炉前包内冲废钢对灰铸铁组织和强度的影响

采用炉前包冲内冲废钢与孕育相结合的方法，研究了炉前包内冲废钢对灰铸铁组织和强度的影响。结果表明：冲废钢可以增加灰铸铁中初生奥氏体的数量，改变初生奥氏体枝晶的分布形态，强化灰铸铁孕育效果，有利于提高灰铸铁的强度。     

深冲汽车簿钢板织构的表述方法

利用晶体学性质,将深冲汽车薄板的两种主要特征织构表述于反极图之中,再利用织构定量技术便能简捷地分析深冲钢板的织构与深冲性能的关系。经验证,此法与ODF定量分析所得的结果一致。     

Extraction of Metal(Ti,V,Mo)-oxyl Cations by Monoacidic Phosphates and Phosphonates

The extraction of TiO~(2+),VO~(2+),VO_2~+ and MoO_2~(2+)by HDEHP,HEHEHP and HDTMPP was studiedrespectively.The orders of extraction ability for the three extractant systems are as follows:for TiO~(2+),VO~(2+):HDEHP>HEHEHP>HDTMPP;for VO_2~+, MoO_2~(2+):HDEHP< HEHEHP< HDTMPP.The experimental results were discussed from the point of view of extractant structures.     

人胎盘泌乳素的提纯及裂解片断免疫学特性的研究

采用人胎盘泌乳素（hPL）-亲和层析和抗人血清抗体-亲和层析技术，从胎盘匀浆中提取hPL纯品，经圆盘电泳、免疫电泳及交叉试验证实为单一成分。溴化氰水解后的产物，以高效液相色谱分析。分离出7个肽链片断峰，分别用hPL和人生长激素（hGH）放免法检测其活性，其中第6肽链片断无hGH免疫活性，若用此片断制备特异抗体，可避免与hGH出现交叉反应。     

异丁烯两段催化氧化制甲基丙烯酸催化剂研究 Ⅰ．异丁烯气相氧化制甲基丙烯醛多元复合氧化物催化剂的研究

本文报导用于异丁烯（叔丁醇）选择氧化制ＭＡＬ（甲基丙烯醛）的多元复合氧化物催化剂的研究结果。同共沉淀法制备的催化剂的活性主要决定于其化学组成，同时受到制备条件的影响。最佳催化剂的主要物相组成为ＰｂＯ３，Ｂｉ２ＭｏＯ３．３、ＮｉＭｏＯ４、Ｓｂ２Ｏ３及ａ－Ｆｅ２Ｏ３小颗粒。在３５５℃，１０００～２５００ｈ－１空速范围，叔丁醇：水：空气＝１：３．３：７（摩尔比）的条件下可获得异丁烯转化率达９６％，ＭＡＬ选择性达８９％的结果。     

Exploiting Batch Processing on Streaming Architectures to Solve 2D Elliptic Finite Element Problems: A Hybridized Discontinuous Galerkin (HDG) Case Study

Numerical methods for elliptic partial differential equations (PDEs) within both continuous and hybridized discontinuous Galerkin (HDG) frameworks share the same general structure: local (elemental) matrix generation followed by a global linear system assembly and solve. The lack of inter-element communication and easily parallelizable nature of the local matrix generation stage coupled with the parallelization techniques developed for the linear system solvers make a numerical scheme for elliptic PDEs a good candidate for implementation on streaming architectures such as modern graphical processing units (GPUs). We propose an algorithmic pipeline for mapping an elliptic finite element method to the GPU and perform a case study for a particular method within the HDG framework. This study provides comparison between CPU and GPU implementations of the method as well as highlights certain performance-crucial implementation details. The choice of the HDG method for the case study was dictated by the computationally-heavy local matrix generation stage as well as the reduced trace-based communication pattern, which together make the method amenable to the fine-grained parallelism of GPUs. We demonstrate that the HDG method is well-suited for GPU implementation, obtaining total speedups on the order of 30–35 times over a serial CPU implementation for moderately sized problems.     

