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Fangcui Zhao

Publications and source records attributed to Fangcui Zhao.

17 recordsLinked to original sources
arXiv2007

Complexities of Human Promoter Sequences

By means of the diffusion entropy approach, we detect the scale-invariance characteristics embedded in the 4737 human promoter sequences. The exponent for the scale-invariance is in a wide range of $[ {0.3,0.9} ]$, which centered at $δ_c = 0.66$. The distribution of the exponent can be separated into left and right branches with respect to the maximum. The left and right branches are asymmetric and can be fitted exactly with Gaussian form with different widths, respectively.

q-bio.OT↗
arXiv2006

Synchronizabilities of Networks: A New index

The random matrix theory is used to bridge the network structures and the dynamical processes defined on them. We propose a possible dynamical mechanism for the enhancement effect of network structures on synchronization processes, based upon which a dynamic-based index of the synchronizability is introduced in the present paper.

cond-mat.stat-mech↗
arXiv2006

Nonlinear Modeling Approach to Human Promoter Sequences

By means of the nonlinear modeling technique (NM technique), we find the nonlinear deterministic structures in the promoter regions (PPRs) of DNA sequences, called deterministic valleys (DVs) in this paper. These DVs prefer to occur much more outside of a special region around TSS. The number, positions and shapes of the DVs are basically different for different PPRs. Generally, these DVs do not occur in the CpG islands, which tells us that they should be special structures with new biological functions rather than the CpG islands. PACS numbers: 87.14.Gg, 87.10.1e, 05.45.2a

q-bio.GN↗
arXiv2005

Scaling Invariance in Wave Functions of Quantum Systems on Complex Networks

Structure-induced features of the wave functions for the quantum systems on complex networks are discussed in this paper. For a quantum system on a network, the state corresponding to the eigenvalue close to the center of the spectrum is used as the representative state to display the impacts of the structure on the wave functions. We consider the Erdos-Renyi, the WS small world and the growing randomly network (GRN) models. It is found that the probability distribution functions (PDF) of the representative state's components can be described with a power-law with an exponential cutoff in a unified way. For Erdos-Renyi networks, with the increase of the connectivity probability $p_{ER} $ the PDF turns from power-law-dominated to exponential-dominated functions. For the WS networks in a special region of the rewiring probability $p_r \in (0,0.2)$, where this model can capture the features of real world networks, and the GRN networks, the PDFs obey almost a perfect power-law. These characteristics can be used as the structure measurements of complex networks. They can also provide useful information on dynamical processes on complex networks.

cond-mat.dis-nn↗
arXiv2005

Collective Chaos Induced by Structures of Complex Networks

Mapping a complex network of $N$coupled identical oscillators to a quantum system, the nearest neighbor level spacing (NNLS) distribution is used to identify collective chaos in the corresponding classical dynamics on the complex network. The classical dynamics on an Erdos-Renyi network with the wiring probability $p_{ER} \le \frac{1}{N}$ is in the state of collective order, while that on an Erdos-Renyi network with $p_{ER} > \frac{1}{N}$ in the state of collective chaos. The dynamics on a WS Small-world complex network evolves from collective order to collective chaos rapidly in the region of the rewiring probability $p_r \in [0.0,0.1]$, and then keeps chaotic up to $p_r = 1.0$. The dynamics on a Growing Random Network (GRN) is in a special state deviates from order significantly in a way opposite to that on WS small-world networks. Each network can be measured by a couple values of two parameters $(β,η)$.

cond-mat.stat-mech↗
arXiv2005

Load Distribution on Small-world Networks

Mapping a complex network to an atomic cluster, the Anderson localization theory is used to obtain the load distribution on a complex network. Based upon an intelligence-limited model we consider the load distribution and the congestion and cascade failures due to attacks and occasional damages. It is found that the eigenvector centrality (EC) is an effective measure to find key nodes for traffic flow processes. The influence of structure of a WS small-world network is investigated in detail.

cond-mat.dis-nn↗
arXiv2005

Scaling Invariance in Spectra of Complex Networks: A Diffusion Factorial Moment Approach

