聚合反应制备键合型纤维素-三(4-甲基苯基甲酸酯)衍生物类手性固定相

合成了带有甲基丙烯酰基的纤维素-三(4-甲基苯基甲酸酯)衍生物(CTMB),并通过聚合反应将其键合在具有双键的硅胶表面,得到手性固定相(CSP)。用所得到的CSP对9种对映体化合物(丁苯酞、酮基布洛芬、反-2,3-二苯环氧乙烷、安息香、安息香类似物、托葛尔碱、华法令、4,4′-二甲氧基-5,6,5′,6′-二次甲二氧基-2,2′-羰甲基甲酯联苯、4,4′-二甲氧基-5,6,5′,6′-二次甲二氧基-2-羰甲基甲酯-2′-羰甲基乙酯联苯)进行了拆分。考察了CSP制备过程中甲基丙烯酰氯的用量、键合方式以及硅胶     

An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Motor Imagery Electroencephalography (MI-EEG) has shown good prospects in neurorehabilitation, and the entropy-based nonlinear dynamic methods have been successfully applied to feature extraction of MI-EEG. Especially based on Multiscale Fuzzy Entropy (MFE), the fuzzy entropies of the τ coarse-grained sequences in τ scale are calculated and averaged to develop the Composite MFE (CMFE) with more feature information. However, the coarse-grained process fails to match the nonstationary characteristic of MI-EEG by a mean filtering algorithm. In this paper, CMFE is improved by assigning the different weight factors to the different sample points in the coarse-grained process, i.e., using the weighted mean filters instead of the original mean filters, which is conductive to signal filtering and feature extraction, and the resulting personalized Weighted CMFE (WCMFE) is more suitable to represent the nonstationary MI-EEG for different subjects. All the WCMFEs of multi-channel MI-EEG are fused in serial to construct the feature vector, which is evaluated by a back-propagation neural network. Based on a public dataset, extensive experiments are conducted, yielding a relatively higher classification accuracy by WCMFE, and the statistical significance is examined by two-sample t-test. The results suggest that WCMFE is superior to the other entropy-based and traditional feature extraction methods.     

Angular auto-correlation functions in molecular crystals and liquids: application to incoherent neutron scattering law

The effect of the local hindered molecular motion in the incoherent neutron scattering spectra in crystals and liquids is studied theoretically on the basis of the angular auto-correlation functions symmetrized on the dynamical point groups. The extended angular jump model simulates the motion. The microscopic properties of the matter, the time scale and the point symmetry of the molecule motion as well as the site symmetry of the molecule, are taken into account with the help of the dynamical variables of the model. The incoherent neutron scattering function is anisotropic in a monocrystalline sample. The scattering function consists of elastic plus quasi-elastic components. The shape of the quasi-elastic scattering spectrum is expressed by the sum of the weighted Lorentzian curves symmetrized with respect to the non-identical irreducible representations of the molecule motion point symmetry group. The elastic part of the intensity is increased by the contribution arising from the molecule motion of the identity representation symmetry. The scattering expression related to the molecule motion symmetry of a perfect cubic group is similar to the well-known expression derived for the scattering from the spherical top molecules exhibiting rotation diffusion.     

The electrostatic persistence length of polymers beyond the OSF limit

We use large-scale Monte Carlo simulations to test scaling theories for the electrostatic persistence length l _e of isolated, uniformly charged polymers with Debye-Hückel intrachain interactions in the limit where the screening length κ^-1 exceeds the intrinsic persistence length of the chains. Our simulations cover a significantly larger part of the parameter space than previous studies. We observe no significant deviations from the prediction l _e∝κ^-2 by Khokhlov and Khachaturian which is based on applying the Odijk-Skolnick-Fixman theories of electrostatic bending rigidity and electrostatically excluded volume to the stretched de Gennes-Pincus-Velasco-Brochard polyelectrolyte blob chain. A linear or sublinear dependence of the persistence length on the screening length can be ruled out. We show that previous results pointing into this direction are due to a combination of excluded-volume and finite chain length effects. The paper emphasizes the role of scaling arguments in the development of useful representations for experimental and simulation data. Received 12 February 2002     

经验模态分解与样本熵在并网型光伏逆变器故障诊断中的应用

摘要：针对光伏并网逆变器电路中故障信号的非线性、非平稳特点,提出一种基于经验模态分解(EMD)和样本熵(SampEn)的故障诊断方法。首先,利用经验模态分解对逆变器的三相输出电压进行分解,得到有限个本征模式分量(IMF),从中选取包含故障主要信息的前几个本征模式分量提取故障信息。然后,计算本征模式分量的样本熵,从而得到用于故障诊断的特征向量;最后,将逆变器开路故障进行分类和编码,将故障特征向量输入BP神经网络进行模式识别,从而达到故障诊断的目的。在Matlab环境下对光伏并网逆变器的故障诊断进行了实验,实验结果证明了文中方法能实现对光伏并网逆变器的故障诊断,且与小波包变换相比,该方法具有诊断效率高和准确度高等特点。     

