Abstract

      The nuclear structure of ^28-40Si isotopes toward neutron dripline has been investigated in framework of shell model with Skyrme-Hrtree-Fock method using certain Skyrme parameterizations. Moreover, investigations of static properties such as nuclear densities for proton, neutron, mass, and, charge densities with their corresponding rms radii, neutron skin thicknesses, binding energies, separation energies, shell gap, and pairing gap have been performed using the most recent Skyrme parameterization. The calculated results have been compared with available experimental data to identify which of these parameterizations introduced equivalent results with the ex

The question of estimation took a great interest in some engineering, statistical applications, various applied, human sciences, the methods provided by it helped to identify and accurately the many random processes.

In this paper, methods were used through which the reliability function, risk function, and estimation of the distribution parameters were used, and the methods are (Moment Method, Maximum Likelihood Method), where an experimental study was conducted using a simulation method for the purpose of comparing the methods to show which of these methods are competent in practical application This is based on the observations generated from the Rayleigh logarithmic distribution (RL) with sample sizes

This paper deals with the preparation of new monomers and polymers which including heterocyclic unit. The diacid chlorides compounds [1-3] were prepared from the reaction of glutaric acid, adipic acid, terephthalic acid with thionyl chloride. Succinic acid reacted with ethanol to produce compound [4]. Compound [4] reacted with hydrazine hydrate to obtain succinic hydrazide [5].Compound [5] reaction with CS2 and KOH in absolute ethanol to produce compound [6].The polymers [7-12] have been created by reacting diacid chlorides compounds [1-3] with compound[5] or [6] in dry pyridine with some drops of DMF. The topology of produced compounds has characterized through their spectral and analytical data as in FT-IR spectra, Thermal analysis [DSC,

In this work, a deep computational study has been conducted to assign several qualities for the graph ⁠. Furthermore, determine the amount of the dihedral subgroups in the Held simple group He through utilizing the attributes of gamma.

The results of the historical review of social and political realities in general show that the practical and procedural applications of social engineering as a particular activity primarily of the social and political characteristics of man and society emerged in modern Western societies before appearing in other societies, These results also show that the emergence of these practical reasons and their applications in the West has also seen the emergence of modern theoretical foundations there, which seems to be the usual and usual context everywhere and in most or not all areas of life. Since the social and political dimensions are intertwined in human life and are in full, comprehensive and lasting harmony, interest in this geometry h

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob