A field experiment was conducted in an agricultural field in Al-Hindia district, Karbala governorate in a silty clay soil during the year 2020. The research included a study of two factors, the first is the depth of plowing at two levels, namely 13 and 20 cm, which represented the main blocks. The second is the tire inflation pressure at two levels, namely (70 and 140 kPa), which represented the secondary blocks. Slippage percentage, field efficiency, leaf area, and 300 grain weight were studied. The experiment was carried out using a split-plot system under a Randomized complete block design, at three replications. The tillage depth of 13 cm exceeds/transcend by giving it the least slippage of (11.01%), the highest field efficiency of (50.09%) and the largest leaf area of (0.48 cm2 ). The tire pressure of 70 kPa obtained the highest field efficiency of (47.79%) and the largest paper area of (0.47 cm2 ). Tire pressure 140 kPa, exceeds / transcends in recording the minimal slippage of (11.39%). As for the bilateral interaction, it impacted all the studied traits except for the weight of (300) grains
This study aimed to show the relationship between mental health and shyness for university students in Baghdad and Al – Mustansiria university which its subject was (200) students , ( 100) males and ( 100) females , Mental Health scale which is constructed by (Al – Janabi 1991) and developed by (Hassan 2006) was used for this aim ,The scale of shyness was built according to a questioner to the students and according to previous publications and studies .
Multiple regulation analysis step - wise was used for data analysis in order to identify the possibility to find single or couple indications for the independent variable (mental
... Show MoreGeneral 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
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This paper investigates an effective computational method (ECM) based on the standard polynomials used to solve some nonlinear initial and boundary value problems appeared in engineering and applied sciences. Moreover, the effective computational methods in this paper were improved by suitable orthogonal base functions, especially the Chebyshev, Bernoulli, and Laguerre polynomials, to obtain novel approximate solutions for some nonlinear problems. These base functions enable the nonlinear problem to be effectively converted into a nonlinear algebraic system of equations, which are then solved using Mathematica®12. The improved effective computational methods (I-ECMs) have been implemented to solve three applications involving nonli
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