基于中温黑体的近红外光纤光谱仪辐射定标的方法研究

中温黑体是红外谱段常用的高精度辐射标尺设备,而近红外是处于其有效辐射范围边缘的谱段,所以该谱段的定标研究相对较少。研究了基于中温黑体的近红外光纤光谱仪辐射定标方法,主旨是探讨定标精度如何受定标模型的结构参数选择的影响,进而为近红外光谱辐射计量溯源提供技术参考。采用50～1 050 ℃的可调中温黑体对近红外光纤光谱仪(950～1 700 nm)进行辐射定标。针对定标的关键环节重点讨论了两个内容,首先是辐射传输模型的几何因子匹配问题,比较分析了传统的双圆盘辐射传输模型和光纤直接耦合模型。对于光纤光谱仪的辐射定标来讲,采用光纤直接耦合形式的辐射传输模型,结构上更简单,耦合效率更高。其次重点分析了辐射定标中模型的结构参数对定标精度的影响,其影响的原因是定标数据本身属性中的尺度结构特征,即通常所说的非线性问题。因此对于定标精度要求较高时,需要采用非线性定标模型进行校正,并尽可能保证测试点采样的尺度均衡,这是小样本数据解释非线性结构关系时无法回避的样本选择问题。数据分析结果表明,定标方程的不同结构参数的选择策略对定标精度有显著影响,校正方程的样本残差标准差带变化范围为±0.1%～±1%。     

The effect of severe plastic deformation and annealing conditions on mechanical properties and restoration phenomena in an ultrafine-grains Fe-28.5%Ni steel

The effect of annealing condition on the microstructure evolution, together with mechanical properties of Fe-28.5Ni steel processed by one and six cycles of accumulative roll bonding (ARB), was explored. The evolution of microstructure was studied by means of Electron Backscattered Diffraction method. The ARB-processed specimens were annealed for 30 minutes between 500°C and 600°C, and the effect of these annealing processes was elaborated. Results showed a significantly reduced martensite phase during ARB process cycles. Moreover, a refined and stabilised ultrafine structure was obtained in 6-cycle ARB-processed specimen. Results also show that by annealing the 1-cycle specimen at 550°C, no significant microstructural change and mechanical properties variation are identified. As such, no significant microstructural change and mechanical properties variation were notified for annealing of 6-cycle ARB-processed specimen at 500°C. However, annealing at 600°C in both conditions led to a complete recrystallisation of the deformed structure. Annealing at 550°C in 6-cycle ARB-processed specimen showed partial recrystallisation as well as an abnormal grain growth characteristic. The abnormal grain growth was seen by annealing of 1-cycle ARB-processed specimen at 600°C. The changing features of dislocations were discussed in these specimens in terms of changing the dynamics of low- and high-angle grain boundaries. Tensile test results also showed a significant increase in the yield/ultimate tensile strengths with the application of ARB cycles. Although ARB process led to a rapid reduction of total elongation, full restoration during high-temperature annealing returned this value back to the initial, non-deformed, condition.     

A comprehensive review on surface modification,structure interface and bonding mechanism of plant cellulose fiber reinforced polymer based composites

Abstract

Plant cellulose fiber polymer composites are readily applied in wide range of applications due to ecological and economical alternative to traditional materials. The considerable amount of residues and organic wastes from agricultural process are still employed as lower energy resource. Organic materials are generally disposed in composting, landfilling or anaerobic digestion. The utilization of these wastes in plant fiber composites shows significant alternative and environmental friendly in nature. The production of plant cellulose fiber composite with higher structural properties is optimized by interfacial bonding between polymer and reinforced fiber. The interface plays a vital role in regulating mechanical properties by distributing bonds and stress transferring, which is one of least understood element of composites. This paper presents the comprehensive review of fiber structures, different modification techniques to reduce the incompatibility between matrix and fiber, assessment of structure interface and bonding, clarifies the interfacial adhesion of cellulose fiber composites.     

Theoretical analysis of trends in hydrogen bonding involving halogen acceptors (F−At) covalently bonded to a group 14 atom (C−Pb)

In this work, extensive quantum-chemical calculations have been carried out to identify and elucidate trends in the hydrogen-bonding (HB) interaction involving halogen acceptors covalently bonded to a group 14 atom. A series of 25 heterodimers composed of MH₃X (where M = C?Pb and X = F?At) and HNC molecules have been selected as model complexes stabilised by the HB interaction occurring between the X atom of MH₃X and the H atom of HNC. The interaction energy (E_int) between MH₃X and HNC in the MH₃X···HNC complexes falls in the range from ?2.7 to ?10.8 kcal/mol, indicating weak or medium strength of HB in these complexes. The strength of HB in the complexes remains consistent with the well-known HB weakening as X gets heavier. Regarding the effect of M on E_int, the gradual strengthening of HB is observed while descending group 14, but only from M = Si to M = Pb. The trends in E_int are compared with various HB-related parameters obtained from vibrational analysis, the natural bond orbital (NBO) method, the symmetry-adapted perturbation theory (SAPT) and the quantum theory of atoms in molecules (QTAIM). The parameters that present clear (possibly linear) relationships with E_int have been selected to characterise the effect of M and X on the HB interaction.     

Quantum Bayesian computation

Quantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed-up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep learning that are fundamental to Bayesian learning. Second, we describe quantum data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Third, we show how quantum algorithms naturally calculate Bayesian quantities of interest such as posterior distributions and marginal likelihoods. Our goal then is to show how quantum algorithms solve statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes and stochastic gradient descent. On the empirical side, we apply a quantum FFT algorithm to Chicago house price data. Finally, we conclude with directions for future research.