聚亚甲基链的温度系数

本文采用旋转异构态模型,考虑排斥体积效应,用 Monte Carlo模拟方法计算了聚亚甲基高分子链的温度系数dln/dT。考虑排斥体积的聚亚甲基链的温度系数大于无扰聚亚甲基链的温度系数,并更接近实验值。     

高效毛细管电泳-电荷耦合器件检测器联用技术研究Ⅳ.Monte Carlo模拟

根据毛细管电泳分离受不确定因素影响较多的特点，应用MonteCarlo模拟方法，探讨了电荷耦合器件检测器操作参数，进样、电压、温度等误差因素对检测信号强度、测定准确度和精密度的影响，比较了峰高和峰体积定量计算方法的相对优劣，对进一步开发和应用电荷耦合器件检测毛细管电泳技术有一定意义。     

A new multivariate variability control chart based on a covariance matrix combination

In the field of multivariate quality control, there are many control charts related to the process mean but few options addressing process variability. Variability control charts have two main drawbacks: the first relates to the number of parameters to tune and the second relates to how changes in the mean affect the performance of these charts. Thus, in this paper, we propose a new multivariate variability control chart, called the multivariate exponentially weighted covariance matrix combination, which solves these two problems. The results show that this new chart performs well in the detection of changes in variance when the mean does not change and outperforms other charts when the mean does change.     

Fast and Accurate Approximation of the Full Conditional for Gamma Shape Parameters

The gamma distribution arises frequently in Bayesian models, but there is not an easy-to-use conjugate prior for the shape parameter of a gamma. This inconvenience is usually dealt with by using either Metropolis–Hastings moves, rejection sampling methods, or numerical integration. However, in models with a large number of shape parameters, these existing methods are slower or more complicated than one would like, making them burdensome in practice. It turns out that the full conditional distribution of the gamma shape parameter is well approximated by a gamma distribution, even for small sample sizes, when the prior on the shape parameter is also a gamma distribution. This article introduces a quick and easy algorithm for finding a gamma distribution that approximates the full conditional distribution of the shape parameter. We empirically demonstrate the speed and accuracy of the approximation across a wide range of conditions. If exactness is required, the approximation can be used as a proposal distribution for Metropolis–Hastings. Supplementary material for this article is available online.     

Absorbed weak polyelectrolytes: Impact of confinement,topology, and chemically specific interactions on ionization,conformation free energy,counterion condensation,and absorption equilibrium

Absorption of weak polyelectrolytes impacts on properties such as ionization, conformations, and counterion (CI) condensation that are important in several areas of applied and fundamental science. We used a weak polyelectrolyte model and Monte Carlo simulations to investigate how the mentioned properties depend on pH or the size of a spherical cavity (SC) permeable to CIs but not to polyelectrolytes; the latter have either linear or starlike topologies and may be allowed to form charged hydrogen bonds (c? H‐bonds) between ionized and neutral monomers. Average ionization decreases upon increasing the number of arms at a constant number of monomers; it instead increases with the arm length in large SC due to CI screening. The way SC size, c? H‐bonds, and pH values interrelate to define ionization is instead more complicate due to arm pairing or clustering when c? H‐bonds are possible. These induce oscillations in the arm local ionization and impact on both monomer and CI distributions in the complete simulation cell in a way that also depends on polymer topology. The impact of ionization on the confinement free energy is also estimated; this highlighted that c? H‐bonding may enhance absorption compared with neutral chains. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019 , 57, 491–510     

Fractional Dynamics Identification via Intelligent Unpacking of the Sample Autocovariance Function by Neural Networks

Many single-particle tracking data related to the motion in crowded environments exhibit anomalous diffusion behavior. This phenomenon can be described by different theoretical models. In this paper, fractional Brownian motion (FBM) was examined as the exemplary Gaussian process with fractional dynamics. The autocovariance function (ACVF) is a function that determines completely the Gaussian process. In the case of experimental data with anomalous dynamics, the main problem is first to recognize the type of anomaly and then to reconstruct properly the physical rules governing such a phenomenon. The challenge is to identify the process from short trajectory inputs. Various approaches to address this problem can be found in the literature, e.g., theoretical properties of the sample ACVF for a given process. This method is effective; however, it does not utilize all of the information contained in the sample ACVF for a given trajectory, i.e., only values of statistics for selected lags are used for identification. An evolution of this approach is proposed in this paper, where the process is determined based on the knowledge extracted from the ACVF. The designed method is intuitive and it uses information directly available in a new fashion. Moreover, the knowledge retrieval from the sample ACVF vector is enhanced with a learning-based scheme operating on the most informative subset of available lags, which is proven to be an effective encoder of the properties inherited in complex data. Finally, the robustness of the proposed algorithm for FBM is demonstrated with the use of Monte Carlo simulations.     

CDT meets Hořava–Lifshitz gravity

The theory of causal dynamical triangulations (CDT) attempts to define a nonperturbative theory of quantum gravity as a sum over spacetime geometries. One of the ingredients of the CDT framework is a global time foliation, which also plays a central role in the quantum gravity theory recently formulated by Ho?ava. We show that the phase diagram of CDT bears a striking resemblance with the generic Lifshitz phase diagram appealed to by Ho?ava. We argue that CDT might provide a unifying nonperturbative framework for anisotropic as well as isotropic theories of quantum gravity.     

Packing Densities and Simulated Tempering for Hard Core Gibbs Point Processes

Monotonicity and convergence properties of the intensity of hard core Gibbs point processes are investigated and compared to the closest packing density. For such processes simulated tempering is shown to be an efficient alternative to commonly used Markov chain Monte Carlo algorithms. Various spatial characteristics of the pure hard core process are studied based on samples obtained with the simulated tempering algorithm.