The objective of the present investigation was to enhance the solubility of practically insoluble mirtazapine by preparing nanosuspension, prepared by using solvent anti solvent technology. Mirtazapine is practically insoluble in water which act as antidepressant .It was prepared as nano particles in order to improve its solubility and dissolution rate. Twenty formulas were prepared and different stabilizing agents were used with different concentrations such as poly vinyl pyrrolidone (PVPK-90), poly vinyl alcohol (PVA), poloxamer 188 and poloxamer 407. The ratios of drug to stabilizers used to prepare the nanoparticles were 1: 1 and 1:2. The prepared nanoparticles were evaluated for particle size, entrapment efficiency, dissolution study, Fourier transform infrared spectroscopy, differential scanning calorimetry, and atomic force microscopy. The percentage of drug entrapment efficiency of F1-F20 was ranged from 78.2% ± 1 to 95.9 % ± 1. The release rate and extent of mirtazapine nanoparticles were inversely proportional to the particle size of the drug i.e. it decreased when particle size increase. It is concluded that the nanoprecipitation have potential to formulate homogenous nanosuspensions with uniform-sized stable nanoparticles of mirtazapine. The prepared nanosuspension showed enhanced dissolution which may lead to enhanced solubility of mirtazapine.
The research involves using phenol – formaldehyde (Novolak) resin as matrix for making composite material, while glass fiber type (E) was used as reinforcing materials. The specimen of the composite material is reinforced with (60%) ratio of glass fiber.
The impregnation method is used in test sample preparation, using molding by pressure presses.
All samples were exposure to (Co60) gamma rays of an average energy (2.5)Mev. The total doses were (208, 312 and 728) KGy.
The mechanical tests (bending, bending strength, shear force, impact strength and surface indentation) were performed on un irradiated and irrad
... Show MoreIn this research , we study the inverse Gompertz distribution (IG) and estimate the survival function of the distribution , and the survival function was evaluated using three methods (the Maximum likelihood, least squares, and percentiles estimators) and choosing the best method estimation ,as it was found that the best method for estimating the survival function is the squares-least method because it has the lowest IMSE and for all sample sizes
In this research , we study the inverse Gompertz distribution (IG) and estimate the survival function of the distribution , and the survival function was evaluated using three methods (the Maximum likelihood, least squares, and percentiles estimators) and choosing the best method estimation ,as it was found that the best method for estimating the survival function is the squares-least method because it has the lowest IMSE and for all sample sizes
The current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets,
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