This exceeds the simple dichotomy of either modeling the mechanism by comparing DNA sequence walks as Fractal Brownian Motion (fbm) processes. The 2-D mappings of the DNA sequences for this research are from Iterated Function System (IFS) (Also known as the ``Chaos Game Representation'' (CGR)) mappings of the DNA sequences. This technique converts a 1-D sequence into a 2-D representation that preserves subsequence structure and provides a visual representation. The second step of this analysis involves the application of Wavelet Packet Transforms, a recently developed technique from the field of signal processing. A multi-fractal model is built by using wavelet transforms to estimate the Hurst exponent, H. The Hurst exponent is a non-parametric measurement of the dynamism of a system. This procedure is used to evaluate gene- coding events in the DNA sequence of cystic fibrosis mutations. The H exponent is calculated for various mutation sites in this gene. The results of this study indicate the presence of anti-persistent, random walks and persistent ``sub-periods'' in the sequence. This indicates the hypothesis of a multi-fractal model of DNA information encoding warrants further consideration. This work examines the model's behavior in both pathological (mutations) and non-pathological (healthy) base pair sequences of the cystic fibrosis gene. These mutations both natural and synthetic were introduced by computer manipulation of the original base pair text files. The results show that disease severity and system ``information dynamics'' correlate. These results have implications for genetic engineering as well as in mathematical biology. They suggest that there is scope for more multi-fractal models to be developed.

Multiplicative multifractal modeling and discrimination of human neuronal activity

International Nuclear Information System (INIS)

Zheng Yi; Gao Jianbo; Sanchez, Justin C.; Principe, Jose C.; Okun, Michael S.

2005-01-01

Understanding neuronal firing patterns is one of the most important problems in theoretical neuroscience. It is also very important for clinical neurosurgery. In this Letter, we introduce a computational procedure to examine whether neuronal firing recordings could be characterized by cascade multiplicative multifractals. By analyzing raw recording data as well as generated spike train data from 3 patients collected in two brain areas, the globus pallidus externa (GPe) and the globus pallidus interna (GPi), we show that the neural firings are consistent with a multifractal process over certain time scale range (t 1 ,t 2 ), where t 1 is argued to be not smaller than the mean inter-spike-interval of neuronal firings, while t 2 may be related to the time that neuronal signals propagate in the major neural branching structures pertinent to GPi and GPe. The generalized dimension spectrum D q effectively differentiates the two brain areas, both intra- and inter-patients. For distinguishing between GPe and GPi, it is further shown that the cascade model is more effective than the methods recently examined by Schiff et al. as well as the Fano factor analysis. Therefore, the methodology may be useful in developing computer aided tools to help clinicians perform precision neurosurgery in the operating room

Quantitative assessment of Heart Rate Dynamics during meditation: An ECG based study with Multi-fractality and visibility graph

Directory of Open Access Journals (Sweden)

Anirban eBhaduri

2016-02-01

Full Text Available Abstract: Abstract: The cardiac dynamics during meditation is explored quantitatively with two chaos-based non-linear techniques viz. multi-fractal detrended fluctuation analysis and visibility network analysis techniques. The data used are the instantaneous heart rate (in beats/minute of subjects performing Kundalini Yoga and Chi meditation from PhysioNet. The results show consistent differences between the quantitative parameters obtained by both the analysis techniques. This indicates an interesting phenomenon of change in the complexity of the cardiac dynamics during meditation supported with quantitative parameters.The results also produce a preliminary evidence that these techniques can be used as a measure of physiological impact on subjects performing meditation.

Quantitative Assessment of Heart Rate Dynamics during Meditation: An ECG Based Study with Multi-Fractality and Visibility Graph.

Science.gov (United States)

Bhaduri, Anirban; Ghosh, Dipak

2016-01-01

The cardiac dynamics during meditation is explored quantitatively with two chaos-based non-linear techniques viz. multi-fractal detrended fluctuation analysis and visibility network analysis techniques. The data used are the instantaneous heart rate (in beats/minute) of subjects performing Kundalini Yoga and Chi meditation from PhysioNet. The results show consistent differences between the quantitative parameters obtained by both the analysis techniques. This indicates an interesting phenomenon of change in the complexity of the cardiac dynamics during meditation supported with quantitative parameters. The results also produce a preliminary evidence that these techniques can be used as a measure of physiological impact on subjects performing meditation.

Multifractal scaling analysis of autopoisoning reactions over a rough surface

International Nuclear Information System (INIS)

Chaudhari, Ajay; Yan, Ching-Cher Sanders; Lee, S.-L.

2003-01-01

Decay type diffusion-limited reactions (DLR) over a rough surface generated by a random deposition model were performed. To study the effect of the decay profile on the reaction probability distribution (RPD), multifractal scaling analysis has been carried out. The dynamics of these autopoisoning reactions are controlled by the two parameters in the decay function, namely, the initial sticking probability (P ini ) of every site and the decay rate (m). The smaller the decay rate, the narrower is the range of α values in the α-f(α) multifractal spectrum. The results are compared with the earlier work of DLR over a surface of diffusion-limited aggregation (DLA). We also considered here the autopoisoning reactions over a smooth surface for comparing our results, which show clearly how the roughness affects the chemical reactions. The q-τ(q) multifractal curves for the smooth surface are linear whereas those for the rough surface are nonlinear. The range of α values in the case of a rough surface is wider than that of the smooth surface

Multifractal analysis of managed and independent float exchange rates

Science.gov (United States)

Stošić, Darko; Stošić, Dusan; Stošić, Tatijana; Stanley, H. Eugene

2015-06-01

We investigate multifractal properties of daily price changes in currency rates using the multifractal detrended fluctuation analysis (MF-DFA). We analyze managed and independent floating currency rates in eight countries, and determine the changes in multifractal spectrum when transitioning between the two regimes. We find that after the transition from managed to independent float regime the changes in multifractal spectrum (position of maximum and width) indicate an increase in market efficiency. The observed changes are more pronounced for developed countries that have a well established trading market. After shuffling the series, we find that the multifractality is due to both probability density function and long term correlations for managed float regime, while for independent float regime multifractality is in most cases caused by broad probability density function.

Characterizing water fingering phenomena in soils using magnetic resonance imaging and multifractal theory

Directory of Open Access Journals (Sweden)

A. Posadas

2009-02-01

Full Text Available The study of water movement in soils is of fundamental importance in hydrologic science. It is generally accepted that in most soils, water and solutes flow through unsaturated zones via preferential paths or fingers. This paper combines magnetic resonance imaging (MRI with both fractal and multifractal theory to characterize preferential flow in three dimensions. A cubic double-layer column filled with fine and coarse textured sand was placed into a 500 gauss MRI system. Water infiltration through the column (0.15×0.15×0.15 m³ was recorded in steady state conditions. Twelve sections with a voxel volume of 0.1×0.1×10 mm³ each were obtained and characterized using fractal and multifractal theory. The MRI system provided a detailed description of the preferential flow under steady state conditions and was also useful in understanding the dynamics of the formation of the fingers. The f(α multifractal spectrum was very sensitive to the variation encountered at each horizontally-oriented slice of the column and provided a suitable characterization of the dynamics of the process identifying four spatial domains. In conclusion, MRI and fractal and multifractal analysis were able to characterize and describe the preferential flow process in soils. Used together, the two methods provide a good alternative to study flow transport phenomena in soils and in porous media.

Finite-size effect and the components of multifractality in financial volatility

International Nuclear Information System (INIS)

Zhou Weixing

2012-01-01

Highlights: ► The apparent multifractality can be decomposed quantitatively. ► There is a marked finite-size effect in the detection of multifractality. ► The effective multifractality can be further decomposed into two components. ► A time series exhibits effective multifractality only if it possesses nonlinearity. ► The daily DJIA volatility is analyzed as an example. - Abstract: Many financial variables are found to exhibit multifractal nature, which is usually attributed to the influence of temporal correlations and fat-tailedness in the probability distribution (PDF). Based on the partition function approach of multifractal analysis, we show that there is a marked finite-size effect in the detection of multifractality, and the effective multifractality is the apparent multifractality after removing the finite-size effect. We find that the effective multifractality can be further decomposed into two components, the PDF component and the nonlinearity component. Referring to the normal distribution, we can determine the PDF component by comparing the effective multifractality of the original time series and the surrogate data that have a normal distribution and keep the same linear and nonlinear correlations as the original data. We demonstrate our method by taking the daily volatility data of Dow Jones Industrial Average from 26 May 1896 to 27 April 2007 as an example. Extensive numerical experiments show that a time series exhibits effective multifractality only if it possesses nonlinearity and the PDF has an impact on the effective multifractality only when the time series possesses nonlinearity. Our method can also be applied to judge the presence of multifractality and determine its components of multifractal time series in other complex systems.

Investigation of multifractality in the Brazilian stock market

Science.gov (United States)

Maganini, Natália Diniz; Da Silva Filho, Antônio Carlos; Lima, Fabiano Guasti

2018-05-01

Many studies point to a possible new stylized fact for financial time series: the multifractality. Several authors have already detected this characteristic in multiple time series in several countries. With that in mind and based on Multifractal Detrended Fluctuation Analysis (MFDFA) method, this paper analyzes the multifractality in the Brazilian market. This analysis is performed with daily data from IBOVESPA index (Brazilian stock exchange's main index) and other four highly marketable stocks in the Brazilian market (VALE5, ITUB4, BBDC4 and CIEL3), which represent more than 25% of the index composition, making up 1961 observations for each asset in the period from June 26 2009 to May 31 2017. We found that the studied stock prices and Brazilian index are multifractal, but that the multifractality degree is not the same for all the assets. The use of shuffled and surrogated series indicates that for the period and the actions considered the long-range correlations do not strongly influence the multifractality, but the distribution (fat tails) exerts a possible influence on IBOVESPA and CIEL3.

Diffusion and scattering in multifractal clouds

Energy Technology Data Exchange (ETDEWEB)

Lovejoy, S. [McGill Univ., Montreal, Quebec (Canada); Schertzer, D. [Universite Pierre et Marie Curie, Paris (France); Waston, B. [St. Lawrence Univ., Canton, NY (United States)] [and others

1996-04-01

This paper describes investigations of radiative properties of multifractal clouds using two different approaches. In the first, diffusion is considered by examining the scaling properties of one dimensional random walks on media with multifractal diffusivities. The second approach considers the scattering statistics associated with radiative transport.

Multifractal Conceptualisation of Hydro-Meteorological Extremes

Science.gov (United States)

Tchiguirinskaia, I.; Schertzer, D.; Lovejoy, S.

2009-04-01

Hydrology and more generally sciences involved in water resources management, technological or operational developments face a fundamental difficulty: the extreme variability of hydro-meteorological fields. It clearly appears today that this variability is a function of the observation scale and yield hydro-meteorological hazards. Throughout the world, the development of multifractal theory offers new techniques for handling such non-classical variability over wide ranges of time and space scales. The resulting stochastic simulations with a very limited number of parameters well reproduce the long range dependencies and the clustering of rainfall extremes often yielding fat tailed (i.e., an algebraic type) probability distributions. The goal of this work was to investigate the ability of using very short or incomplete data records for reliable statistical predictions of the extremes. In particular we discuss how to evaluate the uncertainty in the empirical or semi-analytical multifractal outcomes. We consider three main aspects of the evaluation, such as the scaling adequacy, the multifractal parameter estimation error and the quantile estimation error. We first use the multiplicative cascade model to generate long series of multifractal data. The simulated samples had to cover the range of the universal multifractal parameters widely available in the scientific literature for the rainfall and river discharges. Using these long multifractal series and their sub-samples, we defined a metric for parameter estimation error. Then using the sets of estimated parameters, we obtained the quantile values for a range of excedance probabilities from 5% to 0.01%. Plotting the error bars on a quantile plot enable an approximation of confidence intervals that would be particularly important for the predictions of multifractal extremes. We finally illustrate the efficiency of such concept on its application to a large database (more than 16000 selected stations over USA and

Cross-Correlations between Energy and Emissions Markets: New Evidence from Fractal and Multifractal Analysis

Directory of Open Access Journals (Sweden)

Gang-Jin Wang

2014-01-01

Full Text Available We supply a new perspective to describe and understand the behavior of cross-correlations between energy and emissions markets. Namely, we investigate cross-correlations between oil and gas (Oil-Gas, oil and CO2 (Oil-CO2, and gas and CO2 (Gas-CO2 based on fractal and multifractal analysis. We focus our study on returns of the oil, gas, and CO2 during the period of April 22, 2005–April 30, 2013. In the empirical analysis, by using the detrended cross-correlation analysis (DCCA method, we find that cross-correlations for Oil-Gas, Oil-CO2, and Gas-CO2 obey a power-law and are weakly persistent. Then, we adopt the method of DCCA cross-correlation coefficient to quantify cross-correlations between energy and emissions markets. The results show that their cross-correlations are diverse at different time scales. Next, based on the multifractal DCCA method, we find that cross-correlated markets have the nonlinear and multifractal nature and that the multifractality strength for three cross-correlated markets is arranged in the order of Gas-CO2 > Oil-Gas > Oil-CO2. Finally, by employing the rolling windows method, which can be used to investigate time-varying cross-correlation scaling exponents, we analyze short-term and long-term market dynamics and find that the recent global financial crisis has a notable influence on short-term and long-term market dynamics.

Price-volume multifractal analysis of the Moroccan stock market

Science.gov (United States)

El Alaoui, Marwane

2017-11-01

In this paper, we analyzed price-volume multifractal cross-correlations of Moroccan Stock Exchange. We chose the period from January 1st 2000 to January 20th 2017 to investigate the multifractal behavior of price change and volume change series. Then, we used multifractal detrended cross-correlations analysis method (MF-DCCA) and multifractal detrended fluctuation analysis (MF-DFA) to analyze the series. We computed bivariate generalized Hurst exponent, Rényi exponent and spectrum of singularity for each pair of indices to measure quantitatively cross-correlations. Furthermore, we used detrended cross-correlations coefficient (DCCA) and cross-correlation test (Q(m)) to analyze cross-correlation quantitatively and qualitatively. By analyzing results, we found existence of price-volume multifractal cross-correlations. The spectrum width has a strong multifractal cross-correlation. We remarked that volume change series is anti-persistent when we analyzed the generalized Hurst exponent for all moments q. The cross-correlation test showed the presence of a significant cross-correlation. However, DCCA coefficient had a small positive value, which means that the level of correlation is not very significant. Finally, we analyzed sources of multifractality and their degree of contribution in the series.

Multifractal Detrended Fluctuation Analysis of Human gait Diseases

Directory of Open Access Journals (Sweden)

Srimonti eDutta

2013-10-01

Full Text Available IIn this paper multifractal detrended fluctuation analysis is used to study the human gait time series for normal and diseased sets. It is observed that long range correlation is primarily responsible for the origin of multifractality. The study reveals that the degree of multifractality is more for normal set compared to diseased set. However the method fails to distinguish between the two diseased sets.

Multifractal spectra in shear flows

Science.gov (United States)

Keefe, L. R.; Deane, Anil E.

1989-01-01

Numerical simulations of three-dimensional homogeneous shear flow and fully developed channel flow, are used to calculate the associated multifractal spectra of the energy dissipation field. Only weak parameterization of the results with the nondimensional shear is found, and this only if the flow has reached its asymptotic development state. Multifractal spectra of these flows coincide with those from experiments only at the range alpha less than 1.

Multifractal analysis for the historic set in topological dynamical systems

International Nuclear Information System (INIS)

Zhou, Xiaoyao; Chen, Ercai

2013-01-01

In this paper the historic set is divided into different level sets and we use topological pressure to describe the size of these level sets. We give an application of these results to dimension theory. Our primary focus is using topological pressure to describe the relative multifractal spectrum of ergodic averages and to give a positive answer to the conjecture posed by Olsen (2003 J. Math. Pures Appl. 82 1591–649). (paper)

Fractal and multifractal analyses of bipartite networks

Science.gov (United States)

Liu, Jin-Long; Wang, Jian; Yu, Zu-Guo; Xie, Xian-Hua

2017-03-01

Bipartite networks have attracted considerable interest in various fields. Fractality and multifractality of unipartite (classical) networks have been studied in recent years, but there is no work to study these properties of bipartite networks. In this paper, we try to unfold the self-similarity structure of bipartite networks by performing the fractal and multifractal analyses for a variety of real-world bipartite network data sets and models. First, we find the fractality in some bipartite networks, including the CiteULike, Netflix, MovieLens (ml-20m), Delicious data sets and (u, v)-flower model. Meanwhile, we observe the shifted power-law or exponential behavior in other several networks. We then focus on the multifractal properties of bipartite networks. Our results indicate that the multifractality exists in those bipartite networks possessing fractality. To capture the inherent attribute of bipartite network with two types different nodes, we give the different weights for the nodes of different classes, and show the existence of multifractality in these node-weighted bipartite networks. In addition, for the data sets with ratings, we modify the two existing algorithms for fractal and multifractal analyses of edge-weighted unipartite networks to study the self-similarity of the corresponding edge-weighted bipartite networks. The results show that our modified algorithms are feasible and can effectively uncover the self-similarity structure of these edge-weighted bipartite networks and their corresponding node-weighted versions.

Joint multifractal analysis based on wavelet leaders

Science.gov (United States)

Jiang, Zhi-Qiang; Yang, Yan-Hong; Wang, Gang-Jin; Zhou, Wei-Xing

2017-12-01

Mutually interacting components form complex systems and these components usually have long-range cross-correlated outputs. Using wavelet leaders, we propose a method for characterizing the joint multifractal nature of these long-range cross correlations; we call this method joint multifractal analysis based on wavelet leaders (MF-X-WL). We test the validity of the MF-X-WL method by performing extensive numerical experiments on dual binomial measures with multifractal cross correlations and bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. Both experiments indicate that MF-X-WL is capable of detecting cross correlations in synthetic data with acceptable estimating errors. We also apply the MF-X-WL method to pairs of series from financial markets (returns and volatilities) and online worlds (online numbers of different genders and different societies) and determine intriguing joint multifractal behavior.

Multifractal and higher-dimensional zeta functions

International Nuclear Information System (INIS)

Véhel, Jacques Lévy; Mendivil, Franklin

2011-01-01

In this paper, we generalize the zeta function for a fractal string (as in Lapidus and Frankenhuijsen 2006 Fractal Geometry, Complex Dimensions and Zeta Functions: Geometry and Spectra of Fractal Strings (New York: Springer)) in several directions. We first modify the zeta function to be associated with a sequence of covers instead of the usual definition involving gap lengths. This modified zeta function allows us to define both a multifractal zeta function and a zeta function for higher-dimensional fractal sets. In the multifractal case, the critical exponents of the zeta function ζ(q, s) yield the usual multifractal spectrum of the measure. The presence of complex poles for ζ(q, s) indicates oscillations in the continuous partition function of the measure, and thus gives more refined information about the multifractal spectrum of a measure. In the case of a self-similar set in R n , the modified zeta function yields asymptotic information about both the 'box' counting function of the set and the n-dimensional volume of the ε-dilation of the set

Introduction to the Multifractal Analysis of Images

OpenAIRE

Lévy Véhel , Jacques

1998-01-01

International audience; After a brief review of some classical approaches in image segmentation, the basics of multifractal theory and its application to image analysis are presented. Practical methods for multifractal spectrum estimation are discussed and some experimental results are given.

Anti-correlation and multifractal features of Spain electricity spot market

NARCIS (Netherlands)

Norouzzadeh, Payam; Dullaert, W.; Rahmani, Bahareh

2007-01-01

We use multifractal detrended fluctuation analysis (MF-DFA) to numerically investigate correlation, persistence, multifractal properties and scaling behavior of the hourly spot prices for the Spain electricity exchange-Compania O Peradora del Mercado de Electricidad (OMEL). Through multifractal

Fractal and multifractal analysis of LiF thin film surface

International Nuclear Information System (INIS)

Yadav, R.P.; Dwivedi, S.; Mittal, A.K.; Kumar, M.; Pandey, A.C.

2012-01-01

Highlights: ► Fractal and multifractal analysis of surface morphologies of the LiF thin films. ► Complexity and roughness of the LiF thin films increases as thickness increases. ► LiF thin films are multifractal in nature. ► Strength of the multifractality increases with thickness of the film. - Abstract: Fractal and multifractal analysis is performed on the atomic force microscopy (AFM) images of the surface morphologies of the LiF thin films of thickness 10 nm, 20 nm, and 40 nm, respectively. Autocorrelation function, height–height correlation function, and two-dimensional multifractal detrended fluctuation analysis (MFDFA) are used for characterizing the surface. It is found that the interface width, average roughness, lateral correlation length, and fractal dimension of the LiF thin film increase with the thickness of the film, whereas the roughness exponent decreases with thickness. Thus, the complexity and roughness of the LiF thin films increases as thickness increases. It is also demonstrated that the LiF thin films are multifractal in nature. Strength of the multifractality increases with thickness of the film.

Multifractal Analysis of Asian Foreign Exchange Markets and Financial Crisis

Science.gov (United States)

Oh, Gabjin; Kwon, Okyu; Jung, Woo-Sung

2012-02-01

We analyze the multifractal spectra of daily foreign exchange rates for Japan, Hong-Kong, Korea, and Thailand with respect to the United States Dollar from 1991 to 2005. We find that the return time series show multifractal spectrum features for all four cases. To observe the effect of the Asian currency crisis, we also estimate the multifractal spectra of limited series before and after the crisis. We find that the Korean and Thai foreign exchange markets experienced a significant increase in multifractality compared to Hong-Kong and Japan. We also show that the multifractality is stronge related to the presence of high values of returns in the series.

Submicron scale tissue multifractal anisotropy in polarized laser light scattering

Science.gov (United States)

Das, Nandan Kumar; Dey, Rajib; Chakraborty, Semanti; Panigrahi, Prasanta K.; Meglinski, Igor; Ghosh, Nirmalya

2018-03-01

The spatial fluctuations of the refractive index within biological tissues exhibit multifractal anisotropy, leaving its signature as a spectral linear diattenuation of scattered polarized light. The multifractal anisotropy has been quantitatively assessed by the processing of relevant Mueller matrix elements in the Fourier domain, utilizing the Born approximation and subsequent multifractal analysis. The differential scaling exponent and width of the singularity spectrum appear to be highly sensitive to the structural multifractal anisotropy at the micron/sub-micron length scales. An immediate practical use of these multifractal anisotropy parameters was explored for non-invasive screening of cervical precancerous alterations ex vivo, with the indication of a strong potential for clinical diagnostic purposes.

Financial market volatility and contagion effect: A copula-multifractal volatility approach

Science.gov (United States)

Chen, Wang; Wei, Yu; Lang, Qiaoqi; Lin, Yu; Liu, Maojuan

2014-03-01

In this paper, we propose a new approach based on the multifractal volatility method (MFV) to study the contagion effect between the U.S. and Chinese stock markets. From recent studies, which reveal that multifractal characteristics exist in both developed and emerging financial markets, according to the econophysics literature we could draw conclusions as follows: Firstly, we estimate volatility using the multifractal volatility method, and find out that the MFV method performs best among other volatility models, such as GARCH-type and realized volatility models. Secondly, we analyze the tail dependence structure between the U.S. and Chinese stock market. The estimated static copula results for the entire period show that the SJC copula performs best, indicating asymmetric characteristics of the tail dependence structure. The estimated dynamic copula results show that the time-varying t copula achieves the best performance, which means the symmetry dynamic t copula is also a good choice, for it is easy to estimate and is able to depict both the upper and lower tail dependence structure. Finally, with the results of the previous two steps, we analyze the contagion effect between the U.S. and Chinese stock markets during the subprime mortgage crisis. The empirical results show that the subprime mortgage crisis started in the U.S. and that its stock market has had an obvious contagion effect on the Chinese stock market. Our empirical results should/might be useful for investors allocating their portfolios.

Multifractal Analysis for the Teichmueller Flow

Energy Technology Data Exchange (ETDEWEB)

Meson, Alejandro M., E-mail: meson@iflysib.unlp.edu.ar; Vericat, Fernando, E-mail: vericat@iflysib.unlp.edu.ar [Instituto de Fisica de Liquidos y Sistemas Biologicos (IFLYSIB) CCT-CONICET, La Plata-UNLP and Grupo de Aplicaciones Matematicas y Estadisticas de la Facultad de Ingenieria (GAMEFI) UNLP (Argentina)

2012-03-15

We present a multifractal description for Teichmueller flows. A key ingredient to do this is the Rauzy-Veech-Zorich reduction theory, which allows to treat the problem in the setting of suspension flows over subshifts. To perform the multifractal analysis we implement a thermodynamic formalism for suspension flows over countable alphabet subshifts a bit different from that developed by Barreira and Iommi.

Multifractal features of spot rates in the Liquid Petroleum Gas shipping market

NARCIS (Netherlands)

Engelen, Steve; Norouzzadeh, Payam; Dullaert, Wout; Rahmani, Bahareh

We investigate for the first time the spot rate dynamics of Very Large Gas Carriers (VLGCs) by means of multifractal detrended fluctuation analysis (MF-DFA) and rescaled range (R/S) analysis. Both non-parametric methods allow for a rigorous statistical analysis of the freight process by detecting

Quantum computation of multifractal exponents through the quantum wavelet transform

International Nuclear Information System (INIS)

Garcia-Mata, Ignacio; Giraud, Olivier; Georgeot, Bertrand

2009-01-01

We study the use of the quantum wavelet transform to extract efficiently information about the multifractal exponents for multifractal quantum states. We show that, combined with quantum simulation algorithms, it enables to build quantum algorithms for multifractal exponents with a polynomial gain compared to classical simulations. Numerical results indicate that a rough estimate of fractality could be obtained exponentially fast. Our findings are relevant, e.g., for quantum simulations of multifractal quantum maps and of the Anderson model at the metal-insulator transition.

Multifractal Scaling of Grayscale Patterns: Lacunarity and Correlation Dimension

Science.gov (United States)

Roy, A.; Perfect, E.

2012-12-01

While fractal models can characterize self-similarity in binary fields, comprised solely of 0's and 1's, the concept of multifractals is needed to quantify scaling behavior in non-binary grayscale fields made up of fractional values. Multifractals are characterized by a spectrum of non-integer dimensions, Dq (-∞ < q < +∞) instead of a single fractal dimension. The gliding-box algorithm is sometimes employed to estimate these different dimensions. This algorithm is also commonly used for computing another parameter, lacunarity, L, which characterizes the distribution of gaps or spaces in patterns, fractals, multifractals or otherwise, as a function of scale (or box-size, x). In the case of 2-dimensional multifractal fields, L has been shown to be theoretically related to the correlation dimension, D2, by dlog(L)/dlog(x) = D2 - 2. Therefore, it is hypothesized that lacunarity analysis can help in delineating multifractal behavior in grayscale patterns. In testing this hypothesis, a set of 2-dimensional multifractal grayscale patterns was generated with known D2 values, and then analyzed for lacunarity by employing the gliding-box algorithm. The D2 values computed using this analysis gave a 1:1 relationship with the known D2 values, thus empirically validating the theoretical relationship between L and D2. Lacunarity analysis was further used to evaluate the multifractal nature of natural grayscale images in the form of soil thin sections that had been previously classified as multifractals based on the standard box counting method. The results indicated that lacunarity analysis is a more sensitive indicator of multifractal behavior in natural grayscale patterns than the box counting approach. A weighted mean of the log-transformed lacunarity values at different scales was employed for differentiating between grayscale patterns with various degrees of scale dependent clustering attributes. This new measure, which expresses lacunarity as a single number, should

Daily extreme temperature multifractals in Catalonia (NE Spain)

International Nuclear Information System (INIS)

Burgueño, A.; Lana, X.; Serra, C.; Martínez, M.D.

2014-01-01

The multifractal character of the daily extreme temperatures in Catalonia (NE Spain) is analyzed by means of the multifractal detrended fluctuation analysis (MF-DFA) applied to 65 thermometric records covering years 1950–2004. Although no clear spatial patterns of the multifractal spectrum parameters appear, factor scores deduced from Principal Component analysis indicate some signs of spatial gradients. Additionally, the daily extreme temperature series are classified depending on their complex time behavior, through four multifractal parameters (Hurst exponent, Hölder exponent with maximum spectrum, spectrum asymmetry and spectrum width). As a synthesis of the three last parameters, a basic measure of complexity is proposed through a normalized Complexity Index. Its regional behavior is found to be free of geographical dependences. This index represents a new step towards the description of the daily extreme temperatures complexity.

Daily extreme temperature multifractals in Catalonia (NE Spain)

Energy Technology Data Exchange (ETDEWEB)

Burgueño, A. [Departament d' Astronomia i Meteorologia, Universitat de Barcelona, Barcelona (Spain); Lana, X., E-mail: francisco.javier.lana@upc.edu [Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Barcelona (Spain); Serra, C. [Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Barcelona (Spain); Martínez, M.D. [Departament de Física Aplicada, Universitat Politècnica de Catalunya, Barcelona (Spain)

2014-02-01

The multifractal character of the daily extreme temperatures in Catalonia (NE Spain) is analyzed by means of the multifractal detrended fluctuation analysis (MF-DFA) applied to 65 thermometric records covering years 1950–2004. Although no clear spatial patterns of the multifractal spectrum parameters appear, factor scores deduced from Principal Component analysis indicate some signs of spatial gradients. Additionally, the daily extreme temperature series are classified depending on their complex time behavior, through four multifractal parameters (Hurst exponent, Hölder exponent with maximum spectrum, spectrum asymmetry and spectrum width). As a synthesis of the three last parameters, a basic measure of complexity is proposed through a normalized Complexity Index. Its regional behavior is found to be free of geographical dependences. This index represents a new step towards the description of the daily extreme temperatures complexity.

(Multi)fractality of Earthquakes by use of Wavelet Analysis

Science.gov (United States)

Enescu, B.; Ito, K.; Struzik, Z. R.

2002-12-01

The fractal character of earthquakes' occurrence, in time, space or energy, has by now been established beyond doubt and is in agreement with modern models of seismicity. Moreover, the cascade-like generation process of earthquakes -with one "main" shock followed by many aftershocks, having their own aftershocks- may well be described through multifractal analysis, well suited for dealing with such multiplicative processes. The (multi)fractal character of seismicity has been analysed so far by using traditional techniques, like the box-counting and correlation function algorithms. This work introduces a new approach for characterising the multifractal patterns of seismicity. The use of wavelet analysis, in particular of the wavelet transform modulus maxima, to multifractal analysis was pioneered by Arneodo et al. (1991, 1995) and applied successfully in diverse fields, such as the study of turbulence, the DNA sequences or the heart rate dynamics. The wavelets act like a microscope, revealing details about the analysed data at different times and scales. We introduce and perform such an analysis on the occurrence time of earthquakes and show its advantages. In particular, we analyse shallow seismicity, characterised by a high aftershock "productivity", as well as intermediate and deep seismic activity, known for its scarcity of aftershocks. We examine as well declustered (aftershocks removed) versions of seismic catalogues. Our preliminary results show some degree of multifractality for the undeclustered, shallow seismicity. On the other hand, at large scales, we detect a monofractal scaling behaviour, clearly put in evidence for the declustered, shallow seismic activity. Moreover, some of the declustered sequences show a long-range dependent (LRD) behaviour, characterised by a Hurst exponent, H > 0.5, in contrast with the memory-less, Poissonian model. We demonstrate that the LRD is a genuine characteristic and is not an effect of the time series probability

High values of disorder-generated multifractals and logarithmically correlated processes

International Nuclear Information System (INIS)

Fyodorov, Yan V.; Giraud, Olivier

2015-01-01

In the introductory section of the article we give a brief account of recent insights into statistics of high and extreme values of disorder-generated multifractals following a recent work by the first author with P. Le Doussal and A. Rosso (FLR) employing a close relation between multifractality and logarithmically correlated random fields. We then substantiate some aspects of the FLR approach analytically for multifractal eigenvectors in the Ruijsenaars–Schneider ensemble (RSE) of random matrices introduced by E. Bogomolny and the second author by providing an ab initio calculation that reveals hidden logarithmic correlations at the background of the disorder-generated multifractality. In the rest we investigate numerically a few representative models of that class, including the study of the highest component of multifractal eigenvectors in the Ruijsenaars–Schneider ensemble

MULTIFRACTAL SOLAR EUV INTENSITY FLUCTUATIONS AND THEIR IMPLICATIONS FOR CORONAL HEATING MODELS

Energy Technology Data Exchange (ETDEWEB)

Cadavid, A. C.; Lawrence, J. K.; Christian, D. J. [Department of Physics and Astronomy, California State University Northridge, 18111 Nordhoff Street, Northridge, CA 91330 (United States); Rivera, Y. J. [Department of Climate and Space Sciences, University of Michigan, Ann Arbor, Michigan 48109-2143 (United States); Jennings, P. J. [5174 S. Slauson Avenue, Culver City, CA 90230 (United States); Rappazzo, A. F., E-mail: ana.cadavid@csun.edu [Department of Earth, Planetary and Space Sciences, University of California Los Angeles, Los Angeles, CA 90095 (United States)

2016-11-10

We investigate the scaling properties of the long-range temporal evolution and intermittency of Atmospheric Imaging Assembly/ Solar Dynamics Observatory intensity observations in four solar environments: an active region core, a weak emission region, and two core loops. We use two approaches: the probability distribution function (PDF) of time series increments and multifractal detrended fluctuation analysis (MF-DFA). Noise taints the results, so we focus on the 171 Å waveband, which has the highest signal-to-noise ratio. The lags between pairs of wavebands distinguish between coronal versus transition region (TR) emission. In all physical regions studied, scaling in the range of 15–45 minutes is multifractal, and the time series are anti-persistent on average. The degree of anti-correlation in the TR time series is greater than that for coronal emission. The multifractality stems from long-term correlations in the data rather than the wide distribution of intensities. Observations in the 335 Å waveband can be described in terms of a multifractal with added noise. The multiscaling of the extreme-ultraviolet data agrees qualitatively with the radiance from a phenomenological model of impulsive bursts plus noise, and also from ohmic dissipation in a reduced magnetohydrodynamic model for coronal loop heating. The parameter space must be further explored to seek quantitative agreement. Thus, the observational “signatures” obtained by the combined tests of the PDF of increments and the MF-DFA offer strong constraints that can systematically discriminate among models for coronal heating.

Multifractal vector fields and stochastic Clifford algebra.

Science.gov (United States)

Schertzer, Daniel; Tchiguirinskaia, Ioulia

2015-12-01

In the mid 1980s, the development of multifractal concepts and techniques was an important breakthrough for complex system analysis and simulation, in particular, in turbulence and hydrology. Multifractals indeed aimed to track and simulate the scaling singularities of the underlying equations instead of relying on numerical, scale truncated simulations or on simplified conceptual models. However, this development has been rather limited to deal with scalar fields, whereas most of the fields of interest are vector-valued or even manifold-valued. We show in this paper that the combination of stable Lévy processes with Clifford algebra is a good candidate to bridge up the present gap between theory and applications. We show that it indeed defines a convenient framework to generate multifractal vector fields, possibly multifractal manifold-valued fields, based on a few fundamental and complementary properties of Lévy processes and Clifford algebra. In particular, the vector structure of these algebra is much more tractable than the manifold structure of symmetry groups while the Lévy stability grants a given statistical universality.

Multifractal vector fields and stochastic Clifford algebra

Energy Technology Data Exchange (ETDEWEB)

Schertzer, Daniel, E-mail: Daniel.Schertzer@enpc.fr; Tchiguirinskaia, Ioulia, E-mail: Ioulia.Tchiguirinskaia@enpc.fr [University Paris-Est, Ecole des Ponts ParisTech, Hydrology Meteorology and Complexity HM& Co, Marne-la-Vallée (France)

2015-12-15

In the mid 1980s, the development of multifractal concepts and techniques was an important breakthrough for complex system analysis and simulation, in particular, in turbulence and hydrology. Multifractals indeed aimed to track and simulate the scaling singularities of the underlying equations instead of relying on numerical, scale truncated simulations or on simplified conceptual models. However, this development has been rather limited to deal with scalar fields, whereas most of the fields of interest are vector-valued or even manifold-valued. We show in this paper that the combination of stable Lévy processes with Clifford algebra is a good candidate to bridge up the present gap between theory and applications. We show that it indeed defines a convenient framework to generate multifractal vector fields, possibly multifractal manifold-valued fields, based on a few fundamental and complementary properties of Lévy processes and Clifford algebra. In particular, the vector structure of these algebra is much more tractable than the manifold structure of symmetry groups while the Lévy stability grants a given statistical universality.

Introduction to multifractal detrended fluctuation analysis in matlab.

Science.gov (United States)

Ihlen, Espen A F

2012-01-01

Fractal structures are found in biomedical time series from a wide range of physiological phenomena. The multifractal spectrum identifies the deviations in fractal structure within time periods with large and small fluctuations. The present tutorial is an introduction to multifractal detrended fluctuation analysis (MFDFA) that estimates the multifractal spectrum of biomedical time series. The tutorial presents MFDFA step-by-step in an interactive Matlab session. All Matlab tools needed are available in Introduction to MFDFA folder at the website www.ntnu.edu/inm/geri/software. MFDFA are introduced in Matlab code boxes where the reader can employ pieces of, or the entire MFDFA to example time series. After introducing MFDFA, the tutorial discusses the best practice of MFDFA in biomedical signal processing. The main aim of the tutorial is to give the reader a simple self-sustained guide to the implementation of MFDFA and interpretation of the resulting multifractal spectra.

Rank-ordered multifractal analysis for intermittent fluctuations with global crossover behavior

International Nuclear Information System (INIS)

Tam, Sunny W. Y.; Chang, Tom; Kintner, Paul M.; Klatt, Eric M.

2010-01-01

The rank-ordered multifractal analysis (ROMA), a recently developed technique that combines the ideas of parametric rank ordering and one-parameter scaling of monofractals, has the capabilities of deciphering the multifractal characteristics of intermittent fluctuations. The method allows one to understand the multifractal properties through rank-ordered scaling or nonscaling parametric variables. The idea of the ROMA technique is applied to analyze the multifractal characteristics of the auroral zone electric-field fluctuations observed by the SIERRA sounding rocket. The observed fluctuations span across contiguous multiple regimes of scales with different multifractal characteristics. We extend the ROMA technique such that it can take into account the crossover behavior - with the possibility of collapsing probability distribution functions - over these contiguous regimes.

Alternative measure of multifractal content and its application in finance

International Nuclear Information System (INIS)

Grech, Dariusz

2016-01-01

An alternative method for analysis of multifractal properties of time series is provided. We propose a new kind of measure of multifractality strength which takes into account the behavior of multifractal profile of the generalized Hurst exponent h(q) for all moment orders q and is not limited only to the edge values of moment orders describing the scaling properties of smallest and largest fluctuations of a given signal in multifractal detrended fluctuation analysis (MFDFA). The meaning of this new measure is clarified and its performance is investigated for synthetic multifractal data and also for examples of real signals originating from stock markets. We provide also the interpretation of the alternative method following the scaling law that links together the geometric mean value of properly normalized standard q-fluctuation function F"2(q; τ) in MFDFA and the window length τ in which detrending of a signal is performed. We discuss in this context the influence of multifractal bias on the new measure, i.e., the influence of effects which give similar observed features as multiscaling properties however, are not generated by temporal multiscaling autocorrelation in data. It is shown that the proposed alternative measure is robust in some extend to nonstationarity in data. As a result one may avoid problems with interpretation of multifractal profile h(q) encountered in many real nonstationary signals investigated in the standard way.

Roadmap for Scaling and Multifractals in Geosciences: still a long way to go ?

Science.gov (United States)

Schertzer, Daniel; Lovejoy, Shaun

2010-05-01

, we will point out more general questions, which can be put together into the following provocative question: how to convert the classical time evolving deterministic PDE's into dynamical multifractal systems? We will argue that this corresponds to an already active field of research, which include: multifractals as generic solutions of nonlinear PDE (exact results for 1D Burgers equation and a few other caricatures of Navier-Stokes equations, prospects for 3D Burgers equations), cascade structures of numerical weather models, links between multifractal processes and random dynamical systems, and the challenging debate on the most relevant stochastic multifractal formalism, whereas there is already a rather general consent about the deterministic one.

Econophysics vs Cardiophysics: the Dual Face of Multifractality

NARCIS (Netherlands)

Z.R. Struzik

2003-01-01

textabstractMultifractality in physiological time series and notably in human adult heart rate has been primarily attributed to the Fourier phase ordering of the signal [1]. In contrast, the primary cause for the width of the multifractal spectrum in financial time series has recently been connected

Multifractals of investor behavior in stock market

Science.gov (United States)

Oh, Gabjin

2017-07-01

In this paper, we analyze the nonlinear properties of investor activity using the multifractal detrended fluctuation analysis (MF-DFA) method. Using the aggregated trading volumes of buying, selling, and normalized net investor trading (NIT) to quantify the characteristics of trader behavior in the KOSPI market, we find that the cumulative distribution functions of all NIT time series, except for individual traders, follow a power-law distribution with an exponent in the range of 2.92 ≤ γ ≤ 3.87. To observe the nonlinear features of investor activity, we also calculate the multifractal spectra for the buyer, seller, and NIT data sets and find that a multifractal structure exists in all of the data, regardless of the investor type studied.

Correlation and multifractality in climatological time series

International Nuclear Information System (INIS)

Pedron, I T

2010-01-01

Climate can be described by statistical analysis of mean values of atmospheric variables over a period. It is possible to detect correlations in climatological time series and to classify its behavior. In this work the Hurst exponent, which can characterize correlation and persistence in time series, is obtained by using the Detrended Fluctuation Analysis (DFA) method. Data series of temperature, precipitation, humidity, solar radiation, wind speed, maximum squall, atmospheric pressure and randomic series are studied. Furthermore, the multifractality of such series is analyzed applying the Multifractal Detrended Fluctuation Analysis (MF-DFA) method. The results indicate presence of correlation (persistent character) in all climatological series and multifractality as well. A larger set of data, and longer, could provide better results indicating the universality of the exponents.

Multifractality, efficiency analysis of Chinese stock market and its cross-correlation with WTI crude oil price

Science.gov (United States)

Zhuang, Xiaoyang; Wei, Yu; Ma, Feng

2015-07-01

In this paper, the multifractality and efficiency degrees of ten important Chinese sectoral indices are evaluated using the methods of MF-DFA and generalized Hurst exponents. The study also scrutinizes the dynamics of the efficiency of Chinese sectoral stock market by the rolling window approach. The overall empirical findings revealed that all the sectoral indices of Chinese stock market exist different degrees of multifractality. The results of different efficiency measures have agreed on that the 300 Materials index is the least efficient index. However, they have a slight diffidence on the most efficient one. The 300 Information Technology, 300 Telecommunication Services and 300 Health Care indices are comparatively efficient. We also investigate the cross-correlations between the ten sectoral indices and WTI crude oil price based on Multifractal Detrended Cross-correlation Analysis. At last, some relevant discussions and implications of the empirical results are presented.

Multifractal structure of multiplicity distributions and negative binomials

International Nuclear Information System (INIS)

Malik, S.; Delhi, Univ.

1997-01-01

The paper presents experimental results of the multifractal structure analysis in proton-emulsion interactions at 800 GeV. The multiplicity moments have a power law dependence on the mean multiplicity in varying bin sizes of pseudorapidity. The values of generalised dimensions are calculated from the slope value. The multifractal characteristics are also examined in the light of negative binomials. The observed multiplicity moments and those derived from the negative-binomial fits agree well with each other. Also the values of D q , both observed and derived from the negative-binomial fits not only decrease with q typifying multifractality but also agree well each other showing consistency with the negative-binomial form

Multifractal distribution of spike intervals for two oscillators coupled by unreliable pulses

International Nuclear Information System (INIS)

Kestler, Johannes; Kinzel, Wolfgang

2006-01-01

Two neurons coupled by unreliable synapses are modelled by leaky integrate-and-fire neurons and stochastic on-off synapses. The dynamics is mapped to an iterated function system. Numerical calculations yield a multifractal distribution of interspike intervals. The covering, information and correlation dimensions are calculated as a function of synaptic strength and transmission probability. (letter to the editor)

Linearization effect in multifractal analysis: Insights from the Random Energy Model

Science.gov (United States)

Angeletti, Florian; Mézard, Marc; Bertin, Eric; Abry, Patrice

2011-08-01

The analysis of the linearization effect in multifractal analysis, and hence of the estimation of moments for multifractal processes, is revisited borrowing concepts from the statistical physics of disordered systems, notably from the analysis of the so-called Random Energy Model. Considering a standard multifractal process (compound Poisson motion), chosen as a simple representative example, we show the following: (i) the existence of a critical order q∗ beyond which moments, though finite, cannot be estimated through empirical averages, irrespective of the sample size of the observation; (ii) multifractal exponents necessarily behave linearly in q, for q>q∗. Tailoring the analysis conducted for the Random Energy Model to that of Compound Poisson motion, we provide explicative and quantitative predictions for the values of q∗ and for the slope controlling the linear behavior of the multifractal exponents. These quantities are shown to be related only to the definition of the multifractal process and not to depend on the sample size of the observation. Monte Carlo simulations, conducted over a large number of large sample size realizations of compound Poisson motion, comfort and extend these analyses.

Multifractal analysis of three-dimensional histogram from color images

International Nuclear Information System (INIS)

Chauveau, Julien; Rousseau, David; Richard, Paul; Chapeau-Blondeau, Francois

2010-01-01

Natural images, especially color or multicomponent images, are complex information-carrying signals. To contribute to the characterization of this complexity, we investigate the possibility of multiscale organization in the colorimetric structure of natural images. This is realized by means of a multifractal analysis applied to the three-dimensional histogram from natural color images. The observed behaviors are confronted to those of reference models with known multifractal properties. We use for this purpose synthetic random images with trivial monofractal behavior, and multidimensional multiplicative cascades known for their actual multifractal behavior. The behaviors observed on natural images exhibit similarities with those of the multifractal multiplicative cascades and display the signature of elaborate multiscale organizations stemming from the histograms of natural color images. This type of characterization of colorimetric properties can be helpful to various tasks of digital image processing, as for instance modeling, classification, indexing.

Multifractal detrended cross-correlation analysis in the MENA area

Science.gov (United States)

El Alaoui, Marwane; Benbachir, Saâd

2013-12-01

In this paper, we investigated multifractal cross-correlations qualitatively and quantitatively using a cross-correlation test and the Multifractal detrended cross-correlation analysis method (MF-DCCA) for markets in the MENA area. We used cross-correlation coefficients to measure the level of this correlation. The analysis concerns four stock market indices of Morocco, Tunisia, Egypt and Jordan. The countries chosen are signatory of the Agadir agreement concerning the establishment of a free trade area comprising Arab Mediterranean countries. We computed the bivariate generalized Hurst exponent, Rényi exponent and spectrum of singularity for each pair of indices to measure quantitatively the cross-correlations. By analyzing the results, we found the existence of multifractal cross-correlations between all of these markets. We compared the spectrum width of these indices; we also found which pair of indices has a strong multifractal cross-correlation.

Multifractal properties of resistor diode percolation.

Science.gov (United States)

Stenull, Olaf; Janssen, Hans-Karl

2002-03-01

Focusing on multifractal properties we investigate electric transport on random resistor diode networks at the phase transition between the nonpercolating and the directed percolating phase. Building on first principles such as symmetries and relevance we derive a field theoretic Hamiltonian. Based on this Hamiltonian we determine the multifractal moments of the current distribution that are governed by a family of critical exponents [psi(l)]. We calculate the family [psi(l)] to two-loop order in a diagrammatic perturbation calculation augmented by renormalization group methods.

Classification of Prolapsed Mitral Valve versus Healthy Heart from Phonocardiograms by Multifractal Analysis

Directory of Open Access Journals (Sweden)

Ana Gavrovska

2013-01-01

Full Text Available Phonocardiography has shown a great potential for developing low-cost computer-aided diagnosis systems for cardiovascular monitoring. So far, most of the work reported regarding cardiosignal analysis using multifractals is oriented towards heartbeat dynamics. This paper represents a step towards automatic detection of one of the most common pathological syndromes, so-called mitral valve prolapse (MVP, using phonocardiograms and multifractal analysis. Subtle features characteristic for MVP in phonocardiograms may be difficult to detect. The approach for revealing such features should be locally based rather than globally based. Nevertheless, if their appearances are specific and frequent, they can affect a multifractal spectrum. This has been the case in our experiment with the click syndrome. Totally, 117 pediatric phonocardiographic recordings (PCGs, 8 seconds long each, obtained from 117 patients were used for PMV automatic detection. We propose a two-step algorithm to distinguish PCGs that belong to children with healthy hearts and children with prolapsed mitral valves (PMVs. Obtained results show high accuracy of the method. We achieved 96.91% accuracy on the dataset (97 recordings. Additionally, 90% accuracy is achieved for the evaluation dataset (20 recordings. Content of the datasets is confirmed by the echocardiographic screening.

Multifractal cross-correlations between crude oil and tanker freight rate

Science.gov (United States)

Chen, Feier; Miao, Yuqi; Tian, Kang; Ding, Xiaoxu; Li, Tingyi

2017-05-01

Analysis of crude oil price and tanker freight rate volatility attract more attention as the mechanism is not only the basis of industrialization but also a vital role in economics, especially after the year 2008 when financial crisis notably blew the maritime transportation. In this paper, we studied the cross-correlations between the West Texas International crude oil (WTI) and Baltic Exchange Dirty Tanker Index (BDTI) employing the Multifractal Detrended Cross-Correlation Analysis (MF-DCCA). Empirical results show that the degree of short-term cross-correlation is higher than that in the long term and that the strength of multifractality after financial crisis is larger than that before. Moreover, the components of multifractal spectrum are quantified with the finite-size effect taken into consideration and an improved method in terms of constructing the surrogated time series provided. Numerical results show that the multifractality is generated mostly from the nonlinear and the fat-tailed probability distribution (PDF) part. Also, it is apparent that the PDF part changes a lot after the financial crisis. The research is contributory to risk management by providing various instructions for participants in shipping markets. Our main contribution is that we investigated both the multifractal features and the origin of multifractality and provided confirming evidence of multifractality through numerical results while applying quantitative analysis based on MF-DCCA; furthermore, the research is contributory to risk management since it provides instructions in both economic market and stock market simultaneously. However, constructing the surrogated series in order to obtain consistence seems less convincing which requires further discussion and attempts.

A multifractal detrended fluctuation analysis of financial market efficiency: Comparison using Dow Jones sector ETF indices

Science.gov (United States)

Tiwari, Aviral Kumar; Albulescu, Claudiu Tiberiu; Yoon, Seong-Min

2017-10-01

This study challenges the efficient market hypothesis, relying on the Dow Jones sector Exchange-Traded Fund (ETF) indices. For this purpose, we use the generalized Hurst exponent and multifractal detrended fluctuation analysis (MF-DFA) methods, using daily data over the timespan from 2000 to 2015. We compare the sector ETF indices in terms of market efficiency between short- and long-run horizons, small and large fluctuations, and before and after the global financial crisis (GFC). Our findings can be summarized as follows. First, there is clear evidence that the sector ETF markets are multifractal in nature. We also find a crossover in the multifractality of sector ETF market dynamics. Second, the utilities and consumer goods sector ETF markets are more efficient compared with the financial and telecommunications sector ETF markets, in terms of price prediction. Third, there are noteworthy discrepancies in terms of market efficiency, between the short- and long-term horizons. Fourth, the ETF market efficiency is considerably diminished after the global financial crisis.

Multifractal Model of Soil Water Erosion

Science.gov (United States)

Oleshko, Klaudia

2017-04-01

Breaking of solid surface symmetry during the interaction between the rainfall of high erosivity index and internally unstable volcanic soil/vegetation systems, results in roughness increasing as well as fertile horizon loosing. In these areas, the sustainability of management practices depends on the ability to select and implement the precise indicators of soil erodibility and vegetation capacity to protect the system against the extreme damaging precipitation events. Notwithstanding, the complex, non-linear and scaling nature of the phenomena involved in the interaction among the soil, vegetation and precipitation is still not taken into account by the numerous commonly used empirical, mathematical and computer simulation models: for instance, by the universal soil loss equation (USLE). The soil erodibility factor (K-factor) is still measuring by a set of empirical, dimensionless parameters and indexes, without taking into account the scaling (frequently multifractal) origin of a broad range of heterogeneous, anisotropic and dynamical phenomena involved in hydric erosion. Their mapping is not representative of this complex system spatial variability. In our research, we propose to use the toolbox of fractals and multifractals techniques in vista of its ability to measure the scale invariance and type/degree of soil, vegetation and precipitation symmetry breaking. The hydraulic units are chosen as the precise measure of soil/vegetation stability. These units are measured and modeled for soils with contrasting architecture, based on their porosity/permeability (Poroperm) as well as retention capacity relations. The simple Catalog of the most common Poroperm relations is proposed and the main power law relations among the elements of studied system are established and compared for some representative agricultural and natural Biogeosystems of Mexico. All resulted are related with the Mandelbrot' Baby Theorem in order to construct the universal Phase Diagram which

Multifractal characteristics of NDVI maps in space and time in the Community of Madrid (Spain)

Science.gov (United States)

Sotoca, Juan J. Martin; Saa-Requejo, Antonio; Grau, Juan B.; Tarquis, Ana M.

2015-04-01

Satellite information has contributed to improve our understanding of the spatial variability of hydro-climatic and ecological processes. Vegetation activity is tightly coupled with climate, hydro-ecological fluxes, and terrain dynamics in river basins at a wide range of space-time scales (Scheuring and Riedi, 1994). Indices of vegetation activity are constructed using satellite information of reflectance of the relevant spectral bands which enhance the contribution of vegetation being Normalized Difference Vegetation Index (NDVI) widely used. How can we study such a complex system? Multifractals and fractals are related techniques mainly used in physics to characterize the scaling behaviour of a system; they differ in that fractals look at the geometry of presence/absence patterns, while multifractals look at the arrangement of quantities such as population or biomass densities (Saravia et al., 2012). Scaling laws are an emergent general feature of ecological systems; they reflect constraints in their organization that can provide tracks about the underlying mechanisms (Solé and Bascompte, 2006). In this work, we have applied these techniques to study the spatial pattern through one year of NDVI maps. A rectangular area that includes the Community of Madrid and part of the surroundings, consisting of 300 x 280 pixels with a resolution of 500 x 500 m2 has been selected and monthly NDVI maps analyzed using the multifractal spectrum and the map of singularities (Cheng and Agterberg, 1996). The results show a cyclical pattern in the multifractal behaviour and singularity points related to river basin networks (Martín-Sotoca, 2014). References Cheng, Q. and Agterberg, F.P. (1996). Multifractal modeling and spatial statistics. Math. Geol. Vol 28, 1-16. Martín-Sotoca, J.J. (2014) Estructura Espacial de la Sequía en Pastos y sus Aplicaciones en el Seguro Agrario. Master Thesis, UPM (In Spanish). Saravia LA, Giorgi A, Momo F.: Multifractal growth in periphyton

The Multifractal Structure of Small-Scale Artificial Ionospheric Turbulence

Directory of Open Access Journals (Sweden)

Vybornov F. I.

2013-03-01

Full Text Available We present the results of investigation of a multifractal structure of the artificial ionospheric turbulence when the midlatitude ionosphere is affected by high-power radio waves. The experimental studies were performed on the basis of the SURA heating facility with the help of radio sounding of the disturbed region of ionospheric plasma by signals from the Earth’s orbital satellities. In the case of vertical radio sounding of the disturbed ionosphere region, the measured multipower and generalized multifractal spectra of turbulence coincide well with similar multifractal characteristics of the ionosperic turbulence under the natural conditions. In the case of oblique sounding of the disturbance region at small angles between the line of sight to the satellite and the direction of the Earth’s magnetic field, a nonuniform structure of the small-scale turbulence with a relatively narrow multipower spectrum and small variations in the generalized multifractal spectrum of the electron density was detected.

Aging in autonomic control by multifractal studies of cardiac interbeat intervals in the VLF band

International Nuclear Information System (INIS)

Makowiec, Danuta; Kryszewski, Stanisław; Rynkiewicz, Andrzej; Wdowczyk-Szulc, Joanna; Żarczyńska-Buchowiecka, Marta; Gałąska, Rafał

2011-01-01

The heart rate responds dynamically to various intrinsic and environmental stimuli. The autonomic nervous system is said to play a major role in this response. Multifractal analysis offers a novel method to assess the response of cardiac interbeat intervals. Twenty-four hour ECG recordings of RR interbeat intervals (of 48 elderly volunteers (age 65–94), 40 middle-aged persons (age 45–53) and 36 young adults (age 18–26)) were investigated to study the effect of aging on autonomic regulation during normal activity in healthy adults. Heart RR-interval variability in the very low frequency (VLF) band (32–420 RR intervals) was evaluated by multifractal tools. The nocturnal and diurnal signals of 6 h duration were studied separately. For each signal, the analysis was performed twice: for a given signal and for the integrated signal. A multifractal spectrum was quantified by the h max value at which a multifractal spectrum attained its maximum, width of a spectrum, Hurst exponent, extreme events h left and distance between the maxima of a signal and its integrated counterpart. The following seven characteristics are suggested as quantifying the age-related decrease in the autonomic function ('int' refers to the integrated signal): (a) h sleep max − h max wake > 0.05 for a signal; (b) h int max > 1.15 for wake; (c) h int max − h max > 0.85 for sleep; (d) Hurst wake − Hurst sleep < 0.01; (e) width wake > 0.07; (f) width int < 0.30 for sleep; (g) h int left > 0.75. Eighty-one percent of elderly people had at least four of these properties, and ninety-two percent of young people had three or less. This shows that the multifractal approach offers a concise and reliable index of healthy aging for each individual. Additionally, the applied method yielded insights into dynamical changes in the autonomic regulation due to the circadian cycle and aging. Our observations support the hypothesis that imbalance in the autonomic control due to healthy aging could

Multifractal in Volatility of Family Business Stocks Listed on Casablanca STOCK Exchange

Science.gov (United States)

Lahmiri, Salim

In this paper, we check for existence of multifractal in volatility of Moroccan family business stock returns and in volatility of Casablanca market index returns based on multifractal detrended fluctuation analysis (MF-DFA) technique. Empirical results show strong evidence of multifractal characteristics in volatility series of both family business stocks and market index. In addition, it is found that small variations in volatility of family business stocks are persistent, whilst small variations in volatility of market index are anti-persistent. However, large variations in family business volatility and market index volatility are both anti-persistent. Furthermore, multifractal spectral analysis based results show strong evidence that volatility in Moroccan family business companies exhibits more multifractality than volatility in the main stock market. These results may provide insightful information for risk managers concerned with family business stocks.

Lorentz violations in multifractal spacetimes

Energy Technology Data Exchange (ETDEWEB)

Calcagni, Gianluca [Instituto de Estructura de la Materia, CSIC, Madrid (Spain)

2017-05-15

Using the recent observation of gravitational waves (GW) produced by a black-hole merger, we place a lower bound on the energy above which a multifractal spacetime would display an anomalous geometry and, in particular, violations of Lorentz invariance. In the so-called multifractional theory with q-derivatives, we show that the deformation of dispersion relations is much stronger than in generic quantum-gravity approaches (including loop quantum gravity) and, contrary to the latter, present observations on GWs can place very strong bounds on the characteristic scales at which spacetime deviates from standard Minkowski. The energy at which multifractal effects should become apparent is E{sub *} > 10{sup 14} GeV (thus improving previous bounds by 12 orders of magnitude) when the exponents in the measure are fixed to their central value 1 / 2. We also estimate, for the first time, the effect of logarithmic oscillations in the measure (corresponding to a discrete spacetime structure) and find that they do not change much the bounds obtained in their absence, unless the amplitude of the oscillations is fine tuned. This feature, unavailable in known quantum-gravity scenarios, may help the theory to avoid being ruled out by gamma-ray burst (GRB) observations, for which E{sub *} > 10{sup 17} GeV or greater. (orig.)

Analyzing the Cross-Correlation Between Onshore and Offshore RMB Exchange Rates Based on Multifractal Detrended Cross-Correlation Analysis (MF-DCCA)

Science.gov (United States)

Xie, Chi; Zhou, Yingying; Wang, Gangjin; Yan, Xinguo

We use the multifractal detrended cross-correlation analysis (MF-DCCA) method to explore the multifractal behavior of the cross-correlation between exchange rates of onshore RMB (CNY) and offshore RMB (CNH) against US dollar (USD). The empirical data are daily prices of CNY/USD and CNH/USD from May 1, 2012 to February 29, 2016. The results demonstrate that: (i) the cross-correlation between CNY/USD and CNH/USD is persistent and its fluctuation is smaller when the order of fluctuation function is negative than that when the order is positive; (ii) the multifractal behavior of the cross-correlation between CNY/USD and CNH/USD is significant during the sample period; (iii) the dynamic Hurst exponents obtained by the rolling windows analysis show that the cross-correlation is stable when the global economic situation is good and volatile in bad situation; and (iv) the non-normal distribution of original data has a greater effect on the multifractality of the cross-correlation between CNY/USD and CNH/USD than the temporary correlation.

Multifractal signal reconstruction based on singularity power spectrum

International Nuclear Information System (INIS)

Xiong, Gang; Yu, Wenxian; Xia, Wenxiang; Zhang, Shuning

2016-01-01

Highlights: • We propose a novel multifractal reconstruction method based on singularity power spectrum analysis (MFR-SPS). • The proposed MFR-SPS method has better power characteristic than the algorithm in Fraclab. • Further, the SPS-ISE algorithm performs better than the SPS-MFS algorithm. • Based on the proposed MFR-SPS method, we can restructure singularity white fractal noise (SWFN) and linear singularity modulation (LSM) multifractal signal, in equivalent sense, similar with the linear frequency modulation(LFM) signal and WGN in the Fourier domain. - Abstract: Fractal reconstruction (FR) and multifractal reconstruction (MFR) can be considered as the inverse problem of singularity spectrum analysis, and it is challenging to reconstruct fractal signal in accord with multifractal spectrum (MFS). Due to the multiple solutions of fractal reconstruction, the traditional methods of FR/MFR, such as FBM based method, wavelet based method, random wavelet series, fail to reconstruct fractal signal deterministically, and besides, those methods neglect the power spectral distribution in the singular domain. In this paper, we propose a novel MFR method based singularity power spectrum (SPS). Supposing the consistent uniform covering of multifractal measurement, we control the traditional power law of each scale of wavelet coefficients based on the instantaneous singularity exponents (ISE) or MFS, simultaneously control the singularity power law based on the SPS, and deduce the principle and algorithm of MFR based on SPS. Reconstruction simulation and error analysis of estimated ISE, MFS and SPS show the effectiveness and the improvement of the proposed methods compared to those obtained by the Fraclab package.

The weather and climate: emergent laws and multifractal cascades

Science.gov (United States)

Lovejoy, Shaun; Schertzer, Daniel

2013-04-01

Science in general and physics and geophysics in particular are hierarchies of interlocking theories and models with low level, fundamental laws such as quantum mechanics and statistical mechanics providing the underpinnings for the emergence of the qualitatively new, higher level laws of thermodynamics and continuum mechanics that provide the current bases for modelling the weather and climate. Yest it was the belief of generations of turbulence pioneers (notably Richardson, Kolmogorov, Obhukhov, Corrsin, Bolgiano) that at sufficiently high levels of nonlinearity (quantified by the Reynold's number, of the order 10**12 in the atmosphere) that new even higher level laws would emerge describing "fully developed turbulence". However for atmospheric applications, the pioneers' eponymous laws suffered from two basic restrictions - isotropy and homogeneity - that prevented them from being valid over wide ranges of scale. Over the last thirty years both of these restrictions have been overcome - the former with the generalization from isotropic to strongly anisotropic notions of scale (to account notably for stratification), and from homogeneity to strong heterogeneity (intermittency) via multifractal cascades. In this presentation we give an overview of recent developments and analyses covering huge ranges of space-time scales (including weather, macroweather and climate time scales). We show how the combination of strong anisotropy and strong intermittency commonly leads to the "phenomenological fallacy" in which morphology is confounded with mechanism. With the help of stochastic models, we show how processes with vastly different large and small scale morphologies can arise from a unique multifractal dynamical mechanisms [Lovejoy and Schertzer, 2013]. References: Lovejoy, S., and D. Schertzer (2013), The Weather and Climate: Emergent Laws and Multifractal Cascades, 480 pp., Cambridge University Press, Cambridge.

A Renormalization-Group Interpretation of the Connection between Criticality and Multifractals

Science.gov (United States)

Chang, Tom

2014-05-01

Turbulent fluctuations in space plasmas beget phenomena of dynamic complexity. It is known that dynamic renormalization group (DRG) may be employed to understand the concept of forced and/or self-organized criticality (FSOC), which seems to describe certain scaling features of space plasma turbulence. But, it may be argued that dynamic complexity is not just a phenomenon of criticality. It is therefore of interest to inquire if DRG may be employed to study complexity phenomena that are distinctly more complicated than dynamic criticality. Power law scaling generally comes about when the DRG trajectory is attracted to the vicinity of a fixed point in the phase space of the relevant dynamic plasma parameters. What happens if the trajectory lies within a domain influenced by more than one single fixed point or more generally if the transformation underlying the DRG is fully nonlinear? The global invariants of the group under such situations (if they exist) are generally not power laws. Nevertheless, as we shall argue, it may still be possible to talk about local invariants that are power laws with the nonlinearity of transformation prescribing a specific phenomenon as crossovers. It is with such concept in mind that we may provide a connection between the properties of dynamic criticality and multifractals from the point of view of DRG (T. Chang, Chapter VII, "An Introduction to Space Plasma Complexity", Cambridge University Press, 2014). An example in terms of the concepts of finite-size scaling (FSS) and rank-ordered multifractal analysis (ROMA) of a toy model shall be provided. Research partially supported by the US National Science Foundation and the European Community's Seventh Framework Programme (FP7/ 2007-2013) under Grant agreement no. 313038/STORM.

Multi-fractal analysis of highway traffic data

Institute of Scientific and Technical Information of China (English)

Shang Peng-Jian; Shen Jin-Sheng

2007-01-01

The purpose of the present study is to investigate the presence of multi-fractal behaviours in the traffic time series not only by statistical approaches but also by geometrical approaches. The pointwise H(o)lder exponent of a function is calculated by developing an algorithm for the numerical evaluation of H(o)lder exponent of time series. The traffic time series observed on the Beijing Yuquanying highway are analysed. The results from all these methods indicate that the traffic data exhibit the multi-fractal behaviour.

Asymmetric multi-fractality in the U.S. stock indices using index-based model of A-MFDFA

International Nuclear Information System (INIS)

Lee, Minhyuk; Song, Jae Wook; Park, Ji Hwan; Chang, Woojin

2017-01-01

Highlights: • ‘Index-based A-MFDFA’ model is proposed to assess the asymmetric multi-fractality. • The asymmetric multi-fractality in the U.S. stock indices are investigated using ‘Index-based’ and ‘Return-based’ A-MFDFA. • The asymmetric feature is more significantly identified by ‘Index-based’ model than ‘return-based’ model. • Source of multi-fractality and time-varying features are analyzed. - Abstract: We detect the asymmetric multi-fractality in the U.S. stock indices based on the asymmetric multi-fractal detrended fluctuation analysis (A-MFDFA). Instead using the conventional return-based approach, we propose the index-based model of A-MFDFA where the trend based on the evolution of stock index rather than stock price return plays a role for evaluating the asymmetric scaling behaviors. The results show that the multi-fractal behaviors of the U.S. stock indices are asymmetric and the index-based model detects the asymmetric multi-fractality better than return-based model. We also discuss the source of multi-fractality and its asymmetry and observe that the multi-fractal asymmetry in the U.S. stock indices has a time-varying feature where the degree of multi-fractality and asymmetry increase during the financial crisis.

Measuring efficiency of international crude oil markets: A multifractality approach

Science.gov (United States)

Niere, H. M.

2015-01-01

The three major international crude oil markets are treated as complex systems and their multifractal properties are explored. The study covers daily prices of Brent crude, OPEC reference basket and West Texas Intermediate (WTI) crude from January 2, 2003 to January 2, 2014. A multifractal detrended fluctuation analysis (MFDFA) is employed to extract the generalized Hurst exponents in each of the time series. The generalized Hurst exponent is used to measure the degree of multifractality which in turn is used to quantify the efficiency of the three international crude oil markets. To identify whether the source of multifractality is long-range correlations or broad fat-tail distributions, shuffled data and surrogated data corresponding to each of the time series are generated. Shuffled data are obtained by randomizing the order of the price returns data. This will destroy any long-range correlation of the time series. Surrogated data is produced using the Fourier-Detrended Fluctuation Analysis (F-DFA). This is done by randomizing the phases of the price returns data in Fourier space. This will normalize the distribution of the time series. The study found that for the three crude oil markets, there is a strong dependence of the generalized Hurst exponents with respect to the order of fluctuations. This shows that the daily price time series of the markets under study have signs of multifractality. Using the degree of multifractality as a measure of efficiency, the results show that WTI is the most efficient while OPEC is the least efficient market. This implies that OPEC has the highest likelihood to be manipulated among the three markets. This reflects the fact that Brent and WTI is a very competitive market hence, it has a higher level of complexity compared against OPEC, which has a large monopoly power. Comparing with shuffled data and surrogated data, the findings suggest that for all the three crude oil markets, the multifractality is mainly due to long

Regularities of Multifractal Measures

Indian Academy of Sciences (India)

First, we prove the decomposition theorem for the regularities of multifractal Hausdorff measure and packing measure in R R d . This decomposition theorem enables us to split a set into regular and irregular parts, so that we can analyze each separately, and recombine them without affecting density properties. Next, we ...

Multifractal characteristics of optical turbulence measured through a single beam holographic process

OpenAIRE

Perez, Dario G.; Barille, Regis; Morille, Yohann; Zielinska, Sonia; Ortyl, Ewelina

2014-01-01

We have previously shown that azopolymer thin films exposed to coherent light that has travelled through a turbulent medium produces a surface relief grating containing information about the intensity of the turbulence; for instance, a relation between the refractive index structure constant C2 as a function of the surface parameters was obtained. In this work, we show that these films capture much more information about the turbulence dynamics. Multifractal detrended fluctuation and fractal ...

Multifractal Detrended Cross-Correlation Analysis of agricultural futures markets

International Nuclear Information System (INIS)

He Lingyun; Chen Shupeng

2011-01-01

Highlights: → We investigated cross-correlations between China's and US agricultural futures markets. → Power-law cross-correlations are found between the geographically far but correlated markets. → Multifractal features are significant in all the markets. → Cross-correlation exponent is less than averaged GHE when q 0. - Abstract: We investigated geographically far but temporally correlated China's and US agricultural futures markets. We found that there exists a power-law cross-correlation between them, and that multifractal features are significant in all the markets. It is very interesting that the geographically far markets show strong cross-correlations and share much of their multifractal structure. Furthermore, we found that for all the agricultural futures markets in our studies, the cross-correlation exponent is less than the averaged generalized Hurst exponents (GHE) when q 0.

Multifractal properties of diffusion-limited aggregates and random multiplicative processes

International Nuclear Information System (INIS)

Canessa, E.

1991-04-01

We consider the multifractal properties of irreversible diffusion-limited aggregation (DLA) from the point of view of the self-similarity of fluctuations in random multiplicative processes. In particular we analyse the breakdown of multifractal behaviour and phase transition associated with the negative moments of the growth probabilities in DLA. (author). 20 refs, 5 figs

Spatial Characterization of Landscapes through Multifractal Analysis of DEM

Directory of Open Access Journals (Sweden)

P. L. Aguado

2014-01-01

Full Text Available Landscape evolution is driven by abiotic, biotic, and anthropic factors. The interactions among these factors and their influence at different scales create a complex dynamic. Landscapes have been shown to exhibit numerous scaling laws, from Horton’s laws to more sophisticated scaling of heights in topography and river network topology. This scaling and multiscaling analysis has the potential to characterise the landscape in terms of the statistical signature of the measure selected. The study zone is a matrix obtained from a digital elevation model (DEM (map 10 × 10 m, and height 1 m that corresponds to homogeneous region with respect to soil characteristics and climatology known as “Monte El Pardo” although the water level of a reservoir and the topography play a main role on its organization and evolution. We have investigated whether the multifractal analysis of a DEM shows common features that can be used to reveal the underlying patterns and information associated with the landscape of the DEM mapping and studied the influence of the water level of the reservoir on the applied analysis. The results show that the use of the multifractal approach with mean absolute gradient data is a useful tool for analysing the topography represented by the DEM.

Coupled uncertainty provided by a multifractal random walker

International Nuclear Information System (INIS)

Koohi Lai, Z.; Vasheghani Farahani, S.; Movahed, S.M.S.; Jafari, G.R.

2015-01-01

The aim here is to study the concept of pairing multifractality between time series possessing non-Gaussian distributions. The increasing number of rare events creates “criticality”. We show how the pairing between two series is affected by rare events, which we call “coupled criticality”. A method is proposed for studying the coupled criticality born out of the interaction between two series, using the bivariate multifractal random walk (BiMRW). This method allows studying dependence of the coupled criticality on the criticality of each individual system. This approach is applied to data sets of gold and oil markets, and inflation and unemployment. - Highlights: • The coupled criticality between two systems is modeled by the bivariate multifractal random walk. • This coupled criticality is generally directed. • This coupled criticality is inversely proportional to the criticality of either of the systems. • The coupled criticality can emerge when at least one of the systems posses a Gaussian distribution

Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis

Directory of Open Access Journals (Sweden)

Mao-Gui Hu

2009-10-01

Full Text Available Satellite remote sensing (RS is an important contributor to Earth observation, providing various kinds of imagery every day, but low spatial resolution remains a critical bottleneck in a lot of applications, restricting higher spatial resolution analysis (e.g., intraurban. In this study, a multifractal-based super-resolution reconstruction method is proposed to alleviate this problem. The multifractal characteristic is common in Nature. The self-similarity or self-affinity presented in the image is useful to estimate details at larger and smaller scales than the original. We first look for the presence of multifractal characteristics in the images. Then we estimate parameters of the information transfer function and noise of the low resolution image. Finally, a noise-free, spatial resolutionenhanced image is generated by a fractal coding-based denoising and downscaling method. The empirical case shows that the reconstructed super-resolution image performs well indetail enhancement. This method is not only useful for remote sensing in investigating Earth, but also for other images with multifractal characteristics.

Influence of urban morphology on total noise pollution: multifractal description.

Science.gov (United States)

Ariza-Villaverde, Ana B; Jiménez-Hornero, Francisco J; Gutiérrez De Ravé, Eduardo

2014-02-15

Exposure to ambient noise levels above 65 dB can cause public health problems. The spatial distribution of this kind of pollution is linked to various elements which make up the urban form, such as construction density, the existence of open spaces and the shape and physical position of buildings. Since urban morphology displays multifractal behaviour, the present research studies for the first time the relationship between total noise pollution and urban features, such as street width and building height by means of a joint multifractal spectrum in two neighbourhoods of the city of Cordoba (Andalusia, Spain). According to the results, the joint multifractal spectrum reveals a positive correlation between the total noise pollution and the street width to building height ratio, this being more evident when urban morphology is regular. The information provided by the multifractal analysis completes the description obtained by using urban indexes and landscape metrics and might be useful for urban planning once the linkage between both frameworks has been done. Copyright © 2013 Elsevier B.V. All rights reserved.

Multifractal characterisation and classification of bread crumb digital images

OpenAIRE

Baravalle, Rodrigo Guillermo; Delrieux, Claudio Augusto; Gómez, Juan Carlos

2017-01-01

Adequate models of the bread crumb structure can be critical for understanding flow and transport processes in bread manufacturing, creating synthetic bread crumb images for photo-realistic rendering, evaluating similarities, and establishing quality features of different bread crumb types. In this article, multifractal analysis, employing the multifractal spectrum (MFS), has been applied to study the structure of the bread crumb in four varieties of bread (baguette, sliced, bran, and sandwic...

Variability of multifractal parameters in an urban precipitation monitoring network

Science.gov (United States)

Licznar, Paweł; De Michele, Carlo; Dżugaj, Dagmara; Niesobska, Maria

2014-05-01

Precipitation especially over urban areas is considered a highly non-linear process, with wide variability over a broad range of temporal and spatial scales. Despite obvious limitations of rainfall gauges location at urban sites, rainfall monitoring by gauge networks is a standard solution of urban hydrology. Often urban precipitation gauge networks are formed by modern electronic gauges and connected to control units of centralized urban drainage systems. Precipitation data, recorded online through these gauge networks, are used in so called Real-Time-Control (RTC) systems for the development of optimal strategies of urban drainage outflows management. As a matter of fact, the operation of RTC systems is motivated mainly by the urge of reducing the severity of urban floods and combined sewerage overflows, but at the same time, it creates new valuable precipitation data sources. The variability of precipitation process could be achieved by investigating multifractal behavior displayed by the temporal structure of precipitation data. There are multiply scientific communications concerning multifractal properties of point-rainfall data from different worldwide locations. However, very little is known about the close variability of multifractal parameters among closely located gauges, at the distances of single kilometers. Having this in mind, here we assess the variability of multifractal parameters among gauges of the urban precipitation monitoring network in Warsaw, Poland. We base our analysis on the set of 1-minute rainfall time series recorded in the period 2008-2011 by 25 electronic weighing type gauges deployed around the city by the Municipal Water Supply and Sewerage Company in Warsaw as a part of local RTC system. The presence of scale invariance and multifractal properties in the precipitation process was investigated with spectral analysis, functional box counting method and studying the probability distributions and statistical moments of the rainfall

Multifractal analysis of a GCM climate

Science.gov (United States)

Carl, P.

2003-04-01

Multifractal analysis using the Wavelet Transform Modulus Maxima (WTMM) approach is being applied to the climate of a Mintz--Arakawa type, coarse resolution, two--layer AGCM. The model shows a backwards running period multiplication scenario throughout the northern summer, subsequent to a 'hard', subcritical Hopf bifurcation late in spring. This 'route out of chaos' (seen in cross sections of a toroidal phase space structure) is born in the planetary monsoon system which inflates the seasonal 'cycle' into these higher order structures and is blamed for the pronounced intraseasonal--to--centennial model climate variability. Previous analyses of the latter using advanced modal decompositions showed regularity based patterns in the time--frequency plane which are qualitatively similar to those obtained from the real world. The closer look here at the singularity structures, as a fundamental diagnostic supplement, aims at both more complete understanding (and quantification) of the model's qualitative dynamics and search for further tools of model intercomparison and verification in this respect. Analysing wavelet is the 10th derivative of the Gaussian which might suffice to suppress regular patterns in the data. Intraseasonal attractors, studied in time series of model precipitation over Central India, show shifting and braodening singularity spectra towards both more violent extreme events (premonsoon--monsoon transition) and weaker events (late summer to postmonsoon transition). Hints at a fractal basin boundary are found close to transition from period--2 to period--1 in the monsoon activity cycle. Interannual analyses are provided for runs with varied solar constants. To address the (in--)stationarity issue, first results are presented with a windowed multifractal analysis of longer--term runs ("singularity spectrogram").

Statistical classifiers on multifractal parameters for optical diagnosis of cervical cancer

Science.gov (United States)

Mukhopadhyay, Sabyasachi; Pratiher, Sawon; Kumar, Rajeev; Krishnamoorthy, Vigneshram; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-06-01

An augmented set of multifractal parameters with physical interpretations have been proposed to quantify the varying distribution and shape of the multifractal spectrum. The statistical classifier with accuracy of 84.17% validates the adequacy of multi-feature MFDFA characterization of elastic scattering spectroscopy for optical diagnosis of cancer.

Apparent scale correlations in a random multifractal process

DEFF Research Database (Denmark)

Cleve, Jochen; Schmiegel, Jürgen; Greiner, Martin

2008-01-01

We discuss various properties of a homogeneous random multifractal process, which are related to the issue of scale correlations. By design, the process has no built-in scale correlations. However, when it comes to observables like breakdown coefficients, which are based on a coarse......-graining of the multifractal field, scale correlations do appear. In the log-normal limit of the model process, the conditional distributions and moments of breakdown coefficients reproduce the observations made in fully developed small-scale turbulence. These findings help to understand several puzzling empirical details...

Multifractal analysis of oceanic chlorophyll maps remotely sensed from space

Directory of Open Access Journals (Sweden)

L. de Montera

2011-03-01

Full Text Available Phytoplankton patchiness has been investigated with multifractal analysis techniques. We analyzed oceanic chlorophyll maps, measured by the SeaWiFS orbiting sensor, which are considered to be good proxies for phytoplankton. The study area is the Senegalo-Mauritanian upwelling region, because it has a low cloud cover and high chlorophyll concentrations. Multifractal properties are observed, from the sub-mesoscale up to the mesoscale, and are found to be consistent with the Corssin-Obukhov scale law of passive scalars. This result indicates that, in this specific region and within this scale range, turbulent mixing would be the dominant effect leading to the observed variability of phytoplankton fields. Finally, it is shown that multifractal patchiness can be responsible for significant biases in the nonlinear source and sink terms involved in biogeochemical numerical models.

Dual-induced multifractality in online viewing activity

Science.gov (United States)

Qin, Yu-Hao; Zhao, Zhi-Dan; Cai, Shi-Min; Gao, Liang; Stanley, H. Eugene

2018-01-01

Although recent studies have found that the long-term correlations relating to the fat-tailed distribution of inter-event times exist in human activity and that these correlations indicate the presence of fractality, the property of fractality and its origin have not been analyzed. We use both detrended fluctuation analysis and multifractal detrended fluctuation analysis to analyze the time series in online viewing activity separating from Movielens and Netflix. We find long-term correlations at both the individual and communal levels and that the extent of correlation at the individual level is determined by the activity level. These long-term correlations also indicate that there is fractality in the pattern of online viewing. We first find a multifractality that results from the combined effect of the fat-tailed distribution of inter-event times (i.e., the times between successive viewing actions of individuals) and the long-term correlations in online viewing activity and verify this finding using three synthesized series. Therefore, it can be concluded that the multifractality in online viewing activity is caused by both the fat-tailed distribution of inter-event times and the long-term correlations and that this enlarges the generic property of human activity to include not just physical space but also cyberspace.

Multifractal analysis of forest fires in complex regions

Science.gov (United States)

Vega Orozco, C. D.; Kanevski, M.; Golay, J.; Tonini, M.; Conedera, M.

2012-04-01

Forest fires can be studied as point processes where the ignition points represent the set of locations of the observed events in a defined study region. Their spatial and temporal patterns can be characterized by their fractal properties; which quantify the global aspect of the geometry of the support data. However, a monofractal dimension can not completely describe the pattern structure and related scaling properties. Enhancements in fractal theory had developed the multifractal concept which describes the measures from which interlinked fractal sets can be retrieved and characterized by their fractal dimension and singularity strength [1, 2]. The spatial variability of forest fires is conditioned by an intermixture of human, topographic, meteorological and vegetation factors. This heterogeneity makes fire patterns complex scale-invariant processes difficult to be depicted by a single scale. Therefore, this study proposes an exploratory data analysis through a multifractal formalism to characterize and quantify the multiscaling behaviour of the spatial distribution pattern of this phenomenon in a complex region like the Swiss Alps. The studied dataset is represented by 2,401 georeferenced forest fire ignition points in canton Ticino, Switzerland, in a 40-years period from 1969 to 2008. Three multifractal analyses are performed: one assesses the multiscaling behaviour of fire occurrence probability of the support data (raw data) and four random patterns simulated within three different support domains; second analysis studies the multifractal behavior of patterns from anthropogenic and natural ignited fires (arson-, accident- and lightning-caused fires); and third analysis aims at detecting scale-dependency of the size of burned area. To calculate the generalized dimensions, Dq, a generalization of the box counting methods is carried out based on the generalization of Rényi information of the qth order moment of the probability distribution. For q > 0, Dq

Multifractals in Western Major STOCK Markets Historical Volatilities in Times of Financial Crisis

Science.gov (United States)

Lahmiri, Salim

In this paper, the generalized Hurst exponent is used to investigate multifractal properties of historical volatility (CHV) in stock market price and return series before, during and after 2008 financial crisis. Empirical results from NASDAQ, S&P500, TSE, CAC40, DAX, and FTSE stock market data show that there is strong evidence of multifractal patterns in HV of both price and return series. In addition, financial crisis deeply affected the behavior and degree of multifractality in volatility of Western financial markets at price and return levels.

Price-volume multifractal analysis and its application in Chinese stock markets

Science.gov (United States)

Yuan, Ying; Zhuang, Xin-tian; Liu, Zhi-ying

2012-06-01

An empirical research on Chinese stock markets is conducted using statistical tools. First, the multifractality of stock price return series, ri(ri=ln(Pt+1)-ln(Pt)) and trading volume variation series, vi(vi=ln(Vt+1)-ln(Vt)) is confirmed using multifractal detrended fluctuation analysis. Furthermore, a multifractal detrended cross-correlation analysis between stock price return and trading volume variation in Chinese stock markets is also conducted. It is shown that the cross relationship between them is also found to be multifractal. Second, the cross-correlation between stock price Pi and trading volume Vi is empirically studied using cross-correlation function and detrended cross-correlation analysis. It is found that both Shanghai stock market and Shenzhen stock market show pronounced long-range cross-correlations between stock price and trading volume. Third, a composite index R based on price and trading volume is introduced. Compared with stock price return series ri and trading volume variation series vi, R variation series not only remain the characteristics of original series but also demonstrate the relative correlation between stock price and trading volume. Finally, we analyze the multifractal characteristics of R variation series before and after three financial events in China (namely, Price Limits, Reform of Non-tradable Shares and financial crisis in 2008) in the whole period of sample to study the changes of stock market fluctuation and financial risk. It is found that the empirical results verified the validity of R.

Multifractality and quantum diffusion from self-consistent theory of localization

Energy Technology Data Exchange (ETDEWEB)

Suslov, I. M., E-mail: suslov@kapitza.ras.ru [Kapitza Institute for Physical Problems (Russian Federation)

2015-11-15

Multifractal properties of wave functions in a disordered system can be derived from self-consistent theory of localization by Vollhardt and Wölfle. A diagrammatic interpretation of results allows to obtain all scaling relations used in numerical experiments. The arguments are given that the one-loop Wegner result for a space dimension d = 2 + ϵ is exact, so the multifractal spectrum is strictly parabolical. The σ-models are shown to be deficient at the four-loop level and the possible reasons of that are discussed. The extremely slow convergence to the thermodynamic limit is demonstrated. The open question on the relation between multifractality and a spatial dispersion of the diffusion coefficient D(ω, q) is resolved in the compromise manner due to ambiguity of the D(ω, q) definition. Comparison is made with the extensive numerical material.

A Multifractal Detrended Fluctuation Analysis of the Ising Financial Markets Model with Small World Topology

International Nuclear Information System (INIS)

Zhang Ang-Hui; Li Xiao-Wen; Su Gui-Feng; Zhang Yi

2015-01-01

We present a multifractal detrended fluctuation analysis (MFDFA) of the time series of return generated by our recently-proposed Ising financial market model with underlying small world topology. The result of the MFDFA shows that there exists obvious multifractal scaling behavior in produced time series. We compare the MFDFA results for original time series with those for shuffled series, and find that its multifractal nature is due to two factors: broadness of probability density function of the series and different correlations in small- and large-scale fluctuations. This may provide new insight to the problem of the origin of multifractality in financial time series. (paper)

The foreign exchange market: return distributions, multifractality, anomalous multifractality and the Epps effect

Science.gov (United States)

Drożdż, Stanisław; Kwapień, Jarosław; Oświȩcimka, Paweł; Rak, Rafał

2010-10-01

We present a systematic study of various statistical characteristics of high-frequency returns from the foreign exchange market. This study is based on six exchange rates forming two triangles: EUR-GBP-USD and GBP-CHF-JPY. It is shown that the exchange rate return fluctuations for all of the pairs considered are well described by the non-extensive statistics in terms of q-Gaussians. There exist some small quantitative variations in the non-extensivity q-parameter values for different exchange rates (which depend also on the time scales studied), and this can be related to the importance of a given exchange rate in the world's currency trade. Temporal correlations organize the series of returns such that they develop the multifractal characteristics for all of the exchange rates, with a varying degree of symmetry of the singularity spectrum f(α), however. The most symmetric spectrum is identified for the GBP/USD. We also form time series of triangular residual returns and find that the distributions of their fluctuations develop disproportionately heavier tails as compared to small fluctuations, which excludes description in terms of q-Gaussians. The multifractal characteristics of these residual returns reveal such anomalous properties as negative singularity exponents and even negative singularity spectra. Such anomalous multifractal measures have so far been considered in the literature in connection with diffusion-limited aggregation and with turbulence. Studying the cross-correlations among different exchange rates, we found that market inefficiency on short time scales leads to the occurrence of the Epps effect on much longer time scales, but comparable to the ones for the stock market. Although the currency market is much more liquid than the stock markets and has a much greater transaction frequency, the building up of correlations takes up to several hours—a duration that does not differ much from what is observed in the stock markets. This may suggest

A Macroscopic Multifractal Analysis of Parabolic Stochastic PDEs

Science.gov (United States)

Khoshnevisan, Davar; Kim, Kunwoo; Xiao, Yimin

2018-05-01

It is generally argued that the solution to a stochastic PDE with multiplicative noise—such as \\dot{u}= 1/2 u''+uξ, where {ξ} denotes space-time white noise—routinely produces exceptionally-large peaks that are "macroscopically multifractal." See, for example, Gibbon and Doering (Arch Ration Mech Anal 177:115-150, 2005), Gibbon and Titi (Proc R Soc A 461:3089-3097, 2005), and Zimmermann et al. (Phys Rev Lett 85(17):3612-3615, 2000). A few years ago, we proved that the spatial peaks of the solution to the mentioned stochastic PDE indeed form a random multifractal in the macroscopic sense of Barlow and Taylor (J Phys A 22(13):2621-2626, 1989; Proc Lond Math Soc (3) 64:125-152, 1992). The main result of the present paper is a proof of a rigorous formulation of the assertion that the spatio-temporal peaks of the solution form infinitely-many different multifractals on infinitely-many different scales, which we sometimes refer to as "stretch factors." A simpler, though still complex, such structure is shown to also exist for the constant-coefficient version of the said stochastic PDE.

A Macroscopic Multifractal Analysis of Parabolic Stochastic PDEs

Science.gov (United States)

Khoshnevisan, Davar; Kim, Kunwoo; Xiao, Yimin

2018-04-01

It is generally argued that the solution to a stochastic PDE with multiplicative noise—such as \\dot{u}= 1/2 u''+uξ, where {ξ} denotes space-time white noise—routinely produces exceptionally-large peaks that are "macroscopically multifractal." See, for example, Gibbon and Doering (Arch Ration Mech Anal 177:115-150, 2005), Gibbon and Titi (Proc R Soc A 461:3089-3097, 2005), and Zimmermann et al. (Phys Rev Lett 85(17):3612-3615, 2000). A few years ago, we proved that the spatial peaks of the solution to the mentioned stochastic PDE indeed form a random multifractal in the macroscopic sense of Barlow and Taylor (J Phys A 22(13):2621-2626, 1989; Proc Lond Math Soc (3) 64:125-152, 1992). The main result of the present paper is a proof of a rigorous formulation of the assertion that the spatio-temporal peaks of the solution form infinitely-many different multifractals on infinitely-many different scales, which we sometimes refer to as "stretch factors." A simpler, though still complex, such structure is shown to also exist for the constant-coefficient version of the said stochastic PDE.

Analyzing the LiF thin films deposited at different substrate temperatures using multifractal technique

Energy Technology Data Exchange (ETDEWEB)

Yadav, R.P. [Department of Physics, University of Allahabad, Allahabad, UP 211002 (India); Dwivedi, S., E-mail: suneetdwivedi@gmail.com [K Banerjee Centre of Atmospheric and Ocean Studies, University of Allahabad, Allahabad, UP 211002 (India); Mittal, A.K. [Department of Physics, University of Allahabad, Allahabad, UP 211002 (India); K Banerjee Centre of Atmospheric and Ocean Studies, University of Allahabad, Allahabad, UP 211002 (India); Kumar, Manvendra [Nanotechnology Application Centre, University of Allahabad, Allahabad, UP 211002 (India); Pandey, A.C. [K Banerjee Centre of Atmospheric and Ocean Studies, University of Allahabad, Allahabad, UP 211002 (India); Nanotechnology Application Centre, University of Allahabad, Allahabad, UP 211002 (India)

2014-07-01

The Atomic Force Microscopy technique is used to characterize the surface morphology of LiF thin films deposited at substrate temperatures 77 K, 300 K and 500 K, respectively. It is found that the surface roughness of thin film increases with substrate temperature. The multifractal nature of the LiF thin film at each substrate temperature is investigated using the backward two-dimensional multifractal detrended moving average analysis. The strength of multifractility and the non-uniformity of the height probabilities of the thin films increase as the substrate temperature increases. Both the width of the multifractal spectrum and the difference of fractal dimensions of the thin films increase sharply as the temperature reaches 500 K, indicating that the multifractility of the thin films becomes more pronounced at the higher substrate temperatures with greater cluster size. - Highlights: • Analyzing LiF thin films using multifractal detrended moving average technique • Surface roughness of LiF thin film increases with substrate temperature. • LiF thin films at each substrate temperature exhibit multifractality. • Multifractility becomes more pronounced at the higher substrate temperatures.

Estimation of the global regularity of a multifractional Brownian motion

DEFF Research Database (Denmark)

Lebovits, Joachim; Podolskij, Mark

This paper presents a new estimator of the global regularity index of a multifractional Brownian motion. Our estimation method is based upon a ratio statistic, which compares the realized global quadratic variation of a multifractional Brownian motion at two different frequencies. We show that a ...... that a logarithmic transformation of this statistic converges in probability to the minimum of the Hurst functional parameter, which is, under weak assumptions, identical to the global regularity index of the path....

Degree distributions of the visibility graphs mapped from fractional Brownian motions and multifractal random walks

International Nuclear Information System (INIS)

Ni Xiaohui; Jiang Zhiqiang; Zhou Weixing

2009-01-01

The dynamics of a complex system is usually recorded in the form of time series, which can be studied through its visibility graph from a complex network perspective. We investigate the visibility graphs extracted from fractional Brownian motions and multifractal random walks, and find that the degree distributions exhibit power-law behaviors, in which the power-law exponent α is a linear function of the Hurst index H of the time series. We also find that the degree distribution of the visibility graph is mainly determined by the temporal correlation of the original time series with minor influence from the possible multifractal nature. As an example, we study the visibility graphs constructed from three Chinese stock market indexes and unveil that the degree distributions have power-law tails, where the tail exponents of the visibility graphs and the Hurst indexes of the indexes are close to the α∼H linear relationship.

Multifractal analysis of multiparticle emission data in the framework of visibility graph and sandbox algorithm

Science.gov (United States)

Mali, P.; Manna, S. K.; Mukhopadhyay, A.; Haldar, P. K.; Singh, G.

2018-03-01

Multiparticle emission data in nucleus-nucleus collisions are studied in a graph theoretical approach. The sandbox algorithm used to analyze complex networks is employed to characterize the multifractal properties of the visibility graphs associated with the pseudorapidity distribution of charged particles produced in high-energy heavy-ion collisions. Experimental data on 28Si+Ag/Br interaction at laboratory energy Elab = 14 . 5 A GeV, and 16O+Ag/Br and 32S+Ag/Br interactions both at Elab = 200 A GeV, are used in this analysis. We observe a scale free nature of the degree distributions of the visibility and horizontal visibility graphs associated with the event-wise pseudorapidity distributions. Equivalent event samples simulated by ultra-relativistic quantum molecular dynamics, produce degree distributions that are almost identical to the respective experiment. However, the multifractal variables obtained by using sandbox algorithm for the experiment to some extent differ from the respective simulated results.

EGS Richardson AGU Chapman NVAG3 Conference: Nonlinear Variability in Geophysics: scaling and multifractal processes

Directory of Open Access Journals (Sweden)

D. Schertzer

1994-01-01

Full Text Available 1. The conference The third conference on "Nonlinear VAriability in Geophysics: scaling and multifractal processes" (NVAG 3 was held in Cargese, Corsica, Sept. 10-17, 1993. NVAG3 was joint American Geophysical Union Chapman and European Geophysical Society Richardson Memorial conference, the first specialist conference jointly sponsored by the two organizations. It followed NVAG1 (Montreal, Aug. 1986, NVAG2 (Paris, June 1988; Schertzer and Lovejoy, 1991, five consecutive annual sessions at EGS general assemblies and two consecutive spring AGU meeting sessions. As with the other conferences and workshops mentioned above, the aim was to develop confrontation between theories and experiments on scaling/multifractal behaviour of geophysical fields. Subjects covered included climate, clouds, earthquakes, atmospheric and ocean dynamics, tectonics, precipitation, hydrology, the solar cycle and volcanoes. Areas of focus included new methods of data analysis (especially those used for the reliable estimation of multifractal and scaling exponents, as well as their application to rapidly growing data bases from in situ networks and remote sensing. The corresponding modelling, prediction and estimation techniques were also emphasized as were the current debates about stochastic and deterministic dynamics, fractal geometry and multifractals, self-organized criticality and multifractal fields, each of which was the subject of a specific general discussion. The conference started with a one day short course of multifractals featuring four lectures on a Fundamentals of multifractals: dimension, codimensions, codimension formalism, b Multifractal estimation techniques: (PDMS, DTM, c Numerical simulations, Generalized Scale Invariance analysis, d Advanced multifractals, singular statistics, phase transitions, self-organized criticality and Lie cascades (given by D. Schertzer and S. Lovejoy, detailed course notes were sent to participants shortly after the

EGS Richardson AGU Chapman NVAG3 Conference: Nonlinear Variability in Geophysics: scaling and multifractal processes

Science.gov (United States)

Schertzer, D.; Lovejoy, S.

1. The conference The third conference on "Nonlinear VAriability in Geophysics: scaling and multifractal processes" (NVAG 3) was held in Cargese, Corsica, Sept. 10-17, 1993. NVAG3 was joint American Geophysical Union Chapman and European Geophysical Society Richardson Memorial conference, the first specialist conference jointly sponsored by the two organizations. It followed NVAG1 (Montreal, Aug. 1986), NVAG2 (Paris, June 1988; Schertzer and Lovejoy, 1991), five consecutive annual sessions at EGS general assemblies and two consecutive spring AGU meeting sessions. As with the other conferences and workshops mentioned above, the aim was to develop confrontation between theories and experiments on scaling/multifractal behaviour of geophysical fields. Subjects covered included climate, clouds, earthquakes, atmospheric and ocean dynamics, tectonics, precipitation, hydrology, the solar cycle and volcanoes. Areas of focus included new methods of data analysis (especially those used for the reliable estimation of multifractal and scaling exponents), as well as their application to rapidly growing data bases from in situ networks and remote sensing. The corresponding modelling, prediction and estimation techniques were also emphasized as were the current debates about stochastic and deterministic dynamics, fractal geometry and multifractals, self-organized criticality and multifractal fields, each of which was the subject of a specific general discussion. The conference started with a one day short course of multifractals featuring four lectures on a) Fundamentals of multifractals: dimension, codimensions, codimension formalism, b) Multifractal estimation techniques: (PDMS, DTM), c) Numerical simulations, Generalized Scale Invariance analysis, d) Advanced multifractals, singular statistics, phase transitions, self-organized criticality and Lie cascades (given by D. Schertzer and S. Lovejoy, detailed course notes were sent to participants shortly after the conference). This

Multifractal characteristics of multiparticle production in heavy-ion collisions at SPS energies

Science.gov (United States)

Khan, Shaista; Ahmad, Shakeel

Entropy, dimensions and other multifractal characteristics of multiplicity distributions of relativistic charged hadrons produced in ion-ion collisions at SPS energies are investigated. The analysis of the experimental data is carried out in terms of phase space bin-size dependence of multiplicity distributions following the Takagi’s approach. Yet another method is also followed to study the multifractality which, is not related to the bin-width and (or) the detector resolution, rather involves multiplicity distribution of charged particles in full phase space in terms of information entropy and its generalization, Rényi’s order-q information entropy. The findings reveal the presence of multifractal structure — a remarkable property of the fluctuations. Nearly constant values of multifractal specific heat “c” estimated by the two different methods of analysis followed indicate that the parameter “c” may be used as a universal characteristic of the particle production in high energy collisions. The results obtained from the analysis of the experimental data agree well with the predictions of Monte Carlo model AMPT.

Fractals and multifractals in physics

International Nuclear Information System (INIS)

Arcangelis, L. de.

1987-01-01

We present a general introduction to the world of fractals. The attention is mainly devoted to stress how fractals do indeed appear in the real world and to find quantitative methods for characterizing their properties. The idea of multifractality is also introduced and it is presented in more details within the framework of the percolation problem

Multifractal resilience and viability

Science.gov (United States)

Tchiguirinskaia, I.; Schertzer, D. J. M.

2017-12-01

The term resilience has become extremely fashionable and there had been many attempts to provide operational definition and in fact metrics going beyond a set of more or less ad-hoc indicators. The viability theory (Aubin and Saint-Pierre, 2011) have been used to give a rather precise mathematical definition of resilience (Deffuant and Gilbert, 2011). However, it does not grasp the multiscale nature of resilience that is rather fundamental as particularly stressed by Folke et al (2010). In this communication, we first recall a preliminary attempt (Tchiguirinskaia et al., 2014) to define multifractal resilience with the help of the maximal probable singularity. Then we extend this multifractal approach to the capture basin of the viability, therefore the resilient basin. Aubin, J P, A. Bayen, and P Saint-Pierre (2011). Viability Theory. New Directions. Springer, Berlin,. Deffuant, G. and Gilbert, N. (eds) (2011) Viability and Resilience of Complex Systems. Springer Berlin.Folke, C., S R Carpenter, B Walker, M Sheffer, T Chapin, and J Rockstroem (2010). Resilience thinking: integrating re- silience, adaptability and transformability. Ecology and So- ciety, 14(4):20, Tchiguirinskaia,I., D. Schertzer, , A. Giangola-Murzyn and T. C. Hoang (2014). Multiscale resilience metrics to assess flood. Proceedings of ICCSA 2014, Normandie University, Le Havre, France -.

Multifractal Detrended Fluctuation Analysis of Self-Potential Field Prior to the M 6.5, October 24, 1993 Earthquake in MÉXICO

Science.gov (United States)

Cervantes, F.; González-Trejo, J. I.; Real-Ramírez, C. A.; Hoyos-Reyes, L. F.; Area de Sistemas Computacionales

2013-05-01

In the current literature on seismo electromagnetic, it has been reported many earthquakes which present electromagnetic anomalies as probable precursors of their occurrences. Although this methodology remains yet under discussion, is relevant to study many particular cases. In this work, we report a multifractal detrended fluctuation analysis (MFDFA) of electroseismic signals recorded in the Acapulco station during 1993. In October 24, 1993, occurred and earthquake (EQ) with M 6.5, with epicenter at (16.54 N, 98.98 W), 100Km away from the mentioned station. The multifractal spectrum identifies the deviations in fractal structure within time periods with large and small fluctuations. We discuss the dynamical meaning of this analysis and its possible relation with the mentioned EQ.

Multifractal structure of multiparticle production in the branching models

International Nuclear Information System (INIS)

Chiu, C.B.; Hwa, R.C.

1990-01-01

A procedure is described for the multifractal analysis of data on multiparticle production obtained at high energy either in experiment or in Monte Carlo simulation. It is shown how the spectrum f(α) of the rapidity-density index α can be determined from the multiplicity fluctuation of the rapidity distribution, as the resolution is changed. The branching model is used to illustrate the procedure. It is found that the φ 3 model has a narrower f(α) than the gluon model, suggesting that multifractality is a useful arena for confrontation between theory and experiment. 13 refs., 2 figs

Multi-fractal measures of city-size distributions based on the three-parameter Zipf model

International Nuclear Information System (INIS)

Chen Yanguang; Zhou Yixing

2004-01-01

A multi-fractal framework of urban hierarchies is presented to address the rank-size distribution of cities. The three-parameter Zipf model based on a pair of exponential-type scaling laws is generalized to multi-scale fractal measures. Then according to the equivalent relationship between Zipf's law and Pareto distribution, a set of multi-fractal equations are derived using dual conversion and the Legendre transform. The US city population data coming from the 2000 census are employed to verify the multi-fractal models and the results are satisfying. The multi-fractal measures reveal some strange symmetry regularity of urban systems. While explaining partially the remains of the hierarchical step-like frequency distribution of city sizes suggested by central place theory, the mathematical framework can be interpreted with the entropy-maximizing principle and some related ideas from self-organization

MFDFA and Lacunarity Analysis of Synthetic Multifractals and Pre-Cancerous Tissues

Science.gov (United States)

Roy, A.; Das, N.; Ghosh, N.

2017-12-01

Multifractal Detrended Fluctuation Analysis (MFDFA) has been employed for evaluating complex variations in the refractive index (RI) of human pre-cancerous tissues. While this was primarily aimed towards the early diagnosis of cancer in the human cervix, question remains whether multifractal analysis alone can be conclusively used for distinguishing between various grades of pre-cancerous tissues. Lacunarity is a parameter that was developed for multiscale analysis of data and has been shown to be theoretically related to the correlation dimension, D2, by dlog(L)/dlog(x) = D2 - 2. Further, research has proven that not only can Lacunarity be used as a preliminary indicator of multifractal behavior but it also distinguishes between images with similar correlation dimension values. In order to compare the efficacy of the two approaches namely, MFDFA and Lacunarity, in distinguishing between pre-cancerous tissues of various grades, we test these techniques on a set of 2-dimensional theoretical random multifractal fields. MFDFA is employed for computing the width of the singularity spectrum f(α), which is a measure of multifractal behavior. A weighted mean of the log-transformed lacunarity values at different scales is employed for differentiating between patterns with the same correlation dimension but differences in texture. The two different techniques are then applied to images containing RI values of biopsy samples from human cervical tissues that were histo-pathologically characterized as grade-I and grade-II pre-cancerous cells. The results show that the two approaches are complementary to one another when it comes to multi-scale analysis of complex natural patterns manifested in the images of such pre-cancerous cells.

Multifractal analysis of vertical profiles of soil penetration resistance at the field scale

Directory of Open Access Journals (Sweden)

G. M. Siqueira

2013-07-01

Full Text Available Soil penetration resistance (PR is widely used as an indirect indicator of soil strength. Soil PR is linked to basic soil properties and correlated to root growth and plant production, and as such it is extensively used as a practical tool for assessing soil compaction and to evaluate the effects of soil management. This study investigates how results from multifractal analysis can quantify key elements of depth-dependent soil PR profiles and how this information can be used at the field scale. We analysed multifractality of 50 PR vertical profiles, measured from 0 to 60 cm depth and randomly located on a 6.5 ha sugar cane field in northeastern Brazil. The scaling property of each profile was typified by singularity, and Rényi spectra estimated by the method of moments. The Hurst exponent was used to parameterize the autocorrelation of the vertical PR data sets. The singularity and Rènyi spectra showed that the vertical PR data sets exhibited a well-defined multifractal structure. Hurst exponent values were close to 1, ranging from 0.944 to 0.988, indicating strong persistence in PR variation with soil depth. Also, the Hurst exponent was negatively and significantly correlated to coefficient of variation (CV, skewness and maximum values of the depth-dependent PR. Multifractal analysis added valuable information to describe the spatial arrangement of depth-dependent penetrometer data sets, which was not taken into account by classical statistical indices. Multifractal parameters were mapped over the experimental field and compared with mean and maximum values of PR. Combination of spatial variability survey and multifractal analysis appear to be useful to manage soil compaction.

Multifractal analysis of vertical profiles of soil penetration resistance at the field scale

Science.gov (United States)

Siqueira, G. M.; Silva, E. F. F.; Montenegro, A. A. A.; Vidal Vázquez, E.; Paz-Ferreiro, J.

2013-07-01

Soil penetration resistance (PR) is widely used as an indirect indicator of soil strength. Soil PR is linked to basic soil properties and correlated to root growth and plant production, and as such it is extensively used as a practical tool for assessing soil compaction and to evaluate the effects of soil management. This study investigates how results from multifractal analysis can quantify key elements of depth-dependent soil PR profiles and how this information can be used at the field scale. We analysed multifractality of 50 PR vertical profiles, measured from 0 to 60 cm depth and randomly located on a 6.5 ha sugar cane field in northeastern Brazil. The scaling property of each profile was typified by singularity, and Rényi spectra estimated by the method of moments. The Hurst exponent was used to parameterize the autocorrelation of the vertical PR data sets. The singularity and Rènyi spectra showed that the vertical PR data sets exhibited a well-defined multifractal structure. Hurst exponent values were close to 1, ranging from 0.944 to 0.988, indicating strong persistence in PR variation with soil depth. Also, the Hurst exponent was negatively and significantly correlated to coefficient of variation (CV), skewness and maximum values of the depth-dependent PR. Multifractal analysis added valuable information to describe the spatial arrangement of depth-dependent penetrometer data sets, which was not taken into account by classical statistical indices. Multifractal parameters were mapped over the experimental field and compared with mean and maximum values of PR. Combination of spatial variability survey and multifractal analysis appear to be useful to manage soil compaction.

The Kantian brain: brain dynamics from a neurophenomenological perspective.

