http://www.bibsonomy.org/burst/user/statphys23/analysisBibSonomy BuRST Feed for /user/statphys23/analysis2008-07-26T04:44:44+02:00http://www.bibsonomy.org/bibtex/21d6aee2eeb95ba05326c9b098fb036d2/statphys23statphys232007-06-20T10:16:09+02:00spectral analysis statphys23 problem topic-3 bazarov hydrodynamics <span style="color:#555555;">S. <a href="http://www.bibsonomy.org/author/Velasco">Velasco</a> and B. <a href="http://www.bibsonomy.org/author/Jim\&#039;enez De Cisneros">Jim'enez De Cisneros</a> and J.M.M. <a href="http://www.bibsonomy.org/author/Roco">Roco</a> and J.A. <a href="http://www.bibsonomy.org/author/White">White</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulySpectral analysis of Bazarov's piston2007spectral analysis statphys23 problem topic-3 bazarov hydrodynamics We consider a hard-disk gas contained in an adiabatically isolated cylinder with a movable, frictionles, adiabatic piston under constant external pressure. The power spectrum of the position of a piston at equilibrium is investigated. The analysis is made by means of molecular dynamics simulations and hydrodynamics. For different piston masses we obtain results for the main times scales involved in the problem: the piston frequency and its damping constant.http://www.bibsonomy.org/bibtex/2e0bb75631ffe99d7f57d403cac3bf2ab/statphys23statphys232007-06-20T10:16:09+02:00renewal random topic-3 signal statphys23 telegraph power theory spectrum analysis function mittag-leffler <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Manzini">Manzini</a> and E. <a href="http://www.bibsonomy.org/author/Scalas">Scalas</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyMittag-Leffler time-spaced random telegraph signal2007renewal random topic-3 signal statphys23 telegraph power theory spectrum analysis function mittag-leffler We discuss the power spectrum of a random telegraph signal where waiting times are distributed according to the Mittag-Leffler function. The Mittag-Leffler function is a generalization of the exponential distribution that interpolates between a stretched exponential at low waiting times and a power law at high waiting-times. The Mittag-Leffler distribution is defined as follows in terms of its complementary cumulative distribution function (also called survival function): $$ E_{\beta} (-t^{\beta}) = \sum_{n=0}^{\infty} \frac{(-t^{\beta})^n}{\Gamma (\beta n+1)}, $$ where $0 < \beta \leq 1$. For $\beta =1$, the (one-parameter) Mittag-Leffler function coincides with the exponential survival function. The first moment of the Mittag-Leffler distribution is already infinite.http://www.bibsonomy.org/bibtex/226d733662118f1784c159e846f6552d5/statphys23statphys232007-06-20T10:16:09+02:00topic-11 series time modeling statphys23 walks random stochastic econophysics analysis <span style="color:#555555;">B.P. <a href="http://www.bibsonomy.org/author/Boris Podobnik">Boris Podobnik</a> and H.E. <a href="http://www.bibsonomy.org/author/Stanley">Stanley</a> and I.G. <a href="http://www.bibsonomy.org/author/Grosse">Grosse</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyA Stochastic Process with a Size-Dependent Standard Deviation for Growth Rates2007topic-11 series time modeling statphys23 walks random stochastic econophysics analysis Stationary and nonstationary stochastic processes [P. Jung, {\it Rev. Mod. Phys.} {\bf 234}, 175 (1993)] occur in a variety of phenomena as different as Brownian motion [A. Einstein, {\it Ann. Phys.} {\bf 17}, 549 (1905)], Johnson noise [J. Johnson, {\it Phys. Rev.} {\bf 32}, 97 (1928)], stellar dynamics [S. Chandrasekhar, {\it Rev. Mod. Phys.} {\bf 15}, 1 (1943)], and quantum optics [H. Risken, in {\it Progress in Optics}. Besides in physics, stochastic processes have been successfully applied in economics for modeling and thus explaining diverse levels of economics systems, ranging from the ``micro'' level of company products to the ``macro'' level of company sizes and even national economies. Recently, Fu et al.\ [D. Fu et al., {\it Proc. Natl. Acad. Sci. USA\/} {\bf 102}, 18801 (2005)] show that for different economic variables from both the micro and the macro level, the distribution of logarithmic growth rates are approximately (i) exponential in the central part, (ii) power-law decaying in the tails, and that there is (iii) a monotonically decreasing power-law relation between the company sales and the standard deviation of logarithmic growth rates. Fu et al.\ propose a process recently cited in the Handbook of Industrial Organization [Volume 3, edited by Robert Porter and Mark Armstrong] for modeling the empirical observations (i) and (ii), but this model fails to reproduce observation (iii). For modeling observations (i)--(iii), we propose the multiplicative stochastic process of logarithmic growth rates \begin{equation} R_t \equiv \ln\left({S_t\over S_{t-1}}\right)=\mu_0\Delta t + (S_{t-1})^\gamma\sigma_0\eta_t\Delta t, \end{equation} where $\sigma$, $\gamma$, and $\mu$ are three parameters, $\eta_t$ is an i.i.d.\ Gaussian noise, and $S_t $ is the random variable. When the parameter $\gamma$ introduced for modeling the dependence of the standard deviation $\sigma(R_t)$ on the size $S_t$ is set equal to zero, the stochastic process reduces to geometric Brownian motion, the most widely employed stochastic process in finance. The process can also be related to the Ornstein-Uhlenbeck process, a well-known stochastic process introduced in physics. For different time series of logarithmic growth rates $R_{t}$ with $\gamma=-0.15$, we calculate the average size $\langle S \rangle$ and the standard deviation $\sigma(R_t)$. Fig.