Myrtle plant was washed, dried, and powdered after harvesting to produce a fine powder used in water treatment. An alcoholic extract was created from the myrtle plant using ethanol, which was then analyzed using GC-Mass, Fourier Transform Infrared spectroscopy, and ultraviolet-visible spectroscopy to identify the active components. Zinc nanoparticles were created using alcoholic extract. FT-IR, UV-Vis, SEM, EDX, and TEM were used to characterize zinc nanoparticles. Using a continuous processing procedure, zinc nanoparticles with myrtle extract and powder were employed to clean polluted water containing pesticides and antibiotic. First, 2 g of zinc nanoparticles was mixed with 20 ml of polluted water and the result was (Tetra 44%, Levo 32%), after that used 4 g (Tetra 100%, Levo 100%). Next, myrtle plant was used to treat water (Tetro 100%, Levo 100%). As compared myrtle powder with zinc nanoparticle, it was found that myrtle plant was preferred in water treatment.
In this paper, the problem of resource allocation at Al-Raji Company for soft drinks and juices was studied. The company produces several types of tasks to produce juices and soft drinks, which need machines to accomplish these tasks, as it has 6 machines that want to allocate to 4 different tasks to accomplish these tasks. The machines assigned to each task are subject to failure, as these machines are repaired to participate again in the production process. From past records of the company, the probability of failure machines at each task was calculated depending on company data information. Also, the time required for each machine to complete each task was recorded. The aim of this paper is to determine the minimum expected ti
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تعد صناعة السمنت في العراق من اقدم الصناعات الحديثة واكثرها تطورا وتقدما ومن اقواها تاثيرا في الاقتصاد القومي. واذ توفر في صناعة السمنت العراقي كافة المستلزمات الناجحة من حيث توفر المواد الاولية والخبرات الفنية والتقنية واسواق ثابتة وراسخة محليا وعالميا فقد كان من المفروض ان يتم التوسع في هذه الصناعة، وان التخطيط لهذه الصناعة امرا ضروريا خاصة وان مادة السمنت هي احدى اهم المواد الرئيسة التي يؤثر توفره
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Pseudomonas aeruginosa is a common and major opportunistic human pathogen, its causes many and dangersinfectious diseases due to death in some timesex: cystic fibrosis , wounds inflammation , burns inflammation , urinary tract infection , other many infections otitis external , Endocarditis , nosocomial infection and also causes other blood infections (Bacteremia). thereforebecomes founding fast and exact identification of P. aeruginosafrom samples culture very important.However, identification of this species may be problematic due to the marked phenotypic variabilitydemonstrated by samples isolates and the presence of other closely related species. To facilitate species identification, we used 16S ribosomal DNA(rRNA) sequence data
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A new adsorbent was developed by integrating algae biomass (AG) into a chitosan (CN) matrix, followed by structural enhancement via crosslinking with pyromellitic dianhydride (PMDA) through a hydrothermal synthesis approach. This process resulted in the formation of a robust AG@CN-PMDA composite with improved physicochemical characteristics suitable for advanced adsorption applications. The AG@CN-PMDA composite was evaluated for its efficiency in removal of the cationic dye methyl violet 2B (MV 2B) from aqueous solution. The adsorption process was refined through the Box-Behnken design (RSM-BBD), evaluating three essential parameters: adsorbent dosage (A: 0.02–0.1 g/100 mL), pH (B: 4–10), and time (C: 5–20 min). The ideal conditions f
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Issam al-Din al-Asfrani's footnote
On the interpretation of the oval
Imam
Issam al-Din Ibrahim Arbashah al-Asfrani
(Th 159 e)
Surah Al-Baqarah (verse 55-911)
In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi
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