In this research, a novel synthesis of CaONPs has been developed via an environmentally friendly, green method. Garlic extract (Allium sativum) was used as a green-reducing and stabilizing agent for CaONPs. The average particle size of CaONPs was approximately 24.42 nm. The synthesized CaONPs were identified by using Fourier transform infrared (FT-IR) spectroscopy, U.V.-vis spectrum, X-ray diffraction (XRD), Field Emission-Scanning Electron Microscopy (FE-SEM), Transmission Electron Microscopy, transmission electron microscopy (TEM), Energy Dispersive X-ray spectroscopy (EDX), Atomic Force Microscopy (AFM), and zeta potential (Zp) analysis. The current study highlights the notable applications for CaONPs. First, an antimicrobial assay revea

KE Sharquie, AA Noaimi, ER Shwail, J Clin Exp Dermatol Res, 2012 - Cited by 41

Face recognition and identity verification are now critical components of current security and verification technology. The main objective of this review is to identify the most important deep learning techniques that have contributed to the improvement in the accuracy and reliability of facial recognition systems, as well as highlighting existing problems and potential future research areas. An extensive literature review was conducted with the assistance of leading scientific databases such as IEEE Xplore, ScienceDirect, and SpringerLink and covered studies from the period 2015 to 2024. The studies of interest were related to the application of deep neural networks, i.e., CNN, Siamese, and Transformer-based models, in face recogni

Abstract

   Mitoxantrone is an antitumor agent used in the treatment of breast and prostate cancer, acute leukemia, lymphoma, and also in the treatment of multiple sclerosis due to its immunosuppressive properties. The mitoxantrone's cardiotoxicity is irreversible, dose-dependent, and it may occur years after treatment. Zinc is considered as an essential mineral for cell division and the synthesis of DNA and protein; furthermore, such mineral has an important role in states of cardiovascular diseases; and may have protective effects in coronary artery disease and cardiomyopathy.

Objective: The current study is designed to investigate effects of two different doses of zinc sulfat

Seeds of the two rice genotypes namely Amber 33 (A33) and Amber Baghdad (AB) were divided into two groups; the first was presoaked in different concentrations of ethyl methane sulphonate (EMS) as chemical mutagen for different duration times (3, 6 and 12) hrs, and the other was exposed to different exposure times of ultra violate (UV-B) radiation (280-320 nm) as physical mutagen for different times (20, 40 and 60) min at room temperature. Treated and non-treated seeds were transferred into the callus induction medium containing 2.5 mg/L 2,4- dichlorophenoxy acetic acid (2,4-D) and 0.5 mg/L benzyl adenine (BA) under aseptic conditions. Calli were divided into two groups the first was treated with several EMS concentrations (0.0, 0.50. 1.0, 1

Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

Methods of estimating statistical distribution have attracted many researchers when it comes to fitting a specific distribution to data. However, when the data belong to more than one component, a popular distribution cannot be fitted to such data. To tackle this issue, mixture models are fitted by choosing the correct number of components that represent the data. This can be obvious in lifetime processes that are involved in a wide range of engineering applications as well as biological systems. In this paper, we introduce an application of estimating a finite mixture of Inverse Rayleigh distribution by the use of the Bayesian framework when considering the model as Markov chain Monte Carlo (MCMC). We employed the Gibbs sampler and