The study aims to find out the effectiveness of using the Google classroom educational platform in teaching mathematics curricula from the viewpoint of teachers in the Governorate of Al Dhahirah, Sultanate of Oman. The researcher adopted the descriptive-analytical approach. To collect the needed data, a questionnaire of two dimensions was used. It includes (13) items to measure the effectiveness of using the Google classroom in teaching mathematics curricula from the teacher's point of view and includes (10) items to measure the difficulties of using the Google classroom in teaching mathematics curricula from the teachers' point of view. These tools were applied to (32) male and (31) female as the study sample. They represent mathematics

Risperidone is an atypical antipsychotic drug that is used for treating schizophrenia, bipolar mania, and autism. Risperidone rebalances dopamine and serotonin to improve thinking, mood, and behavior by working on dopamine and serotonin α₂receptor antagonism. Risperidone has poor solubility and high permeability through the intestine, so it belongs to Biopharmaceutical Classification System (BCS) class II exhibits poor oral biopharmaceutical properties.

 The aim of the present work was to improve solubility and dissolution of Risperidone by preparing nanosuspension using different stabilizers and different solvents‎ in a method known as solvent-antisolvent precipitation method.  Twenty-eight formulas were prepared

In the present study twenty samples of human urine were taken
from healthy male and female with different of: ages, occupation and
place of residence. These samples were collected from the hospital to
measure the concentration of radon gas in human urine by using one
of solid state nuclear track detectors LR-115.
The results obtained of the concentrations of radon in healthy human
urine are varying from 2.12×10-3 Bq.l-1 to 4.42×10-3 Bq.l-1 and
these values are less than the allowed limits 12.3×10-3 Bq.l-1.

Three mesoporous silica with different functional group were prepared by one-step synthesis based on the simultaneous hydrolysis and condensation of sodium silicate with organo - silane in the presence of template surfactant polydimethylsiloxane - polyethyleneoxide (PDMS - PEO). The prepared materials were characterized by Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), atomic force microscopy (AFM) and nitrogen adsorption/desorption experiments. The results indicate that the preparation of methyl and phenyl functionalized silica were successful and the mass of methyl and phenyl groups bonded to the silica structure are 15, 38 mmol per gram silica. The average diameter of the silica particles are 103.51,

Pathological blood clot in blood vessels, which often leads to cardiovascular diseases, are one of the most common causes of death in humans. Therefore, enzymatic therapy to degrade blood clots is vital. To achieve this goal, bromelain was immobilized and used for the biodegradation of blood clots. Bromelain was extracted from the pineapple fruit pulp (Ananas comosus) and purified by ion exchange chromatography after precipitation with ammonium sulphate (0-80 %), resulting in a yield of 70%, purification fold of 1.42, and a specific activity of 1175 U/mg. Bromelain was covalently immobilized on functionalized multi-walled carbon nanotubes (MWCNT), with an enzyme loading of 71.35%. The results of the characterization of free and immobilized

            A simple, rapid, accurate and sensitive spectrophotometric method for the determination of thiaminehydrochloride has been developed. The method is based on the formation of the Schiff’s base between the primary amino group present in thiamine hydrochloride and aldehyde group present in the vanillin reagent to produce a yellow colored complex having maximum absorption at 390 nm. Beer^’s law has obeaid over the concentration range of 2-28µg/mL, with molar absorptivity of 0.96x10⁴L/mol.cm. The average recovery which is a measure of accuracy is 100±1.3% and the relative standard deviation (RSD) is less than1.5 .The present method is considered to be

In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we