空位在金刚石近(001)表面扩散的分子动力学模拟

用分子动力学方法模拟了空位在金刚石近(001)表面的扩散过程,研究了温度对空位扩散的影响.结果表明,当温度为1000K左右时,位于近表面第二层上的空位开始向表面运动;当温度在1400—2000K时,空位完全扩散到表面.这与实验结果和其他计算结果符合得很好.同时发现,温度为1400—1800K时,空位的扩散经历了两次迁移运动,其分别对应了均方位移图中的两个极大值.在不施加任何约束的条件下得到了空位的动态扩散路径,空位在金刚石近(001)表面的扩散势垒约为042eV.并探讨了一定温度下空位数目增多及其不同排列 关键词： 金刚石 空位 扩散 分子动力学     

Optimal design of a convergent-barrel cold spray nozzle by numerical method

A convergent-barrel (CB) cold spray nozzle was designed through numerical simulation. It was found that the main factors influencing significantly particle velocity and temperature include the length and diameter of the barrel section, the nature of the accelerating gas and its pressure and temperature, and the particle size. Particles can achieve a relatively low velocity but a high temperature under the same gas pressure using a CB nozzle compared to a convergent-divergent (CD) nozzle. The experiment results with Cu powder using the designed CB nozzle confirmed that particle deposition can be realized under a lower gas pressure with a CB nozzle.     

神经网络在可靠性分析中的应用

应用人工神经网络对舰炮发生故障的发射炮弹数进行了预测。借助前馈神经网络对非线性函数的逼近能力，对发生故障的发射炮弹数进行了单步预测，并与使用灰色数列预测的结果进行了对比，验证了人工神经网络进行可靠性预测的可行性。     

Performance and simulation of an on-board guided-wave optical processor for airborne SAR applications

This paper describes the performance and simulation of a compact integrated optical processor for the real-time reconstruction of two-dimensional images in airborne stripmap synthetic aperture radar applications. The functional behavior of the processor is explained in some detail. The design criteria are briefly given. The simulation step allowed the main processor characteristics and properties to be identified. A number of comparisons were obtained in airborne SAR mission scenarios between the predictions of the optical device and those achieved by the modern electronic digital approach, based on the wavefront reconstruction method by matched filtering.     

A new method for constructing infinite families of k-tight optimal double loop networks

The double loop network (DLN) is a circulant digraph with n nodes and outdegree 2. DLN has been widely used in the designing of local area networks and distributed systems. In this paper, a new method for constructing infinite families of k-tight optimal DLN is presented. For k = 0,1,…,40, the infinite families of k-tight optimal DLN can be constructed by the new method, where the number nk(t,a) of their nodes is a polynomial of degree 2 in t and contains a parameter a. And a conjecture is proposed.     

Effect of Notoginsenoside-Rg1 on the Expression of Several Proteins in the Striatum of Rat Models with Parkinson＇s Disease

After establishing hemi-Parkinsonian rat models, the relationships between neuron death and the expression of several proteins, such as c-Fos, GFAP, GDNF, NF-κB and some cytokines were determined. Therapeutics experiments with notoginsenoside-Rg1 were carried out. The research results show that the expressions of GFAP, NF-Kκ and c-Fos will obviously increase in the lesion side of the striatum and the expression of GDNF will decrease, which implies that the signal transduction pathway may participate in the apoptosis in neurons. The levels of some cytokines such as TNF-α, IL-1β in the striatum of PD rat models increased compared to those of normal rats. The results of the therapeutics experiments show that notoginsenoside-Rg1 may repress the immune inflammation response and regulate the immune function through the neuro-immune molecular network. Therefore, notoginsenoside-Rg1 can be used as an effective drug for anti-oxidation and anti-inflammation, and can be used in the therapy of Parkinson＇s disease（PD）.     

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.     

基于二次插值的非饱和土壤水流问题的特征差分方法及数值模拟

本文针对一类非饱和土壤水流问题,提出了基于二次插值的特征差分格式,得到了严谨的L2模误差估计.并作了数值试验,指明方法的有效性.     

Reconstruction of the Point-Spread Function of the Human Eye Using a New Phase-Retrieval Algorithm

The double-pass method is thought to obtain the point spread function (PSF) in human eyes based on two techniques: the symmetric double-pass method using the same pupil size and the asymmetric double-pass method using a different pupil size. The symmetric double-pass method provides autocorrelation of the retinal PSF and, thus, the modulation transfer function. The asymmetric double-pass method provides low-frequency partial-phase information and the partial phase-retrieval algorithm is applied to obtain the complete-phase information, and to estimate the PSF. The partial phase-retrieval algorithm is based on the iteration method proposed by Fineup and Kowalczyk and requires a lengthy computation. In this study, we propose a new high-speed phase-retrieval algorithm based on the property that the real and imaginary parts of optical transfer functions (OTFs) continuously change in value.     

Numerical simulation of the spatiotemporal evolution of a nanoparticle–plasma system

A one-dimensional nanodusty plasma was modeled by self-consistently coupling a plasma model with nanoparticle growth, charging, and transport models. As nanoparticles grow from subnanometer to tens of nm in diameter, the numerical results predict a rich spatiotemporal structure, including four distinct temporal phases: a charge-limited phase, a charge accumulation phase, an early ion drag phase, and a sheath interaction phase.