水杨酸分子NIR－FT－SERS与VIS－SERS的比较研究

水杨酸分子ＮＩＲ－ＦＴ－ＳＥＲＳ与ＶＩＳ－ＳＥＲＳ的比较研究方炎，汪媛，胡凤霞（首都师范大学实验中心，首都师范大学综合所北京１０００３７）ＴｈｅＣｏｍｐａｒｉｓｏｎｏｆＶＩＳ－ＳＥＲＳｔｏＮＩＲ－ＦＴ－ＳＥＲＳｏｆＭｏｌｅｃｕｌｅｓＡｄｓｏｒｂｅｄｏ...     

简立方格子上界面的自旋S（1）反铁Ising模型研究

本依据献［１］用自旋Ｓ（１）Ｉｓｉｎｇ模型研究了具有近邻（Ｊ１）和次近邻（ＲＪ１）相互作用的简立方格子的（１００）、（１１０）和（１１１０界面性质。在体内为反铁磁相互作用下，分析了界面的基态，并用Ｍ－Ｃ方法计算了界面的低温性质，给出了ＳＣ格子的三类Ｔ－Ｒ界相图，由此讨论了二类界面相变（宏观刻面和粗化）及其在氯化钠晶体界面上的应用。     

简立方格子上界面的自旋S（1）反铁磁Ising模型研究

本文依据文献［１］用自旋Ｓ（１）Ｉｓｉｎｇ模型研究了具有近邻（Ｊ１）和次近邻（ＲＪ１）相互作用的简立方格子的（１００）、（１１０）和（１１１）界面性质．在体内为反铁磁相互作用下，分析了界面的基态，并用Ｍ－Ｃ方法计算了界面的低温性质，给出了ＳＣ格子的三类Ｔ－Ｒ界面相图，由此讨论了二类界面相变（宏观刻面和粗化）及其在氯化钠晶体界面上的应用．     

高Tc氧化物超导体的性参数的研究

在高Ｔｃ氧化物超导体的研究中，引入分子平均电负性ＸＭＯ、单位氧原子上阳离子加合电离势∑ＩＭ和原子部分电荷δ０及δＭ的计算方法，并对ＹＢＣＯ、ＹＢＳＣＯ、ＢＩＳＣＣＯ、ＴＬＢＣＣＯ、ＴＩＲＥＣＣＯ、ＴＩＲＥＳＣＣＯ及ＲＥＢＣＯ等系列的Ｔｃ变化规律，进行了研究。     

表面活性剂从碱性氰化液中萃取金机理研究

本文利用Ｆｏｕｒｉｅｒ变换红外光谱（ＦＴＩＲ）、放射性同位素示踪实验、核磁共振（ＮＭＲ）等方法研究了萃金过程及负载金有机相的溶液结构，实验结果表明：伴随萃取过程，有机相金含量逐渐增加，有机相含水量逐渐增加，磷酸三丁脂（ＴＢＰ）中磷氧双键（Ｐ＝Ｏ）伸缩振动吸收峰向低频移动；核磁共振（ＮＭＲ）＾３１Ｐ的化学位移向高场移动。表明在有机相的聚集体中ＴＢＰ的磷氧基与氰化金阴离子有相互作用。     

电池正极材料中Li~+运动特性的~7Li NMR研究

利用变温７ＬｉＮＭＲ实验对Ｌｉ电池正极材料ＬｉＭｎ２Ｏ４和ＬｉＣｏＯ２中Ｌｉ＋的运动特性进行了研究，结果表明，随实验温度的提高，ＬｉＭｎ２Ｏ４的７ＬｉＮＭＲ谱线窄化，表明其中Ｌｉ＋迁移能力增加，而ＬｉＣｏＯ２的谱线无变化．此外随温度提高，ＬｉＭｎ２Ｏ４的７Ｌｉ的Ｔ１变短，而ＬｉＣｏＯ２的Ｔ１变长，产生这种不同变化趋势的原因在于ＬｉＭｎ２Ｏ４和ＬｉＣｏＯ２晶相结构的差异造成其中Ｌｉ＋迁移能力的差别，它们各自的相关时间τｃ使７Ｌｉ核的Ｔ１分别位于Ｔ１－τｃ曲线极小点两侧     

聚丙烯腈热裂解产物的ESR和固体￣（13）CNMR研究

用电子自旋共振（ＥＳＲ）和固体￣（１３）ＣＮＭＲ研究了聚丙烯腈（ＰＡＮ）在不同裂解温度（Ｔ＿Ｐ）下的结构变化，当Ｔ＿Ｐ从２５０℃增加到６００℃，ＥＳＲ信号线形没有变化，ｇ值与Ｔ＿Ｐ无关，ΔＨ＿（ＰＰ）减小，自旋浓度（Ｎ＿Ｓ）开始增加，Ｔ＿Ｐ进一步升高，Ｎ＿Ｓ又减少．￣（１３）ＣＮＭＲ谱指出在不同Ｔ＿Ｐ下谱峰发生很大变化，与另文报道的红外及Ｘ－射经衍射结果一致，随Ｔ＿Ｐ升高，ＰＡＮ逐渐脱去小分子形成具有环化共轭结构的产物．     

