Fibonacci序列的统计属性和电子输运系数

对按膨胀规律A→AB和B→A生成的Fibonacci序列,采用一维随机行走模型数值计算了序列的自相关函数以及自行定义的准标准偏差.利用Hurst分析法研究了序列的再标度范围函数及其Hurst指数,并将结果与一维随机二元序列进行了对比.发现这些统计量有奇特的准周期振荡行为以及小于05的Hurst指数,直接论证了Fibonacci序列具有关联、标度不变及自相似等性质.从Anderson紧束缚模型出发,采用传输矩阵方法研究了Fibonacci序列的电子输运特性,讨论了输运系数对能量及其序列长度的依赖关系.研究 关键词： Fibonacci序列 统计属性 电子输运系数     

Fibonacci链的电子结构特性

对由递推关系S_m+1={S_m|S_m-1}生成的Fibonacci链,从Anderson紧束缚模型出发,用负本征值理论及三对角高阶厄米矩阵本征值理论,对电子的态密度和能级结构进行数值研究,直观简洁地论证其三分叉的能带结构.用重整化群方法,结合散射理论,研究链中电子的局域长度和输运系数,发现具有不同局域属性的能态.一些特定的能量区间值存在扩展态,其相应的输运系数接近1.绝大部分能量对应的电子具有很小或几乎为零的局域长度,说明链中存在相当数量的局域态.定性得出电子输运系数随Fibonacci链参数变化的规律.     

DNA分子链的电子局域性质及电导的研究

将一维随机二元固体模型应用于DNA分子链，利用传输矩阵方法来研究系统电子态的局域性质并进而讨论系统的导电性质.对一个链长为50000个碱基对的DNA序列，数值分析了局域长度和电导随碱基对的摩尔百分数、本征能量和无序度的变化关系.结果表明，系统的局域长度和电导强烈地依赖于能量，在能带中心部分局域长度大于边沿部分.无序度也在一定程度上影响着局域长度，双方成反向变化的关系.对有限长度的DNA分子链，局域长度体现出明显的对碱基对摩尔百分数的依赖关系，对正常成分比例的随机DNA序列，在所有能量范围内系统的态都是局域的，系统的电导很小，系统呈现绝缘体行为.仅当一种碱基对在序列中所占比例很小时，系统中可以发现与特定分立能量值相对应的“扩展态”存在，处在这些态下的系统有较大的电导，但这些扩展态是不稳定的，在热力学极限之下会消失. 关键词： DNA分子链 电子局域 局域长度 电导     

无序度对一维长程关联无序系统中局域化-退局域化转变的影响

对长程幂律关联能量序列进行了修正,使其能体现出无序度在一维长程关联无序系统中的影响,并利用重正化群方法,计算了能反映该系统局域化-退局域化转变的Lyapunov指数.结果表明,在由于关联指数p的影响而在系统中出现的局域化向退局域化的转变中,无序度起着相反的作用.当关联指数p一定而无序度W增大时,系统中心能区范围内由于长程关联而引起的扩展态逐渐向局域态转变.当无序度W增大到某一临界值W_c时,系统中所有本征态均转变为局 关键词： 长程关联 Lyapunov指数 无序度 局域化-退局域化转变     

反馈脉冲棘轮的能量转化效率研究

研究了反馈脉冲棘轮的定向输运及能量转化效率.详细讨论了弹簧自由长度、耦合强度及脉冲相位等参量对耦合布朗粒子定向输运性能的影响.研究发现,一定自由长度和耦合强度都能促进反馈脉冲棘轮的定向输运,并能使耦合粒子拖动负载做功时的能量转化效率达到最大.此外,通过调节脉冲相位能使反馈棘轮在一个演化周期内获得两次流反转,且合适的相位还能增强反馈棘轮的定向输运.所得结论不仅可为实验上设计合适的反馈脉冲作用来优化棘轮的定向输运性能,而且还能为生物医疗上药物的精准投放提供一定的理论参考.     

spin-Peierls系统低能量激发谱的理论研究

用玻色化技术和量子自洽方法研究了spin-Peierls系统的低能量激发谱,计算了二聚化相的基态、单粒子激发态和双粒子束缚态的能量、阻挫对其低能量行为的影响及其各自的自旋-自旋关联函数.结果表明,随着阻挫的增大,spin-Peierls系统中的基态能会逐渐减小,单粒子激发态能隙和双粒子束缚态能隙却会增大.双粒子束缚态和基态的关联函数具有类似的短程关联,而单粒子激发态的关联函数具有长程关联.因此导出,单粒子激发态为自旋三重态,双粒子束缚态与基态类似为自旋单态,它存在于双粒子连续激发态的下边.该结果与Ain等 关键词： spin-Peierls系统 束缚态 关联函数     

一维长程关联无序系统中的电子态

利用傅里叶滤波法在一维Anderson无序系统中产生了具有幂律谱密度公式s(q)∝q^-p形式的长程关联随机能量序列，并利用传输矩阵方法计算了系统中引入了长程关联后的局域长度，同时应用负本征值理论对系统中的电子态密度进行了分析，并分别把计算结果与系统中不具有长程关联时的局域长度与电子态密度进行了比较.结果表明，长程幂律关联的引入对电子态的性质产生了很大的影响，当关联指数p≥2.0时，在系统能带中心范围内发生了部分局域态向退局域态的转变，而同时电子态密度也发生了很大的变化，出现了六个范霍夫奇点，系统的能带范围也相应地得到展宽. 关键词： 无序系统 长程关联 局域长度 电子态密度     

嵌入锂原子的zigzag型单壁碳纳米管的电子传导特性

运用基于第一性原理的密度泛函理论（DFT）的非平衡格林函数（NEGF）方法对Li原子嵌入后的zigzag型单壁碳纳米管(SWCNT)的电子输运性质进行了研究.在构建和优化了Li原子嵌入的zigzag型单壁碳纳米管的电子输运模型后,研究了该系统的电子传输概率、能态密度、电子透射谱,还研究了电子能量和偏置电压设置与变化对其电子输运特性的影响.结果显示zigzag型单壁碳纳米管嵌入Li原子后,电子输运特性发生了较大变化,具有电子输运拓宽效应和量子台阶复苏效应. 关键词： Li原子 碳纳米管 电子输运 拓宽效应     

单光子计数型CCD的蒙特卡罗模拟

建立了适用于研究PI-LCX:1300型单光子计数型CCD量子效率及多像素事件的蒙特卡罗模拟模型,采用蒙特卡罗程序Geant4对0.5~30.0 keV能量区间的X射线在CCD芯片中的输运进行了模拟研究。研究了X射线在CCD芯片中的能量沉积谱,给出了CCD探测X射线的效率曲线,其结果与厂家提供的效率曲线一致。研究了Si片厚度对探测效率的影响,结果表明在有效探测范围内,Si片越厚探测效率越高,而对较高能量的X射线,此趋势不明显。研究了能量沉积分布在多个像素中的问题,结果表明周围像素中的能量沉积主要由中心像素的特征X射线及瑞利散射X射线所贡献,在5~30 keV之间X射线能量越高,能量沉积效率越低,多像素污染效果越弱。     

p型Na掺杂各向异性ZnO的电子性质与热电输运性质

采用密度泛函理论的第一性原理计算研究了p型Na掺杂各向异性ZnO的能带结构、光学性质、介电性质、总态密度和不同原子的分态密度,并系统分析了其热电输运性质。计算分析结果表明,p型Na掺杂ZnO为p型直接带隙半导体,其带隙增大到1.3eV,其对于光子的吸收限向低能量光子移动,体系费米能级附近的态密度大幅度提高,这主要是p态电子贡献的;在费米能级附近的导带和价带中都出现了新的能级,这些新的能级主要由Nas、Nap、Znp、Znd和Op电子形成,且他们之间存在着强耦合相互作用。Na掺杂ZnO的电输运性质具有各向异性;其价带和导带中的载流子有效质量均较大;载流子输运主要由Nas、Nap、Znp和Op电子完成。     

Long-range correlations and charge transport properties of DNA sequences

By using Hurst's analysis and transfer approach, the rescaled range functions and Hurst exponents of human chromosome 22 and enterobacteria phage lambda DNA sequences are investigated and the transmission coefficients, Landauer resistances and Lyapunov coefficients of finite segments based on above genomic DNA sequences are calculated. In a comparison with quasiperiodic and random artificial DNA sequences, we find that λ-DNA exhibits anticorrelation behavior characterized by a Hurst exponent 0.5<H<1 while, as far as the segments selected in our Letter are concerned, Ch22 sequence displays a transition from correlation behavior to anticorrelation behavior. The resonant peaks of the transmission coefficient in genomic sequences can survive in longer sequence length than in random sequences but in shorter sequence length than in quasiperiodic sequences. It is shown that the genomic sequences have long-range correlation properties to some extent but the correlations are not strong enough to maintain the scale invariance properties.     

Long range correlation and possible electron conduction through DNA sequences

Long range correlation analysis and charge conductivity investigation are applied to sequences in 16 chromosomes in the Saccharomyces cerevisiae genome. DNA sequence data are analyzed via Hurst’s analysis and Detrended Fluctuation Analysis (DFA) analysis. Super diffusive nature of mapping sequences are evident with the measured Hurst exponent H to be around the value of 0.60 for all sequences in the 16 chromosomes. The DFA result is consistent with the result from the Hurst analysis. Tight binding models are applied for the investigation of charge conduction through DNA sequences. The overall averaged transmission coefficients, 〈T_N〉_av, calculated from sixteen chromosomes are shown to be significantly different from values calculated from random as well as periodic sequences. Sequences from the S. cerevisiae genome promise better charge conduction ability than random sequences. Finally, delocalized electronic wave function patterns are also shown through calculations using the tight binging model. Slightly delocalized electronic wavefunctions are seen on sequences in sixteen chromosomes, as compared with those obtained from random sequences on the same eigenenergies.     