Science.gov (United States)

Fazelpour, Sina; Thompson, Evan

2015-04-01

Current research on spontaneous, self-generated brain rhythms and dynamic neural network coordination cast new light on Immanuel Kant's idea of the 'spontaneity' of cognition, that is, the mind's capacity to organize and synthesize sensory stimuli in novel, unprecedented ways. Nevertheless, determining the precise nature of the brain-cognition mapping remains an outstanding challenge. Neurophenomenology, which uses phenomenological information about the variability of subjective experience in order to illuminate the variability of brain dynamics, offers a promising method for addressing this challenge. Copyright © 2014 Elsevier Ltd. All rights reserved.

Singularity spectra of fractional Brownian motions as a multi-fractal

International Nuclear Information System (INIS)

Kim, T.S.; Kim, S.

2004-01-01

Fractional Brownian motion acts as a random process with statistical self-similarity in time and self-affinity in shape. From these properties, the complicated patterns can be suitably represented by it with a minimal parameter and less memory. By considering its statistical property through the power spectrum density we can see that this process is not stationary, even though its differential motion is stationary. So in this paper, by taking the wavelet transform instead of Fourier transformation we investigate its multi-fractal spectrum as a multi-fractal model

Fractional governing equations of transient groundwater flow in confined aquifers with multi-fractional dimensions in fractional time

OpenAIRE

M. L. Kavvas; T. Tu; A. Ercan; J. Polsinelli

2017-01-01

Using fractional calculus, a dimensionally consistent governing equation of transient, saturated groundwater flow in fractional time in a multi-fractional confined aquifer is developed. First, a dimensionally consistent continuity equation for transient saturated groundwater flow in fractional time and in a multi-fractional, multidimensional confined aquifer is developed. For the equation of water flux within a multi-fractional multidimensional confined aquifer, a dimensionally...

Multifractal analysis of visibility graph-based Ito-related connectivity time series.

Science.gov (United States)

Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano

2016-02-01

In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.

Empirical method to measure stochasticity and multifractality in nonlinear time series

Science.gov (United States)

Lin, Chih-Hao; Chang, Chia-Seng; Li, Sai-Ping

2013-12-01

An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm, and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.

Multifractal characteristics of optical turbulence measured through a single beam holographic process.

Science.gov (United States)

Pérez, Darío G; Barillé, Regis; Morille, Yohann; Zielińska, Sonia; Ortyl, Ewelina

2014-08-11

We have previously shown that azopolymer thin films exposed to coherent light that has travelled through a turbulent medium produces a surface relief grating containing information about the intensity of the turbulence; for instance, a relation between the refractive index structure constant C(n)2 as a function of the surface parameters was obtained. In this work, we show that these films capture much more information about the turbulence dynamics. Multifractal detrended fluctuation and fractal dimension analysis from images of the surface roughness produced by the light on the azopolymer reveals scaling properties related to those of the optical turbulence.

Information flow dynamics in the brain

Science.gov (United States)

Rabinovich, Mikhail I.; Afraimovich, Valentin S.; Bick, Christian; Varona, Pablo

2012-03-01

Timing and dynamics of information in the brain is a hot field in modern neuroscience. The analysis of the temporal evolution of brain information is crucially important for the understanding of higher cognitive mechanisms in normal and pathological states. From the perspective of information dynamics, in this review we discuss working memory capacity, language dynamics, goal-dependent behavior programming and other functions of brain activity. In contrast with the classical description of information theory, which is mostly algebraic, brain flow information dynamics deals with problems such as the stability/instability of information flows, their quality, the timing of sequential processing, the top-down cognitive control of perceptual information, and information creation. In this framework, different types of information flow instabilities correspond to different cognitive disorders. On the other hand, the robustness of cognitive activity is related to the control of the information flow stability. We discuss these problems using both experimental and theoretical approaches, and we argue that brain activity is better understood considering information flows in the phase space of the corresponding dynamical model. In particular, we show how theory helps to understand intriguing experimental results in this matter, and how recent knowledge inspires new theoretical formalisms that can be tested with modern experimental techniques.

Influence of Age and Aerobic Fitness on the Multifractal Characteristics of Electrocardiographic RR Time-Series

Directory of Open Access Journals (Sweden)

Michael James Lewis

2013-05-01

Full Text Available Multifractal properties of electrocardiographic inter-beat (RR time-series offer insight into its long-term correlation structure, independently of RR variability. Here we quantify multifractal characteristics of RR data during 24-hour diurnal-nocturnal activity in healthy participants. We tested the hypotheses that (1 age, gender and aerobic fitness influence RR multifractal properties, and that (2 these are influenced by circadian variation.Seventy adults (39 males aged 19-58 years and of various fitness levels were monitored using 24-hour ECG. Participants were dichotomised by median age and fitness for sub-group analysis. Gender and fitness were independent of age (p=0.1, p>0.5. Younger/older group ages were substantially different (p<0.0005 and were independent of gender and fitness. Multifractality was quantified using the probability spectrum of Hölder exponents (h, from which modal h (h* and the full-width and half-widths at half-maximum measures (FWHM, HWHM+ and HWHM- were derived. FWHM decreased (p=0.004 and h* increased (p=0.011 in older people, indicating diminished long-range RR correlations and weaker anti-persistent behavior. Anti-persistent correlation (h* was strongest in the youngest/fittest individuals and weakest in the oldest/least fit individuals (p=0.015. Long-range correlation (HWHM+/FWHM was strongest in the fittest males and weakest in the least fit females (p=0.007-0.033.Multifractal RR characteristics in our healthy participants showed strong age-dependence with diminished long-range anti-persistent correlation in older people. Circadian variation of these characteristics was influenced by fitness and gender: fitter males and females of all ages had the greatest degree of multifractality or long-range order. Multifractal characterisation appears to be a useful method for exploring the physiological basis of long-term correlation structure in RR time-series as well as the benefits thereon of physical fitness training.

Multiscale characterization of pore spaces using multifractals analysis of scanning electronic microscopy images of carbonates

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M. S. Jouini

2011-12-01

Full Text Available Pore spaces heterogeneity in carbonates rocks has long been identified as an important factor impacting reservoir productivity. In this paper, we study the heterogeneity of carbonate rocks pore spaces based on the image analysis of scanning electron microscopy (SEM data acquired at various magnifications. Sixty images of twelve carbonate samples from a reservoir in the Middle East were analyzed. First, pore spaces were extracted from SEM images using a segmentation technique based on watershed algorithm. Pores geometries revealed a multifractal behavior at various magnifications from 800x to 12 000x. In addition, the singularity spectrum provided quantitative values that describe the degree of heterogeneity in the carbonates samples. Moreover, for the majority of the analyzed samples, we found low variations (around 5% in the multifractal dimensions for magnifications between 1700x and 12 000x. Finally, these results demonstrate that multifractal analysis could be an appropriate tool for characterizing quantitatively the heterogeneity of carbonate pore spaces geometries. However, our findings show that magnification has an impact on multifractal dimensions, revealing the limit of applicability of multifractal descriptions for these natural structures.

Multifractal diffusion entropy analysis: Optimal bin width of probability histograms

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Jizba, Petr; Korbel, Jan

2014-11-01

In the framework of Multifractal Diffusion Entropy Analysis we propose a method for choosing an optimal bin-width in histograms generated from underlying probability distributions of interest. The method presented uses techniques of Rényi’s entropy and the mean squared error analysis to discuss the conditions under which the error in the multifractal spectrum estimation is minimal. We illustrate the utility of our approach by focusing on a scaling behavior of financial time series. In particular, we analyze the S&P500 stock index as sampled at a daily rate in the time period 1950-2013. In order to demonstrate a strength of the method proposed we compare the multifractal δ-spectrum for various bin-widths and show the robustness of the method, especially for large values of q. For such values, other methods in use, e.g., those based on moment estimation, tend to fail for heavy-tailed data or data with long correlations. Connection between the δ-spectrum and Rényi’s q parameter is also discussed and elucidated on a simple example of multiscale time series.

Multifractal characterization of single wall carbon nanotube thin films surface upon exposure to optical parametric oscillator laser irradiation

International Nuclear Information System (INIS)

Ţălu, Ştefan; Marković, Zoran; Stach, Sebastian; Todorović Marković, B.; Ţălu, Mihai

2014-01-01

This study presents a multifractal approach, obtained with atomic force microscopy analysis, to characterize the structural evolution of single wall carbon nanotube thin films upon exposure to optical parametric oscillator laser irradiation at wavelength of 430 nm. Microstructure and morphological changes of carbon nanotube films deposited on different substrates (mica and TGX grating) were recorded by atomic force microscope. A detailed methodology for surface multifractal characterization, which may be applied for atomic force microscopy data, was presented. Multifractal analysis of surface roughness revealed that carbon nanotube films surface has a multifractal geometry at various magnifications. The generalized dimension D q and the singularity spectrum f(α) provided quantitative values that characterize the local scale properties of carbon nanotube films surface morphology at nanometer scale. Multifractal analysis provides different yet complementary information to that offered by traditional surface statistical parameters.

From Mathematical Monsters to Generalized Scale Invariance in Geophysics: Highlights of the Multifractal Saga

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Schertzer, D. J.; Tchiguirinskaia, I.; Lovejoy, S.

2013-12-01

Fractals and multifractals are very illustrative of the profound synergies between mathematics and geophysics. The book ';Fractal Geometry of Nature' (Mandelbrot, 1982) brilliantly demonstrated the genericity in geophysics of geometric forms like Cantor set, Peano curve and Koch snowflake, which were once considered as mathematical monsters. However, to tame the geophysical monsters (e.g. extreme weather, floods, earthquakes), it was required to go beyond geometry and a unique fractal dimension. The concept of multifractal was coined in the course of rather theoretical debates on intermittency in hydrodynamic turbulence, sometimes with direct links to atmospheric dynamics. The latter required a generalized notion of scale in order to deal both with scale symmetries and strong anisotropies (e.g. time vs. space, vertical vs. horizontal). It was thus possible to show that the consequences of intermittency are of first order, not just 'corrections' with respect to the classical non-intermittent modeling. This was in fact a radical paradigm shift for geophysics: the extreme variability of geophysical fields over wide ranges of scale, which had long been so often acknowledged and deplored, suddenly became handy. Recent illustrations are the possibility to track down in large date sets the Higgs boson of intermittence, i.e. a first order multifractal phase transition leading to self-organized criticality, and to simulate intermittent vector fields with the help of Lie cascades, based for instance on random Clifford algebra. It is rather significant that this revolution is no longer limited to fundamental and theoretical problems of geophysics, but now touches many applications including environmental management, in particular for urban management and resilience. These applications are particularly stimulating when taken in their full complexity.

Multifractality and herding behavior in the Japanese stock market

International Nuclear Information System (INIS)

Cajueiro, Daniel O.; Tabak, Benjamin M.

2009-01-01

In this paper we present evidence of multifractality and herding behavior for a large set of Japanese stocks traded in the Tokyo Stock Exchange. We find evidence that herding behavior occurs in periods of extreme market movements. Therefore, based on the intuition behind the tests to detect herding phenomenon developed, for instance, in Christie and Huang [Christie W, Huang R. Following the pied pier: do individual returns herd around the market? Financ Analysts J 1995;51:31-7] and Chang et al. [Chang EC, Cheng JW, Khorana A. Examination of herd behavior in equity markets: an international perspective. J Bank Finance 2000;24:1651-99], we suggest that herding behavior may be one of the causes of multifractality.

Quantitative assessment of submicron scale anisotropy in tissue multifractality by scattering Mueller matrix in the framework of Born approximation

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Das, Nandan Kumar; Dey, Rajib; Chakraborty, Semanti; Panigrahi, Prasanta K.; Meglinski, Igor; Ghosh, Nirmalya

2018-04-01

A number of tissue-like disordered media exhibit local anisotropy of scattering in the scaling behavior. Scaling behavior contains wealth of fractal or multifractal properties. We demonstrate that the spatial dielectric fluctuations in a sample of biological tissue exhibit multifractal anisotropy. Multifractal anisotropy encoded in the wavelength variation of the light scattering Mueller matrix and manifesting as an intriguing spectral diattenuation effect. We developed an inverse method for the quantitative assessment of the multifractal anisotropy. The method is based on the processing of relevant Mueller matrix elements in Fourier domain by using Born approximation, followed by the multifractal analysis. The approach promises for probing subtle micro-structural changes in biological tissues associated with the cancer and precancer, as well as for non-destructive characterization of a wide range of scattering materials.

Robust transient dynamics and brain functions

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Mikhail I Rabinovich

2011-06-01

Full Text Available In the last few decades several concepts of Dynamical Systems Theory (DST have guided psychologists, cognitive scientists, and neuroscientists to rethink about sensory motor behavior and embodied cognition. A critical step in the progress of DST application to the brain (supported by modern methods of brain imaging and multi-electrode recording techniques has been the transfer of its initial success in motor behavior to mental function, i.e., perception, emotion, and cognition. Open questions from research in genetics, ecology, brain sciences, etc. have changed DST itself and lead to the discovery of a new dynamical phenomenon, i.e., reproducible and robust transients that are at the same time sensitive to informational signals. The goal of this review is to describe a new mathematical framework -heteroclinic sequential dynamics- to understand self-organized activity in the brain that can explain certain aspects of robust itinerant behavior. Specifically, we discuss a hierarchy of coarse-grain models of mental dynamics in the form of kinetic equations of modes. These modes compete for resources at three levels: (i within the same modality, (ii among different modalities from the same family (like perception, and (iii among modalities from different families (like emotion and cognition. The analysis of the conditions for robustness, i.e., the structural stability of transient (sequential dynamics, give us the possibility to explain phenomena like the finite capacity of our sequential working memory -a vital cognitive function-, and to find specific dynamical signatures -different kinds of instabilities- of several brain functions and mental diseases.

Multifractality as a Measure of Complexity in Solar Flare Activity

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Sen, Asok K.

2007-03-01

In this paper we use the notion of multifractality to describe the complexity in H α flare activity during the solar cycles 21, 22, and 23. Both northern and southern hemisphere flare indices are analyzed. Multifractal behavior of the flare activity is characterized by calculating the singularity spectrum of the daily flare index time series in terms of the Hölder exponent. The broadness of the singularity spectrum gives a measure of the degree of multifractality or complexity in the flare index data. The broader the spectrum, the richer and more complex is the structure with a higher degree of multifractality. Using this broadness measure, complexity in the flare index data is compared between the northern and southern hemispheres in each of the three cycles, and among the three cycles in each of the two hemispheres. Other parameters of the singularity spectrum can also provide information about the fractal properties of the flare index data. For instance, an asymmetry to the left or right in the singularity spectrum indicates a dominance of high or low fractal exponents, respectively, reflecting a relative abundance of large or small fluctuations in the total energy emitted by the flares. Our results reveal that in the even (22nd) cycle the singularity spectra are very similar for the northern and southern hemispheres, whereas in the odd cycles (21st and 23rd) they differ significantly. In particular, we find that in cycle 21, the northern hemisphere flare index data have higher complexity than its southern counterpart, with an opposite pattern prevailing in cycle 23. Furthermore, small-scale fluctuations in the flare index time series are predominant in the northern hemisphere in the 21st cycle and are predominant in the southern hemisphere in the 23rd cycle. Based on these findings one might suggest that, from cycle to cycle, there exists a smooth switching between the northern and southern hemispheres in the multifractality of the flaring process. This new

Multifractal fluctuations in joint angles during infant spontaneous kicking reveal multiplicativity-driven coordination

International Nuclear Information System (INIS)

Stephen, Damian G.; Hsu, Wen-Hao; Young, Diana; Saltzman, Elliot L.; Holt, Kenneth G.; Newman, Dava J.; Weinberg, Marc; Wood, Robert J.; Nagpal, Radhika; Goldfield, Eugene C.

2012-01-01

Previous research has considered infant spontaneous kicking as a form of exploration. According to this view, spontaneous kicking provides information about motor degrees of freedom and may shape multijoint coordinations for more complex movement patterns such as gait. Recent work has demonstrated that multifractal, multiplicative fluctuations in exploratory movements index energy flows underlying perceptual-motor information. If infant spontaneous kicking is exploratory and occasions an upstream flow of information from the motor periphery, we expected not only that multiplicativity of fluctuations at the hip should promote multiplicativity of fluctuations at more distal joints (i.e., reflecting downstream effects of neural control) but also that multiplicativity at more distal joints should promote multiplicativity at the hip. Multifractal analysis demonstrated that infant spontaneous kicking in four typically developing infants for evidence of multiplicative fluctuations in multiple joint angles along the leg (i.e., hip, knee, and ankle) exhibited multiplicativity. Vector autoregressive modeling demonstrated that only one leg exhibited downstream effects but that both legs exhibited upstream effects. These results confirm the exploratory aspect of infant spontaneous kicking and suggest chaotic dynamics in motor coordination. They also resonate with existing models of chaos-controlled robotics and noise-based interventions for rehabilitating motor coordination in atypically developing patients.

Symmetries and stochastic symmetry breaking in multifractal geophysics: analysis and simulation with the help of the Lévy-Clifford algebra of cascade generators..

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Schertzer, D. J. M.; Tchiguirinskaia, I.

2016-12-01

Multifractal fields, whose definition is rather independent of their domain dimension, have opened a new approach of geophysics enabling to explore its spatial extension that is of prime importance as underlined by the expression "spatial chaos". However multifractals have been until recently restricted to be scalar valued, i.e. to one-dimensional codomains. This has prevented to deal with the key question of complex component interactions and their non trivial symmetries. We first emphasize that the Lie algebra of stochastic generators of cascade processes enables us to generalize multifractals to arbitrarily large codomains, e.g. flows of vector fields on large dimensional manifolds. In particular, we have recently investigated the neat example of stable Levy generators on Clifford algebra that have a number of seductive properties, e.g. universal statistical and robust algebra properties, both defining the basic symmetries of the corresponding fields (Schertzer and Tchiguirinskaia, 2015). These properties provide a convenient multifractal framework to study both the symmetries of the fields and how they stochastically break the symmetries of the underlying equations due to boundary conditions, large scale rotations and forcings. These developments should help us to answer to challenging questions such as the climatology of (exo-) planets based on first principles (Pierrehumbert, 2013), to fully address the question of the limitations of quasi- geostrophic turbulence (Schertzer et al., 2012) and to explore the peculiar phenomenology of turbulent dynamics of the atmosphere or oceans that is neither two- or three-dimensional. Pierrehumbert, R.T., 2013. Strange news from other stars. Nature Geoscience, 6(2), pp.8183. Schertzer, D. et al., 2012. Quasi-geostrophic turbulence and generalized scale invariance, a theoretical reply. Atmos. Chem. Phys., 12, pp.327336. Schertzer, D. & Tchiguirinskaia, I., 2015. Multifractal vector fields and stochastic Clifford algebra

Surface roughness and morphology of dental nanocomposites polished by four different procedures evaluated by a multifractal approach

Energy Technology Data Exchange (ETDEWEB)

Ţălu, Ştefan, E-mail: stefan_ta@yahoo.com [Technical University of Cluj-Napoca, Faculty of Mechanical Engineering, Department of AET, Discipline of Descriptive Geometry and Engineering Graphics, 103-105 B-dul Muncii St., Cluj-Napoca 400641, Cluj (Romania); Stach, Sebastian, E-mail: sebastian.stach@us.edu.pl [University of Silesia, Faculty of Computer Science and Materials Science, Institute of Informatics, Department of Biomedical Computer Systems, Będzińska 39, 41-205 Sosnowiec (Poland); Lainović, Tijana, E-mail: tijana.lainovic@gmail.com [University of Novi Sad, Faculty of Medicine, School of Dentistry, Hajduk Veljkova 3, 21000 Novi Sad (Serbia); Vilotić, Marko, E-mail: markovil@uns.ac.rs [University of Novi Sad, Faculty of Technical Sciences, Department for Production Engineering, Trg Dositeja Obradovića 6, 21000 Novi Sad (Serbia); Blažić, Larisa, E-mail: larisa.blazic@gmail.com [University of Novi Sad, Faculty of Medicine, School of Dentistry, Clinic of Dentistry of Vojvodina, Department of Restorative Dentistry and Endodontics, Hajduk Veljkova 3, 21000 Novi Sad (Serbia); Alb, Sandu Florin, E-mail: albflorin@yahoo.com [“Iuliu Haţieganu” University of Medicine and Pharmacy, Faculty of Dentistry, Department of Periodontology, 8 Victor Babeş St., 400012 Cluj-Napoca (Romania); Kakaš, Damir, E-mail: kakasdam@uns.ac.rs [University of Novi Sad, Faculty of Technical Sciences, Department for Production Engineering, Trg Dositeja Obradovića 6, 21000 Novi Sad (Serbia)

2015-03-01

Graphical abstract: - Highlights: • Multifractals are good indicators of polished dental composites 3-D surface structure. • The nanofilled composite had superior 3-D surface properties than the nanohybrid one. • Composite polishing with diamond paste created improved 3-D multifractal structure. • Recommendation: polish the composite with diamond paste if using the one-step tool. • Multifractal analysis could become essential in designing new dental surfaces. - Abstract: The objective of this study was to determine the effect of different dental polishing methods on surface texture parameters of dental nanocomposites. The 3-D surface morphology was investigated by atomic force microscopy (AFM) and multifractal analysis. Two representative dental resin-based nanocomposites were investigated: a nanofilled and a nanohybrid composite. The samples were polished by two dental polishing protocols using multi-step and one-step system. Both protocols were then followed by diamond paste polishing. The 3-D surface roughness of samples was studied by AFM on square areas of topography on the 80 × 80 μm{sup 2} scanning area. The multifractal spectrum theory based on computational algorithms was applied for AFM data and multifractal spectra were calculated. The generalized dimension D{sub q} and the singularity spectrum f(α) provided quantitative values that characterize the local scale properties of dental nanocomposites polished by four different dental polishing protocols at nanometer scale. The results showed that the larger the spectrum width Δα (Δα = α{sub max} − α{sub min}) of the multifractal spectra f(α), the more non-uniform was the surface morphology. Also, the 3-D surface topography was described by statistical parameters, according to ISO 25178-2:2012. The 3-D surface of samples had a multifractal nature. Nanofilled composite had lower values of height parameters than nanohybrid composites, due to its composition. Multi-step polishing protocol

Surface roughness and morphology of dental nanocomposites polished by four different procedures evaluated by a multifractal approach

International Nuclear Information System (INIS)

Ţălu, Ştefan; Stach, Sebastian; Lainović, Tijana; Vilotić, Marko; Blažić, Larisa; Alb, Sandu Florin; Kakaš, Damir

2015-01-01

Graphical abstract: - Highlights: • Multifractals are good indicators of polished dental composites 3-D surface structure. • The nanofilled composite had superior 3-D surface properties than the nanohybrid one. • Composite polishing with diamond paste created improved 3-D multifractal structure. • Recommendation: polish the composite with diamond paste if using the one-step tool. • Multifractal analysis could become essential in designing new dental surfaces. - Abstract: The objective of this study was to determine the effect of different dental polishing methods on surface texture parameters of dental nanocomposites. The 3-D surface morphology was investigated by atomic force microscopy (AFM) and multifractal analysis. Two representative dental resin-based nanocomposites were investigated: a nanofilled and a nanohybrid composite. The samples were polished by two dental polishing protocols using multi-step and one-step system. Both protocols were then followed by diamond paste polishing. The 3-D surface roughness of samples was studied by AFM on square areas of topography on the 80 × 80 μm 2 scanning area. The multifractal spectrum theory based on computational algorithms was applied for AFM data and multifractal spectra were calculated. The generalized dimension D q and the singularity spectrum f(α) provided quantitative values that characterize the local scale properties of dental nanocomposites polished by four different dental polishing protocols at nanometer scale. The results showed that the larger the spectrum width Δα (Δα = α max − α min ) of the multifractal spectra f(α), the more non-uniform was the surface morphology. Also, the 3-D surface topography was described by statistical parameters, according to ISO 25178-2:2012. The 3-D surface of samples had a multifractal nature. Nanofilled composite had lower values of height parameters than nanohybrid composites, due to its composition. Multi-step polishing protocol created a better

Fractional governing equations of transient groundwater flow in confined aquifers with multi-fractional dimensions in fractional time

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M. L. Kavvas

2017-10-01

Full Text Available Using fractional calculus, a dimensionally consistent governing equation of transient, saturated groundwater flow in fractional time in a multi-fractional confined aquifer is developed. First, a dimensionally consistent continuity equation for transient saturated groundwater flow in fractional time and in a multi-fractional, multidimensional confined aquifer is developed. For the equation of water flux within a multi-fractional multidimensional confined aquifer, a dimensionally consistent equation is also developed. The governing equation of transient saturated groundwater flow in a multi-fractional, multidimensional confined aquifer in fractional time is then obtained by combining the fractional continuity and water flux equations. To illustrate the capability of the proposed governing equation of groundwater flow in a confined aquifer, a numerical application of the fractional governing equation to a confined aquifer groundwater flow problem was also performed.

Multifractal analysis of CoFe2O4/2DBS/H2O ferrofluid from TEM and SANS measurements

International Nuclear Information System (INIS)

Stan, C.; Cristescu, C.P.; Balasoiu, M.; Ivankov, O.I.

2015-01-01

Preliminary investigation on the morphological properties and the multifractal characteristics of CoFe 2 O 4 nanoparticles, coated with a double layer of dodecylbenzenesulphonic acid and dispersed in double distillated water, is presented. TEM images of the sample are analyzed and the computed multifractal spectrum reveals universal multifractality. A comparison with the fractal approach applied to SANS data is presented, and consistency of results is demonstrated.

Influence of the atomic force microscope tip on the multifractal analysis of rough surfaces

International Nuclear Information System (INIS)

Klapetek, Petr; Ohlidal, Ivan; Bilek, Jindrich

2004-01-01

In this paper, the influence of atomic force microscope tip on the multifractal analysis of rough surfaces is discussed. This analysis is based on two methods, i.e. on the correlation function method and the wavelet transform modulus maxima method. The principles of both methods are briefly described. Both methods are applied to simulated rough surfaces (simulation is performed by the spectral synthesis method). It is shown that the finite dimensions of the microscope tip misrepresent the values of the quantities expressing the multifractal analysis of rough surfaces within both the methods. Thus, it was concretely shown that the influence of the finite dimensions of the microscope tip changed mono-fractal properties of simulated rough surface to multifractal ones. Further, it is shown that a surface reconstruction method developed for removing the negative influence of the microscope tip does not improve the results obtained in a substantial way. The theoretical procedures concerning both the methods, i.e. the correlation function method and the wavelet transform modulus maxima method, are illustrated for the multifractal analysis of randomly rough gallium arsenide surfaces prepared by means of the thermal oxidation of smooth gallium arsenide surfaces and subsequent dissolution of the oxide films

To be and not to be: scale correlations in random multifractal processes

DEFF Research Database (Denmark)

Cleve, Jochen; Schmiegel, Jürgen; Greiner, Martin

We discuss various properties of a random multifractal process, which are related to the issue of scale correlations. By design, the process is homogeneous, non-conservative and has no built-in scale correlations. However, when it comes to observables like breakdown coefficients, which are based...... on a coarse-graining of the multifractal field, scale correlations do appear. In the log-normal limit of the model process, the conditional distributions and moments of breakdown coefficients reproduce the observations made in fully developed small-scale turbulence. These findings help to understand several...

Multiscale multifractal DCCA and complexity behaviors of return intervals for Potts price model

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Wang, Jie; Wang, Jun; Stanley, H. Eugene

2018-02-01

To investigate the characteristics of extreme events in financial markets and the corresponding return intervals among these events, we use a Potts dynamic system to construct a random financial time series model of the attitudes of market traders. We use multiscale multifractal detrended cross-correlation analysis (MM-DCCA) and Lempel-Ziv complexity (LZC) perform numerical research of the return intervals for two significant China's stock market indices and for the proposed model. The new MM-DCCA method is based on the Hurst surface and provides more interpretable cross-correlations of the dynamic mechanism between different return interval series. We scale the LZC method with different exponents to illustrate the complexity of return intervals in different scales. Empirical studies indicate that the proposed return intervals from the Potts system and the real stock market indices hold similar statistical properties.

Multifractal detrended Cross Correlation Analysis of Foreign Exchange and SENSEX fluctuation in Indian perspective

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Dutta, Srimonti; Ghosh, Dipak; Chatterjee, Sucharita

2016-12-01

The manuscript studies autocorrelation and cross correlation of SENSEX fluctuations and Forex Exchange Rate in respect to Indian scenario. Multifractal detrended fluctuation analysis (MFDFA) and multifractal detrended cross correlation analysis (MFDXA) were employed to study the correlation between the two series. It was observed that the two series are strongly cross correlated. The change of degree of cross correlation with time was studied and the results are interpreted qualitatively.

Statistical and Multifractal Evaluation of Soil Compaction in a Vineyard

Science.gov (United States)

Marinho, M.; Raposo, J. R.; Mirás Avalos, J. M.; Paz González, A.

2012-04-01

One of the detrimental effects caused by agricultural machines is soil compaction, which can be defined by an increase in soil bulk density. Soil compaction often has a negative impact on plant growth, since it reduces the macroporosity and soil permeability and increases resistance to penetration. Our research explored the effect of the agricultural machinery on soil when trafficking through a vineyard at a small spatial scale, based on the evaluation of the soil compaction status. The objectives of this study were: i) to quantify soil bulk density along transects following wine row, wheel track and outside track, and, ii) to characterize the variability of the bulk density along these transects using multifractal analysis. The field work was conducted at the experimental farm of EVEGA (Viticulture and Enology Centre of Galicia) located in Ponte San Clodio, Leiro, Orense, Spain. Three parallel transects were marked on positions with contrasting machine traffic effects, i.e. vine row, wheel-track and outside-track. Undisturbed samples were collected in 16 points of each transect, spaced 0.50 m apart, for bulk density determination using the cylinder method. Samples were taken in autumn 2011, after grape harvest. Since soil between vine rows was tilled and homogenized beginning spring 2011, cumulative effects of traffic during the vine growth period could be evaluated. The distribution patterns of soil bulk density were characterized by multifractal analysis carried out by the method of moments. Multifractality was assessed by several indexes derived from the mass exponent, τq, the generalized dimension, Dq, and the singularity spectrum, f(α), curves. Mean soil bulk density values determined for vine row, outside-track and wheel-track transects were 1.212 kg dm-3, 1.259 kg dm-3and 1.582 kg dm-3, respectively. The respective coefficients of variation (CV) for these three transects were 7.76%, 4.82% and 2.03%. Therefore mean bulk density under wheel-track was 30

Comparison of the multifractal characteristics of heavy metals in soils within two areas of contrasting economic activities in China

Science.gov (United States)

Li, Xiaohui; Li, Xiangling; Yuan, Feng; Jowitt, Simon M.; Zhou, Taofa; Yang, Kui; Zhou, Jie; Hu, Xunyu; Li, Yang

2016-09-01

Industrial and agricultural activities can generate heavy metal pollution that can cause a number of negative environmental and health impacts. This means that evaluating heavy metal pollution and identifying the sources of these pollutants, especially in urban or developed areas, is an important first step in mitigating the effects of these contaminating but necessary economic activities. Here, we present the results of a heavy metal (Cu, Pb, Zn, Cd, As, and Hg) soil geochemical survey in Hefei city. We used a multifractal spectral technique to identify and compare the multifractality of heavy metal concentrations of soils within the industrial Daxing and agricultural Yicheng areas. This paper uses three multifractal parameters (Δα, Δf(α), and τ''(1)) to indicate the overall amount of multifractality within the soil geochemical data. The results show all of the elements barring Hg have larger Δα, Δf(α), and τ''(1) values in the Daxing area compared to the Yicheng area. The degree of multifractality suggests that the differing economic activities in Daxing and Yicheng generate very different heavy metal pollution loads. In addition, the industrial Daxing area contains significant Pb and Cd soil contamination, whereas Hg is the main heavy metal present in soils within the Yicheng area, indicating that differing clean-up procedures and approaches to remediating these polluted areas are needed. The results also indicate that multifractal modelling and the associated generation of multifractal parameters can be a useful approach in the evaluation of heavy metal pollution in soils.

Mapping how local perturbations influence systems-level brain dynamics.

Science.gov (United States)

Gollo, Leonardo L; Roberts, James A; Cocchi, Luca

2017-10-15

The human brain exhibits a distinct spatiotemporal organization that supports brain function and can be manipulated via local brain stimulation. Such perturbations to local cortical dynamics are globally integrated by distinct neural systems. However, it remains unclear how local changes in neural activity affect large-scale system dynamics. Here, we briefly review empirical and computational studies addressing how localized perturbations affect brain activity. We then systematically analyze a model of large-scale brain dynamics, assessing how localized changes in brain activity at the different sites affect whole-brain dynamics. We find that local stimulation induces changes in brain activity that can be summarized by relatively smooth tuning curves, which relate a region's effectiveness as a stimulation site to its position within the cortical hierarchy. Our results also support the notion that brain hubs, operating in a slower regime, are more resilient to focal perturbations and critically contribute to maintain stability in global brain dynamics. In contrast, perturbations of peripheral regions, characterized by faster activity, have greater impact on functional connectivity. As a parallel with this region-level result, we also find that peripheral systems such as the visual and sensorimotor networks were more affected by local perturbations than high-level systems such as the cingulo-opercular network. Our findings highlight the importance of a periphery-to-core hierarchy to determine the effect of local stimulation on the brain network. This study also provides novel resources to orient empirical work aiming at manipulating functional connectivity using non-invasive brain stimulation. Copyright © 2017 Elsevier Inc. All rights reserved.

Multifractal modeling of the production of concentrated sugar syrup crystal

International Nuclear Information System (INIS)

Bi Sheng; Gao Jianbo

2016-01-01

High quality, concentrated sugar syrup crystal is produced in a critical step in cane sugar production: the clarification process. It is characterized by two variables: the color of the produced sugar and its clarity degree. We show that the temporal variations of these variables follow power-law distributions and can be well modeled by multiplicative cascade multifractal processes. These interesting properties suggest that the degradation in color and clarity degree has a system-wide cause. In particular, the cascade multifractal model suggests that the degradation in color and clarity degree can be equivalently accounted for by the initial “impurities” in the sugarcane. Hence, more effective cleaning of the sugarcane before the clarification stage may lead to substantial improvement in the effect of clarification. (paper)

Multifractal Analysis of Seismically Induced Soft-Sediment Deformation Structures Imaged by X-Ray Computed Tomography

Science.gov (United States)

Nakashima, Yoshito; Komatsubara, Junko

Unconsolidated soft sediments deform and mix complexly by seismically induced fluidization. Such geological soft-sediment deformation structures (SSDSs) recorded in boring cores were imaged by X-ray computed tomography (CT), which enables visualization of the inhomogeneous spatial distribution of iron-bearing mineral grains as strong X-ray absorbers in the deformed strata. Multifractal analysis was applied to the two-dimensional (2D) CT images with various degrees of deformation and mixing. The results show that the distribution of the iron-bearing mineral grains is multifractal for less deformed/mixed strata and almost monofractal for fully mixed (i.e. almost homogenized) strata. Computer simulations of deformation of real and synthetic digital images were performed using the egg-beater flow model. The simulations successfully reproduced the transformation from the multifractal spectra into almost monofractal spectra (i.e. almost convergence on a single point) with an increase in deformation/mixing intensity. The present study demonstrates that multifractal analysis coupled with X-ray CT and the mixing flow model is useful to quantify the complexity of seismically induced SSDSs, standing as a novel method for the evaluation of cores for seismic risk assessment.

Multifractal temporally weighted detrended cross-correlation analysis to quantify power-law cross-correlation and its application to stock markets

Science.gov (United States)

Wei, Yun-Lan; Yu, Zu-Guo; Zou, Hai-Long; Anh, Vo

2017-06-01

A new method—multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA)—is proposed to investigate multifractal cross-correlations in this paper. This new method is based on multifractal temporally weighted detrended fluctuation analysis and multifractal cross-correlation analysis (MFCCA). An innovation of the method is applying geographically weighted regression to estimate local trends in the nonstationary time series. We also take into consideration the sign of the fluctuations in computing the corresponding detrended cross-covariance function. To test the performance of the MF-TWXDFA algorithm, we apply it and the MFCCA method on simulated and actual series. Numerical tests on artificially simulated series demonstrate that our method can accurately detect long-range cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWXDFA, we apply it on time series from stock markets and find that power-law cross-correlation between stock returns is significantly multifractal. A new coefficient, MF-TWXDFA cross-correlation coefficient, is also defined to quantify the levels of cross-correlation between two time series.

Comparative Multifractal Detrended Fluctuation Analysis of Heavy Ion Interactions at a Few GeV to a Few Hundred GeV

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Gopa Bhoumik

2016-01-01

Full Text Available We have studied the multifractality of pion emission process in 16O-AgBr interactions at 2.1 AGeV  and  60 AGeV, 12C-AgBr  and  24Mg-AgBr interactions at 4.5 AGeV, and 32S-AgBr interactions at 200 AGeV using Multifractal Detrended Fluctuation Analysis (MFDFA method which is capable of extracting the actual multifractal property filtering out the average trend of fluctuation. The analysis reveals that the pseudorapidity distribution of the shower particles is multifractal in nature for all the interactions; that is, pion production mechanism has inbuilt multiscale self-similarity property. We have employed MFDFA method for randomly generated events for 32S-AgBr interactions at 200 AGeV. Comparison of expt. results with those obtained from randomly generated data set reveals that the source of multifractality in our data is the presence of long range correlation. Comparing the results obtained from different interactions, it may be concluded that strength of multifractality decreases with projectile mass for the same projectile energy and for a particular projectile it increases with energy. The values of ordinary Hurst exponent suggest that there is long range correlation present in our data for all the interactions.

Simultaneous multifractal decompositions for the spectra of local entropies and ergodic averages

International Nuclear Information System (INIS)

Meson, Alejandro; Vericat, Fernando

2009-01-01

We consider different multifractal decompositions of the form K α i ={x:g i (x)=α i },i=1,2,...,d, and we study the dimension spectrum corresponding to the multiparameter decomposition K α = intersection i=1 d K α i ,α=(α 1 ,...,α d ). Then for an homeomorphism f : X → X and potentials φ, ψ : X → R we analyze the decompositions K α + ={x:lim n→∞ 1/n (S n + (φ))(x)=α},K β - ={x:lim n→∞ 1/n (S n - (ψ))(x)=β}, where 1/n (S n + (φ)),1/n (S n - (ψ)) are ergodic averages using forward and backward orbits of f respectively. We must emphasize that the analysis, in any case, is done without requiring conditions of hyperbolicity for the dynamical system or Hoelder continuity on the potentials. We illustrate with an application to galactic dynamics: a set of stars (which do not interact among them) moving in a galactic field.

Study on multi-fractal fault diagnosis based on EMD fusion in hydraulic engineering

International Nuclear Information System (INIS)

Lu, Shibao; Wang, Jianhua; Xue, Yangang

2016-01-01

Highlights: • The measured shafting vibration data signal of the hydroelectric generating set is acquired through EMD. • The vibration signal waveform is identified and purified with EMD to obtain approximation coefficient of various fault signals. • The multi-fractal spectrum provides the distributed geometrical or probabilistic information of point. • EMD provides the real information for the next subsequent analysis and recognition. - Abstract: The vibration signal analysis of the hydraulic turbine unit aims at extracting the characteristic information of the unit vibration. The effective signal processing and information extraction are the key to state monitoring and fault diagnosis of the hydraulic turbine unit. In this paper, the vibration fault diagnosis model is established, which combines EMD, multi-fractal spectrum and modified BP neural network; the vibration signal waveform is identified and purified with EMD to obtain approximation coefficient of various fault signals; the characteristic vector of the vibration fault is acquired with the multi-fractal spectrum algorithm, which is classified and identified as input vector of BP neural network. The signal characteristics are extracted through the waveform, the diagnosis and identification are carried out in combination of the multi-fractal spectrum to provide a new method for fault diagnosis of the hydraulic turbine unit. After the application test, the results show that the method can improve the intelligence and humanization of diagnosis, enhance the man–machine interaction, and produce satisfactory identification result.

Rank-Ordered Multifractal Analysis (ROMA of probability distributions in fluid turbulence

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C. C. Wu

2011-04-01

Full Text Available Rank-Ordered Multifractal Analysis (ROMA was introduced by Chang and Wu (2008 to describe the multifractal characteristic of intermittent events. The procedure provides a natural connection between the rank-ordered spectrum and the idea of one-parameter scaling for monofractals. This technique has successfully been applied to MHD turbulence simulations and turbulence data observed in various space plasmas. In this paper, the technique is applied to the probability distributions in the inertial range of the turbulent fluid flow, as given in the vast Johns Hopkins University (JHU turbulence database. In addition, a new way of finding the continuous ROMA spectrum and the scaled probability distribution function (PDF simultaneously is introduced.

Multifractal analysis of electronic transitions in a family of quasiperiodic potentials

International Nuclear Information System (INIS)

Thakur, P.K.; Brouers, F.; Ananthakrishna, G.

1989-12-01

We analyze the nature of extended, localized and critical states in an extension of the Aubry model where mobility edges have been reported. We calculate the multifractal spectra of exact eigenstates of this model for varying chain lengths and confirm the existence of mobility edges. Moreover we are able to show that the localized states can exhibit a behaviour rather different from the usual exponentially decaying states in a random potential. Lyapounov exponents or participation ratios are unable to produce such information. Our results also indicate that a stable multifractal distribution is a general feature of crossovers of states of different nature. This conjecture should be confirmed in other models. (author). 17 refs, 8 figs

Multifractal detrended cross-correlation between the Chinese domestic and international gold markets based on DCCA and DMCA methods

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Cao, Guangxi; Han, Yan; Chen, Yuemeng; Yang, Chunxia

2014-05-01

Based on the daily price data of Shanghai and London gold spot markets, we applied detrended cross-correlation analysis (DCCA) and detrended moving average cross-correlation analysis (DMCA) methods to quantify power-law cross-correlation between domestic and international gold markets. Results show that the cross-correlations between the Chinese domestic and international gold spot markets are multifractal. Furthermore, forward DMCA and backward DMCA seems to outperform DCCA and centered DMCA for short-range gold series, which confirms the comparison results of short-range artificial data in L. Y. He and S. P. Chen [Physica A 390 (2011) 3806-3814]. Finally, we analyzed the local multifractal characteristics of the cross-correlation between Chinese domestic and international gold markets. We show that multifractal characteristics of the cross-correlation between the Chinese domestic and international gold markets are time-varying and that multifractal characteristics were strengthened by the financial crisis in 2007-2008.

The Virtual Mouse Brain: A Computational Neuroinformatics Platform to Study Whole Mouse Brain Dynamics.

Science.gov (United States)

Melozzi, Francesca; Woodman, Marmaduke M; Jirsa, Viktor K; Bernard, Christophe

2017-01-01

Connectome-based modeling of large-scale brain network dynamics enables causal in silico interrogation of the brain's structure-function relationship, necessitating the close integration of diverse neuroinformatics fields. Here we extend the open-source simulation software The Virtual Brain (TVB) to whole mouse brain network modeling based on individual diffusion magnetic resonance imaging (dMRI)-based or tracer-based detailed mouse connectomes. We provide practical examples on how to use The Virtual Mouse Brain (TVMB) to simulate brain activity, such as seizure propagation and the switching behavior of the resting state dynamics in health and disease. TVMB enables theoretically driven experimental planning and ways to test predictions in the numerous strains of mice available to study brain function in normal and pathological conditions.

A Smoothing Technique for the Multifractal Analysis of a Medium Voltage Feeders Electric Current

Science.gov (United States)

de Santis, Enrico; Sadeghian, Alireza; Rizzi, Antonello

2017-12-01

The current paper presents a data-driven detrending technique allowing to smooth complex sinusoidal trends from a real-world electric load time series before applying the Detrended Multifractal Fluctuation Analysis (MFDFA). The algorithm we call Smoothed Sort and Cut Fourier Detrending (SSC-FD) is based on a suitable smoothing of high power periodicities operating directly in the Fourier spectrum through a polynomial fitting technique of the DFT. The main aim consists of disambiguating the characteristic slow varying periodicities, that can impair the MFDFA analysis, from the residual signal in order to study its correlation properties. The algorithm performances are evaluated on a simple benchmark test consisting of a persistent series where the Hurst exponent is known, with superimposed ten sinusoidal harmonics. Moreover, the behavior of the algorithm parameters is assessed computing the MFDFA on the well-known sunspot data, whose correlation characteristics are reported in literature. In both cases, the SSC-FD method eliminates the apparent crossover induced by the synthetic and natural periodicities. Results are compared with some existing detrending methods within the MFDFA paradigm. Finally, a study of the multifractal characteristics of the electric load time series detrendended by the SSC-FD algorithm is provided, showing a strong persistent behavior and an appreciable amplitude of the multifractal spectrum that allows to conclude that the series at hand has multifractal characteristics.

Understanding the source of multifractality in financial markets

Czech Academy of Sciences Publication Activity Database

Baruník, Jozef; Aste, T.; Di Matteo, T.; Liu, R.

2012-01-01

Roč. 391, č. 17 (2012), s. 4234-4251 ISSN 0378-4371 R&D Projects: GA ČR GA402/09/0965 Institutional research plan: CEZ:AV0Z10750506 Keywords : Multifractality * Financial markets * Hurst exponent Subject RIV: AH - Economics Impact factor: 1.676, year: 2012 http://www.sciencedirect.com/science/article/pii/S0378437112002890

Invariant measures in brain dynamics

International Nuclear Information System (INIS)

Boyarsky, Abraham; Gora, Pawel

2006-01-01

This note concerns brain activity at the level of neural ensembles and uses ideas from ergodic dynamical systems to model and characterize chaotic patterns among these ensembles during conscious mental activity. Central to our model is the definition of a space of neural ensembles and the assumption of discrete time ensemble dynamics. We argue that continuous invariant measures draw the attention of deeper brain processes, engendering emergent properties such as consciousness. Invariant measures supported on a finite set of ensembles reflect periodic behavior, whereas the existence of continuous invariant measures reflect the dynamics of nonrepeating ensemble patterns that elicit the interest of deeper mental processes. We shall consider two different ways to achieve continuous invariant measures on the space of neural ensembles: (1) via quantum jitters, and (2) via sensory input accompanied by inner thought processes which engender a 'folding' property on the space of ensembles

Assessment of brain phenylalanine dynamics in phenylketonuria patients

International Nuclear Information System (INIS)

Bik-Multanowski, M.; Pietrzyk, J. J.; Pasowicz, M.; Banys, R.P.

2006-01-01

Phenylketonuria (PKU) is the most common inborn error of metabolism in man. Brain phenylalanine kinetics can determine neurological treatment outcome in phenylketonuria. The aim of our study wa sto test a simplified magnetic resonance spectroscopy method for assessment of brain phenylalanine dynamics in PKU patients. Brain phenylalanine concentration (measured by means of magnetic resonance spectroscopy) and blood phenylalanine concentrations changes occurring within 24 hours after oral phenylalanine loading were analyzed in 5 PKU patients. The brain/blood phenylalanine ratio in 3 persons with normal intelligence was lower than in 2 with borderline intelligence or mild mental retardation. In our opinion the proposed method could be useful for assessment of brain phenylalanine dynamics in PKU patients. (author)

Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom.

Science.gov (United States)

Dikker, Suzanne; Wan, Lu; Davidesco, Ido; Kaggen, Lisa; Oostrik, Matthias; McClintock, James; Rowland, Jess; Michalareas, Georgios; Van Bavel, Jay J; Ding, Mingzhou; Poeppel, David

2017-05-08

The human brain has evolved for group living [1]. Yet we know so little about how it supports dynamic group interactions that the study of real-world social exchanges has been dubbed the "dark matter of social neuroscience" [2]. Recently, various studies have begun to approach this question by comparing brain responses of multiple individuals during a variety of (semi-naturalistic) tasks [3-15]. These experiments reveal how stimulus properties [13], individual differences [14], and contextual factors [15] may underpin similarities and differences in neural activity across people. However, most studies to date suffer from various limitations: they often lack direct face-to-face interaction between participants, are typically limited to dyads, do not investigate social dynamics across time, and, crucially, they rarely study social behavior under naturalistic circumstances. Here we extend such experimentation drastically, beyond dyads and beyond laboratory walls, to identify neural markers of group engagement during dynamic real-world group interactions. We used portable electroencephalogram (EEG) to simultaneously record brain activity from a class of 12 high school students over the course of a semester (11 classes) during regular classroom activities (Figures 1A-1C; Supplemental Experimental Procedures, section S1). A novel analysis technique to assess group-based neural coherence demonstrates that the extent to which brain activity is synchronized across students predicts both student class engagement and social dynamics. This suggests that brain-to-brain synchrony is a possible neural marker for dynamic social interactions, likely driven by shared attention mechanisms. This study validates a promising new method to investigate the neuroscience of group interactions in ecologically natural settings. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Multifractal aspects of the scaling laws in fully developed compressible turbulence

International Nuclear Information System (INIS)

Shivamoggi, B.K.

1995-01-01

In this paper, multifractal aspects of the scalings laws in fully developed compressible turbulence are considered. Compressibility effects on known results of incompressible turbulence are pointed out. copyright 1995 Academic Press, Inc

A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory.

Science.gov (United States)

Li, Jingchao; Cao, Yunpeng; Ying, Yulong; Li, Shuying

2016-01-01

Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately.

Multifractal spectra in homogeneous shear flow

Science.gov (United States)

Deane, A. E.; Keefe, L. R.

1988-01-01

Employing numerical simulations of 3-D homogeneous shear flow, the associated multifractal spectra of the energy dissipation, scalar dissipation and vorticity fields were calculated. The results for (128) cubed simulations of this flow, and those obtained in recent experiments that analyzed 1- and 2-D intersections of atmospheric and laboratory flows, are in some agreement. A two-scale Cantor set model of the energy cascade process which describes the experimental results from 1-D intersections quite well, describes the 3-D results only marginally.

Usefulness of dynamic magnetic resonance imaging in brain tumors

International Nuclear Information System (INIS)

Joo, Yang Gu; Suh, Soo Jhi; Zeon, Seok Kil; Woo, Sung Ku; Kim, Hong; Kim, Jung Sik; Lee, Sung Moon; Lee, Hee Jung; Takahashi, Mutsumasa

1994-01-01

To investigate the usefulness of dynamic MR imaging in the differential diagnosis of brain tumors. Dynamic MR imaging was performed in 43 patients with histopathologically proved brain tumors. Serial images were sequentially obtained every 30 seconds for 3-5 minutes with use of spin-echo technique(TR 200msec/TE 15msec) after rapid injection of Gd-DTPA in a dose of 0.1mmol/kg body weight. Dynamics of contrast enhancement of the brain tumors were analyzed visually and by the sequential contrast enhancement ratio(CER). On the dynamic MR imaging, contrast enhancement pattern of the gliomas showed gradual increase in signal intensity(SI) till 180 seconds and usually had a longer time to peak of the CER. The SI of metastatic brain tumors increased steeply till 30 seconds and then rapidly or gradually decreased and the tumors had a shorter time to peak of the CER. Meningiomas showed a rapid ascent in SI till 30 to 60 seconds and then made a plateau or slight descent of the CER. Lymphomas and germinomas showed relatively rapid increase of SI till 30 seconds and usually had a longer time peak of the CER. Dynamic MR imaging with Gd-DTPA may lead to further information about the brain tumors as the sequential contrast enhancement pattern and CER parameters seem to be helpful in discriminating among the brain tumors

Multifractal Approach to Time Clustering of Earthquakes. Application to Mt. Vesuvio Seismicity

Science.gov (United States)

Codano, C.; Alonzo, M. L.; Vilardo, G.

The clustering structure of the Vesuvian earthquakes occurring is investigated by means of statistical tools: the inter-event time distribution, the running mean and the multifractal analysis. The first cannot clearly distinguish between a Poissonian process and a clustered one due to the difficulties of clearly distinguishing between an exponential distribution and a power law one. The running mean test reveals the clustering of the earthquakes, but looses information about the structure of the distribution at global scales. The multifractal approach can enlighten the clustering at small scales, while the global behaviour remains Poissonian. Subsequently the clustering of the events is interpreted in terms of diffusive processes of the stress in the earth crust.

THE APPLICATION OF WAVELET-MULTIFRACTAL ANALYSIS IN PROBLEMS OF METAL STRUCTURE

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VOLCHUK V. N.

2015-09-01

Full Text Available Raising of problem. In order to obtain acceptable results of the evaluation of the metal structure developed methodology should include the use of both classical and modern methods of its evaluation and the properties of the produced goods. Thus, to establish the relationship between mechanical properties and structural elements of metal to use multifractal theory. The proposed method is the most appropriate to quantify the majority of real structures, which are integral approximation figures Euclid introduces some uncertainty, and therefore not always acceptable in practical problems of modern materials science. According to the proposed method, each of heterogeneous objects, which are the structures most metals can be characterized by variety of statistical Renyi dimensions. The range of dimensions multifractals interpreted as some of the physical laws, which have a separate statistical properties that make it possible to their financial performance. Application of statistical dimensions of the structural elements for the assessment of qualitative characteristics of metal contributes to their formalization as a function of the fractal dimension. This in turn makes it possible to identify and anticipate the physical and mechanical properties of the metal without producing special mechanical tests. Purpose  obtain information about the possible application of wavelet-multifractal analysis to assess the microstructure of the metal. Conclusion. Using the methods of wavelet multifractal analysis, a statistical evaluation of the structural elements of steel St3ps. An analysis of the characteristics of uniformity, consistency and regularity of the structural elements has shown that most of the change observed in the samples subjected to accelerated cooling water in the temperature range of the intermediate (bainitic conversion 550 – 4500С, less - in samples cooled in the temperature range 650 pearlite transformation  6000С and the smallest

Repair, redistribution and repopulation in V79 spheroids during multifraction irradiation

International Nuclear Information System (INIS)

Brown, R.C.; Durand, R.E.

1994-01-01

We used cells growing as multicell spheroids to determine whether the initial radiation response would be predictive for multifraction exposures, or whether other factors including repopulation rate should be considered. Potential problems of hypoxia and reoxygenation were avoided by using small spheroids which had not yet developed radiobiologically hypoxic regions. Repair and redistribution dominated the responses in the first two or three exposures, with repopulation playing a minor role. As the fractionation schedule was extended, however, repopulation between fractions largely determined the number of viable cells per spheroid. We conclude that the radiation response of cells from untreated spheroids provides a general indication of net sensitivity, but that repair and redistribution produces considerable variation in radiosensitivity throughout a fractionation protocol. Ultimately, repopulation effects may dominate the multifraction response. (Author)

Multifractal Modeling of Turbulent Mixing

Science.gov (United States)

Samiee, Mehdi; Zayernouri, Mohsen; Meerschaert, Mark M.