~1(a) shows that, due to $\gamma < 0$, $\sigma(R_t)$ versus $\langle S\rangle$ scales as a power law $\sigma(R_t) \propto \langle S\rangle^{\beta}$, where $\beta=\gamma$. We find in Fig.~1(b) that for $\gamma=-0.15$ the central part of distribution $P(R_t|S_0)$ can be approximated by an exponential distribution, and Fig.~1(c) shows that the far tails of $P(R_t|S_0)$ can be approximated by power-laws, where the parameter $\sigma$ controls the power-law exponent. We also find in Fig.~1(d) that four important macroeconomic variables, (export, import, debt, and investments) exhibit the same properties (i)-(iii).http://www.bibsonomy.org/bibtex/29f981e8e4703173c0b24b57ce76371cc/statphys23statphys232007-06-20T10:16:09+02:00analysis language network topic-11 complex statphys23 apparatus textual <span style="color:#555555;">S.M.G. <a href="http://www.bibsonomy.org/author/Caldeira">Caldeira</a> and D.M.B. <a href="http://www.bibsonomy.org/author/Coutinho">Coutinho</a> and G.M. <a href="http://www.bibsonomy.org/author/Teixeira">Teixeira</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyA Complex Network Model of Words to Describe the Dynamics of Text Construction2007analysis language network topic-11 complex statphys23 apparatus textual This work explores interdisciplinary dialogue between Physics and Psychoanalysis on the dynamics of text construction. Human language must be considered a complex object of knowledge. Physics offers concepts and instruments that allow modeling the language apparatus as a complex network. Language complexity is evidenced by an intricate system composed of elements (words) that interact in small groups (sentences), arriving at a bigger, auto-organized organism (text), thus producing emergent order (sense). Each sentence is a conceptual unit, where new sentences are connected with old ones by means of shared words, forming a network. Analyzing unconscious phenomena, Freud describes a “reticular fabric”, a network with vertices, edges and interstices, emphasizing quantitative differences between tracks through which the neuron information passes, generating preferential ways, where “difference of essence is substituted by one of destination and place”. Freud’s hypothesis is that, in speech and writing, the process of choosing words is unconscious and determined by easiness of connection between representans (words corresponding to objects, not as meaning, but as marks). With networks theory, we analyzed different samples of written texts, in search of emergent properties. To allow building networks, the texts received a previous treatment to eliminate grammatical words and reduce them to canonic form. Statistical analyses used Degree Distribution, Diameter, Frequency of Pairs, Critical Centrality and Betweenness, as measures for identifying words of bigger value for the network, as well as ratio of new words. The dynamics of written text construction was analyzed by adding new sentences and words, measuring parameters in each stage. All texts presented a redundancy pattern responsible for the topology of the network, but the expanded texts continued to present new concepts, suggesting a similar behavior between them, including oral discourses. The exception was Joyce’s Ulisses whose new word increases as a function of new sentences presented an exponent extremely high. In line with Freud’s hypothesis, results indicate that the network topology is composed by frequency of word repetition and not by structure of sentences or use of grammatical words.http://www.bibsonomy.org/bibtex/2bfb8741e77da75c0673ea00800d27407/statphys23statphys232007-06-20T10:16:09+02:00processes statphys23 fractal turbulence analysis topic-11 scaling levy time series <span style="color:#555555;">K. <a href="http://www.bibsonomy.org/author/Kiyani">Kiyani</a> and S.C. <a href="http://www.bibsonomy.org/author/Chapman">Chapman</a> and B. <a href="http://www.bibsonomy.org/author/Hnat">Hnat</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyConditioning of data outliers to identify and obtain the scaling behaviour of statistically self-similar and multifractal, non-Gaussian processes from finite length time-series2007processes statphys23 fractal turbulence analysis topic-11 scaling levy time series We address the generic problem of extracting the scaling exponents of a stationary non-Gaussian process realised by a time-series of finite length, where information about the process is not known \emph{a priori}. Estimating the scaling exponents relies upon estimating the moments, or more typically structure functions, of the probability density of the differenced time-series. If the probability density is heavy tailed, outliers strongly influence the scaling behaviour of the moments. From an operational point of view, we wish to recover the scaling exponents of the underlying process by excluding a minimal population of the outliers. This method is particularly sensitive in distinguishing and quantifying self-affine, or self-similar, scaling from weak multifractality. We will illustrate this with two synthetically