CIV.—The formation of chlorinated amines by the reduction of nitro-compounds

Hurst, Winifred Grace; Thorpe, Jocelyn Field

doi:10.1039/ct9150700934

Cited by 109 publications

(137 citation statements)

References 0 publications

Supporting

Mentioning

136

Contrasting

Unclassified

Order By: Relevance

“…This point is underscored by the fact that the knowledge of the Hurst exponent [9,10] does not provide sufficient information about the physical processes underlying the data. Indeed, the expression, < [x(t 1 ) − x(t 2 )] 2 >∼ |t 2 − t 1 | 2H , which defines the Hurst exponent H, in fact, does not provide a clear and unique information about the random process x(t).…”

Section: Introductionmentioning

confidence: 98%

“…One of the most popular is the Hurst exponent H [9,10] measuring the "roughness" of multifractal data and allowing one to distinguish between conventional diffusion (H=0.5), subdiffusion (H < 0.5) and superdiffusion (H > 0.5), as discussed earlier in Section 1. However, the presently available results for the Hurst exponent, computed largely from the AE index are rather controversial, can not distinguish between sub-and super-diffusion regimes.…”

Section: Coupled Solar Wind -Magnetosphere Systemmentioning

confidence: 99%

See 1 more Smart Citation

Multiscale behavior and fractional kinetics from the data of solar wind–magnetosphere coupling

Zaslavsky

Guzdar

Edelnman

et al. 2008

Communications in Nonlinear Science and Numerical Simulation

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 98%

Section: Coupled Solar Wind -Magnetosphere Systemmentioning

confidence: 99%

Multiscale behavior and fractional kinetics from the data of solar wind–magnetosphere coupling

Zaslavsky

Guzdar

Edelnman

et al. 2008

Communications in Nonlinear Science and Numerical Simulation

View full text Add to dashboard Cite

“…The average of rescaled range in all the boxes of equal size n, is obtained and denoted by < R/S >. Repeat the above computation over different box size n to provide a relationship between < R/S > and n. According to Hurst's experimental study [64], a power-law relation between < R/S > and the box size n indicates the presence of scaling: < R/S >∼ n α . Figure 14 shows the results of R/S, DFA-1 and DFA-2 on the same generated noises.…”

Section: Appendix A: Noisementioning

confidence: 99%

Effect of trends on detrended fluctuation analysis

et al. 2001

View full text Add to dashboard Cite

Detrended fluctuation analysis (DFA) is a scaling analysis method used to estimate long-range power-law correlation exponents in noisy signals. Many noisy signals in real systems display trends, so that the scaling results obtained from the DFA method become difficult to analyze. We systematically study the effects of three types of trends -linear, periodic, and power-law trends, and offer examples where these trends are likely to occur in real data. We compare the difference between the scaling results for artificially generated correlated noise and correlated noise with a trend, and study how trends lead to the appearance of crossovers in the scaling behavior. We find that crossovers result from the competition between the scaling of the noise and the "apparent" scaling of the trend. We study how the characteristics of these crossovers depend on (i) the slope of the linear trend; (ii) the amplitude and period of the periodic trend; (iii) the amplitude and power of the power-law trend and (iv) the length as well as the correlation properties of the noise. Surprisingly, we find that the crossovers in the scaling of noisy signals with trends also follow scaling laws -i.e. long-range power-law dependence of the position of the crossover on the parameters of the trends. We show that the DFA result of noise with a trend can be exactly determined by the superposition of the separate results of the DFA on the noise and on the trend, assuming that the noise and the trend are not correlated. If this superposition rule is not followed, this is an indication that the noise and the superimposed trend are not independent, so that removing the trend could lead to changes in the correlation properties of the noise. In addition, we show how to use DFA appropriately to minimize the effects of trends, and how to recognize if a crossover indicates indeed a transition from one type to a different type of underlying correlation, or the crossover is due to a trend without any transition in the dynamical properties of the noise.

show abstract

“…They represented the properties of flickering in a two-dimensional parameter space and showed that CVs of different classes tend to occupy different regions of this space. Later Tamburini et al (2009, hereafter TDB09) used the R/S rescaled range analysis (Hurst 1951;Hurst et al 1965) for the first time as a complementary tool to characterize the degree of persistence/anti-persistence of the white light flickering of the IP-class CV V709 Cas. Motivated by the interesting results presented in these works, we have therefore used the same statistical techniques to study the X-ray flickering properties of CVs as a whole as well as a function of their individual classes to link them with physical parameters.…”

Section: Introductionmentioning

confidence: 99%

Wavelet andR/Sanalysis of the X-ray flickering of cataclysmic variables

Anzolin¹,

Tamburini

Martino³

et al. 2010

A&A

View full text Add to dashboard Cite

Context. Recently, wavelets and R/S analysis have been used as statistical tools to characterize the optical flickering of cataclysmic variables. Aims. Here we present the first comprehensive study of the statistical properties of X-ray flickering of cataclysmic variables in order to link them with physical parameters. Methods. We analyzed a sample of 97 X-ray light curves of 75 objects of all classes observed with the XMM-Newton space telescope. By using the wavelets analysis, each light curve has been characterized by two parameters, α and Σ, that describe the energy distribution of flickering on different timescales and the strength at a given timescale, respectively. We also used the R/S analysis to determine the Hurst exponent of each light curve and define their degree of stochastic memory in time.Results. The X-ray flickering is typically composed of long time scale events (1.5 < ∼ α < ∼ 3), with very similar strengths in all the subtypes of cataclysmic variables (−3 < ∼ Σ < ∼ −1.5). The X-ray data are distributed in a much smaller area of the α − Σ parameter space with respect to those obtained with optical light curves. The tendency of the optical flickering in magnetic systems to show higher Σ values than the non-magnetic systems is not encountered in the X-rays. The Hurst exponents estimated for all light curves of the sample are larger than those found in the visible, with a peak at 0.82. In particular, we do not obtain values lower than 0.5. The X-ray flickering presents a persistent memory in time, which seems to be stronger in objects containing magnetic white dwarf primaries. Conclusions. The similarity of the X-ray flickering in objects of different classes together with the predominance of a persistent stochastic behavior can be explained in terms of magnetically-driven accretion processes acting in a considerable fraction of the analyzed objects.

show abstract

CIV.—The formation of chlorinated amines by the reduction of nitro-compounds

Cited by 109 publications

References 0 publications

Multiscale behavior and fractional kinetics from the data of solar wind–magnetosphere coupling

Multiscale behavior and fractional kinetics from the data of solar wind–magnetosphere coupling

Effect of trends on detrended fluctuation analysis

Wavelet andR/Sanalysis of the X-ray flickering of cataclysmic variables

Contact Info

Product

Resources

About