Large quantities of contaminated carwash wastewater are produced per day from carwash places. Extensively it contains large quantities of chemicals from detergents, oil, grease, heavy metals, suspended solids, types of hydrocarbons, and biological contents. A novel electrocoagulation treatment by foil electrodes was conducted to remove COD, turbidity, Total Dissolved Solids (TDS) from contaminated carwash wastewater and decrease its Electrical Conductivity (EC). A thin layer of aluminum foil is used as an electrode in this treatment process. The effects of different voltage and treatment times were studied. The best result was found at a voltage of 30 volts and treatment time 90 minute where the removal efficiency of COD

The corrosion behavior and corrosion inhibition of α-brass (65.3% Cu, 34.4% Zn and others 0.3%) in 0.6 mol.dm-3 NaCl solution have been investigated using potentiostatic polarization technique, the main results obtained were expressed in terms of corrosion (Ec) and corrosion current (ic). The research was performed in neutral and slightly acidic media [pH=7 and pH=4] over the temperature range (288-318)K. It was found that the rate of corrosion increases with the increase of acidity and the increase of temperature. The rate of corrosion increased with the increase of temperature in conformity with Arrhenius equation. Values of activation energy (Ea*), pre-exponential factor (A) and entropy of activation (∆S*) have been derived f

Four samples were collected from the wastewater of State Battery Manufacturing Company (SBMC); Babylon 2 factory in AL-Waziriya district, as triplicates. Physical and chemical measurements were carried out such as temperature, pH, Lead concentrations and their ranges were: (19.5-34.5) °C, (6.1-6.4) and (4.5-6.5) mg/L, respectively. Six dominant Bacillus spp. isolates were isolated from these samples; namely, Bacillus subtilis N1, Bacillus subtilis N2, Bacillus subtilis N3, Bacillus cereus N4, Bacillus cereus N5 , Bacillus cereus N6. These isolates were capable of removing Lead from aqueous solutions in a capacity reached 27.6 ± 1.4, 10.1 ± 1.7, 74.5 ± 0.7, 8.93 ± 2.8, 8.1 ± 3.5, 1.6± 0.7 mg/L, respectively. Whereas cell walls,

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w