generated reference models: the first of which is manifestly self-similar, an $\alpha$-stable Levy process; and the second, manifestly multifractal, a cascade \emph{p}-model. We show that for the symmetric $\alpha$-stable Levy process, the Levy exponent is recovered in up to the 6th order moment after only ~0.1-0.5 percent of the data are excluded. The scaling properties of the excluded outliers can also be tested to provide additional information about the system. Unlike the self-similar Levy process, which shows a convergence of all its exponents for each moment order, the multifractal \emph{p}-model process shows a divergence of its exponents as the outlying data points are excluded. Importantly then, successively removing outlying data points does not convert the multifractal \emph{p}-model time-series into a self-similar process. Although this is expected for a multifractal, as the Hurst exponent is locally dependent on the position in the time-series, we will show that outliers distort the scaling of these processes too and that conditioning is also needed. We will be discussing a way of distinguishing this multifractal scaling thus presenting a unified treatment of the handling and remedying of extreme data outliers. As a practical application of the above technique we quantify the scaling of magnetic energy density in the inertial range of solar wind turbulence seen in situ at 1 AU with respect to solar activity. At solar maximum, when the coronal magnetic field is dynamic and topologically complex, we find self-similar scaling in the solar wind, whereas at solar minimum, when the coronal fields are more ordered, we find multifractality. We propose that the self-similar scaling seen at solar maximum is indicative of the non-trivial evolution of the early stages of the development of turbulence being represented near 1 AU in the elliptic at solar maximum, and thus reflects the fractal structure of the processes which drive the interplanetary solar wind at its solar origin. More importantly, this quantifies the solar wind signature that is of direct coronal origin, and distinguishes it from that of local MHD turbulence, with quantitative implications for our understanding of coronal heating of the solar wind. [1] K. Kiyani, S. C. Chapman and B. Hnat , Phys. Rev. E 74(5), 047611 (2006) [2] B. Hnat, S. C. Chapman, G. Rowlands, N. W. Watkins, and W. M. Farrell, Geophys. Res. Lett. 29 (2002) [3] K. Kiyani, S. C. Chapman, B. Hnat, R. M. Nicol, (2007)http://arxiv.org/abs/physics/0702123http://www.bibsonomy.org/bibtex/293f22e7dccd0d6124b5caaf24090af4a/statphys23statphys232007-06-20T10:16:09+02:00topic-11 heart statphys23 rate multifractal fetal analysis <span style="color:#555555;">M.K. <a href="http://www.bibsonomy.org/author/Yum">Yum</a> and K. <a href="http://www.bibsonomy.org/author/Kim">Kim</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyDevelopmental characteristics of inhomogeneous local irregularities in fetal heart rates2007topic-11 heart statphys23 rate multifractal fetal analysis Developmental characteristics of fetal heart rate (FHR) dynamics were not extensively studied. We noticed that local irregularity behavior during a short-term period is quite inhomogeneous in mature fetuses. We studied the developmental property of the inhomogeneous local irregularity of FHR. FHR data of 450 normal fetuses aged between 23 and 40 weeks of gestation were studied. Multifractal analysis of FHR using structure function of FHR and calculated generalized very-short-term (<16 heartbeats) Holder exponent. All negative order Holder exponents showed statistically significant negative correlations with gestational weeks. All positive order Holder exponents showed significant positive correlations with gestatinal weeks. The result indicates that developmental change of the local irregularity occurs inhomogenously throughout the FHR. As fetus matures, when FHR changes small during the very-short-term period, the change becomes more irregular, and when FHR changes great during the period, the change becomes less irregular.http://www.bibsonomy.org/bibtex/2ab06de286d5aae0d9062f7374c9315ad/statphys23statphys232007-06-20T10:16:09+02:00www topic-11 modeling analysis large scale statphys23 network <span style="color:#555555;">B.M.T. <a href="http://www.bibsonomy.org/author/Gon\c Calves">Gonc Calves</a> and J.J. <a href="http://www.bibsonomy.org/author/Ramasco">Ramasco</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyModelling the behavior of individual websurfers2007www topic-11 modeling analysis large scale statphys23 network We perform a large scale analysis of the behavior of individual users as they navigate through a World Wide Web domain. The logs of the webserver responsible for serving Emory University's web content under the \emph{emory.edu} domain and all subdomains over a period of $294$ consecutive days is analyzed. The resulting bipartite graph contained $3,179,671$ IP addresses connected to $2,562,398$ unique URLs through $53,582,121$ individual and timestamped page requests. Limited information on the referer URLs is also available. The topology and the dynamics of the network evolution is analyzed and both numerical and analytical modeling strategies are proposed. The resulting properties of the simulated network are compared with the original empirical results.http://www.bibsonomy.org/bibtex/25f7f2e450c532cf709eb7830573d5cff/statphys23statphys232007-06-20T10:16:09+02:00topic-11 statphys23 atmospheric multifractals dynamics detrended pollutants fluctuation analysis nonlinear <span style="color:#555555;">A. <a href="http://www.bibsonomy.org/author/Munoz Diosdado">Munoz Diosdado</a> and G. <a href="http://www.bibsonomy.org/author/Galvez Coyt">Galvez Coyt</a> and H. <a href="http://www.bibsonomy.org/author/Reyes Cruz">Reyes Cruz</a> and D. <a href="http://www.bibsonomy.org/author/Bueno Hernandez">Bueno Hernandez</a> and J.L. <a href="http://www.bibsonomy.org/author/Del Rio Correa">Del Rio Correa</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyNonlinear analysis of time series of atmospheric pollutants in Mexico City2007topic-11 statphys23 atmospheric multifractals dynamics detrended pollutants fluctuation analysis nonlinear Atmospheric pollution is an important problem in the metropolitan area of Mexico City. Concentrations of ozone, sulfur dioxide, carbon monoxide, nitrogen dioxide and PM10 particles (particles smaller than 10 micrometers) have been measured in approximately thirty monitoring stations in different places of the city since 1986, although the data before 1990 are not very confident. Each hour a concentration measure is measured. In this work we have analyzed with nonlinear dynamics techniques the concentration time series of atmospheric pollutants since 1990 to 2005. We have used detrended fluctuation analysis (DFA) and the Higuchi's algorithm in order to characterize the correlations in these time series. We have found that there are long-large correlations in the time series of ozone, sulfur dioxide, carbon monoxide, nitrogen dioxide and PM10 particles. However, there are significant differences between the correlational dynamics of each pollutant; we have found greater correlations in the time series of PM10 and ozone. We have concluded that the permanence time of PM10 and ozone is greater than the permanence time of sulfur dioxide, carbon monoxide and nitrogen dioxide. We have also studied the annual variation of the correlation exponents and we have obtained that there is a qualitative correlation between the values of the correlation exponents and the concentration values of the pollutants. In order of better characterize the nonlinear properties of the time series of these complex systems; we analyzed the time series by using the Chhabra and Jensen multifractal formalism. All the time series (annual and total) are multifractal, however, the multifractality degree (the width of the multifractal spectrum) is different for each pollutant. The degree of multifractality has been associated with the complexity of the time series, in these sense we have encountered that time series of sulfur dioxide are more complex than time series of other pollutants. Although the width of the multifractal spectrum is not a measure of the concentration levels, when we analyzed the plots of the spectrum width of the annual time series versus the year, the greater values of the width coincide with the greater values of concentration of the atmospheric pollutants.http://www.bibsonomy.org/bibtex/25ae1977caa04035a8055e4e43a644bf5/statphys23statphys232007-06-20T10:16:09+02:00analysis geomorphology topic-11 statphys23 earth fractals statistical moon <span style="color:#555555;">A. <a href="http://www.bibsonomy.org/author/Baldassarri">Baldassarri</a> and M. <a href="http://www.bibsonomy.org/author/Montuori">Montuori</a> and O. <a href="http://www.bibsonomy.org/author/Prieto-ballesteros">Prieto-ballesteros</a> and S.C. <a href="http://www.bibsonomy.org/author/Manrubia">Manrubia</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyReading the geometry of landscapes: Global topography reveals the action of geological processes on Earth2007analysis geomorphology topic-11 statphys23 earth fractals statistical moon The analysis of the geometry of natural relief aids in the qualitative interpretation of the geological processes that have acted through time. However, an underlying, recurrent and still open question is whether there is a one-to-one relationship between the quantitative properties of landscapes and the dominant geomorphological processes that originate them. We show that the geometry of isolines (curves at fixed elevation) is an appropriate observable to disantagle such a relationship. A global fractal analysis of terrestrial isolines yields a clear identification of trenches and abyssal plains, differentiates oceanic ridges from continental slopes and platforms, localizes coastlines and river systems, and isolates areas at high elevation (or latitude) subjected to the erosive action of ice. Further comparison with geometrical properties of the lunar landscape supports the existence of a one-to-one correspondence between principal geomorphic processes and landforms.http://www.bibsonomy.org/bibtex/23ac4612c53ad8caea069c822447f5a55/statphys23statphys232007-06-20T10:16:09+02:00statphys23 statistics topic-3 aris-taylor dispersion lattice-boltzmann analysis random equation beadpack langevin <span style="color:#555555;">R.S. <a href="http://www.bibsonomy.org/author/Maier">Maier</a> and D.M. <a href="http://www.bibsonomy.org/author/Kroll">Kroll</a> and H.T. <a href="http://www.bibsonomy.org/author/Davis">Davis</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulySimulations and Theory of Dispersion in Flow in Beadpacks2007statphys23 statistics topic-3 aris-taylor dispersion lattice-boltzmann analysis random equation beadpack langevin The flow of liquid through a cylindrical random beadpack is simulated using the Lattice-Boltzmann technique. Dispersion of tracer particles is simulated using the Langevin equation. In contradiction of conventional wisdom, we find that the longitudinal dispersivity is a strong function of the ratio R/d, where R is the radius of the cylinder and d is the diameter of the beads, even beyond R/d values of 50. With the Aris-Taylor analysis of the convective dispersion equation, we are able to show that this effect arises from the rapidly varying boundary layer near the wall of the cylinder. The reason the radius dependence of the dispersion has not received much attention is that in most beadpack experiments the dispersive flow is no fully developed. We give the criteria for fully developed flow in terms of R/d, flow rate and time.http://www.bibsonomy.org/bibtex/278bb94a8a995f41f87b886037d4dc145/statphys23statphys232007-06-20T10:16:09+02:00roughness scaling growth analysis instability fracture topic-4 statphys23 <span style="color:#555555;">N. <a href="http://www.bibsonomy.org/author/Mallick">Mallick</a> and P.P. <a href="http://www.bibsonomy.org/author/Cortet">Cortet</a> and S. <a href="http://www.bibsonomy.org/author/Santucci">Santucci</a> and S.G. <a href="http://www.bibsonomy.org/author/Roux">Roux</a> and L. <a href="http://www.bibsonomy.org/author/Vanel">Vanel</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyInluence of growth dynamics on fracture roughness2007roughness scaling growth analysis instability fracture topic-4 statphys23 Since the early description of rough fractures as self-affine surfaces, the existence of universal roughness exponents has been strongly debated. There are now many experimental evidences for a non-universal value of the roughness exponent of fracture surfaces (influence of the heterogenity of the material structure, the anisotropy of the fracturation process...). Also, a recent analysis suggests that in rupture of paper, the crack interface would be multifractal. We study the roughness of a crack interface in a sheet of paper during a creep experiment. The paper sheet is loaded with an initial crack at its center in a tensile machine with a constant force (mode I). This force is chosen in order to have a stable crack. Nevertheless, the crack grows very slowly (about $10^{-4}\,\rm{m.s}^{-1}$) by a thermal activation process (subcritical growth), and when it reaches a critical length, there is a transition in the growth regime, which becomes very fast (about $300\,\rm{m.s}^{-1}$) and is driven by mechanical instability. After digitization of the \textit{post mortem} sample, we are able to extract subcritical and fast growth parts of the signal and analyse them comparatively. Roughness exponents are reliably estimated using the first order cumulant, a quantity recently introduced in the turbulence literature. We show that this quantity should be a beter estimator of the roughness exponent than the second order structure function usually used. Using a large data set, we find a significant difference in fracture roughness between the slow (sub-critical) and the fast growth regime. In the subcritical growth part we find a roughness exponent of about $0.70$ while in the fast growth part this exponent is about $0.64$. We also study the influence of paper structure, sample geometry and loading. Finally, we discuss the relevance of multifractality, by reviewing very recent signal processing tools that have been developped to characterize scaling properties.http://www.bibsonomy.org/bibtex/265675c976623270a0e654462016a480c/statphys23statphys232007-06-20T10:16:09+02:00topic-10 cardiovascular systems statphys23 non-stationarities system series complex time analysis quasi-periodicities <span style="color:#555555;">A.Y. <a href="http://www.bibsonomy.org/author/Schumann">Schumann</a> and J.W. <a href="http://www.bibsonomy.org/author/Kantelhardt">Kantelhardt</a> and A. <a href="http://www.bibsonomy.org/author/Bauer">Bauer</a> and G. <a href="http://www.bibsonomy.org/author/Schmidt">Schmidt</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyMultivariate Phase Rectified Signal Averaging2007topic-10 cardiovascular systems statphys23 non-stationarities system series complex time analysis quasi-periodicities The behaviour of many natural complex systems is characterized by nonstationarities and phase shifts making a conventional analysis of periodicities not fully reliable. Recently, the method of Phase Rectified Signal Averaging (PRSA) [1] has been introduced for the extraction of non-stationary oscillations out of noisy signals with varying mean. As an example, PRSA was shown to be superior in risk classification of sudden cardiac death after initial myocardial infarction [2]. The main advantage of the PRSA is its capability to analyze separately periodicities occurring around increases (or, alternatively, decreases) of the signal. We now suggest a multivariate form of PRSA to study the relationships (i.e., interactions, partial syncronization, etc.) between two or more complex signals. We compare this method with cross correlation and cross spectra techniques and also discuss the application of multivariate PRSA in a recent baroreflex regulation study. 