Crystallinity and dielectric relaxations in semi-crystalline poly(ether ether ketone)

The Maxwell-Wagner-Sillars (MWS) relaxation is studied for semi-crystalline polymers poly (ether ether ketone) (PEEK), in the range 20 Hz-1 MHz and temperature varying from 80 to 330 °C. The parameter is the crystallization condition in the case of PEEK, which is a semi-crystalline polymer considered as a particulate composite. The relaxation found in the semi-crystalline polymers above the α relaxation of the PEEK is ascribed to the trapping of conductive carriers at the interface between crystalline lamellae and the amorphous matrix. The study of PEEK microstructure is based on differential calorimetry and X-rays diffraction. Two lamellae populations have been detected, that depends on the crystallization temperature and its duration. The crystallinity rate is increasing with crystallization temperature and duration. In dielectric studies, the use of the electric modulus instead of permittivity allows us to minimize the ionic conduction and then leads to the appearance of the interfacial relaxation. According to our measurements, the crystallinity rate is not the main factor of the interfacial relaxation intensity, which also depends on the nature and degree of perfection of the lamellae.     

Transport through artificial single-molecule magnets: Spin-pair state sequential tunneling and Kondo effects

The transport properties of an artificial single-molecule magnet based on a CdTe quantum dot doped with a single Mn+2 ion(S=5/2) are investigated by the non-equilibrium Green function method.We consider a minimal model where the Mn-hole exchange coupling is strongly anisotropic so that spin-flip is suppressed and the impurity spin S and a hole spin s entering the quantum dot are coupled into spin pair states with(2S+1) sublevels.In the sequential tunneling regime,the differential conductance exhibits(2S+1) possible peaks,corresponding to resonance tunneling via(2S+1) sublevels.At low temperature,Kondo physics dominates transport and(2S+1) Kondo peaks occur in the local density of states and conductance.These peaks originate from the spin-singlet state formed by the holes in the leads and on the dot via higher-order processes and are related to the parallel and antiparallel spin pair states.     

基于传播圆补偿像散的被动锁模激光器谐振腔设计方法

由于超短脉冲激光器的谐振腔大都采用多镜折叠的形式,像散已成为影响锁模激光器性能优劣的重要问题.本文提出了一种基于传播圆补偿像散的被动锁模激光器谐振腔设计方法,该方法简单、直观、高效,容易找到补偿像散的最佳位置.理论研究表明,当SESAM位于子午和弧矢传播圆交汇处附近时,SESAM处的子午光斑和弧矢光斑大小几乎相等,像散得到补偿.该谐振腔对外界干扰引起的腔镜振动和热透镜焦距的变化均不敏感,谐振腔的抗干扰性很强.实验研究表明,当SESAM位于子午和弧矢传播圆交汇处附近时,锁模激光器可获得稳定连续的锁模激光脉冲,且激光器的抗干扰性很强.本文的理论研究与实验结果相一致.     

The optical properties and energy transition process in nanocomposite of polyvinyl-pyrrolidone polymer and Mn-doped ZnS

This study has been carried out on the optical properties of polyvinyl-pyrrolidone (PVP), the energy transition process in nanocomposite of PVP capped ZnS:Mn nanocrystalline and the influence of the PVP concentration on the optical properties of the PVP capped ZnS:Mn nanocrystalline thin films synthesized by the wet chemical method. The microstructures of the samples were investigated by X-ray diffraction, the atomic absorption spectroscopy, and transmission electron microscopy. The results showed that the prepared samples belonged to the sphalerite structure with the average particle size of about 2–3 nm. The optical properties of samples are studied by measuring absorption, photoluminescence (PL) spectra and time-resolved PL spectra in the wavelength range from 200 to 700 nm at 300 K. From data of the absorption spectra, the absorption edge of PVP polymer was found about of 230 nm. The absorption edge of PVP capped ZnS:Mn nanoparticles shifted from 322 to 305 nm when the PVP concentration increases. The luminescence spectra of PVP showed a blue emission with peak maximum at 394 nm. The luminescence spectra of ZnS:Mn–PVP exhibits a blue emission with peak maximum at 437 nm and an orange–yellow emission of ion Mn²⁺ with peak maximum at 600 nm. While the PVP coating did not affect the microstructure of ZnS:Mn nanomaterial, the PL spectra of the PVP capped ZnS:Mn samples were found to be affected strongly by the PVP concentration.     

铬和镍的添加对Fe3Al合金力学性能影响的DFT研究

利用第一原理研究了过渡金属元素 Cr 或 Ni 在 Fe₃Al合金中的优先占位行为及其合金化效应. 计算结果表明: Cr 或 Ni 的取代有助于Fe₃Al 合金体系更稳定, Cr 优先占据 Fe_I 位, Ni 优先占据 Fe_II位. Fe₂NiAl-II 具有最小的剪切模量G, 杨氏模量E和G/B值, 因此Fe₂NiAl-II合金的韧性、延展性最佳. 态密度和电荷密度图表明, 过渡金属元素的取代提高了它们与近邻基体原子之间的相互作用, 削弱了Al和Fe的相互作用.     

