In this research, the theme for employing a simple and sensitive method is to employ a new Schiff base ligand (N’-(4- (dimethyl amino) benzylidene)-3, 5-dinitrobenzohydrazide) to estimate Ni (II) to form orange complex (N-(4-(dimethyl amino) benzylidene)-3, 5-dinitrobenzohydrazide nickel (II) chloride) in acid medium (hydrochloric acid), it gives an absorption peak at the wavelength 485 nm. The preferred conditions were studied to form the complex and obtain the highest absorbance including concentration of Schiff base ligand, the best medium for complex formation, effects of addition sequence on complex formation, the effect of temperature on the absorbance of the complex formed, and the setting time of the formed complex. The obtained results show the extent of the scatter plot 0.03–6 ppm and linear range 0.03–5 ppm. The relative standard deviation RSD % was calculated and the ratio was less than 4% for n = 8, the percentage recovery was calculated and the results were within acceptable percentages, correlation coefficient equal to 0.99995 was obtained, while the estimation coefficient was equal to 0.9999 and percentage capital R-squared explained variation as a percentage/total variation equal to 99.99. The method is considered one of the successful methods for estimation of Ni (II) in its aqueous solution also in different specimens in which it is found at the lowest cost, and by using a newly prepared Schiff base ligand and for the first time it is used in the estimation of Ni (II).
To evaluate and improve the efficiency of photovoltaic solar modules connected with linear pipes for water supply, a three-dimensional numerical simulation is created and simulated via commercial software (Ansys-Fluent). The optimization utilizes the principles of the 1st and 2nd laws of thermodynamics by employing the Response Surface Method (RSM). Various design parameters, including the coolant inlet velocity, tube diameter, panel dimensions, and solar radiation intensity, are systematically varied to investigate their impacts on energetic and exergitic efficiencies and destroyed exergy. The relationship between the design parameters and the system responses is validated through the development of a predictive model. Both single and mult
... Show MoreBackground: Suffering from recurrent boils (furunclosis) is a common problem in our locality as it is noticed by many dermatologists especially in association with increasingly hot weather. The most common causative organisms are staphylococci. Objective: The aim of the study was to shed the light upon this problem and compare two systemic therapeutic agents for the prevention of recurrence, doxycycline and rifampicin. Patient and method: One hundred thirty-five (135) Patients with recurrent boils from Al-Yarmouk teaching hospital dermatology outpatient department were included in this study; age ranged from 10 to 64 years old and out of total patients 32 were males and 103 were females. Patients were assessed by full history and cl
... Show MoreSchiff bases, named after Hugo Schiff, are aldehyde- or ketone-like compounds in which the carbonyl group is replaced by imine or azomethine group. They are widely used for industrial purposes and also have a broad range of applications as antioxidants. An overview of antioxidant applications of Schiff bases and their complexes is discussed in this review. A brief history of the synthesis and reactivity of Schiff bases and their complexes is presented. Factors of antioxidants are illustrated and discussed. Copyright © 2016 John Wiley & Sons, Ltd.
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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