1) A. Bauer et al., Physica A 364, 423 (2006)\\ 2) A. Bauer et al., The Lancet 367, 1674 (2006)http://www.bibsonomy.org/bibtex/2004f9734df8daadc2c24d473c1c7c880/statphys23statphys232007-06-20T10:16:09+02:00values analysis series statphys23 fluctuation extreme processes stochastic phenomena topic-11 time <span style="color:#555555;">C. <a href="http://www.bibsonomy.org/author/Pennetta">Pennetta</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyStatistics of the Extreme Values in Presence of Intermediate-Term Correlations2007values analysis series statphys23 fluctuation extreme processes stochastic phenomena topic-11 time The return time statistics (RTS) of the extreme values in time series with long-term correlations has been recently studied by Bunde et al. [1] and Altmann and Kantz [2]. These authors found that the return intervals of the extreme values follow a stretched exponential distribution with a value of the distribution exponent practically coincident with the correlation exponent of the time series. In this talk, the RTS of time series characterized by finite-term correlations with non-exponential decay will be considered. Precisely, the results will be discussed of numerical analyses of the return intervals of extreme values associated with the resistance fluctuations displayed by a resistor in a nonequilibrium stationary states [3]. These results show that when the auto-correlation function displays a non-exponential and non-power-law decay, the distribution of the return times of extreme values still keeps the stretched exponential form, with an exponent largely independent of the threshold [3]. Thus, the stretched exponential distribution cannot be considered an exclusive feature of long-term correlated time series. 1) A. Bunde et al., {\em Physica A}, {\bf 330}, 1 (2003) and A. Bunde et al., {\em Phys. Rev. Lett.}, {\bf 94}, 048701 (2005). \\ 2) E. G. Altmann, H. Kantz, {\em Phys. Rev.} E, {\bf 71}, 056106 (2005). \\ 3) C. Pennetta, {\em Eur. Phys. J.} B, {\bf 50}, 95 (2006).http://www.bibsonomy.org/bibtex/2ae381688ccf551cedf24836c2128028d/statphys23statphys232007-06-20T10:16:09+02:00topic-11 time social statistical economic analysis systems statphys23 financial series complex <span style="color:#555555;">V. <a href="http://www.bibsonomy.org/author/Alfi">Alfi</a> and A. <a href="http://www.bibsonomy.org/author/Petri">Petri</a> and L. <a href="http://www.bibsonomy.org/author/Pietronero">Pietronero</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyA sum rule approach to detect complex correlation in time series2007topic-11 time social statistical economic analysis systems statphys23 financial series complex A basic problem in the analysis of time series consists in unveiling and characterizing correlations among the variables at different times. In practice inmost cases this consists in considering the two point correlations over a long time series. Often complex properties are related to the long time behavior of these correlations. However, in many systems, like for example financial time series, simple correlations are intrinsically excluded by the arbitrage hypothesis. This leaves space for subtle complex correlations which are clearly difficult to detect. The usual approach is to focus on the pair correlations for grouped variables like in the problem of volatility clustering. Also in this case the availability of long time series is fundamental. This poses another problem because the stationarity hypothesis is not always appropriate. Inspired by these problems we introduce a new method to detect complex correlations in time series of finite size. The method comes from the Spitzerกวs identity which controls the extremal values for sums of random variables. The basic idea is that a deviation from this identity is a sign of correlations in the variables and it corresponds to a sort of sum rule for correlations of any extension also in non stationary processes. We have tested the method which has only four point correlations. The application to real financial data shows that the method is a practical tool to detect correlations of any type even in finite time series. This is usually not possible with the standard statistical tools.http://www.bibsonomy.org/bibtex/257a369a3ff7324fab4fa2a76acde873e/statphys23statphys232007-06-20T10:16:09+02:00statphys23 complexity scaling beat rate meditation heart analysis topic-11 <span style="color:#555555;">A. <a href="http://www.bibsonomy.org/author/Sarkar">Sarkar</a> and P. <a href="http://www.bibsonomy.org/author/Barat">Barat</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyEffect of meditation on human heartbeat rate2007statphys23 complexity scaling beat rate meditation heart analysis topic-11 Physiological signals are endpoint manifestations of cellular events that depict how physiological systems vary over time. Extraordinary complexity of the physiological signals and the recognition that physiologic time series contain hidden information has stimulated growing interest in applying concepts and techniques of statistical and nonlinear physics to a wide range of biomedical problems. The human heartbeat is one important example of complex physiologic fluctuations which is considered as a suitable