Microstructure and Microchemistry of Polymer-Derived Crystalline SiC Fibers

Stoichiometric and near-stoichiometric silicon carbide fibers, derived from the polymer polycarbosilane, have been characterized by scanning Auger and transmission electron microscope techniques. Excess carbon usually appeared in the fibers, but it could be eliminated by controlling processing conditions. The fibers consisted mainly of submicrometer-sized SiC microcrystals, with a considerably twinned and stacking-faulted β-SiC structure. Free carbon, which appeared in the form of graphite, was the only detectable intergranular phase in the nonstoichioinetric SiC fibers, while the stoichiometric SiC fibers contained no detectable second phase whatsoever.     

Ammoxidation of toluene over Y-Ba-Cu-Co-O perovskites

Ammoxidation of toluene over the perovskites YBa₂Cu₃O_6.1, YBa₂Cu₂CoO_6.7 and YBaCuCoO_4.9 was investigated at 400 °C. At low partial pressures of O₂ benzonitrile was selectively formed, while CO₂ was the main product at high pressures of O₂. Systematic differences in activity were observed for the three phases and are related to the crystal contents of Cu and Co. At low O₂ pressures, Cu-sites are active for nitrile formation, while Co-sites give CO₂. At high O₂ pressures, the activity for CO₂ of Cu-sites increases more than that of Co-sites due to filling of near-surface oxygen vacancies.     

Terpolymers. II. Mechanical properties and transition temperatures of terpolymers of vinyl stearate,vinyl acetate,and vinyl chloride

Vinyl stearate was studied as a major internal plasticizer in terpolymers containing vinyl acetate and vinyl chloride. The terpolymers were prepared by systematically replacing vinyl acetate by close increments of vinyl stearate starting with combinations of vinyl acetate and vinyl chloride, in increments, over all compositions. For comparison of properties, a complete range of copolymers of vinyl stearate and vinyl chloride, as well as mixtures of poly(vinyl chloride) and di-2-ethylhexyl phthalate (DOP) were also made. The external plasticizer was more efficient in reducing the glass temperature than was vinyl stearate. The decline in T_g with weight fraction of plasticizer was linear for the copolymers and terpolymers but concave downward with the liquid diluent. The linear decline was shown to involve mere additivity of the free volume contributed by each side-chain methylene (or methyl) group in both vinyl esters to reducing T_g. The mechanism of the diluent system was more complex. However, the magnitude of the reduction of tensile modulus at a given weight fraction of DOP could be equaled or exceeded by the same amount of vinyl stearate, by increasing the vinyl acetate content of the base copolymer to 40 mole-% or more. Unfortunately, the ultimate strengths and elongations of internally plasticized systems were reduced more than those of the mixtures at comparable compositions. Vinyl stearate was found to markedly retard photolytic degradation compared to both vinyl acetate and the external plasticizer in unstabilized samples having nearly the same thermal treatment. The effect was greater than could be ascribed to dilution by the long alkyl group. The production of a stearoyl radical more stable than the radicals initiating dehydrochlorination is suggested as a possible mechanism.     

Isolation kernel: the X factor in efficient and effective large scale online kernel learning

Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. Current state-of-the-art large scale online kernel learning focuses on improving efficiency. Two key approaches to gain efficiency through approximation are (1) limiting the number of support vectors, and (2) using an approximate feature map. They often employ a kernel with a feature map with intractable dimensionality. While these approaches can deal with large scale datasets efficiently, this outcome is achieved by compromising predictive accuracy because of the approximation. We offer an alternative approach that puts the kernel used at the heart of the approach. It focuses on creating a sparse and finite-dimensional feature map of a kernel called Isolation Kernel. Using this new approach, to achieve the above aim of large scale online kernel learning becomes extremely simple—simply use Isolation Kernel instead of a kernel having a feature map with intractable dimensionality. We show that, using Isolation Kernel, large scale online kernel learning can be achieved efficiently without sacrificing accuracy.

     

Conspiracy vs science: A large-scale analysis of online discussion cascades

World Wide Web - With the emergence and rapid proliferation of social media platforms and social networking sites, recent years have witnessed a surge of misinformation spreading in our daily life....     

Mutual spectral residual approach for multifocus image fusion

Multifocus image fusion is the process of obtaining a single image from multiple partially focused images such that the newly formed image consists of the well-defined information extracted from each source image. This paper proposes the use of saliency of the source images based on Mutual Spectral Residual. Spectral Residual brings out the unique/salient features of an image in frequency domain. The idea of proposed mutual spectral residual is to emphasize the relative unique features of a source image with respect to the other source images. The relative unique features are utilized to form saliency maps for each source image. These saliency maps can clearly indicate the focused and defocused parts of an image. Based on the saliency maps obtained, the image fusion takes place in spatial domain. Visual inspection and quantitative evaluation of the fused images obtained by the proposed method, using different evaluation metrics, demonstrate its effectiveness over several existing image fusion methods.