The catalytically supported combustor for lean mixtures

This study aims to investigate advantages that the catalytically supported combustor can have. A catalytic combustor was prepared which consisted of a catalyst bed and a thermal combustor downstream of the catalyst bed. The catalyst bed consisted of two stages, a Pd catalyst in the first stage and a Pt catalyst in the second stage. The results showed that the catalytically supported combustion had some advantages. One of them was that autoignition occurred in the thermal combustor so that an igniter was not necessary to start flame ignition. Another was that the catalytically supported combustor offered stable combustion for lean mixtures. NO_x emissions were low because of the stable lean combustion. Copyright © 1999 John Wiley & Sons, Ltd.     

Efficient normal estimation using variable‐size operator

Shading an object is to simulate the behaviour of light incident on its surfaces. It is necessary to calculate normal vectors on the surfaces of the object for shading it. Since objects do not contain surface inclination in voxel‐based representation, a normal vector for each voxel must be estimated from the relative position of its neighbouring voxels that have the same data value. The previously devised methods that use fixed‐size gradient operators can estimate normal vectors accurately only in a limited area and may cause some errors and artefacts. In this paper we propose an efficient normal estimation method using an extended central difference operator whose size can vary according to the arrangement of surface‐comprising voxels. This method calculates normals more accurately than the previous methods and its computation time is shorter than that of methods that guarantee the same image quality. In order to show the improvement, we compare the quality of resulting images and processing time by implementing the newly proposed method and the previous methods and then applying them to some volume data. Copyright © 1999 John Wiley & Sons, Ltd.     

Physicochemical and functional characteristics of a novel fermented pepper (Capsiccum annuum L.) leaves-based beverage using lactic acid bacteria

Two lactic acid bacteria (LAB), Lactobacillus homohiochii JBCC 25 and L. homohiochii JBCC 46, were isolated from a naturally fermented vinegar beverage. Fermented pepper leaves beverage (FPLB-3) which indicated good assessment based on a sensory evaluation test was prepared using the mixed strains. The prepared FPLB-3 showed enhanced functional aspects based on γ-aminobutyric acid (23.6 mg%), total polyphenol (2,828.2 gallic acid equivalents μg/g), and flavonoid contents (2,008.8 catechin equivalents μg/g). Moreover, FPLB-3 showed significantly stronger scavenging activities for the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical (84.6%) and superoxide radical (90.8%) and outstanding inhibitory activity against α-glucosidase of 65.9% at 50 mg/mL compared to the raw material. Based on the results, the newly developed FPLB using Wongi-1 pepper leaves and Lactobacillus is expected to be a novel functional food with natural antioxidant and antidiabetic capacities.     

Indium gallium zinc oxide (IGZO)-based Ohmic contact formation on n-type gallium antimony (GaSb)

In this paper, Ohmic-like contact on n-type GaSb with on/off-current ratio of 1.64 is presented, which is formed at 500 °C by inserting IGZO between metal (Ni) and GaSb. The resulting Ohmic contact is systematically investigated by TOF-SIMS, HSC chemistry simulation, XPS, TEM, AFM, and J–V measurements. Two main factors contributing to the Ohmic contact formation are (1) InSb (or InGaSb) with narrow energy bandgap (providing low electron and hole barrier heights) formed by In diffusion from IGZO and Sb released by Ga oxidation, and (2) free Sb working as traps that induces tunneling current.     

Detection of shellcodes in drive-by attacks using kernel machines

In this paper, we propose a light-weight framework using kernel machines for the detection of shellcodes used in drive-by download attacks. As the shellcodes are passed in webpages as JavaScript strings, we studied the effectiveness of the proposed approach on about 9850 shellcodes and 10000 JavaScript strings collected from the wild. Our analysis shows that the trained SVMs (Support Vector Machines) classified with an accuracy of over 99 %. Our evaluation of the trained SVM models with different proportions of training datasets proved to perform consistently with an average accuracy of 99.51 % and the proposed static approach proved to be effective against detecting even the polymorphic shellcode variants. The performance of our approach was compared to an emulation based approach and observed that our approach performed with slightly better accuracies by consuming about 33 % of the time consumed by the emulation based approach.