: The Aluminium (Al) material emerged as a plasmonic material in the wavelength ranges from the ultraviolet to the visible bands in different on-chip plasmonic applications. In this paper, we demonstrate the effect of using Al on the electromagnetic (EM) field distribution of a compact hybrid plasmonic waveguide (HPW) acting as a polarization rotator. We compare the performance of Al with other familiar metals that are widely used as plasmonic materials, which are Silver (Ag) and Gold (Au). Furthermore, we study the effect of reducing the geometrical dimensions of the used materials on the EM field distributions inside the HPW and, consequently, on the efficiency of the polarization rotation. We perform the study based on the Finite Element Method (FEM) using COMSOL software at an operation wavelength of 700 nm. This paper verifies that the Al could be used as an efficient plasmonic material in integrated single-photon sources for quantum key distribution systems.
In this research, the effect of changing the flood level of Al-Shuwaija marsh was studied using the geographic information systems, specifically the QGIS program, and the STRM digital elevation model with a spatial analysis accuracy of 28 meters, was used to study the marsh. The hydraulic factors that characterize the marsh and affecting on the flooding such as the ranks of the water channels feeding the marsh and the degree of slope and flat areas in it are studied. The area of immersion water, the mean depth, and the accumulated water volume are calculated for each immersion level, thereby, this study finds the safe immersion level for this marsh was determined.
In this work, we study several features of the non-zero divisor graphs (ℵZD- graph) for the ring Zn of integer modulo n. For instance, the clique number, radius, girth, domination number, and the local clustering coefficient are determined. Furthermore, we present an algorithm that calculates the clique number and draws the non-zero divisor for the ring Zn.
The objective of this paper is to study the stability of SIS epidemic model involving treatment. Two types of such eco-epidemiological models are introduced and analyzed. Boundedness of the system is established. The local and global dynamical behaviors are performed. The conditions of persistence of the models are derived.
The dried fruit peel of pomegranate in Punicaceae family was fractionated chromatographically on Sephadex-LH-20 column .Gallic acid (trihydroxybenzoic acid) and its related galloyl esters such as gallotannin(i.e. β-penta-O-galloyl–D-glucose) were obtained homogenously. Different concentrations of gallic acid and gallotannin were used to determine their inhibitory effect on human serum cholinesterase. The enzyme activity was measured according to the method reported by the WHO .The inhibitory effect of these compounds on the activity of human serum cholinesterase have been studied in vitro .The inhibitory effect was remarkably clear with increasing concentration of gallic acid .Whereas galloyl ester showed n
... Show MoreThis book presents the problem of tooth decay due to bacteria Streptococcus mutans one of methods of treatment using 3 extracts of S. persica (miswak) (aqueous, acetone and methanol) and prove its effectiveness and its impact on the gtf (B, C, and D) genes that code the glucosyltransferase (Gtf) enzymes that cause decay membrane compared to the usual means used for the prevention of tooth decay
The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm
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