The functional properties of the defatted powder and protein isolate of germinated mung bean seeds were studied and the estimation of the amino acids was carried out. The results showed a significant increase in the values of the amino acids leucine, lysine, phenylalanine and valine. The results of studying the functional properties of the protein isolate and defatted powder showed that there were significant differences between the sample The defatted and protein isolate, if the water absorption capacity of the defatted mung powder was 2.5% water/gm protein and the water absorption capacity of the protein isolate was 3%ml water/g protein, the fat binding capacity of the defatted powder was 0.3 ml fat/g protein and the isolate The ratio was

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 behavior of the first-order black and gray solitons propagtedin optical fiber in the presence of frequency chirp is studied analytically and numerically results show that phase profile of black solitons changes abruptly

The adsorption isotherms and kinetic uptakes of CO₂ were measured. Adsorption isotherms were measured at two temperatures 309 K and 333 K and over a pressure range of 1 to 7 bar. Experimental data of CO₂ adsorption isotherms were modeled using Langmuir, Freundlich and Temkin. Based on coefficient of correlation it was found that Langmuir isotherm model was well suited with the experimental data of CO₂ adsorption isotherms.  In addition, Adsorption kinetic of CO₂ mixture with N₂ containing 10 % by volume CO₂ and 90 % by volume N₂ were determined in a temperature 36 °C and under the atmospheric pressure .When the flow rate was increased from

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