Crystal electric field excitations in ferromagnetic CeTX compounds

A ferromagnetic ground state was identified for the compounds CeCuGe (T_C=10 K), CeCuSi (T_C=15 K) [F. Yang, et al., J. Appl. Phys. 69 (1991) 4705] and CeAuGe (T_C=10 K) [R. Pottgen, J. Magn. Magn. Mater. 152 (1996) 196]. The observed saturation magnetic moment values at low temperatures for all three compounds are considerably less than the theoretically expected value g_JJ=2.14μ_B for the free Ce³⁺ ion involving the entire six-fold J=5/2 multiplet, and thus provide a first indication of partial lifting of the f-electron level degeneracy in these compounds. Specific heat data yield crystal electric field (CEF) excitation energies (Δ_Sch) equivalent to 140 K for CeCuGe, 110 K for CeCuSi and 280 K for CeAuGe. To confirm the presence of CEF excitations directly, we have carried out inelastic neutron scattering (INS) measurements on all three compounds, using the HET spectrometer at ISIS Facility. Here, we present a detailed analysis of the INS spectra of CeCuSi on the basis of a CEF model and the detailed analysis of the INS of the other two compounds will be reported elsewhere.     

Passive Tracking of Multiple Underwater Targets in Incomplete Detection and Clutter Environment

A major advantage of the use of passive sonar in the tracking multiple underwater targets is that they can be kept covert, which reduces the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the complexity of data association between measurements and targets make the problem of underwater passive multiple target tracking challenging. To deal with these problems, the cardinalized probability hypothesis density (CPHD) recursion, which is based on Bayesian information theory, is developed to handle the data association uncertainty, and to acquire existing targets’ numbers and states (e.g., position and velocity). The key idea of the CPHD recursion is to simultaneously estimate the targets’ intensity and the probability distribution of the number of targets. The CPHD recursion is the first moment approximation of the Bayesian multiple targets filter, which avoids the data association procedure between the targets and measurements including clutter. The Bayesian-filter-based extended Kalman filter (EKF) is applied to deal with the nonlinear bearing and Doppler measurements. The experimental results show that the EKF-based CPHD recursion works well in the underwater passive multiple target tracking system in cluttered and noisy environments.     

选择性激光烧结成型件密度的支持向量回归预测

根据不同工艺参数（层厚、扫描间距、激光功率、扫描速度、加工环境温度、层与层之间的加工时间间隔和扫描方式）下的选择性激光烧结成型件密度的实测数据集,应用基于粒子群算法寻优的支持向量回归（SVR）方法,建立了加工工艺参数与成型件密度间的预测模型,并与BP神经网络模型进行了比较.结果表明：基于相同的训练样本和检验样本,成型件密度的SVR模型比其BP神经网络模型具有更强的内部拟合能力和更高的预测精度;增加训练样本数有助于提高SVR预测模型的泛化能力;基于留一交叉验证法的SVR模型的预测误差最小.因此,SVR是一种预测选择性激光烧结成型件密度的有效方法. 关键词： 选择性激光烧结 密度 支持向量机 回归分析     

基于拓扑结构的碱金属化合物摩尔磁化率的支持向量回归研究

基于经典电动力学导出的表征简单离子磁化率的磁性点价g_i所构建的分子磁性连接性指数?^mF及45种碱金属化合物的摩尔磁化率χ_m的实测数据集,利用粒子群寻优的支持向量回归（SVR）方法,建立了基于⁰F和¹F的碱金属化合物χ_m的预测模型,并与基于多元线性回归（MLR）模型的计算结果进行了比较.结果显示,基于9次交叉验证的SVR模型预测的平均绝对误差、平均相对误差绝对值以及均方根误差均比MLR模型小,表明SVR模型的回归预测能力优于MLR.研究表明,磁性连接性指数^mF是一种合适的分子描述符,SVR是一种预测碱金属化合物χ_m的有效方法. 关键词： 碱金属化合物 摩尔磁化率 支持向量回归 预测     

