Synthesis and Liquid Crystalline Behavior of Random Copolymer of Poly(ethylene oxide) Macromonomer and Liquid Crystalline Monomer by the Photon Transmission Technique

Random copolymers of poly(ethylene oxide) macromonomer with p‐vinylbenzyl end‐functional group (PEOVB) and liquid crystalline monomer, namely 6‐(4‐cyanobiphenyl‐4′‐oxy)hexyl acrylate (COA), were prepared by conventional free radical polymerization. A living anionic polymerization technique was employed for the synthesis of PEO macromonomers bearing p‐vinylbenzyl moiety at one end. The photon transmission method was also applied to study the phase transitions of COA monomer and its random copolymer with PEO. It was found that, for both samples, the nematic‐smectic A transition is continuous, but the critical fluctuation regions do not allow to obtain 3D XY values. Instead, we have obtained the values close to mean field regime. Scaling of thermal hystersis for random copolymer sample near the nematic‐isotropic transition was studied as well. Thermal hysteresis loops were produced under linearly varying temperature. It was shown that the areas of the hysteresis loops scale with the temperature scanning rate with an exponent being equal to 0.614 which is in good agreement with the field‐theoretical value.     

扩散模型和凝聚模型耦合作用下胶体凝聚动力学的Monte Carlo模拟研究

以扩散模型(Ds(γ)=D0×sγ)和凝聚模型(Pij(σ)=P0×(i×j)σ)为基础,对胶体体系随时间的演变、团簇大小分布及其标度关系、团簇的重均大小S(t)的变化规律以及模型对最终分形维数的影响四个角度进行了比较研究,发现扩散指数γ0和凝聚概率指数σ0对胶体的凝聚动力学过程有相似的影响.本文在较宽的γ和σ取值范围内,对胶体的凝聚动力学进行了模拟研究,对慢速凝聚向快速凝聚的转化机理作了定量分析,并进一步分析了在团簇-团簇凝聚(CCA)模型下,得到类似扩散置限凝聚(DLA)模型的凝聚体的物理意义,结果表明:(1)γ0代表了体系中团簇或单粒做"定向运动"而非无规则的布朗运动的情况.这种"定向运动"的推动力可能来自于大团簇产生的强"长程范德华力"、"电场力"等,或来自于体系边界处的外力场的作用.(2)当σ0时,体系成为先快后慢的慢速凝聚,这可能对应大团簇为一排斥中心,即胶体颗粒存在"排斥力场"的现象.(3)证实了团簇的重均大小在凝聚过程的早期按指数规律增长,而后期按幂函数规律增长的实验现象.模拟研究还表明,胶体体系的凝聚动力学过程,在σ0时是一个存在正反馈机制的非线性动力学过程,而在σ0时则体现出负反馈的特征.     

一种超混沌映射的单双参量自适应同步

利用参量自适应法实现了超混沌系统Kawakami映射的同步.通过严密的数学推导,给出了Kawakami映射在单参量自适应下实现同步的充分条件,并在双参量自适应控制下系统同样实现了同步.数值模拟结果显示,该映射输出具有类似于白噪声的宽谱特性,因此比较适合用于保密通信.     

An Exploratory Study on the Complexity and Machine Learning Predictability of Stock Market Data

This paper shows if and how the predictability and complexity of stock market data changed over the last half-century and what influence the M1 money supply has. We use three different machine learning algorithms, i.e., a stochastic gradient descent linear regression, a lasso regression, and an XGBoost tree regression, to test the predictability of two stock market indices, the Dow Jones Industrial Average and the NASDAQ (National Association of Securities Dealers Automated Quotations) Composite. In addition, all data under study are discussed in the context of a variety of measures of signal complexity. The results of this complexity analysis are then linked with the machine learning results to discover trends and correlations between predictability and complexity. Our results show a decrease in predictability and an increase in complexity for more recent years. We find a correlation between approximate entropy, sample entropy, and the predictability of the employed machine learning algorithms on the data under study. This link between the predictability of machine learning algorithms and the mentioned entropy measures has not been shown before. It should be considered when analyzing and predicting complex time series data, e.g., stock market data, to e.g., identify regions of increased predictability.     

Nonlinear Measures to Evaluate Upright Postural Stability: A Systematic Review

Conventional biomechanical analyses of human movement have been generally derived from linear mathematics. While these methods can be useful in many situations, they fail to describe the behavior of the human body systems that are predominately nonlinear. For this reason, nonlinear analyses have become more prevalent in recent literature. These analytical techniques are typically investigated using concepts related to variability, stability, complexity, and adaptability. This review aims to investigate the application of nonlinear metrics to assess postural stability. A systematic review was conducted of papers published from 2009 to 2019. Databases searched were PubMed, Google Scholar, Science-Direct and EBSCO. The main inclusion consisted of: Sample entropy, fractal dimension, Lyapunov exponent used as nonlinear measures, and assessment of the variability of the center of pressure during standing using force plate. Following screening, 43 articles out of the initial 1100 were reviewed including 33 articles on sample entropy, 10 articles on fractal dimension, and 4 papers on the Lyapunov exponent. This systematic study shows the reductions in postural regularity related to aging and the disease or injures in the adaptive capabilities of the movement system and how the predictability changes with different task constraints.     

用动态光散射跟踪钙-海藻酸水溶液凝胶化

海藻酸(alginate)是一种天然多糖,是直链键合的β-D-甘露糖醛酸(M)和α-L-古洛糖醛酸(G)的无规嵌段共聚物[1].在海藻酸水溶液中加入钙、铜、锌、铅等二价正离子,能够形成凝胶;其中钙-海藻酸凝胶在细胞输送、组织工程等领域受到人们的关注[2,3].影响海藻酸凝胶化的因素包括海藻酸的分子量和分子量分布、M/G值和序列分布、溶液浓度、正离子种类与浓度等[4,5].     

Critical behaviour of the order parameters at the SmC* to SmA phase transition in a ferroelectric liquid crystal mixture

Critical behaviour of the order parameters has been investigated in the ferroelectric liquid crystal mixture ZLI‐3654 in a 7.5 µm thick planar cell. The temperature dependence of the primary (tilt angle) and secondary (spontaneous polarisation) order parameters is considered. The critical exponent (β) has been evaluated from the fitting of the temperature dependence of the experimental data for both tilt angle and spontaneous polarisation. Experimental results are compared with the predictions of the de Gennes and Landau models.     

连续损伤力学基临界奇异指数与破坏时间预测

响应量在临近破坏时呈现出临界幂律奇异性加速特征,是一种被广泛证实的灾变破坏前兆,并被火山、滑坡和岩石破坏实验等后验预测结果证实为一种对破坏时间进行短临期预测的可行方法.但是,奇异性指数测量值的较大分散性导致了对其具体取值的争议和预测效果的不确定性.因此,理解奇异性指数取值特征及其内在物理控制因素,成为了一个核心问题.本文基于连续介质损伤力学和材料时间相关失效特征,构建了刻画损伤加速发展通向破坏过程的力学模型.导出了恒名义应力蠕变加载和控制名义应力随时间线性增大两种典型加载方式下,损伤和应变率加速发展通向破坏的临界幂律奇异性前兆特征.阐明了临界幂律奇异性指数取值依赖于材料损伤与承受真应力之间的非线性关系这一内在物理根源,表明了实际测量中奇异性指数的分散性不完全归结于测量数据误差,而是有着内在物理控制因素.针对破坏前奇异性指数的不确定性,建议了在未知奇异性指数条件下预测破坏时间的方法,并基于花岗岩脆性蠕变破坏实验进行了验证和说明.