奥氏体化温度对钢中马氏体组织形态的影响

用光学显微术研究了奥氏体化温度对３０、４０、６０、Ｔ８、Ｔ１２钢马氏体组织形态的影响。结果表明，奥氏体化温度越高，获得板条马氏体的量越多，奥氏体化温度超过某个临界值时，中高碳钢均可获得全部板条马氏体组织。钢的含碳量越高，获得全部板条马氏体的临界奥氏体温度越高。在该温度以下淬火时，３０、４０、６０钢先形成板条马氏体，后形成片状马氏体；Ｔ８、Ｔ１２钢先形成片状马氏体，后形成板条马氏体     

集成系统中工艺卡片填写与工序图绘制的研究

利用VBA实现CAD和CAPP系统集成，讨论了基于Word平台进行工艺卡填写，并在此基础上进行工艺卡填写和工序图并行输出的一般问题。工艺文件的填写是在CAPP数据库的支持下，直接读取CAD所提供的CAPP工艺数据文件，经推理机推理自动生产工艺过程；工序图绘制是在特征图表级和参数化绘图原理的基础上进行的。     

集成有源负载放大器的研究

本文重点介绍了集成有源负载放大器的分析方法,并以镜像电流源为例详细介绍、对比了两种方法(图解分析法和微变等效电路分析法)。此外,介绍了用厄雷电压计算 Av 的简便方法。     

物理学与美学

探索物理学中美的因素,领悟自然科学之美,激发科学创新精神。     

生命伦理学及其四大基本原则

生命伦理学起源于1950年代,1970年代成为一门独立的学科.我国于1980年代初起步.生命伦理学的英文bioethics,由bio和ethics两个词组成,意即在进行生命科学研究时既要遵循生命科学规律,又要合乎伦理学的原则.     

A STAGE-STRUCTURED SI ECO-EPIDEMIOLOGICAL MODEL WITH TIME DELAY AND IMPULSIVE CONTROLLING*

This paper formulates a robust stage-structured SI eco-epidemiological model with periodic constant pulse releasing of infectious pests with pathogens. The authors show that the conditions for global attractivity of the ＇pest-eradication＇ periodic solution and permanence of the system depend on time delay, hence, the authors call it ＂profitless＂. Further, the authors present a pest management strategy in which the pest population is kept under the economic threshold level （ETL） when the pest population is uniformly persistent. By numerical analysis, the authors also show that constant maturation time delay for the susceptible pests and pulse releasing of the infectious pests can bring obvious effects on the dynamics of system.     

铝厂污水零排放研究

本文从水质方面分析了西南某铝厂污水零排放的可行性。为了实现污水零排放,对该铝厂东片区、氧化铝厂、热电厂三大污水处理系统进行改进。从现场试验监测及统计分析结果表明,改进后的处理系统处理的水质满足再生水水质指标要求,该铝厂污水零排放的实现创造了极大的经济和社会效益。     

文学的存在方式现象学

文学的存在方式是指文学赖以存在的整体结构。通过对文学创作活动(事件)、文学接受活动(事件)的现象学描述,建构了文学的存在方式———文学存在的四维整体结构和文学存在之主体间的交流结构,后两者共同组建着文学的存在整体。     

观赏植物矮化培养研究

通过试验筛选出适合万寿菊、满天星快速繁殖的培养基．矮壮素可使满天星和万寿菊丛苗数量减少，植株生长明显受抑制，使其节间缩短，叶片增厚，叶色浓绿．矮壮素还有使万寿菊根系发达粗壮，分支较少的作用．500mg／L的矮壮素比较适宜矮化培养，既达到了矮化的效果又不至于阻碍植株正常生长，且试管苗株形紧凑、美观．     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.