The mass collision energy loss (dE/dX), the mass radiative energy loss (Srad/) and the total mass stopping power of electrons in the energy range of 0.01 MeV up to 1000 MeV has been calculated for Lung, Urea and Skin. The results of the present work for the mass collision stopping power of electrons in Lung, Urea and Skin are in excellent agreement with the standard results given by ESTAR program, where the maximum percentage error between the present calculated values and that of ESTAR program in Lung tissue, Urea and Skin tissue is 0.27%, 0.3% and 0.8% respectively. The mass radiative energy loss of electrons in the same energy range is also calculated using a modified equation, and the results are found to be in very good agreem

Several stress-strain models were used to predict the strengths of steel fiber reinforced concrete, which are distinctive of the material. However, insufficient research has been done on the influence of hybrid fiber combinations (comprising two or more distinct fibers) on the characteristics of concrete. For this reason, the researchers conducted an experimental program to determine the stress-strain relationship of 30 concrete samples reinforced with two distinct fibers (a hybrid of polyvinyl alcohol and steel fibers), with compressive strengths ranging from 40 to 120 MPa. A total of 80% of the experimental results were used to develop a new empirical stress-strain model, which was accomplished through the application of the parti

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

Atheists have spread in the modern era, so that atheism has become a bad phenomenon in the world in general and in Islamic societies in particular, so the research aims to study the individual and social effects left by atheism on the atheists themselves, and the research included multiple axes: atheism linguistically and idiomatically, atheism in the Qur’an Noble and Modern (and Contemporary) Atheism Statistics: and the reasons for atheism: Studying the phenomenon of atheism in Iraq as a model, then studying the effects of atheism: on the individual first, then atheism and its impact on society, then the conclusion, recommendations, sources and references

With the great development in the field of the Internet, the talk about the new media and its implications began, And its interactive services have made the future of media material sometimes participating in it and manufacturing it at other times,
the public is seeking information and choosing the appropriate ones, as well as exchanging messages with the sender after what the role of the receiver is just receiving information only.
This study aims to demonstrate the effects of using digital media in various forms and types to construct the value system of Iraqi society through the identification of the following aims:
Identify the most popular digital media for the Iraqi public in their daily lives on the Internet.
Identify

The aim of the study is to reveal the effect of the constructivist learning Model on the achievement and reflective thinking of the fifth grade literary Preparatory students in History subject. A random sample was chosen which consisted of 64 students divided into experimental and control groups, each group consisted of 32 students. The experimental group was taught via the constructivist learning model, and the control group was taught via the traditional method. The experiment was lasted for Eight weeks, each week taught two lessons. The researcher adopted the experimental design with partial control. The two groups were equalized statistically. The researcher used two instruments, the achievement test and the reflective thinking test.

The aim of the study is to reveal the effect of the constructivist learning Model on the achievement and reflective thinking of the fifth grade literary Preparatory students in History subject. A random sample was chosen which consisted of 64 students divided into experimental and control groups, each group consisted of 32 students. The experimental group was taught via the constructivist learning model, and the control group was taught via the traditional method. The experiment was lasted for Eight weeks, each week taught two lessons. The researcher adopted the experimental design with partial control. The two groups were equalized statistically. The researcher used two instruments, the achievement test and the reflective thinking test.

The accurate identification of internal and external pressures in thick-walled hyperelastic vessels is a challenging inverse problem with significant implications for structural health monitoring, biomedical devices, and soft robotics. Conventional analytical and numerical approaches address the forward problem effectively but offer limited means for recovering unknown load conditions from observable deformations. In this study, we introduce a Graph-FEM/ML framework that couples high-fidelity finite element simulations with machine learning models to infer normalized internal and external pressures from measurable boundary deformations. A dataset of 1386 valid samples was generated through Latin Hypercube Sampling of geometric and l