An experimental study of dynamic tensile properties of glass fiber at low-temperatures

Tensile impact experiments of EC8.0−24×7 glass fiber bundles at different low temperaturesT(14°C, −40°C and −10°C) and strain rates ɛ were carried out, and complete stress-strain curves were obtained. Within the range of the experiment temperatures and strain rates, it is found that the initial modulusE, the ultimate strength σ_max and the unstable strain ɛ _b of the glass fiber bundles all increase with ɛ at an identicalT. At an identical ɛ, with the decrease ofT, E and σ_max increase; but ɛ _b increases when 10°C>T>−40°C and decreases when −40°C>T>−100°C. The strain-rate- and temperature-dependent bimodal Weibull statistical constitutive theory was adopted for the statistical analysis of the experimental results, and the Weibull parameters of single fiber were obtained. The results show that the bimodal Weibull distribution function is suitable to represent the strength distribution of the glass fiber at low temperature and different strain rates. The differences in the mechanical properties between EC8.0−24×7 and EC5.5−12 ×14 glass fiber bundles were also discussed. Project supported by the National Natural Science Foundation of China (No. 19772058).     

One-dimensional non-nearest-neighbor random walks in the presence of traps

A one-dimensional lattice random walk in the presence ofm equally spaced traps is considered. The step length distribution is a symmetric exponential. An explicit analytic expression is obtained for the probability that the random walk will be trapped at thejth trapping site.     

两指标双F统计过程控制图

当产品质量指标服从二元正态分时,可用T2控制图与Λ控制图联合判断产品生产的过程是否处于受控状态。本文利用T2统计量与F统计量、Λ统计量与F统计量之间的关系,得到了两指标情形下两类基于F分布统计量的统计过程控制图,简称双F统计过程控制图,并给出了控制图应用实例。     

ON THE SINGULAR DIRECTIONS OF VALUE DISTRIBUTION OF HOLOMORPHIC CURVES IN P^n（C）

This article proves the existence of singular directions of value distribution theory for some transcendental holomorphic curves in the n-dimensional complex projective space Pn(C). An example is given to complement these results.     

用于ψ＇→γ＇xcJ→γ＇γJ/ψ过程中多极分析的产生子

为了实验研究粲偶素ψ＇级联衰变过程ψ＇→γ＇xcJ→γ＇γJ/ψ,一个用于正确描述全部末态角分布的产生子是必要的．可以通过分析角分布的关联来确定这一过程的多极展开参数（或者等效的螺旋度振幅），而从中可以知道高阶电磁多极矩和相对论修正的贡献，同时可以检验ψ＇是不是ψ＇（2S）和ψ＇（1D）的混合．介绍了一个能完全描述这一过程的角分布的产生子，同时讨论了测量多极参数的测量方法．这些都可以被用在BESⅢ或者CLEOc的分析当中．     

梯度棒折射率分布系数的计算和验证

本文推导了用离子交换法制作的径向折射率梯度棒的g,h_4和h_6与材料特性相联系的计算公式。根据测定的材料参数,并用本文公式算出的g值与用成像法测得的g值相比较,其结果基本相符。     

Extreme behavior of bivariate elliptical distributions

This paper exploits a stochastic representation of bivariate elliptical distributions in order to obtain asymptotic results which are determined by the tail behavior of the generator. Under certain specified assumptions, we present the limiting distribution of componentwise maxima, the limiting upper copula, and a bivariate version of the classical peaks over threshold result.     

Study on the Baryon State X Produced in the Process J／ψ→p＋X,X→p＋P

The decay process J/ψ→p X X→p P，where p,p and P are the proton,antiproton and pseudoscalar states,respectively,has been studied in terms of the angular distribution and the generalized moment analysis methods.The result shows that we can identify the spin,but cannot determine the parity of the baryon resonance state X produced in the process J/ψ→p X X→p P.     

Monte Carlo方法研究低能电子束曝光沉积能分布规律

建立一个描述低能电子在多元多层介质中散射的物理模型,运用MonteCarlo方法模拟低能电子在靶体胶衬底中的复杂散射过程,在此基础上通过大量计算研究入射束能、胶层厚度、衬底材料等不同曝光条件对抗蚀剂沉积能密度分布的影响,获得沉积能分布规律:适量的低束能、薄胶层、低原子序数衬底可以使前散射电子对胶中沉积能密度分布的贡献增大、背散射电子的贡献减小,从而提高曝光分辨率. 关键词： 电子束曝光 MonteCarlo方法 低能电子散射 能量沉积     

Temperature effect on deposition rate of silicon nitride films

Temperature effects on deposition rate of silicon nitride films were characterized by building a neural network prediction model. The silicon nitride films were deposited by using a plasma enhanced chemical vapor deposition system and process parameter effects were systematically characterized by 2⁶⁻¹ fractional factorial experiment. The process parameters involved include a radio frequency power, pressure, temperature, SiH₄, N₂, and NH₃ flow rates. The prediction performance of generalized regression neural network was drastically improved by optimizing multi-valued training factors using a genetic algorithm. Several 3D plots were generated to investigate parameter effects at various temperatures. Predicted variations were experimentally validated. The temperature effect on the deposition rate was a complex function of parameters but N₂ flow rate. Larger decreases in the deposition rate with the temperature were only noticed at lower SiH₄ (or higher NH₃) flow rates. Typical effects of SiH₄ or NH₃ flow rate were only observed at higher or lower temperatures. A comparison with the refractive index model facilitated a selective choice of either SiH₄ or NH₃ for process optimization.