‎تم في هذه الدراسة ، تزيين ‏رقائق أكسيد الجرافين (‏GO‏) بجسيمات ‏كوبلتيت النيكل النانوية ‏NiCo2O4‎‏(‏NC‏) عن طريق الترسيب في ‏الموقع ، وتم استخدام المتراكب ‏المحضر (‏NC: GO‏) كسطح ماز لإزالة ‏صبغة الميثيل الخضراء ( ‏MG‏) من ‏المحاليل المائية. تم التحقق من ‏التغطية الناجحة لأوكسيد الجرافين ‏بجزيئات كوبلتيت النيكل النانوية ‏‏(‏NC‏) باستخدام دراسات ‏FT-IR‏ وحيود ‏الأشعة السينية (‏XRD‏). كانت أحجام  ‏الجسيم

In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like iden

Zeolite Y nanoparticles were synthesized by sol - gel method. Dffirent samples using two silica sources were prepared.
Sodium metasilicate (Na2SiO3) (48% silica) and silicic acid silica (H2SiO3) (75% silica) were employed as silica
source and aluminum nitrate (Al(NO3)3.9H2O) was the aluminum source with tetrapropylammonium hydroxide
(TPAOH) as templating agent.
The synihesized-samples were characterized by X-ray diffraction, showed the requirement of diffirent aging time for
complete crystallization to be achieved. Transmission Electronic Microscope (TEM) images, showed the particles were
in the same range of 30 - 75 nm. FT-IR spectroscory, showed the synthesized samples having the zeolite Y crystal
properties. The i

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K