The open hole well log data (Resistivity, Sonic, and Gamma Ray) of well X in Euphrates subzone within the Mesopotamian basin are applied to detect the total organic carbon (TOC) of Zubair Formation in the south part of Iraq. The mathematical interpretation of the logs parameters helped in detecting the TOC and source rock productivity. As well, the quantitative interpretation of the logs data leads to assigning to the organic content and source rock intervals identification. The reactions of logs in relation to the increasing of TOC can be detected through logs parameters. By this way, the TOC can be predicted with an increase in gamma-ray, sonic, neutron, and resistivity, as well as a decrease in the density log. In calculating TOC content, sonic/resistivity overlay technique was used. The results detected that the upper and lower parts (3300-3460 and 3570-3700 respectively) of the formation were the principal source rock in this location. The TOC results from logs are ranged respectively from 1-6 and 1-4 wt % for the upper and lower parts from the formation. These results are compared with TOC from (58) samples of Rock -Eval Pyrolysis, which showed a close pattern of increasing and decreasing in TOC values. This comparison was made so as to enhance the results of this technique. In addition, this tool revealed the possible lithology of the studied intervals, where the logs originally would give an indication to the lithology, as such high TOC is significant to relatively low energy environments. TOC calculation showed that the upper and lower packages represent source-seal rocks, while the middle had good reservoir properties. This relation may indicate a locally stratigraphic trap, and a need for further detailed studies.
Sixty urine samples were collected from women with urinary tract infection in different ages. The aims of this study were determined the dominancy of pathogens isolated from urine of women with UTI and evaluating the antibacterial activity of Rosmarinus officinalis L. essential oil against these pathogenic isolates. Identification of bacteria was done on Chromogenic orientation agar while disc diffusion method was used for determination the sensitivity of bacterial isolates to antibiotics and Agar well diffusion method was used for evaluation the antibacterial effect of Rosemary essential oil on these isolates. The results showed that 50% of women infected with Escherichia coli, it was dominants in ages above 15 years old while Staphylococc
... Show MoreIn this study, novel Schiff base complexes with Zn(II) and Co(II) ions were successfully synthesized. The malonic acid dihydrazide was converted into the Schiff base ligand by combining it with 1-hydroxy-2-naphthaldehyde, and the last step required reacting it with the appropriate metal(II) chloride to produce pure target complexes. The generated complexes were thoroughly characterized using FTIR, 1H-NMR, 13C-NMR, GC-mass, and UV-Vis spectroscopies. In order to photo-stabilize polystyrene (PS) and reduce the photodegradation of its polymeric chains, these chemicals have been used in this work. The efficiency of the generated complexes as photo-stabilizers was evaluated using a variety of techniques, including FTIR, weight loss, visc
... Show MoreThirty nine (12.8%) isolates of Staphylococcus aureus were isolated from 304 healthy human (Nasal swabs). It was found that percentage of males that have S. aureus is more than female's percentage. These isolates (39) were tested with different tests. Twenty seven isolates (69.23 %) were positive for Staphylococcus protein —A (SPA) ,thirty seven ( 94.8 %) were positive for tube coagulase , thirty five ( 89.7 % ) were positive with clumping factor and thirty two ( 82.05 %) had 13 — hemolytic on blood agar. It was found that 100% of the isolates (39 isolates) were positive with one, two or three tests (tube coagulase, clumping factor and SPA).
House 21 fungal isolates fungus to the analyst Albroca output of manufactured blood clot from the Blama human blood showed positive fungi to test analyzes blood clot variation in times where decomposition recorded fungi
A novel Schiff base ligand (DBC) synthesized from 4-chlorobenzoic acid, along with its Cu (II) and Co (II) complexes, was prepared and characterized using FT-IR, 1H and 13C-NMR, UV-Vis spectroscopy, as well as magnetic and conductivity measurements. Based on this, a tetrahedral structure of [M(DBC)Cl2] was proposed for the complexes. Antioxidant activity of the compounds was assessed and compared to ascorbic acid, revealing that the copper complex exhibited superior antioxidant properties compared to the cobalt complex and the ligand. Furthermore, the antibiofilm potential of the copper and cobalt complexes was assessed against five clinically relevant bacterial species (P.aeruginosa, E.coli, K.pneumoniae, S.aureus and S.typhi) usin
... Show MoreThirty one samples of gum swabs were collected from patients with tooth caries (5-30 years old) from the College of Science (Biology department )- University of Baghdad- Iraq for the period from October 2018 to December 2018. , The samples were transported, after inoculation in a transport media (nutrient broth), to the laboratory of the College of Science and then cultured on mannitol salt agar and blood agar). The isolates belonging to Staphylococcus spp. were identified by biochemical tests and Vitek 2 compact system, while the more antibiotic resistant isolates were identified by using Polymerase Chain Reaction(ï´¾PCR) and sequencing of 16SrRNA . The results showed sharp UV absorption peaks at 330 - 340nm and AFM at 5
... Show MoreProblem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a
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