Generalized Transfer Subspace Learning Through Low-Rank Constraint

It is expensive to obtain labeled real-world visual data for use in training of supervised algorithms. Therefore, it is valuable to leverage existing databases of labeled data. However, the data in the source databases is often obtained under conditions that differ from those in the new task. Transfer learning provides techniques for transferring learned knowledge from a source domain to a target domain by finding a mapping between them. In this paper, we discuss a method for projecting both source and target data to a generalized subspace where each target sample can be represented by some combination of source samples. By employing a low-rank constraint during this transfer, the structure of source and target domains are preserved. This approach has three benefits. First, good alignment between the domains is ensured through the use of only relevant data in some subspace of the source domain in reconstructing the data in the target domain. Second, the discriminative power of the source domain is naturally passed on to the target domain. Third, noisy information will be filtered out during knowledge transfer. Extensive experiments on synthetic data, and important computer vision problems such as face recognition application and visual domain adaptation for object recognition demonstrate the superiority of the proposed approach over the existing, well-established methods.     

Increased α-linolenic acid intake increases tissue α-linolenic acid content and apparent oxidation with little effect on tissue docosahexaenoic acid in the guinea pig

The essential fatty acids do not have identical roles in nutrition. Linoleic acid (LA) accumulates throughout the body of most mammals, whereas α-linolenic acid (ALA) is rarely found in tissue lipids to the same extent as LA. It has been argued that this is the result of metabolism of ALA to docosahexaenoic acid (DHA) or that ALA is rapidly β-oxidized to acetyl CoA and CO₂. In this study, we consider the effect of high and low ALA levels on the tissue distribution of ALA and other n-3 polyunsaturated fatty acids (PUFA) in all tissues. Guinea pigs were fed one of two defined diets for 3 wk from wearning with both diets containing 1.8% (by weight) of LA and either 1.7% ALA or 0.03% ALA. The high ALA diet was associated with significantly increased ALA levels in all tissues except the brain and significantly increased levels of long-chain n-3 PUFA in all tissues except intestines, brain, carcass, and skin. The long-chain n-3 PUFA content of the whole body was less than 5% of that of the ALA content in both diet groups, and the major long-chain n-3 PUFA (>66% of total) in the body was 22∶5n−3. The brain was the only tissue where the DHA content exceeded that of 22∶5n−3. On the low ALA diet, there appeared to be conservation of ALA based on a comparison of the ratio of LA to ALA in the tissues compared with that in the diet. On the high ALA diet there was a loss of ALA relative to LA in the tissues compared with the diet. These studies suggest that the low levels of tissue ALA in the guinea pig are likely the result of β-oxidation or excretion via the skin and fur rather than metabolism to DHA.     

Study of collective user behaviour in Twitter: a fuzzy approach

The study of collective user behaviours in social networking sites has become an increasing important topic in social media mining. Understanding such behaviours has its potential to extract actionable patterns that can be beneficial to develop effective marketing strategies, optimise user experiences and maximise website revenues. With the rapid development of micro-blogging, Twitter has become a richer source of intelligence that can be used to study collective user behaviour, due to its efficient and meaningful user-to-user interactions. However, the classical statistical methods have some drawbacks in bridging the gap between user-generated data and human analysts who mostly use linguistic terms to analyse data and model/summarise knowledge learned. To address this gap, this work proposes a new approach, which employs the mass assignment theory-based fuzzy association rules algorithm (MASS-FARM), for the first time, to extract useful interaction behaviour of Twitter users. The influential factors (including activity time, number of friends/followers and the number of tweets) are represented as fuzzy granules, and the associations amongst are studied by employing MASS-FARM. The collective user behaviours are analysed in the Reply category and the Non-Reply category, respectively. The applicability and usefulness of the proposed method are demonstrated via an empirical study on a collected Twitter data set. The derived results are also discussed and compared with existing works.     

基于条件随机场的低图像质量车牌字符分割

提出一种基于条件随机场的车牌字符分割算法,能够对光照不均、相机拍摄角度造成的低图像质量的车牌图像,特别是日益增多的车牌边框与字符相连接车牌图像进行有效的字符分割。算法首先进行车牌图像校正,然后利用标注车牌数据进行模型学习,对车牌图像像素列进行分类识别,最后组合成车牌字符分割结果。理论分析与实验结果验证了算法的有效性。