The interaction of charged particles with the chemical elements involved in the synthesis of human tissues is one of the modern techniques in radiation therapy. One of these charged particles are alpha particles, where recent studies have confirmed their ability to generate radiation in a highly toxic localized manner because of its high ionization and short its range. In this work, We focused our study on the interaction of alpha particles with liquid water; since the water represents over 80% of the most-soft tissues, as well as, hydrogen, oxygen, and nitrogen ,because they are key chemical elements involved in the synthesis of most human tissues. The mass stopping powers of alpha particle with HଶO , COଶ, Oଶ, Hଶ and Nଶhave

To damp the low-frequency oscillations which occurred due to the disturbances in the electrical power system, the generators are equipped with Power System Stabilizer (PSS) that provide supplementary feedback stabilizing signals. The low-frequency oscillations in power system are classified as local mode oscillations, intra-area mode oscillation, and interarea mode oscillations. A suitable PSS model was selected considering the low frequencies oscillation in the inter-area mode based on conventional PSS and Fuzzy Logic Controller. Two types of (FIS) Mamdani and suggeno were considered in this paper. The software of the methods was executed using MATLAB R2015a package.

To move forward on the path of goodness and peace, we must realize that, in the midst of the great diversity of cultures and forms of human life in the world, that we form one human nation, which God Almighty created to worship Him on His earth and under His heavens and to enjoy His bounties and natural resources that God Almighty has bestowed upon that nation. On one land, and it is governed by one common destiny. Every country has been endowed with a natural resource by God Almighty that distinguishes it from the other country to live in prosperity if these wealth are distributed equally among the members of the same society and societal justice is achieved. We must join together to work for the establishment of a sustainable global commu

This research is divided into a process of classifying and filtering most of the studies that dealt with sustainable professional development and dividing them into several sections to determine the categories for which sustainable professional development is measured, which categories did not address it, the extent to which the samples deal with and their number of the target variable, as well as the variables that suit sustainable professional development and the variables that did not Study it yet. And also the quality of studies in terms of the methodology used so that there is a reference for every researcher who intends to study sustainable professional development with studies, their characteristics, categories, tools and methodology

Today the NOMA has exponential growth in the use of Optical Visible Light Communication (OVLC) due to good features such as high spectral efficiency, low BER, and flexibility. Moreover, it creates a huge demand for electronic devices with high-speed processing and data rates, which leads to more FPGA power consumption. Therefore; it is a big challenge for scientists and researchers today to recover this problem by reducing the FPGA power and size of the devices. The subject matter of this article is producing an algorithm model to reduce the power consumption of (Field Programmable Gate Array) FPGA used in the design of the Non-Orthogonal Multiple Access (NOMA) techniques applied in (OVLC) systems combined with a blue laser. However, The po

In the current digitalized world, cloud computing becomes a feasible solution for the virtualization of cloud computing resources.  Though cloud computing has many advantages to outsourcing an organization’s information, but the strong security is the main aspect of cloud computing. Identity authentication theft becomes a vital part of the protection of cloud computing data. In this process, the intruders violate the security protocols and perform attacks on the organizations or user’s data. The situation of cloud data disclosure leads to the cloud user feeling insecure while using the cloud platform. The different traditional cryptographic techniques are not able to stop such kinds of attacks. BB84 protocol is the first quantum cry

Amplitude variation with offset (AVO) analysis is an 1 efficient tool for hydrocarbon detection and identification of elastic rock properties and fluid types. It has been applied in the present study using reprocessed pre-stack 2D seismic data (1992, Caulerpa) from north-west of the Bonaparte Basin, Australia. The AVO response along the 2D pre-stack seismic data in the Laminaria High NW shelf of Australia was also investigated. Three hypotheses were suggested to investigate the AVO behaviour of the amplitude anomalies in which three different factors; fluid substitution, porosity and thickness (Wedge model) were tested. The AVO models with the synthetic gathers were analysed using log information to find which of these is the

The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic

Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings