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We investigate a set of complex heart rate time series from healthy human in different behaviour states with the detrended fluctuation analysis and diffusion entropy (DE) method. It is proposed that the scaling properties are influenced by behaviour states. The memory detected by DE exhibits an approximately same pattern after a detrending procedure. Both of them demonstrate the long-range strong correlations in heart rate. These findings may be helpful to understand the underlying dynamical evolution process in the heart rate control system, as well as to model the cardiac dynamic process.

We study the dynamical properties of heart rate variability (HRV) in sleep by analysing the scaling behaviour with the multifractal detrended fluctuation analysis method. It is well known that heart rate is regulated by the interaction of two branches of the autonomic nervous system: the parasympathetic and sympathetic nervous systems. By investigating the multifractal properties of light, deep, rapid-eye-movement (REM) sleep and wake stages, we firstly find an increasing multifractal behaviour during REM sleep which may be caused by augmented sympathetic activities relative to non-REM sleep. In addition, the investigation of long-range correlations of HRV in sleep with second order detrended fluctuation analysis presents irregular phenomena. These findings may be helpful to understand the underlying regulating mechanism of heart rate by autonomic nervous system during wake–sleep transitions.

We study the statistical properties of volatility of price fluctuation for the Hang-Seng index in the Hong Kong stock market, they are measured by locally averaging over a time window T, the absolute value of price change over a short time interval Δt. The data include minute-by-minute records of the Hang-Seng index from 3 January 1994 to 28 May 1997. We find that the cumulative distribution of the volatility is consistent with the asymptotic power-law behaviour, characterized by the power exponent μ = 2.12±0.04, different from that found in the previous studies as μ≈3. The volatility distribution remains the same asymptotic power-law behaviour for the time scales from T = 10 min to T = 80 min. Furthermore, we investigate the volatility correlations by using the power spectrum analysis and detrended fluctuation analysis. Both the methods show a long-range power-law decay with the exponent α = 0.636±0.002.

The study of epidemic spreading in node-property heterogeneous adaptive network shows that node-property heterogeneity can greatly increase the epidemic threshold, and the initial network can adaptively self-organize into a more robust degree heterogeneous network structure. The difference in epidemic spreading between homogeneous and heterogeneous node-property adaptive networks is of great importance for understanding the threshold increasing in the heterogeneous node-property adaptive network. In this paper, we study the transient spreading process in the heterogeneous node-property adaptive network. In order to capture the core-periphery structure in the network, we define two hierarchical structure indicators. When both indicators are of large values in the network, not only is the network of strong core-periphery property, but also less susceptible nodes are more likely to be in the core area of the network. By combining them with various network structure properties, such as the average degree ratio and static threshold of transient network, we analyze the evolution of network structure and show the self-organizing formation process of robust degree heterogeneous structure by numerical simulations. We find that the threshold increase is basically due to the formation of core-periphery structure, where the less susceptible nodes are more likely to be reallocated to the core area of the network by rewiring. In view of this, we propose a new preference rewiring strategy. The results show that the new strategy can increase the epidemic threshold by faciliating the formation of core-periphery structure, which verifies the correctness of the transient network structure analysis. It not only helps to deeply understand the epidemic spreading process in the node-property heterogeneous adaptive network, but also provides new ideas for putting forward the strategy of controlling epidemics.

We investigate a set of complex heart rate time series from healthy human in different behaviour states with the detrended fluctuation analysis and diffusion entropy (DE) method. It is proposed that the scaling properties are influenced by behaviour states. The memory detected by DE exhibits an approximately same pattern after a detrending procedure. Both of them demonstrate the long-range strong correlations in heart rate. These findings may be helpful to understand the underlying dynamical evolution process in the heart rate control system, as well as to model the cardiac dynamic process.

The two phase behavior in financial markets actually means the bifurcation phenomenon, which represents the change of the conditional probability from an unimodal to a bimodal distribution. In this paper, the bifurcation phenomenon in Hang-Seng index is carefully investigated. It is observed that the bifurcation phenomenon in financial index is not universal, but specific under certain conditions. The phenomenon just emerges when the power-law exponent of absolute increment distribution is between 1 and 2 with appropriate period. Simulations on a randomly generated time series suggest the bifurcation phenomenon itself is subject to the statistics of absolute increment, thus it may not be able to reflect the essential financial behaviors. However, even under the same distribution of absolute increment, the range where bifurcation phenomenon occurs is far different from real market to artificial data, which may reflect certain market information., 8 pages and 5 figures

Evaluation of node importance is helpful to improve the invulnerability and robustness of complex networked systems. At present, the classic ranking methods of quantitatively analyzing node importance are based on the centrality measurements of network topology, such as degree, betweenness, closeness, eigenvector, etc. Therefore, they often restrict the unknown topological information and are not convenient to use in large-scale real networked systems. In this paper, according to the idea of self-avoiding random walking, we propose a novel and simplified ranking method integrated with label propagation and local topological information, in which the number of labels that node collects from propagating process quantitatively denotes the ranking order. Moreover, the proposed method is able to characterize the structural influence and importance of node in complex networked system because it comprehensively considers both the direct neighbors of node and the topological relation of node to other ones. Through performing the experiments on three benchmark networks, we obtain interesting results derived from four common evaluating indices, i. e., the coefficient of giant component, the spectral distance, the links of node, and the fragility, which indicate that the proposed method is much more efficient and effective for ranking influential nodes than the acquaintance algorithm.