2017-11-01

Stochastic processes in random media are emerging as interesting tools for modeling anomalous transport phenomena. Applications include intermittent passive scalar transport with background noise in turbulent flows, which are observed in atmospheric boundary layers, turbulent mixing in reactive flows, and long-range dependent flow fields in disordered/fractal environments. In this work, we propose a nonlocal scalar transport equation involving the fractional Laplacian, where the corresponding fractional index is linked to the multifractal structure of the nonlinear passive scalar power spectrum. This work was supported by the AFOSR Young Investigator Program (YIP) award (FA9550-17-1-0150) and partially by MURI/ARO (W911NF-15-1-0562).

Different Multifractal Scaling of the 0 cm Average Ground Surface Temperature of Four Representative Weather Stations over China

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Lei Jiang

2013-01-01

Full Text Available The temporal scaling properties of the daily 0 cm average ground surface temperature (AGST records obtained from four selected sites over China are investigated using multifractal detrended fluctuation analysis (MF-DFA method. Results show that the AGST records at all four locations exhibit strong persistence features and different scaling behaviors. The differences of the generalized Hurst exponents are very different for the AGST series of each site reflecting the different scaling behaviors of the fluctuation. Furthermore, the strengths of multifractal spectrum are different for different weather stations and indicate that the multifractal behaviors vary from station to station over China.

Predicting individual brain maturity using dynamic functional connectivity

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Jian eQin

2015-07-01

Full Text Available Neuroimaging-based functional connectivity (FC analyses have revealed significant developmental trends in specific intrinsic connectivity networks linked to cognitive and behavioral maturation. However, knowledge of how brain functional maturation is associated with FC dynamics at rest is limited. Here, we examined age-related differences in the temporal variability of FC dynamics with data publicly released by the Nathan Kline Institute (NKI (n=183, ages 7-30 and showed that dynamic inter-region interactions can be used to accurately predict individual brain maturity across development. Furthermore, we identified a significant age-dependent trend underlying dynamic inter-network FC, including increasing variability of the connections between the visual network, default mode network (DMN and cerebellum as well as within the cerebellum and DMN and decreasing variability within the cerebellum and between the cerebellum and DMN as well as the cingulo-opercular network. Overall, the results suggested significant developmental changes in dynamic inter-network interaction, which may shed new light on the functional organization of typical developmental brains.

Multifractality and value-at-risk forecasting of exchange rates

Science.gov (United States)

Batten, Jonathan A.; Kinateder, Harald; Wagner, Niklas

2014-05-01

This paper addresses market risk prediction for high frequency foreign exchange rates under nonlinear risk scaling behaviour. We use a modified version of the multifractal model of asset returns (MMAR) where trading time is represented by the series of volume ticks. Our dataset consists of 138,418 5-min round-the-clock observations of EUR/USD spot quotes and trading ticks during the period January 5, 2006 to December 31, 2007. Considering fat-tails, long-range dependence as well as scale inconsistency with the MMAR, we derive out-of-sample value-at-risk (VaR) forecasts and compare our approach to historical simulation as well as a benchmark GARCH(1,1) location-scale VaR model. Our findings underline that the multifractal properties in EUR/USD returns in fact have notable risk management implications. The MMAR approach is a parsimonious model which produces admissible VaR forecasts at the 12-h forecast horizon. For the daily horizon, the MMAR outperforms both alternatives based on conditional as well as unconditional coverage statistics.

Cosmic microwave background and inflation in multi-fractional spacetimes

Energy Technology Data Exchange (ETDEWEB)

Calcagni, Gianluca [Instituto de Estructura de la Materia,CSIC, Serrano 121, 28006 Madrid (Spain); Kuroyanagi, Sachiko [Department of Physics, Nagoya University,Chikusa, Nagoya 464-8602 (Japan); Institute for Advanced Research, Nagoya University,Chikusa, Nagoya 464-8602 (Japan); Tsujikawa, Shinji [Department of Physics, Faculty of Science, Tokyo University of Science,1-3, Kagurazaka, Shinjuku-ku, Tokyo 162-8601 (Japan)

2016-08-18

We use FIRAS and Planck 2015 data to place observational bounds on inflationary scenarios in multi-fractional spacetimes with q-derivatives. While a power-law expansion in the geometric time coordinate is subject to the usual constraints from the tensor-to-scalar ratio, model-independent best fits of the black-body and scalar spectra yield upper limits on the free parameters of the multi-fractal measure of the theory. When the measure describing the fractal spacetime geometry is non-oscillating, information on the CMB black-body spectrum places constraints on the theory independent from but weaker than those obtained from the Standard Model, astrophysical gravitational waves and gamma-ray bursts (GRBs). When log oscillations are included and the measure describes a discrete fractal spacetime at microscopic scales, we obtain the first observational constraints on the amplitudes of such oscillations and find, in general, strong constraints on the multi-scale geometry and on the dimension of space. These results complete the scan and reduction of the parameter space of the theory. Black-body bounds are obtained also for the theory with weighted derivatives.

Cosmic microwave background and inflation in multi-fractional spacetimes

International Nuclear Information System (INIS)

Calcagni, Gianluca; Kuroyanagi, Sachiko; Tsujikawa, Shinji

2016-01-01

We use FIRAS and Planck 2015 data to place observational bounds on inflationary scenarios in multi-fractional spacetimes with q-derivatives. While a power-law expansion in the geometric time coordinate is subject to the usual constraints from the tensor-to-scalar ratio, model-independent best fits of the black-body and scalar spectra yield upper limits on the free parameters of the multi-fractal measure of the theory. When the measure describing the fractal spacetime geometry is non-oscillating, information on the CMB black-body spectrum places constraints on the theory independent from but weaker than those obtained from the Standard Model, astrophysical gravitational waves and gamma-ray bursts (GRBs). When log oscillations are included and the measure describes a discrete fractal spacetime at microscopic scales, we obtain the first observational constraints on the amplitudes of such oscillations and find, in general, strong constraints on the multi-scale geometry and on the dimension of space. These results complete the scan and reduction of the parameter space of the theory. Black-body bounds are obtained also for the theory with weighted derivatives.

Multifractal investigation of continuous seismic signal recorded at El Hierro volcano (Canary Islands) during the 2011-2012 pre- and eruptive phases

Science.gov (United States)

Telesca, Luciano; Lovallo, Michele; Martì Molist, Joan; López Moreno, Carmen; Abella Meléndez, Rafael

2015-02-01

The Multifractal Detrended Fluctuation Analysis (MF-DFA) is an effective method that allows detecting multifractality in non-stationary signals. We applied the MF-DFA to the continuous seismic signal recorded at El Hierro volcano (Canary Islands), which was affected by a submarine monogenetic eruption in October 2011. We investigated the multifractal properties of the continuous seismic signal before the onset of the eruption and after. We analysed three frames of the signal, one measured before the onset of eruption that occurred on October 10, 2011; and two after, but corresponding to two distinct eruptive episodes, the second one started on November 22, 2011 and lasting until late February 2012. The results obtained show a striking difference in the width of the multifractal spectrum, which is generally used to quantify the multifractal degree of a signal: the multifractal spectra of the signal frames recorded during the eruptive episodes are almost identical and much narrower than that of the signal frame measured before the onset of the eruption. Such difference indicates that the seismic signal recorded during the unrest reflects mostly the fracturing of the host rock under the overpressure exerted by the intruding magma, while that corresponding to the eruptive phases was mostly influenced by the flow of magma through the plumbing system, even some fracturing remains, not being possible to distinguish among the two eruptive episodes in terms of rock fracture mechanics.

Energy landscape and dynamics of brain activity during human bistable perception.

Science.gov (United States)

Watanabe, Takamitsu; Masuda, Naoki; Megumi, Fukuda; Kanai, Ryota; Rees, Geraint

2014-08-28

Individual differences in the structure of parietal and prefrontal cortex predict the stability of bistable visual perception. However, the mechanisms linking such individual differences in brain structures to behaviour remain elusive. Here we demonstrate a systematic relationship between the dynamics of brain activity, cortical structure and behaviour underpinning bistable perception. Using fMRI in humans, we find that the activity dynamics during bistable perception are well described as fluctuating between three spatially distributed energy minimums: visual-area-dominant, frontal-area-dominant and intermediate states. Transitions between these energy minimums predicted behaviour, with participants whose brain activity tend to reflect the visual-area-dominant state exhibiting more stable perception and those whose activity transits to frontal-area-dominant states reporting more frequent perceptual switches. Critically, these brain activity dynamics are correlated with individual differences in grey matter volume of the corresponding brain areas. Thus, individual differences in the large-scale dynamics of brain activity link focal brain structure with bistable perception.

The effects of observational correlated noises on multifractal detrended fluctuation analysis

Science.gov (United States)

Gulich, Damián; Zunino, Luciano

2012-08-01

We have numerically investigated the effects that observational correlated noises have on the generalized Hurst exponents, h(q), estimated by using the multifractal generalization of detrended fluctuation analysis (MF-DFA). More precisely, artificially generated stochastic binomial multifractals with increased amount of colored noises were analyzed via MF-DFA. It has been recently shown that for moderate additions of white noise, the generalized Hurst exponents are significantly underestimated for qeffects of additive noise, short- term memory and periodic trends, Physica A 390 (2011) 2480-2490]. In this paper, we have found that h(q) with q≥2 are also affected when correlated noises are considered. This is due to the fact that the spurious correlations influence the scaling behaviors associated to large fluctuations. The results obtained are significant for practical situations, where noises with different correlations are inherently present.

Characterization of turbulence stability through the identification of multifractional Brownian motions

Science.gov (United States)

Lee, K. C.

2013-02-01

Multifractional Brownian motions have become popular as flexible models in describing real-life signals of high-frequency features in geoscience, microeconomics, and turbulence, to name a few. The time-changing Hurst exponent, which describes regularity levels depending on time measurements, and variance, which relates to an energy level, are two parameters that characterize multifractional Brownian motions. This research suggests a combined method of estimating the time-changing Hurst exponent and variance using the local variation of sampled paths of signals. The method consists of two phases: initially estimating global variance and then accurately estimating the time-changing Hurst exponent. A simulation study shows its performance in estimation of the parameters. The proposed method is applied to characterization of atmospheric stability in which descriptive statistics from the estimated time-changing Hurst exponent and variance classify stable atmosphere flows from unstable ones.

Characterization of turbulence stability through the identification of multifractional Brownian motions

Directory of Open Access Journals (Sweden)

K. C. Lee

2013-02-01

Full Text Available Multifractional Brownian motions have become popular as flexible models in describing real-life signals of high-frequency features in geoscience, microeconomics, and turbulence, to name a few. The time-changing Hurst exponent, which describes regularity levels depending on time measurements, and variance, which relates to an energy level, are two parameters that characterize multifractional Brownian motions. This research suggests a combined method of estimating the time-changing Hurst exponent and variance using the local variation of sampled paths of signals. The method consists of two phases: initially estimating global variance and then accurately estimating the time-changing Hurst exponent. A simulation study shows its performance in estimation of the parameters. The proposed method is applied to characterization of atmospheric stability in which descriptive statistics from the estimated time-changing Hurst exponent and variance classify stable atmosphere flows from unstable ones.

A non linear analysis of human gait time series based on multifractal analysis and cross correlations

International Nuclear Information System (INIS)

Munoz-Diosdado, A

2005-01-01

We analyzed databases with gait time series of adults and persons with Parkinson, Huntington and amyotrophic lateral sclerosis (ALS) diseases. We obtained the staircase graphs of accumulated events that can be bounded by a straight line whose slope can be used to distinguish between gait time series from healthy and ill persons. The global Hurst exponent of these series do not show tendencies, we intend that this is because some gait time series have monofractal behavior and others have multifractal behavior so they cannot be characterized with a single Hurst exponent. We calculated the multifractal spectra, obtained the spectra width and found that the spectra of the healthy young persons are almost monofractal. The spectra of ill persons are wider than the spectra of healthy persons. In opposition to the interbeat time series where the pathology implies loss of multifractality, in the gait time series the multifractal behavior emerges with the pathology. Data were collected from healthy and ill subjects as they walked in a roughly circular path and they have sensors in both feet, so we have one time series for the left foot and other for the right foot. First, we analyzed these time series separately, and then we compared both results, with direct comparison and with a cross correlation analysis. We tried to find differences in both time series that can be used as indicators of equilibrium problems

A non linear analysis of human gait time series based on multifractal analysis and cross correlations

Energy Technology Data Exchange (ETDEWEB)

Munoz-Diosdado, A [Department of Mathematics, Unidad Profesional Interdisciplinaria de Biotecnologia, Instituto Politecnico Nacional, Av. Acueducto s/n, 07340, Mexico City (Mexico)

2005-01-01

We analyzed databases with gait time series of adults and persons with Parkinson, Huntington and amyotrophic lateral sclerosis (ALS) diseases. We obtained the staircase graphs of accumulated events that can be bounded by a straight line whose slope can be used to distinguish between gait time series from healthy and ill persons. The global Hurst exponent of these series do not show tendencies, we intend that this is because some gait time series have monofractal behavior and others have multifractal behavior so they cannot be characterized with a single Hurst exponent. We calculated the multifractal spectra, obtained the spectra width and found that the spectra of the healthy young persons are almost monofractal. The spectra of ill persons are wider than the spectra of healthy persons. In opposition to the interbeat time series where the pathology implies loss of multifractality, in the gait time series the multifractal behavior emerges with the pathology. Data were collected from healthy and ill subjects as they walked in a roughly circular path and they have sensors in both feet, so we have one time series for the left foot and other for the right foot. First, we analyzed these time series separately, and then we compared both results, with direct comparison and with a cross correlation analysis. We tried to find differences in both time series that can be used as indicators of equilibrium problems.

Multifractals embedded in short time series: An unbiased estimation of probability moment

Science.gov (United States)

Qiu, Lu; Yang, Tianguang; Yin, Yanhua; Gu, Changgui; Yang, Huijie

2016-12-01

An exact estimation of probability moments is the base for several essential concepts, such as the multifractals, the Tsallis entropy, and the transfer entropy. By means of approximation theory we propose a new method called factorial-moment-based estimation of probability moments. Theoretical prediction and computational results show that it can provide us an unbiased estimation of the probability moments of continuous order. Calculations on probability redistribution model verify that it can extract exactly multifractal behaviors from several hundred recordings. Its powerfulness in monitoring evolution of scaling behaviors is exemplified by two empirical cases, i.e., the gait time series for fast, normal, and slow trials of a healthy volunteer, and the closing price series for Shanghai stock market. By using short time series with several hundred lengths, a comparison with the well-established tools displays significant advantages of its performance over the other methods. The factorial-moment-based estimation can evaluate correctly the scaling behaviors in a scale range about three generations wider than the multifractal detrended fluctuation analysis and the basic estimation. The estimation of partition function given by the wavelet transform modulus maxima has unacceptable fluctuations. Besides the scaling invariance focused in the present paper, the proposed factorial moment of continuous order can find its various uses, such as finding nonextensive behaviors of a complex system and reconstructing the causality relationship network between elements of a complex system.

Multifractal modelling and 3D lacunarity analysis

International Nuclear Information System (INIS)

Hanen, Akkari; Imen, Bhouri; Asma, Ben Abdallah; Patrick, Dubois; Hedi, Bedoui Mohamed

2009-01-01

This study presents a comparative evaluation of lacunarity of 3D grey level models with different types of inhomogeneity. A new method based on the 'Relative Differential Box Counting' was developed to estimate the lacunarity features of grey level volumes. To validate our method, we generated a set of 3D grey level multifractal models with random, anisotropic and hierarchical properties. Our method gives a lacunarity measurement correlated with the theoretical one and allows a better model classification compared with a classical approach.

A multifractal formalism for countable alphabet subshifts

International Nuclear Information System (INIS)

Meson, Alejandro; Vericat, Fernando

2009-01-01

We study here the multifractal spectrum of local entropies for subshifts with an infinite alphabet. The description of this spectrum is obtained from the Legendre transform of a free energy map and Gibbs states associated with adequate potentials. The lack of compactness in the symbolic space necessitates modifications to the description for the compact case, i.e. for finite alphabet. In particular, the class of potentials must be restricted to a narrower one than that considered for the compact case

The brain as a dynamic physical system.

Science.gov (United States)

McKenna, T M; McMullen, T A; Shlesinger, M F

1994-06-01

The brain is a dynamic system that is non-linear at multiple levels of analysis. Characterization of its non-linear dynamics is fundamental to our understanding of brain function. Identifying families of attractors in phase space analysis, an approach which has proven valuable in describing non-linear mechanical and electrical systems, can prove valuable in describing a range of behaviors and associated neural activity including sensory and motor repertoires. Additionally, transitions between attractors may serve as useful descriptors for analysing state changes in neurons and neural ensembles. Recent observations of synchronous neural activity, and the emerging capability to record the spatiotemporal dynamics of neural activity by voltage-sensitive dyes and electrode arrays, provide opportunities for observing the population dynamics of neural ensembles within a dynamic systems context. New developments in the experimental physics of complex systems, such as the control of chaotic systems, selection of attractors, attractor switching and transient states, can be a source of powerful new analytical tools and insights into the dynamics of neural systems.

Universal multifractality in multiparticle production

International Nuclear Information System (INIS)

Florkowski, W.; Hwa, R.C.

1991-01-01

The G moments for the multifractal analysis of multiparticle production are investigated in a model-independent way. By successive bin splitting and assuming the existence of a multiplicity splitting function that depends on multiplicity, but applicable at all steps of the splittings, we study the ergodicity of horizontal and vertical averaging, and derive a universality relation for the G moments. It relates the G moments for different initial multiplicities to a common scaling function Γ q (ξ). The experimental verification of this scaling property would, on the one hand, signify self-similarity in the data, and, on the other, provide a convenient function for comparison not only among different experiments, but also between theory and experiment

Beyond Fractals and 1/f Noise: Multifractal Analysis of Complex Physiological Time Series

Science.gov (United States)

Ivanov, Plamen Ch.; Amaral, Luis A. N.; Ashkenazy, Yosef; Stanley, H. Eugene; Goldberger, Ary L.; Hausdorff, Jeffrey M.; Yoneyama, Mitsuru; Arai, Kuniharu

2001-03-01

We investigate time series with 1/f-like spectra generated by two physiologic control systems --- the human heartbeat and human gait. We show that physiological fluctuations exhibit unexpected ``hidden'' structures often described by scaling laws. In particular, our studies indicate that when analyzed on different time scales the heartbeat fluctuations exhibit cascades of branching patterns with self-similar (fractal) properties, characterized by long-range power-law anticorrelations. We find that these scaling features change during sleep and wake phases, and with pathological perturbations. Further, by means of a new wavelet-based technique, we find evidence of multifractality in the healthy human heartbeat even under resting conditions, and show that the multifractal character and nonlinear properties of the healthy heart are encoded in the Fourier phases. We uncover a loss of multifractality for a life-threatening condition, congestive heart failure. In contrast to the heartbeat, we find that the interstride interval time series of healthy human gait, a voluntary process under neural regulation, is described by a single fractal dimension (such as classical 1/f noise) indicating monofractal behavior. Thus our approach can help distinguish physiological and physical signals with comparable frequency spectra and two-point correlations, and guide modeling of their control mechanisms.

Fundamental Dynamical Modes Underlying Human Brain Synchronization

Directory of Open Access Journals (Sweden)

Catalina Alvarado-Rojas

2012-01-01

Full Text Available Little is known about the long-term dynamics of widely interacting cortical and subcortical networks during the wake-sleep cycle. Using large-scale intracranial recordings of epileptic patients during seizure-free periods, we investigated local- and long-range synchronization between multiple brain regions over several days. For such high-dimensional data, summary information is required for understanding and modelling the underlying dynamics. Here, we suggest that a compact yet useful representation is given by a state space based on the first principal components. Using this representation, we report, with a remarkable similarity across the patients with different locations of electrode placement, that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes. In this space, transitions between behavioral states occur through specific trajectories from one mode to another. These findings suggest that, at a coarse level of temporal resolution, the different brain states are correlated with several dominant synchrony patterns which are successively activated across wake-sleep states.

Entropy and Multifractality in Relativistic Ion-Ion Collisions

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Shaista Khan

2018-01-01

Full Text Available Entropy production in multiparticle systems is investigated by analyzing the experimental data on ion-ion collisions at AGS and SPS energies and comparing the findings with those reported earlier for hadron-hadron, hadron-nucleus, and nucleus-nucleus collisions. It is observed that the entropy produced in limited and full phase space, when normalized to maximum rapidity, exhibits a kind of scaling which is nicely supported by Monte Carlo model HIJING. Using Rényi’s order q information entropy, multifractal characteristics of particle production are examined in terms of generalized dimensions, Dq. Nearly the same values of multifractal specific heat, c, observed in hadronic and ion-ion collisions over a wide range of incident energies suggest that the quantity c might be used as a universal characteristic of multiparticle production in hadron-hadron, hadron-nucleus, and nucleus-nucleus collisions. The analysis is extended to the study of spectrum of scaling indices. The findings reveal that Rényi’s order q information entropy could be another way to investigate the fluctuations in multiplicity distributions in terms of spectral function f(α, which has been argued to be a convenient function for comparison sake not only among different experiments but also between the data and theoretical models.

Multifractal rainfall extremes: Theoretical analysis and practical estimation

International Nuclear Information System (INIS)

Langousis, Andreas; Veneziano, Daniele; Furcolo, Pierluigi; Lepore, Chiara

2009-01-01

We study the extremes generated by a multifractal model of temporal rainfall and propose a practical method to estimate the Intensity-Duration-Frequency (IDF) curves. The model assumes that rainfall is a sequence of independent and identically distributed multiplicative cascades of the beta-lognormal type, with common duration D. When properly fitted to data, this simple model was found to produce accurate IDF results [Langousis A, Veneziano D. Intensity-duration-frequency curves from scaling representations of rainfall. Water Resour Res 2007;43. (doi:10.1029/2006WR005245)]. Previous studies also showed that the IDF values from multifractal representations of rainfall scale with duration d and return period T under either d → 0 or T → ∞, with different scaling exponents in the two cases. We determine the regions of the (d, T)-plane in which each asymptotic scaling behavior applies in good approximation, find expressions for the IDF values in the scaling and non-scaling regimes, and quantify the bias when estimating the asymptotic power-law tail of rainfall intensity from finite-duration records, as was often done in the past. Numerically calculated exact IDF curves are compared to several analytic approximations. The approximations are found to be accurate and are used to propose a practical IDF estimation procedure.

Multifractality to Photonic Crystal & Self-Organization to Metamaterials through Anderson Localizations & Group/Gauge Theory

Science.gov (United States)

Hidajatullah-Maksoed, Widastra

2015-04-01

Arthur Cayley at least investigate by creating the theory of permutation group[F:∖∖Group_theory.htm] where in cell elements addressing of the lattice Qmf used a Cayley tree, the self-afine object Qmf is described by the combination of the finite groups of rotation & inversion and the infinite groups of translation & dilation[G Corso & LS Lacena: ``Multifractal lattice and group theory'', Physica A: Statistical Mechanics &Its Applications, 2005, v 357, issue I, h 64-70; http://www.sciencedirect.com/science/articel/pii/S0378437105005005 ] hence multifractal can be related to group theory. Many grateful Thanks to HE. Mr. Drs. P. SWANTORO & HE. Mr. Ir. SARWONO KUSUMAATMADJA.

Multifractal modelling and 3D lacunarity analysis

Energy Technology Data Exchange (ETDEWEB)

Hanen, Akkari, E-mail: bettaieb.hanen@topnet.t [Laboratoire de biophysique, TIM, Faculte de Medecine (Tunisia); Imen, Bhouri, E-mail: bhouri_imen@yahoo.f [Unite de recherche ondelettes et multifractals, Faculte des sciences (Tunisia); Asma, Ben Abdallah, E-mail: asma.babdallah@cristal.rnu.t [Laboratoire de biophysique, TIM, Faculte de Medecine (Tunisia); Patrick, Dubois, E-mail: pdubois@chru-lille.f [INSERM, U 703, Lille (France); Hedi, Bedoui Mohamed, E-mail: medhedi.bedoui@fmm.rnu.t [Laboratoire de biophysique, TIM, Faculte de Medecine (Tunisia)

2009-09-28

This study presents a comparative evaluation of lacunarity of 3D grey level models with different types of inhomogeneity. A new method based on the 'Relative Differential Box Counting' was developed to estimate the lacunarity features of grey level volumes. To validate our method, we generated a set of 3D grey level multifractal models with random, anisotropic and hierarchical properties. Our method gives a lacunarity measurement correlated with the theoretical one and allows a better model classification compared with a classical approach.

Network dynamics with BrainX(3): a large-scale simulation of the human brain network with real-time interaction.

Science.gov (United States)

Arsiwalla, Xerxes D; Zucca, Riccardo; Betella, Alberto; Martinez, Enrique; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F M J

2015-01-01

BrainX(3) is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX(3) in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX(3) can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

Science.gov (United States)

Arsiwalla, Xerxes D.; Zucca, Riccardo; Betella, Alberto; Martinez, Enrique; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F. M. J.

2015-01-01

BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas. PMID:25759649

Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain Network with Real-Time Interaction

Directory of Open Access Journals (Sweden)

Xerxes D. Arsiwalla

2015-02-01

Full Text Available BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for real-time exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably, due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

Perturbation of whole-brain dynamics in silico reveals mechanistic differences between brain states

NARCIS (Netherlands)

Deco, Gustavo; Cabral, Joana; Saenger, Victor M; Boly, Melanie; Tagliazucchi, Enzo; Laufs, Helmut; Van Someren, Eus; Jobst, Beatrice; Stevner, Angus; Kringelbach, Morten L

2017-01-01

Human neuroimaging research has revealed that wakefulness and sleep involve very different activity patterns. Yet, it is not clear why brain states differ in their dynamical complexity, e.g. in the level of integration and segregation across brain networks over time. Here, we investigate the

Perturbation of whole-brain dynamics in silico reveals mechanistic differences between brain states

NARCIS (Netherlands)

Deco, Gustavo; Cabral, Joana; Saenger, Victor M; Boly, Melanie; Tagliazucchi, Enzo; Laufs, Helmut; Van Someren, Eus; Jobst, Beatrice M; Stevner, Angus B A; Kringelbach, Morten L

2018-01-01

Human neuroimaging research has revealed that wakefulness and sleep involve very different activity patterns. Yet, it is not clear why brain states differ in their dynamical complexity, e.g. in the level of integration and segregation across brain networks over time. Here, we investigate the

Complexity measures of music

Science.gov (United States)

Pease, April; Mahmoodi, Korosh; West, Bruce J.

2018-03-01

We present a technique to search for the presence of crucial events in music, based on the analysis of the music volume. Earlier work on this issue was based on the assumption that crucial events correspond to the change of music notes, with the interesting result that the complexity index of the crucial events is mu ~ 2, which is the same inverse power-law index of the dynamics of the brain. The search technique analyzes music volume and confirms the results of the earlier work, thereby contributing to the explanation as to why the brain is sensitive to music, through the phenomenon of complexity matching. Complexity matching has recently been interpreted as the transfer of multifractality from one complex network to another. For this reason we also examine the mulifractality of music, with the observation that the multifractal spectrum of a computer performance is significantly narrower than the multifractal spectrum of a human performance of the same musical score. We conjecture that although crucial events are demonstrably important for information transmission, they alone are not suficient to define musicality, which is more adequately measured by the multifractality spectrum.

Log-Normality and Multifractal Analysis of Flame Surface Statistics

Science.gov (United States)

Saha, Abhishek; Chaudhuri, Swetaprovo; Law, Chung K.

2013-11-01

The turbulent flame surface is typically highly wrinkled and folded at a multitude of scales controlled by various flame properties. It is useful if the information contained in this complex geometry can be projected onto a simpler regular geometry for the use of spectral, wavelet or multifractal analyses. Here we investigate local flame surface statistics of turbulent flame expanding under constant pressure. First the statistics of local length ratio is experimentally obtained from high-speed Mie scattering images. For spherically expanding flame, length ratio on the measurement plane, at predefined equiangular sectors is defined as the ratio of the actual flame length to the length of a circular-arc of radius equal to the average radius of the flame. Assuming isotropic distribution of such flame segments we convolute suitable forms of the length-ratio probability distribution functions (pdfs) to arrive at corresponding area-ratio pdfs. Both the pdfs are found to be near log-normally distributed and shows self-similar behavior with increasing radius. Near log-normality and rather intermittent behavior of the flame-length ratio suggests similarity with dissipation rate quantities which stimulates multifractal analysis. Currently at Indian Institute of Science, India.

Black holes in multi-fractional and Lorentz-violating models

Energy Technology Data Exchange (ETDEWEB)

Calcagni, Gianluca [CSIC, Instituto de Estructura de la Materia, Madrid (Spain); Rodriguez Fernandez, David [Universidad de Oviedo, Department of Physics, Oviedo (Spain); Ronco, Michele [Universita di Roma ' ' La Sapienza' ' , Dipartimento di Fisica, Rome (Italy); INFN, Rome (Italy)

2017-05-15

We study static and radially symmetric black holes in the multi-fractional theories of gravity with q-derivatives and with weighted derivatives, frameworks where the spacetime dimension varies with the probed scale and geometry is characterized by at least one fundamental length l{sub *}. In the q-derivatives scenario, one finds a tiny shift of the event horizon. Schwarzschild black holes can present an additional ring singularity, not present in general relativity, whose radius is proportional to l{sub *}. In the multi-fractional theory with weighted derivatives, there is no such deformation, but non-trivial geometric features generate a cosmological-constant term, leading to a de Sitter-Schwarzschild black hole. For both scenarios, we compute the Hawking temperature and comment on the resulting black-hole thermodynamics. In the case with q-derivatives, black holes can be hotter than usual and possess an additional ring singularity, while in the case with weighted derivatives they have a de Sitter hair of purely geometric origin, which may lead to a solution of the cosmological constant problem similar to that in unimodular gravity. Finally, we compare our findings with other Lorentz-violating models. (orig.)

Black holes in multi-fractional and Lorentz-violating models

International Nuclear Information System (INIS)

Calcagni, Gianluca; Rodriguez Fernandez, David; Ronco, Michele

2017-01-01

We study static and radially symmetric black holes in the multi-fractional theories of gravity with q-derivatives and with weighted derivatives, frameworks where the spacetime dimension varies with the probed scale and geometry is characterized by at least one fundamental length l_*. In the q-derivatives scenario, one finds a tiny shift of the event horizon. Schwarzschild black holes can present an additional ring singularity, not present in general relativity, whose radius is proportional to l_*. In the multi-fractional theory with weighted derivatives, there is no such deformation, but non-trivial geometric features generate a cosmological-constant term, leading to a de Sitter-Schwarzschild black hole. For both scenarios, we compute the Hawking temperature and comment on the resulting black-hole thermodynamics. In the case with q-derivatives, black holes can be hotter than usual and possess an additional ring singularity, while in the case with weighted derivatives they have a de Sitter hair of purely geometric origin, which may lead to a solution of the cosmological constant problem similar to that in unimodular gravity. Finally, we compare our findings with other Lorentz-violating models. (orig.)

ABC of multi-fractal spacetimes and fractional sea turtles

Energy Technology Data Exchange (ETDEWEB)

Calcagni, Gianluca [Instituto de Estructura de la Materia, CSIC, Madrid (Spain)

2016-04-15

We clarify what it means to have a spacetime fractal geometry in quantum gravity and show that its properties differ from those of usual fractals. A weak and a strong definition of multi-scale and multi-fractal spacetimes are given together with a sketch of the landscape of multi-scale theories of gravitation. Then, in the context of the fractional theory with q-derivatives, we explore the consequences of living in a multi-fractal spacetime. To illustrate the behavior of a non-relativistic body, we take the entertaining example of a sea turtle. We show that, when only the time direction is fractal, sea turtles swim at a faster speed than in an ordinary world, while they swim at a slower speed if only the spatial directions are fractal. The latter type of geometry is the one most commonly found in quantum gravity. For time-like fractals, relativistic objects can exceed the speed of light, but strongly so only if their size is smaller than the range of particle-physics interactions. We also find new results about log-oscillating measures, the measure presentation and their role in physical observations and in future extensions to nowhere-differentiable stochastic spacetimes. (orig.)

ABC of multi-fractal spacetimes and fractional sea turtles

International Nuclear Information System (INIS)

Calcagni, Gianluca

2016-01-01

We clarify what it means to have a spacetime fractal geometry in quantum gravity and show that its properties differ from those of usual fractals. A weak and a strong definition of multi-scale and multi-fractal spacetimes are given together with a sketch of the landscape of multi-scale theories of gravitation. Then, in the context of the fractional theory with q-derivatives, we explore the consequences of living in a multi-fractal spacetime. To illustrate the behavior of a non-relativistic body, we take the entertaining example of a sea turtle. We show that, when only the time direction is fractal, sea turtles swim at a faster speed than in an ordinary world, while they swim at a slower speed if only the spatial directions are fractal. The latter type of geometry is the one most commonly found in quantum gravity. For time-like fractals, relativistic objects can exceed the speed of light, but strongly so only if their size is smaller than the range of particle-physics interactions. We also find new results about log-oscillating measures, the measure presentation and their role in physical observations and in future extensions to nowhere-differentiable stochastic spacetimes. (orig.)

Black holes in multi-fractional and Lorentz-violating models.

Science.gov (United States)

Calcagni, Gianluca; Rodríguez Fernández, David; Ronco, Michele

2017-01-01

We study static and radially symmetric black holes in the multi-fractional theories of gravity with q -derivatives and with weighted derivatives, frameworks where the spacetime dimension varies with the probed scale and geometry is characterized by at least one fundamental length [Formula: see text]. In the q -derivatives scenario, one finds a tiny shift of the event horizon. Schwarzschild black holes can present an additional ring singularity, not present in general relativity, whose radius is proportional to [Formula: see text]. In the multi-fractional theory with weighted derivatives, there is no such deformation, but non-trivial geometric features generate a cosmological-constant term, leading to a de Sitter-Schwarzschild black hole. For both scenarios, we compute the Hawking temperature and comment on the resulting black-hole thermodynamics. In the case with q -derivatives, black holes can be hotter than usual and possess an additional ring singularity, while in the case with weighted derivatives they have a de Sitter hair of purely geometric origin, which may lead to a solution of the cosmological constant problem similar to that in unimodular gravity. Finally, we compare our findings with other Lorentz-violating models.

ABC of multi-fractal spacetimes and fractional sea turtles

Science.gov (United States)

Calcagni, Gianluca

2016-04-01

We clarify what it means to have a spacetime fractal geometry in quantum gravity and show that its properties differ from those of usual fractals. A weak and a strong definition of multi-scale and multi-fractal spacetimes are given together with a sketch of the landscape of multi-scale theories of gravitation. Then, in the context of the fractional theory with q-derivatives, we explore the consequences of living in a multi-fractal spacetime. To illustrate the behavior of a non-relativistic body, we take the entertaining example of a sea turtle. We show that, when only the time direction is fractal, sea turtles swim at a faster speed than in an ordinary world, while they swim at a slower speed if only the spatial directions are fractal. The latter type of geometry is the one most commonly found in quantum gravity. For time-like fractals, relativistic objects can exceed the speed of light, but strongly so only if their size is smaller than the range of particle-physics interactions. We also find new results about log-oscillating measures, the measure presentation and their role in physical observations and in future extensions to nowhere-differentiable stochastic spacetimes.

Multifractal detrended cross-correlation analysis for epileptic patient in seizure and seizure free status

International Nuclear Information System (INIS)

Ghosh, Dipak; Dutta, Srimonti; Chakraborty, Sayantan

2014-01-01

Highlights: • We analyze EEG of patients during seizure and in seizure free interval. • Data from different sections of the brain and seizure activity was analyzed. • Assessment of cross-correlation in seizure and seizure free interval using MF-DXA technique. - Abstract: This paper reports a study of EEG data of epileptic patients in terms of multifractal detrended cross-correlation analysis (MF-DXA). The EEG clinical data were obtained from the EEG Database available with the Clinic of Epileptology of the University Hospital of Bonn, Germany. The data sets (C, D, and E) were taken from five epileptic patients undergoing presurgical evaluations. The data sets consist of intracranial EEG recordings during seizure-free intervals (interictal periods) from within the epileptogenic zone (D) and from the hippocampal formation of the opposite hemisphere of the epileptic patients’ brain, respectively (C). The data set (E) was recorded during seizure activity (ictal periods). MF-DXA is a very rigorous and robust tool for assessment of cross-correlation among two nonlinear time series. The study reveals the degree of cross-correlation is more among seizure and seizure free interval in epileptogenic zone. These data are very significant for diagnosis, onset and prognosis of epileptic patients

Assessment of 48 Stock markets using adaptive multifractal approach

Science.gov (United States)

Ferreira, Paulo; Dionísio, Andreia; Movahed, S. M. S.

2017-11-01

In this paper, Stock market comovements are examined using cointegration, Granger causality tests and nonlinear approaches in context of mutual information and correlations. Since underlying data sets are affected by non-stationarities and trends, we also apply Adaptive Multifractal Detrended Fluctuation Analysis (AMF-DFA) and Adaptive Multifractal Detrended Cross-Correlation Analysis (AMF-DXA). We find only 170 pair of Stock markets cointegrated, and according to the Granger causality and mutual information, we realize that the strongest relations lies between emerging markets, and between emerging and frontier markets. According to scaling exponent given by AMF-DFA, h(q = 2) > 1, we find that all underlying data sets belong to non-stationary process. According to Efficient Market Hypothesis (EMH), only 8 markets are classified in uncorrelated processes at 2 σ confidence interval. 6 Stock markets belong to anti-correlated class and dominant part of markets has memory in corresponding daily index prices during January 1995 to February 2014. New-Zealand with H = 0 . 457 ± 0 . 004 and Jordan with H = 0 . 602 ± 0 . 006 are far from EMH. The nature of cross-correlation exponents based on AMF-DXA is almost multifractal for all pair of Stock markets. The empirical relation, Hxy ≤ [Hxx +Hyy ] / 2, is confirmed. Mentioned relation for q > 0 is also satisfied while for q behavior of markets for small fluctuations is affected by contribution of major pair. For larger fluctuations, the cross-correlation contains information from both local (internal) and global (external) conditions. Width of singularity spectrum for auto-correlation and cross-correlation are Δαxx ∈ [ 0 . 304 , 0 . 905 ] and Δαxy ∈ [ 0 . 246 , 1 . 178 ] , respectively. The wide range of singularity spectrum for cross-correlation confirms that the bilateral relation between Stock markets is more complex. The value of σDCCA indicates that all pairs of stock market studied in this time interval

Multiscale multifractal multiproperty analysis of financial time series based on Rényi entropy

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Yujun, Yang; Jianping, Li; Yimei, Yang

This paper introduces a multiscale multifractal multiproperty analysis based on Rényi entropy (3MPAR) method to analyze short-range and long-range characteristics of financial time series, and then applies this method to the five time series of five properties in four stock indices. Combining the two analysis techniques of Rényi entropy and multifractal detrended fluctuation analysis (MFDFA), the 3MPAR method focuses on the curves of Rényi entropy and generalized Hurst exponent of five properties of four stock time series, which allows us to study more universal and subtle fluctuation characteristics of financial time series. By analyzing the curves of the Rényi entropy and the profiles of the logarithm distribution of MFDFA of five properties of four stock indices, the 3MPAR method shows some fluctuation characteristics of the financial time series and the stock markets. Then, it also shows a richer information of the financial time series by comparing the profile of five properties of four stock indices. In this paper, we not only focus on the multifractality of time series but also the fluctuation characteristics of the financial time series and subtle differences in the time series of different properties. We find that financial time series is far more complex than reported in some research works using one property of time series.

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior

DEFF Research Database (Denmark)

Hansen, Sofie Therese; Hansen, Lars Kai

2016-01-01

the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging......Electroencephalography (EEG) can capture brain dynamics in high temporal resolution. By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding...

Neural dynamics in Parkinsonian brain: The boundary between synchronized and nonsynchronized dynamics

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Park, Choongseok; Worth, Robert M.; Rubchinsky, Leonid L.

2011-04-01

Synchronous oscillatory dynamics is frequently observed in the human brain. We analyze the fine temporal structure of phase-locking in a realistic network model and match it with the experimental data from Parkinsonian patients. We show that the experimentally observed intermittent synchrony can be generated just by moderately increased coupling strength in the basal ganglia circuits due to the lack of dopamine. Comparison of the experimental and modeling data suggest that brain activity in Parkinson's disease resides in the large boundary region between synchronized and nonsynchronized dynamics. Being on the edge of synchrony may allow for easy formation of transient neuronal assemblies.

New trends in applied harmonic analysis sparse representations, compressed sensing, and multifractal analysis

CERN Document Server

Cabrelli, Carlos; Jaffard, Stephane; Molter, Ursula

2016-01-01

This volume is a selection of written notes corresponding to courses taught at the CIMPA School: "New Trends in Applied Harmonic Analysis: Sparse Representations, Compressed Sensing and Multifractal Analysis". New interactions between harmonic analysis and signal and image processing have seen striking development in the last 10 years, and several technological deadlocks have been solved through the resolution of deep theoretical problems in harmonic analysis. New Trends in Applied Harmonic Analysis focuses on two particularly active areas that are representative of such advances: multifractal analysis, and sparse representation and compressed sensing. The contributions are written by leaders in these areas, and covers both theoretical aspects and applications. This work should prove useful not only to PhD students and postdocs in mathematics and signal and image processing, but also to researchers working in related topics.

Multifractal analysis of vertical total electron content (VTEC at equatorial region and low latitude, during low solar activity

Directory of Open Access Journals (Sweden)

M. J. A. Bolzan

2013-01-01

Full Text Available This paper analyses the multifractal aspects of the GPS data (measured during a period of low solar activity obtained from two Brazilian stations: Belém (01.3° S, 48.3° W and São José dos Campos (SJC (23.2° S, 45.9° W. The results show that the respective geographic sites show important scaling differences as well as similarities when their multifractal signatures for vertical total electron content (VTEC are compared. The f(α spectra have a narrow shape for great scales, which indicates the predominance of deterministic phenomena, such as solar rotation (27 days over intermittent phenomena. Furthermore, the f(α spectra for both sites have a strong multifractality degree at small scales. This strong multifractality degree observed at small scales (1 to 12 h at both sites is because the ionosphere over Brazil is a non-equilibrium system. The differences found were that Belém presented a stronger multifractality at small scales (1 h to 12 h compared with SJC, particularly in 2006. The reason for this behaviour may be associated with the location of Belém, near the geomagnetic equator, where at this location the actions of X-rays, ultraviolet, and another wavelength from the Sun are more direct, strong, and constant throughout the whole year. Although the SJC site is near ionospheric equatorial anomaly (IEA peaks, this interpretation could explain the higher values found for the intermittent parameter μ for Belém compared with SJC. Belém also showed the presence of one or two flattening regions for f(α spectra at the same scales mentioned before. These differences and similarities also were interpreted in terms of the IEA content, where this phenomenon is an important source of intermittence due the presence of the VTEC peaks at ±20° geomagnetic latitudes.

Mechanistic Insights into Human Brain Impact Dynamics through Modal Analysis

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Laksari, Kaveh; Kurt, Mehmet; Babaee, Hessam; Kleiven, Svein; Camarillo, David

2018-03-01

Although concussion is one of the greatest health challenges today, our physical understanding of the cause of injury is limited. In this Letter, we simulated football head impacts in a finite element model and extracted the most dominant modal behavior of the brain's deformation. We showed that the brain's deformation is most sensitive in low frequency regimes close to 30 Hz, and discovered that for most subconcussive head impacts, the dynamics of brain deformation is dominated by a single global mode. In this Letter, we show the existence of localized modes and multimodal behavior in the brain as a hyperviscoelastic medium. This dynamical phenomenon leads to strain concentration patterns, particularly in deep brain regions, which is consistent with reported concussion pathology.

True and apparent scaling: The proximity of the Markov-switching multifractal model to long-range dependence

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Liu, Ruipeng; Di Matteo, T.; Lux, Thomas

2007-09-01

In this paper, we consider daily financial data of a collection of different stock market indices, exchange rates, and interest rates, and we analyze their multi-scaling properties by estimating a simple specification of the Markov-switching multifractal (MSM) model. In order to see how well the estimated model captures the temporal dependence of the data, we estimate and compare the scaling exponents H(q) (for q=1,2) for both empirical data and simulated data of the MSM model. In most cases the multifractal model appears to generate ‘apparent’ long memory in agreement with the empirical scaling laws.

Connectome-harmonic decomposition of human brain activity reveals dynamical repertoire re-organization under LSD.

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Atasoy, Selen; Roseman, Leor; Kaelen, Mendel; Kringelbach, Morten L; Deco, Gustavo; Carhart-Harris, Robin L

2017-12-15

Recent studies have started to elucidate the effects of lysergic acid diethylamide (LSD) on the human brain but the underlying dynamics are not yet fully understood. Here we used 'connectome-harmonic decomposition', a novel method to investigate the dynamical changes in brain states. We found that LSD alters the energy and the power of individual harmonic brain states in a frequency-selective manner. Remarkably, this leads to an expansion of the repertoire of active brain states, suggestive of a general re-organization of brain dynamics given the non-random increase in co-activation across frequencies. Interestingly, the frequency distribution of the active repertoire of brain states under LSD closely follows power-laws indicating a re-organization of the dynamics at the edge of criticality. Beyond the present findings, these methods open up for a better understanding of the complex brain dynamics in health and disease.

Nano-Modeling and Computation in Bio and Brain Dynamics

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Paolo Di Sia

2016-04-01

Full Text Available The study of brain dynamics currently utilizes the new features of nanobiotechnology and bioengineering. New geometric and analytical approaches appear very promising in all scientific areas, particularly in the study of brain processes. Efforts to engage in deep comprehension lead to a change in the inner brain parameters, in order to mimic the external transformation by the proper use of sensors and effectors. This paper highlights some crossing research areas of natural computing, nanotechnology, and brain modeling and considers two interesting theoretical approaches related to brain dynamics: (a the memory in neural network, not as a passive element for storing information, but integrated in the neural parameters as synaptic conductances; and (b a new transport model based on analytical expressions of the most important transport parameters, which works from sub-pico-level to macro-level, able both to understand existing data and to give new predictions. Complex biological systems are highly dependent on the context, which suggests a “more nature-oriented” computational philosophy.

Investigating the Intersession Reliability of Dynamic Brain-State Properties.

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Smith, Derek M; Zhao, Yrian; Keilholz, Shella D; Schumacher, Eric H

2018-06-01

Dynamic functional connectivity metrics have much to offer to the neuroscience of individual differences of cognition. Yet, despite the recent expansion in dynamic connectivity research, limited resources have been devoted to the study of the reliability of these connectivity measures. To address this, resting-state functional magnetic resonance imaging data from 100 Human Connectome Project subjects were compared across 2 scan days. Brain states (i.e., patterns of coactivity across regions) were identified by classifying each time frame using k means clustering. This was done with and without global signal regression (GSR). Multiple gauges of reliability indicated consistency in the brain-state properties across days and GSR attenuated the reliability of the brain states. Changes in the brain-state properties across the course of the scan were investigated as well. The results demonstrate that summary metrics describing the clustering of individual time frames have adequate test/retest reliability, and thus, these patterns of brain activation may hold promise for individual-difference research.

Multifractal analysis of the Korean agricultural market

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Kim, Hongseok; Oh, Gabjin; Kim, Seunghwan

2011-11-01

We have studied the long-term memory effects of the Korean agricultural market using the detrended fluctuation analysis (DFA) method. In general, the return time series of various financial data, including stock indices, foreign exchange rates, and commodity prices, are uncorrelated in time, while the volatility time series are strongly correlated. However, we found that the return time series of Korean agricultural commodity prices are anti-correlated in time, while the volatility time series are correlated. The n-point correlations of time series were also examined, and it was found that a multifractal structure exists in Korean agricultural market prices.

Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain Network with Real-Time Interaction

OpenAIRE

Xerxes D. Arsiwalla; Riccardo eZucca; Alberto eBetella; Enrique eMartinez; David eDalmazzo; Pedro eOmedas; Gustavo eDeco; Gustavo eDeco; Paul F.M.J. Verschure; Paul F.M.J. Verschure

2015-01-01

BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimula...

Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

OpenAIRE

Arsiwalla, Xerxes D.; Zucca, Riccardo; Betella, Alberto; Martínez, Enrique, 1961-; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F. M. J.

2015-01-01

BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimula...

Multifractal features of spot rates in the Liquid Petroleum Gas shipping market

International Nuclear Information System (INIS)

Engelen, Steve; Norouzzadeh, Payam; Rahmani, Bahareh; Dullaert, Wout

2011-01-01

We investigate for the first time the spot rate dynamics of Very Large Gas Carriers (VLGCs) by means of multifractal detrended fluctuation analysis (MF-DFA) and rescaled range (R/S) analysis. Both non-parametric methods allow for a rigorous statistical analysis of the freight process by detecting correlation, scaling and fluctuation behavior regardless of nonlinearity issues. By applying different data-frequencies and a temporal framework, the Hurst exponents indicate that freight rates exhibit trend-reinforcement and persistence subject to limited time-dependency and controlled volatility. The found long-range dependence corroborates that a predictive freight model can be built undermining the efficient market hypothesis. Memory effects seem to each time build up until they are interrupted by seasonal transitions, stochastic events or cycles which all spark a sudden loss in correlations or increase in nonlinearities. The surrogate and shuffling data procedures demonstrate that, dependent on the data-frequency used, memory effects and fat-tail distributions should be contained differently in freight rate models. (author)

From standard alpha-stable Lévy motions to horizontal visibility networks: dependence of multifractal and Laplacian spectrum

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Zou, Hai-Long; Yu, Zu-Guo; Anh, Vo; Ma, Yuan-Lin

2018-05-01

In recent years, researchers have proposed several methods to transform time series (such as those of fractional Brownian motion) into complex networks. In this paper, we construct horizontal visibility networks (HVNs) based on the -stable Lévy motion. We aim to study the relations of multifractal and Laplacian spectrum of transformed networks on the parameters and of the -stable Lévy motion. First, we employ the sandbox algorithm to compute the mass exponents and multifractal spectrum to investigate the multifractality of these HVNs. Then we perform least squares fits to find possible relations of the average fractal dimension , the average information dimension and the average correlation dimension against using several methods of model selection. We also investigate possible dependence relations of eigenvalues and energy on , calculated from the Laplacian and normalized Laplacian operators of the constructed HVNs. All of these constructions and estimates will help us to evaluate the validity and usefulness of the mappings between time series and networks, especially between time series of -stable Lévy motions and HVNs.

Multifractal analysis of plasma turbulence in biasing experiments on Castor tokamak

Czech Academy of Sciences Publication Activity Database

Budaev, V.P.; Dufková, Edita; Nanobashvili, S.; Weinzettl, Vladimír; Zajac, Jaromír

2005-01-01

Roč. 55, C (2005), s. 1615-1621 ISSN 0011-4626. [Workshop “Electric Fields, Structures and Relaxation in Edge Plasmas". Tarragona, 5.7.2005-5.7.2005] Institutional research plan: CEZ:AV0Z20430508 Keywords : plasma turbulence * multifractal analysis Subject RIV: BL - Plasma and Gas Discharge Physics Impact factor: 0.360, year: 2005

Dynamic Data Visualization with Weave and Brain Choropleths.

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Dianne Patterson

Full Text Available This article introduces the neuroimaging community to the dynamic visualization workbench, Weave (https://www.oicweave.org/, and a set of enhancements to allow the visualization of brain maps. The enhancements comprise a set of brain choropleths and the ability to display these as stacked slices, accessible with a slider. For the first time, this allows the neuroimaging community to take advantage of the advanced tools already available for exploring geographic data. Our brain choropleths are modeled after widely used geographic maps but this mashup of brain choropleths with extant visualization software fills an important neuroinformatic niche. To date, most neuroinformatic tools have provided online databases and atlases of the brain, but not good ways to display the related data (e.g., behavioral, genetic, medical, etc. The extension of the choropleth to brain maps allows us to leverage general-purpose visualization tools for concurrent exploration of brain images and related data. Related data can be represented as a variety of tables, charts and graphs that are dynamically linked to each other and to the brain choropleths. We demonstrate that the simplified region-based analyses that underlay choropleths can provide insights into neuroimaging data comparable to those achieved by using more conventional methods. In addition, the interactive interface facilitates additional insights by allowing the user to filter, compare, and drill down into the visual representations of the data. This enhanced data visualization capability is useful during the initial phases of data analysis and the resulting visualizations provide a compelling way to publish data as an online supplement to journal articles.

Abnormal rich club organization and functional brain dynamics in schizophrenia.

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van den Heuvel, Martijn P; Sporns, Olaf; Collin, Guusje; Scheewe, Thomas; Mandl, René C W; Cahn, Wiepke; Goñi, Joaquín; Hulshoff Pol, Hilleke E; Kahn, René S

2013-08-01

The human brain forms a large-scale structural network of regions and interregional pathways. Recent studies have reported the existence of a selective set of highly central and interconnected hub regions that may play a crucial role in the brain's integrative processes, together forming a central backbone for global brain communication. Abnormal brain connectivity may have a key role in the pathophysiology of schizophrenia. To examine the structure of the rich club in schizophrenia and its role in global functional brain dynamics. Structural diffusion tensor imaging and resting-state functional magnetic resonance imaging were performed in patients with schizophrenia and matched healthy controls. Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands. Forty-eight patients and 45 healthy controls participated in the study. An independent replication data set of 41 patients and 51 healthy controls was included to replicate and validate significant findings. MAIN OUTCOME(S) AND MEASURES: Measures of rich club organization, connectivity density of rich club connections and connections linking peripheral regions to brain hubs, measures of global brain network efficiency, and measures of coupling between brain structure and functional dynamics. Rich club organization between high-degree hub nodes was significantly affected in patients, together with a reduced density of rich club connections predominantly comprising the white matter pathways that link the midline frontal, parietal, and insular hub regions. This reduction in rich club density was found to be associated with lower levels of global communication capacity, a relationship that was absent for other white matter pathways. In addition, patients had an increase in the strength of structural connectivity-functional connectivity coupling. Our findings provide novel biological evidence that schizophrenia is characterized by a selective

The weather and Climate: emergent laws and multifractal cascades

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Lovejoy, S.

