Amoxicillin 1 was treated with thiosemicarbazide and Phosphoryl chloride to obtain a new derivatives that contains 1,3,4-thiadiazole moiety 2. Schiff bases compounds were synthesized by the reaction of compound 2 with different aldehydes such as benzaldehyde and some substituted Benzaldehyde; p-hydroy, p-Chloro, p-Nitro, p-Dimethylamino, p-Methyl, p-Methoxy, p-Ethoxy to give compounds 3a-h. The obtained compounds have tested towards gram -ve and gram +ve bacteria. The compound shows good to moderate result towards the bacteria.

Till now, isatin derivatives have received a lot of interest in organic and medicinal chemistry due to their significant biological and pharmacological activities. Schiff’s and Mannich bases of isatins are an effective group of heterocyclic derivatives that play a significant role in medicinal chemistry as antimicrobial agents. In light of these facts, new Schiff bases and Mannich bases of isatin were synthesized. The monomer Mannich bases; 3(a-e) have been synthesized by reacting isatin with different secondary amines, piperidine, morpholine, and pyrrolidine, dimethylamine, diphenylamine, separately, and formaldehyde, while the dimer (5) formed by using piperazine and formaldehyde which then react separately with Phenylhydrazine

The present paper describes a static model of malware detection and employs a tri-class prevention policy that operates on the EMBER 2024 Win64 partition. A LightGBM classifier trained on 1,040,000 samples and 804 static PE features achieves AUC-ROC = 0.9979, precision = 98.7%, recall = 97.4%, and F1 = 0.9805 on the temporally-split 240,000-sample test set. An innovative Tri-class Threshold Scheme (BLOCK/SUSPICIOUS/ALLOW) obtained by constrained F2-score optimisation on a separate validation set obtains 97.9% malware detection with 98.1% benign pass rate. Efficient model scoring is verified by mean per-sample model inference latency of 2.30 ms (P99: 2.70 ms) measured on pre-extracted EMBER features, future work is characterized by a complet

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

The large number of failure in electrical power plant leads to the sudden stopping of work. In some cases, the necessary reserve materials are not available for maintenance which leads to interrupt of power generation in the electrical power plant unit. The present study, deals with the determination of availability aspects of generator in unit 5 of Al-Dourra electric power plant. In order to evaluate this generator's availability performance, a wide range of studies have been conducted to gather accurate information at the level of detail considered suitable to achieve the availability analysis aim. The Weibull Distribution is used to perform the reliability analysis via Minitab 17, and Artificial Neural Networks (ANNs) by approaching o