The current research discusses the jurisprudence of the argumentative meaning in the Qur’an, and it is one of the general methods of responses in the Qur’an, as well as the argumentative approach to the recitations of the readers and directing their meanings. I have chosen a model for three verses from Surat Al-An’am, in which there are seven frequent recitations and seven odd ones, on a topic related to theology and faith; It forms the centerpiece of the argument verses in Surat Al-An'am; It contains the jurisprudence of dealing with infidels and dissenters, turning away and forbidding insulting what they claim besides God in the event of corruption, and God Almighty’s decree and wisdom in the occurrence of unbeli

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

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

    The motion of fast deuterons in most dense plasma focus devices  ( DPF ) , may be characterized that it has a complex nature in its paths and this phenomena by describing a through gyrating motion with arbitrary changes in magnitude and direction .  In this research , we focused on the  understanding the theoretical concepts which  depend deeply on the experimental results to explain the deuteron motions in the pinch region , and then to use the fundamental physical formulas that are deeply related to the explanation of this motion to prepare a suitable model for calculating the vertical and radial components for deuteron velocity by improving the  Rung – Kutta  Method

In this note, we present a component-wise algorithm combining several recent ideas from signal processing for simultaneous piecewise constants trend, seasonality, outliers, and noise decomposition of dynamical time series. Our approach is entirely based on convex optimisation, and our decomposition is guaranteed to be a global optimiser. We demonstrate the efficiency of the approach via simulations results and real data analysis.

This paper presents a study for the influence of magnetohydrodynamic (MHD) on the oscillating flows of fractional Burgers’ fluid. The fractional calculus approach in the constitutive relationship model is introduced and a fractional Burgers’ model is built. The exact solution of the oscillating motions of a fractional Burgers’ fluid due to cosine and sine oscillations of an infinite flat plate are established with the help of integral transforms (Fourier sine and Laplace transforms). The expressions for the velocity field and the resulting shear stress that have been obtained, presented under integral and series form in terms of the generalized Mittag-Leffler function, satisfy all imposed initial and boundary conditions. Finall

Objective: to assess the Nursing College students' health-protective behaviors (HPBs) and their
association with some sociodemograghic characteristics.
Methodology: A sample of 100 Students (males and females) was selected through a systematic
random sample that were at the third and fourth year of Nursing College in Baghdad University for the
period of April 1st through April 30th 2007. Data were collected through the use of a self-report
instrument that used for Americans as HPBs assessment that contains 23 items. Reliability and validity
of the tool were determined through a pilot study. A descriptive statistical approach (frequencies and
percentages) and inferential statistical approach (chi-square) were used for

 The power factors and electronic thermal conductivities in bismuth telluride (Bi2Te3), lead-telluride (PbTe), and gallium arsenide (GaAs) at room temperature (300K) quantum wires and quantum wells are theoretically investigated. Our formalism rigorously takes into account modification of these power factors and electronic thermal conductivities in free-surface wires and wells due to spatial confinement. From our numerical results, we predict a significant increase of the power factor in quantum wires with diameter w=20 Ã…. The increase is always stronger in quantum wires than in quantum wells of the corresponding dimensions. An unconfined phonon distribution assumed based on the bulk lattice thermal conductivity is then employed

In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is appl