The PAR(p) Interconfigurations model used by the Brazilian Electric Sector

This study discusses the characteristics of the Periodic Autoregressive model, PAR(p), which is used to generate synthetic series of inflow energies that serve as entries for computer platforms that implement the planning and expansion of the operations of the BES – the Brazilian Electric Sector (SEB – Sistema Elétrico Brasileiro). The methodology for the design of a generating plant is presented in addition to the fundamentals of the “PAR(p) Interconfigurations” Model, which is referred to as the Inflow Energy Generation Model (IEGM) in this study. The major contribution of this study is to provide the first scientific discussion of the representation of multiple configurations using the PAR(p) model. For this purpose, several topics related to the time series are discussed, such as the definition of the model order, the matter of stationarity and the need to address possible outliers. Finally, a case study is presented, wherein the results of the estimation and generation of the described model’s scenarios are demonstrated.     

抗真菌蛋白Rs-AFP2基因定点突变及其对大肠杆菌中表达的影响

为了研究遗传密码子对表达调控的影响,利用PCR重叠延伸法,对萝卜抗真菌蛋白Rs-AFP2基因编码序列区的部分核苷酸进行沉默突变,构建突变体Rs-AFPm.序列分析表明,PCR产物全长240bp,有一个阅读框,编码的蛋白由29个氨基酸的信号肽和51个氨基酸的抗真菌蛋白组成.突变体与突变前的Rs-AFP2基因相比,在编码区第3号氨基酸Lys相差一个碱基(TTG→TTA),第5号氨基酸Gln相差一个碱基(CAG→CAA),第6号稀有密码子Arg相差两个碱基(CAG→CGA).重新合成引物,将切除信号肽的Rs-AFP2基因和Rs-AFPm基因与原核表达载体pET-21b(+)分别重组到大肠杆菌BL21菌株.IPTG诱导后,二者均得到了表达.软件分析显示,突变前pETAFPo表达产物占全菌蛋白的3%,突变后pETAFPm的表达产物占全菌蛋白含量的8%;表达蛋白主要以包涵体的形式存在,包涵体经超声波破碎后,蛋白质复性,抑菌结果表明,pETAFPm表达产物的抑菌半径大于pETAFP2表达产物的抑菌半径.这些都说明改造后的Rs-AFPm基因与Rs-AFP2基因相比,已有效地提高表达量.     

抗乙肝病毒表面抗原PreS1(20-47)的单链抗体在转基因烟草根分泌物中的初步表达

为探索利用植物根分泌表达重组蛋白的可行性，构建了含有抗乙肝病毒表面抗原PreS1(20—47)单链抗体(ScFv)基因的表达载体。该ScFv基因转化烟草后在烟草根部细胞的细胞质和内质网中获得表达。实验结果表明，5’端融合ER导向信号肽的重组ScFv可通过根分泌表达。     