Interacting gaps model,dynamics of order book,and stock-market fluctuations

Inspired by order-book models of financial fluctuations, we investigate the Interacting gaps model, which is the schematic one-dimensional system mimicking the order-book dynamics. We find by simulations the power-law tail in return distribution, power-law decay of volatility autocorrelation with exponent 0.5 and Hurst exponent close to 1/2. Surprisingly, when we make a mean-field approximation, i.e. replace the one-dimensional system by effectively infinite-dimensional one, we obtain analytically the return exponent 5/2, in perfect accord with one-dimensional simulations.     

Fractals in DNA sequence analysis

Fractal methods have been successfully used to study many problems in physics,mathematics,engineering,finance,and even in biology,There has been an increasing interest in unravelling the mysteries of DNA;for example,how can we distinguish coding and noncoding sequences,and the problems of classification and evolution relationship of organisms are key problems in bioinformatics,Although much research has been carried out by taking into consideration the long-range correlations in DNA sequences,and the global fractal dimension has been used in these works by other people,the models and methods are somewhat rough and the results are not satisfactory.In recent years,our group has introduced a time series model(statistical point of view)and a visual representation (geometrical point of view) to DNA sequence analysis.We have also used fractal dimension,correlation dimension,the Hurst exponent and the dimension spectrum (multifractal analysis)to discuss problems in this field.In this paper,we introduce these fractal models and methods and the results of DNA sequence analysis.     

Rescaled Range and Transition Matrix Analysis of DNA Sequences

We treat some fractal and statistical features of the DNA sequences. First, a fractal record model of DNA sequence is proposed by mapping,DNA sequences to integer sequences, followed by the R/S analysis of the model and computation of the Hurst exponents. Second, we consider the transition between the four kinds of bases within DNA. The transition matrix analysis of DNA sequences shows that some measures of complexity based on transition proportion matrices are of interest.     

A note on geometric method-based procedures to calculate the Hurst exponent

Geometric method-based procedures, which we will call GM algorithms hereafter, were introduced in M.A. Sánchez-Granero, J.E. Trinidad Segovia, J. García Pérez, Some comments on Hurst exponent and the long memory processes on capital markets, Phys. A 387 (2008) 5543-5551, to calculate the Hurst exponent of a time series. The authors proved that GM algorithms, based on a geometrical approach, are more accurate than classical algorithms, especially with short length time series. The main contribution of this paper is to provide a mathematical background for the validity of these two algorithms to calculate the Hurst exponent H of random processes with stationary and self-affine increments. In particular, we show that these procedures are valid not only for exploring long memory in classical processes such as (fractional) Brownian motions, but also for estimating the Hurst exponent of (fractional) Lévy stable motions.     

Hurst exponent and prediction based on weak-form efficient market hypothesis of stock markets

We empirically investigated the relationships between the degree of efficiency and the predictability in financial time-series data. The Hurst exponent was used as the measurement of the degree of efficiency, and the hit rate calculated from the nearest-neighbor prediction method was used for the prediction of the directions of future price changes. We used 60 market indexes of various countries. We empirically discovered that the relationship between the degree of efficiency (the Hurst exponent) and the predictability (the hit rate) is strongly positive. That is, a market index with a higher Hurst exponent tends to have a higher hit rate. These results suggested that the Hurst exponent is useful for predicting future price changes. Furthermore, we also discovered that the Hurst exponent and the hit rate are useful as standards that can distinguish emerging capital markets from mature capital markets.     

On Hurst exponent estimation under heavy-tailed distributions

In this paper, we show how the sampling properties of the Hurst exponent methods of estimation change with the presence of heavy tails. We run extensive Monte Carlo simulations to find out how rescaled range analysis (R/S), multifractal detrended fluctuation analysis (MF-DFA), detrending moving average (DMA) and generalized Hurst exponent approach (GHE) estimate Hurst exponent on independent series with different heavy tails. For this purpose, we generate independent random series from stable distribution with stability exponent α changing from 1.1 (heaviest tails) to 2 (Gaussian normal distribution) and we estimate the Hurst exponent using the different methods. R/S and GHE prove to be robust to heavy tails in the underlying process. GHE provides the lowest variance and bias in comparison to the other methods regardless the presence of heavy tails in data and sample size. Utilizing this result, we apply a novel approach of the intraday time-dependent Hurst exponent and we estimate the Hurst exponent on high frequency data for each trading day separately. We obtain Hurst exponents for S&P500 index for the period beginning with year 1983 and ending by November 2009 and we discuss the surprising result which uncovers how the market’s behavior changed over this long period.     

Relationship between efficiency and predictability in stock price change

In this study, we evaluate the relationship between efficiency and predictability in the stock market. The efficiency, which is the issue addressed by the weak-form efficient market hypothesis, is calculated using the Hurst exponent and the approximate entropy (ApEn). The predictability corresponds to the hit-rate; this is the rate of consistency between the direction of the actual price change and that of the predicted price change, as calculated via the nearest neighbor prediction method. We determine that the Hurst exponent and the ApEn value are negatively correlated. However, predictability is positively correlated with the Hurst exponent.