Abstract

The aim of this research is to determine how well the Cubing Technique affects the Iraqi EFL students' composition writing, vocabulary, and meta-cognitive awareness of writing strategies. The sample of (64) secondary-school female students in the fifth grade is drawn from two classrooms and split into two equal groups: the experimental group and the control group, each of which consists of (32) students. A quasi-experimental design is applied. The performance test and Meta-cognitive Writing Strategies questionnaire are given as a pre-test for equalizing the two groups after ensuring their validity and reliability. Then, they are administrated as a posttest in both groups. According to the results, the si

The reality of the field of construction projects in Iraq refers to needing for the development of performance in order to improve quality and reduce defects and errors and to control the time and cost, so there is needing for the application of effective methods in this area, one of the methods that can be applied in this area is the manner of Six Sigma. This research aims to enhance the performance and quality improvement for the construction projects by improving performance in the work of the implementation of the concrete structure depending on the Six Sigma methodology, and for the purpose of achieving the aim of the research, the researcher firstly depends on the theoretical study that include the concepts of qual

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

Sequence covering array (SCA) generation is an active research area in recent years. Unlike the sequence-less covering arrays (CA), the order of sequence varies in the test case generation process. This paper reviews the state-of-the-art of the SCA strategies, earlier works reported that finding a minimal size of a test suite is considered as an NP-Hard problem. In addition, most of the existing strategies for SCA generation have a high order of complexity due to the generation of all combinatorial interactions by adopting one-test-at-a-time fashion. Reducing the complexity by adopting one-parameter- at-a-time for SCA generation is a challenging process. In addition, this reduction facilitates the supporting for a higher strength of cove

The aim of this research is to identify the effect of Webinar technique on digital culture in the College of Education for pure sciences at Ibin Haitham, University of Baghdad. The research samples consisted of (68) male and female students from the Chemistry Department who are following classes during the (2019- 2020) academic year. The samples represent (42%) of the total number of (162) students split into control and experimental groups. For this purpose, the scientific contents for testing were determined. The experimental part is based on analysis of the results from experiments in (preliminary standard solutions, refractive index, Beer-Lambert law). To achieve the aim of the research in testing the measure of student's digital cultur

This paper presents the Taguchi approach for optimization of hardness for  shape memory alloy (Cu-Al-Ni) . The influence of  powder metallurgy parameters on hardness has been investigated. Taguchi technique and ANOVA were used for analysis. Nine experimental runs based on Taguchi’s L9 orthogonal array were performed (OA),for two parameters was study (Pressure and sintering temperature) for three different levels (300 ,500 and 700) MPa ,(700 ,800 and  900)^oC respectively . Main effect, signal-to-noise (S/N) ratio was study, and analysis of variance (ANOVA) using  to investigate the micro-hardness characteristics of the shape memory alloy .after application the result of study shown the hei

In this research study the effect of fish on the properties optical films thickness 1200-1800 and calculated energy gap Basra direct transport permitted and forbidden to membranes and urged decreasing values ??of Optical Energy Gap increase fish included accounts optical also calculate the constants visual as factories winding down and the refractive index and reflectivity membranes also by real part and imaginarythe dielectric constant

The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conv

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.