Diffusion bonding of an aluminium-lithium alloy (AA8090) using aluminium-copper alloy interlayers

Diffusion bonds have been produced between sheets of an Al-Li-Cu-Mg-Zr alloy using aluminium-4% copper vapour deposited metallic interlayers. Microstructural changes occurred both in the parent alloy and in the bond interface after diffusion bonding cycles and post-bonding heat treatments were analysed. Different metallographic techniques (light microscopy, scanning and transmission electron microscopy) have been used. Diffusion bonding trials were carried out using the same alloy (AA8090), both in non-superplastic (T6) and superplastic conditions. Differences in their behaviours in relation to diffusion bonding were observed.     

BOPP薄膜生产中的功能母料

简述了BOPP薄膜生产过程中所需要的抗静电母料、防粘连母料和爽滑母料等功能母料，并分析了它们的作用、机理以及在生产中的应用特点。     

Ultrasound‐activated Knoevenagel condensation of malononitrile with carbonylic compounds catalysed by alkaline‐doped saponites

α,β‐Unsaturated nitriles have been synthesized by Knoevenagel condensation of a carbonylic compound with malononitrile, assisted by sonochemical irradiation. Two alkaline‐promoted clays (Li⁺‐ and Cs⁺‐exchanged saponites) have been employed as catalysts. The influence of the carbonylic compound (benzaldehyde or cyclohexanone) and the use of a solvent on the catalytic activity have been studied. Remarkable increase in the conversion values has been found when the reaction is activated by ultrasound, as compared with the thermal activation. In this green, solvent‐free procedure, α,β‐unsaturated nitriles have been produced in very high yields (97%) when the Cs⁺‐saponite is used as catalyst. Copyright © 2004 Society of Chemical Industry     

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction

This paper introduces and evaluates a new class of knowledge model, the recursive Bayesian multinet (RBMN), which encodes the joint probability distribution of a given database. RBMNs extend Bayesian networks (BNs) as well as partitional clustering systems. Briefly, a RBMN is a decision tree with component BNs at the leaves. A RBMN is learnt using a greedy, heuristic approach akin to that used by many supervised decision tree learners, but where BNs are learnt at leaves using constructive induction. A key idea is to treat expected data as real data. This allows us to complete the database and to take advantage of a closed form for the marginal likelihood of the expected complete data that factorizes into separate marginal likelihoods for each family (a node and its parents). Our approach is evaluated on synthetic and real-world databases.     

Evaluation and enhancement of COBRA-TF efficiency for LWR calculations

Detailed representations of the reactor core generate computational meshes with a high number of cells where the fluid dynamics equations must be solved. An exhaustive analysis of the CPU times needed by the thermal-hydraulic subchannel code COBRA-TF for different stages in the solution process has revealed that the solution of the linear system of pressure equations is the most time consuming process. To improve code efficiency two optimized matrix solvers, Super LU library and Krylov non-stationary iterative methods have been implemented in the code and their performance has been tested using a suite of five test cases. The results of performed comparative analyses have demonstrated that for large cases, the implementation of the Bi-Conjugate Gradient Stabilized (Bi-CGSTAB) Krylov method combined with the incomplete LU factorization with dual truncation strategy (ILUT) pre-conditioner reduced the time used by the code for the solution of the pressure matrix by a factor of 20. Both new solvers converge smoothly regardless of the nature of simulated cases and the mesh structures and improve the stability and accuracy of results compared to the classic Gauss–Seidel iterative method. The obtained results indicate that the direct inversion method is the best option for small cases.     

Increasing the Complexity of Magnetic Core/Shell Structured Nanocomposites for Biological Applications

This Review article ponders core/shell structured nanoparticles that can be prepared with features that combine properties of different materials, including ligands that enhance their biocompatibility. These nanocomposites are not classified in terms of synthesis, but rather by how these features are distributed in the final morphology, attending to connected or isolated materials that end up in interacting or not‐interacting functionalities. In particular, we have focused on magnetic core/shell‐structured particles with a directly connected, coupled, or isolated second functionality. The current progress on methods in colloidal solution that have allowed the great development of these multifunctional magnetic and active spheres on biological and biomedical fields is reported.     

裂缝性潜山储层地震描述技术

裂缝是潜山油气藏的重要储集层 ,潜山的油气产出与裂缝的发育情况密切相关 ,因此 ,对裂缝发育控制因素和裂缝发育规律进行了分析和探讨。尽管单条裂缝在地震资料上难以识别 ,但是裂缝带在地震资料上有其特征。描述了裂缝储层的地球物理特征 ,并利用测井约束反演技术、吸收系数技术、地震相分类和相干分析技术对潜山储层进行预测。通过在车古 2 0 1潜山的应用实例分析 ,说明以上 4种储层描述技术比较有效     

Scientific and technological performance evaluation of the Spanish Council for Scientific Research (CSIC) in the field of Biotechnology

An evaluation of the Spanish CSIC performance in Biotechnology, as compared with those of the French CNRS and the Italian CNR, has been carried out to determine the balance between the generation of scientific knowledge and the transfer of technology. This study shows a high scientific productivity mostly in journals with moderate impact factor, a low generation of patents and an insufficient transfer of knowledge to the Spanish companies. Other indicators confirm the existence of competitive human resources in biotechnological research producing scientific knowledge of interest for the development of patents and that cooperates successfully at European level.     

Symbolic parametrization of curves

If algebraic varieties like curves or surfaces are to be manipulated by computers, it is essential to be able to represent these geometric objects in an appropriate way. For some applications an implicit representation by algebraic equations is desirable, whereas for others an explicit or parametric representation is more suitable. Therefore, transformation algorithms from one representation to the other are of utmost importance.We investigate the transformation of an implicit representation of a plane algebraic curve into a parametric representation. Various methods for computing a rational parametrization, if one exists, are described. As a new idea we introduce the concept of working with classes of conjugate (singular or simple) points on curves. All the necessary operations, like determining the multiplicity and the character of the singular points or passing a linear system of curves through these points, can be applied to such classes of conjugate points. Using this idea one can parametrize a curve if one knows only one simple point on it. We do not propose any new method for finding such a simple point. By classical methods a rational point on a rational curve can be computed, if such a point exists. Otherwise, one can express the coordinates of such a point in an algebraic extension of degree 2 over the ground field.