Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

Molasse medium containing different concentrations of (NH4)2 SO4, (NH4)3 PO4, urea, KCI, and P2O5 were compared with the medium used for commercial production of C. utilis in a factory south of Iraq. An efficient medium, which produced 19. 16% dry wt. and 5. 78% protein, was developed. The effect of adding various concentrations of micronutrients (FeSO4, 7T20, MnSO4. 7H20, ZnSO4. 7E20) was also studied. Results showed that FeSo4. 7H20 caused a noticeable increase in both dry wt. and protein content of the yeast.

Gelatin is a valuable substance that is known to have a complex 3D structure, but it has limited adsorption efficiency, which limit its application at physiological temperature. In this paper, gelatin was improved by the addition of glutaraldehyde (GTA) to enhance its ability to adsorb dye in aqueous solution. The adsorption experiments were conducted under different conditions, such as the amount of adsorbent, the concentration of the dye, and the temperature. The study found that the qe by GTA-GE (4.978 to 23.056 mg/g) and GE (4.8 to 21.333 mg/g) increased with the increase of initial dye concentrations but decreased with the amount of adsorbent. The parameters at equilibrium were at a pH of 4 and a dose of adsorbent of 100 mg in

Heat transfer process and fluid flow in a solar chimney used for natural ventilation are investigated numerically and experimentally her in. Solar chimney was designed, manufactured and tested by selecting different positions of air entrance namely: bottom entrance, side entrance, and both side and bottom entrances. The effect of integrating the chimney with paraffin (phase change material) on its thermal behavior has been also investigated. CFD analysis based on finite volume method is used to predict the thermal performance, and fluid flow in two-dimensional solar chimney under unsteady state condition, to identify the effect of different parameters such as solar radiation, and inclination angle. Experimental results show that a solar chi