铜到钼的类钠离子能级结构及软X射线光谱理论计算

本文采用Hartree-Fock自洽场方法,从理论上计算了铜到钼的类钠离子1s~22s~22p~6nl(n=3～6,l=O～5)各能级能量。给出了有可能实现软X射线激光的跃迁(5g—4f,5f—4d,6g—4f,6f—4d)的光谱性质:跃迁波长、振子强度,并和实验值进行了比较,跃迁波长的相对误差不超过千分之二。从计算结果可知,Se的6f—4d跃迁,Br的6g—4f跃迁,Zr的5f—4l跃迁,Nb的5g—4f跃迁都已进入水窗波段。     

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.     

LIBS对钛合金焊缝中成分的原位统计分布分析表征

钛合金凭借其强度高、耐蚀性好、耐热性高等特点已经被广泛应用于航天、海洋、生物医药等诸多领域,其中Ti-6Al-4V（TC4）合金的耐热性、强度、塑性、韧性、成形性、可焊性、耐蚀性和生物相容性均较好,已成为钛合金工业中的王牌合金。钛合金在激光焊接时,加入表面活性剂可以增加焊缝熔深、提高焊接效率、改善焊缝微观组织的不均匀性,但是可能会改变熔合区和焊缝区中元素含量及其分布状态,从而可能会对材料的性能产生一定的影响。运用LIBS分析技术对TC4钛合金焊接试样表面进行面扫描同步获得多元素成分信息,同时结合原位统计分布分析方法（OPA）,实现了对钛合金母材、熔合区、焊缝成分及其分布状态的快速表征,为活性剂的选择和焊接后钛合金的材料性能提供一种新的评价手段。选取了两个使用不同活性剂进行焊接的TC4钛合金薄板试样,选取焊缝纵切面方向作为分析面,采用320目的氧化铝砂纸进行表面处理,利用LIBSOPA系统进行成分分布统计表征。首先,对激发光斑和剥蚀条件进行条件优化,最终选择200 μm的激发光斑、10个预剥蚀脉冲10个剥蚀脉冲进行实验,并建立了钛合金中C,Al,V,Fe,Si和Ti六个元素的校准曲线（其中Si元素主要来自活性剂）;然后对钛合金焊接样品进行了区域扫描,并对元素含量和分布状态进行了统计表征。同时,在钛合金焊接样品的不同部位进行分区取样,采用高频红外法分析C元素含量,并与LIBSOPA结果进行比对,两种测试方法结果吻合。元素Al,V,Fe,Si和Ti分布结果与微束X荧光光谱法对应性较好。运用LIBSOPA 技术实现了对钛合金母材、熔合区、焊缝中多元素的成分分布表征,为快速判定钛合金焊缝中成分及分布状态提供了全新的评价表征手段。     

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A three-channel far-infrared (FIR) HCN laser interferometer has been developed to measure the line averaged electron density on EAST tokamak. Detectors with the properties of high sensitivity, high signal-to-noise ratio, small volume and stability are needed in interferometer device. The application of new Schottky barrier diode (SBD) detector has made the density signal and the signal-to-noise ratio greatly improved. The disturbance caused by the lower hybrid wave can be eliminated by using double copper foil shield and narrow band pass filter. The improved detectors have worked well during the long pulse discharge and H mode discharge.     

多解型重原子法

如果重原子具有高于结构的赝对称性,使用重原子法应获得具有多解形式的轻原子位置,我们引入一个赝结构模型并导出了赝结构衍射结构因子F_M(H)的表达式,给出了校正项|E_P(H)|的估算值。将多解型重原子法应用于一个未知结构,获得了完美的赝结构图象。 关键词：     

EicC对撞光学设计

EicC是中国科学院近代物理研究所计划建造的中国电子-离子对撞机装置,该对撞机质心能位于20 GeV附近,是研究海夸克的最佳能量窗口,同时还可研究胶子和价夸克。EicC对撞粒子为高极化率质子和电子束团,质子环pRing采用八字环设计方案,可以更好地保持极化质子束团极化率,电子环eRing采用跑道形环设计方案,可以更好地利用隧道空间。该装置电子束流能量中心值为3.5 GeV,电子束RMS发射度为水平方向60 nm·rad,垂直方向60 nm·rad,对撞点b函数为水平方向0.4 m,垂直方向0.12 m;质子束流能量中心值20 GeV,质子束RMS发射度为水平方向300 nm·rad,垂直方向180 nm·rad,对撞点b函数为水平方向0.08 m,垂直方向0.04 m,设计亮度2×1033 cm–2s–1。EicC采用双对撞区非对称光学设计,通过对EicC不同色品补偿方案的研究,最终确定了弧区加短直线节共同补偿的色品补偿方案;通过研究对撞点处b函数以及对撞点间相移对动力学孔径的影响,最终得到pRing动力学孔径大于8 s(s为束团RMS尺寸)、eRing动力学孔径大于20 s,满足大于束团尺寸6 s的要求。     

非晶态FeZrB合金的电阻率与温度的关系

本文研究了非晶态(Fe_1-xZr_x)_84.5B_15.5(x=0,0.02,0.04,0.06,0.08,0.1,0.15)和Fe_90-xB_xZr₁₀(x=0,4,10,16,20)合金的电阻率ρ与温度T的关系。实验结果表明,当Zr含量在0.02≤x≤0.08时,ρ-T曲线出现两个线性斜率,在略高于居里温度T_c处出现转折,在T关键词：     

稳态自洽密度分布的三参量解族

本文利用严格的解析方法求得了激光等离子体中稳态自洽密度分布的三参量解族。讨论几种特殊情况,指出Lee等人给出的平台型密度分布只是其中的一种特殊情况。 关键词：     

A MODIFIED SKYRME MODEL

In this paper we study a modified skyrme model with m_π≠0 and compute the static properties of nucleons in this model.The results show that most of theoretical values have been improved,especially 2>^1/2=0.56fm is two times as one from the original Skyrme model and it is close to experimental value.