The analysis of Iraqi light oil (light naphtha) by capillary gas chromatography- mass spectrometry (GC-MS) was performed by the injection of whole naphtha sample without use of solvents. Qualitative analysis and the identification of the hydrocarbon constituents of light naphtha was performed and comparison had been done with American light oil (light naphtha). The obtained results showed a major difference between the two-light naphtha.

Biotreatment using immobilized cells (IC) technology has proved to be the most promising and most economical approach for the removal of many toxic organic pollutants found in petroleum-refinery wastewater (PRW) such as phenol. This study was undertaken to evaluate the degradation of phenol by Pseudomonas cells individually immobilized in two different bio-carrier matrices including polyvinyl alcohol-guar gum (PVA-GG) and polyvinyl alcohol-agar agar (PVA-AA). Results of batch experiments revealed that complete removal of phenol was attained in the first cycle after 150 min using immobilized cells (IC) in both PVA-GG and PVA-AA. Additional cycles were confirmed to evaluate the validity of recycling beads of immob

   Adsorption of lead ions from wastewater by native agricultural waste, precisely tea waste. After the activation and carbonization of tea waste, there was a substantial improvement in surface area and other physical characteristics which include density, bulk density, and porosity. FTIR analysis indicates that the functional groups in tea waste adsorbent are aromatic and carboxylic. It can be concluded that the tea waste could be a good sorbent for the removal of Lead ions from wastewater. Different dosages of the adsorbents were used in the batch studies. A random series of experiments indicated a removal degree efficiency of lead reaching (95 %) at 5 ppm optimum concentration, with adsorbents R² =97.75% for tea. Three mo

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Tuberculosis status as the second leading causes of significant morbidity and mortality from an infectious disease worldwide, after human immunodeficiency virus (HIV). Sample collection was conducted at the Institute of Chest and Respiratory Diseases/Baghdad Medical City in Baghdad. The collection interval was from August to October 2014, 629 suspected TB patients were examined during this period. The results revealed among total 629 specimens, 56 (8.9%) of the specimens were positive by direct examination and 573 (91.1%) negative specimens by smear microscopy. Fifty six DNA samples were extracted from positive ZN smears of sputum specimens and 40 samples from healthy persons (as control) were subjected to molecular diagnosis by real tim

A total of 96 stool samples were collected from children with bloody diarrhea from two hospitals in Baghdad. All samples were surveyed and examined for the presence of the Escherichia coli O157:H7 and differentiate it from other Non -Sorbitol Fermenting Escherichia coli (NSF E. coli). The Bacterial isolates were identifed by using morphological diagnostic methods, Samples were cultured on liquid enrichment medium, incubated at 37C? for 24 hrs, and then cultured on Cefixime Tellurite -Sorbitol MacConkey Agar (CT- SMAC). 32 non-sorbitol fermenting bacterial isolates were obtained of which 11 were identified as Escherichia coli by using traditional biochemical tests and API20E diagnostic system without differentiation between

In this work, silicon nitride (Si₃N₄) thin films were deposited on metallic substrates (aluminium and titanium sheets) by the DC reactive sputtering technique using two different silicon targets (n-type and p-type Si wafers) as well as two Ar:N₂ gas mixing ratios (50:50 and 70:30). The electrical conductivity of the metallic (aluminium and titanium) substrates was measured before and after the deposition of silicon nitride thin films on both surfaces of the substrates. The results obtained from this work showed that the deposited films, in general, reduced the electrical conductivity of the substrates, and the thin films prepared from n-type silicon targets using a 50:50 mixing ratio and deposited on both