In this work, the fusion cross section ,  fusion barrier distribution  and the probability of fusion  have been investigated by coupled channel method  for the systems ⁴⁶Ti+⁶⁴Ni, ⁴⁰Ca+¹⁹⁴Pt and ⁴⁰Ar+¹⁴⁸Sm with semi-classical and quantum mechanical approach using SCF and CCFULL Fortran codes respectively. The results for these calculations are compared with available experimental data. The results show that the quantum calculations agree better with experimental data, especially bellow the Coulomb barrier, for the studied systems while above this barrier, the two codes reproduce the data.

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

The advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given con

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

Cerium (III), Neodymium (III) and Samarium (III) Complexes existent a wide range of implementation that stretch from their play in the medicinal and pharmaceutical area because of their major significant pharmacological characteristic such as antifungal, anti-cancer, anti-bacterial ,anti-human immunodeficiency virus ,antineoplastic, anti-inflammation,inhibition corrosion,in some industrial (polymers, Azo dye).It is likely to open avenuesto research among various disciplines such as physics, electronics, chemistry and materials science by these complexes that contain exquisitely designed organic molecules.This paper reviews the definition, importance and various applications of Cerium (III), Neodymium (III) and Samarium (III) Complexes anddi

Objective: To review and identify the major drivers for COVID-19 vaccine acceptance. Methods: A scoping review of studies of COVID-19 vaccine perceptions and barriers to using the COVID-19 vaccines. Two search engines, including PubMed and Google Scholar, were purposefully searched. Results: Eight studies from different countries were reviewed to categorize factors influencing people's acceptance of COVID-19 according to the Health Belief Model (HBM). Perceived susceptibility, and severity of the disease (COVID-19), in addition to perceived benefits of COVID-19 vaccination and "cues to action", can enhance vaccination acceptance. In contrast, perceived barriers to the COVID-19 vaccine can increase people's hesitancy to be vaccinated

In present days, drug resistance is a major emerging problem in the healthcare sector. Novel antibiotics are in considerable need because present effective treatments have repeatedly failed. Antimicrobial peptides are the biologically active secondary metabolites produced by a variety of microorganisms like bacteria, fungi, and algae, which possess surface activity reduction activity along with this they are having antimicrobial, antifungal, and antioxidant antibiofilm activity. Antimicrobial peptides include a wide variety of bioactive compounds such as Bacteriocins, glycolipids, lipopeptides, polysaccharide-protein complexes, phospholipids, fatty acids, and neutral lipids. Bioactive peptides derived from various natural sources like bacte

Modern agriculture is challenged by soil degradation, nutrient depletion, plant diseases, and excessive dependence on chemical fertilizers and pesticides. By examining different strains of Pantoea, the study highlights their role in promoting plant growth, improving their tolerance to stress, reducing reliance on synthetic agricultural inputs, and contributing to more sustainable and environmentally friendly agricultural practices. Using a combination of practical qualitative methods and reliable quantitative data, the research gathers extensive information on how these microbes impact various crops and key soil health indicators. The improvements in plant growth statistics and nutrient levels are often quite astonishing. The result