This study was aimed to assess the efficiency of N.oleander to remove heavy metals such as Copper (Cu) from wastewater. A toxicity test was conducted outdoor for 65-day to estimate the ability of N.oleander to tolerate Cu in synthetic wastewater. Based on a previous range-finding test, five concentrations were used in this test (0, 50, 100, 300, 510 mg/l). The results showed that maximum values of removal efficiency was found 99.9% on day-49 for the treatment 50 mg/l. Minimum removal efficiency was 94% day-65 for the treatment of 510 mg/l. Water concentration was within the permissible limits of river conservation and were 0.164 at day-35 for the 50 mg/l treatment, decreased thereafter until the end of the observation, and 0.12 at day-65 for the treatment 100 mg/l. the concentrations of water samples exceeded the permissible limits for 300 and 510 mg/l throughout the observation. Bioaccumulation factor (BAF) for N.oleaner was found to be greater than one for all the treatments. Higher translocation factor (TF) were 1.65, 1.73, 2.61 and 2.34 mg/l for 50, 100, 300 and 510 mg/l, respectively. This study revealed that N.oleander can tolerate and treat Cu concentration in wastewater.
In this work, lead oxide nanoparticles were prepared by laser ablation of lead target immersed in deionized water by using pulsed Nd:YAG laser with laser energy 400 mJ/pulse and different laser pulses. The chemical bonding of lead oxide nps was investigated by Fourier Transform Infrared (FTIR); surface morphology and optical properties were investigated by Scanning Electron Microscope (SEM) and UV-Visible spectroscopy respectively, and the size effect of lead oxide nanoparticles was studied on its antibacterial action against two types of bacteria Gram-negitive (Escherichia coli) and Gram-positive (Staphylococcusaurus) by diffusion method. The antibacterial property results show that the antibacterial activity of the Lead oxide NPs was
... Show MoreIn this work, nanostructure aluminum oxide thin films were deposited on glass substrates using a direct current (DC) magnetic reactive sputtering (MRS) technique. A gaseous mixture of argon and oxygen at different mixing ratios was used to synthesize Al2O3 nanoparticles. After extracting Al2O3 powder from the glass substrate, X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), and energy-dispersive spectroscopy (EDS) were used to analyze the structural and morphological properties of the synthesized thin films. The effect of deposition time on the spectral properties, as well as on the size of the nanoparticles, was determined.
The study was conducted to show the effect of using dried rumen powder as a source of animal protein in the diets of common carp (Cyprinus carpio L.) on its performance, in the fish laboratory/College of Agricultural Engineering Sciences/University of Baghdad/ for a period of 70 d, 70 fingerlings were used with an average starting weight of 30±3 g, with a live mass rate of 202±2 g, randomly distributed among five treatments, two replicates for each treatment and seven fish for each replicate. Five diets of almost identical protein content and different percentages of addition of dried rumen powder were added. 25% was added to treatment T2 and 50% to treatment T3 and 75% of the treatment T4 and 100% of the treatment T5
... Show MoreThe subject of the Internet of Things is very important, especially at present, which is why it has attracted the attention of researchers and scientists due to its importance in human life. Through it, a person can do several things easily, accurately, and in an organized manner. The research addressed important topics, the most important of which are the concept of the Internet of Things, the history of its emergence and development, the reasons for its interest and importance, and its most prominent advantages and characteristics. The research sheds light on the structure of the Internet of Things, its structural components, and its most important components. The research dealt with the most important search engines in the Intern
... Show MoreDiabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att
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