Proving termination of GHC programs

A transformational approach for proving termination of parallel logic programs such as GHC programs is proposed. A transformation from GHC programs to term rewriting systems is developed; it exploits the fact that unifications in GHC-resolution correspond to matchings. The termination of a GHC program for a class of queries is implied by the termination of the resulting rewrite system. This approach facilitates the applicability of a wide range of termination techniques developed for rewrite systems in proving termination of GHC programs. The method consists of three steps: (a) deriving moding information from a given GHC program, (b) transforming the GHC program into a term rewriting system using the moding information, and finally (c) proving termination of the resulting rewrite system. Using this method, the termination of many benchmark GHC programs such as quick-sort, merge-sort, merge, split, fair-split and append, etc., can be proved. This is a revised and extended version of Ref. 12). The work was partially supported by the NSF Indo-US grant INT-9416687 Kapur was partially supported by NSF Grant nos. CCR-8906678 and INT-9014074. M. R. K. Krishna Rao, Ph.D.: He currently works as a senior research fellow at Griffith University, Brisbane, Australia. His current interests are in the areas of logic programming, modular aspects and noncopying implementations of term rewriting, learning logic programs from examples and conuterexamples and dynamics of mental states in rational agent architectures. He received his Ph.D in computer science from Tata Institute of Fundamental Research (TIFR), Bombay in 1993 and worked at TIFR and Max Planck Institut für Informatik, Saarbrücken until January 1997. Deepak Kapur, Ph.D.: He currently works as a professor at the State University of New York at Albany. His research interests are in the areas of automated reasoning, term rewriting, constraint solving, algebraic and geometric reasoning and its applications in computer vision, symbolic computation, formal methods, specification and verification. He obtained his Ph.D. in Computer Science from MIT in 1980. He worked at General Electric Corporate Research and Development until 1987. Prof. Kapur is the editor-in-chief of the Journal of Automated Reasoning. He also serves on the editorial boards of Journal of Logic Programming, Journal on Constraints, and Journal of Applicable Algebra in Engineering, Communication and Computer Science. R. K. Shyamasundar, Ph.D.: He currently works as a professor at Tata Institute of Fundamental Research (TIFR), Bombay. His current intersts are in the areas of logic programming, reactive and real time programming, constraint solving, formal methods, specification and verification. He received his Ph.D in computer science from Indian Institute of Science, Bangalore in 1975 and has been a faculty member at Tata Institute of Fundamental Research since then. He has been a visiting/regular faculty member at Technological University of Eindhoven, University of Utrecht, IBM TJ Watson Research Centre, Pennsylvania State University, University of Illinois at Urbana-Champaign, INRIA and ENSMP, France. He has served on (and chaired) Program Committees of many International Conferences and has been on the Editorial Committees.     

网络化制造中基于STEP-NC的编程方法

STEP-NC是由欧美提出的新的数控接口标准，目的是取代旧的数控标准ISO6983。本文首先分析了STEP-NC的两种应用标准：ISO14649（ARM）和STEPAP238（AIM），并且分析了Part21和Part28的应用领域及Part21文件格式和其中的信息产生方法。然後介绍了ST-Developer环境下ROSEC＋＋颠库编程接口及其应用程序的早、晚编联方式，并且以此为基础设计了整个程序的编程流程，重点介绍了工艺规划设计。最后给出了在C＋＋编程环境下利用ROSE库函数写入形成STEP AP238文件的方法。     

建筑工程投标报价优选模型

通过建立区间二次规划数学模型、灰色模型、多目标规划等模型，建立材料价格和用量的选择模型，提供投标单位一种便捷的调整工程造价和控制造价的手段。     

面向对象的程序设计语言CLUSTER86的设计与实现

本文介绍了笔者设计并实现的一种新型的面向对象的程序设计语言,着重讨论了在设计过程中采用的新的概念,并和其它类似语言作了比较.     

27例乙型血友病患者Ⅸ因子基因突变研究

采用ＰＣＲ（聚合酶链反应）及ＧＡＷＴＳ（ＧｅｎｏｍｉｃＡｍｐｌｉｆｉｃａｔｉｏｎｗｉｔｈＴｒａｎｓｃｒｉｐｔｓＳｅｑｕｅｎｃｉｎｇ）技术，研究了江苏、湖北、山东、广东、福建、宁夏六省区２７例乙型血友病患者及其家系成员ＦⅨ（九因子）基因各２．２Ｋｂ　ＤＮＡ序列。在２ｌ例中发现２０种不同类型的突变。其中１２种是未曾报道的新突变。３８％的突变发生在ＣｐＧ二核苷致序列上，进一步证实了ＣｐＧ确系突变热点。同时检出６例女性为致病基因携带者。对开展基因产前诊断及优生优育等，具有重要意义。     

Pruning Algorithms for Rule Learning

Pre-pruning and Post-pruning are two standard techniques for handling noise in decision tree learning. Pre-pruning deals with noise during learning, while post-pruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre- and post-pruning techniques for separate-and-conquer rule learning algorithms and discuss some fundamental problems. The primary goal of this paper is to show how to solve these problems with two new algorithms that combine and integrate pre- and post-pruning.