Electrosteric stabilization of concentrated cement suspensions imparted by a strong anionic polyelectrolyte and a non-ionic polymer

Strong polyelectrolytes, known as superplasticizers, improve the initial fluidity of concentrated cement suspensions through electrostatic stabilization. These polyelectrolytes do not maintain the initial fluidity, however, primarily due to an increase in the ionic strength of the cementitious suspension. Consequently, non-ionic polymers are often used in conjunction with polyelectrolytes to provide steric stabilization and hence to sustain the desired fluidity over a longer time, and this has lead to the development of copolymers with both electrostatic and steric (electrosteric) functionalities. To design such polymers, it is necessary to optimize the balance between electrostatic and steric stabilization to maximize suspension fluidity. We have quantified the effects of a strong anionic polyelectrolyte, melamine formaldehyde sulfonate (MFS), and a non-ionic polymer, hydroxypropylmethylcellulose (HPMC), on the zeta potential of cement particles and the steady shear and low-amplitude rheological properties of concentrated cement suspensions. While the adsorption of MFS onto the cement particle surfaces leads to a sign inversion in the zeta potential, the adsorption of the non-ionic HPMC has no significant effect on the potential. The addition of HPMC to the suspensions substantially reduces the steady shear viscosity and the storage modulus at constant MFS concentration; in addition, there exists an intermediate HPMC concentration that minimizes fluidity. The resulting suspension fluidity is also maintained over a longer time than in the absence of HPMC. This improvement in the stability and fluidity of cement suspensions is attributed to “complementary electrosteric dispersion/stabilization”, and provides insight to the design of polymers with electrosteric functionality.     

地震前后基准站GPS数据异常提取算法

目前对地震前兆异常的研究主要集中在“热”和“电”等方面,很少涉及基准站的GPS数据。然而,已经有学者证明震中附近基准站的GPS时间序列坐标数据中也蕴含着大地震的前兆信息。针对2001~2010年间美国本土发生的具有代表性的三个地震进行了研究,首次将Martingale理论运用于GPS数据处理,提出一种异常提取算法,进而对地震前后,震中附近多个基准站的GPS数据进行分析。实验结果表明算法能够有效的反映大地震前后GPS数据中异常的变化趋势,为使用GPS数据对大地震进行预报提供了更多可能。     

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。     

Towards subject independent continuous sign language recognition: A segment and merge approach

This paper presents a segment-based probabilistic approach to robustly recognize continuous sign language sentences. The recognition strategy is based on a two-layer conditional random field (CRF) model, where the lower layer processes the component channels and provides outputs to the upper layer for sign recognition. The continuously signed sentences are first segmented, and the sub-segments are labeled SIGN or ME (movement epenthesis) by a Bayesian network (BN) which fuses the outputs of independent CRF and support vector machine (SVM) classifiers. The sub-segments labeled as ME are discarded and the remaining SIGN sub-segments are merged and recognized by the two-layer CRF classifier; for this we have proposed a new algorithm based on the semi-Markov CRF decoding scheme. With eight signers, we obtained a recall rate of 95.7% and a precision of 96.6% for unseen samples from seen signers, and a recall rate of 86.6% and a precision of 89.9% for unseen signers.     

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.