界面生长中断对GaAs (111)衬底上 AlGaAs/GaAs量子阱电子自旋寿命的影响

用固源分子束外延技术(SSMBE)在GaAs(111)衬底上,采用不同的界面中断时间生长了多组AlGaAs/GaAs多量子阱样品(MQWs),通过室温发光光谱和时间分辨克尔旋转谱(TRKR)研究了界面生长中断对发光光谱半峰全宽(FWHM)和量子阱中电子自旋弛豫时间(自旋寿命)的影响,发现了自旋寿命随着界面生长中断时间的增加呈现先减小后增加的趋势,此变化趋势与荧光光谱半峰全宽表征的材料质量随中断时间的变化一致,适当的界面生长中断时间能有效的增加GaAs (111)衬底上AlGaAs/GaAs 多量子阱中电子自旋寿命。     

Mathematics learning support and engagement in first year engineering

This paper considers the effects of both free optional mathematics learning support and engagement on the mathematics performance in a foundation mathematics subject of a cohort of engineering students entering university with poor mathematical skills. New engineering students were directed to either a foundation or standard mathematics subject based on the results of a placement test. For students in the foundation subject, it was found that high levels of learning support were associated with greater improvement over the semester. Some form of learning support was used by 57.9% of the students, a reasonably high proportion of the cohort. Some factors for this high level of use of learning support are considered. One possible factor, the placement test, appears to have had a positive effect. Engagement in the subject activities as measured by tutorial attendance and learning management system use was found to have a positive association with final mark. Students who utilized a high level of learning support were more likely to be more engaged with the subject, making it impossible to draw conclusions about improvements being solely due to the use of learning support.     

碳/环氧复合材料在航天有效载荷支撑结构中的应用

为了满足航天有效载荷轻型化、高比强度、高比模量的要求,分析了航天有效载荷支撑结构常用工程材料的优缺点;阐述了碳/环氧复合材料在航天有效载荷承力支撑结构中应用的可行性;介绍了该材料在航天有效载荷中的实际工程应用及相关设计方法。用优化设计后的碳/环氧复合材料支撑机构支撑80 kg重的有效载荷,在正弦振动和随机振动试验过程中碳纤支撑座没有发生破坏,而质量相对于铝合金结构降低了近30%。结果表明,碳/环氧复合材料应用于航天有效载荷支撑结构中是可行的。      

矩阵多项式秩的和的恒等式及其应用

证明了矩阵A的两个多项式秩的和等于它们最大公因式与最小公倍式秩的和,这个结果不仅可以概括近期文献的相关工作,而且可以对应用矩阵多项式求逆矩阵的方法作进一步的研究,同时也可使关于矩阵秩恒等式的最新讨论获得一种简单统一的处理方法.     

Tracking customer portfolio composition: A factor analysis approach

In this paper, we address the changing composition of a customer portfolio taking into account actions undertaken by the company to adapt its service offer to market conditions and/or technological innovations. We present a specific methodology to identify clusters of customers in different periods and then compare them over time. The classification process takes into account both qualitative and quantitative aspects of the consumption levels of the services or products offered by the company. The possibility of period‐to‐period variation in the customer portfolio and the service or product offer is also considered, in order to achieve a more realistic scenario. The core of the proposed methodology is related to the family of exploratory factorial and cluster techniques. The customers are classified by using a bicriterial clustering methodology based on ‘tandem’ analysis (multiple factor analysis+cluster analysis of the main factors). The bicriterial approach allows for a compromise between customers' consumption levels (a quantitative criterion) and their consumption/non‐consumption pattern (a qualitative criterion). The evolution of the customer portfolio composition is explored through multiple correspondence analysis. This technique allows visual comparison of the position of different clusters against time and the identification of key changes in customer consumption behavior. The methodology is tested on realistic customer portfolio scenarios for a major telecommunication company. We simulate various scenarios to show the strengths of our proposal. Copyright © 2009 John Wiley & Sons, Ltd.