丙烯酸酯改性双环戊二烯酚型环氧树脂及其性能研究

通过FT－IR光谱表征了丙烯酸酯改性的DCPD酚型环氧树脂／DDM体系结构，并通过测定改性体系的凝胶时间和分析凝胶时间的影响因素，研究了该体系的反应特性，同时，对改造前后DCPD酚型环氧树脂体系的性能做了分析。     

湿法聚氨酯树脂浆料配方的改进

通过对湿法PU合成革用聚氨酯(PU)树脂浆料配方的改进，降低了浆料的生产成本，浆料的后期加工性能提高，并且PU膜的性能也有所改善。     

近十年常温干法精脱硫专利技术发展动态

收集了近十年来国内的常温干法精脱硫专利，并分类列举出各种脱硫剂的物化性质和几种常温精脱硫工艺，重点阐述了该技术的优缺点及其未来发展方向。     

Allelopathic Potential and Chemical Constituents of Volatile Oil from Ageratum conyzoides

Fresh leaves and volatile oil of the important weed Ageratum conyzoides in south China were highly inhibitory to seedling growth of various cultivated crops, especially in an adverse habitat. The constituents of the volatile oil were analyzed by GC-MS. Eleven components were identified and six main components, precocene I, precocene II, 3,3-dimethyl-5-tert-butylindone, -caryophyllene, -bisabolene, and fenchyl acetate, were isolated by means of column chromatography. Precocene I, precocene II, -caryophyllene, and 3,3-dimethyl-5-tert-butylindone inhibited seedling growth of acceptor plants. Inhibitory activity of the volatile oil was more intense than that of the pure components. Fenchyl acetate and -bisabolene have no inhibitory activity, but when mixed with precocene II, they increased the inhibitory activity to growth of acceptor seedling plants. Experiments show that allelopathic synergism exists among allelochemicals of Ageratum conyzoides.     

外掺氧化镁拱坝温控仿真分析及横缝设置研究

基于现阶段条件下的混凝土热力学参数及温控措施,考虑气温、混凝土性能随龄期的变化及MgO的膨胀性等因素,采用三维有限元法对鱼粮水库拱坝的温度场进行仿真计算分析,计算结果表明,坝体混凝土在自然入仓、冬季保温的情况下,无需采取其它温控措施,通过对比不分缝方案及分缝方案,说明设置诱导缝可改善坝体应力分布情况。通过改变缝的位置更加有利于释放大坝温度应力,且各部位应力均能满足规范要求。     

强化质量意识 提高市场竞争力

介绍了企业在面临国际陶瓷市场竞争激烈的形势下，努力转变发展思路，拓宽市场，强化质量意识。根据市场变化，生产适销对路搞档优质陶瓷产品，增强产品的市场能力，提高了企业的经济效益。     

气相色谱法测定3-氯-1-丙醇

以填充柱气相色谱对 3 氯 1 丙醇进行了分析研究 ,方法变异系数 0 .5 % ,回收率99.5 0 %～ 10 0 .6 7%     

大掺量粉煤灰高性能混凝土的研究

本文从高分散性表面活性剂显著影响粉煤灰的形态效应而大幅降低混凝土用水量的角度出发,利用复合激发剂激发粉煤灰的火山灰效应,结合粉煤灰自身的微集料效应提高混凝土强度的技术手段,探讨了大掺量粉煤灰在混凝土中的应用,分析了高分散性表面活性剂与复合激发剂的组合对大掺量粉煤灰混凝土的作用机理。     

Image super-resolution based on multi-space sparse representation

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However, discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. In this paper, we propose a super-resolution construction based on multi-space sparse representation to efficiently solve the problem. In the representation, image patches are decomposed into a structure component and a texture component represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Experiment results demonstrate that the proposed method significantly improves the PSNR, SSIM and visual quality of reconstructed high-resolution image.     

Know by a handful the whole sack: efficient sampling for top-k influential user identification in large graphs

Influence Maximization aims to find the top-K influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Most existing greedy algorithms mainly focus on computing the exact influence spread, leading to low computational efficiency and limiting their application to real-world social networks. While in this paper we show that through supervised sampling, we can efficiently estimate the influence spread at only negligible cost of precision, thus significantly reducing the execution time. Motivated by this, we propose ESMCE, a power-law exponent supervised Monte Carlo estimation method. In particular, ESMCE exploits the power-law exponent of the social network to guide the sampling, and employs multiple iterative steps to guarantee the estimation accuracy. Moreover, ESMCE shows excellent scalability and well suits large-scale social networks. Extensive experiments on six real-world social networks demonstrate that, compared with state-of-the-art greedy algorithms, ESMCE is able to achieve almost two orders of magnitude speedup in execution time with only negligible error (2.21 % on average) in influence spread.