Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

Turning tradition into an instrument of research: The editorship of William Nicholson (1753–1815)

Mainly known for its links to the periodical market and radical politics, this article recontextualizes the editorship of William Nicholson (1753–1815) in terms of its roots in the metropolitan natural philosophical circles of the second half of the 18th century as well as its impact on experimenters and men of science after 1797. The article argues that Nicholson's editorship of the Journal of Natural Philosophy, Chemistry, and the Arts was a means to expand his philosophical significance among natural philosophers at home and abroad—and was, in fact, a form of epistemological subversion that challenged the “Banksian Learned Empire.”     

基于投影寻踪的径向基函数网络在参考腾发量预测中的应用

径向基函数网络(RBFN)已广泛应用于参考腾发量预测等领域，但常用的K均值聚类和自组织特征映射等方法在求取径向基函数网络隐层节点中心时存在较大不足。针对这一问题，本文引入投影寻踪方法，在投影降维的基础上实现对大量高维数据的聚类，建立了基于投影寻踪的径向基函数网络模型，并将该模型应用于山西潇河灌区参考腾发量的预测，研究了不同气象因子输入对参考腾发量预测精度的影响。结果表明，基于投影寻踪的径向基函数网络具有较强的适用性，只需使用最高温度、最低温度、日照时数和旬序数作为输入因子，就能以较高的精度预测参考腾发量。     

国有科研单位人才流失原因分析及对策

近年来,国有科研单位,尤其是地处经济不发达地区的国有科研单位,出现了较为严重的人才流失现象,一定程度上影响和制约了国有科研单位的发展。通过对国有科研单位人才流失原因的分析,提出了相应的对策。     

一种基于Normal基椭圆曲线密码芯片的设计

文章设计了一款椭圆曲线密码芯片。实现了GF（2^233）域上normal基椭圆曲线数字签名和认证。并支持椭圆曲线参数的用户配置。在VLSI的实现上，提出了一种新的可支持GF（2^233）域和GF（p）域并行运算的normal基椭圆曲线VLSI架构。其架构解决了以往GF（p）CA算迟后于GF（2^233）域运算的问题，从而提高了整个芯片的运算吞吐率。基于SMIC 0．18μm最坏的工艺，综合后关键路径最大时延3．8ns，面积18mm^2；考虑布局布线的影响，芯片的典型的情况下，每秒可实现8000次签名或4500次认证。     

径向基函数神经网络的再学习算法及其应用

为了应用径向基函数神经网络逐步地识别待研究系统,文章针对径向基函数神经网络的再学习算法开展了深入的研究.应用严格的数学推理方法,将径向基函数神经网络的再学习问题转化为矩阵求逆的附加运算.详细给出了径向基函数神经网络再学习算法中增加新训练样本和增加新基函数的数学公式,同时对如何获取新的训练样本进行了研究.     

Cartesian grid methods using radial basis functions for solving Poisson, Helmholtz, and diffusion–convection equations

Four methods that solve the Poisson, Helmholtz, and diffusion–convection problems on Cartesian grid by collocation with radial basis functions are presented. Each problem is split into a problem with an inhomogeneous equation and homogeneous boundary conditions, and a problem with a homogeneous equation and inhomogeneous boundary conditions. The former problem is solved by collocation with multiquadrics, whereas the latter problem is solved by collocation with either multiquadrics or fundamental solutions. It is found that methods that make use of fundamental solutions for collocation yield more accurate solutions that are less sensitive to the shape parameter of multiquadrics and node arrangement. Additional collocation appears to improve the quality of solutions.     

Nonreactive atom/molecule-surface scattering within the finite basis wave packet method

A quantum wave packet code for studying nonreactive scattering of closed-shell atoms or diatomic molecules from a rigid surface is described. The time evolution relies on the Chebychev propagator. Up to 5 collider degrees of freedom, 3 in translation and 2 in rotation, are treated in a pseudospectral way with the momentum or finite basis representation as the primary space. Potential matrix elements are efficiently evaluated by means of sequential 1D transformations between momentum and coordinate spaces. Fast Fourier transforms are performed for the translational and azimuthal coordinates whereas a Gauss-Legendre transform is used for the polar coordinate. This pseudospectral strategy minimizes memory requirements because no off-diagonal Hamiltonian matrix elements need to be stored. In addition, a wide variety of physical systems can be studied since no particular functional form is imposed for the interaction potential.     

Neural Computation of the MoM Matrix Elements for Planar Configurations

In this paper a new artificial neural network (ANN) based model for the calculation of the method of moments (MoM) matrix elements is presented. Training sets that characterize the matrix elements are first constructed. These sets are then utilized to effectively train two radial basis function (RBF) neural networks to accurately estimate all the elements of the MoM matrix for any mesh used. The potential of the proposed approach is demonstrated in the case of a narrow microstrip line. The current distribution on the microstrip line produced by the trained RBF networks agrees very well with the exact distribution. In addition, the proposed ANN model is much faster than the conventional MoM procedure.     

电子技术实验教学中科学素质与创新能力的培养

分析了当前高校电子技术实验教学中存在的一些问题，总结和介绍了作者在电子技术实验教学改革中针对提高学生科学素质和创新能力所做的一些工作尝试，并对进一步改革电子技术实验教学提出了设想。