2016-12-01

In the atmosphere, nonlinear terms are typically about a trillion times larger than linear ones; we anticipate the emergence of high level turbulence laws. The classical turbulence laws were restricted to homogeneous and isotropic systems; to apply them to the atmosphere they must be generalized to account for strong anisotropy (especially stratification) and variability (intermittency). Over the last 30 years, using scaling symmetry principles and multifractal cascades, this has been done. While hitherto they were believed applicable only up to ≈ 100 m, (generalized) turbulence laws now anisotropic and multifractal, they cover spatial scales up planetary in extent and in time well beyond weather scales to include the climate. These higher level laws are stochastic in nature and provide the theoretical basis both for stochastic parametrizations as well as stochastic forecasting. In the time domain the emergent laws for fluctuations DT (for example in temperature T) have means T > ≈ DtH i.e. they are scaling (power laws) in the time interval Dt. We find find exponents H>0 (fluctuations increase with scale) up to ≈ Dt ≈10 days (the lifetime of planetary scale structures, the analogous transition in the ocean is at Dt ≈ 1 year on Mars it is Dt ≈ 2 sols). At larger Dt, there is a transition to a new "macroweather" regime with H≈30 years (anthropocene; larger in the pre-industrial epoch), new climate processes begin to dominate, leading to H>0. "The climate is what you expect, the weather is what you get": the climate is thought to be a kind of "average weather". However this "expected" behavior is macroweather, not the climate. On the contrary, the climate is the new even lower frequency regime at scales Dt> 30 yrs and it has statistical properties very similar to the weather. At these scales, "macroweather is what you expect, the climate is what you get". The scaling in the macroweather regime implies that there is a long-term memory. We show how the

Multifractal scaling at the Kolmogorov microscale in fully developed compressible turbulence

International Nuclear Information System (INIS)

Shivamoggi, B.K.

1995-01-01

In this paper, some aspects of multifractal scaling at the Kolmogorov microscale in fully developed compressible turbulence are considered. These considerations, on the one hand, provide an insight into the mechanism of compressible turbulence, and on the other hand enable one to determine the robustness of some known results in incompressible turbulence. copyright 1995 Academic Press, Inc

Information dynamics of brain-heart physiological networks during sleep

Science.gov (United States)

Faes, L.; Nollo, G.; Jurysta, F.; Marinazzo, D.

2014-10-01

This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

Characterizing scaling properties of complex signals with missed data segments using the multifractal analysis

Science.gov (United States)

Pavlov, A. N.; Pavlova, O. N.; Abdurashitov, A. S.; Sindeeva, O. A.; Semyachkina-Glushkovskaya, O. V.; Kurths, J.

2018-01-01

The scaling properties of complex processes may be highly influenced by the presence of various artifacts in experimental recordings. Their removal produces changes in the singularity spectra and the Hölder exponents as compared with the original artifacts-free data, and these changes are significantly different for positively correlated and anti-correlated signals. While signals with power-law correlations are nearly insensitive to the loss of significant parts of data, the removal of fragments of anti-correlated signals is more crucial for further data analysis. In this work, we study the ability of characterizing scaling features of chaotic and stochastic processes with distinct correlation properties using a wavelet-based multifractal analysis, and discuss differences between the effect of missed data for synchronous and asynchronous oscillatory regimes. We show that even an extreme data loss allows characterizing physiological processes such as the cerebral blood flow dynamics.

Hartree-Fock study of the Anderson metal-insulator transition in the presence of Coulomb interaction: Two types of mobility edges and their multifractal scaling exponents

Science.gov (United States)

Lee, Hyun-Jung; Kim, Ki-Seok

2018-04-01

We investigate the role of Coulomb interaction in the multifractality of Anderson metal-insulator transition, where the Coulomb interaction is treated within the Hartree-Fock approximation, but disorder effects are taken into account exactly. An innovative technical aspect in our simulation is to utilize the Ewald-sum technique, which allows us to introduce the long-range nature of the Coulomb interaction into Hartree-Fock self-consistent equations of order parameters more accurately. This numerical simulation reproduces the Altshuler-Aronov correction in a metallic state and the Efros-Shklovskii pseudogap in an insulating phase, where the density of states ρ (ω ) is evaluated in three dimensions. Approaching the quantum critical point of a metal-insulator transition from either the metallic or insulting phase, we find that the density of states is given by ρ (ω ) ˜|ω| 1 /2 , which determines one critical exponent of the McMillan-Shklovskii scaling theory. Our main result is to evaluate the eigenfunction multifractal scaling exponent αq, given by the Legendre transformation of the fractal dimension τq, which characterizes the scaling behavior of the inverse participation ratio with respect to the system size L . Our multifractal analysis leads us to identify two kinds of mobility edges, one of which occurs near the Fermi energy and the other of which appears at a high energy, where the density of states at the Fermi energy shows the Coulomb-gap feature. We observe that the multifractal exponent at the high-energy mobility edge remains to be almost identical to that of the Anderson localization transition in the absence of Coulomb interactions. On the other hand, we find that the multifractal exponent near the Fermi energy is more enhanced than that at the high-energy mobility edge, suspected to result from interaction effects. However, both the multifractal exponents do not change even if the strength of the Coulomb interaction varies. We also show that the

From Brain-Environment Connections to Temporal Dynamics and Social Interaction: Principles of Human Brain Function.

Science.gov (United States)

Hari, Riitta

2017-06-07

Experimental data about brain function accumulate faster than does our understanding of how the brain works. To tackle some general principles at the grain level of behavior, I start from the omnipresent brain-environment connection that forces regularities of the physical world to shape the brain. Based on top-down processing, added by sparse sensory information, people are able to form individual "caricature worlds," which are similar enough to be shared among other people and which allow quick and purposeful reactions to abrupt changes. Temporal dynamics and social interaction in natural environments serve as further essential organizing principles of human brain function. Copyright © 2017 Elsevier Inc. All rights reserved.

Avalanching Systems with Longer Range Connectivity: Occurrence of a Crossover Phenomenon and Multifractal Finite Size Scaling

Directory of Open Access Journals (Sweden)

Simone Benella

2017-07-01

Full Text Available Many out-of-equilibrium systems respond to external driving with nonlinear and self-similar dynamics. This near scale-invariant behavior of relaxation events has been modeled through sand pile cellular automata. However, a common feature of these models is the assumption of a local connectivity, while in many real systems, we have evidence for longer range connectivity and a complex topology of the interacting structures. Here, we investigate the role that longer range connectivity might play in near scale-invariant systems, by analyzing the results of a sand pile cellular automaton model on a Newman–Watts network. The analysis clearly indicates the occurrence of a crossover phenomenon in the statistics of the relaxation events as a function of the percentage of longer range links and the breaking of the simple Finite Size Scaling (FSS. The more complex nature of the dynamics in the presence of long-range connectivity is investigated in terms of multi-scaling features and analyzed by the Rank-Ordered Multifractal Analysis (ROMA.

Multifractal analysis of 2D gray soil images

Science.gov (United States)

González-Torres, Ivan; Losada, Juan Carlos; Heck, Richard; Tarquis, Ana M.

2015-04-01

Soil structure, understood as the spatial arrangement of soil pores, is one of the key factors in soil modelling processes. Geometric properties of individual and interpretation of the morphological parameters of pores can be estimated from thin sections or 3D Computed Tomography images (Tarquis et al., 2003), but there is no satisfactory method to binarized these images and quantify the complexity of their spatial arrangement (Tarquis et al., 2008, Tarquis et al., 2009; Baveye et al., 2010). The objective of this work was to apply a multifractal technique, their singularities (α) and f(α) spectra, to quantify it without applying any threshold (Gónzalez-Torres, 2014). Intact soil samples were collected from four horizons of an Argisol, formed on the Tertiary Barreiras group of formations in Pernambuco state, Brazil (Itapirema Experimental Station). The natural vegetation of the region is tropical, coastal rainforest. From each horizon, showing different porosities and spatial arrangements, three adjacent samples were taken having a set of twelve samples. The intact soil samples were imaged using an EVS (now GE Medical. London, Canada) MS-8 MicroCT scanner with 45 μm pixel-1 resolution (256x256 pixels). Though some samples required paring to fit the 64 mm diameter imaging tubes, field orientation was maintained. References Baveye, P.C., M. Laba, W. Otten, L. Bouckaert, P. Dello, R.R. Goswami, D. Grinev, A. Houston, Yaoping Hu, Jianli Liu, S. Mooney, R. Pajor, S. Sleutel, A. Tarquis, Wei Wang, Qiao Wei, Mehmet Sezgin. Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray microtomography data. Geoderma, 157, 51-63, 2010. González-Torres, Iván. Theory and application of multifractal analysis methods in images for the study of soil structure. Master thesis, UPM, 2014. Tarquis, A.M., R.J. Heck, J.B. Grau; J. Fabregat, M.E. Sanchez and J.M. Antón. Influence of Thresholding in Mass and Entropy Dimension of 3-D

A multifractal approach to characterize cumulative rainfall and tillage effects on soil surface micro-topography and to predict depression storage

Directory of Open Access Journals (Sweden)

E. Vidal Vázquez

2010-10-01

Full Text Available Most of the indices currently employed for assessing soil surface micro-topography, such as random roughness (RR, are merely descriptors of its vertical component. Recently, multifractal analysis provided a new insight for describing the spatial configuration of soil surface roughness. The main objective of this study was to test the ability of multifractal parameters to assess in field conditions the decay of initial surface roughness induced by natural rainfall under different soil tillage systems. In addition, we evaluated the potential of the joint use of multifractal indices plus RR to improve predictions of water storage in depressions of the soil surface (MDS. Field experiments were performed on an Oxisol at Campinas, São Paulo State (Brazil. Six tillage treatments, namely, disc harrow, disc plough, chisel plough, disc harrow + disc level, disc plough + disc level and chisel plough + disc level were tested. In each treatment soil surface micro-topography was measured four times, with increasing amounts of natural rainfall, using a pin meter. The sampling scheme was a square grid with 25 × 25 mm point spacing and the plot size was 1350 × 1350 mm (≈1.8 m², so that each data set consisted of 3025 individual elevation points. Duplicated measurements were taken per treatment and date, yielding a total of 48 experimental data sets. MDS was estimated from grid elevation data with a depression-filling algorithm. Multifractal analysis was performed for experimental data sets as well as for oriented and random surface conditions obtained from the former by removing slope and slope plus tillage marks, respectively. All the investigated microplots exhibited multifractal behaviour, irrespective of surface condition, but the degree of multifractality showed wide differences between them. Multifractal parameters provided valuable information for characterizing the spatial features of soil micro-topography as they were able to

Spatiotemporal Dynamics of Dendritic Spines in the Living Brain

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Chia-Chien eChen

2014-05-01

Full Text Available Dendritic spines are ubiquitous postsynaptic sites of most excitatory synapses in the mammalian brain, and thus may serve as structural indicators of functional synapses. Recent works have suggested that neuronal coding of memories may be associated with rapid alterations in spine formation and elimination. Technological advances have enabled researchers to study spine dynamics in vivo during development as well as under various physiological and pathological conditions. We believe that better understanding of the spatiotemporal patterns of spine dynamics will help elucidate the principles of experience-dependent circuit modification and information processing in the living brain.

Monofractal or multifractal: a case study of spatial distribution of mining-induced seismic activity

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M. Eneva

1994-01-01

Full Text Available Using finite data sets and limited size of study volumes may result in significant spurious effects when estimating the scaling properties of various physical processes. These effects are examined with an example featuring the spatial distribution of induced seismic activity in Creighton Mine (northern Ontario, Canada. The events studied in the present work occurred during a three-month period, March-May 1992, within a volume of approximate size 400 x 400 x 180 m3. Two sets of microearthquake locations are studied: Data Set 1 (14,338 events and Data Set 2 (1654 events. Data Set 1 includes the more accurately located events and amounts to about 30 per cent of all recorded data. Data Set 2 represents a portion of the first data set that is formed by the most accurately located and the strongest microearthquakes. The spatial distribution of events in the two data sets is examined for scaling behaviour using the method of generalized correlation integrals featuring various moments q. From these, generalized correlation dimensions are estimated using the slope method. Similar estimates are made for randomly generated point sets using the same numbers of events and the same study volumes as for the real data. Uniform and monofractal random distributions are used for these simulations. In addition, samples from the real data are randomly extracted and the dimension spectra for these are examined as well. The spectra for the uniform and monofractal random generations show spurious multifractality due only to the use of finite numbers of data points and limited size of study volume. Comparing these with the spectra of dimensions for Data Set 1 and Data Set 2 allows us to estimate the bias likely to be present in the estimates for the real data. The strong multifractality suggested by the spectrum for Data Set 2 appears to be largely spurious; the spatial distribution, while different from uniform, could originate from a monofractal process. The spatial

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns.

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Liu, Jin; Liao, Xuhong; Xia, Mingrui; He, Yong

2018-02-01

The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease. © 2017 Wiley Periodicals, Inc.

Interference of scalp and skull with dynamic isotope studies of brain

International Nuclear Information System (INIS)

Oldendorf, W.H.

1972-01-01

The overlying scalp and skull create artifacts in external brain counting measurements in which isotope appears in these tissues. It is much less of a problem in dynamic studies since high levels of superficial isotope are not found in the first few seconds after introduction into the blood by any anatomic route. Diffusible tracers concentrate somewhat less in the scalp and skull than in the brain immediately after injection by any route. Nondiffusible tracers of low molecular weight attain a much higher concentration in the scalp than the brain only after about the first minute because of passage from plasma to scalp extracellular fluid. This equilibration does not occur in brain because of the blood-brain barrier. Scalp and skull thus create much less of a problem with brief dynamic studies than with chronic long-term studies. Some physical considerations of external collimation are discussed, and means are suggested to minimize superficial isotope contributions to brain counts. (U.S.)

Dynamic Multi-Coil Shimming of the Human Brain at 7 Tesla

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Juchem, Christoph; Nixon, Terence W.; McIntyre, Scott; Boer, Vincent O.; Rothman, Douglas L.; de Graaf, Robin A.

2011-01-01

High quality magnetic field homogenization of the human brain (i.e. shimming) for MR imaging and spectroscopy is a demanding task. The susceptibility differences between air and tissue are a longstanding problem as they induce complex field distortions in the prefrontal cortex and the temporal lobes. To date, the theoretical gains of high field MR have only been realized partially in the human brain due to limited magnetic field homogeneity. A novel shimming technique for the human brain is presented that is based on the combination of non-orthogonal basis fields from 48 individual, circular coils. Custom-built amplifier electronics enabled the dynamic application of the multi-coil shim fields in a slice-specific fashion. Dynamic multi-coil (DMC) shimming is shown to eliminate most of the magnetic field inhomogeneity apparent in the human brain at 7 Tesla and provided improved performance compared to state-of-the-art dynamic shim updating with zero through third order spherical harmonic functions. The novel technique paves the way for high field MR applications of the human brain for which excellent magnetic field homogeneity is a prerequisite. PMID:21824794

Multifractal analysis of 2001 Mw 7 . 7 Bhuj earthquake sequence in Gujarat, Western India

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Aggarwal, Sandeep Kumar; Pastén, Denisse; Khan, Prosanta Kumar

2017-12-01

The 2001 Mw 7 . 7 Bhuj mainshock seismic sequence in the Kachchh area, occurring during 2001 to 2012, has been analyzed using mono-fractal and multi-fractal dimension spectrum analysis technique. This region was characterized by frequent moderate shocks of Mw ≥ 5 . 0 for more than a decade since the occurrence of 2001 Bhuj earthquake. The present study is therefore important for precursory analysis using this sequence. The selected long-sequence has been investigated first time for completeness magnitude Mc 3.0 using the maximum curvature method. Multi-fractal Dq spectrum (Dq ∼ q) analysis was carried out using effective window-length of 200 earthquakes with a moving window of 20 events overlapped by 180 events. The robustness of the analysis has been tested by considering the magnitude completeness correction term of 0.2 to Mc 3.0 as Mc 3.2 and we have tested the error in the calculus of Dq for each magnitude threshold. On the other hand, the stability of the analysis has been investigated down to the minimum magnitude of Mw ≥ 2 . 6 in the sequence. The analysis shows the multi-fractal dimension spectrum Dq decreases with increasing of clustering of events with time before a moderate magnitude earthquake in the sequence, which alternatively accounts for non-randomness in the spatial distribution of epicenters and its self-organized criticality. Similar behavior is ubiquitous elsewhere around the globe, and warns for proximity of a damaging seismic event in an area. OS: Please confirm math roman or italics in abs.

A challenge to chaotic itinerancy from brain dynamics

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Kay, Leslie M.

2003-09-01

Brain hermeneutics and chaotic itinerancy proposed by Tsuda are attractive characterizations of perceptual dynamics in the mammalian olfactory system. This theory proposes that perception occurs at the interface between itinerant neural representation and interaction with the environment. Quantifiable application of these dynamics has been hampered by the lack of definable history and action processes which characterize the changes induced by behavioral state, attention, and learning. Local field potentials measured from several brain areas were used to characterize dynamic activity patterns for their use as representations of history and action processes. The signals were recorded from olfactory areas (olfactory bulb, OB, and pyriform cortex) and hippocampal areas (entorhinal cortex and dentate gyrus, DG) in the brains of rats. During odor-guided behavior the system shows dynamics at three temporal scales. Short time-scale changes are system-wide and can occur in the space of a single sniff. They are predictable, associated with learned shifts in behavioral state and occur periodically on the scale of the intertrial interval. These changes occupy the theta (2-12 Hz), beta (15-30 Hz), and gamma (40-100 Hz) frequency bands within and between all areas. Medium time-scale changes occur relatively unpredictably, manifesting in these data as alterations in connection strength between the OB and DG. These changes are strongly correlated with performance in associated trial blocks (5-10 min) and may be due to fluctuations in attention, mood, or amount of reward received. Long time-scale changes are likely related to learning or decline due to aging or disease. These may be modeled as slow monotonic processes that occur within or across days or even weeks or years. The folding of different time scales is proposed as a mechanism for chaotic itinerancy, represented by dynamic processes instead of static connection strengths. Thus, the individual maintains continuity of

CHAOS EXPANSION FOR MULTIFRACTIONAL L ´EVY PROCESSES%多分数L´evy过程的混沌展开

Institute of Scientific and Technical Information of China (English)

吕学斌; 马树建

2016-01-01

In this paper, we study the chaos expansion for multifractional L´evy processes. By using the white noise analysis, we give the chaos expansion of multifractional L´evy Processes. Moreover, we derive their L´evy-Hermite transforms and Malliavin derivatives.%本文研究了多分数L´evy过程的混沌展开。利用白噪声分析方法，给出了多分数L´evy过程的混沌展开。进一步地，给出其L´evy-Hermite变换和Malliavin导数。

Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan.

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Elizabeth N Davison

2016-11-01

Full Text Available Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism-hypergraph cardinality-we investigate individual variations in two separate, complementary data sets. The first data set ("multi-task" consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set ("age-memory", in which 95 individuals, aged 18-75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.

Multifractal analyis of soil invertebrates along a transect under different land uses

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Machado Siqueira, Glécio; Alves Silva, Raimunda; Vidal-Vázquez, Eva; Paz-González, Antonio

2017-04-01

Soil fauna play a central role in many essential ecosystem processes. Land use and management can have a dramatic effect upon soil invertebrate community. Indices based on soil invertebrates abundance and diversity are fundamental for soil quality assessment. Many soil properties and attributes have been shown to exhibit spatial variabilityThe aim of this study was to analyze the scaling heterogeneity of the soil invertebrate community sampled using pitfall traps across a transect. The field study was conducted at Mata Roma municipality, Maranhão State, Brazil. Transects were marked under seven different agricultural/forestry land uses (millet, soybean, maize, eucalyptus, pasture, secondary savannah and native savannah). Native vegetation was considered as a reference, whereas the agricultural fields showed a range of soil use intensities. Along these transects 130 pitfall per land use were installed. First, differences in community assemblages and composition under different land use systems were evaluated using classical indices. Then, the spatial distribution of soil fauna trapped by pitfall techniques, characterized through generalized dimension, Dq, and singularity spectra, f(α) - α, showed a well-defined multifractal structure. Differences in scaling heterogeneity and other multifractal characteristics were examined in relation to land use intensification.

Clinical values of CT and dynamic CT in brain infarction

International Nuclear Information System (INIS)

Lim, Soo Il; Jang, Do; Seo, Eun Joo; Sohn, Myung Hee; Choi, Ki Chul

1985-01-01

With the advent of faster scan time and new computer program, a scanning technique called 'dynamic computed tomography' has become possible. Dynamic computed tomography consisted of performing multiple rapid sequence scans after injection of contrast material. The authors have evaluated the clinical usefulness of computed tomography and dynamic computed tomography of 93 patients with brain infarction and/or ischemia during the period of 17 months from April 1983 to August 1984 in Department of Radiology, Chonbuk National University Hospital. The results were as follows; 1. The age distribution ranged from 18 years to 78 years. Among them the most common age group was between 50 years and 59 years (40.9%). 2. The sites of brain infarction were cerebral lobes (63 cases,68), basal ganglia (15 cases, 16.1%) and multiple sites (6 cases, 6.4%). The common affected site was middle cerebral artery territories. 3. The contrast enhancement of acute infarction was noted in 14 cases (17.5%) which occurred commonly between 3 days and 2 weeks from ictus. 4. The patterns of time-density curve in brain infarction and/or ischemia were as follow: a. Depression of slow wash-in phase was 20 cases (59%). b. Lower peak concentration was 17 cases (50%). c. Lower and delayed peak concentration was 7 cases (21%). d. No definite peak concentration was 6 cases (18%). First three patterns of time-density curve were thought as relatively characteristic curve of brain infarction and/or ischemia. 5. Two cases that showed negative findings on precontrast CT scan appeared to be positive findings as hypodensity on postcontrast CT scan and were confirmed as brain infarction by dynamic CT. 6. The diagnostic entity of dynamic CT scan were as follows; a. large artery thrombotic infarction were 23 cases (58%). b. lacnar infarction were 6 cases (15%). c. ischemia were 5 cases (13%). d. normal were 5 cases (13%). In six cases of lacunar infarction which was double hypodensity on pre-and postcontrast CT

Clinical values of CT and dynamic CT in brain infarction

Energy Technology Data Exchange (ETDEWEB)

Lim, Soo Il; Jang, Do; Seo, Eun Joo; Sohn, Myung Hee; Choi, Ki Chul [Chonbuk National University College of Medicine, Jeonju (Korea, Republic of)

1985-04-15

With the advent of faster scan time and new computer program, a scanning technique called 'dynamic computed tomography' has become possible. Dynamic computed tomography consisted of performing multiple rapid sequence scans after injection of contrast material. The authors have evaluated the clinical usefulness of computed tomography and dynamic computed tomography of 93 patients with brain infarction and/or ischemia during the period of 17 months from April 1983 to August 1984 in Department of Radiology, Chonbuk National University Hospital. The results were as follows; 1. The age distribution ranged from 18 years to 78 years. Among them the most common age group was between 50 years and 59 years (40.9%). 2. The sites of brain infarction were cerebral lobes (63 cases,68), basal ganglia (15 cases, 16.1%) and multiple sites (6 cases, 6.4%). The common affected site was middle cerebral artery territories. 3. The contrast enhancement of acute infarction was noted in 14 cases (17.5%) which occurred commonly between 3 days and 2 weeks from ictus. 4. The patterns of time-density curve in brain infarction and/or ischemia were as follow: a. Depression of slow wash-in phase was 20 cases (59%). b. Lower peak concentration was 17 cases (50%). c. Lower and delayed peak concentration was 7 cases (21%). d. No definite peak concentration was 6 cases (18%). First three patterns of time-density curve were thought as relatively characteristic curve of brain infarction and/or ischemia. 5. Two cases that showed negative findings on precontrast CT scan appeared to be positive findings as hypodensity on postcontrast CT scan and were confirmed as brain infarction by dynamic CT. 6. The diagnostic entity of dynamic CT scan were as follows; a. large artery thrombotic infarction were 23 cases (58%). b. lacnar infarction were 6 cases (15%). c. ischemia were 5 cases (13%). d. normal were 5 cases (13%). In six cases of lacunar infarction which was double hypodensity on pre

Multifractal detrended cross-correlation analysis on gold, crude oil and foreign exchange rate time series

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Pal, Mayukha; Madhusudana Rao, P.; Manimaran, P.

2014-12-01

We apply the recently developed multifractal detrended cross-correlation analysis method to investigate the cross-correlation behavior and fractal nature between two non-stationary time series. We analyze the daily return price of gold, West Texas Intermediate and Brent crude oil, foreign exchange rate data, over a period of 18 years. The cross correlation has been measured from the Hurst scaling exponents and the singularity spectrum quantitatively. From the results, the existence of multifractal cross-correlation between all of these time series is found. We also found that the cross correlation between gold and oil prices possess uncorrelated behavior and the remaining bivariate time series possess persistent behavior. It was observed for five bivariate series that the cross-correlation exponents are less than the calculated average generalized Hurst exponents (GHE) for q0 and for one bivariate series the cross-correlation exponent is greater than GHE for all q values.

Multifractal spectra of scanning electron microscope images of SnO2 thin films prepared by pulsed laser deposition

International Nuclear Information System (INIS)

Chen, Z.W.; Lai, J.K.L.; Shek, C.H.

2005-01-01

The concept of fractal geometry has proved useful in describing structures and processes in experimental systems. In this Letter, the surface topographies of SnO 2 thin films prepared by pulsed laser deposition for various substrate temperatures were measured by scanning electron microscope (SEM). Multifractal spectra f(α) show that the higher the substrate temperature, the wider the spectrum, and the larger the Δf(Δf=f(α min )-f(α max )). It is apparent that the nonuniformity of the height distribution increases with the increasing substrate temperature, and the liquid droplets of SnO 2 thin films are formed on previous thin films. These results show that the SEM images can be characterized by the multifractal spectra

Detecting brain dynamics during resting state: a tensor based evolutionary clustering approach

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Al-sharoa, Esraa; Al-khassaweneh, Mahmood; Aviyente, Selin

2017-08-01

Human brain is a complex network with connections across different regions. Understanding the functional connectivity (FC) of the brain is important both during resting state and task; as disruptions in connectivity patterns are indicators of different psychopathological and neurological diseases. In this work, we study the resting state functional connectivity networks (FCNs) of the brain from fMRI BOLD signals. Recent studies have shown that FCNs are dynamic even during resting state and understanding the temporal dynamics of FCNs is important for differentiating between different conditions. Therefore, it is important to develop algorithms to track the dynamic formation and dissociation of FCNs of the brain during resting state. In this paper, we propose a two step tensor based community detection algorithm to identify and track the brain network community structure across time. First, we introduce an information-theoretic function to reduce the dynamic FCN and identify the time points that are similar topologically to combine them into a tensor. These time points will be used to identify the different FC states. Second, a tensor based spectral clustering approach is developed to identify the community structure of the constructed tensors. The proposed algorithm applies Tucker decomposition to the constructed tensors and extract the orthogonal factor matrices along the connectivity mode to determine the common subspace within each FC state. The detected community structure is summarized and described as FC states. The results illustrate the dynamic structure of resting state networks (RSNs), including the default mode network, somatomotor network, subcortical network and visual network.

Short-term mechanisms influencing volumetric brain dynamics

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Nikki Dieleman

2017-01-01

Full Text Available With the use of magnetic resonance imaging (MRI and brain analysis tools, it has become possible to measure brain volume changes up to around 0.5%. Besides long-term brain changes caused by atrophy in aging or neurodegenerative disease, short-term mechanisms that influence brain volume may exist. When we focus on short-term changes of the brain, changes may be either physiological or pathological. As such determining the cause of volumetric dynamics of the brain is essential. Additionally for an accurate interpretation of longitudinal brain volume measures by means of neurodegeneration, knowledge about the short-term changes is needed. Therefore, in this review, we discuss the possible mechanisms influencing brain volumes on a short-term basis and set-out a framework of MRI techniques to be used for volumetric changes as well as the used analysis tools. 3D T1-weighted images are the images of choice when it comes to MRI of brain volume. These images are excellent to determine brain volume and can be used together with an analysis tool to determine the degree of volume change. Mechanisms that decrease global brain volume are: fluid restriction, evening MRI measurements, corticosteroids, antipsychotics and short-term effects of pathological processes like Alzheimer's disease, hypertension and Diabetes mellitus type II. Mechanisms increasing the brain volume include fluid intake, morning MRI measurements, surgical revascularization and probably medications like anti-inflammatory drugs and anti-hypertensive medication. Exercise was found to have no effect on brain volume on a short-term basis, which may imply that dehydration caused by exercise differs from dehydration by fluid restriction. In the upcoming years, attention should be directed towards studies investigating physiological short-term changes within the light of long-term pathological changes. Ultimately this may lead to a better understanding of the physiological short-term effects of

Characterizing slope morphology using multifractal technique: a study from the western continental margin of India.

Digital Repository Service at National Institute of Oceanography (India)

Chakraborty, B.; Karisiddaiah, S.M.; Menezes, A.A.A.; Haris, K.; Gokul, G.S.; Fernandes, W.A.; Kavitha, G.

margin of India. Geo-Mar Lett 26: 114–119. doi: 10.1007/s00367-006-0022-6 Cheng Q, Agterberg FP (1996) Comparison between two types of multifractal modeling. Math Geol 28 (8): 1001–1015 Dandapath S, Chakraborty B, Maslov N, Karisiddaiah SM...

Improved moment scaling estimation for multifractal signals

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D. Veneziano

2009-11-01

Full Text Available A fundamental problem in the analysis of multifractal processes is to estimate the scaling exponent K(q of moments of different order q from data. Conventional estimators use the empirical moments μ^_r^q=⟨ | ε_r(τ|^q⟩ of wavelet coefficients ε_r(τ, where τ is location and r is resolution. For stationary measures one usually considers "wavelets of order 0" (averages, whereas for functions with multifractal increments one must use wavelets of order at least 1. One obtains K^(q as the slope of log( μ^_r^q against log(r over a range of r. Negative moments are sensitive to measurement noise and quantization. For them, one typically uses only the local maxima of | ε_r(τ| (modulus maxima methods. For the positive moments, we modify the standard estimator K^(q to significantly reduce its variance at the expense of a modest increase in the bias. This is done by separately estimating K(q from sub-records and averaging the results. For the negative moments, we show that the standard modulus maxima estimator is biased and, in the case of additive noise or quantization, is not applicable with wavelets of order 1 or higher. For these cases we propose alternative estimators. We also consider the fitting of parametric models of K(q and show how, by splitting the record into sub-records as indicated above, the accuracy of standard methods can be significantly improved.

Dynamic glucose enhanced (DGE) MRI for combined imaging of blood-brain barrier break down and increased blood volume in brain cancer.

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Xu, Xiang; Chan, Kannie W Y; Knutsson, Linda; Artemov, Dmitri; Xu, Jiadi; Liu, Guanshu; Kato, Yoshinori; Lal, Bachchu; Laterra, John; McMahon, Michael T; van Zijl, Peter C M

2015-12-01

Recently, natural d-glucose was suggested as a potential biodegradable contrast agent. The feasibility of using d-glucose for dynamic perfusion imaging was explored to detect malignant brain tumors based on blood brain barrier breakdown. Mice were inoculated orthotopically with human U87-EGFRvIII glioma cells. Time-resolved glucose signal changes were detected using chemical exchange saturation transfer (glucoCEST) MRI. Dynamic glucose enhanced (DGE) MRI was used to measure tissue response to an intravenous bolus of d-glucose. DGE images of mouse brains bearing human glioma showed two times higher and persistent changes in tumor compared with contralateral brain. Area-under-curve (AUC) analysis of DGE delineated blood vessels and tumor and had contrast comparable to the AUC determined using dynamic contrast enhanced (DCE) MRI with GdDTPA, both showing a significantly higher AUC in tumor than in brain (P blood volume and permeability with respect to normal brain. We expect DGE will provide a low-risk and less expensive alternative to DCE MRI for imaging cancer in vulnerable populations, such as children and patients with renal impairment. © 2015 Wiley Periodicals, Inc.

Dynamic Glucose Enhanced (DGE) MRI for Combined Imaging of Blood Brain Barrier Break Down and Increased Blood Volume in Brain Cancer

Science.gov (United States)

Xu, Xiang; Chan, Kannie WY; Knutsson, Linda; Artemov, Dmitri; Xu, Jiadi; Liu, Guanshu; Kato, Yoshinori; Lal, Bachchu; Laterra, John; McMahon, Michael T.; van Zijl, Peter C.M.

2015-01-01

Purpose Recently, natural d-glucose was suggested as a potential biodegradable contrast agent. The feasibility of using d-glucose for dynamic perfusion imaging was explored to detect malignant brain tumors based on blood brain barrier breakdown. Methods Mice were inoculated orthotopically with human U87-EGFRvIII glioma cells. Time-resolved glucose signal changes were detected using chemical exchange saturation transfer (glucoCEST) MRI. Dynamic glucose enhanced (DGE) MRI was used to measure tissue response to an intravenous bolus of d-glucose. Results DGE images of mouse brains bearing human glioma showed two times higher and persistent changes in tumor compared to contralateral brain. Area-under-curve (AUC) analysis of DGE delineated blood vessels and tumor and had contrast comparable to the AUC determined using dynamic contrast enhanced (DCE) MRI with GdDTPA, both showing a significantly higher AUC in tumor than in brain (pblood volume and permeability with respect to normal brain. We expect DGE will provide a low-risk and less expensive alternative to DCE MRI for imaging cancer in vulnerable populations, such as children and patients with renal impairment. PMID:26404120

Dynamic relationship between Japanese Yen exchange rates and market anxiety: A new perspective based on MF-DCCA

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Lu, Xinsheng; Sun, Xinxin; Ge, Jintian

2017-05-01

This paper investigates the dynamic relationship between Japanese Yen exchange rates and market anxiety during the period from January 5, 1998 to April 18, 2016. A quantitative technique of multifractal detrended cross-correlation analysis (MF-DCCA) is used to explore the multifractal features of the cross-correlations between USD/JPY, AUD/JPY exchange rates and the market anxiety gauge VIX. The investigation shows that the causal relationship between Japanese Yen exchange rates and VIX are bidirectional in general, and the cross-correlations between the two sets of time series are multifractal. Strong evidence suggests that the cross-correlation exponents tend to exhibit different volatility patterns in response to diverse external shocks such as financial distress and widening in interest rate spread, suggesting that the cross-correlated behavior between Japanese Yen exchange rates and VIX are susceptible to economic uncertainties and risks. In addition, the performances of two market anxiety gauges, the VIX and the TED spread, are compared and the sources of multifractality are also traced. Thus, this paper contributes to the literature by shedding light on the unique driving forces of the Yen exchange rate fluctuations in the international foreign exchange market.

Network dynamics in the healthy and epileptic developing brain

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Richard Rosch

2018-03-01

Full Text Available Electroencephalography (EEG allows recording of cortical activity at high temporal resolution. EEG recordings can be summarized along different dimensions using network-level quantitative measures, such as channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different timescales and can be tracked dynamically. Here we describe the dynamics of network state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n = 8, age: 1–8 months. We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity. We further show that EEGs from different patient groups and controls may be distinguishable on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups. These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in the future inform the clinical use of quantitative EEG for diagnosis.

Impaired social brain network for processing dynamic facial expressions in autism spectrum disorders

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Sato Wataru

2012-08-01

Full Text Available Abstract Background Impairment of social interaction via facial expressions represents a core clinical feature of autism spectrum disorders (ASD. However, the neural correlates of this dysfunction remain unidentified. Because this dysfunction is manifested in real-life situations, we hypothesized that the observation of dynamic, compared with static, facial expressions would reveal abnormal brain functioning in individuals with ASD. We presented dynamic and static facial expressions of fear and happiness to individuals with high-functioning ASD and to age- and sex-matched typically developing controls and recorded their brain activities using functional magnetic resonance imaging (fMRI. Result Regional analysis revealed reduced activation of several brain regions in the ASD group compared with controls in response to dynamic versus static facial expressions, including the middle temporal gyrus (MTG, fusiform gyrus, amygdala, medial prefrontal cortex, and inferior frontal gyrus (IFG. Dynamic causal modeling analyses revealed that bi-directional effective connectivity involving the primary visual cortex–MTG–IFG circuit was enhanced in response to dynamic as compared with static facial expressions in the control group. Group comparisons revealed that all these modulatory effects were weaker in the ASD group than in the control group. Conclusions These results suggest that weak activity and connectivity of the social brain network underlie the impairment in social interaction involving dynamic facial expressions in individuals with ASD.

Fault Diagnosis of Broken Rotor Bars in Squirrel-Cage Induction Motor of Hoister Based on Duffing Oscillator and Multifractal Dimension

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Zhike Zhao

2014-07-01

Full Text Available This paper is to propose a novel fault diagnosis method for broken rotor bars in squirrel-cage induction motor of hoister, which is based on duffing oscillator and multifractal dimension. Firstly, based on the analysis of the structure and performance of modified duffing oscillator, the end of transitional slope from chaotic area to large-scale cycle area is selected as the optimal critical threshold of duffing oscillator by bifurcation diagrams and Lyapunov exponent. Secondly, the phase transformation duffing oscillator from chaos to intermittent chaos is sensitive to the signals, whose frequency difference is quite weak from the reference signal. The spectrums of the largest Lyapunov exponents and bifurcation diagrams of the duffing oscillator are utilized to analyze the variance in different parameters of frequency. Finally, this paper is to analyze the characteristics of both single fractal (box-counting dimension and multifractal and make a comparison between them. Multifractal detrended fluctuation analysis is applied to detect extra frequency component of current signal. Experimental results reveal that the method is effective for early detection of broken rotor bars in squirrel-cage induction motor of hoister.

Neurofeedback Tunes Scale-Free Dynamics in Spontaneous Brain Activity.

Science.gov (United States)

Ros, T; Frewen, P; Théberge, J; Michela, A; Kluetsch, R; Mueller, A; Candrian, G; Jetly, R; Vuilleumier, P; Lanius, R A

2017-10-01

Brain oscillations exhibit long-range temporal correlations (LRTCs), which reflect the regularity of their fluctuations: low values representing more random (decorrelated) while high values more persistent (correlated) dynamics. LRTCs constitute supporting evidence that the brain operates near criticality, a state where neuronal activities are balanced between order and randomness. Here, healthy adults used closed-loop brain training (neurofeedback, NFB) to reduce the amplitude of alpha oscillations, producing a significant increase in spontaneous LRTCs post-training. This effect was reproduced in patients with post-traumatic stress disorder, where abnormally random dynamics were reversed by NFB, correlating with significant improvements in hyperarousal. Notably, regions manifesting abnormally low LRTCs (i.e., excessive randomness) normalized toward healthy population levels, consistent with theoretical predictions about self-organized criticality. Hence, when exposed to appropriate training, spontaneous cortical activity reveals a residual capacity for "self-tuning" its own temporal complexity, despite manifesting the abnormal dynamics seen in individuals with psychiatric disorder. Lastly, we observed an inverse-U relationship between strength of LRTC and oscillation amplitude, suggesting a breakdown of long-range dependence at high/low synchronization extremes, in line with recent computational models. Together, our findings offer a broader mechanistic framework for motivating research and clinical applications of NFB, encompassing disorders with perturbed LRTCs. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

Directory of Open Access Journals (Sweden)

Kai Liu

2018-03-01

Full Text Available The separation of coal and gangue is an important process of the coal preparation technology. The conventional way of manual selection and separation of gangue from the raw coal can be replaced by computer vision technology. In the literature, research on image recognition and classification of coal and gangue is mainly based on the grayscale and texture features of the coal and gangue. However, there are few studies on characteristics of coal and gangue from the perspective of their outline differences. Therefore, the multifractal detrended fluctuation analysis (MFDFA method is introduced in this paper to extract the geometric features of coal and gangue. Firstly, the outline curves of coal and gangue in polar coordinates are detected and achieved along the centroid, thereby the multifractal characteristics of the series are analyzed and compared. Subsequently, the modified local singular spectrum widths Δ h of the outline curve series are extracted as the characteristic variables of the coal and gangue for pattern recognition. Finally, the extracted geometric features by MFDFA combined with the grayscale and texture features of the images are compared with other methods, indicating that the recognition rate of coal gangue images can be increased by introducing the geometric features.

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics.

Science.gov (United States)

He, Bin; Sohrabpour, Abbas; Brown, Emery; Liu, Zhongming

2018-06-04

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

Fractal and multifractal characteristics of swift heavy ion induced self-affine nanostructured BaF{sub 2} thin film surfaces

Energy Technology Data Exchange (ETDEWEB)

Yadav, R. P.; Mittal, A. K. [Department of Physics, University of Allahabad, Allahabad 211002 (India); Kumar, Manvendra, E-mail: kmanav@gmail.com; Pandey, A. C. [Nanotechnology Application Centre, University of Allahabad, Allahabad 211002 (India)

2015-08-15

Fractal and multifractal characteristics of self-affine surfaces of BaF{sub 2} thin films, deposited on crystalline Si 〈1 1 1〉 substrate at room temperature, were studied. Self-affine surfaces were prepared by irradiation of 120 MeV Ag{sup 9+} ions which modified the surface morphology at nanometer scale. The surface morphology of virgin thin film and those irradiated with different ion fluences are characterized by atomic force microscopy technique. The surface roughness (interface width) shows monotonic decrease with ion fluences, while the other parameters, such as lateral correlation length, roughness exponent, and fractal dimension, did not show either monotonic decrease or increase in nature. The self-affine nature of the films is further confirmed by autocorrelation function. The power spectral density of thin films surfaces exhibits inverse power law variation with spatial frequency, suggesting the existence of fractal component in surface morphology. The multifractal detrended fluctuation analysis based on the partition function approach is also performed on virgin and irradiated thin films. It is found that the partition function exhibits the power law behavior with the segment size. Moreover, it is also seen that the scaling exponents vary nonlinearly with the moment, thereby exhibiting the multifractal nature.

Complexity and multifractal behaviors of multiscale-continuum percolation financial system for Chinese stock markets

Science.gov (United States)

Zeng, Yayun; Wang, Jun; Xu, Kaixuan

2017-04-01

A new financial agent-based time series model is developed and investigated by multiscale-continuum percolation system, which can be viewed as an extended version of continuum percolation system. In this financial model, for different parameters of proportion and density, two Poisson point processes (where the radii of points represent the ability of receiving or transmitting information among investors) are applied to model a random stock price process, in an attempt to investigate the fluctuation dynamics of the financial market. To validate its effectiveness and rationality, we compare the statistical behaviors and the multifractal behaviors of the simulated data derived from the proposed model with those of the real stock markets. Further, the multiscale sample entropy analysis is employed to study the complexity of the returns, and the cross-sample entropy analysis is applied to measure the degree of asynchrony of return autocorrelation time series. The empirical results indicate that the proposed financial model can simulate and reproduce some significant characteristics of the real stock markets to a certain extent.

Dynamic brain connectivity is a better predictor of PTSD than static connectivity.

Science.gov (United States)

Jin, Changfeng; Jia, Hao; Lanka, Pradyumna; Rangaprakash, D; Li, Lingjiang; Liu, Tianming; Hu, Xiaoping; Deshpande, Gopikrishna

2017-09-01

Using resting-state functional magnetic resonance imaging, we test the hypothesis that subjects with post-traumatic stress disorder (PTSD) are characterized by reduced temporal variability of brain connectivity compared to matched healthy controls. Specifically, we test whether PTSD is characterized by elevated static connectivity, coupled with decreased temporal variability of those connections, with the latter providing greater sensitivity toward the pathology than the former. Static functional connectivity (FC; nondirectional zero-lag correlation) and static effective connectivity (EC; directional time-lagged relationships) were obtained over the entire brain using conventional models. Dynamic FC and dynamic EC were estimated by letting the conventional models to vary as a function of time. Statistical separation and discriminability of these metrics between the groups and their ability to accurately predict the diagnostic label of a novel subject were ascertained using separate support vector machine classifiers. Our findings support our hypothesis that PTSD subjects have stronger static connectivity, but reduced temporal variability of connectivity. Further, machine learning classification accuracy obtained with dynamic FC and dynamic EC was significantly higher than that obtained with static FC and static EC, respectively. Furthermore, results also indicate that the ease with which brain regions engage or disengage with other regions may be more sensitive to underlying pathology than the strength with which they are engaged. Future studies must examine whether this is true only in the case of PTSD or is a general organizing principle in the human brain. Hum Brain Mapp 38:4479-4496, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

The dynamic system corresponding to LOD and AAM.

Science.gov (United States)

Liu, Shida; Liu, Shikuo; Chen, Jiong

2000-02-01

Using wavelet transform, the authors can reconstruct the 1-D map of a multifractal object. The wavelet transform of LOD and AAM shows that at 20 years scale, annual scale and 2 - 3 years scale, the jump points of LOD and AAM accord with each other very well, and their reconstructing 1-D mapping dynamic system are also very similar.

Nonlinear dynamics of the atmospheric pollutants in Mexico City

Science.gov (United States)

Muñoz-Diosdado, Alejandro; Barrera-Ferrer, Amilcar; Angulo-Brown, Fernando

2014-05-01

The atmospheric pollution in the Metropolitan Zone of Mexico City (MZMC) is a serious problem with social, economical and political consequences, in virtue that it is the region which concentrates both the greatest country population and a great part of commercial and industrial activities. According to the World Health Organization, maximum permissible concentrations of atmospheric pollutants are exceeded frequently. In the MZMC, the environmental monitoring has been limited to criteria pollutants, named in this way due to when their levels are measured in the atmosphere, they indicate in a precise way the air quality. The Automatic Atmospheric Monitoring Network monitors and registers the values of pollutants concentration in air in the MZMC. Actually, it is integrated by approximately 35 automatic-equipped remote stations, which report an every-hour register. Local and global invariant quantities have been widely used to describe the fractal properties of diverse time series. In the study of certain time series, many times it is assumed that they are monofractal, which means that they can be described only with one fractal dimension. But this hypothesis is unrealistic because a lot of time series are heterogeneous and non stationary, so their scaling properties are not the same throughout time and therefore they may require more fractal dimensions for their description. Complexity of the atmospheric pollutants dynamics suggests us to analyze its time series of hourly concentration registers with the multifractal formalism. So, in this work, air concentration time series of MZMC criteria pollutants were studied with the proposed method. The chosen pollutants to perform this analysis are ozone, sulfur dioxide, carbon monoxide, nitrogen dioxide and PM10 (particles less than 10 micrometers). We found that pollutants air concentration time series are multifractal. When we calculate the degree of multifractality for each time series we know that while more

Measurement of brain perfusion, blood volume, and blood-brain barrier permeability, using dynamic contrast-enhanced T(1)-weighted MRI at 3 tesla

DEFF Research Database (Denmark)

Larsson, Henrik B W; Courivaud, Frédéric; Rostrup, Egill

2009-01-01

Assessment of vascular properties is essential to diagnosis and follow-up and basic understanding of pathogenesis in brain tumors. In this study, a procedure is presented that allows concurrent estimation of cerebral perfusion, blood volume, and blood-brain permeability from dynamic T(1)-weighted...... on a pixel-by-pixel basis of cerebral perfusion, cerebral blood volume, and blood-brain barrier permeability.......Assessment of vascular properties is essential to diagnosis and follow-up and basic understanding of pathogenesis in brain tumors. In this study, a procedure is presented that allows concurrent estimation of cerebral perfusion, blood volume, and blood-brain permeability from dynamic T(1)-weighted...... imaging of a bolus of a paramagnetic contrast agent passing through the brain. The methods are applied in patients with brain tumors and in healthy subjects. Perfusion was estimated by model-free deconvolution using Tikhonov's method (gray matter/white matter/tumor: 72 +/- 16/30 +/- 8/56 +/- 45 mL/100 g...

The mean-variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI.

Science.gov (United States)

Thompson, William H; Fransson, Peter

2015-01-01

When studying brain connectivity using fMRI, signal intensity time-series are typically correlated with each other in time to compute estimates of the degree of interaction between different brain regions and/or networks. In the static connectivity case, the problem of defining which connections that should be considered significant in the analysis can be addressed in a rather straightforward manner by a statistical thresholding that is based on the magnitude of the correlation coefficients. More recently, interest has come to focus on the dynamical aspects of brain connectivity and the problem of deciding which brain connections that are to be considered relevant in the context of dynamical changes in connectivity provides further options. Since we, in the dynamical case, are interested in changes in connectivity over time, the variance of the correlation time-series becomes a relevant parameter. In this study, we discuss the relationship between the mean and variance of brain connectivity time-series and show that by studying the relation between them, two conceptually different strategies to analyze dynamic functional brain connectivity become available. Using resting-state fMRI data from a cohort of 46 subjects, we show that the mean of fMRI connectivity time-series scales negatively with its variance. This finding leads to the suggestion that magnitude- versus variance-based thresholding strategies will induce different results in studies of dynamic functional brain connectivity. Our assertion is exemplified by showing that the magnitude-based strategy is more sensitive to within-resting-state network (RSN) connectivity compared to between-RSN connectivity whereas the opposite holds true for a variance-based analysis strategy. The implications of our findings for dynamical functional brain connectivity studies are discussed.

Removing divergences in the negative moments of the multi-fractal parition function with the wavelet transformation

NARCIS (Netherlands)

Z.R. Struzik

1998-01-01

textabstractWe present a promising technique which is capable of accessing the divergence free component of the partition function for the negative moments of the multi-fractal analysis of data using the wavelet transformation. It is based on implicitly bounding the local logarithmic slope of the

New Perspectives on Spontaneous Brain Activity: Dynamic Networks and Energy Matter.

Science.gov (United States)

Tozzi, Arturo; Zare, Marzieh; Benasich, April A

2016-01-01

Spontaneous brain activity has received increasing attention as demonstrated by the exponential rise in the number of published article on this topic over the last 30 years. Such "intrinsic" brain activity, generated in the absence of an explicit task, is frequently associated with resting-state or default-mode networks (DMN)s. The focus on characterizing spontaneous brain activity promises to shed new light on questions concerning the structural and functional architecture of the brain and how they are related to "mind". However, many critical questions have yet to be addressed. In this review, we focus on a scarcely explored area, specifically the energetic requirements and constraints of spontaneous activity, taking into account both thermodynamical and informational perspectives. We argue that the "classical" definitions of spontaneous activity do not take into account an important feature, that is, the critical thermodynamic energetic differences between spontaneous and evoked brain activity. Spontaneous brain activity is associated with slower oscillations compared with evoked, task-related activity, hence it exhibits lower levels of enthalpy and "free-energy" (i.e., the energy that can be converted to do work), thus supporting noteworthy thermodynamic energetic differences between spontaneous and evoked brain activity. Increased spike frequency during evoked activity has a significant metabolic cost, consequently, brain functions traditionally associated with spontaneous activity, such as mind wandering, require less energy that other nervous activities. We also review recent empirical observations in neuroscience, in order to capture how spontaneous brain dynamics and mental function can be embedded in a non-linear dynamical framework, which considers nervous activity in terms of phase spaces, particle trajectories, random walks, attractors and/or paths at the edge of the chaos. This takes us from the thermodynamic free-energy, to the realm of "variational

Brain Responses to Dynamic Facial Expressions: A Normative Meta-Analysis

Directory of Open Access Journals (Sweden)

Oksana Zinchenko

2018-06-01

Full Text Available Identifying facial expressions is crucial for social interactions. Functional neuroimaging studies show that a set of brain areas, such as the fusiform gyrus and amygdala, become active when viewing emotional facial expressions. The majority of functional magnetic resonance imaging (fMRI studies investigating face perception typically employ static images of faces. However, studies that use dynamic facial expressions (e.g., videos are accumulating and suggest that a dynamic presentation may be more sensitive and ecologically valid for investigating faces. By using quantitative fMRI meta-analysis the present study examined concordance of brain regions associated with viewing dynamic facial expressions. We analyzed data from 216 participants that participated in 14 studies, which reported coordinates for 28 experiments. Our analysis revealed bilateral fusiform and middle temporal gyri, left amygdala, left declive of the cerebellum and the right inferior frontal gyrus. These regions are discussed in terms of their relation to models of face processing.

Brain Responses to Dynamic Facial Expressions: A Normative Meta-Analysis.

Science.gov (United States)

Zinchenko, Oksana; Yaple, Zachary A; Arsalidou, Marie

2018-01-01

Identifying facial expressions is crucial for social interactions. Functional neuroimaging studies show that a set of brain areas, such as the fusiform gyrus and amygdala, become active when viewing emotional facial expressions. The majority of functional magnetic resonance imaging (fMRI) studies investigating face perception typically employ static images of faces. However, studies that use dynamic facial expressions (e.g., videos) are accumulating and suggest that a dynamic presentation may be more sensitive and ecologically valid for investigating faces. By using quantitative fMRI meta-analysis the present study examined concordance of brain regions associated with viewing dynamic facial expressions. We analyzed data from 216 participants that participated in 14 studies, which reported coordinates for 28 experiments. Our analysis revealed bilateral fusiform and middle temporal gyri, left amygdala, left declive of the cerebellum and the right inferior frontal gyrus. These regions are discussed in terms of their relation to models of face processing.

The mean–variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI

Science.gov (United States)

Thompson, William H.; Fransson, Peter

2015-01-01

When studying brain connectivity using fMRI, signal intensity time-series are typically correlated with each other in time to compute estimates of the degree of interaction between different brain regions and/or networks. In the static connectivity case, the problem of defining which connections that should be considered significant in the analysis can be addressed in a rather straightforward manner by a statistical thresholding that is based on the magnitude of the correlation coefficients. More recently, interest has come to focus on the dynamical aspects of brain connectivity and the problem of deciding which brain connections that are to be considered relevant in the context of dynamical changes in connectivity provides further options. Since we, in the dynamical case, are interested in changes in connectivity over time, the variance of the correlation time-series becomes a relevant parameter. In this study, we discuss the relationship between the mean and variance of brain connectivity time-series and show that by studying the relation between them, two conceptually different strategies to analyze dynamic functional brain connectivity become available. Using resting-state fMRI data from a cohort of 46 subjects, we show that the mean of fMRI connectivity time-series scales negatively with its variance. This finding leads to the suggestion that magnitude- versus variance-based thresholding strategies will induce different results in studies of dynamic functional brain connectivity. Our assertion is exemplified by showing that the magnitude-based strategy is more sensitive to within-resting-state network (RSN) connectivity compared to between-RSN connectivity whereas the opposite holds true for a variance-based analysis strategy. The implications of our findings for dynamical functional brain connectivity studies are discussed. PMID:26236216

Dynamic effective connectivity of inter-areal brain circuits.

Directory of Open Access Journals (Sweden)

Demian Battaglia

Full Text Available Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity (or, more precisely, causal connectivity, related to the elusive question "Which areas cause the present activity of which others?". Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal. We show then that transitions between effective connectivity configurations (like, for instance, reversal in the direction of inter-areal interactions can be triggered reliably by brief perturbation inputs, properly timed with respect to an ongoing local oscillation, without the need for plastic synaptic changes. Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer. Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas. Going beyond these early

Cascade-Driven Series with Narrower Multifractal Spectra Than Their Surrogates: Standard Deviation of Multipliers Changes Interactions across Scales

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Jun Taek Lee

2017-01-01

Full Text Available Multifractal (or singularity spectra widths w allow diagnosing cascade structure through comparing original series’ widths wOrig to surrogate series’ widths wSurr. However, interpretations of 0Multifractal detrended fluctuation analysis (MF-DFA and Chhabra and Jensen’s method provided two estimates of wOrig for 200 simulated series at each value 0.1≤σ≤1.1 incrementing by 0.05. Increasing σ draws wOrig away from wSurr

Brain-wide neuronal dynamics during motor adaptation in zebrafish.