肿瘤细胞诱导分化剂HMBA对自旋标记物TEMPO与蛋白质结构的影响…

在将ＴＥＭＰＯ掺入肿瘤细胞用以研究化学诱导分化剂六亚甲基二乙酰胺诱导分化机理的实验中，观察到一种强的单峰波谱，ＴＥＭＰＯ掺入到蛋白质分子的内部可能形成了局部的高浓度，肿瘤细胞诱导分化剂ＨＭＢＡ在诱导浓度范围内可使一些蛋白质分子的构象发生变化，改变ＴＥＭＰＯ与蛋白质分子的结合。     

~(14)N~(16)O分子X~2Π_(3/2)R(1.5)v=0→1及~(15)N~(16)O分子X~2Π_(3/2)Q(1.5)v=0→1跃迁的FLMR光谱

本文采用法拉弟效应的激光磁共振光谱技术，研究了－氧化氮分子１４Ｎ１６ＯＸ２Π３／２Ｒ（１．５）ｖ＝０→１和同位素分子１５Ｎ１６ＯＸ２Π３／２Ｑ（１．５）ｖ＝０→１跃迁的ＦＬＭＲ光谱，实验给出了样品浓度和信号强度之间的关系及调制磁场强度与ＦＬＭＲ信号强度之间的关系。     

应用TLC—FT—SERS对萝芙木中利血平成分的研究

本文报道了应用薄层色谱（ＴＬＣ）与傅里叶变换表面增强拉曼光谱（ＦＴ－ＳＥＲＳ）联用技术,获得了中草药萝芙木有效成分利血平光谱研究的新方法。研究表明：在薄层原位约２μｇ的样品即可获得利血平分子的特征振动谱带,并阐述了样品分子在表面增强基底银微粒表面的吸附模式,从而说明ＴＬＣ－ＳＥＲＳ结合对中草药化学成分进行高灵敏度指纹检测的可靠性和优越性。     

Complementarity of the maldacena and randall-sundrum pictures

We apply random-matrix-theory (RMT) to the analysis of evolution of wave packets in energy space. We study the crossover from ballistic behavior to saturation, the possibility of having an intermediate diffusive behavior, and the feasibility of strong localization effect. Both theoretical considerations and numerical results are presented. Using quantal-classical correspondence considerations we question the validity of the emerging dynamical picture. In particular, we claim that the appearance of the intermediate diffusive behavior is possibly an artifact of the RMT strategy.     

One-factor model for the cross-correlation matrix in the Vietnamese stock market

Random matrix theory (RMT) has been applied to the analysis of the cross-correlation matrix of a financial time series. The most important findings of previous studies using this method are that the eigenvalue spectrum largely follows that of random matrices but the largest eigenvalue is at least one order of magnitude higher than the maximum eigenvalue predicted by RMT. In this work, we investigate the cross-correlation matrix in the Vietnamese stock market using RMT and find similar results to those of studies realized in developed markets (US, Europe, Japan) , , , , , , , ,  and  as well as in other emerging markets, ,  and . Importantly, we found that the largest eigenvalue could be approximated by the product of the average cross-correlation coefficient and the number of stocks studied. We demonstrate this dependence using a simple one-factor model. The model could be extended to describe other characteristics of the realistic data.     

Global correlation matrix spectra of the surface temperature of the oceans from random matrix theory to Poisson fluctuations

In this work we use the random matrix theory (RMT) to correctly describe the behavior of spectral statistical properties of the sea surface temperature of oceans. This oceanographic variable plays an important role in the global climate system. The data were obtained from National Oceanic and Atmospheric Administration (NOAA) and delimited for the period 1982 to 2016. The results show that oceanographic systems presented specific β values that can be used to classify each ocean according to its correlation behavior. The nearest-neighbors spacing of correlation matrix for north, central and south of the three oceans get close to a RMT distribution. However, the regions delimited in the Antarctic pole exhibited the distribution of the nearest-neighbors spacing well described by the Poisson model, which shows a statistical change of RMT to Poisson fluctuations.     

Comparing the structure of an emerging market with a mature one under global perturbation

In this paper we investigate the Tehran stock exchange (TSE) and Dow Jones Industrial Average (DJIA) in terms of perturbed correlation matrices. To perturb a stock market, there are two methods, namely local and global perturbation. In the local method, we replace a correlation coefficient of the cross-correlation matrix with one calculated from two Gaussian-distributed time series, whereas in the global method, we reconstruct the correlation matrix after replacing the original return series with Gaussian-distributed time series. The local perturbation is just a technical study. We analyze these markets through two statistical approaches, random matrix theory (RMT) and the correlation coefficient distribution. By using RMT, we find that the largest eigenvalue is an influence that is common to all stocks and this eigenvalue has a peak during financial shocks. We find there are a few correlated stocks that make the essential robustness of the stock market but we see that by replacing these return time series with Gaussian-distributed time series, the mean values of correlation coefficients, the largest eigenvalues of the stock markets and the fraction of eigenvalues that deviate from the RMT prediction fall sharply in both markets. By comparing these two markets, we can see that the DJIA is more sensitive to global perturbations. These findings are crucial for risk management and portfolio selection.     

Statistical properties of cross-correlation in the Korean stock market

We investigate the statistical properties of the cross-correlation matrix between individual stocks traded in the Korean stock market using the random matrix theory (RMT) and observe how these affect the portfolio weights in the Markowitz portfolio theory. We find that the distribution of the cross-correlation matrix is positively skewed and changes over time. We find that the eigenvalue distribution of original cross-correlation matrix deviates from the eigenvalues predicted by the RMT, and the largest eigenvalue is 52 times larger than the maximum value among the eigenvalues predicted by the RMT. The b₄₇₃\beta_{473} coefficient, which reflect the largest eigenvalue property, is 0.8, while one of the eigenvalues in the RMT is approximately zero. Notably, we show that the entropy function E(s)E(\sigma) with the portfolio risk σ for the original and filtered cross-correlation matrices are consistent with a power-law function, E(σ) ~ s^-g\sigma^{-\gamma}, with the exponent γ ~ 2.92 and those for Asian currency crisis decreases significantly.     

Eigenvalue density of cross-correlations in Sri Lankan financial market

We apply the universal properties with Gaussian orthogonal ensemble (GOE) of random matrices namely spectral properties, distribution of eigenvalues, eigenvalue spacing predicted by random matrix theory (RMT) to compare cross-correlation matrix estimators from emerging market data. The daily stock prices of the Sri Lankan All share price index and Milanka price index from August 2004 to March 2005 were analyzed. Most eigenvalues in the spectrum of the cross-correlation matrix of stock price changes agree with the universal predictions of RMT. We find that the cross-correlation matrix satisfies the universal properties of the GOE of real symmetric random matrices. The eigen distribution follows the RMT predictions in the bulk but there are some deviations at the large eigenvalues. The nearest-neighbor spacing and the next nearest-neighbor spacing of the eigenvalues were examined and found that they follow the universality of GOE. RMT with deterministic correlations found that each eigenvalue from deterministic correlations is observed at values, which are repelled from the bulk distribution.     

Entanglement production in quantized chaotic systems

Quantum chaos is a subject whose major goal is to identify and to investigate different quantum signatures of classical chaos. Here we study entanglement production in coupled chaotic systems as a possible quantum indicator of classical chaos. We use coupled kicked tops as a model for our extensive numerical studies. We find that, in general, chaos in the system produces more entanglement. However, coupling strength between two subsystems is also a very important parameter for entanglement production. Here we show how chaos can lead to large entanglement which is universal and describable by random matrix theory (RMT). We also explain entanglement production in coupled strongly chaotic systems by deriving a formula based on RMT. This formula is valid for arbitrary coupling strengths, as well as for sufficiently long time. Here we investigate also the effect of chaos on the entanglement production for the mixed initial state. We find that many properties of the mixed-state entanglement production are qualitatively similar to the pure state entanglement production. We however still lack an analytical understanding of the mixed-state entanglement production in chaotic systems.     

Superconducting terminals as sensitive probes for scarred states

When a quantum-chaotic normal conductor is coupled to a superconductor, the random-matrix theory (RMT) predicts that a gap opens up in the excitation spectrum which is of universal size E(g)(RMT) approximately 0.3 Planck/t(D), where t(D) is the mean scattering time between Andreev reflections. We show that a scarred state of long lifetime t(S)>t(D) suppresses the excitation gap over a window DeltaE approximately 2E(g)(RMT) which can be much larger than the narrow resonance width GammaS= Planck/t(S) of the scar in the normal system. The minimal value of the excitation gap within this window is given by GammaS/2相似文献   

Random matrix theory filters in portfolio optimisation: A stability and risk assessment

Random matrix theory (RMT) filters, applied to covariance matrices of financial returns, have recently been shown to offer improvements to the optimisation of stock portfolios. This paper studies the effect of three RMT filters on the realised portfolio risk, and on the stability of the filtered covariance matrix, using bootstrap analysis and out-of-sample testing.We propose an extension to an existing RMT filter, (based on Krzanowski stability), which is observed to reduce risk and increase stability, when compared to other RMT filters tested. We also study a scheme for filtering the covariance matrix directly, as opposed to the standard method of filtering correlation, where the latter is found to lower the realised risk, on average, by up to 6.7%.We consider both equally and exponentially weighted covariance matrices in our analysis, and observe that the overall best method out-of-sample was that of the exponentially weighted covariance, with our Krzanowski stability-based filter applied to the correlation matrix. We also find that the optimal out-of-sample decay factors, for both filtered and unfiltered forecasts, were higher than those suggested by Riskmetrics [J.P. Morgan, Reuters, Riskmetrics technical document, Technical Report, 1996. http://www.riskmetrics.com/techdoc.html], with those for the latter approaching a value of α=1.In conclusion, RMT filtering reduced the realised risk, on average, and in the majority of cases when tested out-of-sample, but increased the realised risk on a marked number of individual days–in some cases more than doubling it.