marker for estimation of autonomic nervous system. Recent studies reveal that under normal conditions, heartbeat rate fluctuations may display extended correlations of the type typically exhibited by dynamical systems far from equilibrium. In this paper, we study the effect of a particular form of mediation (Chinese Chi) on the heartbeat rate. We have applied two well known methods of scaling analysis (1) Detrended Fluctuation Analysis (DFA) and (2) Diffusion Entropy Analysis (DEA) on the 8 different heartbeat rate data sets (pre meditation and during meditation). The analyses revealed that the mediation affects the long range correlation exhibited by a normal heart. Moreover, DEA showed an appearance of periodicity in the heartbeat rate during meditation. We have also quantified the complexity of the heart rate variability using the multiscale entropy analysis and recurrence analysis. The heart beat during meditation is found to be more.http://www.bibsonomy.org/bibtex/209bf4c5ca4ef99ca3248ae0688f2e882/statphys23statphys232007-06-20T10:16:09+02:00statphys23 topic-5 noise analysis processes stochastic series measurement time <span style="color:#555555;">D. <a href="http://www.bibsonomy.org/author/Kleinhans">Kleinhans</a> and R. <a href="http://www.bibsonomy.org/author/Friedrich">Friedrich</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyEstimation of Drift and Diffusion function in presence of measurement noise2007statphys23 topic-5 noise analysis processes stochastic series measurement time The understanding of complex systems greatly has benefited from the concept of order parameters, that obey stochastic partial differential equations [1]. Recently, a method for the direct estimation of these equation from measured data sets has been proposed [2]. However, this procedure involves the estimation of the moments of the transition probability density functions (pdfs) in the limit of infinitesimal small increments in time, that frequently are not accessible from discrete measurements. Moreover, measurement noise seriously impacts the transition pdfs at small time increments and, therefore, tampers the results of the estimation procedure. This contribution addresses the progress of two recent works with respect to this shortcoming. First, an iterative method was proposed, that avoids the limiting procedure and, therefore, is less sensitive to measurement noise [3]. It is based on the iterative optimisation of the transition pdfs in reference to the pdfs, that directly can be obtained form the measured data set. Recently, the conformance of this procedure with maximum likelihood methods could be demonstrated [4]. Second, the former method could be extended for noisy data [5]. Thereby, the increasing impacts of measurement noise on the transition pdfs at small time increments can be utilised for the simultaneous estimation of the noise amplitude and the process' dynamics. For the Ornstein-Uhlenbeck process, closed expressions for the estimation procedure could be derived, that permit the proper reconstruction even in case of high noise amplitudes.\\ 1) H.~Haken. \newblock {\em Synergetics}. \newblock Springer Series in Synergetics. Springer-Verlag, Berlin, 2004. \newblock Introduction and advanced topics, Reprint of the third (1983) edition [{\it Synergetics}] and the first (1983) edition [{\it Advanced synergetics}].\\ 2) S.~Siegert, R.~Friedrich, and J.~Peinke. \newblock Analysis of datasets of stochastic systems. \newblock {\em Physics Letters A}, 243:275--280, 1998.\\ 3) D.~Kleinhans, R.~Friedrich, A.~Nawroth, and J.~Peinke. \newblock An iterative procedure for the estimation of drift and diffusion coefficients of langevin processes. \newblock {\em Phys Lett A}, 346:42--46, 2005.\\ 4) D.~Kleinhans and R.~Friedrich. \newblock Maximum likelihood estimation of drift and diffusion functions. \newblock {\em (to be published in Phys. Lett. A)}, preprint available at http://arxiv.org/abs/physics/0611102.\\ 5) F.~Boettcher, J.~Peinke, D.~Kleinhans, R.~Friedrich, P.G.~Lind, and M.~Haase. \newblock Reconstruction of complex dynamical systems affected by strong measurement noise. \newblock {\em Phys. Rev. Lett.}, 97:090603, 2006.http://www.bibsonomy.org/bibtex/23a2bfc17b8a4f738ee033242eca64dfe/statphys23statphys232007-06-20T10:16:09+02:00function topic-1 statphys23 entropy analysis statistical mechanics survival nonextensive <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Uchinami">Uchinami</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyNonextensive Statistical Mechanics in Survival Analysis2007function topic-1 statphys23 entropy analysis statistical mechanics survival nonextensive The probability distribution which the lifetime data obey can be described approximately by the logistic system, and the method to analyze these data by its probability distribution is known as survival analysis$^{1)}$. We take notice of the logistic system where its time dependence of population size $N(t)$ is described by ${\displaystyle \frac{dN}{dt}=-\mu(t)N}$ with the force of mortality $\mu(t)$. Tsallis et al. have recently developed the nonextensive statistical mechanics$^{2)}$. According to this formulation, we introduce the $q-$exponential function $y=f(x)=e_q^{-x}\equiv \left[ 1+(q-1)x\right]^{-\frac{1}{q-1}}$, and its inverse function, i.e., the $q-$logarithmic function $y=f^{-1}(x)=-\log_q x\equiv \frac{(1/x)^{q-1}-1}{q-1}$. \begin{flushleft} {\bf (A)\ In the case of $\mu(t)=\lambda_q\left[N(t)\right]^{q-1}$} \end{flushleft} In this case, we define the modified time $\widetilde{t}_{\rm a}$ by $$\widetilde{t}_{\rm a}\equiv\lambda_q\left[N(0)\right]^{(q-1)}t,$$ so that the Hazard function $\lambda(t)$ and the survival function $S(t)$ in survival analysis can be written respectively by $$\lambda(t)=\left(\frac{\widetilde{t}_{\rm a}}{t}\right)\left(e_q^{-\widetilde{t}_{\rm a}}\right)^{q-1}, \qquad S(t)=e_q^{-\widetilde{t}_{\rm a}}.