Weak Long-range Correlations Research Articles

Fractal correlations in the Covid-19 genome sequence via multivariate rescaled range analysis

An approach based on fractal scaling analysis to characterize the organization of the Covid-19 genome sequences is presented in this work. The method is based on a multivariate version of the fractal rescaled range analysis implemented on a sliding window scheme to detect variations of long-range correlations over the genome sequence domains. As a preliminary step, the nucleotide sequence is mapped in a numerical sequence by following a Voss rule, resulting in a multichannel sequence represented as a binary matrix. Fractal correlations, quantified in terms of the Hurst exponent, depending on the region of the sequence, where the Covid-19 genome sequences are predominantly random, with some patches of weak long-range correlations. The analysis shows that the regions of randomness are more abundant in the Covid-19 sequences than in the primitive SARS sequence, which suggests that the Covid-19 virus possesses a more diverse genomic structure for replication and infection. The analysis constrained to the surface glycoprotein region shows that the Covid-19 sequence is less random as compared to the SARS sequence, which indicates that the Covid-19 virus can undergo more ordered replications of the spike protein. The Omicron variation exhibits an interesting pattern with some randomness similarities with the other SARS and the Covid-19 genome sequences. Overall, the results show that the multivariate rescaled range analysis provides a suitable framework to assess long-term correlations hidden in the internal organization of the Covid-19 genome sequence.

Time-Scaling Properties of Sunshine Duration Based on Detrended Fluctuation Analysis over China

The spatial and temporal variabilities of the daily Sunshine Duration (SSD) time series from the Chinese Meteorological Administration during the 1954–2009 period are examined by the Detrended Fluctuation Analysis (DFA) method. As a whole, weak long-range correlations (LRCs) are found in the daily SSD anomaly records over China. LRCs are also verified by shuffling the SSD records. The proportion of the stations with LRCs accounts for about 97% of the total. Many factors affect the scaling properties of the daily SSD records such as sea-land difference and Tibetan Plateau landform and so on. We find land use and land cover as one of the important factors closely links to LRCs of the SSD. Strong LRCs of the SSD mainly happen in underlying surface of deserts and crops, while weak LRCs occur in forest and grassland. Further studies of scaling behaviors are still necessary to be performed due to the complex underlying surface and climate system.

Current fluctuations in boundary-driven quantum spin chains

Boundary-driven spin chains are paradigmatic non-equilibrium systems in both classical and quantum settings. In general it may not be possible to distinguish classical from quantum transport through monitoring the mean current, as both ballistic as well as diffusive regimes occur in either setting. Here we show that genuine quantum features become manifest in large fluctuations which allow a discrimination between classical and quantum transport: in the classical case, realizations that are characterized by atypically large boundary activity are associated with larger than typical currents, i.e. an enhanced number of events at the boundaries goes together with a large current. Conversely, in the quantum case the Zeno effect leads to the suppression of current in trajectories with large activity at the boundary. We analyze how these different dynamical regimes are reflected in the structure of rare fluctuations. We show furthermore that realizations supporting a large current are generated via weak long-range correlations within the spin chain, typically associated with hyperuniformity.

Long-Range Correlations in Sentence Series from A Story of the Stone.

A sentence is the natural unit of language. Patterns embedded in series of sentences can be used to model the formation and evolution of languages, and to solve practical problems such as evaluating linguistic ability. In this paper, we apply de-trended fluctuation analysis to detect long-range correlations embedded in sentence series from A Story of the Stone, one of the greatest masterpieces of Chinese literature. We identified a weak long-range correlation, with a Hurst exponent of 0.575±0.002 up to a scale of 104. We used the structural stability to confirm the behavior of the long-range correlation, and found that different parts of the series had almost identical Hurst exponents. We found that noisy records can lead to false results and conclusions, even if the noise covers a limited proportion of the total records (e.g., less than 1%). Thus, the structural stability test is an essential procedure for confirming the existence of long-range correlations, which has been widely neglected in previous studies. Furthermore, a combination of de-trended fluctuation analysis and diffusion entropy analysis demonstrated that the sentence series was generated by a fractional Brownian motion.

Entropy of finite random binary sequences with weak long-range correlations.

We study the N-step binary stationary ergodic Markov chain and analyze its differential entropy. Supposing that the correlations are weak we express the conditional probability function of the chain through the pair correlation function and represent the entropy as a functional of the pair correlator. Since the model uses the two-point correlators instead of the block probability, it makes it possible to calculate the entropy of strings at much longer distances than using standard methods. A fluctuation contribution to the entropy due to finiteness of random chains is examined. This contribution can be of the same order as its regular part even at the relatively short lengths of subsequences. A self-similar structure of entropy with respect to the decimation transformations is revealed for some specific forms of the pair correlation function. Application of the theory to the DNA sequence of the R3 chromosome of Drosophila melanogaster is presented.

Stock Indices for Emerging Markets

In the last years a lot of attention was paid to the Hurst exponent and its application to predict prices in financial markets. In this study, we employ the Hurst exponent to quantify the long-term correlations. We use detrended fluctuation analysis (DFA) method to calculate the Hurst exponent, as it is known to be the most efficient [1]. The method itself is described in the next section. The application of DFA for studying the stock market behavior was initiated by Liu et al. [2]. They show that the S&P500 stock index exhibits weak long-range correlations. On the other hand, Grech and Pamula [3] and Grech and Mazur [4] employed the local Hurst exponent behavior in order to predict crashes on the financial market.

Statistical properties of turbulence in a toroidal magnetized ECR plasma

The statistical analyses of fluctuation data measured by electrostatic-probe arrays clearly show that the self-organized criticality (SOC) avalanches are not the dominant behaviors in a toroidal ECR plasma in the SMT (Simple Magnetic Torus) mode of KT-5D device. The f −1 index region in the auto-correlation spectra of the floating potential V f and the ion saturation current I s , which is a fingerprint of a SOC system, ranges only in a narrow frequency band. By investigating the Hurst exponents at increasingly coarse grained time series, we find that at a time scale of τ > 100 μs , there exists no or a very weak long-range correlation over two decades in τ. The difference between the PDFs of I s and V f clearly shows a more global nature of the latter. The transport flux induced by the turbulence suggests that the natural intermittency of turbulent transport maybe independent of the avalanche induced by near criticality. The drift instability is dominant in a SMT plasma generated by means of ECR discharges.

Random sequences with power-law correlations exhibit proteinlike behavior

We use a replica approach to investigate the thermodynamic properties of the random heteropolymers with persistent power-law correlations in monomer sequence. We show that this type of sequences possess proteinlike properties. In particular, we show that they can fold into stable unique three-dimensional structure (the "native" structure, in protein terminology) through two different types of pathways. One is a fast folding pathway and leads directly to the native structure. Another one, a more slower pathway, passes through the microphase separated (MPS) state and includes a number of intermediate glassy states. The scale and the magnitude of the MPS are calculated. The frozen state can be reached only by sequences with weak long-range correlations. The critical value for the correlation exponent is found, above which (strong correlations) freezing is impossible.

Statistical properties of stock market indices of different economies

Daily changes in the logarithm of stock market index from 1997 to 2004 are analyzed for countries from three subgroups of economies classified by the International Monetary Fund (IMF): developing Asian countries, newly industrialized Asian economies and major advanced economies. For all markets, the daily changes are well fitted by a non-Gaussian stable probability density. The time evolution of the standard deviation of the daily changes for each market obeys a power law. However, the developing Asian countries have the smallest stable density characteristic parameters α and the largest exponents b of the power law, except China's SSEC and India's SENSEX. The values of α and b for these two markets are closer to those of the newly industrialized Asian economies; in particular, those for China's SSEC are close to those for Hong Kong's HSI. The values of α and b for the newly industrialized Asian economies are in between those for the developing Asian countries and major advanced economies, consistent with the results for generalized Hurst exponent [Physica A 324 (2003) 183]. The daily changes for the developing Asian countries and newly industrialized Asian economies have a weak long-range correlation, whereas the daily changes for the major advanced economies have a weak long-range anti-correlation.