A new method called diffusion factorial moment (DFM) is used to obtain scaling features embedded in spectra of complex networks. For an Erdos-Renyi network with connecting probability $p_{ER} < \frac{1}{N}$, the scaling parameter is $δ= 0.51$, while for $p_{ER} \ge \frac{1}{N}$ the scaling parameter deviates from it significantly. For WS small-world networks, in the special region $p_r \in [0.05,0.2]$, typical scale invariance is found. For GRN networks, in the range of $θ\in[0.33,049]$, we have $δ=0.6\pm 0.1$. And the value of $δ$ oscillates around $δ=0.6$ abruptly. In the range of $θ\in[0.54,1]$, we have basically $δ>0.7$. Scale invariance is one of the common features of the three kinds of networks, which can be employed as a global measurement of complex networks in a unified way.

cond-mat.stat-mech↗
arXiv2005

Complex Network Approach to Human Promoter Sequences

Based upon the correlation matrix of the human promoter sequences, a complex network is constructed to capture the principal relationships between these promoters. It is a complex network has the properties of the right-skewed degree distribution and the clustering simultaneously, i.e., a hierarchical structure. An eigenvector centrality (EC) based method is used to reconstruct this hierarchical structure.

q-bio.GN↗
arXiv2005

Reconstruct the Hierarchical Structure in a Complex Network

A number of recent works have concentrated on a few statistical properties of complex networks, such as the clustering, the right-skewed degree distribution and the community, which are common to many real world networks. In this paper, we address the hierarchy property sharing among a large amount of networks. Based upon the eigenvector centrality (EC) measure, a method is proposed to reconstruct the hierarchical structure of a complex network. It is tested on the Santa Fe Institute collaboration network, whose structure is well known. We also apply it to a Mathematicians' collaboration network and the protein interaction network of Yeast. The method can detect significantly hierarchical structures in these networks.

physics.soc-ph↗
arXiv2004

Temporal Series Analysis Approach to Spectra of Complex Networks

The spacing of nearest levels of the spectrum of a complex network can be regarded as a time series. Joint use of Multi-fractal Detrended Fluctuation Approach (MF-DFA) and Diffusion Entropy (DE) is employed to extract characteristics from this time series. For the WS (Watts and Strogatz) small-world model, there exist a critical point at rewiring probability . For a network generated in the range, the correlation exponent is in the range of . Above this critical point, all the networks behave similar with that at . For the ER model, the time series behaves like FBM (fractional Brownian motion) noise at . For the GRN (growing random network) model, the values of the long-range correlation exponent are in the range of . For most of the GRN networks the PDF of a constructed time series obeys a Gaussian form. In the joint use of MF-DFA and DE, the shuffling procedure in DE is essential to obtain a reliable result. PACS number(s): 89.75.-k, 05.45.-a, 02.60.-x

cond-mat.stat-mech↗
arXiv2003

Modeling SARS Spreading on Complex Networks

The spreading of SARS will destruct the initial network structure to a new phase, and in turn the spreading process will be weakened effectively and finally halted by this evolution of network structure. This mechanism is called immunity of contact network in this paper. What we can do is to accelerate effectively this process only.

cond-mat.stat-mech↗
arXiv2002

Dynamical Characteristics of a Hodgkin-Huxley neuron

By means of the concepts of factorial moment, return map and NM estimator, we analyze some responses of a HH neuron to various types of spike-train inputs. The corresponding fractal dimensions and values of NM estimators can describe the correlation characteristics quantitatively. It is found that all the intervals of successive output spikes may obey a power-law for some conditions. And in time scale the output series for a HH model for each type of inputs is correlated with typical correlation length and correlation strength. These power-law and correlation characteristics may be useful in the coding and decoding process of HH neurons. These concepts may distinguish different inputs quantitatively. PACS number(s): 87.18.Sn, 84.35.+i, 05.45.Tp

cond-mat.dis-nn↗
arXiv2001

A Brief Discussion on the Crossovers in Detrended Fluctuation Analysis

An analytical formula for the contributions of the trend leftovers in DFA method is presented, based upon which the crossovers in DFA are investigated in detail. This general formula can explain the calculated results with DFA method for some examples in literature very well.

cond-mat.stat-mech↗
arXiv2001

Analysis of DNA chains by means of factorial moments

By means of the concept of Factorial Moments we examine DNA sequences from Yeast to distinguish coding and non-coding regions. It is found that the FM may be a powerful tool for analysis of DNA sequences. PACS numbers: 87.15.Cc,87.10.+e,87.14.Gg

cond-mat.soft↗
arXiv2001

Investigation on gait by means of factorial moments

By means of Factorial Moments(FM), the long-range correlations embedded in gait time series are investigated. It is found that FM is an effective tool to deal with this kind of time series. Keywords: Factorial Moments,Time series, Long-range correlations

cond-mat.dis-nn↗