内禀无序蛋白构象与带电氨基酸残基排布关系——以精氨酸和天冬氨酸组成的随机多肽为例

内禀无序蛋白的结构特征与其氨基酸序列有着密切的联系.其中一个核心问题是正负带电氨基酸残基的排列如何影响无序蛋白或者多肽的构象?为了回答这一问题,本研究以天冬氨酸和精氨酸两种带电残基组成的随机多肽为研究对象,利用全原子蒙特卡罗模拟和温度副本交换采样方法,研究了随机多肽的电荷排布与结构之间的定性关系.结果表明:正负带电残基在序列上混合均匀时,由于肽链内部的静电吸引和排斥相互抵消,肽链倾向于形成无规卷曲的构象;正负带电残基分离时,由于长程静电相互吸引,多肽倾向于形成类β-发卡的形状.     

基于LCEEMD的低信噪比拉曼光谱自适应去噪方法研究

在生物体拉曼光谱快速采集或低功率采集过程中,往往会获得低信噪比拉曼光谱。针对低信噪比光谱数据,提出应用补充总体经验模态方法(CEEMD)分解拉曼光谱,并且依据特征模态分量的归一化排列熵值(NPE)按比例扣除噪声成分的方法,称为局部补充总体均值经验模分解方法(LCEEMD)。LCEEMD方法不仅解决了经验模态(EMD)分解中高频信号与噪声的模态混叠问题,还有效降低了总体经验模态分解法(EEMD)中的残留噪声。仿真数据实验显示,LCEEMD方法在处理10db信噪比模拟光谱时获得了39.615 0 db信噪比,0.001 17标准差和0.999 9相关系数。在人体皮肤拉曼光谱试验中,LCEEMD方法滤波后数据准确呈现出角质层脂质酰胺I带激发拉曼强谱峰以及甘油三酸酯中(CO)酯微弱谱峰。在水稻叶片可溶性糖定量预测模型中,LCEEMD方法取得了0.871 7预测相关系数和0.912 0预测标准误差,优于EMD和EEMD软阈值去噪(0.511 4,1.647 8和0.638 2,1.508 8)。LCEEMD方法实施过程中,根据去噪性能指标反馈调整归一化排列熵阈值,直至获得最佳去噪效果,滤波过程无需参数设置,可以自适应实现。     

Thermal Conductivity of Complex Plasmas Using Novel Evan-Gillan Approach

The thermal conductivity of complex fluid materials(dusty plasmas) has been explored through novel Evan-Gillan homogeneous non-equilibrium molecular dynamic(HNEMD) algorithm. The thermal conductivity coefficient obtained from HNEMD is dependent on various plasma parameters(Γ, κ). The proposed algorithm gives accurate results with fast convergence and small size effect over a wide range of plasma parameters. The cross microscopic heat energy current is discussed in association with variation of temperature(1/Γ) and external perturbations(P_z). The thermal conductivity obtained from HNEMD simulations is found to be very good agreement and more reliable than previously known numerical techniques of equilibrium molecular dynamic, nonequilibrium molecular dynamic simulations. Our new investigations point to an effective conclusion that the thermal conductivity of complex dusty plasmas is dependent on an extensive range of plasma coupling(Γ) and screening parameter(κ) and it varies by the alteration in these parameters.It is also shown that a different approach is used for computations of thermal conductivity in 2D complex plasmas and can be appropriate method for behaviors of complex systems.     

RETRACTED: A Hybrid Nonlinear Active Noise Control Method Using Chebyshev Nonlinear Filter

Investigations into active noise control (ANC) technique have been conducted with the aim of effective control of the low-frequency noise. In practice, however, the performance of currently available ANC systems degrades due to the effects of nonlinearity in the primary and secondary paths, primary noise and louder speaker. This paper proposes a hybrid control structure of nonlinear ANC system to control the non-stationary noise produced by the rotating machinery on the nonlinear primary path. A fast version of ensemble empirical mode decomposition is used to decompose the non-stationary primary noise into intrinsic mode functions, which are expanded using the second-order Chebyshev nonlinear filter and then individually controlled. The convergence of the nonlinear ANC system is also discussed. Simulation results demonstrate that proposed method outperforms the FSLMS and VFXLMS algorithms with respect to noise reduction and convergence rate.     

