Applied Science Private University

About: Applied Science Private University is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Catalysis & Population. The organization has 4124 authors who have published 5299 publications receiving 116167 citations.

Enhanced Electrocatalytic Activity of Pt Subnanoclusters on Graphene Nanosheet Surface

Abstract: Graphene nanosheet (GNS) gives rise to an extraordinary modification to the properties of Pt cluster electrocatalysts supported on it. The Pt/GNS electrocatalyst revealed an unusually high activity for methanol oxidation reaction compared to Pt/carbon black catalyst. The Pt/GNS electrocatalyst also revealed quite a different characteristic for CO oxidation among the measured catalyst samples. It is found that Pt particles below 0.5 nm in size are formed on GNS, which would acquire the specific electronic structures of Pt, modifying its catalytic activities.

Review: Current international research into cellulosic fibres and composites

Abstract: The following paper summarises a number of international research projects being undertaken to understand the mechanical properties of natural cellulose fibres and composite materials. In particular the use of novel techniques, such as Raman spectroscopy, synchrotron x-ray and half-fringe photoelastic methods of measuring the physical and micromechanical properties of cellulose fibres is reported. Current single fibre testing procedures are also reviewed with emphasis on the end-use in papermaking. The techniques involved in chemically modifying fibres to improve interfacial adhesion in composites are also reviewed, and the use of novel fibre sources such as bacterial and animal cellulose. It is found that there is overlap in current international research into this area, and that there are complementary approaches and therefore further combining of these may make further progress possible. In particular a need to measure locally the adhesion properties and deformation processes of fibres in composites, with different chemical treatments, ought to be a focus of future research.

A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications

Abstract: Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. We propose an approach based on self organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science. The proposed approach can handle scenes containing moving backgrounds, gradual illumination variations and camouflage, has no bootstrapping limitations, can include into the background model shadows cast by moving objects, and achieves robust detection for different types of videos taken with stationary cameras. We compare our method with other modeling techniques and report experimental results, both in terms of detection accuracy and in terms of processing speed, for color video sequences that represent typical situations critical for video surveillance systems.