Science.gov (United States)

Ahrens, Misha B; Li, Jennifer M; Orger, Michael B; Robson, Drew N; Schier, Alexander F; Engert, Florian; Portugues, Ruben

2012-05-09

A fundamental question in neuroscience is how entire neural circuits generate behaviour and adapt it to changes in sensory feedback. Here we use two-photon calcium imaging to record the activity of large populations of neurons at the cellular level, throughout the brain of larval zebrafish expressing a genetically encoded calcium sensor, while the paralysed animals interact fictively with a virtual environment and rapidly adapt their motor output to changes in visual feedback. We decompose the network dynamics involved in adaptive locomotion into four types of neuronal response properties, and provide anatomical maps of the corresponding sites. A subset of these signals occurred during behavioural adjustments and are candidates for the functional elements that drive motor learning. Lesions to the inferior olive indicate a specific functional role for olivocerebellar circuitry in adaptive locomotion. This study enables the analysis of brain-wide dynamics at single-cell resolution during behaviour.

Short desynchronization episodes prevail in synchronous dynamics of human brain rhythms.

Science.gov (United States)

Ahn, Sungwoo; Rubchinsky, Leonid L

2013-03-01

Neural synchronization is believed to be critical for many brain functions. It frequently exhibits temporal variability, but it is not known if this variability has a specific temporal patterning. This study explores these synchronization/desynchronization patterns. We employ recently developed techniques to analyze the fine temporal structure of phase-locking to study the temporal patterning of synchrony of the human brain rhythms. We study neural oscillations recorded by electroencephalograms in α and β frequency bands in healthy human subjects at rest and during the execution of a task. While the phase-locking strength depends on many factors, dynamics of synchrony has a very specific temporal pattern: synchronous states are interrupted by frequent, but short desynchronization episodes. The probability for a desynchronization episode to occur decreased with its duration. The transition matrix between synchronized and desynchronized states has eigenvalues close to 0 and 1 where eigenvalue 1 has multiplicity 1, and therefore if the stationary distribution between these states is perturbed, the system converges back to the stationary distribution very fast. The qualitative similarity of this patterning across different subjects, brain states and electrode locations suggests that this may be a general type of dynamics for the brain. Earlier studies indicate that not all oscillatory networks have this kind of patterning of synchronization/desynchronization dynamics. Thus, the observed prevalence of short (but potentially frequent) desynchronization events (length of one cycle of oscillations) may have important functional implications for the brain. Numerous short desynchronizations (as opposed to infrequent, but long desynchronizations) may allow for a quick and efficient formation and break-up of functionally significant neuronal assemblies.

Hypobaric Hypoxia Imbalances Mitochondrial Dynamics in Rat Brain Hippocampus

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Khushbu Jain

2015-01-01

Full Text Available Brain is predominantly susceptible to oxidative stress and mitochondrial dysfunction during hypobaric hypoxia, and therefore undergoes neurodegeneration due to energy crisis. Evidences illustrate a high degree of association for mitochondrial fusion/fission imbalance and mitochondrial dysfunction. Mitochondrial fusion/fission is a recently reported dynamic mechanism which frequently occurs among cellular mitochondrial network. Hence, the study investigated the temporal alteration and involvement of abnormal mitochondrial dynamics (fusion/fission along with disturbed mitochondrial functionality during chronic exposure to hypobaric hypoxia (HH. The Sprague-Dawley rats were exposed to simulated high altitude equivalent to 25000 ft for 3, 7, 14, 21, and 28 days. Mitochondrial morphology, distribution within neurons, enzyme activity of respiratory complexes, Δψm, ADP: ATP, and expression of fission/fusion key proteins were determined. Results demonstrated HH induced alteration in mitochondrial morphology by damaged, small mitochondria observed in neurons with disturbance of mitochondrial functionality and reduced mitochondrial density in neuronal processes manifested by excessive mitochondrial fragmentation (fission and decreased mitochondrial fusion as compared to unexposed rat brain hippocampus. The study suggested that imbalance in mitochondrial dynamics is one of the noteworthy mechanisms occurring in hippocampal neurons during HH insult.

Dynamical duality of type- and token-computation as an abstract brain

Energy Technology Data Exchange (ETDEWEB)

Gunji, Yukio-Pegio [Department of Earth and Planetary Sciences, Faculty of Science, Kobe University (Japan); Graduate School of Science and Technology, Kobe University (Japan)] e-mail: yukio@kobe-u.ac.jp; Miyoshi, Hiroyuki [Department of Computer Sciences, Faculty of Science, Kyoto Sangyo University (Japan); Takahashi, Tatsuji [Graduate School of Science and Technology, Kobe University (Japan); Kamiura, Moto [Graduate School of Science and Technology, Kobe University (Japan)

2006-03-01

In brain science, there are few researches focusing on the theoretical relation between cognition (top-down processing) and perception (bottom-up processing). Philosophically they were regarded as the alternative leading to the dualism of mind and body, while it is an adequate problem for the endo-physics. Qualia are conjectured as the hard problem under those situations. To overcome such an impasse, we propose an abstract brain model featuring the dynamical duality of two parts of computations in a brain, in a term of endo-physics and internal measurement. Two parts of computations are expressed as binary relations, and the relationship between them is expressed as a pair of maps, called an infomorphism [Barwise J, Seligman J. Information flow, the logic of distributed systems. Cambridge University Press 1997]. Dynamical duality is implemented by the interaction between a binary relation and an infomorphism, and that leads to a dynamical change of a pair of binary relations. When a binary relation is expressed as a partial ordered set, one can check whether a part of computation is closed with respect to logical operations (i.e., a lattice) or not. By estimating a binary relation in terms of properties of lattice, we show that dynamical infomorphism robustly develops to a pair of logical computations corresponding to type cognition and non-logical one corresponding to qualia perception. It implies origin of differentiation and robust co-existence of type-cognition and qualia-perception. It is easy to see that dynamical infomorphism can also develop to a pair of singleton sets corresponding to savants' special cognitive style.

Dynamical duality of type- and token-computation as an abstract brain

International Nuclear Information System (INIS)

Gunji, Yukio-Pegio; Miyoshi, Hiroyuki; Takahashi, Tatsuji; Kamiura, Moto

2006-01-01

In brain science, there are few researches focusing on the theoretical relation between cognition (top-down processing) and perception (bottom-up processing). Philosophically they were regarded as the alternative leading to the dualism of mind and body, while it is an adequate problem for the endo-physics. Qualia are conjectured as the hard problem under those situations. To overcome such an impasse, we propose an abstract brain model featuring the dynamical duality of two parts of computations in a brain, in a term of endo-physics and internal measurement. Two parts of computations are expressed as binary relations, and the relationship between them is expressed as a pair of maps, called an infomorphism [Barwise J, Seligman J. Information flow, the logic of distributed systems. Cambridge University Press 1997]. Dynamical duality is implemented by the interaction between a binary relation and an infomorphism, and that leads to a dynamical change of a pair of binary relations. When a binary relation is expressed as a partial ordered set, one can check whether a part of computation is closed with respect to logical operations (i.e., a lattice) or not. By estimating a binary relation in terms of properties of lattice, we show that dynamical infomorphism robustly develops to a pair of logical computations corresponding to type cognition and non-logical one corresponding to qualia perception. It implies origin of differentiation and robust co-existence of type-cognition and qualia-perception. It is easy to see that dynamical infomorphism can also develop to a pair of singleton sets corresponding to savants' special cognitive style

Quantitative theory of driven nonlinear brain dynamics.

Science.gov (United States)

Roberts, J A; Robinson, P A

2012-09-01

Strong periodic stimuli such as bright flashing lights evoke nonlinear responses in the brain and interact nonlinearly with ongoing cortical activity, but the underlying mechanisms for these phenomena are poorly understood at present. The dominant features of these experimentally observed dynamics are reproduced by the dynamics of a quantitative neural field model subject to periodic drive. Model power spectra over a range of drive frequencies show agreement with multiple features of experimental measurements, exhibiting nonlinear effects including entrainment over a range of frequencies around the natural alpha frequency f(α), subharmonic entrainment near 2f(α), and harmonic generation. Further analysis of the driven dynamics as a function of the drive parameters reveals rich nonlinear dynamics that is predicted to be observable in future experiments at high drive amplitude, including period doubling, bistable phase-locking, hysteresis, wave mixing, and chaos indicated by positive Lyapunov exponents. Moreover, photosensitive seizures are predicted for physiologically realistic model parameters yielding bistability between healthy and seizure dynamics. These results demonstrate the applicability of neural field models to the new regime of periodically driven nonlinear dynamics, enabling interpretation of experimental data in terms of specific generating mechanisms and providing new tests of the theory. Copyright © 2012 Elsevier Inc. All rights reserved.

Nonlinear multi-analysis of agent-based financial market dynamics by epidemic system

Science.gov (United States)

Lu, Yunfan; Wang, Jun; Niu, Hongli

2015-10-01

Based on the epidemic dynamical system, we construct a new agent-based financial time series model. In order to check and testify its rationality, we compare the statistical properties of the time series model with the real stock market indices, Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index. For analyzing the statistical properties, we combine the multi-parameter analysis with the tail distribution analysis, the modified rescaled range analysis, and the multifractal detrended fluctuation analysis. For a better perspective, the three-dimensional diagrams are used to present the analysis results. The empirical research in this paper indicates that the long-range dependence property and the multifractal phenomenon exist in the real returns and the proposed model. Therefore, the new agent-based financial model can recurrence some important features of real stock markets.

On a possible mechanism of the brain for responding to dynamical features extracted from input signals

International Nuclear Information System (INIS)

Liu Zengrong; Chen Guanrong

2003-01-01

Based on the general theory of nonlinear dynamical systems, a possible mechanism for responding to some dynamical features extracted from input signals in brain activities is described and discussed. This mechanism is first converted to a nonlinear dynamical configuration--a generalized synchronization of complex dynamical systems. Then, some general conditions for achieving such synchronizations are derived. It is shown that dynamical systems have potentials of producing different responses for different features extracted from various input signals, which may be used to describe brain activities. For illustration, some numerical examples are given with simulation figures

Multifractal-based nuclei segmentation in fish images.

Science.gov (United States)

Reljin, Nikola; Slavkovic-Ilic, Marijeta; Tapia, Coya; Cihoric, Nikola; Stankovic, Srdjan

2017-09-01

The method for nuclei segmentation in fluorescence in-situ hybridization (FISH) images, based on the inverse multifractal analysis (IMFA) is proposed. From the blue channel of the FISH image in RGB format, the matrix of Holder exponents, with one-by-one correspondence with the image pixels, is determined first. The following semi-automatic procedure is proposed: initial nuclei segmentation is performed automatically from the matrix of Holder exponents by applying predefined hard thresholding; then the user evaluates the result and is able to refine the segmentation by changing the threshold, if necessary. After successful nuclei segmentation, the HER2 (human epidermal growth factor receptor 2) scoring can be determined in usual way: by counting red and green dots within segmented nuclei, and finding their ratio. The IMFA segmentation method is tested over 100 clinical cases, evaluated by skilled pathologist. Testing results show that the new method has advantages compared to already reported methods.

Nonlinear dynamics of the brain: emotion and cognition

Energy Technology Data Exchange (ETDEWEB)

Rabinovich, Mikhail I; Muezzinoglu, M K [Institute for Nonlinear Science, University of California, San Diego, CA (United States)

2010-07-08

Experimental investigations of neural system functioning and brain activity are standardly based on the assumption that perceptions, emotions, and cognitive functions can be understood by analyzing steady-state neural processes and static tomographic snapshots. The new approaches discussed in this review are based on the analysis of transient processes and metastable states. Transient dynamics is characterized by two basic properties, structural stability and information sensitivity. The ideas and methods that we discuss provide an explanation for the occurrence of and successive transitions between metastable states observed in experiments, and offer new approaches to behavior analysis. Models of the emotional and cognitive functions of the brain are suggested. The mathematical object that represents the observed transient brain processes in the phase space of the model is a structurally stable heteroclinic channel. The possibility of using the suggested models to construct a quantitative theory of some emotional and cognitive functions is illustrated. (reviews of topical problems)

Nonlinear dynamics of the brain: emotion and cognition

International Nuclear Information System (INIS)

Rabinovich, Mikhail I; Muezzinoglu, M K

2010-01-01

Experimental investigations of neural system functioning and brain activity are standardly based on the assumption that perceptions, emotions, and cognitive functions can be understood by analyzing steady-state neural processes and static tomographic snapshots. The new approaches discussed in this review are based on the analysis of transient processes and metastable states. Transient dynamics is characterized by two basic properties, structural stability and information sensitivity. The ideas and methods that we discuss provide an explanation for the occurrence of and successive transitions between metastable states observed in experiments, and offer new approaches to behavior analysis. Models of the emotional and cognitive functions of the brain are suggested. The mathematical object that represents the observed transient brain processes in the phase space of the model is a structurally stable heteroclinic channel. The possibility of using the suggested models to construct a quantitative theory of some emotional and cognitive functions is illustrated. (reviews of topical problems)

Multi-fractal analysis and lacunarity spectrum of the dark matter haloes in the SDSS-DR7

International Nuclear Information System (INIS)

Chacón-Cardona, C.A.; Casas-Miranda, R.A.; Muñoz-Cuartas, J.C.

2016-01-01

Highlights: • We analysed the dark matter in Seventh Data Release of the Sloan Digital Sky Survey. • From the initial sample with 412,468 galaxies, 339,505 dark matter haloes were used. • We found the multifractal and the lacunarity spectrum as radial distance function. • The dark matter set did not achieve at the physical dimension of the space. - Abstract: The dark matter halo distribution of the nearby universe is used to study the fractal behaviour in the proximate universe. The data, which is based on four volume-limited galaxy samples was obtained by Muñoz-Cuartas and Mueller (2012) from the Seventh Data Release of the Sloan Digital Sky Survey (SDSS-DR7). In order to know the fractal behaviour of the observed universe, from the initial sample which contains 412,468 galaxies and 339,505 dark matter haloes were used as input for the fractal calculations. Using this data we use the sliding-window technique for the dark matter distribution and compute the multi-fractal dimension and the lacunarity spectrum and use it to study its dependence on radial distance in every sample. The transition to homogeneity is not observed in the dark matter halo distribution obtained from the SDSS-DR7 volume-limited galaxy samples; in its place the dark matter halo distribution exhibits a persistent multi-fractal behaviour where the measured dimension does not arrive at the value of the physical dimension of the space, for all structure parameter values of the analysed set, at least up to radial distances of the ordered from 165 Mpc/h from the available centres of each sample. Our results and their implications are discussed in the context of the formation of large-scale structures in the universe.

Volatility-constrained multifractal detrended cross-correlation analysis: Cross-correlation among Mainland China, US, and Hong Kong stock markets

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Cao, Guangxi; Zhang, Minjia; Li, Qingchen

2017-04-01

This study focuses on multifractal detrended cross-correlation analysis of the different volatility intervals of Mainland China, US, and Hong Kong stock markets. A volatility-constrained multifractal detrended cross-correlation analysis (VC-MF-DCCA) method is proposed to study the volatility conductivity of Mainland China, US, and Hong Kong stock markets. Empirical results indicate that fluctuation may be related to important activities in real markets. The Hang Seng Index (HSI) stock market is more influential than the Shanghai Composite Index (SCI) stock market. Furthermore, the SCI stock market is more influential than the Dow Jones Industrial Average stock market. The conductivity between the HSI and SCI stock markets is the strongest. HSI was the most influential market in the large fluctuation interval of 1991 to 2014. The autoregressive fractionally integrated moving average method is used to verify the validity of VC-MF-DCCA. Results show that VC-MF-DCCA is effective.

Multifractal detrended fluctuation analysis of analog random multiplicative processes

Energy Technology Data Exchange (ETDEWEB)

Silva, L.B.M.; Vermelho, M.V.D. [Instituto de Fisica, Universidade Federal de Alagoas, Maceio - AL, 57072-970 (Brazil); Lyra, M.L. [Instituto de Fisica, Universidade Federal de Alagoas, Maceio - AL, 57072-970 (Brazil)], E-mail: marcelo@if.ufal.br; Viswanathan, G.M. [Instituto de Fisica, Universidade Federal de Alagoas, Maceio - AL, 57072-970 (Brazil)

2009-09-15

We investigate non-Gaussian statistical properties of stationary stochastic signals generated by an analog circuit that simulates a random multiplicative process with weak additive noise. The random noises are originated by thermal shot noise and avalanche processes, while the multiplicative process is generated by a fully analog circuit. The resulting signal describes stochastic time series of current interest in several areas such as turbulence, finance, biology and environment, which exhibit power-law distributions. Specifically, we study the correlation properties of the signal by employing a detrended fluctuation analysis and explore its multifractal nature. The singularity spectrum is obtained and analyzed as a function of the control circuit parameter that tunes the asymptotic power-law form of the probability distribution function.

Recent advances in applying mass spectrometry and systems biology to determine brain dynamics.

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Scifo, Enzo; Calza, Giulio; Fuhrmann, Martin; Soliymani, Rabah; Baumann, Marc; Lalowski, Maciej

2017-06-01

Neurological disorders encompass various pathologies which disrupt normal brain physiology and function. Poor understanding of their underlying molecular mechanisms and their societal burden argues for the necessity of novel prevention strategies, early diagnostic techniques and alternative treatment options to reduce the scale of their expected increase. Areas covered: This review scrutinizes mass spectrometry based approaches used to investigate brain dynamics in various conditions, including neurodegenerative and neuropsychiatric disorders. Different proteomics workflows for isolation/enrichment of specific cell populations or brain regions, sample processing; mass spectrometry technologies, for differential proteome quantitation, analysis of post-translational modifications and imaging approaches in the brain are critically deliberated. Future directions, including analysis of cellular sub-compartments, targeted MS platforms (selected/parallel reaction monitoring) and use of mass cytometry are also discussed. Expert commentary: Here, we summarize and evaluate current mass spectrometry based approaches for determining brain dynamics in health and diseases states, with a focus on neurological disorders. Furthermore, we provide insight on current trends and new MS technologies with potential to improve this analysis.

An extended algebraic variational multiscale-multigrid-multifractal method (XAVM4) for large-eddy simulation of turbulent two-phase flow

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Rasthofer, U.; Wall, W. A.; Gravemeier, V.

2018-04-01

A novel and comprehensive computational method, referred to as the eXtended Algebraic Variational Multiscale-Multigrid-Multifractal Method (XAVM4), is proposed for large-eddy simulation of the particularly challenging problem of turbulent two-phase flow. The XAVM4 involves multifractal subgrid-scale modeling as well as a Nitsche-type extended finite element method as an approach for two-phase flow. The application of an advanced structural subgrid-scale modeling approach in conjunction with a sharp representation of the discontinuities at the interface between two bulk fluids promise high-fidelity large-eddy simulation of turbulent two-phase flow. The high potential of the XAVM4 is demonstrated for large-eddy simulation of turbulent two-phase bubbly channel flow, that is, turbulent channel flow carrying a single large bubble of the size of the channel half-width in this particular application.

Do resting brain dynamics predict oddball evoked-potential?

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Lee Tien-Wen

2011-11-01

Full Text Available Abstract Background The oddball paradigm is widely applied to the investigation of cognitive function in neuroscience and in neuropsychiatry. Whether cortical oscillation in the resting state can predict the elicited oddball event-related potential (ERP is still not clear. This study explored the relationship between resting electroencephalography (EEG and oddball ERPs. The regional powers of 18 electrodes across delta, theta, alpha and beta frequencies were correlated with the amplitude and latency of N1, P2, N2 and P3 components of oddball ERPs. A multivariate analysis based on partial least squares (PLS was applied to further examine the spatial pattern revealed by multiple correlations. Results Higher synchronization in the resting state, especially at the alpha spectrum, is associated with higher neural responsiveness and faster neural propagation, as indicated by the higher amplitude change of N1/N2 and shorter latency of P2. None of the resting quantitative EEG indices predict P3 latency and amplitude. The PLS analysis confirms that the resting cortical dynamics which explains N1/N2 amplitude and P2 latency does not show regional specificity, indicating a global property of the brain. Conclusions This study differs from previous approaches by relating dynamics in the resting state to neural responsiveness in the activation state. Our analyses suggest that the neural characteristics carried by resting brain dynamics modulate the earlier/automatic stage of target detection.

Modeling the complexity of acoustic emission during intermittent plastic deformation: Power laws and multifractal spectra.

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Kumar, Jagadish; Ananthakrishna, G

2018-01-01

Scale-invariant power-law distributions for acoustic emission signals are ubiquitous in several plastically deforming materials. However, power-law distributions for acoustic emission energies are reported in distinctly different plastically deforming situations such as hcp and fcc single and polycrystalline samples exhibiting smooth stress-strain curves and in dilute metallic alloys exhibiting discontinuous flow. This is surprising since the underlying dislocation mechanisms in these two types of deformations are very different. So far, there have been no models that predict the power-law statistics for discontinuous flow. Furthermore, the statistics of the acoustic emission signals in jerky flow is even more complex, requiring multifractal measures for a proper characterization. There has been no model that explains the complex statistics either. Here we address the problem of statistical characterization of the acoustic emission signals associated with the three types of the Portevin-Le Chatelier bands. Following our recently proposed general framework for calculating acoustic emission, we set up a wave equation for the elastic degrees of freedom with a plastic strain rate as a source term. The energy dissipated during acoustic emission is represented by the Rayleigh-dissipation function. Using the plastic strain rate obtained from the Ananthakrishna model for the Portevin-Le Chatelier effect, we compute the acoustic emission signals associated with the three Portevin-Le Chatelier bands and the Lüders-like band. The so-calculated acoustic emission signals are used for further statistical characterization. Our results show that the model predicts power-law statistics for all the acoustic emission signals associated with the three types of Portevin-Le Chatelier bands with the exponent values increasing with increasing strain rate. The calculated multifractal spectra corresponding to the acoustic emission signals associated with the three band types have a maximum

The dynamic brain: from spiking neurons to neural masses and cortical fields.

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Gustavo Deco

2008-08-01

Full Text Available The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space-time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI, electroencephalogram (EEG, and magnetoencephalogram (MEG. Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the

EGS Richardson AGU Chapman NVAG3 Conference: Nonlinear Variability in Geophysics: scaling and multifractal processes

OpenAIRE

D. Schertzer; S. Lovejoy; S. Lovejoy

1994-01-01

1. The conference The third conference on "Nonlinear VAriability in Geophysics: scaling and multifractal processes" (NVAG 3) was held in Cargese, Corsica, Sept. 10-17, 1993. NVAG3 was joint American Geophysical Union Chapman and European Geophysical Society Richardson Memorial conference, the first specialist conference jointly sponsored by the two organizations. It followed NVAG1 (Montreal, Aug. 1986), NVAG2 (Paris, June 1988; Schertzer and Lovejoy, 1991), five consecutive annual ...

EGS Richardson AGU Chapman NVAG3 Conference: Nonlinear Variability in Geophysics: scaling and multifractal processes

OpenAIRE

Schertzer , D; Lovejoy , S.

1994-01-01

International audience; 1. The conference The third conference on "Nonlinear VAriability in Geophysics: scaling and multifractal processes" (NVAG 3) was held in Cargese, Corsica, Sept. 10-17, 1993. NVAG3 was joint American Geophysical Union Chapman and European Geophysical Society Richardson Memorial conference, the first specialist conference jointly sponsored by the two organizations. It followed NVAG1 (Montreal, Aug. 1986), NVAG2 (Paris, June 1988; Schertzer and Lovejoy, 1991), five conse...

Extracting sensitive spectrum bands of rapeseed using multiscale multifractal detrended fluctuation analysis

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Jiang, Shan; Wang, Fang; Shen, Luming; Liao, Guiping; Wang, Lin

2017-03-01

Spectrum technology has been widely used in crop non-destructive testing diagnosis for crop information acquisition. Since spectrum covers a wide range of bands, it is of critical importance to extract the sensitive bands. In this paper, we propose a methodology to extract the sensitive spectrum bands of rapeseed using multiscale multifractal detrended fluctuation analysis. Our obtained sensitive bands are relatively robust in the range of 534 nm-574 nm. Further, by using the multifractal parameter (Hurst exponent) of the extracted sensitive bands, we propose a prediction model to forecast the Soil and plant analyzer development values ((SPAD), often used as a parameter to indicate the chlorophyll content) and an identification model to distinguish the different planting patterns. Three vegetation indices (VIs) based on previous work are used for comparison. Three evaluation indicators, namely, the root mean square error, the correlation coefficient, and the relative error employed in the SPAD values prediction model all demonstrate that our Hurst exponent has the best performance. Four rapeseed compound planting factors, namely, seeding method, planting density, fertilizer type, and weed control method are considered in the identification model. The Youden indices calculated by the random decision forest method and the K-nearest neighbor method show that our Hurst exponent is superior to other three Vis, and their combination for the factor of seeding method. In addition, there is no significant difference among the five features for other three planting factors. This interesting finding suggests that the transplanting and the direct seeding would make a big difference in the growth of rapeseed.

A close look at brain dynamics: cells and vessels seen by in vivo two-photon microscopy.

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Fumagalli, Stefano; Ortolano, Fabrizio; De Simoni, Maria-Grazia

2014-10-01

The cerebral vasculature has a unique role in providing a constant supply of oxygen and nutrients to ensure normal brain functions. Blood vessels that feed the brain are far from being simply channels for passive transportation of fluids. They form complex structures made up of different cell types. These structures regulate blood supply, local concentrations of O2 and CO2, transport of small molecules, trafficking of plasma cells and fine cerebral functions in normal and diseased brains. Until few years ago, analysis of these functions has been typically based on post mortem techniques, whose interpretation is limited by the need for tissue processing at specific times. For a reliable and effective picture of the dynamic processes in the central nervous system, real-time information in vivo is required. There are now few in vivo systems, among which two-photon microscopy (2-PM) is a truly innovative tool for studying the brain. 2-PM has been used to dissect specific aspects of vascular and immune cell dynamics in the context of neurological diseases, providing exciting results that could not have been obtained with conventional methods. This review summarizes the latest findings on vascular and immune system action in the brain, with particular focus on the dynamic responses after ischemic brain injury. 2-PM has helped define the hierarchical architecture of the brain vasculature, the dynamic interaction between the vasculature and immune cells recruited to lesion sites, the effects of blood flow on neuronal and microglial activity and the ability of cells of the neurovascular unit to regulate blood flow. Copyright © 2014 Elsevier Ltd. All rights reserved.

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia

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Van Snellenberg, Jared X.; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa

2016-01-01

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during an n-back working-memory task) and positron emission tomography using the radiotracer [11C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. SIGNIFICANCE

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia.

Science.gov (United States)

Cassidy, Clifford M; Van Snellenberg, Jared X; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa; Horga, Guillermo

2016-04-13

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during ann-back working-memory task) and positron emission tomography using the radiotracer [(11)C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. It is unclear

The Independent and Shared Mechanisms of Intrinsic Brain Dynamics: Insights From Bistable Perception

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Teng Cao

2018-04-01

Full Text Available In bistable perception, constant input leads to alternating perception. The dynamics of the changing perception reflects the intrinsic dynamic properties of the “unconscious inferential” process in the brain. Under the same condition, individuals differ in how fast they experience the perceptual alternation. In this study, testing many forms of bistable perception in a large number of observers, we investigated the key question of whether there is a general and common mechanism or multiple and independent mechanisms that control the dynamics of the inferential brain. Bistable phenomena tested include binocular rivalry, vase-face, Necker cube, moving plaid, motion induced blindness, biological motion, spinning dancer, rotating cylinder, Lissajous-figure, rolling wheel, and translating diamond. Switching dynamics for each bistable percept was measured in 100 observers. Results show that the switching rates of subsets of bistable percept are highly correlated. The clustering of dynamic properties of some bistable phenomena but not an overall general control of switching dynamics implies that the brain’s inferential processes are both shared and independent – faster in constructing 3D structure from motion does not mean faster in integrating components into an objects.

Correlations in magnitude series to assess nonlinearities: Application to multifractal models and heartbeat fluctuations

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Bernaola-Galván, Pedro A.; Gómez-Extremera, Manuel; Romance, A. Ramón; Carpena, Pedro

2017-09-01

The correlation properties of the magnitude of a time series are associated with nonlinear and multifractal properties and have been applied in a great variety of fields. Here we have obtained the analytical expression of the autocorrelation of the magnitude series (C|x |) of a linear Gaussian noise as a function of its autocorrelation (Cx). For both, models and natural signals, the deviation of C|x | from its expectation in linear Gaussian noises can be used as an index of nonlinearity that can be applied to relatively short records and does not require the presence of scaling in the time series under study. In a model of artificial Gaussian multifractal signal we use this approach to analyze the relation between nonlinearity and multifractallity and show that the former implies the latter but the reverse is not true. We also apply this approach to analyze experimental data: heart-beat records during rest and moderate exercise. For each individual subject, we observe higher nonlinearities during rest. This behavior is also achieved on average for the analyzed set of 10 semiprofessional soccer players. This result agrees with the fact that other measures of complexity are dramatically reduced during exercise and can shed light on its relationship with the withdrawal of parasympathetic tone and/or the activation of sympathetic activity during physical activity.

SELECTION OF SCALE OF PICTURE OF STRUCTURE FOR ITS MULTIFRACTAL ANALYSIS

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VOLCHUK V. N.

2015-11-01

Full Text Available Problem statement. Each scale level detectesthe new features of the structure of the material describing of it quality. For example, features of the grain structure are revealed in different kind of steel on microstruc ture level, and its parameters greatly influences on the strength properties of the metal. Thus, to select the scale of representation of a fractal object, for instance the elements of structure of roll iron or steel is necessary to determine the interval (1, where observed its self-similarity, and on this interval should be selected the scale, the use of which will allow him to choose adequate fractal dimension. For optimal scale structure of repose is taken one in which at least two adjacent points of the series (2, the fractal dimension is minimal differences between them. This is explained by the fact that this is best observed property of self-similarity structure. An example of the selection of the scale representation of the structure of cast iron rolls execution of SPHN (a and execution SSHN (b is shown on interval of increases in the range of x 100 to x1000 with a predetermined pitch Δl = 100. The implementation of this phase of research allowed to determine experimentally the optimal scale of representation of structure of iron roll with increasing x 200 for multifractal analysis of its elements: inclusion of the plate and nodular graphit, carbides. Purpose To determine the optimal scale structure representation for iron roll multifractal analysis of its elements: inclusion of the plate and nodular carbides. Conclusion. It was found that the fractal dimension of the structural elements of the test ranged from experimental error 5÷7%, which testifies to the universality of this assessment, and therefore reliability and economic benefits, in terms of the equipping of laboratories expensive metallurgical microscopes with higher resolution.

Task-based core-periphery organization of human brain dynamics.

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Danielle S Bassett

Full Text Available As a person learns a new skill, distinct synapses, brain regions, and circuits are engaged and change over time. In this paper, we develop methods to examine patterns of correlated activity across a large set of brain regions. Our goal is to identify properties that enable robust learning of a motor skill. We measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities, we find that the complex reconfiguration patterns of the brain's putative functional modules that control learning can be described parsimoniously by the combined presence of a relatively stiff temporal core that is composed primarily of sensorimotor and visual regions whose connectivity changes little in time and a flexible temporal periphery that is composed primarily of multimodal association regions whose connectivity changes frequently. The separation between temporal core and periphery changes over the course of training and, importantly, is a good predictor of individual differences in learning success. The core of dynamically stiff regions exhibits dense connectivity, which is consistent with notions of core-periphery organization established previously in social networks. Our results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior.

Dynamic Functional Imaging of Brain Glucose Utilization using fPET-FDG

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Villien, Marjorie; Wey, Hsiao-Ying; Mandeville, Joseph B.; Catana, Ciprian; Polimeni, Jonathan R.; Sander, Christin Y.; Zürcher, Nicole R.; Chonde, Daniel B.; Fowler, Joanna S.; Rosen, Bruce R.; Hooker, Jacob M.

2014-01-01

Glucose is the principal source of energy for the brain and yet the dynamic response of glucose utilization to changes in brain activity is still not fully understood. Positron emission tomography (PET) allows quantitative measurement of glucose metabolism using 2-[18F]-fluorodeoxyglucose (FDG). However, FDG PET in its current form provides an integral (or average) of glucose consumption over tens of minutes and lacks the temporal information to capture physiological alterations associated with changes in brain activity induced by tasks or drug challenges. Traditionally, changes in glucose utilization are inferred by comparing two separate scans, which significantly limits the utility of the method. We report a novel method to track changes in FDG metabolism dynamically, with higher temporal resolution than exists to date and within a single session. Using a constant infusion of FDG, we demonstrate that our technique (termed fPET-FDG) can be used in an analysis pipeline similar to fMRI to define within-session differential metabolic responses. We use visual stimulation to demonstrate the feasibility of this method. This new method has a great potential to be used in research protocols and clinical settings since fPET-FDG imaging can be performed with most PET scanners and data acquisition and analysis is straightforward. fPET-FDG is a highly complementary technique to MRI and provides a rich new way to observe functional changes in brain metabolism. PMID:24936683

Measuring complexity with multifractals in texts. Translation effects

International Nuclear Information System (INIS)

Ausloos, M.

2012-01-01

Highlights: ► Two texts in English and one in Esperanto are transformed into 6 time series. ► D(q) and f(alpha) of such (and shuffled) time series are obtained. ► A model for text construction is presented based on a parametrized Cantor set. ► The model parameters can also be used when examining machine translated texts. ► Suggested extensions to higher dimensions: in 2D image analysis and on hypertexts. - Abstract: Should quality be almost a synonymous of complexity? To measure quality appears to be audacious, even very subjective. It is hereby proposed to use a multifractal approach in order to quantify quality, thus through complexity measures. A one-dimensional system is examined. It is known that (all) written texts can be one-dimensional nonlinear maps. Thus, several written texts by the same author are considered, together with their translation, into an unusual language, Esperanto, and asa baseline their corresponding shuffled versions. Different one-dimensional time series can be used: e.g. (i) one based on word lengths, (ii) the other based on word frequencies; both are used for studying, comparing and discussing the map structure. It is shown that a variety in style can be measured through the D(q) and f(α) curves characterizing multifractal objects. This allows to observe on the one hand whether natural and artificial languages significantly influence the writing and the translation, and whether one author’s texts differ technically from each other. In fact, the f(α) curves of the original texts are similar to each other, but the translated text shows marked differences. However in each case, the f(α) curves are far from being parabolic, – in contrast to the shuffled texts. Moreover, the Esperanto text has more extreme values. Criteria are thereby suggested for estimating a text quality, as if it is a time series only. A model is introduced in order to substantiate the findings: it consists in considering a text as a random Cantor set

The multifractal structure of satellite sea surface temperature maps can be used to obtain global maps of streamlines

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A. Turiel

2009-10-01

Full Text Available Nowadays Earth observation satellites provide information about many relevant variables of the ocean-climate system, such as temperature, moisture, aerosols, etc. However, to retrieve the velocity field, which is the most relevant dynamical variable, is still a technological challenge, specially in the case of oceans. New processing techniques, emerged from the theory of turbulent flows, have come to assist us in this task. In this paper, we show that multifractal techniques applied to new Sea Surface Temperature satellite products opens the way to build maps of ocean currents with unprecedented accuracy. With the application of singularity analysis, we show that global ocean circulation patterns can be retrieved in a daily basis. We compare these results with high-quality altimetry-derived geostrophic velocities, finding a quite good correspondence of the observed patterns both qualitatively and quantitatively; and this is done for the first time on a global basis, even for less active areas. The implications of this findings from the perspective both of theory and of operational applications are discussed.

Principal States of Dynamic Functional Connectivity Reveal the Link Between Resting-State and Task-State Brain: An fMRI Study.

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Cheng, Lin; Zhu, Yang; Sun, Junfeng; Deng, Lifu; He, Naying; Yang, Yang; Ling, Huawei; Ayaz, Hasan; Fu, Yi; Tong, Shanbao

2018-01-25

Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain's dynamic and adaptive activities. In this study, we adopted [Formula: see text]-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns. In order to test this hypothesis, this study examined the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent (BOLD)-fMRI data from 26 healthy subjects during rest (REST) and a hand closing-and-opening (HCO) task. Two principal FC states in REST and one principal FC state in HCO were identified. The first principal FC state in REST was found similar to that in HCO, which appeared to represent intrinsic network architecture and validated the broadly similar spatial patterns between REST and HCO. However, the second FC principal state in REST with much shorter "dwell time" implied the transient functional relationship between DMN and MN during REST. In addition, a more frequent shifting between two principal FC states indicated that brain network dynamically maintained a "default mode" in the motor system during REST, whereas the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity during HCO, validating the distinct temporal patterns between REST and HCO. Our results further demonstrated that dynamic FC analysis could offer unique

Dynamic Quantitative T1 Mapping in Orthotopic Brain Tumor Xenografts

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Kelsey Herrmann

2016-04-01

Full Text Available Human brain tumors such as glioblastomas are typically detected using conventional, nonquantitative magnetic resonance imaging (MRI techniques, such as T2-weighted and contrast enhanced T1-weighted MRI. In this manuscript, we tested whether dynamic quantitative T1 mapping by MRI can localize orthotopic glioma tumors in an objective manner. Quantitative T1 mapping was performed by MRI over multiple time points using the conventional contrast agent Optimark. We compared signal differences to determine the gadolinium concentration in tissues over time. The T1 parametric maps made it easy to identify the regions of contrast enhancement and thus tumor location. Doubling the typical human dose of contrast agent resulted in a clearer demarcation of these tumors. Therefore, T1 mapping of brain tumors is gadolinium dose dependent and improves detection of tumors by MRI. The use of T1 maps provides a quantitative means to evaluate tumor detection by gadolinium-based contrast agents over time. This dynamic quantitative T1 mapping technique will also enable future quantitative evaluation of various targeted MRI contrast agents.

Dynamics of Gut-Brain Communication Underlying Hunger.

Science.gov (United States)

Beutler, Lisa R; Chen, Yiming; Ahn, Jamie S; Lin, Yen-Chu; Essner, Rachel A; Knight, Zachary A

2017-10-11

Communication between the gut and brain is critical for homeostasis, but how this communication is represented in the dynamics of feeding circuits is unknown. Here we describe nutritional regulation of key neurons that control hunger in vivo. We show that intragastric nutrient infusion rapidly and durably inhibits hunger-promoting AgRP neurons in awake, behaving mice. This inhibition is proportional to the number of calories infused but surprisingly independent of macronutrient identity or nutritional state. We show that three gastrointestinal signals-serotonin, CCK, and PYY-are necessary or sufficient for these effects. In contrast, the hormone leptin has no acute effect on dynamics of these circuits or their sensory regulation but instead induces a slow modulation that develops over hours and is required for inhibition of feeding. These findings reveal how layers of visceral signals operating on distinct timescales converge on hypothalamic feeding circuits to generate a central representation of energy balance. Copyright © 2017 Elsevier Inc. All rights reserved.

Repair during multifraction exposures: spheroids versus monolayers

International Nuclear Information System (INIS)

Durand, R.E.

1984-01-01

Many type of mammalian cells, when grown in culture as multicell spheroids, display an increased ability to accumulate and repair sublethal radiation damage which has been called the ''contact effect''. Since this effect has the potential to markedly modify the multifraction radiation response of cells in V79 spheroids relative to cells in monolayer cultures, an investigation was made of regimens ranging from 1 to 100 fractions. Effective dose rates were chosen near 1 Gy h -1 to inhibit cell progression and thus simplify analysis of the results. As expected, larger doses per fraction produced more net cell killing in both systems than lower doses per fraction. Additionally, less killing of spheroid cells was observed in all regimens, in accord with their greater potential for repair. However, when the data were expressed as isoeffect curves, the spheroid and monolayer curves converged as the number of fractions increased. Thus, quite similar inherent sensitivity and repair capabilities would be predicted for ultra-low doses per fraction. High precision techniques for defining survival after doses of radiation from 0.2 to 1 Gy were, however, still able to demonstrate a survival advantage for cells grown as spheroids. (author)

Nonlinear Dynamic Theory of Acute Cell Injuries and Brain Ischemia

Science.gov (United States)

Taha, Doaa; Anggraini, Fika; Degracia, Donald; Huang, Zhi-Feng

2015-03-01

Cerebral ischemia in the form of stroke and cardiac arrest brain damage affect over 1 million people per year in the USA alone. In spite of close to 200 clinical trials and decades of research, there are no treatments to stop post-ischemic neuron death. We have argued that a major weakness of current brain ischemia research is lack of a deductive theoretical framework of acute cell injury to guide empirical studies. A previously published autonomous model based on the concept of nonlinear dynamic network was shown to capture important facets of cell injury, linking the concept of therapeutic to bistable dynamics. Here we present an improved, non-autonomous formulation of the nonlinear dynamic model of cell injury that allows multiple acute injuries over time, thereby allowing simulations of both therapeutic treatment and preconditioning. Our results are connected to the experimental data of gene expression and proteomics of neuron cells. Importantly, this new model may be construed as a novel approach to pharmacodynamics of acute cell injury. The model makes explicit that any pro-survival therapy is always a form of sub-lethal injury. This insight is expected to widely influence treatment of acute injury conditions that have defied successful treatment to date. This work is supported by NIH NINDS (NS081347) and Wayne State University President's Research Enhancement Award.

Devil's carpet of topological entropy and complexity of global dynamical behavior

International Nuclear Information System (INIS)

Cao, K.-F.; Zhang, X.-S.; Zhou Zhong; Peng, S.-L.

2003-01-01

For bimodal maps the concept of an equal topological entropy class (ETEC) is established by the dual star products. All the infinitely many ETEC plateaus and single points are harmonically organized in the kneading parameter plane, they construct a multifractal devil's carpet, which possesses a perfect subregion similarity and a dual central symmetry. The entropy devil's carpet reveals the complexity of global dynamical behavior in the whole parameter plane of bimodal systems

Encoding of Physics Concepts: Concreteness and Presentation Modality Reflected by Human Brain Dynamics

OpenAIRE

Lai, Kevin; She, Hsiao-Ching; Chen, Sheng-Chang; Chou, Wen-Chi; Huang, Li-Yu; Jung, Tzyy-Ping; Gramann, Klaus

2012-01-01

Previous research into working memory has focused on activations in different brain areas accompanying either different presentation modalities (verbal vs. non-verbal) or concreteness (abstract vs. concrete) of non-science concepts. Less research has been conducted investigating how scientific concepts are learned and further processed in working memory. To bridge this gap, the present study investigated human brain dynamics associated with encoding of physics concepts, taking both presentati...

The multifractal nature of plume structure in high-Rayleigh-number convection

Science.gov (United States)

Puthenveettil, Baburaj A.; Ananthakrishna, G.; Arakeri, Jaywant H.

2005-03-01

The geometrically different planforms of near-wall plume structure in turbulent natural convection, visualized by driving the convection using concentration differences across a membrane, are shown to have a common multifractal spectrum of singularities for Rayleigh numbers in the range 1010-1011 at Schmidt number of 602. The scaling is seen for a length scale range of 25 and is independent of the Rayleigh number, the flux, the strength and nature of the large-scale flow, and the aspect ratio. Similar scaling is observed for the plume structures obtained in the presence of a weak flow across the membrane. This common non-trivial spatial scaling is proposed to be due to the same underlying generating process for the near-wall plume structures.

Dynamic changes in brain activity during prism adaptation.

Science.gov (United States)

Luauté, Jacques; Schwartz, Sophie; Rossetti, Yves; Spiridon, Mona; Rode, Gilles; Boisson, Dominique; Vuilleumier, Patrik

2009-01-07

Prism adaptation does not only induce short-term sensorimotor plasticity, but also longer-term reorganization in the neural representation of space. We used event-related fMRI to study dynamic changes in brain activity during both early and prolonged exposure to visual prisms. Participants performed a pointing task before, during, and after prism exposure. Measures of trial-by-trial pointing errors and corrections allowed parametric analyses of brain activity as a function of performance. We show that during the earliest phase of prism exposure, anterior intraparietal sulcus was primarily implicated in error detection, whereas parieto-occipital sulcus was implicated in error correction. Cerebellum activity showed progressive increases during prism exposure, in accordance with a key role for spatial realignment. This time course further suggests that the cerebellum might promote neural changes in superior temporal cortex, which was selectively activated during the later phase of prism exposure and could mediate the effects of prism adaptation on cognitive spatial representations.

Characterizing Detrended Fluctuation Analysis of multifractional Brownian motion

Science.gov (United States)

Setty, V. A.; Sharma, A. S.

2015-02-01

The Hurst exponent (H) is widely used to quantify long range dependence in time series data and is estimated using several well known techniques. Recognizing its ability to remove trends the Detrended Fluctuation Analysis (DFA) is used extensively to estimate a Hurst exponent in non-stationary data. Multifractional Brownian motion (mBm) broadly encompasses a set of models of non-stationary data exhibiting time varying Hurst exponents, H(t) as against a constant H. Recently, there has been a growing interest in time dependence of H(t) and sliding window techniques have been used to estimate a local time average of the exponent. This brought to fore the ability of DFA to estimate scaling exponents in systems with time varying H(t) , such as mBm. This paper characterizes the performance of DFA on mBm data with linearly varying H(t) and further test the robustness of estimated time average with respect to data and technique related parameters. Our results serve as a bench-mark for using DFA as a sliding window estimator to obtain H(t) from time series data.

Multifractal analysis of implied volatility in index options

Science.gov (United States)

Oh, GabJin

2014-06-01

In this paper, we analyze the statistical and the non-linear properties of the log-variations in implied volatility for the CAC40, DAX and S& P500 daily index options. The price of an index option is generally represented by its implied volatility surface, including its smile and skew properties. We utilize a Lévy process model as the underlying asset to deepen our understanding of the intrinsic property of the implied volatility in the index options and estimate the implied volatility surface. We find that the options pricing models with the exponential Lévy model can reproduce the smile or sneer features of the implied volatility that are observed in real options markets. We study the variation in the implied volatility for at-the-money index call and put options, and we find that the distribution function follows a power-law distribution with an exponent of 3.5 ≤ γ ≤ 4.5. Especially, the variation in the implied volatility exhibits multifractal spectral characteristics, and the global financial crisis has influenced the complexity of the option markets.

Source space analysis of event-related dynamic reorganization of brain networks.

Science.gov (United States)

Ioannides, Andreas A; Dimitriadis, Stavros I; Saridis, George A; Voultsidou, Marotesa; Poghosyan, Vahe; Liu, Lichan; Laskaris, Nikolaos A

2012-01-01

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain.

Science.gov (United States)

Luo, Huichun; Huang, Yongzhi; Du, Xueying; Zhang, Yunpeng; Green, Alexander L; Aziz, Tipu Z; Wang, Shouyan

2018-01-01

In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep

Brain-heart linear and nonlinear dynamics during visual emotional elicitation in healthy subjects.

Science.gov (United States)

Valenza, G; Greco, A; Gentili, C; Lanata, A; Toschi, N; Barbieri, R; Sebastiani, L; Menicucci, D; Gemignani, A; Scilingo, E P

2016-08-01

This study investigates brain-heart dynamics during visual emotional elicitation in healthy subjects through linear and nonlinear coupling measures of EEG spectrogram and instantaneous heart rate estimates. To this extent, affective pictures including different combinations of arousal and valence levels, gathered from the International Affective Picture System, were administered to twenty-two healthy subjects. Time-varying maps of cortical activation were obtained through EEG spectral analysis, whereas the associated instantaneous heartbeat dynamics was estimated using inhomogeneous point-process linear models. Brain-Heart linear and nonlinear coupling was estimated through the Maximal Information Coefficient (MIC), considering EEG time-varying spectra and point-process estimates defined in the time and frequency domains. As a proof of concept, we here show preliminary results considering EEG oscillations in the θ band (4-8 Hz). This band, indeed, is known in the literature to be involved in emotional processes. MIC highlighted significant arousal-dependent changes, mediated by the prefrontal cortex interplay especially occurring at intermediate arousing levels. Furthermore, lower and higher arousing elicitations were associated to not significant brain-heart coupling changes in response to pleasant/unpleasant elicitations.

Characteristics of time-activity curves obtained from dynamic 11C-methionine PET in common primary brain tumors.

Science.gov (United States)

Nomura, Yuichi; Asano, Yoshitaka; Shinoda, Jun; Yano, Hirohito; Ikegame, Yuka; Kawasaki, Tomohiro; Nakayama, Noriyuki; Maruyama, Takashi; Muragaki, Yoshihiro; Iwama, Toru

2018-07-01

The aim of this study was to assess whether dynamic PET with 11 C-methionine (MET) (MET-PET) is useful in the diagnosis of brain tumors. One hundred sixty patients with brain tumors (139 gliomas, 9 meningiomas, 4 hemangioblastomas and 8 primary central nervous system lymphomas [PCNSL]) underwent dynamic MET-PET with a 3-dimensional acquisition mode, and the maximum tumor MET-standardized uptake value (MET-SUV) was measured consecutively to construct a time-activity curve (TAC). Furthermore, receiver operating characteristic (ROC) curves were generated from the time-to-peak (TTP) and the slope of the curve in the late phase (SLOPE). The TAC patterns of MET-SUVs (MET-TACs) could be divided into four characteristic types when MET dynamics were analyzed by dividing the MET-TAC into three phases. MET-SUVs were significantly higher in early and late phases in glioblastoma compared to anaplastic astrocytoma, diffuse astrocytoma and the normal frontal cortex (P dynamic MET-PET study could be helpful in the non-invasive discrimination of brain tumor subtypes, in particular gliomas.

Models of neural dynamics in brain information processing - the developments of 'the decade'

International Nuclear Information System (INIS)

Borisyuk, G N; Borisyuk, R M; Kazanovich, Yakov B; Ivanitskii, Genrikh R

2002-01-01

Neural network models are discussed that have been developed during the last decade with the purpose of reproducing spatio-temporal patterns of neural activity in different brain structures. The main goal of the modeling was to test hypotheses of synchronization, temporal and phase relations in brain information processing. The models being considered are those of temporal structure of spike sequences, of neural activity dynamics, and oscillatory models of attention and feature integration. (reviews of topical problems)

Multifractal character of the electronic states in disordered two-dimensional systems

International Nuclear Information System (INIS)

Tit, N.; Schreiber, M.

1994-08-01

The nature of electronic states in disordered two-dimensional (2D) systems is investigated. To this aim, we present our calculations of both density of states and dc-conductivity for square lattices modelling the Anderson Hamiltonian with on-site energies randomly chosen from a box distribution of width W. For weak disorder (W), the eigenfunctions calculated by means of the Lanczos diagonalization algorithm display spatial fluctuations reflecting their (multi)fractal behaviour. For increasing disorder or energy the observed increase of the curdling of the wavefunction reflects its stronger localization. Our dc-conductivity results suggest a critical fractal dimension d * c =1.48±0.05 to discriminate between the exponentially and the power-law localized states. Consequences of the localization on transport properties are also discussed. (author). 30 refs, 10 figs, 1 tab

Development of a realistic, dynamic digital brain phantom for CT perfusion validation

Science.gov (United States)

Divel, Sarah E.; Segars, W. Paul; Christensen, Soren; Wintermark, Max; Lansberg, Maarten G.; Pelc, Norbert J.

2016-03-01

Physicians rely on CT Perfusion (CTP) images and quantitative image data, including cerebral blood flow, cerebral blood volume, and bolus arrival delay, to diagnose and treat stroke patients. However, the quantification of these metrics may vary depending on the computational method used. Therefore, we have developed a dynamic and realistic digital brain phantom upon which CTP scans can be simulated based on a set of ground truth scenarios. Building upon the previously developed 4D extended cardiac-torso (XCAT) phantom containing a highly detailed brain model, this work consisted of expanding the intricate vasculature by semi-automatically segmenting existing MRA data and fitting nonuniform rational B-spline surfaces to the new vessels. Using time attenuation curves input by the user as reference, the contrast enhancement in the vessels changes dynamically. At each time point, the iodine concentration in the arteries and veins is calculated from the curves and the material composition of the blood changes to reflect the expected values. CatSim, a CT system simulator, generates simulated data sets of this dynamic digital phantom which can be further analyzed to validate CTP studies and post-processing methods. The development of this dynamic and realistic digital phantom provides a valuable resource with which current uncertainties and controversies surrounding the quantitative computations generated from CTP data can be examined and resolved.

Protection of cortex by overlying meninges tissue during dynamic indentation of the adolescent brain.

Science.gov (United States)

MacManus, David B; Pierrat, Baptiste; Murphy, Jeremiah G; Gilchrist, Michael D

2017-07-15

Traumatic brain injury (TBI) has become a recent focus of biomedical research with a growing international effort targeting material characterization of brain tissue and simulations of trauma using computer models of the head and brain to try to elucidate the mechanisms and pathogenesis of TBI. The meninges, a collagenous protective tri-layer, which encloses the entire brain and spinal cord has been largely overlooked in these material characterization studies. This has resulted in a lack of accurate constitutive data for the cranial meninges, particularly under dynamic conditions such as those experienced during head impacts. The work presented here addresses this lack of data by providing for the first time, in situ large deformation material properties of the porcine dura-arachnoid mater composite under dynamic indentation. It is demonstrated that this tissue is substantially stiffer (shear modulus, μ=19.10±8.55kPa) and relaxes at a slower rate (τ 1 =0.034±0.008s, τ 2 =0.336±0.077s) than the underlying brain tissue (μ=6.97±2.26kPa, τ 1 =0.021±0.007s, τ 2 =0.199±0.036s), reducing the magnitudes of stress by 250% and 65% for strains that arise during indentation-type deformations in adolescent brains. We present the first mechanical analysis of the protective capacity of the cranial meninges using in situ micro-indentation techniques. Force-relaxation tests are performed on in situ meninges and cortex tissue, under large strain dynamic micro-indentation. A quasi-linear viscoelastic model is used subsequently, providing time-dependent mechanical properties of these neural tissues under loading conditions comparable to what is experienced in TBI. The reported data highlights the large differences in mechanical properties between these two tissues. Finite element simulations of the indentation experiments are also performed to investigate the protective capacity of the meninges. These simulations show that the meninges protect the underlying brain tissue

Multifractal Downscaling of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau.