染料掺杂光子晶体荧光带隙边缘的激射研究

利用激光全息光刻技术, 在重铬酸盐明胶 感光材料中制备了掺杂有机染料的层状光子晶体. 在532 nm纳秒脉冲激光激励下, 样品的荧光光谱表现出良好的带隙特征; 随着抽运能量的增加, 在荧光带隙带边位置获得了激射光, 并进一步研究了光子晶体的带边位置与染料荧光峰的匹配对激射的影响.带边位置越靠近染料的荧光峰, 激射阈值越低, 反之则不易产生激射.该研究为超低阈值光子晶体激光器的发展提供了思路和方法. 关键词： 全息光刻 光子晶体 荧光带隙 低阈值激射     

Optimal Randomness for Stochastic Configuration Network (SCN) with Heavy-Tailed Distributions

Stochastic Configuration Network (SCN) has a powerful capability for regression and classification analysis. Traditionally, it is quite challenging to correctly determine an appropriate architecture for a neural network so that the trained model can achieve excellent performance for both learning and generalization. Compared with the known randomized learning algorithms for single hidden layer feed-forward neural networks, such as Randomized Radial Basis Function (RBF) Networks and Random Vector Functional-link (RVFL), the SCN randomly assigns the input weights and biases of the hidden nodes in a supervisory mechanism. Since the parameters in the hidden layers are randomly generated in uniform distribution, hypothetically, there is optimal randomness. Heavy-tailed distribution has shown optimal randomness in an unknown environment for finding some targets. Therefore, in this research, the authors used heavy-tailed distributions to randomly initialize weights and biases to see if the new SCN models can achieve better performance than the original SCN. Heavy-tailed distributions, such as Lévy distribution, Cauchy distribution, and Weibull distribution, have been used. Since some mixed distributions show heavy-tailed properties, the mixed Gaussian and Laplace distributions were also studied in this research work. Experimental results showed improved performance for SCN with heavy-tailed distributions. For the regression model, SCN-Lévy, SCN-Mixture, SCN-Cauchy, and SCN-Weibull used less hidden nodes to achieve similar performance with SCN. For the classification model, SCN-Mixture, SCN-Lévy, and SCN-Cauchy have higher test accuracy of 91.5%, 91.7% and 92.4%, respectively. Both are higher than the test accuracy of the original SCN.     

Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the processing of complex signals, enhance the control of complex systems, increase the performance of optimization, and even extend the enabling of the potential for creativity. In this article, the authors discuss the fractional dynamics, FOT and rich fractional stochastic models. First, the use of fractional dynamics in big data analytics for quantifying big data variability stemming from the generation of complex systems is justified. Second, we show why fractional dynamics is needed in machine learning and optimal randomness when asking: “is there a more optimal way to optimize?”. Third, an optimal randomness case study for a stochastic configuration network (SCN) machine-learning method with heavy-tailed distributions is discussed. Finally, views on big data and (physics-informed) machine learning with fractional dynamics for future research are presented with concluding remarks.     

量子点单光子源的光纤耦合

半导体量子点在低温下产生谱线细锐的激子发光可制备单光子源.光纤耦合可避免低温共聚焦装置扫描定位和振动影响,是实现单光子源即插即用和组件化的关键技术.在耦合工艺上,基于微区定位标记发展出拉锥光纤与光子晶体腔或波导侧向耦合、大数值孔径锥形端面光纤与量子点样片垂直耦合等技术;然而,上述工艺需要多维度精密调节以避免柔软光纤的畸形弯曲实现对准和高效耦合.陶瓷插针或石英V槽封装的光纤无弯曲且具有大平滑端面,只要与单量子点样片对准贴合就可保证垂直收光, V槽封装的排式光纤还可通过盲对粘合避免扫描对准,耦合简单.本文在前期排式光纤粘合少对数分布Bragg反射镜(distributed Bragg reflector, DBR)微柱样片实现单光子输出基础上,经理论模拟采用多对数DBR腔提升样片垂直出光和光纤收光效率,使光纤输出单光子计数率大大提升.     

Statistical and Visual Analysis of Audio,Text, and Image Features for Multi-Modal Music Genre Recognition

We present a multi-modal genre recognition framework that considers the modalities audio, text, and image by features extracted from audio signals, album cover images, and lyrics of music tracks. In contrast to pure learning of features by a neural network as done in the related work, handcrafted features designed for a respective modality are also integrated, allowing for higher interpretability of created models and further theoretical analysis of the impact of individual features on genre prediction. Genre recognition is performed by binary classification of a music track with respect to each genre based on combinations of elementary features. For feature combination a two-level technique is used, which combines aggregation into fixed-length feature vectors with confidence-based fusion of classification results. Extensive experiments have been conducted for three classifier models (Naïve Bayes, Support Vector Machine, and Random Forest) and numerous feature combinations. The results are presented visually, with data reduction for improved perceptibility achieved by multi-objective analysis and restriction to non-dominated data. Feature- and classifier-related hypotheses are formulated based on the data, and their statistical significance is formally analyzed. The statistical analysis shows that the combination of two modalities almost always leads to a significant increase of performance and the combination of three modalities in several cases.