$$ For this survival function $S(t)=e_q^{-\widetilde{t}_{\rm a}}$, we define its inverse function, that is, nonextensive entropy $\widetilde{S}_q(t)$ as $\widetilde{S}_q(t)\equiv -\log_q S(t)$ by using the $q-$logarithmic function, so that the nonextensive entropy can be expressed simply as $$\widetilde{S}_q(t)=\widetilde{t}_{\rm a}.$$ We can confirm that this nonextensive entropy satisfy a pseudo-additive property. When we pay attention to the limit of $q\to 1$, in particular, we have $\lim_{q\to 1}\widetilde{t}_{\rm a}=\lambda_1 t$, and also have $\lim_{q\to 1}e_q^{-\widetilde{t}_{\rm a}}=e^{-\lambda_1 t}$ in which $e^{-\lambda_1 t}$ denotes the ordinary exponential function. As a result of these behaviors, we see that various quantities in survival analysis and the nonextensive entropy are independent of $N(0)$ which means the initial condition. That is to say, this implies the property referred to as the lack of memory property. \begin{flushleft} {\bf (B)\ In the case of $\mu(t)=\lambda_1+\lambda_q\left[N(t)\right]^{q-1}$} \end{flushleft} In this case, we define the modified time $\widetilde{t}_{\rm b}$ by $$\widetilde{t}_{\rm b}\equiv \frac{\lambda_q\left[N(0)\right]^{q-1}}{\lambda_1}\frac{1-e^{-(q-1)\lambda_1t}}{q-1}.$$ So the Hazard function $\lambda(t)$ and the survival function $S(t)$ in this case can be written as $$\lambda(t)=\lambda_1+\lambda_q\left[N(0)\right]^{q-1}e^{-(q-1)\lambda_1t}\left(e_q^{-\widetilde{t}_{\rm b}}\right)^{q-1}$$ $$\quad S(t)=e^{-\lambda_1t}\times e_q^{-\widetilde{t}_{\rm b}}$$ respectively. This form of the survival function shows that it can be expressed as the product of the $q-$exponential function $S^{(q)}(t)\equiv e_q^{-\widetilde{t}_{\rm b}}$ and the ordinary exponential function $S^{(1)}\equiv e^{-\lambda_1t}$, that is, $$S(t)=S^{(1)}\times S^{(q)}(t).$$ For this survival function $S(t)=S^{(1)}S^{(q)}(t)$, the corresponding nonextensive entropy $\widetilde{S}(t)\equiv -\log_qS(t)$ which is defined by using the $q-$logarithm has the following expression: $$\widetilde{S}(t)=\frac{\left(1/S(t)\right)^{q-1}-1}{q-1}=$$ $$=\widetilde{S}_q^{(1)}+\widetilde{S}_q^{(q)}(t)+(q-1)\widetilde{S}_q^{(1)}\widetilde{S}_q^{(q)}(t),$$ where $\widetilde{S}_q^{(1)}(t)$ and $\widetilde{S}_q^{(q)}$ are $$ \widetilde{S}_q^{(1)}(t)= -\log_qS^{(1)}(t) =\frac{1-e^{(q-1)\lambda_1t}}{q-1}$$ $$\quad \widetilde{S}_q^{(q)}(t)=-\log_qS^{(q)}=\widetilde{t}_{\rm b},$$ respectively. When we pay attention to the limit of $q\to 1$, also, we can develop similar considerations as the case {\bf (A)}. Therefore, we have $\lim_{q\to 1}\widetilde{t}_{\rm b}=\lambda_1 t$. As a result, we obtain in this limit that the Harzard function $\lambda(t)$ is given by $\lambda(t)=2\lambda_1$, the survival function $S(t)$ is given by $S(t)=e^{-2\lambda_1 t}$, and the nonextensive entropy $\widetilde{S}(t)$ is given by $\widetilde{S}(t)=2\lambda_1 t$. In this limit of the case {\bf (B)}, we also show the lack of memory property which is independent of the initial condition $N(0)$.\\ J. F. Lawless, \textit{Statistical Models and Methods for Lifetime Data}, 2nd ed. (John Wiley \& Sons, 2003)\\ C. Tsallis, in \textit{Nonextensive Entropy}, edited by M. Gell-Mann and C. Tsallis (Oxford University Press, 2004)http://www.bibsonomy.org/bibtex/25cbe474e740f7fbe0e885a908133dba4/statphys23statphys232007-06-20T10:16:09+02:00statphys23 time analysis electromagnetic trap series microsplasmas topic-5 confined <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Romero-Bastida">Romero-Bastida</a> and M.A. <a href="http://www.bibsonomy.org/author/Olivares-robles">Olivares-robles</a> </span><em>Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, </em><em>Genova, Italy, </em><em>9-13 July2007. </em>Wed Jun 20 10:16:09 CEST 2007Genova, ItalyAbstract Book of the XXIII IUPAP International Conference on Statistical Physics9-13 JulyMicroscopic dynamics of ion motion in microplasmas from nonlinear time-series analysis.2007statphys23 time analysis electromagnetic trap series microsplasmas topic-5 confined The problem of characterizing the dynamical regime, either regular or chaotic, of a Hamiltonian many-degrees-of-freedom system is investigated by analyzing the computer simulated position and velocity time series of ions confined in a Penning trap and forming so-called microplasmas. The ($\varepsilon,\tau$) entropy, which measures the amount of information generated by unit time at different scales $\tau$ of time and $\varepsilon$ of the observable, is numerically computed by methods of nonlinear time-series analysis using the position and velocity signals of a single ion for different trap geometries, as well as for various values of both the energy of the system and ion number. Results obtained from the aforementioned time series are compared and discussed.