The research is an attempt to investigate experimentally the influence of teacher’s errors correction and students’ errors correction on teaching English at the College of Physical Education for Women. Errors are seen as a natural way for developing any language but teachers are puzzled the way they can correct these errors. So, this research gives some idea of using two types of errors correction. The sample of the research is female students of the first year stage at the College of Physical Education for Women of the academic year 2009-2010. The whole population of the research is (94) students while the sample is (64). Thus, the sample represents 68% from the population of the research. The sample represents It is hypothesized that there are no significant differences between the experimental group which has been taught according students’ errors correction technique and the control group which has been taught according the teacher’s error correction technique in teaching English. To fulfill the aim of the research an experiment has been designed with two groups of (64) students chosen randomly from first year students. Both groups have matched in terms of age, the level of subjects’ achievement in previous year (the Baccalaureate Exam), and the academic type of study in the secondary school. The experiment lasted nine weeks. A post-test has been constructed in the last week for both groups after insuring its validity and reliability. After analyzing the results statistically, it has been found that there are significant differences between the two groups in their achievement in the test. Accordingly, the null hypothesis has been rejected. Finally, instructors are recommended to use students’ correction errors for developing their students’ achievement and knowledge in English language.
General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k
... Show MoreChalcopyrite thin films ternary Silver Indium Diselenide AgInSe2 (AIS) pure and Aluminum Al doped with ratio 0.03 was prepared using thermal evaporation with a vacuum of 7*10-6 torr on glass with (400) nm thickness for study the structural and optical properties. X-ray diffraction was used to show the inflance of Al ratio dopant on structural properties. X-ray diffraction show that thin films AIS pure, Al doped at RT and annealing at 573 K are polycrystalline with tetragonal structure with preferential orientation (112). raise the crystallinity degree. AFM used to study the effect of Al on surfaces roughness and Grain Size Optical properties such as the optical band gap, absorption coefficient, Extinction coefficient, refractive ind
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Tests were performed on asphalt concrete specimens with (101.6 mm in diameter and 101.6 mm in height), and the results were implemented for calculating permanent deformation and resilient modulus under repeated compressive stress with different levels of stresses (0.068, 0.138 and 0.206) MPa at 40 ºC. Two types of additives namely (carbon black-asphalt) and (SBR-asphalt) were tried as rejuvenators with three percentages of (0.5, 1 and 1.5) % by weight of asphalt cement along with two ratios of AC (1 and 2) % have been implemented as rejuvenator and blended with the reclaimed asphalt concrete. Aged materials were obtained from the site. 100% Reclaimed Asphalt Pavement material from the reclaimed mixture is implemented. A
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Autorías: Nuha Mohsin Dhahi, Ahmed Thare Hani, Muwafaq Obayes Khudhair. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 6, 2022. Artículo de Revista en Dialnet.
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The present experimental work is conducted to examine the influence of adding Alumina (Al2O3) nanoparticles and Titanium oxide (TiO2) nanoparticles each alone to diesel fuel on the characteristic of the emissions. The size of both Alumina and Titanium oxide nanoparticles which have been added to diesel fuel to obtain nano-fuel is about 20 nm and 25 nm respectively. Three doses of (Al2O3) and (TiO2) were prepared (25, 50, and 100) ppm. The nanoparticles mixed with gas oil fuel by mechanical homogenous (manual electrical mixer) and ultrasonic processor. The study reveals that the adding of Aluminum oxide (Al2O3) and Titanium oxide (TiO2) to g
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The Influence of Some Vitamins and Biochemical Parameters on Iraqi Females’ Patients with Malignant Breast Cancer"
Duration of each developmental stage of the house dust mite Dermatophagoides pteronyssinus together with the mortality percentage were observed at a combination of five different temperatures namely 20C°, 22.5C°, 25C°, 27.5C° and 30C° and four different humidities namely 55%, 75%, 85% and 95% r. h. Results showed that temperature had the greatest effect on the life cycle period. The higher the temperature the shorter the life cycle was aid versa verea. On the other hand, humidity seems to be less effectiveness, though at the higher temperature and humidity no development was occured. Mortality among all temperatures and humidities appeared nearly the same, but at higher temperature and higher humidity and because of mould g
... Show MoreThe normalized difference vegetation index (NDVI) is an effective graphical indicator that can be used to analyze remote sensing measurements using a space platform, in order to investigate the trend of the live green vegetation in the observed target. In this research, the change detection of vegetation in Babylon city was done by tracing the NDVI factor for temporal Landsat satellite images. These images were used and utilized in two different terms: in March 19th in 2015 and March 5th in 2020. The Arc-GIS program ver. 10.7 was adopted to analyze the collected data. The final results indicate a spatial variation in the (NDVI), where it increases from (1666.91 𝑘𝑚2) in 2015 to (1697.01 𝑘𝑚2)) in 2020 between the t
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