A mixture of algae biomass (Chrysophyta, Cyanophyta, and Chlorophyte) has been investigated for its possible adsorption removal of cationic dyes (methylene blue, MB). Effect of pH (1-8), biosorbent dosage (0.2-2 g/100ml), agitated speed (100-300), particle size (1304-89μm), temperature (20-40˚C), initial dye concentration (20-300 mg/L), and sorption–desorption were investigated to assess the algal-dye sorption mechanism. Different pre-treatments, alkali, protonation, and CaCl2 have been experienced in order to enhance the adsorption capacity as well as the stability of the algal biomass. Equilibrium isotherm data were analyzed using Langmuir, Freundlich, and Temkin models. The maximum dye-sorption capacity was 26.65 mg/g at pH= 5, 25

In the current study, three types of algae namely Tetradesmus nygaardi (MZ801740), Scenedesmus quadricauda (MZ801741) and Coelastrella sp (MZ801742) were extracted by 95% ethanol and hexane against two types of gram positive and two types of gram negative bacteria by wells diffusion methods. Eleven concentrations from the extract of algae (2, 5, 10, 15, 20, 25, 30, 35, 40, 45 and 50 mg/ml) were utilized. It was noticed that ethanolic extraction was more effective than hexane in Scenedesmus quadricauda than the two other mentioned algal species against all pathogenic bacteria, Acintobacter baumanii (ATCC: 19606), Klebsiella pneumonia (ATCC: 13883) Enterococcus faecalis (ATCC: 29212) and Staphylococc

The objective of the study was to predict crop coefficient (K) values for cucumber inside the greenhouse during the growing season 2014, using watermarks gypsum blocks and atmometer c apparatus during the growing stages and to compare the predicted values of the crop coefficient with different methods and approaches. The study was conducted in the greenhouses field within Al-Mahawil Township, 70 km south of Baghdad, Iraq. The watermarks soil water sensors and atmometer apparatus were used to measure crop evapotranspiration and reference evapotranspiration on daily basis, respectively. The comparison and the statistical analysis between the calculated K in this study and values obtained from greenhouse gave a good agreement. The root mean

   Asphaltenes are a solubility class described as a component of crude oil with undesired characteristics. In this study, Sharqy Baghdad heavy oil upgrading was achieved utilizing the solvent deasphalting approach as asphaltenes are insoluble in paraffinic solvents; they may be removed from heavy crude oil by adding N-Hexane as a solvent to create deasphalted oil (DAO)of higher quality. This method is known as Solvent De-asphalting (SDA). Different effects have been assessed for the SDA process, such as solvent to oil ratio (4-16/1 ml/g), the extraction temperature (23 ºC) room temperature and (68 ºC) reflux temperature at (0.5 h mixing time with 400 rpm mixing speed). The best solvent deasphalting results were obtained at room temp

Thin films of In2O3-CdO at various CdO contents (0.01, 0.02, 0.03, 0.04 and 0.05) were deposited on transparent substrate which is glass using chemical spray pyrolysis deposition method at substrate temperature 150oC. The structural properties was studied to characterize the prepared materials by XRD analysis. Surface morphology has been illustrated using scanning electron microscopy which proved the nanosize of prepared materials. This materials have been used as gas sensor for toxic gas which is hydrogen sulfide H2S. The sensitivity and response speed have been investigated with addition of CdO nanoparticles. © 2021, S.C. Virtual Company of Phisics S.R.L. All rights reserved.

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are