Predicting ships' CO2 emissions using feature‐oriented methods

Shipping companies are forced by the current EU regulation to set up a system for monitoring, reporting, and verification of harmful emissions from their fleet. In this regulatory background, data collected from onboard sensors can be utilized to assess the ship's operating conditions and quantify its CO₂ emission levels. The standard approach for analyzing such data sets is based on summarizing the measurements obtained during a given voyage by the average value. However, this compression step may lead to significant information loss since most variables present a dynamic profile that is not well approximated by the average value only. Therefore, in this work, we test two feature‐oriented methods that are able to extract additional features, namely, profile‐driven features (PdF) and statistical pattern analysis (SPA). A real data set from a Ro‐Pax ship is then considered to test the selected methods. The data set is segregated according to the voyage distance into short, medium, and long routes. Both PdF and SPA are compared with the standard approach, and the results demonstrate the benefits of employing more systematic and informative feature‐oriented methods. For the short route, no method is able to predict CO₂ emissions in a satisfactory way, whereas for the medium and long routes, regression models built using features obtained from both PdF and SPA improve their prediction performance. In particular, for the long route, the standard approach failed to provide reasonably good predictions.     

Painlevé VI,Painlevé III,and the Hankel determinant associated with a degenerate Jacobi unitary ensemble

This paper studies the Hankel determinant generated by a perturbed Jacobi weight, which is closely related to the largest and smallest eigenvalue distribution of the degenerate Jacobi unitary ensemble. By using the ladder operator approach for the orthogonal polynomials, we find that the logarithmic derivative of the Hankel determinant satisfies a nonlinear second-order differential equation, which turns out to be the Jimbo–Miwa–Okamoto σ-form of the Painlevé VI equation by a translation transformation. We also show that, after a suitable double scaling, the differential equation is reduced to the Jimbo–Miwa–Okamoto σ-form of the Painlevé III. In the end, we obtain the asymptotic behavior of the Hankel determinant as t→1⁻ and t→0⁺ in two important cases, respectively.     

正则黑洞熵与相变

应用隧道效应对黑洞Hawking辐射研究得到的辐射谱,对非旋转黑洞的正则熵进行讨论.所得熵遵守Bekenstein-Hawking 面积定律,并带有修正项,主要修正项与面积的对数成正比,另有与黑洞热容量有关的修正项.利用所给出的正则熵,对黑洞的相变进行讨论,得到当黑洞的热容量出现发散时,正则熵在该处并不出现复数,由此认为此相变为二级相变. 关键词： 正则系综 量子修正 黑洞相变     

Polymer Transport in Random Flow

The dynamics of polymers in a random smooth flow is investigated in the framework of the Hookean dumbbell model. The analytical expression of the time-dependent probability density function of polymer elongation is derived explicitly for a Gaussian, rapidly changing flow. When polymers are in the coiled state the pdf reaches a stationary state characterized by power-law tails both for small and large arguments compared to the equilibrium length. The characteristic relaxation time is computed as a function of the Weissenberg number. In the stretched state the pdf is unstationary and exhibits multiscaling. umerical simulations for the two-dimensional Navier–Stokes flow confirm the relevance of theoretical results obtained for the -correlated model.     

On the Distribution of Eigenvalues of Grand Canonical Density Matrices

Using physical arguments and partition theoretic methods, we demonstrate under general conditions, that the eigenvalues w(m) of the grand canonical density matrix decay rapidly with their index m, like w(m)exp[–B ^–1(ln m)^1+1/ ], where B and are positive constants, O(1), which may be computed from the spectrum of the Hamiltonian. We compute values of B and for several physical models, and confirm our theoretical predictions with numerical experiments. Our results have implications in a variety of questions, including the behaviour of fluctuations in ensembles, and the convergence of numerical density matrix renormalization group techniques.