焊膏印刷工艺参数的选择与优化

表面贴装工艺流程的关键工序之一就是焊膏印刷,其工艺控制的好坏直接影响着装配的线路板的质量。通过对印刷工艺参数的分析,利用试验设计的方法来找出关键工艺参数,并加以优化设定,从而为控制工序参数奠定基础。     

结合RDF实现对XML文档的RBAC

文章提出了一种面向XML文档的基于XMLschema并结合RDF的访问控制模型,它实现了对XML文档的细粒度的安全访问控制,同时提供了对XML文档中associationsecurityobject的安全访问控制。     

全光网中的光信噪比估算与系统设计

指出了WDM光传送网中 ,级联光上下路节点或光交叉连接节点后 ,系统构成上与传统级联线路放大器情况下的差异以及常用光信噪比评估公式的近似性。对WDM光传送网中线路给出了一个等效模型 ,并详细推导了光信噪比的计算公式 ,同时计入了光放大器的自发辐射噪声引起的饱和效应。最后针对不同系统配置情况进行了光信噪比的估算 ,分析了估算结果及其对线路设计的指导意义     

软锰矿氧化硫化钡制高纯碳酸锰和氢氧化钡工艺研究

软锰矿同硫化钡溶液在较低温度下进行氧化还原反应，当硫化钡含量控制在26-75g／L范围内，软锰矿与硫化钡的物质的量比为2．5-3，反应时间50-60min，软锰矿被硫化钡还原为MnO，其还原率可达93．5％以上；还原后的锰用硫酸浸取，溶液经除杂后，与碳酸铵反应而制成高纯碳酸锰。硫化钡则被氧化为氢氧化钡，经蒸发、结晶而制得入水氢氧化钡，其回收利用率高速95％。     

一种高分辨率视频转换系统的设计

介绍了一种视频信号转换系统,本系统能将医学专用显示设备上播放的非标准单色模拟视频信号转换为标准电视视频信号、标准SVGA视频信号,也可直接输出标准数字视频信号。使非标准单色模拟视频信号能够在通用隔行或逐行显示设备上显示,解决了医学系统产生的视频信号在教学、演示或传输方面的需要。本系统在多家医院试用,达到设计功能。     

添加剂对工业齿轮油性能影响的研究

研究了硫化烯烃、磷酸酯胺盐、硫磷酸酯极压抗磨剂,含氮杂环衍生物多功能添加剂和防锈剂等,对工业齿轮油抗乳化性能、极压抗磨性能、防锈性能的影响;以及硫化烯烃、磷酸酯盐极压抗磨剂与含氮杂环衍生物多功能添加剂之间的协和效应。试验结果表明:1.2%~1.6%复合添加剂调制的CKD220工业齿轮油中,磷酸脂盐的正确选用可以提高齿轮油的抗乳化性能、防锈性能和减少防锈剂用量。硫化烯烃与磷酸脂盐、含氮杂环衍生物多功能添加剂的合理组合,可以提高齿轮油的极压抗磨性能和防锈性能,减少添加剂总加剂量。     

基于CB的串行口和接口板端口程序应用

串行口及接口板端口是计算机通信的主要途径 ,也是目前计算机工程测控中通用的基本方法。C Builder (简称CB)编程语言以其功能完备、开发效率高的特点而成为目前流行的程序开发工具 ,介绍了利用CB语言编写串行口和端口通信的程序的方法 ,并对具体的各方法编写了相应的示例。     

数字电视用中分辨率74cm纯平彩色显像管BC配列不良的分析与对策

通过对74cm中分辨率纯平彩色显像管BC配列局部严重群散不良的调查分析,确定荫罩受力塌陷是主要原因,通过改良设计、工艺优化及其它方面的综合对策,使以上技术难题得到满意的解决,整管不良率由4.78%下降到0.172%。     

ADN氧化剂的合成及其在推进剂中的应用

综述了ADN的合成、性质及其在推进剂中的应用，认为球形ADN具有能量高、感度低、特征信号低，对环境无污染等优点，将其作为氧化剂在高能低特征信号推进剂中有着广阔的应用前景。     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto