扩展基于AD9874数字接收机谱宽的二次抽取算法

针对基于AD9874芯片的数字接收机的最小谱宽和谱宽允许设置的数目均不能满足常规临床用磁共振成像技术的要求,本文从软件角度提出了一种扩展数字接收机谱宽的二次抽取算法. 该算法具体实现过程主要包括两个步骤：(1) 自适应过采样倍率和自动谱宽调整;(2) 数字滤波和抽取. 为了验证这个二次抽取算法的有效性,本文分别用单脉冲实验和磁共振成像实验加以了验证.     

测量冷原子温度的理论与近似拟合公式的误差分析

对短程飞行时间法(tim e-of-flight,TOF)中推算冷原子温度的理论拟合公式与近似拟合公式进行了误差分析与比较。研究表明:对于使用短程飞行吸收光谱信号推测冷原子团温度,当探测光光斑半径与冷原子团高斯半径之比k小于0.2时,理论拟合公式和近似拟合公式能很好的相符,随着探测光光斑半径与冷原子团高斯半径比值的逐渐增大,用近似拟合公式所得TOF吸收信号与用理论拟合公式所得TOF吸收信号的误差也将逐渐增大,当比值为0.5时,用近似拟合公式所得TOF吸收信号的误差将增大到20%。     

线聚焦——实现实用X射线激光饱和输出的关键技术

自从X射线激光问世以来,在产生机制从准稳态到瞬态,抽运方式从横向抽运到纵向抽运,再到全新的掠入射抽运方式的过程中,大增益的饱和输出一直是研究的根本目标.线聚焦问题是实现辐照均匀化进而达到饱和输出的关键.伴随着抽运方式的发展,先后出现了一系列对应的线聚焦器件,取得了不错的线聚焦效果.文章对X射线激光发展历史上出现的主要线聚焦器件进行了总结.     

动态激光衍射法测量细丝直径

基于夫琅禾费衍射理论和巴比涅互补原理,提出了利用激光衍射对细丝直径进行动态测量的方法.该方法具有测量精度高、速度快、非接触、使用方便且易于微机联接实现自动化测量等优点.     

吸积盘的X射线辐射对周围星际物质的离化研究

利用辐射流体力学程序对三倍频纳秒激光与靶物质相互作用进行了模拟研究，得到了可以产生黑体辐射谱分布的激光等离子体X射线辐射靶的最佳厚度；数值模拟研究了黑体谱分布的X射线辐射场对等离子体系统平均离化度分布的影响，它有助于深入理解天体物理中吸积盘对它周围星际物质的离化影响. 关键词： 辐射流体力学 激光等离子体 X射线辐射 吸积盘 离化     

方位伺服系统扭转振动固有频率的分析

本文阐述了伺服系统结构设计中估算系统固有频率的必要性,介绍了一种分析和计算经纬仪方位伺服系统扭转振动固有频率的方法。     

Nanomaterials in Cerebrovascular Disease Diagnose and Treatment

Cerebrovascular diseases (CVDs) are among the most serious diseases with high mortality and disability rates. The prevalent diagnosis and treatment methods of CVDs include imaging and interventional therapy. With the development of nanotechnology, large numbers of nanomaterials have been applied to the diagnosis and treatment of CVDs, mainly including carbon nanotubes, quantum dots, fullerenes, and dendrimers. In this review, the applications of nanomaterials in the field of diagnosis and treatment of CVDs, mainly including drug target delivery, imaging, therapy, endovascular treatment, and angiogenesis, are summarized. The applications of nanomaterials in the field of CVD are almost in the laboratory, and more effort is needed for clinical translation. The aim of this review is to provide useful information for future research and equipment development.     

Amorphous High-Entropy Hydroxides of Tunable Wide Solar Absorption for Solar Water Evaporation

The high-entropy materials have raised much attention in recent years due to their extraordinary performances in mechanical, catalysis, energy storage fields. Herein, a new type of high-entropy hydroxides (e.g., NiFeCoMnAl(OH)_x) that are amorphous and capable of broad solar absorption is reported. A facile one-pot co-precipitation method is employed to synthesize these amorphous high-entropy hydroxides (a-HEHOs) under ambient conditions. The a-HEHOs thus obtained display widely tunable bandgap (e.g., from 2.6 to 1.1 eV) due to their high-entropy and amorphous characteristics, enabling efficient light absorbance and photothermal conversion in the solar regime. Further solar water evaporation measurements show that the a-HEHOs delivered a considerable energy conversion efficiency of 55%, comparable to black titanium oxides that are synthesized using more complex and expensive methods.     

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.