Liquisolid compact is the most promising technique for increasing dissolution rate and bioavailability of poorly soluble drugs.Clopidogrel bisulfate is an oral antiplatelets used for treatment and prophylaxis of cardiovacular and peripheral vascular diseases related to platelets aggreagation.Clopidogrel has low solubility at high pH media of intestine and low bioavailability of a bout 50% after oral doses.The purpose of this work was to enhance dissolution pattern of clopidogrel through its formulation into liquisolid tablets.A mathematical model was used to calculate the optimum quantities of tween 80 , carrier (Avicel PH 102) and coating material (Aerosil 200) needed to prepare acceptably flowing and compactible powder mixtures.The liq

Clotrimazole (CLO) is an antimycotic imidazole derivative applied locally for the treatment of vaginal yeast infections. In this study, CLO was formulated as vaginal mucoadhesive hydrogel, using different types of mucoadhesive polymers to ensure prolonged contact between active ingredient and vaginal mucosa.

Physicochemical properties of the prepared formulas were evaluated as a visual inspection, pH, swelling index, spreadability, and mucoadhesive characteristics, in addition to an in-vitro drug release. The influence of type and concentration of polymers as CMC-Na (1.5, 2.5, and 3.5%w/w), carbopol 940( 0.25, 0.5, and 1 %w/w) and poloxamer 407 (15, 25, 30%w/w) on CLO release  from the prepared gels were also invest

Iron oxide(Fe3O4) nanoparticles of different sizes and shapes were synthesized by solve-hydrothermal reaction assisted by microwave irradiation using ferrous ammonium sulfate as a metal precursor, oleic acid as dispersing agent, ethanol as reducing agent and NaOH as precipitating agent at pH=12. The synthesized Fe3O4 nano particles were characterized by X-ray diffraction (XRD), FTIR and thermal analysis TG-DTG. Sizes and shapes of Fe3O4 nanoparticles were characterized by Scanning Electron Microscopy (SEM), and atomic force microscopy (AFM).

This study focuses on the implementation of interfaces for human machine interaction (HMI) control and monitor automatic production line. The automatic production line can performance feeding, transportation, sorting functions. The objectives of this study are implemented two SCADA/HMI system using two different software. TIA portal software is used to build HMI, alarm, and trends in touch panel which is helped an operator to control and monitor the production line. LabVIEW software is used to build HMI and trends in the computer screen and is linked with Microsoft Excel (ME) to generate information table helped to monitor the performance of the pneumatic equipment. The production line can do performance feeding, transportation, sorting fun

The -mixing of  - transition in Er 168 populated in Er)n,n(Er 168168  reaction is calculated in the present work by using a2- ratio method. This method has used in previou studies [4, 5, 6, 7] in case that the second transition is pure or for that transition which can be considered as pure only, but in one work we applied this method for two cases, in the first one for pure transition and in the 2nd one for non pure transitions. We take into accunt the experimental a2- coefficient for p revious works and -values for one transition only [1]. The results obtained are, in general, in agood agreement within associated errors, with those reported previously [1], the discrepancies that occur are due to inaccuracies existing

The trading banks in Iraq invest their funds according to regulations imposed by the Central Bank in Iraq in different financial fields like stock exchanges, acquire stocks as assets that could be sold at any time as well as make loans and contributing in corporations establishment also magnitude foreign capital through direct contacts with foreign exchange markets.

We can summarize the problem of this paper as shortage in mathematical models that used in studying and analyzing these investments and according to this problem we used (a constructed mathematical model ) consists of three major indicators: profitability of total investment assets which is divided into three sub-indicators: owners equity risk indicator, debits risk i

     In this research, silver nanoparticles (AgNPs) were manufactured using aqueous extract of mushroom Pleurotus ostreatus. Anticancer potential of AgNPs was investigated versus human breast cancer cell line (MCF-7). Cytotoxic response was assessed by MTT assay. AgNPs showed inhibition effect at the following concentrations 12.5, 25, 50, 100 and 200 µg/ml versus MCF-7 cell line, and all treatments had a positive result. The MCF-7 cells were inhibited up to 85.14 % at the concentration 200 μg/ml of AgNPs which reduced cells viability to 14.86%, while 12.5 μg/ml of AgNPs caused 24.23% cells inhibition with reduction of cells viability to 75.77%.

    This research aims to review the importance of estimating the nonparametric regression function using so-called Canonical Kernel which depends on re-scale the smoothing parameter, which has a large and important role in Kernel  and give the sound amount of smoothing .

We has been shown the importance of this method through the application of these concepts on real data refer to international exchange rates to the U.S. dollar against the Japanese yen for the period from January 2007 to March 2010. The results demonstrated preference the nonparametric estimator with Gaussian on the other nonparametric and parametric regression estima

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings