The Cu(II) was found using a quick and uncomplicated procedure that involved reacting it with a freshly synthesized ligand to create an orange complex that had an absorbance peak of 481.5 nm in an acidic solution. The best conditions for the formation of the complex were studied from the concentration of the ligand, medium, the eff ect of the addition sequence, the eff ect of temperature, and the time of complex formation. The results obtained are scatter plot extending from 0.1–9 ppm and a linear range from 0.1–7 ppm. Relative standard deviation (RSD%) for n = 8 is less than 0.5, recovery % (R%) within acceptable values, correlation coeffi cient (r) equal 0.9986, coeffi cient of determination (r2) equal to 0.9973, and percentage capita

Background: Chemotherapeutic medication treatment for cancer is typically used in conjunction with other techniques as part of a routine regimen. It is well established that the capacity of different chemotherapeutic drugs to induce apoptosis is correlated with their anticancer efficacy. Quinazolinone-based drugs have demonstrated excellent responses from several cancer cell types. These substances have a lot of potential for use as building blocks in the creation of apoptosis inducers. Objective: To assess the new quinazolinone derivatives (M1 and M2) that were recently synthesized for their potential to halt wound healing and to use the acridine orange/propidium iodide (AO/PI) double stain to assess their capacity to induce apopto

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

         Allopurinol derivative were prepared by reacting the (1-chloroacetyl)-2-Hydropyrazolo{3,4-d}pyrimidine-4-oneiwith 5- methoxy- 2-aminoibenzothiazoleiunder certain conditions to obtain new compound  ( N- (2-aminoacetyl (5-methoxy) benzothiazole -2yl) (A₄), Reaction of 5-(P-dimethyl amine benzene)-2-amino-1,3,4- oxadiazole in the presence of potassium carbonate anhydrous to yield new  compound (N-(2- aminoacetyl-5-(P-dimethyl amine benzene )-1,3,4-oxadiazoles-2-yl)(A₃₀) and Azo compound (N-(5-(Azo-2-hydroxy-5-amino benzene)-1,3-Diazol-2yl)Allopurinol(A₄₆). The structure of prepared compounds were confirmed by (FT-IR)

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

This study involves the synthesis of a new class of silicon polymers, designated as P1-P7, derived from dichlorodimethylsilane (DCDMS) in combination with various organic compounds (Schiff bases prepared from different amines and appropriate aldehydes or ketones) [I-V] through condensation polymerization. The structures of all monomers and polymers were characterization by FTIR and 1HNMR spectroscopy (for some polymers). The results of thermogravimetric analysis (TGA) and differential scanning calorimetry DSC test show stable thermal behaviour. Polymers with a higher concentration of aromatic rings in their repeating structural units exhibited a higher temperature for weight loss, indicating increased thermal stability. Thermal meas

The research to knows some biomechanics variables in different spots with and without players in basketball youth players and analysis by using destructive method in surfing study and the research were applied for jump shoot from one of basketball players in ( middle , left , right ) in side zone and out of zone also from three point shoot with and without defense and we depend on successful shoot on analyze .The results and conclusions that center of weight of the player on standby on high and knee angel and hips were more wide also the two angle of wrist , elbow on start of shooting be more wide with defender more than without defender .the maximum high center of weight and shooting angle and ball entrance being less degree with defender