固体粉末的分形几何体尖端烧结模型

本文提出一个分形几何模型(Fractal geometry model)。用它描述固体粉末体系的结构特征,并在此基础上建立了单金属粉末体系烧结过程的动力学模型。以单分子反应为例,研究了与烧结有关的若干因素。将理论预测与已有实验进行比较,得到了较满意的结果。     

科研成果管理软件设计中的共性问题

通过科研成果管理系统的实现,讨论了设计中必须考虑的软件管理能力、预警管理功能及一系列共性问题。     

基于峰态的盲信号分离

就一种基于信号峰态(kurtosis)极值化的盲信号分离方法进行了探讨．首先介绍算法理论，然后提出了一种简化快速的最速下降算法，解决了收敛指向性的问题，并将对混合语音信号的分离扩展到混合图像信号的分离．实验结果表明，同其他基于峰态的盲分离算法相比，其分离效果好，而且在收敛速度、运算复杂度方面有显著的优势．     

DSP实现SPWM波发生的软件开发与应用

介绍了采用TMS320LF2407芯片、通过查表和实时计算两种方法发生SPWM波，并给出了部分程序源代码．比较了两个法实现SPWM波发生的优缺点．并分析了每种方法的应用范围．     

铁纳米线阵列的结构与微观磁性

采用电化学方法制备了平行排列的Fe纳米线阵列。利用透射电镜、X—射线衍射、物性测量系统和穆斯堡尔谱分别对样品的形貌、晶体结构、宏观磁性和微观磁性进行了研究。结果表明样品形状均匀，但沿纳米线方向有晶体织构，且存在着很强的形状各向异性。利用结果，结合计算机模拟，对样品中原子磁矩的分布进行了讨论。     

荧光磁粉无损检测自动化系统的实现

对如何实现凸轮轴荧光磁粉无损检测自动化进行了讨论。给出了以微计算机为中心的自动控制系统，系统以主计算机控制PLC磁粉探伤机和步进电机，使工件的探伤过程与图像处理过程并行运行，实现了裂纹磁痕的自动识别。     

Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets

We present the first analysis of the human proteome with regard to interactions between proteins. We also compare the human interactome with the available interaction datasets from yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans) and fly (Drosophila melanogaster). Of >70,000 binary interactions, only 42 were common to human, worm and fly, and only 16 were common to all four datasets. An additional 36 interactions were common to fly and worm but were not observed in humans, although a coimmunoprecipitation assay showed that 9 of the interactions do occur in humans. A re-examination of the connectivity of essential genes in yeast and humans indicated that the available data do not support the presumption that the number of interaction partners can accurately predict whether a gene is essential. Finally, we found that proteins encoded by genes mutated in inherited genetic disorders are likely to interact with proteins known to cause similar disorders, suggesting the existence of disease subnetworks. The human interaction map constructed from our analysis should facilitate an integrative systems biology approach to elucidating the cellular networks that contribute to health and disease states.