This research explored the performance of steel fiber concrete-filled stainless-steel tube columns stiffened with embedded carbon steel T-sections with various steel fiber ratios under biaxial bending conditions. A numerical parametric analysis was adopted, using finite element modeling with Abaqus CAE/2021 to evaluate the effects of the fiber ratio (ranging from 0% to 1.5%) on the load-bearing capacity and deflection behavior of columns. In addition, the compressive strength of concrete ranged between 45 and 65 MPa. An increase in the fiber ratio led to a substantial improvement in the ultimate load-bearing capacity (up to 24%), a reduction in deflection (of approximately 49%), and an improvement in column ductility, which were obt

To evaluate and improve the efficiency of photovoltaic solar modules connected with linear pipes for water supply, a three-dimensional numerical simulation is created and simulated via commercial software (Ansys-Fluent). The optimization utilizes the principles of the 1st and 2nd laws of thermodynamics by employing the Response Surface Method (RSM). Various design parameters, including the coolant inlet velocity, tube diameter, panel dimensions, and solar radiation intensity, are systematically varied to investigate their impacts on energetic and exergitic efficiencies and destroyed exergy. The relationship between the design parameters and the system responses is validated through the development of a predictive model. Both single and mult

This paper is concerned with the numerical solutions of the vorticity transport equation (VTE) in two-dimensional space with homogenous Dirichlet boundary conditions. Namely, for this problem, the Crank-Nicolson finite difference equation is derived.  In addition, the consistency and stability of the Crank-Nicolson method are studied. Moreover, a numerical experiment is considered to study the convergence of the Crank-Nicolson scheme and to visualize the discrete graphs for the vorticity and stream functions. The analytical result shows that the proposed scheme is consistent, whereas the numerical results show that the solutions are stable with small space-steps and at any time levels.

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

فقد تناولت في موضوعي هذه الآية الكريمة فقد جاء في هذه الآية تعليم من الله عز وجل لرسوله (ص) فلكل داع الى الله من أمته, اسلوباٌ يدعوا به الناس ومحاجة الكافرين بالقرآن, وفيها بيان من الله عز وجل بأنه سيري الناس في المستقبل بعض آياته في كونه, وهي آيات دالات على أن القران حق منزل من عند الله جل جلاله, وليس من وضع البشر, فالناس عاجز عن معرفة الآيات الباهرات التي سيريها الله عز وجل للناس في كونه , وقد أخبرهم عنها في القرآ

ءأرﻘﻟا ةﺎﯾﺣﺑ ًﺎﻘﯾﺛو ًﻻﺎﺻﺗا لﺻﺗﺗ. نﻣ ﮫﺑﺗﺎﮐﻟﻟ ﻲﺻﺧﺷﻟا ﻊﺑﺎطﻟا ﻲﻔﺣﺻﻟا دوﻣﻌﻟا لﻣﺣﯾ ا فﻟﺗﺧﻣﻟ ﮫﻟوﺎﻧﺗ لﻼﺧ وا ﮫﺋارا وا هرظﻧ ﺔﮭﺟو لﻣﺣﺗ ﻲﺗﻟا ﺔﯾﻣوﯾﻟا ثادﺣﻻاو ﺎﯾﺎﺿﻘﻟ ﺢﺿﻔﺑ موﻘﯾو ثادﺣﻻاو ﺔﯾﺑﻟﺳﻟا رھاوظﻟﻟ ىدﺻﺗﯾ وا، ءيرﺎﻘﻟا ﯽﻟا ﮫﺑرﺎﺟﺗ وا هرﺎﮐﻓا ءﺎطﺧﻻا دﺻرﯾ بﯾﻗرﺑ ﮫﺑﺷا وھو، ءيرﺟﻟا دﻘﻧﻟا نﻋ مﻧﯾ بوﻟﺳﺎﺑ ﺔﺋطﺎﺧﻟا تﺎﺳرﺎﻣﻣﻟا ﺎﮭﺣدﻣﯾو تﺎﯾﺑﺎﺟﯾﻻا ﯽﻟﻋ