Science.gov (United States)

Duffaut Espinosa, L A; Posadas, A N; Carbajal, M; Quiroz, R

2017-01-01

In this paper, a multifractal downscaling technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This downscaling procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the downscaling process. The results were validated against on-site measurements of meteorological stations distributed in the area under study.

Dynamic regulation of NMDAR function in the adult brain by the stress hormone corticosterone

Directory of Open Access Journals (Sweden)

Yiu Chung eTse

2012-03-01

Full Text Available Stress and corticosteroids dynamically modulate the expression of synaptic plasticity at glutamatergic synapses in the developed brain. Together with alpha-amino-3-hydroxy-methyl-4-isoxazole propionic acid receptors (AMPAR, N-methyl-D-aspartate receptors (NMDAR are critical mediators of synaptic function and are essential for the induction of many forms of synaptic plasticity. Regulation of NMDAR function by cortisol/corticosterone (CORT may be fundamental to the effects of stress on synaptic plasticity. Recent reports of the efficacy of NMDAR antagonists in treating certain stress-associated psychopathologies further highlight the importance of understanding the regulation of NMDAR function by CORT. Knowledge of how corticosteroids regulate NMDAR function within the adult brain is relatively sparse, perhaps due to a common belief that NMDAR function is relatively stable in the adult brain. We review recent results from our laboratory and others demonstrating dynamic regulation of NMDAR function by CORT in the adult brain. In addition, we consider the issue of how differences in the early life environment may program differential sensitivity to modulation of NMDAR function by CORT and how this may influence synaptic function during stress. Findings from these studies demonstrate that NMDAR function in the adult hippocampus remains sensitive to even brief exposures to CORT and that the capacity for modulation of NMDAR may be programmed, in part, by the early life environment. Modulation of NMDAR function may contribute to dynamic regulation of synaptic plasticity and adaptation in the face of stress, however enhanced NMDAR function may be implicated in mechanisms of stress related psychopathologies including depression.

[Screening based on response surface methodology of multi-fractions traditional Chinese medicine with anti-influenza virus neuraminidase activity: take shuanghuanglian injection as an example].

Science.gov (United States)

Qiu, Ling-Ling; Chen, Long-Hu; Yan, Dan; Zhang, Ping; Tan, Man-Rong; Li, Zheng-Ming; Xiao, Xiao-He

2012-04-01

This study aimed to establish a novel method to screen out the combined components of multi-fractions traditional Chinese medicine (TCM), so that the internal relationship between multi-ingredients could be objectively assessed and the proportioning ratio could be optimized. Taking antiviral effect on neuraminidase activity of influenza virus as the evaluating indicator and using Box-Behnken response surface methodology, the main effective ingredients of Shuanghuanglian injection (SHL) were screened. Meanwhile, the relationship between active ingredients was discussed. Taking SHL as a comparison, the optimum proportioning ratio was predicted. The results indicated that chlorogenic acid, cryptochlorogenic acid, caffeic acid and baicalin have comparatively strong antiviral activity against influenza virus. Moreover, antagonistic action existed between chlorogenic acid and cryptochlorogenic acid, whereas synergistic action between caffeic acid and other components. The optimum proportioning ratio resulted from fitted model is: chlorogenic acid, cryptochlorogenic acid, caffeic acid and baicalin (107 microg x mL(-1) : 279 microg x mL(-1) : 7.99 microg x mL(-1) : 92 microg x mL(-1)). The antiviral activity of the recombined components is stronger than that of SHL, which was consistent with the experiment results (P < 0.05). Box-Behnken response surface methodology has the advantages of general-screening, high-performance and accurate-prediction etc, which is appropriate for screening the combined components of multi-fractions TCM and the optimization of the proportioning ratio. The proposed method can serve as a technological support for the development of modern multi-fractions TCM.

Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

Directory of Open Access Journals (Sweden)

Andreas A. Ioannides

2012-01-01

Full Text Available How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

Mechanical characterization of the P56 mouse brain under large-deformation dynamic indentation

Science.gov (United States)

MacManus, David B.; Pierrat, Baptiste; Murphy, Jeremiah G.; Gilchrist, Michael D.

2016-02-01

The brain is a complex organ made up of many different functional and structural regions consisting of different types of cells such as neurons and glia, as well as complex anatomical geometries. It is hypothesized that the different regions of the brain exhibit significantly different mechanical properties, which may be attributed to the diversity of cells and anisotropy of neuronal fibers within individual brain regions. The regional dynamic mechanical properties of P56 mouse brain tissue in vitro and in situ at velocities of 0.71-4.28 mm/s, up to a deformation of 70 μm are presented and discussed in the context of traumatic brain injury. The experimental data obtained from micro-indentation measurements were fit to three hyperelastic material models using the inverse Finite Element method. The cerebral cortex elicited a stiffer response than the cerebellum, thalamus, and medulla oblongata regions for all velocities. The thalamus was found to be the least sensitive to changes in velocity, and the medulla oblongata was most compliant. The results show that different regions of the mouse brain possess significantly different mechanical properties, and a significant difference also exists between the in vitro and in situ brain.

Spatiotemporal dynamics of large-scale brain activity

Science.gov (United States)

Neuman, Jeremy

Understanding the dynamics of large-scale brain activity is a tough challenge. One reason for this is the presence of an incredible amount of complexity arising from having roughly 100 billion neurons connected via 100 trillion synapses. Because of the extremely high number of degrees of freedom in the nervous system, the question of how the brain manages to properly function and remain stable, yet also be adaptable, must be posed. Neuroscientists have identified many ways the nervous system makes this possible, of which synaptic plasticity is possibly the most notable one. On the other hand, it is vital to understand how the nervous system also loses stability, resulting in neuropathological diseases such as epilepsy, a disease which affects 1% of the population. In the following work, we seek to answer some of these questions from two different perspectives. The first uses mean-field theory applied to neuronal populations, where the variables of interest are the percentages of active excitatory and inhibitory neurons in a network, to consider how the nervous system responds to external stimuli, self-organizes and generates epileptiform activity. The second method uses statistical field theory, in the framework of single neurons on a lattice, to study the concept of criticality, an idea borrowed from physics which posits that in some regime the brain operates in a collectively stable or marginally stable manner. This will be examined in two different neuronal networks with self-organized criticality serving as the overarching theme for the union of both perspectives. One of the biggest problems in neuroscience is the question of to what extent certain details are significant to the functioning of the brain. These details give rise to various spatiotemporal properties that at the smallest of scales explain the interaction of single neurons and synapses and at the largest of scales describe, for example, behaviors and sensations. In what follows, we will shed some

Dynamic correlations between heart and brain rhythm during Autogenic meditation

OpenAIRE

Kim, Dae-Keun; Lee, Kyung-Mi; Kim, Jongwha; Whang, Min-Cheol; Kang, Seung Wan

2013-01-01

This study is aimed to determine significant physiological parameters of brain and heart under meditative state, both in each activities and their dynamic correlations. Electrophysiological changes in response to meditation were explored in 12 healthy volunteers who completed 8 weeks of a basic training course in autogenic meditation. Heart coherence, representing the degree of ordering in oscillation of heart rhythm intervals, increased significantly during meditation. Relative EEG alpha pow...

Multifractality, imperfect scaling and hydrological properties of rainfall time series simulated by continuous universal multifractal and discrete random cascade models

Directory of Open Access Journals (Sweden)

F. Serinaldi

2010-12-01

Full Text Available Discrete multiplicative random cascade (MRC models were extensively studied and applied to disaggregate rainfall data, thanks to their formal simplicity and the small number of involved parameters. Focusing on temporal disaggregation, the rationale of these models is based on multiplying the value assumed by a physical attribute (e.g., rainfall intensity at a given time scale L, by a suitable number b of random weights, to obtain b attribute values corresponding to statistically plausible observations at a smaller L/b time resolution. In the original formulation of the MRC models, the random weights were assumed to be independent and identically distributed. However, for several studies this hypothesis did not appear to be realistic for the observed rainfall series as the distribution of the weights was shown to depend on the space-time scale and rainfall intensity. Since these findings contrast with the scale invariance assumption behind the MRC models and impact on the applicability of these models, it is worth studying their nature. This study explores the possible presence of dependence of the parameters of two discrete MRC models on rainfall intensity and time scale, by analyzing point rainfall series with 5-min time resolution. Taking into account a discrete microcanonical (MC model based on beta distribution and a discrete canonical beta-logstable (BLS, the analysis points out that the relations between the parameters and rainfall intensity across the time scales are detectable and can be modeled by a set of simple functions accounting for the parameter-rainfall intensity relationship, and another set describing the link between the parameters and the time scale. Therefore, MC and BLS models were modified to explicitly account for these relationships and compared with the continuous in scale universal multifractal (CUM model, which is used as a physically based benchmark model. Monte Carlo simulations point out

EXOPLANETARY DETECTION BY MULTIFRACTAL SPECTRAL ANALYSIS

Energy Technology Data Exchange (ETDEWEB)

Agarwal, Sahil; Wettlaufer, John S. [Program in Applied Mathematics, Yale University, New Haven, CT (United States); Sordo, Fabio Del [Department of Astronomy, Yale University, New Haven, CT (United States)

2017-01-01

Owing to technological advances, the number of exoplanets discovered has risen dramatically in the last few years. However, when trying to observe Earth analogs, it is often difficult to test the veracity of detection. We have developed a new approach to the analysis of exoplanetary spectral observations based on temporal multifractality, which identifies timescales that characterize planetary orbital motion around the host star and those that arise from stellar features such as spots. Without fitting stellar models to spectral data, we show how the planetary signal can be robustly detected from noisy data using noise amplitude as a source of information. For observation of transiting planets, combining this method with simple geometry allows us to relate the timescales obtained to primary and secondary eclipse of the exoplanets. Making use of data obtained with ground-based and space-based observations we have tested our approach on HD 189733b. Moreover, we have investigated the use of this technique in measuring planetary orbital motion via Doppler shift detection. Finally, we have analyzed synthetic spectra obtained using the SOAP 2.0 tool, which simulates a stellar spectrum and the influence of the presence of a planet or a spot on that spectrum over one orbital period. We have demonstrated that, so long as the signal-to-noise-ratio ≥ 75, our approach reconstructs the planetary orbital period, as well as the rotation period of a spot on the stellar surface.

Observations of the dynamics of ionic potassium-38 in brain

International Nuclear Information System (INIS)

Duncan, C.C.; Lambrecht, R.M.; Bennett, G.W.; Rescigno, A.; Ment, L.R.

1984-01-01

Short time course potassium dynamics in brain were investigated in the cat. 38 K (T1/2 . 7.6m) was prepared on the BNL 60'' cyclotron by the 40 Ar(p, 3n) 38 K reaction. Positron decay in brain was measured by the limited angle of view positron camera (LAPC). Radioactivity corrected for physical decay following intravenous bolus injection of 38 K showed an initial peak followed by a washout phase with a subsequent monotonic increase. The slope of the washout phase was linearly related to PaCO2 and the subsequent monotonic increase paralleled the arterial concentration of the tracer. No significant changes in 38 K radioactivity were determined following coma producing levels of phenobarbital or seizure producing doses of potassium penicillin as compared to control

Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

Science.gov (United States)

Alonso, Carmelo; Tarquis, Ana M.; Zúñiga, Ignacio; Benito, Rosa M.

2017-03-01

Several studies have shown that vegetation indexes can be used to estimate root zone soil moisture. Earth surface images, obtained by high-resolution satellites, presently give a lot of information on these indexes, based on the data of several wavelengths. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends the possible data archives from the present time to several decades back. Because of this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. In this work, four band images have been considered, which are involved in these vegetation indexes, and were taken by satellites Ikonos-2 and Landsat-7 of the same geographic location, to study the effect of both spatial (pixel size) and radiometric (number of bits coding the image) resolution on these wavelength bands as well as two vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). In order to do so, a multi-fractal analysis of these multi-spectral images was applied in each of these bands and the two indexes derived. The results showed that spatial resolution has a similar scaling effect in the four bands, but radiometric resolution has a larger influence in blue and green bands than in red and near-infrared bands. The NDVI showed a higher sensitivity to the radiometric resolution than EVI. Both were equally affected by the spatial resolution. From both factors, the spatial resolution has a major impact in the multi-fractal spectrum for all the bands and the vegetation indexes. This information should be taken in to account when vegetation indexes based on different satellite sensors are obtained.

Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis

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Estrella Olmedo

2018-03-01

Full Text Available A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS Sea Surface Salinity (SSS fields over the North Atlantic Ocean and the Mediterranean Sea. The debiased non-Bayesian retrieval mitigates the systematic errors produced by the contamination of the land over the sea. In addition, this retrieval improves the coverage by means of multiyear statistical filtering criteria. This methodology allows obtaining SMOS SSS fields in the Mediterranean Sea. However, the resulting SSS suffers from a seasonal (and other time-dependent bias. This time-dependent bias has been characterized by means of specific Empirical Orthogonal Functions (EOFs. Finally, high resolution Sea Surface Temperature (OSTIA SST maps have been used for improving the spatial and temporal resolution of the SMOS SSS maps. The presented methodology practically reduces the error of the SMOS SSS in the Mediterranean Sea by half. As a result, the SSS dynamics described by the new SMOS maps in the Algerian Basin and the Balearic Front agrees with the one described by in situ SSS, and the mesoscale structures described by SMOS in the Alboran Sea and in the Gulf of Lion coincide with the ones described by the high resolution remotely-sensed SST images (AVHRR.

Lethal and sublethal cellular injury in multifraction irradiation

International Nuclear Information System (INIS)

Withers, H.R.

1975-01-01

Work has been carried out on cellular injury in multifraction irradiation of mouse tissues and compared with similar work on human skin reported earlier by Dutreix et al (Eur. J. Cancer.; 9:159 (1973)). In agreement with Dutreix et al it is emphasized that the absolute amount of sublethal injury repaired per fractionation interval (Dsub(r)) is not as important to radiotherapists as the change in the amount repaired (ΔDsub(r)) when the dose-per-fraction is altered. It was found that although there is a critical divergence at low doses, the data for mouse tissues are similar to those previously given for human skin and support the conclusions: (i) That the capacity of many normal cells for accumulating and repairing sublethal radiation injury is probably not greatly different. (ii) That fixed exponents used for fraction number and time in iso-effect formulae are inaproporiate. At low doses-per-fraction, repair of sublethal injury is complete, or nearly so, and hence, additional fractionation of dose does not give appreciable additional sparing, whereas rapidly-regenerating tissues, due to the lengthening of overall time, would continue being spared by repopulation. (U.K.)

Fractals as macroscopic manifestation of squeezed coherent states and brain dynamics

International Nuclear Information System (INIS)

Vitiello, Giuseppe

2012-01-01

Recent results on the relation between self-similarity and squeezed coherent states are presented. I consider fractals which are generated iteratively according to a prescribed recipe, the so-called deterministic fractals. Fractal properties are incorporated in the framework of the theory of the entire analytical functions and deformed coherent states. Conversely, fractal properties of squeezed coherent states are recognized. This sheds some light on the understanding of the dynamical origin of fractals and their global nature emerging from local deformation processes. The self-similarity in brain background activity suggested by laboratory observations of power-law distributions of power spectral densities of electrocorticograms is also discussed and accounted in the frame of the dissipative many-body model of brain.

The brain stem function in patients with brain bladder; Clinical evaluation using dynamic CT scan and auditory brainstem response

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Takahashi, Toshihiro (Yokohama City Univ. (Japan). Faculty of Medicine)

1990-11-01

A syndrome of detrusor-sphincter dyssynergia (DSD) is occasionally found in patients with brain bladder. To evaluate the brain stem function in cases of brain bladder, urodynamic study, dynamic CT scan of the brain stem (DCT) and auditory brainstem response (ABR) were performed. The region of interest of DCT aimed at the posterolateral portion of the pons. The results were analysed in contrast with the presense of DSD in urodynamic study. DCT studies were performed in 13 cases with various brain diseases and 5 control cases without neurological diseases. Abnormal patterns of the time-density curve consisted of low peak value, prolongation of filling time and low rapid washout ratio (low clearance ratio) of the contrast medium. Four of 6 cases with DSD showed at least one of the abnormal patterns of the time-density curve bilaterally. In 7 cases without DSD none showed bilateral abnormality of the curve and in 2 of 7 cases only unilateral abnormality was found. ABR was performed in 8 patients with brain diseases. The interpeak latency of the wave I-V (I-V IPL) was considered to be prolonged in 2 cases with DSD compared to that of 4 without DSD. In 2 cases with DSD who had normal DCT findings, measurement of the I-V IPL was impossible due to abnormal pattern of the ABR wave. Above mentioned results suggests the presence of functional disturbance at the posterolateral portion of the pons in cases of brain bladder with DSD. (author).

Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease.

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Jie, Biao; Liu, Mingxia; Shen, Dinggang

2018-07-01

Functional connectivity networks (FCNs) using resting-state functional magnetic resonance imaging (rs-fMRI) have been applied to the analysis and diagnosis of brain disease, such as Alzheimer's disease (AD) and its prodrome, i.e., mild cognitive impairment (MCI). Different from conventional studies focusing on static descriptions on functional connectivity (FC) between brain regions in rs-fMRI, recent studies have resorted to dynamic connectivity networks (DCNs) to characterize the dynamic changes of FC, since dynamic changes of FC may indicate changes in macroscopic neural activity patterns in cognitive and behavioral aspects. However, most of the existing studies only investigate the temporal properties of DCNs (e.g., temporal variability of FC between specific brain regions), ignoring the important spatial properties of the network (e.g., spatial variability of FC associated with a specific brain region). Also, emerging evidence on FCNs has suggested that, besides temporal variability, there is significant spatial variability of activity foci over time. Hence, integrating both temporal and spatial properties of DCNs can intuitively promote the performance of connectivity-network-based learning methods. In this paper, we first define a new measure to characterize the spatial variability of DCNs, and then propose a novel learning framework to integrate both temporal and spatial variabilities of DCNs for automatic brain disease diagnosis. Specifically, we first construct DCNs from the rs-fMRI time series at successive non-overlapping time windows. Then, we characterize the spatial variability of a specific brain region by computing the correlation of functional sequences (i.e., the changing profile of FC between a pair of brain regions within all time windows) associated with this region. Furthermore, we extract both temporal variabilities and spatial variabilities from DCNs as features, and integrate them for classification by using manifold regularized multi

Brain-computer interface analysis of a dynamic visuo-motor task.

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Logar, Vito; Belič, Aleš

2011-01-01

The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could

"Shared Destiny": The Dynamics of Relationships in Families of Patients With Brain Injury.

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Segev, Einav; Levinger, Miriam; Hochman, Yael

2018-01-01

This qualitative research focused on the relationships between family members of patients with acquired brain injury (ABI). The aim was to explore the dynamics between caregivers of the family member with a brain injury during rehabilitation hospitalization, and the relationships between them and the rest of the extended family. Twenty semistructured interviews were conducted with family members. In each family, the spouse of the patient and another family member involved in caregiving were interviewed. The importance of the relationships between family members during rehabilitation hospitalization justifies the examination undertaken in this research. Findings point at the change that took place in the relationships between family members because of the need to cope with a relative's injury. It is possible that direct intervention in the dynamics of the relationship, especially between the family of origin and the nuclear family of the injured person, can benefit extended families in coping with the crisis.

Characterizing Motif Dynamics of Electric Brain Activity Using Symbolic Analysis

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Massimiliano Zanin

2014-10-01

Full Text Available Motifs are small recurring circuits of interactions which constitute the backbone of networked systems. Characterizing motif dynamics is therefore key to understanding the functioning of such systems. Here we propose a method to define and quantify the temporal variability and time scales of electroencephalogram (EEG motifs of resting brain activity. Given a triplet of EEG sensors, links between them are calculated by means of linear correlation; each pattern of links (i.e., each motif is then associated to a symbol, and its appearance frequency is analyzed by means of Shannon entropy. Our results show that each motif becomes observable with different coupling thresholds and evolves at its own time scale, with fronto-temporal sensors emerging at high thresholds and changing at fast time scales, and parietal ones at low thresholds and changing at slower rates. Finally, while motif dynamics differed across individuals, for each subject, it showed robustness across experimental conditions, indicating that it could represent an individual dynamical signature.

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series.

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Thompson, William Hedley; Fransson, Peter

2016-12-01

Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box-Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed.

Clustering structures of large proteins using multifractal analyses based on a 6-letter model and hydrophobicity scale of amino acids

International Nuclear Information System (INIS)

Yang Jianyi; Yu Zuguo; Anh, Vo

2009-01-01

The Schneider and Wrede hydrophobicity scale of amino acids and the 6-letter model of protein are proposed to study the relationship between the primary structure and the secondary structural classification of proteins. Two kinds of multifractal analyses are performed on the two measures obtained from these two kinds of data on large proteins. Nine parameters from the multifractal analyses are considered to construct the parameter spaces. Each protein is represented by one point in these spaces. A procedure is proposed to separate large proteins in the α, β, α + β and α/β structural classes in these parameter spaces. Fisher's linear discriminant algorithm is used to assess our clustering accuracy on the 49 selected large proteins. Numerical results indicate that the discriminant accuracies are satisfactory. In particular, they reach 100.00% and 84.21% in separating the α proteins from the {β, α + β, α/β} proteins in a parameter space; 92.86% and 86.96% in separating the β proteins from the {α + β, α/β} proteins in another parameter space; 91.67% and 83.33% in separating the α/β proteins from the α + β proteins in the last parameter space.

Complex Dynamics in Physiological Systems: From Heart to Brain

CERN Document Server

Dana, Syamal K; Kurths, Jürgen

2009-01-01

Nonlinear dynamics has become an important field of research in recent years in many areas of the natural sciences. In particular, it has potential applications in biology and medicine; nonlinear data analysis has helped to detect the progress of cardiac disease, physiological disorders, for example episodes of epilepsy, and others. This book focuses on the current trends of research concerning the prediction of sudden cardiac death and the onset of epileptic seizures, using the nonlinear analysis based on ECG and EEG data. Topics covered include the analysis of cardiac models and neural models. The book is a collection of recent research papers by leading physicists, mathematicians, cardiologists and neurobiologists who are actively involved in using the concepts of nonlinear dynamics to explore the functional behaviours of heart and brain under normal and pathological conditions. This collection is intended for students in physics, mathematics and medical sciences, and researchers in interdisciplinary areas...

REVIEWS OF TOPICAL PROBLEMS: Nonlinear dynamics of the brain: emotion and cognition

Science.gov (United States)

Rabinovich, Mikhail I.; Muezzinoglu, M. K.

2010-07-01

Experimental investigations of neural system functioning and brain activity are standardly based on the assumption that perceptions, emotions, and cognitive functions can be understood by analyzing steady-state neural processes and static tomographic snapshots. The new approaches discussed in this review are based on the analysis of transient processes and metastable states. Transient dynamics is characterized by two basic properties, structural stability and information sensitivity. The ideas and methods that we discuss provide an explanation for the occurrence of and successive transitions between metastable states observed in experiments, and offer new approaches to behavior analysis. Models of the emotional and cognitive functions of the brain are suggested. The mathematical object that represents the observed transient brain processes in the phase space of the model is a structurally stable heteroclinic channel. The possibility of using the suggested models to construct a quantitative theory of some emotional and cognitive functions is illustrated.

Study of the Fractal and Multifractal Scaling Intervening in the Description of Fracture Experimental Data Reported by the Classical Work: Nature 308, 721–722(1984

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Liliana Violeta Constantin

2012-01-01

Full Text Available Starting from the experimental data referring to the main parameters of the fracture surfaces of some 300-grade maraging steel reported by the classical work published in Nature 308, 721–722(1984, this work studied (a the multifractal scaling by the main parameters of the slit islands of fracture surfaces produced by a uniaxial tensile loading and (b the dependence of the impact energy to fracture and of the fractal dimensional increment on the temperature of the studied steels heat treatment, for the fracture surfaces produced by Charpy impact. The obtained results were analyzed, pointing out the spectral (size distribution of the found slit islands in the frame of some specific clusters (fractal components of the multifractal scaling of representative points of the logarithms of the slit islands areas and perimeters, respectively.

Nonlinear dynamics and chaotic phenomena an introduction

CERN Document Server

Shivamoggi, Bhimsen K

2014-01-01

This book starts with a discussion of nonlinear ordinary differential equations, bifurcation theory and Hamiltonian dynamics. It then embarks on a systematic discussion of the traditional topics of modern nonlinear dynamics  -- integrable systems, Poincaré maps, chaos, fractals and strange attractors. The Baker’s transformation, the logistic map and Lorenz system are discussed in detail in view of their central place in the subject. There is a detailed discussion of solitons centered around the Korteweg-deVries equation in view of its central place in integrable systems. Then, there is a discussion of the Painlevé property of nonlinear differential equations which seems to provide a test of integrability. Finally, there is a detailed discussion of the application of fractals and multi-fractals to fully-developed turbulence -- a problem whose understanding has been considerably enriched by the application of the concepts and methods of modern nonlinear dynamics. On the application side, there is a special...

Linear and Nonlinear Analysis of Brain Dynamics in Children with Cerebral Palsy

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Sajedi, Firoozeh; Ahmadlou, Mehran; Vameghi, Roshanak; Gharib, Masoud; Hemmati, Sahel

2013-01-01

This study was carried out to determine linear and nonlinear changes of brain dynamics and their relationships with the motor dysfunctions in CP children. For this purpose power of EEG frequency bands (as a linear analysis) and EEG fractality (as a nonlinear analysis) were computed in eyes-closed resting state and statistically compared between 26…

Symmetry Breaking in Space-Time Hierarchies Shapes Brain Dynamics and Behavior.

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Pillai, Ajay S; Jirsa, Viktor K

2017-06-07

In order to maintain brain function, neural activity needs to be tightly coordinated within the brain network. How this coordination is achieved and related to behavior is largely unknown. It has been previously argued that the study of the link between brain and behavior is impossible without a guiding vision. Here we propose behavioral-level concepts and mechanisms embodied as structured flows on manifold (SFM) that provide a formal description of behavior as a low-dimensional process emerging from a network's dynamics dependent on the symmetry and invariance properties of the network connectivity. Specifically, we demonstrate that the symmetry breaking of network connectivity constitutes a timescale hierarchy resulting in the emergence of an attractive functional subspace. We show that behavior emerges when appropriate conditions imposed upon the couplings are satisfied, justifying the conductance-based nature of synaptic couplings. Our concepts propose design principles for networks predicting how behavior and task rules are represented in real neural circuits and open new avenues for the analyses of neural data. Copyright © 2017 Elsevier Inc. All rights reserved.

A study on Improvisation in a Musical performance using Multifractal Detrended Cross Correlation Analysis

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Sanyal, Shankha; Banerjee, Archi; Patranabis, Anirban; Banerjee, Kaushik; Sengupta, Ranjan; Ghosh, Dipak

2016-11-01

MFDFA (the most rigorous technique to assess multifractality) was performed on four Hindustani music samples played on same 'raga' sung by the same performer. Each music sample was divided into six parts and 'multifractal spectral width' was determined for each part corresponding to the four samples. The results obtained reveal that different parts of all the four sound signals possess spectral width of widely varying values. This gives a cue of the so called 'musical improvisation' in all music samples, keeping in mind they belong to the bandish part of the same raga. Formal compositions in Hindustani raga are juxtaposed with the improvised portions, where an artist manoeuvers his/her own creativity to bring out a mood that is specific for that particular performance, which is known as 'improvisation'. Further, this observation hints at the association of different emotions even in the same bandish of the same raga performed by the same artist, this interesting observation cannot be revealed unless rigorous non-linear technique explores the nature of musical structure. In the second part, we applied MFDXA technique to explore more in-depth about 'improvisation' and association with emotion. This technique is applied to find the degree of cross-correlation (γx) between the different parts of the samples. Pronounced correlation has been observed in the middle parts of the all the four samples evident from higher values of γx ​whereas the other parts show weak correlation. This gets further support from the values of spectral width from different parts of the sample - width of those parts is significantly different from other parts. This observation is extremely new both in respect of musical structure of so called improvisation and associated emotion. The importance of this study in application area of cognitive music therapy is immense.

Common resting brain dynamics indicate a possible mechanism underlying zolpidem response in severe brain injury

Science.gov (United States)

Williams, Shawniqua T; Conte, Mary M; Goldfine, Andrew M; Noirhomme, Quentin; Gosseries, Olivia; Thonnard, Marie; Beattie, Bradley; Hersh, Jennifer; Katz, Douglas I; Victor, Jonathan D; Laureys, Steven; Schiff, Nicholas D

2013-01-01

Zolpidem produces paradoxical recovery of speech, cognitive and motor functions in select subjects with severe brain injury but underlying mechanisms remain unknown. In three diverse patients with known zolpidem responses we identify a distinctive pattern of EEG dynamics that suggests a mechanistic model. In the absence of zolpidem, all subjects show a strong low frequency oscillatory peak ∼6–10 Hz in the EEG power spectrum most prominent over frontocentral regions and with high coherence (∼0.7–0.8) within and between hemispheres. Zolpidem administration sharply reduces EEG power and coherence at these low frequencies. The ∼6–10 Hz activity is proposed to arise from intrinsic membrane properties of pyramidal neurons that are passively entrained across the cortex by locally-generated spontaneous activity. Activation by zolpidem is proposed to arise from a combination of initial direct drug effects on cortical, striatal, and thalamic populations and further activation of underactive brain regions induced by restoration of cognitively-mediated behaviors. DOI: http://dx.doi.org/10.7554/eLife.01157.001 PMID:24252875

Multifractality and autoregressive processes of dry spell lengths in Europe: an approach to their complexity and predictability

Science.gov (United States)

Lana, X.; Burgueño, A.; Serra, C.; Martínez, M. D.

2017-01-01

Dry spell lengths, DSL, defined as the number of consecutive days with daily rain amounts below a given threshold, may provide relevant information about drought regimes. Taking advantage of a daily pluviometric database covering a great extension of Europe, a detailed analysis of the multifractality of the dry spell regimes is achieved. At the same time, an autoregressive process is applied with the aim of predicting DSL. A set of parameters, namely Hurst exponent, H, estimated from multifractal spectrum, f( α), critical Hölder exponent, α 0, for which f( α) reaches its maximum value, spectral width, W, and spectral asymmetry, B, permits a first clustering of European rain gauges in terms of the complexity of their DSL series. This set of parameters also allows distinguishing between time series describing fine- or smooth-structure of the DSL regime by using the complexity index, CI. Results of previous monofractal analyses also permits establishing comparisons between smooth-structures, relatively low correlation dimensions, notable predictive instability and anti-persistence of DSL for European areas, sometimes submitted to long droughts. Relationships are also found between the CI and the mean absolute deviation, MAD, and the optimum autoregressive order, OAO, of an ARIMA( p, d,0) autoregressive process applied to the DSL series. The detailed analysis of the discrepancies between empiric and predicted DSL underlines the uncertainty over predictability of long DSL, particularly for the Mediterranean region.

Coherent changes of multifractal properties of continuous acoustic emission at failure of heterogeneous materials

Science.gov (United States)

Panteleev, Ivan; Bayandin, Yuriy; Naimark, Oleg

2017-12-01

This work performs a correlation analysis of the statistical properties of continuous acoustic emission recorded in different parts of marble and fiberglass laminate samples under quasi-static deformation. A spectral coherent measure of time series, which is a generalization of the squared coherence spectrum on a multidimensional series, was chosen. The spectral coherent measure was estimated in a sliding time window for two parameters of the acoustic emission multifractal singularity spectrum: the spectrum width and the generalized Hurst exponent realizing the maximum of the singularity spectrum. It is shown that the preparation of the macrofracture focus is accompanied by the synchronization (coherent behavior) of the statistical properties of acoustic emission in allocated frequency intervals.

Dynamic changes in DNA demethylation in the tree shrew (Tupaia belangeri chinensis) brain during postnatal development and aging.

Science.gov (United States)

Wei, Shu; Hua, Hai-Rong; Chen, Qian-Quan; Zhang, Ying; Chen, Fei; Li, Shu-Qing; Li, Fan; Li, Jia-Li

2017-03-18

Brain development and aging are associated with alterations in multiple epigenetic systems, including DNA methylation and demethylation patterns. Here, we observed that the levels of the 5-hydroxymethylcytosine (5hmC) ten-eleven translocation (TET) enzyme-mediated active DNA demethylation products were dynamically changed and involved in postnatal brain development and aging in tree shrews ( Tupaia belangeri chinensis ). The levels of 5hmC in multiple anatomic structures showed a gradual increase throughout postnatal development, whereas a significant decrease in 5hmC was found in several brain regions in aged tree shrews, including in the prefrontal cortex and hippocampus, but not the cerebellum. Active changes in Tet mRNA levels indicated that TET2 and TET3 predominantly contributed to the changes in 5hmC levels. Our findings provide new insight into the dynamic changes in 5hmC levels in tree shrew brains during postnatal development and aging processes.

Cortical Network Dynamics of Perceptual Decision-Making in the Human Brain

Directory of Open Access Journals (Sweden)

Markus eSiegel

2011-02-01

Full Text Available Goal-directed behavior requires the flexible transformation of sensory evidence about our environment into motor actions. Studies of perceptual decision-making have shown that this transformation is distributed across several widely separated brain regions. Yet, little is known about how decision-making emerges from the dynamic interactions among these regions. Here, we review a series of studies, in which we characterized the cortical network interactions underlying a perceptual decision process in the human brain. We used magnetoencephalography (MEG to measure the large-scale cortical population dynamics underlying each of the sub-processes involved in this decision: the encoding of sensory evidence and action plan, the mapping between the two, and the attentional selection of task-relevant evidence. We found that these sub-processes are mediated by neuronal oscillations within specific frequency ranges. Localized gamma-band oscillations in sensory and motor cortices reflect the encoding of the sensory evidence and motor plan. Large-scale oscillations across widespread cortical networks mediate the integrative processes connecting these local networks: Gamma- and beta-band oscillations across frontal, parietal and sensory cortices serve the selection of relevant sensory evidence and its flexible mapping onto action plans. In sum, our results suggest that perceptual decisions are mediated by oscillatory interactions within overlapping local and large-scale cortical networks.

Patient Specific Characteristic of Brain Dynamic in Interpretation of Long Term EEG Analysis

Czech Academy of Sciences Publication Activity Database

Komárek, V.; Paluš, Milan; Hrnčíř, Z.

2004-01-01

Roč. 45, Suppl. 3 (2004), s. 51 ISSN 0013-9580. [European Congress on Epileptology /6./. 30.05.2004-03.06.2004, Vienna] R&D Projects: GA MŠk ME 701 Institutional research plan: CEZ:AV0Z1030915 Keywords : brain dynamic * long term EEG analysis Subject RIV: FH - Neurology

The aesthetic experience as a characteristic feature of brain dynamics

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Giuseppe Vitiello

2015-05-01

Full Text Available The brain constructs within itself an understanding of its surround which constitutes its own world. This is described as its Double in the frame of the dissipative quantum model of brain, where the perception-action arc in the Merleau-Ponty’s phenomenology of perception finds its formal description. In the dialog with the Double, the continuous attempt to reach the equilibrium shows that the real goal pursued by the brain activity is the aesthetical experience, the most harmonious “to-be-in-the-world” reached through reciprocal actions, the aesthetical dimension characterized by the “pleasure” of the perception. Aesthetical pleasure unavoidably implies disclosure, to manifest “signs”, artistic communication. An interpersonal, collective level of consciousness then arises, a larger stage where the actors are mutually dependent. The coherent structure of the brain background state manifests itself in the auto-similarity properties of fractal structures. These are observed to occur also in a large number of natural phenomena and systems. The conception of Nature divided in separated domains is replaced by the vision of Nature unified by laws of form implied by the underlying quantum dynamics of the coherent vacuum, an integrated ecological vision.

Models of neural dynamics in brain information processing - the developments of 'the decade'

Energy Technology Data Exchange (ETDEWEB)

Borisyuk, G N; Borisyuk, R M; Kazanovich, Yakov B [Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Pushchino, Moscow region (Russian Federation); Ivanitskii, Genrikh R [Institute for Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, Moscow region (Russian Federation)

2002-10-31

Neural network models are discussed that have been developed during the last decade with the purpose of reproducing spatio-temporal patterns of neural activity in different brain structures. The main goal of the modeling was to test hypotheses of synchronization, temporal and phase relations in brain information processing. The models being considered are those of temporal structure of spike sequences, of neural activity dynamics, and oscillatory models of attention and feature integration. (reviews of topical problems)

Global brain dynamics during social exclusion predict subsequent behavioral conformity.

Science.gov (United States)

Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O'Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

2018-02-01

Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences.

Longitudinal Dynamics of 3-Dimensional Components of Selfhood After Severe Traumatic Brain Injury: A qEEG Case Study.

Science.gov (United States)

Fingelkurts, Andrew A; Fingelkurts, Alexander A

2017-09-01

In this report, we describe the case of a patient who sustained extremely severe traumatic brain damage with diffuse axonal injury in a traffic accident and whose recovery was monitored during 6 years. Specifically, we were interested in the recovery dynamics of 3-dimensional components of selfhood (a 3-dimensional construct model for the complex experiential selfhood has been recently proposed based on the empirical findings on the functional-topographical specialization of 3 operational modules of brain functional network responsible for the self-consciousness processing) derived from the electroencephalographic (EEG) signal. The analysis revealed progressive (though not monotonous) restoration of EEG functional connectivity of 3 modules of brain functional network responsible for the self-consciousness processing, which was also paralleled by the clinically significant functional recovery. We propose that restoration of normal integrity of the operational modules of the self-referential brain network may underlie the positive dynamics of 3 aspects of selfhood and provide a neurobiological mechanism for their recovery. The results are discussed in the context of recent experimental studies that support this inference. Studies of ongoing recovery after severe brain injury utilizing knowledge about each separate aspect of complex selfhood will likely help to develop more efficient and targeted rehabilitation programs for patients with brain trauma.

Lagged and instantaneous dynamical influences related to brain structural connectivity

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Carmen eAlonso Montes

2015-07-01

Full Text Available Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Indeed, different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI provides a structural connectivity (SC coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI accounts for the variations in the blood-oxygenation-level-dependent T2* signal, providing functional connectivity (FC. Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience.To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas to be compared with FC connectivity matrices obtained by three different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM, linear correlations (C and partial correlations (PC. We also considered the possibility of using lagged correlations in time series; in particular, we compared a lagged version of eSEM and Granger causality (GC. Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C, nor PC provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.

Altered oscillatory brain dynamics after repeated traumatic stress

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Ruf Martina

2007-10-01

Full Text Available Abstract Background Repeated traumatic experiences, e.g. torture and war, lead to functional and structural cerebral changes, which should be detectable in cortical dynamics. Abnormal slow waves produced within circumscribed brain regions during a resting state have been associated with lesioned neural circuitry in neurological disorders and more recently also in mental illness. Methods Using magnetoencephalographic (MEG-based source imaging, we mapped abnormal distributions of generators of slow waves in 97 survivors of torture and war with posttraumatic stress disorder (PTSD in comparison to 97 controls. Results PTSD patients showed elevated production of focally generated slow waves (1–4 Hz, particularly in left temporal brain regions, with peak activities in the region of the insula. Furthermore, differential slow wave activity in right frontal areas was found in PTSD patients compared to controls. Conclusion The insula, as a site of multimodal convergence, could play a key role in understanding the pathophysiology of PTSD, possibly accounting for what has been called posttraumatic alexithymia, i.e., reduced ability to identify, express and regulate emotional responses to reminders of traumatic events. Differences in activity in right frontal areas may indicate a dysfunctional PFC, which may lead to diminished extinction of conditioned fear and reduced inhibition of the amygdala.

Multifractal analysis of radar rainfall fields over the area of Rome

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G. Calenda

2005-01-01

Full Text Available A scale-invariance analysis of space and time rainfall events monitored by meteorological radar over the area of Rome (Italy is proposed. The study of the scale-invariance properties of intense precipitation storms, particularly important in flood forecast and risk mitigation, allows to transfer rainfall information from the large scale predictive meteorological models to the small scale hydrological rainfall-runoff models. Precipitation events are monitored using data collected by the polarimetric Doppler radar Polar 55C (ISAC-CNR, located 15 km Southeast from downtown. The meteorological radar provides the estimates of rainfall intensity over an area of about 10 000 km2 at a resolution of 2×2 km2 in space and 5 min in time. Many precipitation events have been observed from autumn 2001 up to now. A scale-invariance analysis is performed on some of these events with the aim at exploring the multifractal properties and at understanding their dependence on the meteorological large-scale conditions.

Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain.

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Tauste Campo, Adrià; Principe, Alessandro; Ley, Miguel; Rocamora, Rodrigo; Deco, Gustavo

2018-04-01

Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide crossvalidated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial electroencephalography (iEEG) recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable ("degenerate"), and this phase is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to be particularly associated with the activity of the resected regions in patients with validated postsurgical outcome. Our approach characterizes preseizure network dynamics as a cascade of 2 sequential events providing new insights into seizure prediction and control.

Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.

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Liu, Yu-Ting; Lin, Yang-Yin; Wu, Shang-Lin; Chuang, Chun-Hsiang; Lin, Chin-Teng

2016-02-01

This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

Brain dynamics of post-task resting state are influenced by expertise: Insights from baseball players.

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Muraskin, Jordan; Dodhia, Sonam; Lieberman, Gregory; Garcia, Javier O; Verstynen, Timothy; Vettel, Jean M; Sherwin, Jason; Sajda, Paul

2016-12-01

Post-task resting state dynamics can be viewed as a task-driven state where behavioral performance is improved through endogenous, non-explicit learning. Tasks that have intrinsic value for individuals are hypothesized to produce post-task resting state dynamics that promote learning. We measured simultaneous fMRI/EEG and DTI in Division-1 collegiate baseball players and compared to a group of controls, examining differences in both functional and structural connectivity. Participants performed a surrogate baseball pitch Go/No-Go task before a resting state scan, and we compared post-task resting state connectivity using a seed-based analysis from the supplementary motor area (SMA), an area whose activity discriminated players and controls in our previous results using this task. Although both groups were equally trained on the task, the experts showed differential activity in their post-task resting state consistent with motor learning. Specifically, we found (1) differences in bilateral SMA-L Insula functional connectivity between experts and controls that may reflect group differences in motor learning, (2) differences in BOLD-alpha oscillation correlations between groups suggests variability in modulatory attention in the post-task state, and (3) group differences between BOLD-beta oscillations that may indicate cognitive processing of motor inhibition. Structural connectivity analysis identified group differences in portions of the functionally derived network, suggesting that functional differences may also partially arise from variability in the underlying white matter pathways. Generally, we find that brain dynamics in the post-task resting state differ as a function of subject expertise and potentially result from differences in both functional and structural connectivity. Hum Brain Mapp 37:4454-4471, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals

Nonlinear dynamics of laser systems with elements of a chaos: Advanced computational code

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Buyadzhi, V. V.; Glushkov, A. V.; Khetselius, O. Yu; Kuznetsova, A. A.; Buyadzhi, A. A.; Prepelitsa, G. P.; Ternovsky, V. B.

2017-10-01

A general, uniform chaos-geometric computational approach to analysis, modelling and prediction of the non-linear dynamics of quantum and laser systems (laser and quantum generators system etc) with elements of the deterministic chaos is briefly presented. The approach is based on using the advanced generalized techniques such as the wavelet analysis, multi-fractal formalism, mutual information approach, correlation integral analysis, false nearest neighbour algorithm, the Lyapunov’s exponents analysis, and surrogate data method, prediction models etc There are firstly presented the numerical data on the topological and dynamical invariants (in particular, the correlation, embedding, Kaplan-York dimensions, the Lyapunov’s exponents, Kolmogorov’s entropy and other parameters) for laser system (the semiconductor GaAs/GaAlAs laser with a retarded feedback) dynamics in a chaotic and hyperchaotic regimes.

Unveiling the development of intracranial injury using dynamic brain EIT: an evaluation of current reconstruction algorithms.

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Li, Haoting; Chen, Rongqing; Xu, Canhua; Liu, Benyuan; Tang, Mengxing; Yang, Lin; Dong, Xiuzhen; Fu, Feng

2017-08-21

Dynamic brain electrical impedance tomography (EIT) is a promising technique for continuously monitoring the development of cerebral injury. While there are many reconstruction algorithms available for brain EIT, there is still a lack of study to compare their performance in the context of dynamic brain monitoring. To address this problem, we develop a framework for evaluating different current algorithms with their ability to correctly identify small intracranial conductivity changes. Firstly, a simulation 3D head phantom with realistic layered structure and impedance distribution is developed. Next several reconstructing algorithms, such as back projection (BP), damped least-square (DLS), Bayesian, split Bregman (SB) and GREIT are introduced. We investigate their temporal response, noise performance, location and shape error with respect to different noise levels on the simulation phantom. The results show that the SB algorithm demonstrates superior performance in reducing image error. To further improve the location accuracy, we optimize SB by incorporating the brain structure-based conductivity distribution priors, in which differences of the conductivities between different brain tissues and the inhomogeneous conductivity distribution of the skull are considered. We compare this novel algorithm (called SB-IBCD) with SB and DLS using anatomically correct head shaped phantoms with spatial varying skull conductivity. Main results and Significance: The results showed that SB-IBCD is the most effective in unveiling small intracranial conductivity changes, where it can reduce the image error by an average of 30.0% compared to DLS.

Linking brain, mind and behavior.

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Makeig, Scott; Gramann, Klaus; Jung, Tzyy-Ping; Sejnowski, Terrence J; Poizner, Howard

2009-08-01

Cortical brain areas and dynamics evolved to organize motor behavior in our three-dimensional environment also support more general human cognitive processes. Yet traditional brain imaging paradigms typically allow and record only minimal participant behavior, then reduce the recorded data to single map features of averaged responses. To more fully investigate the complex links between distributed brain dynamics and motivated natural behavior, we propose the development of wearable mobile brain/body imaging (MoBI) systems that continuously capture the wearer's high-density electrical brain and muscle signals, three-dimensional body movements, audiovisual scene and point of regard, plus new data-driven analysis methods to model their interrelationships. The new imaging modality should allow new insights into how spatially distributed brain dynamics support natural human cognition and agency.

The Application of Fractal and Multifractal Theory in Hydraulic-Flow-Unit Characterization and Permeability Estimation

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Chen, X.; Yao, G.; Cai, J.

2017-12-01

Pore structure characteristics are important factors in influencing the fluid transport behavior of porous media, such as pore-throat ratio, pore connectivity and size distribution, moreover, wettability. To accurately characterize the diversity of pore structure among HFUs, five samples selected from different HFUs (porosities are approximately equal, however permeability varies widely) were chosen to conduct micro-computerized tomography test to acquire direct 3D images of pore geometries and to perform mercury injection experiments to obtain the pore volume-radii distribution. To characterize complex and high nonlinear pore structure of all samples, three classic fractal geometry models were applied. Results showed that each HFU has similar box-counting fractal dimension and generalized fractal dimension in the number-area model, but there are significant differences in multifractal spectrums. In the radius-volume model, there are three obvious linear segments, corresponding to three fractal dimension values, and the middle one is proved as the actual fractal dimension according to the maximum radius. In the number-radius model, the spherical-pore size distribution extracted by maximum ball algorithm exist a decrease in the number of small pores compared with the fractal power rate rather than the traditional linear law. Among the three models, only multifractal analysis can classify the HFUs accurately. Additionally, due to the tightness and low-permeability in reservoir rocks, connate water film existing in the inner surface of pore channels commonly forms bound water. The conventional model which is known as Yu-Cheng's model has been proved to be typically not applicable. Considering the effect of irreducible water saturation, an improved fractal permeability model was also deduced theoretically. The comparison results showed that the improved model can be applied to calculate permeability directly and accurately in such unconventional rocks.

Chaos game representation of functional protein sequences, and simulation and multifractal analysis of induced measures

International Nuclear Information System (INIS)

Zu-Guo, Yu; Qian-Jun, Xiao; Long, Shi; Jun-Wu, Yu; Anh, Vo

2010-01-01

Investigating the biological function of proteins is a key aspect of protein studies. Bioinformatic methods become important for studying the biological function of proteins. In this paper, we first give the chaos game representation (CGR) of randomly-linked functional protein sequences, then propose the use of the recurrent iterated function systems (RIFS) in fractal theory to simulate the measure based on their chaos game representations. This method helps to extract some features of functional protein sequences, and furthermore the biological functions of these proteins. Then multifractal analysis of the measures based on the CGRs of randomly-linked functional protein sequences are performed. We find that the CGRs have clear fractal patterns. The numerical results show that the RIFS can simulate the measure based on the CGR very well. The relative standard error and the estimated probability matrix in the RIFS do not depend on the order to link the functional protein sequences. The estimated probability matrices in the RIFS with different biological functions are evidently different. Hence the estimated probability matrices in the RIFS can be used to characterise the difference among linked functional protein sequences with different biological functions. From the values of the D q curves, one sees that these functional protein sequences are not completely random. The D q of all linked functional proteins studied are multifractal-like and sufficiently smooth for the C q (analogous to specific heat) curves to be meaningful. Furthermore, the D q curves of the measure μ based on their CGRs for different orders to link the functional protein sequences are almost identical if q ≥ 0. Finally, the C q curves of all linked functional proteins resemble a classical phase transition at a critical point. (cross-disciplinary physics and related areas of science and technology)

Short-term mechanisms influencing volumetric brain dynamics

NARCIS (Netherlands)

Dieleman, Nikki; Koek, Huiberdina L.; Hendrikse, Jeroen

2017-01-01

With the use of magnetic resonance imaging (MRI) and brain analysis tools, it has become possible to measure brain volume changes up to around 0.5%. Besides long-term brain changes caused by atrophy in aging or neurodegenerative disease, short-term mechanisms that influence brain volume may exist.

Dynamic changes in water ADC, energy metabolism, extracellular space volume and tortuosity in neonatal rat brain during irreversible ischemia

NARCIS (Netherlands)

Toorn, van der A.; Syková, E.; Dijkhuizen, R.M.; Voríšek, I.; Vargová, L.; Skobisová, E.; Lookeren Campagne, van M.; Reese, T.; Nicolaij, K.

1996-01-01

To obtain a better understanding of the mechanisms underlying early changes in the brain water apparent diffusion coefficient (ADC) observed in cerebral ischemia, dynamic changes in the ADC of water and in the energy status were measured at postnatal day 8 or 9 in neonatal rat brains after cardiac

Accurate determination of blood–brain barrier permeability using dynamic contrast-enhanced T1-weighted MRI

DEFF Research Database (Denmark)

Cramer, Stig P; Larsson, Henrik B W

2014-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used to estimate permeability in situations with subtle blood-brain barrier (BBB) leakage. However, the method's ability to differentiate such low values from zero is unknown, and no consensus exists on optimal selection...

Temporal scaling and spatial statistical analyses of groundwater level fluctuations

Science.gov (United States)

Sun, H.; Yuan, L., Sr.; Zhang, Y.

2017-12-01

Natural dynamics such as groundwater level fluctuations can exhibit multifractionality and/or multifractality due likely to multi-scale aquifer heterogeneity and controlling factors, whose statistics requires efficient quantification methods. This study explores multifractionality and non-Gaussian properties in groundwater dynamics expressed by time series of daily level fluctuation at three wells located in the lower Mississippi valley, after removing the seasonal cycle in the temporal scaling and spatial statistical analysis. First, using the time-scale multifractional analysis, a systematic statistical method is developed to analyze groundwater level fluctuations quantified by the time-scale local Hurst exponent (TS-LHE). Results show that the TS-LHE does not remain constant, implying the fractal-scaling behavior changing with time and location. Hence, we can distinguish the potentially location-dependent scaling feature, which may characterize the hydrology dynamic system. Second, spatial statistical analysis shows that the increment of groundwater level fluctuations exhibits a heavy tailed, non-Gaussian distribution, which can be better quantified by a Lévy stable distribution. Monte Carlo simulations of the fluctuation process also show that the linear fractional stable motion model can well depict the transient dynamics (i.e., fractal non-Gaussian property) of groundwater level, while fractional Brownian motion is inadequate to describe natural processes with anomalous dynamics. Analysis of temporal scaling and spatial statistics therefore may provide useful information and quantification to understand further the nature of complex dynamics in hydrology.

Recognizing brain motor imagery activities by identifying chaos properties of oxy-hemoglobin dynamics time series

International Nuclear Information System (INIS)

Khoa, Truong Quang Dang; Yuichi, Nakamura; Masahiro, Nakagawa

2009-01-01

In recent years, functional near-infrared spectroscopy (NIRS) has been introduced as a new neuroimaging modality with which to conduct functional brain-imaging studies. With its advanced features, NIRS signal processing has become a very attractive field in computational science. This work explores nonlinear physical aspects of cerebral hemodynamic changes over the time series of NIRS. Detecting the presence of chaos in a dynamical system is an important problem in studying the irregular or chaotic motion that is generated by nonlinear systems whose dynamical laws uniquely determine the time of evolution of a state of the system. The strategy results directly from the definition of the largest Lyapunov exponent. The Lyapunov exponents quantify the exponential divergence of initially close state-space trajectories and estimate the amount of chaos in a system. The method is an application of the Rosenstein algorithm, an efficient method for calculating the largest Lyapunov exponent from an experimental time series. In the present paper, the authors focus mainly on the detection of chaos characteristics of the time series associated to hemoglobin dynamics. Furthermore, the chaos parameters obtained can be used to identify the active state period of the human brain.

Mitochondrial dynamics in the regulation of neurogenesis: From development to the adult brain.

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Khacho, Mireille; Slack, Ruth S

2018-01-01

Mitochondria are classically known to be the cellular energy producers, but a renewed appreciation for these organelles has developed with the accumulating discoveries of additional functions. The importance of mitochondria within the brain has been long known, particularly given the high-energy demanding nature of neurons. The energy demands imposed by neurons require the well-orchestrated morphological adaptation and distribution of mitochondria. Recent studies now reveal the importance of mitochondrial dynamics not only in mature neurons but also during neural development, particularly during the process of neurogenesis and neural stem cell fate decisions. In this review, we will highlight the recent findings that illustrate the importance of mitochondrial dynamics in neurodevelopment and neural stem cell function. Developmental Dynamics 247:47-53, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Multifractal spatial patterns and diversity in an ecological succession.

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Leonardo Ariel Saravia

Full Text Available We analyzed the relationship between biodiversity and spatial biomass heterogeneity along an ecological succession developed in the laboratory. Periphyton (attached microalgae biomass spatial patterns at several successional stages were obtained using digital image analysis and at the same time we estimated the species composition and abundance. We show that the spatial pattern was self-similar and as the community developed in an homogeneous environment the pattern is self-organized. To characterize it we estimated the multifractal spectrum of generalized dimensions D(q. Using D(q we analyze the existence of cycles of heterogeneity during succession and the use of the information dimension D(1 as an index of successional stage. We did not find cycles but the values of D(1 showed an increasing trend as the succession developed and the biomass was higher. D(1 was also negatively correlated with Shannon's diversity. Several studies have found this relationship in different ecosystems but here we prove that the community self-organizes and generates its own spatial heterogeneity influencing diversity. If this is confirmed with more experimental and theoretical evidence D(1 could be used as an index, easily calculated from remote sensing data, to detect high or low diversity areas.

Dynamic contrast-enhanced quantitative perfusion measurement of the brain using T-1-weighted MRI at 3T

DEFF Research Database (Denmark)

Larsson, H.B.W.; Hansen, A.E.; Berg, H.K.

2008-01-01

Purpose: To develop a method for the measurement of brain perfusion based on dynamic contrast-enhanced T-1-weighted MR imaging. Materials and Methods: Dynamic imaging of the first pass of a bolus of a paramagnetic contrast agent was performed using a 3T whole-body magnet and a T-1-weighted fast...... field echo sequence. The input function was obtained from the internal carotid artery. An initial T-1 measurement was performed in order to convert the MR signal to concentration of the contrast agent. Pixelwise and region of interest (ROI)based calculation of cerebral perfusion (CBF) was performed...... inside the infarct core was, 9 mL/100g/min in one of the stroke patients. The other stroke patient had postischemic hyperperfusion and CBF was 140 mL/100g/min. Conclusion: Absolute values of brain perfusion can be obtained using dynamic contrast-enhanced MRI. These values correspond,to expected values...

A preliminary studyof the site-dependence of the multifractalfeatures of geoelectric measurements

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M. Macchiato

2004-06-01

Full Text Available Multifractal analysis was performed to characterize the fluctuations in dynamics of the hourly time variability of self-potential signals measured from January 2001 to September 2002 by three stations installed in the Basilicata region (Southern Italy. Two stations (Giuliano and Tito are located in a seismic area, and one (Laterza in an aseismic area. Multifractal formalism leads to the identification of a set of parameters derived from the shape of the multifractal spectrum (the maximum a0, the asymmetry B and the width W and measuring the «complexity» of the signals. Furthermore, the multifractal parameters seem to discriminate self-potential signals measured in seismic areas from those recorded in aseismic areas.

Multifractal characterizations of nonstationary and intermittency in geophysical fields: Observed, retrieved, or simulated

International Nuclear Information System (INIS)

Davis, A.; Wiscombe, W.; Cahalan, R.; Marshak, A.

1994-01-01

Geophysical data rarely show any smoothness at any scale, and this often makes comparison with theoretical model output difficult. However, highly fluctuating signals and fractual structures are typical of open dissipative systems with nonlinear dynamics, the focus of most geophysical research. High levels of variability are excited over a large range of scales by the combined actions of external forcing and internal instability. At very small scales we expect geophysical fields to be smooth, but these are rarely resolved with available instrumentation or simulation tools; nondifferentiable and even discontinuous models are therefore in order. We need methods of statistically analyzing geophysical data, whether measured in situ, remotely sensed or even generated by a computer model, that are adapted to these characteristics. An important preliminary task is to define statistically stationary features in generally nonstationary signals. We first discuss a simple criterion for stationarity in finite data streams that exhibit power law energy spectra and then, guided by developments in turbulence studies, we advocate the use of two ways of analyzing the scale dependence of statistical information: singular measures and qth order structure functions. In nonstationary situations, the approach based on singular measures seeks power law behavior in integrals over all possible scales of a nonnegative stationary field derived from the data, leading to a characterization of the intermittency in this field. In contrast, the approach based on structure functions uses the signal itself, seeking power laws for the statistical moments of absolute increments over arbitrarily large scales, leading to a characterization of the prevailing nonstationarity in both quantitative and qualitative terms. We explain graphically, step by step, both multifractal statistics which are largely complementary to each other. 45 refs., 13 figs., 2 tabs

Space-filling, multifractal, localized thermal spikes in Si, Ge and ZnO

Science.gov (United States)

Ahmad, Shoaib; Abbas, Muhammad Sabtain; Yousuf, Muhammad; Javeed, Sumera; Zeeshan, Sumaira; Yaqub, Kashif

2018-04-01

The mechanism responsible for the emission of clusters from heavy ion irradiated solids is proposed to be thermal spikes. Collision cascade-based theories describe atomic sputtering but cannot explain the consistently observed experimental evidence for significant cluster emission. Statistical thermodynamic arguments for thermal spikes are employed here for qualitative and quantitative estimation of the thermal spike-induced cluster emission from Si, Ge and ZnO. The evolving cascades and spikes in elemental and molecular semiconducting solids are shown to have fractal characteristics. Power law potential is used to calculate the fractal dimension. With the loss of recoiling particles' energy the successive branching ratios get smaller. The fractal dimension is shown to be dependent upon the exponent of the power law interatomic potential D = 1/2m. Each irradiating ion has the probability of initiating a space-filling, multifractal thermal spike that may sublime a localized region near the surface by emitting clusters in relative ratios that depend upon the energies of formation of respective surface vacancies.

Nonlinear dynamics and damage induced properties of soft matter with application in oncology

Science.gov (United States)

Naimark, O.

2017-09-01

Molecular-morphological signs of oncogenesis could be linked to multiscale collective effects in molecular, cell and tissue related to defects (damage) dynamics. It was shown that nonlinear behavior of biological systems can be linked to the existence of characteristic collective open state modes providing the coherent expression dynamics. New type of criticality in nonequilibrium systems with defects—structural-scaling transition allows the definition of the `driving force' for a biological soft matter related to consolidated open states. The set of collective open states (breathers, autosolitons and blow-up modes) in the molecular ensembles provides the collective expression dynamics to attract the entire system (cell, tissue) toward a few preferred global states. The co-existence of three types of collective modes determines the multifractal scenario of biological soft matter dynamics. The appearance of `globally convergent' dynamics corresponding to the coherent behavior of multiscale blow-up open states (blow-up gene expression) leads to anomalous localized softening (blow-up localized damage) and the subjection of the cells (or tissue) to monofractal dynamics. This dynamics can be associated with cancer progression.

Parametric scaling from species to growth-form diversity: an interesting analogy with multifractal functions.

Science.gov (United States)

Ricotta, Carlo; Pacini, Alessandra; Avena, Giancarlo

2002-01-01

We propose a measure of divergence from species to life-form diversity aimed at summarizing the ecological similarity among different plant communities without losing information on traditional taxonomic diversity. First, species and life-form relative abundances within a given plant community are determined. Next, using Rényi's generalized entropy, the diversity profiles of the analyzed community are computed both from species and life-form relative abundances. Finally, the speed of decrease from species to life-form diversity is obtained by combining the outcome of both profiles. Interestingly, the proposed measure shows some formal analogies with multifractal functions developed in statistical physics for the analysis of spatial patterns. As an application for demonstration, a small data set from a plant community sampled in the archaeological site of Paestum (southern Italy) is used.

Resting-state EEG oscillatory dynamics in fragile X syndrome: abnormal functional connectivity and brain network organization.

Directory of Open Access Journals (Sweden)

Melle J W van der Molen

Full Text Available Disruptions in functional connectivity and dysfunctional brain networks are considered to be a neurological hallmark of neurodevelopmental disorders. Despite the vast literature on functional brain connectivity in typical brain development, surprisingly few attempts have been made to characterize brain network integrity in neurodevelopmental disorders. Here we used resting-state EEG to characterize functional brain connectivity and brain network organization in eight males with fragile X syndrome (FXS and 12 healthy male controls. Functional connectivity was calculated based on the phase lag index (PLI, a non-linear synchronization index that is less sensitive to the effects of volume conduction. Brain network organization was assessed with graph theoretical analysis. A decrease in global functional connectivity was observed in FXS males for upper alpha and beta frequency bands. For theta oscillations, we found increased connectivity in long-range (fronto-posterior and short-range (frontal-frontal and posterior-posterior clusters. Graph theoretical analysis yielded evidence of increased path length in the theta band, suggesting that information transfer between brain regions is particularly impaired for theta oscillations in FXS. These findings are discussed in terms of aberrant maturation of neuronal oscillatory dynamics, resulting in an imbalance in excitatory and inhibitory neuronal circuit activity.

Pathologic changes in the lung following single and multi-fraction irradiation

International Nuclear Information System (INIS)

Travis, E.L.; Harley, R.A.; Fenn, J.O.; Klobukowski, C.J.; Hargrove, H.B.

1977-01-01

The limiting factor in the treatment of malignant disease with irradiation is the tolerance of normal tissue irradiated. In the present study the right lungs of rats were exposed to single doses of 2000 rad of x-radiation, to 10 x 200 rad, or to 5 x 400 rad. Animals from each group were sacrificed monthly for 6 months post exposure. Sections of lung were examined by light microscopy (LM) and by scanning or transmission electron microscopy (SEM and TEM). A focal exudative lesion was seen at 2 months after the single dose; it progressed to a proliferative and then reparative, fibrotic lesion by 6 months. Changes in epithelial lung components, particularly the presence of Type II pneumocytes, were found with both LM and TM. Vascular changes were less pronounced. A striking finding was the presence of mast cells in the alveolar walls. Neither of the multi-fraction schedules produced any of these changes, except hyperplasia of Type II cells following 5 x 400 rad. The possible implication of Type II and mast cells in radiation pneumonitis and fibrosis is discussed

Assessment of brain metastases by means of dynamic susceptibility contrast enhanced MRI

International Nuclear Information System (INIS)

Knopp, M.; Wenz, F.; Debus, J.; Hentrich, H.R.

2002-01-01

Full text: To assess if pre therapeutic measurements of regional cerebral blood flow (rCBF) and volume (rCVB) are able to predict the response of brain metastases to radiation therapy and to assess the influence of radiosurgery on rCBF and rCBV on brain metastases and normal surrounding tissue. We examined 25 patients with brain metastases prior to high dose radiosurgery with conventional T1 and T2 weighted MRI and dynamic susceptibility contrast enhanced MRI (DSC MRI). For DSC MRI 55 T2*w GE images of two sections were acquired after bolus administration of 0.1 mmol/kg gadoteridol (ProHance) for the simultaneous measurement of brain feeding arteries and brain tissue. This allowed an absolute quantification of rCBF and rCBV. Follow-up examinations were performed 6 weeks and 3 months after radiotherapy and the acquired perfusion data were related to a 3 point scale of treatment outcome. Radiosurgery was performed by a linear accelerator with a 80% isodose of 18-20 Gv. For treatment planning the heads of the patients were immobilized by a cask mask to avoid head movement. DSC MRI was able to assess perfusion data in all patients. Higher pre therapeutic rCBV seems to predict a poor treatment outcome. After radiosurgery patients with tumor remission and stable disease presented a decrease of rCBV over time regardless of temporary tumor volume increase. Patients with tumor progression at the 3 month followup presented an increase of rCBV. Effects on normal surrounding tissue could not be observed. DSC MRI using Gadoteridol allows the non-invasive assessment of rCBV and rCBF of brain metastases and its changes due to radiosurgery. The method may also be able to predict treatment outcome. Furthermore radiofrequency effects on surrounding unaffected tissue can be monitored. Copyright (2002) Blackwell Science Pty Ltd

Brain Dynamics An Introduction to Models and Simualtions

CERN Document Server

Haken, Hermann

2008-01-01

Brain Dynamics serves to introduce graduate students and nonspecialists from various backgrounds to the field of mathematical and computational neurosciences. Some of the advanced chapters will also be of interest to the specialists. The book approaches the subject through pulse-coupled neural networks, with at their core the lighthouse and integrate-and-fire models, which allow for the highly flexible modelling of realistic synaptic activity, synchronization and spatio-temporal pattern formation. Topics also include pulse-averaged equations and their application to movement coordination. The book closes with a short analysis of models versus the real neurophysiological system. The second edition has been thoroughly updated and augmented by two extensive chapters that discuss the interplay between pattern recognition and synchronization. Further, to enhance the usefulness as textbook and for self-study, the detailed solutions for all 34 exercises throughout the text have been added.

Analysis of the sensitivity to rainfall spatio-temporal variability of an operational urban rainfall-runoff model in a multifractal framework

Science.gov (United States)

Gires, A.; Tchiguirinskaia, I.; Schertzer, D. J.; Lovejoy, S.

2011-12-01

In large urban areas, storm water management is a challenge with enlarging impervious areas. Many cities have implemented real time control (RTC) of their urban drainage system to either reduce overflow or limit urban contamination. A basic component of RTC is hydraulic/hydrologic model. In this paper we use the multifractal framework to suggest an innovative way to test the sensitivity of such a model to the spatio-temporal variability of its rainfall input. Indeed the rainfall variability is often neglected in urban context, being considered as a non-relevant issue at the scales involve. Our results show that on the contrary the rainfall variability should be taken into account. Universal multifractals (UM) rely on the concept of multiplicative cascade and are a standard tool to analyze and simulate with a reduced number of parameters geophysical processes that are extremely variable over a wide range of scales. This study is conducted on a 3 400 ha urban area located in Seine-Saint-Denis, in the North of Paris (France). We use the operational semi-distributed model that was calibrated by the local authority (Direction Eau et Assainnissement du 93) that is in charge of urban drainage. The rainfall data comes from the C-Band radar of Trappes operated by Météo-France. The rainfall event of February 9th, 2009 was used. A stochastic ensemble approach was implemented to quantify the uncertainty on discharge associated to the rainfall variability occurring at scales smaller than 1 km x 1 km x 5 min that is usually available with C-band radar networks. An analysis of the quantiles of the simulated peak flow showed that the uncertainty exceeds 20 % for upstream links. To evaluate a potential gain from a direct use of the rainfall data available at the resolution of X-band radar, we performed similar analysis of the rainfall fields of the degraded resolution of 9 km x 9 km x 20 min. The results show a clear decrease in uncertainty when the original resolution of C

Resting state brain dynamics and its transients: a combined TMS-EEG study.

Science.gov (United States)

Bonnard, Mireille; Chen, Sophie; Gaychet, Jérôme; Carrere, Marcel; Woodman, Marmaduke; Giusiano, Bernard; Jirsa, Viktor

2016-08-04

The brain at rest exhibits a spatio-temporally rich dynamics which adheres to systematic behaviours that persist in task paradigms but appear altered in disease. Despite this hypothesis, many rest state paradigms do not act directly upon the rest state and therefore cannot confirm hypotheses about its mechanisms. To address this challenge, we combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to study brain's relaxation toward rest following a transient perturbation. Specifically, TMS targeted either the medial prefrontal cortex (MPFC), i.e. part of the Default Mode Network (DMN) or the superior parietal lobule (SPL), involved in the Dorsal Attention Network. TMS was triggered by a given brain state, namely an increase in occipital alpha rhythm power. Following the initial TMS-Evoked Potential, TMS at MPFC enhances the induced occipital alpha rhythm, called Event Related Synchronisation, with a longer transient lifetime than TMS at SPL, and a higher amplitude. Our findings show a strong coupling between MPFC and the occipital alpha power. Although the rest state is organized around a core of resting state networks, the DMN functionally takes a special role among these resting state networks.

Fractal dynamics of heartbeat time series of young persons with metabolic syndrome

Science.gov (United States)

Muñoz-Diosdado, A.; Alonso-Martínez, A.; Ramírez-Hernández, L.; Martínez-Hernández, G.

2012-10-01

Many physiological systems have been in recent years quantitatively characterized using fractal analysis. We applied it to study heart variability of young subjects with metabolic syndrome (MS); we examined the RR time series (time between two R waves in ECG) with the detrended fluctuation analysis (DFA) method, the Higuchi's fractal dimension method and the multifractal analysis to detect the possible presence of heart problems. The results show that although the young persons have MS, the majority do not present alterations in the heart dynamics. However, there were cases where the fractal parameter values differed significantly from the healthy people values.

Dynamic functional brain connectivity for face perception

NARCIS (Netherlands)

Yang, Yuan; Qiu, Yihong; Schouten, Alfred C.

2015-01-01

Face perception is mediated by a distributed brain network comprised of the core system at occipito-temporal areas and the extended system at other relevant brain areas involving bilateral hemispheres. In this study we explored how the brain connectivity changes over the time for face-sensitive

Analytic degree distributions of horizontal visibility graphs mapped from unrelated random series and multifractal binomial measures

Science.gov (United States)

Xie, Wen-Jie; Han, Rui-Qi; Jiang, Zhi-Qiang; Wei, Lijian; Zhou, Wei-Xing

2017-08-01

Complex network is not only a powerful tool for the analysis of complex system, but also a promising way to analyze time series. The algorithm of horizontal visibility graph (HVG) maps time series into graphs, whose degree distributions are numerically and analytically investigated for certain time series. We derive the degree distributions of HVGs through an iterative construction process of HVGs. The degree distributions of the HVG and the directed HVG for random series are derived to be exponential, which confirms the analytical results from other methods. We also obtained the analytical expressions of degree distributions of HVGs and in-degree and out-degree distributions of directed HVGs transformed from multifractal binomial measures, which agree excellently with numerical simulations.

Relationship research between meteorological disasters and stock markets based on a multifractal detrending moving average algorithm

Science.gov (United States)

Li, Qingchen; Cao, Guangxi; Xu, Wei

2018-01-01

Based on a multifractal detrending moving average algorithm (MFDMA), this study uses the fractionally autoregressive integrated moving average process (ARFIMA) to demonstrate the effectiveness of MFDMA in the detection of auto-correlation at different sample lengths and to simulate some artificial time series with the same length as the actual sample interval. We analyze the effect of predictable and unpredictable meteorological disasters on the US and Chinese stock markets and the degree of long memory in different sectors. Furthermore, we conduct a preliminary investigation to determine whether the fluctuations of financial markets caused by meteorological disasters are derived from the normal evolution of the financial system itself or not. We also propose several reasonable recommendations.

Dynamic change of serum protein S100b and its clinical significance in patients with traumatic brain injury

Institute of Scientific and Technical Information of China (English)

CHEN Da-qing; ZHU Lie-lie

2005-01-01

Objective: To analyze the dynamic change of serum protein S100b in patients with traumatic brain injury and its clinical value in assessing brain damage. Methods: According to Glasgow coma scale (GCS), 102 cases of traumatic brain injury were divided into mild brain injury group (GCS≥13, n=31, Group A), moderate brain injury group (8brain injury group (GCS≤8, n=34, Group C). Serial S100b concentrations were analyzed by enzyme-linked immunosorbent assay (ELISA) in blood samples taken on admission, 12 h, 24 h, 48 h, 72 h and 7 days after traumatic brain injury. Results: The severe brain injury group showed significantly higher concentration of serum S100b, with earlier increase and longer duration, than the mild and moderate brain injury groups. The patients with higher S100b exhibited lower GCS scores and poor clinical prognosis. The increase in S100b could emerge before clinical image evidence indicated so. Conclusions: Serum S100b can be used as a sensitive index for assessment and prediction of traumatic brain injury severity and prognosis.

Detrended analysis of shower track distribution in nucleus-nucleus interactions at CERN SPS energy

International Nuclear Information System (INIS)

Mali, P.; Manna, S.K.; Haldar, P.K.; Mukhopadhyay, A.; Singh, G.

2017-01-01

We have studied the charged particle density fluctuations in "1"6O+Ag(Br) and "3"2S+Ag(Br) interactions at 200A GeV incident energy in the laboratory frame by using the detrended methods. These methods can extract (multi)fractal properties of the underlying distributions after filtering out the average trend of fluctuations associated. Multifractal parameters obtained from data analysis are systematically compared with event samples generated by the Ultra-relativistic Quantum Molecular Dynamics (UrQMD) model, where Bose–Einstein correlation (BEC) effect is mimicked via a charge reassignment algorithm implemented as an after burner. Both the experimental and the simulated data are subjected to two different statistical techniques namely the multifractal detrended fluctuation analysis (MFDFA) and multifractal detrended moving average (MFDMA) analysis. The results indicate that for both the interactions considered the pseudorapidity distributions of the shower tracks are multifractal in nature. Qualitatively, both methods of analysis and both interactions considered, result in similar behavior of multifractal parameters. We do however notice significant quantitative differences in certain cases.

Novel modeling of task versus rest brain state predictability using a dynamic time warping spectrum: comparisons and contrasts with other standard measures of brain dynamics

Directory of Open Access Journals (Sweden)

Martin eDinov

2016-05-01

Full Text Available Dynamic time warping, or DTW, is a powerful and domain-general sequence alignment method for computing a similarity measure. Such dynamic programming-based techniques like DTW are now the backbone and driver of most bioinformatics methods and discoveries. In neuroscience it has had far less use, though this has begun to change. We wanted to explore new ways of applying DTW, not simply as a measure with which to cluster or compare similarity between features but in a conceptually different way. We have used DTW to provide a more interpretable spectral description of the data, compared to standard approaches such as the Fourier and related transforms. The DTW approach and standard discrete Fourier transform (DFT are assessed against benchmark measures of neural dynamics. These include EEG microstates, EEG avalanches and the sum squared error (SSE from a multilayer perceptron (MLP prediction of the EEG timeseries, and simultaneously acquired FMRI BOLD signal. We explored the relationships between these variables of interest in an EEG-FMRI dataset acquired during a standard cognitive task, which allowed us to explore how DTW differentially performs in different task settings. We found that despite strong correlations between DTW and DFT-spectra, DTW was a better predictor for almost every measure of brain dynamics. Using these DTW measures, we show that predictability is almost always higher in task than in rest states, which is consistent to other theoretical and empirical findings, providing additional evidence for the utility of the DTW approach.

Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase 18F-FET PET accuracy without dynamic scans.

Science.gov (United States)

Lohmann, Philipp; Stoffels, Gabriele; Ceccon, Garry; Rapp, Marion; Sabel, Michael; Filss, Christian P; Kamp, Marcel A; Stegmayr, Carina; Neumaier, Bernd; Shah, Nadim J; Langen, Karl-Josef; Galldiks, Norbert

2017-07-01

We investigated the potential of textural feature analysis of O-(2-[ 18 F]fluoroethyl)-L-tyrosine ( 18 F-FET) PET to differentiate radiation injury from brain metastasis recurrence. Forty-seven patients with contrast-enhancing brain lesions (n = 54) on MRI after radiotherapy of brain metastases underwent dynamic 18 F-FET PET. Tumour-to-brain ratios (TBRs) of 18 F-FET uptake and 62 textural parameters were determined on summed images 20-40 min post-injection. Tracer uptake kinetics, i.e., time-to-peak (TTP) and patterns of time-activity curves (TAC) were evaluated on dynamic PET data from 0-50 min post-injection. Diagnostic accuracy of investigated parameters and combinations thereof to discriminate between brain metastasis recurrence and radiation injury was compared. Diagnostic accuracy increased from 81 % for TBR mean alone to 85 % when combined with the textural parameter Coarseness or Short-zone emphasis. The accuracy of TBR max alone was 83 % and increased to 85 % after combination with the textural parameters Coarseness, Short-zone emphasis, or Correlation. Analysis of TACs resulted in an accuracy of 70 % for kinetic pattern alone and increased to 83 % when combined with TBR max . Textural feature analysis in combination with TBRs may have the potential to increase diagnostic accuracy for discrimination between brain metastasis recurrence and radiation injury, without the need for dynamic 18 F-FET PET scans. • Textural feature analysis provides quantitative information about tumour heterogeneity • Textural features help improve discrimination between brain metastasis recurrence and radiation injury • Textural features might be helpful to further understand tumour heterogeneity • Analysis does not require a more time consuming dynamic PET acquisition.

Measurement of brain oxygenation changes using dynamic T1-weighted imaging

DEFF Research Database (Denmark)

Haddock, Bryan; Larsson, Henrik B W; Hansen, Adam E

2013-01-01

Magnetic resonance imaging (MRI) has proven useful in evaluating oxygenation in several types of tissue and blood. This study evaluates brain tissue oxygenation changes between normoxia and hyperoxia in healthy subjects using dynamic T1 and T2*-weighted imaging sequences. The change in FiO2 induced...... by hyperoxia caused a significant decrease in T1. A model to determine changes in tissue oxygen tension from the T1-weighted MRI signal is presented based on previous findings that T1 is sensitive to oxygen tension whereas T2* is sensitive to blood saturation. The two sequences produce results with different...... regional and temporal dynamics. These differences combined with results from simulations of the T1 signal intensities, indicate an increase in extravascular oxygen tension during hyperoxia. This study concludes that T1 and T2* responses to FiO2 serve as independent biomarkers of oxygen physiology...

The Virtual Brain: a simulator of primate brain network dynamics.

Science.gov (United States)

Sanz Leon, Paula; Knock, Stuart A; Woodman, M Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R; Jirsa, Viktor

2013-01-01

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

The Virtual Brain: a simulator of primate brain network dynamics

Science.gov (United States)

Sanz Leon, Paula; Knock, Stuart A.; Woodman, M. Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R.; Jirsa, Viktor

2013-01-01

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications. PMID:23781198

Dynamic correlations between heart and brain rhythm during Autogenic meditation

Science.gov (United States)

Kim, Dae-Keun; Lee, Kyung-Mi; Kim, Jongwha; Whang, Min-Cheol; Kang, Seung Wan

2013-01-01

This study is aimed to determine significant physiological parameters of brain and heart under meditative state, both in each activities and their dynamic correlations. Electrophysiological changes in response to meditation were explored in 12 healthy volunteers who completed 8 weeks of a basic training course in autogenic meditation. Heart coherence, representing the degree of ordering in oscillation of heart rhythm intervals, increased significantly during meditation. Relative EEG alpha power and alpha lagged coherence also increased. A significant slowing of parietal peak alpha frequency was observed. Parietal peak alpha power increased with increasing heart coherence during meditation, but no such relationship was observed during baseline. Average alpha lagged coherence also increased with increasing heart coherence during meditation, but weak opposite relationship was observed at baseline. Relative alpha power increased with increasing heart coherence during both meditation and baseline periods. Heart coherence can be a cardiac marker for the meditative state and also may be a general marker for the meditative state since heart coherence is strongly correlated with EEG alpha activities. It is expected that increasing heart coherence and the accompanying EEG alpha activations, heart brain synchronicity, would help recover physiological synchrony following a period of homeostatic depletion. PMID:23914165

Dynamic correlations between heart and brain rhythm during Autogenic meditation.

Science.gov (United States)

Kim, Dae-Keun; Lee, Kyung-Mi; Kim, Jongwha; Whang, Min-Cheol; Kang, Seung Wan

2013-01-01

This study is aimed to determine significant physiological parameters of brain and heart under meditative state, both in each activities and their dynamic correlations. Electrophysiological changes in response to meditation were explored in 12 healthy volunteers who completed 8 weeks of a basic training course in autogenic meditation. Heart coherence, representing the degree of ordering in oscillation of heart rhythm intervals, increased significantly during meditation. Relative EEG alpha power and alpha lagged coherence also increased. A significant slowing of parietal peak alpha frequency was observed. Parietal peak alpha power increased with increasing heart coherence during meditation, but no such relationship was observed during baseline. Average alpha lagged coherence also increased with increasing heart coherence during meditation, but weak opposite relationship was observed at baseline. Relative alpha power increased with increasing heart coherence during both meditation and baseline periods. Heart coherence can be a cardiac marker for the meditative state and also may be a general marker for the meditative state since heart coherence is strongly correlated with EEG alpha activities. It is expected that increasing heart coherence and the accompanying EEG alpha activations, heart brain synchronicity, would help recover physiological synchrony following a period of homeostatic depletion.

Investigating dynamical information transfer in the brain following a TMS pulse: Insights from structural architecture.

Science.gov (United States)

Amico, Enrico; Van Mierlo, Pieter; Marinazzo, Daniele; Laureys, Steven

2015-01-01

Transcranial magnetic stimulation (TMS) has been used for more than 20 years to investigate connectivity and plasticity in the human cortex. By combining TMS with high-density electroencephalography (hd-EEG), one can stimulate any cortical area and measure the effects produced by this perturbation in the rest of the cerebral cortex. The purpose of this paper is to investigate changes of information flow in the brain after TMS from a functional and structural perspective, using multimodal modeling of source reconstructed TMS/hd-EEG recordings and DTI tractography. We prove how brain dynamics induced by TMS is constrained and driven by its structure, at different spatial and temporal scales, especially when considering cross-frequency interactions. These results shed light on the function-structure organization of the brain network at the global level, and on the huge variety of information contained in it.

Emotional recognition from dynamic facial, vocal and musical expressions following traumatic brain injury.

Science.gov (United States)

Drapeau, Joanie; Gosselin, Nathalie; Peretz, Isabelle; McKerral, Michelle

2017-01-01

To assess emotion recognition from dynamic facial, vocal and musical expressions in sub-groups of adults with traumatic brain injuries (TBI) of different severities and identify possible common underlying mechanisms across domains. Forty-one adults participated in this study: 10 with moderate-severe TBI, nine with complicated mild TBI, 11 with uncomplicated mild TBI and 11 healthy controls, who were administered experimental (emotional recognition, valence-arousal) and control tasks (emotional and structural discrimination) for each domain. Recognition of fearful faces was significantly impaired in moderate-severe and in complicated mild TBI sub-groups, as compared to those with uncomplicated mild TBI and controls. Effect sizes were medium-large. Participants with lower GCS scores performed more poorly when recognizing fearful dynamic facial expressions. Emotion recognition from auditory domains was preserved following TBI, irrespective of severity. All groups performed equally on control tasks, indicating no perceptual disorders. Although emotional recognition from vocal and musical expressions was preserved, no correlation was found across auditory domains. This preliminary study may contribute to improving comprehension of emotional recognition following TBI. Future studies of larger samples could usefully include measures of functional impacts of recognition deficits for fearful facial expressions. These could help refine interventions for emotional recognition following a brain injury.

New aspects of fenestrated vasculature and tissue dynamics in the sensory circumventricular organs of adult brains

Directory of Open Access Journals (Sweden)

Seiji eMiyata

2015-10-01

Full Text Available The blood–brain barrier (BBB generally consists of endothelial tight junction barriers that prevent the free entry of blood-derived substances, thereby maintaining the extracellular environment of the brain. However, the circumventricular organs (CVOs, which are located along the midlines of the brain ventricles, lack these endothelial barriers and have fenestrated capillaries; therefore, they have a number of essential functions, including the transduction of information between the blood circulation and brain. Previous studies have demonstrated the extensive contribution of the CVOs to body fluid and thermal homeostasis, energy balance, the chemoreception of blood-derived substances, and neuroinflammation. In this review, recent advances have been discussed in fenestrated capillary characterization and dynamic tissue reconstruction accompanied by angiogenesis and neurogliogenesis in the sensory CVOs of adult brains. The sensory CVOs, including the organum vasculosum of the lamina terminalis (OVLT, subfornical organ (SFO, and area postrema (AP, have size-selective and heterogeneous vascular permeabilities. Astrocyte-/tanycyte-like neural stem cells (NSCs sense blood- and cerebrospinal fluid-derived information through the transient receptor potential vanilloid 1, a mechanical/osmotic receptor, Toll-like receptor 4, a lipopolysaccharide receptor, and Nax, a Na-sensing Na channel. They also express tight junction proteins and densely and tightly surround mature neurons to protect them from blood-derived neurotoxic substances, indicating that the NSCs of the CVOs perform BBB functions while maintaining the capacity to differentiate into new neurons and glial cells. In addition to neurogliogenesis, the density of fenestrated capillaries is regulated by angiogenesis, which is accompanied by the active proliferation and sprouting of endothelial cells. Vascular endothelial growth factor (VEGF signaling may be involved in angiogenesis and

Brain systems for probabilistic and dynamic prediction: computational specificity and integration.

Directory of Open Access Journals (Sweden)

Jill X O'Reilly

2013-09-01

Full Text Available A computational approach to functional specialization suggests that brain systems can be characterized in terms of the types of computations they perform, rather than their sensory or behavioral domains. We contrasted the neural systems associated with two computationally distinct forms of predictive model: a reinforcement-learning model of the environment obtained through experience with discrete events, and continuous dynamic forward modeling. By manipulating the precision with which each type of prediction could be used, we caused participants to shift computational strategies within a single spatial prediction task. Hence (using fMRI we showed that activity in two brain systems (typically associated with reward learning and motor control could be dissociated in terms of the forms of computations that were performed there, even when both systems were used to make parallel predictions of the same event. A region in parietal cortex, which was sensitive to the divergence between the predictions of the models and anatomically connected to both computational networks, is proposed to mediate integration of the two predictive modes to produce a single behavioral output.

The multiple time scales of sleep dynamics as a challenge for modelling the sleeping brain.

Science.gov (United States)

Olbrich, Eckehard; Claussen, Jens Christian; Achermann, Peter

2011-10-13

A particular property of the sleeping brain is that it exhibits dynamics on very different time scales ranging from the typical sleep oscillations such as sleep spindles and slow waves that can be observed in electroencephalogram (EEG) segments of several seconds duration over the transitions between the different sleep stages on a time scale of minutes to the dynamical processes involved in sleep regulation with typical time constants in the range of hours. There is an increasing body of work on mathematical and computational models addressing these different dynamics, however, usually considering only processes on a single time scale. In this paper, we review and present a new analysis of the dynamics of human sleep EEG at the different time scales and relate the findings to recent modelling efforts pointing out both the achievements and remaining challenges.

Multifractal chaotic attractors in a system of delay-differential equations modeling road traffic.

Science.gov (United States)

Safonov, Leonid A.; Tomer, Elad; Strygin, Vadim V.; Ashkenazy, Yosef; Havlin, Shlomo

2002-12-01

We study a system of delay-differential equations modeling single-lane road traffic. The cars move in a closed circuit and the system's variables are each car's velocity and the distance to the car ahead. For low and high values of traffic density the system has a stable equilibrium solution, corresponding to the uniform flow. Gradually decreasing the density from high to intermediate values we observe a sequence of supercritical Hopf bifurcations forming multistable limit cycles, corresponding to flow regimes with periodically moving traffic jams. Using an asymptotic technique we find approximately small limit cycles born at Hopf bifurcations and numerically preform their global continuations with decreasing density. For sufficiently large delay the system passes to chaos following the Ruelle-Takens-Newhouse scenario (limit cycles-two-tori-three-tori-chaotic attractors). We find that chaotic and nonchaotic attractors coexist for the same parameter values and that chaotic attractors have a broad multifractal spectrum. (c) 2002 American Institute of Physics.

Multifractal analysis of Asian markets during 2007-2008 financial crisis

Science.gov (United States)

Hasan, Rashid; Mohammad, Salim M.

2015-02-01

2007-2008 US financial crisis adversely affected the stock markets all over the world. Asian markets also came under pressure and were differently affected. As markets under stress could reveal features that remain hidden under normal conditions, we use MF-DFA technique to investigate the multifractal structure of the US and seven Asian stock markets during the crisis period. The overall period of study, from 01 July 2002 to 31 December 2013, is divided into three sub-periods: pre-crisis period, crisis period and post-crisis period. We find during the crisis period markets of the US, Japan, Hong Kong, S. Korea and Indonesia show very strong non-linearity for positive values of the moment q. We calculate the singularity spectra, f(α) for the three sub-periods for all markets. During the crisis period, we observe that the peaks of the f(α) spectra shift to lower values of α and markets of the US, Japan, Hong Kong, Korea and Indonesia exhibit increased long range correlations of large fluctuations in index returns. We also study the impact of the crisis on the power law exponent in the tail region of the cumulative return distribution and find that by excluding the crisis period from the overall data sets, the tail exponent increases across all markets.

The effect of Gaussian white noise on the fractality of fluctuations in the plasma of a symmetrical discharge

International Nuclear Information System (INIS)

Stan, Cristina; Cristescu, Cristina Maria; Alexandroaei, D.; Cristescu, C.P.

2014-01-01

Highlights: •We study the white Gaussian noise effect on the fractality of plasma fluctuations. •Multifractality strength is increased by the noise, at all inter-anode voltages. •New positive influence of noise resulting in an increasing of the predictability. •Identifying the fluctuations nature: chaotic or stochastic by multifractal analysis. •Noise changes the position of the maximum in the singularity spectra. - Abstract: In this work we investigate the influence of white Gaussian noise on the fluctuations in the plasma of a symmetrical discharge using multifractal detrended fluctuation analysis. We observe that in the range of noise intensity used in our study, the multifractality strength is increased by the noise, at all values of the inter-anode voltage, both for original and filtered time-series. This is interpreted as a new positive influence of noise because this effect can be understood as an increasing in the predictability on the dynamics in a time-series. A constructive influence of noise can appear only for fluctuations with underlying chaotic dynamics. The shuffling analysis demonstrates that the multifractality is purely a consequence of the correlations of the fluctuations. The noise influence is also observed in the change of the position of the maximum in the singularity spectra. The multifractal detrended cross correlation between light intensity and current intensity demonstrates that the fluctuations in both parameters are generated by the same physical processes though they are very different in nature: one is a local parameter and the other is a global one

BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.

Science.gov (United States)

Zhao, Ming; Rattanatamrong, Prapaporn; DiGiovanna, Jack; Mahmoudi, Babak; Figueiredo, Renato J; Sanchez, Justin C; Príncipe, José C; Fortes, José A B

2008-01-01

Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.

Handbook of Brain Connectivity

CERN Document Server

Jirsa, Viktor K

2007-01-01

Our contemporary understanding of brain function is deeply rooted in the ideas of the nonlinear dynamics of distributed networks. Cognition and motor coordination seem to arise from the interactions of local neuronal networks, which themselves are connected in large scales across the entire brain. The spatial architectures between various scales inevitably influence the dynamics of the brain and thereby its function. But how can we integrate brain connectivity amongst these structural and functional domains? Our Handbook provides an account of the current knowledge on the measurement, analysis and theory of the anatomical and functional connectivity of the brain. All contributors are leading experts in various fields concerning structural and functional brain connectivity. In the first part of the Handbook, the chapters focus on an introduction and discussion of the principles underlying connected neural systems. The second part introduces the currently available non-invasive technologies for measuring struct...

Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR

Science.gov (United States)

Mérida, Inés; Reilhac, Anthonin; Redouté, Jérôme; Heckemann, Rolf A.; Costes, Nicolas; Hammers, Alexander

2017-04-01

In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [18F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [18F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach with a simplified single-atlas method (SingleAtlas). We evaluated the impact of pseudo-CT accuracy on reconstructed PET images, compared to PET data reconstructed with real CT, at the regional and voxel levels for the following: radioactivity images; time-activity curves; and kinetic parameters (non-displaceable binding potential, BPND). On static [18F]FDG, the mean bias for MaxProb ranged between 0 and 1% for 73 out of 84 regions assessed, and exceptionally peaked at 2.5% for only one region. Statistical parametric map analysis of MaxProb-corrected PET data showed significant differences in less than 0.02% of the brain volume, whereas SingleAtlas-corrected data showed significant differences in 20% of the brain volume. On dynamic [18F]MPPF, most regional errors on BPND ranged from -1 to  +3% (maximum bias 5%) for the MaxProb method. With SingleAtlas, errors were larger and had higher variability in most regions. PET quantification bias increased over the duration of the dynamic scan for SingleAtlas, but not for MaxProb. We show that this effect is due to the interaction of the spatial tracer-distribution heterogeneity variation over time with the degree of accuracy of the attenuation maps. This

Encoding of physics concepts: concreteness and presentation modality reflected by human brain dynamics.

Directory of Open Access Journals (Sweden)

Kevin Lai

Full Text Available Previous research into working memory has focused on activations in different brain areas accompanying either different presentation modalities (verbal vs. non-verbal or concreteness (abstract vs. concrete of non-science concepts. Less research has been conducted investigating how scientific concepts are learned and further processed in working memory. To bridge this gap, the present study investigated human brain dynamics associated with encoding of physics concepts, taking both presentation modality and concreteness into account. Results of this study revealed greater theta and low-beta synchronization in the anterior cingulate cortex (ACC during encoding of concrete pictures as compared to the encoding of both high and low imageable words. In visual brain areas, greater theta activity accompanying stimulus onsets was observed for words as compared to pictures while stronger alpha suppression was observed in responses to pictures as compared to words. In general, the EEG oscillation patterns for encoding words of different levels of abstractness were comparable but differed significantly from encoding of pictures. These results provide insights into the effects of modality of presentation on human encoding of scientific concepts and thus might help in developing new ways to better teach scientific concepts in class.

Encoding of physics concepts: concreteness and presentation modality reflected by human brain dynamics.

Science.gov (United States)

Lai, Kevin; She, Hsiao-Ching; Chen, Sheng-Chang; Chou, Wen-Chi; Huang, Li-Yu; Jung, Tzyy-Ping; Gramann, Klaus

2012-01-01

Previous research into working memory has focused on activations in different brain areas accompanying either different presentation modalities (verbal vs. non-verbal) or concreteness (abstract vs. concrete) of non-science concepts. Less research has been conducted investigating how scientific concepts are learned and further processed in working memory. To bridge this gap, the present study investigated human brain dynamics associated with encoding of physics concepts, taking both presentation modality and concreteness into account. Results of this study revealed greater theta and low-beta synchronization in the anterior cingulate cortex (ACC) during encoding of concrete pictures as compared to the encoding of both high and low imageable words. In visual brain areas, greater theta activity accompanying stimulus onsets was observed for words as compared to pictures while stronger alpha suppression was observed in responses to pictures as compared to words. In general, the EEG oscillation patterns for encoding words of different levels of abstractness were comparable but differed significantly from encoding of pictures. These results provide insights into the effects of modality of presentation on human encoding of scientific concepts and thus might help in developing new ways to better teach scientific concepts in class.

Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility

Science.gov (United States)

Ma, Feng; Wei, Yu; Huang, Dengshi; Chen, Yixiang

2014-07-01

In this paper, by taking the 5-min high frequency data of the Shanghai Composite Index as example, we compare the forecasting performance of HAR-RV and Multifractal volatility, Realized volatility, Realized Bipower Variation and their corresponding short memory model with rolling windows forecasting method and the Model Confidence Set which is proved superior to SPA test. The empirical results show that, for six loss functions, HAR-RV outperforms other models. Moreover, to make the conclusions more precise and robust, we use the MCS test to compare the performance of their logarithms form models, and find that the HAR-log(RV) has a better performance in predicting future volatility. Furthermore, by comparing the two models of HAR-RV and HAR-log(RV), we conclude that, in terms of performance forecasting, the HAR-log(RV) model is the best model among models we have discussed in this paper.

Non-stationary dynamics in the bouncing ball: A wavelet perspective

Energy Technology Data Exchange (ETDEWEB)

Behera, Abhinna K., E-mail: abhinna@iiserkol.ac.in; Panigrahi, Prasanta K., E-mail: pprasanta@iiserkol.ac.in [Department of Physical Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur 741246 (India); Sekar Iyengar, A. N., E-mail: ansekar.iyengar@saha.ac.in [Plasma Physics Division, Saha Institute of Nuclear Physics (SINP), Sector 1, Block-AF, Bidhannagar, Kolkata 700064 (India)

2014-12-01

The non-stationary dynamics of a bouncing ball, comprising both periodic as well as chaotic behavior, is studied through wavelet transform. The multi-scale characterization of the time series displays clear signatures of self-similarity, complex scaling behavior, and periodicity. Self-similar behavior is quantified by the generalized Hurst exponent, obtained through both wavelet based multi-fractal detrended fluctuation analysis and Fourier methods. The scale dependent variable window size of the wavelets aptly captures both the transients and non-stationary periodic behavior, including the phase synchronization of different modes. The optimal time-frequency localization of the continuous Morlet wavelet is found to delineate the scales corresponding to neutral turbulence, viscous dissipation regions, and different time varying periodic modulations.

Dynamic correlations between heart and brain rhythm during Autogenic meditation

Directory of Open Access Journals (Sweden)

Daekeun eKim

2013-07-01

Full Text Available This study is aimed to determine significant physiological parameters of brain and heart under meditative state, both in each activities and their dynamic correlations. Electrophysiological changes in response to meditation were explored in 12 healthy volunteers who completed 8 weeks of a basic training course in autogenic meditation. Heart coherence, representing the degree of ordering in oscillation of heart rhythm intervals, increased significantly during meditation. Relative EEG alpha power and alpha lagged coherence also increased. A significant slowing of parietal peak alpha frequency was observed. Parietal peak alpha power increased with increasing heart coherence during meditation, but no such relationship was observed during baseline. Average alpha lagged coherence also increased with increasing heart coherence during meditation, but, again, no significant relationship was observed at baseline. Relative alpha power increased with increasing heart coherence during both meditation and baseline periods. Heart coherence can be a cardiac marker for the meditative state and also may be a general marker for the meditative state since heart coherence is strongly correlated with EEG alpha activities. It is expected that increasing heart coherence and the accompanying EEG alpha activations, heart brain synchronicity, would help recover physiological synchrony following a period of homeostatic depletion.

A self-affine multi-fractal wave turbulence discrimination method using data from single point fast response sensors in a nocturnal atmospheric boundary layer

OpenAIRE

Kamada, Ray; Decaria, Alex Joseph

1992-01-01

We present DA, a self-affine, multi-fractal which may become the first routine wave/turbulence discriminant for time series data. Using nocturnal atmospheric data, we show the advantages of D A over self-similar fractals and standard turbulence measures such as FFTs, Richardson number, Brunt-Vaisala frequency, buoyancy length scale, variances, turbulent kinetic energy, and phase averaging. DA also shows promise in resolving "wave-break" events. Since it uses local basis functions, DA may be...

Brain activity dynamics in human parietal regions during spontaneous switches in bistable perception.

Science.gov (United States)

Megumi, Fukuda; Bahrami, Bahador; Kanai, Ryota; Rees, Geraint

2015-02-15

The neural mechanisms underlying conscious visual perception have been extensively investigated using bistable perception paradigms. Previous functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) studies suggest that the right anterior superior parietal (r-aSPL) and the right posterior superior parietal lobule (r-pSPL) have opposite roles in triggering perceptual reversals. It has been proposed that these two areas are part of a hierarchical network whose dynamics determine perceptual switches. However, how these two parietal regions interact with each other and with the rest of the brain during bistable perception is not known. Here, we investigated such a model by recording brain activity using fMRI while participants viewed a bistable structure-from-motion stimulus. Using dynamic causal modeling (DCM), we found that resolving such perceptual ambiguity was specifically associated with reciprocal interactions between these parietal regions and V5/MT. Strikingly, the strength of bottom-up coupling between V5/MT to r-pSPL and from r-pSPL to r-aSPL predicted individual mean dominance duration. Our findings are consistent with a hierarchical predictive coding model of parietal involvement in bistable perception and suggest that visual information processing underlying spontaneous perceptual switches can be described as changes in connectivity strength between parietal and visual cortical regions. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Automated Processing of Dynamic Contrast-Enhanced MRI: Correlation of Advanced Pharmacokinetic Metrics with Tumor Grade in Pediatric Brain Tumors.

Science.gov (United States)

Vajapeyam, S; Stamoulis, C; Ricci, K; Kieran, M; Poussaint, T Young

2017-01-01

Pharmacokinetic parameters from dynamic contrast-enhanced MR imaging have proved useful for differentiating brain tumor grades in adults. In this study, we retrospectively reviewed dynamic contrast-enhanced perfusion data from children with newly diagnosed brain tumors and analyzed the pharmacokinetic parameters correlating with tumor grade. Dynamic contrast-enhanced MR imaging data from 38 patients were analyzed by using commercially available software. Subjects were categorized into 2 groups based on pathologic analyses consisting of low-grade (World Health Organization I and II) and high-grade (World Health Organization III and IV) tumors. Pharmacokinetic parameters were compared between the 2 groups by using linear regression models. For parameters that were statistically distinct between the 2 groups, sensitivity and specificity were also estimated. Eighteen tumors were classified as low-grade, and 20, as high-grade. Transfer constant from the blood plasma into the extracellular extravascular space (K trans ), rate constant from extracellular extravascular space back into blood plasma (K ep ), and extracellular extravascular volume fraction (V e ) were all significantly correlated with tumor grade; high-grade tumors showed higher K trans , higher K ep , and lower V e . Although all 3 parameters had high specificity (range, 82%-100%), K ep had the highest specificity for both grades. Optimal sensitivity was achieved for V e , with a combined sensitivity of 76% (compared with 71% for K trans and K ep ). Pharmacokinetic parameters derived from dynamic contrast-enhanced MR imaging can effectively discriminate low- and high-grade pediatric brain tumors. © 2017 by American Journal of Neuroradiology.

The Virtual Brain: a simulator of primate brain network dynamics

Directory of Open Access Journals (Sweden)

Paula eSanz Leon

2013-06-01

Full Text Available We present TheVirtualBrain (TVB, a neuroinformatics platform for full brainnetwork simulations using biologically realistic connectivity. This simulationenvironment enables the model-based inference of neurophysiological mechanismsacross different brain scales that underlie the generation of macroscopicneuroimaging signals including functional MRI (fMRI, EEG and MEG. Researchersfrom different backgrounds can benefit from an integrative software platformincluding a supporting framework for data management (generation,organization, storage, integration and sharing and a simulation core writtenin Python. TVB allows the reproduction and evaluation of personalizedconfigurations of the brain by using individual subject data. Thispersonalization facilitates an exploration of the consequences of pathologicalchanges in the system, permitting to investigate potential ways to counteractsuch unfavorable processes. The architecture of TVB supports interaction withMATLAB packages, for example, the well known Brain Connectivity Toolbox. TVBcan be used in a client-server configuration, such that it can be remotelyaccessed through the Internet thanks to its web-basedHTML5, JS and WebGL graphical user interface. TVB is alsoaccessible as a standalone cross-platform Python library and application, andusers can interact with the scientific core through the scripting interfaceIDLE, enabling easy modeling, development and debugging of the scientifickernel. This second interface makes TVB extensible by combining it with otherlibraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to thedevelopment of TVB, the architecture and features of its major softwarecomponents as well as potential neuroscience applications.

320-Multidetector row whole-head dynamic subtracted CT angiography and whole-brain CT perfusion before and after carotid artery stenting: Technical note

International Nuclear Information System (INIS)

San Millan Ruiz, Diego; Murphy, Kieran; Gailloud, Philippe

2010-01-01

Introduction: Multidetector CT (MDCT) is increasingly used for the investigation of neurovascular disorders, but restricted z-axis coverage (3.2 cm for 64-MDCT) currently limits perfusion to a small portion of the brain close to the circle of Willis, and precludes dynamic angiographic appreciation of the entire brain circulation. We illustrate the clinical potential of recently developed 320-MDCT extending the z-axis coverage to 16 cm in a patient with symptomatic carotid artery stenosis. Methods: In a 74-year-old patient presenting with critical symptomatic stenosis of the left CCA, pre- and post-carotid artery stenting whole-head subtracted dynamic MDCT angiography and perfusion were obtained in addition to CT angiography of the supra-aortic trunks. Both whole-head subtracted MDCT angiography and perfusion demonstrated delayed left ICA circulation, which normalized after carotid stenting. Discussion: 320-MDCT offers unprecedented z-axis coverage allowing for whole-brain perfusion and subtracted dynamic angiography of the entire intracranial circulation. These innovations can consolidate the role of MDCT as a first intention imaging technique for cerebrovascular disorders, in particular for the acute management of stroke.

Whole-brain dynamic CT angiography and perfusion imaging

Energy Technology Data Exchange (ETDEWEB)

Orrison, W.W. [CHW Nevada Imaging Company, Nevada Imaging Centers, Spring Valley, Las Vegas, NV (United States); College of Osteopathic Medicine, Touro University Nevada, Henderson, NV (United States); Department of Health Physics and Diagnostic Sciences, University of Nevada Las Vegas, Las Vegas, NV (United States); Department of Medical Education, University of Nevada School of Medicine, Reno, NV (United States); Snyder, K.V.; Hopkins, L.N. [Department of Neurosurgery, Millard Fillmore Gates Circle Hospital, Buffalo, NY (United States); Roach, C.J. [School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV (United States); Advanced Medical Imaging and Genetics (Amigenics), Las Vegas, NV (United States); Ringdahl, E.N. [Department of Psychology, University of Nevada Las Vegas, Las Vegas, NV (United States); Nazir, R. [Shifa International Hospital, Islamabad (Pakistan); Hanson, E.H., E-mail: eric.hanson@amigenics.co [College of Osteopathic Medicine, Touro University Nevada, Henderson, NV (United States); Department of Health Physics and Diagnostic Sciences, University of Nevada Las Vegas, Las Vegas, NV (United States); Advanced Medical Imaging and Genetics (Amigenics), Las Vegas, NV (United States)

2011-06-15

The availability of whole brain computed tomography (CT) perfusion has expanded the opportunities for analysing the haemodynamic parameters associated with varied neurological conditions. Examples demonstrating the clinical utility of whole-brain CT perfusion imaging in selected acute and chronic ischaemic arterial neurovascular conditions are presented. Whole-brain CT perfusion enables the detection and focused haemodynamic analyses of acute and chronic arterial conditions in the central nervous system without the limitation of partial anatomical coverage of the brain.

Transfer of spatio-temporal multifractal properties of rainfall to simulated surface runoff

Science.gov (United States)

Gires, Auguste; Giangola-Murzyn, Agathe; Richard, Julien; Abbes, Jean-Baptiste; Tchiguirinskaia, Ioulia; Schertzer, Daniel; Willinger, Bernard; Cardinal, Hervé; Thouvenot, Thomas

2014-05-01

In this paper we suggest to use scaling laws and more specifically Universal Multifractals (UM) to analyse in a spatio-temporal framework both the radar rainfall and the simulated surface runoff. Such tools have been extensively used to analyse and simulate geophysical fields extremely variable over wide range of spatio-temporal scales such as rainfall, but have not often if ever been applied to surface runoff. Such novel combined analysis helps to improve the understanding of the rainfall-runoff relationship. Two catchments of the chair "Hydrology for resilient cities" sponsored by Véolia, and of the European Interreg IV RainGain project are used. They are both located in the Paris area: a 144 ha flat urban area in the Seine-Saint-Denis County, and a 250 ha urban area with a significant portion of forest located on a steep hillside of the Bièvre River. A fully distributed urban hydrological model currently under development called Multi-Hydro is implemented to represent the catchments response. It consists in an interacting core between open source software packages, each of them representing a portion of the water cycle in urban environment. The fully distributed model is tested with pixels of size 5, 10 and 20 m. In a first step the model is validated for three rainfall events that occurred in 2010 and 2011, for which the Météo-France radar mosaic with a resolution of 1 km in space and 5 min in time is available. These events generated significant surface runoff and some local flooding. The sensitivity of the model to the rainfall resolution is briefly checked by stochastically generating an ensemble of realistic downscaled rainfall fields (obtained by continuing the underlying cascade process which is observed on the available range of scales) and inputting them into the model. The impact is significant on both the simulated sewer flow and surface runoff. Then rainfall fields are generated with the help of discrete multifractal cascades and inputted in the

Implantable fiber-optic interface for parallel multisite long-term optical dynamic brain interrogation in freely moving mice