Autorías: Imad Kadhim Khlaif, Israa Gameel Hussein, Talib Faissal Shnawa. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 5, 2022. Artículo de Revista en Dialnet.

 The aim of this research is to identify the extent to which the Conventional and Islamic banks are committed to implement the requirements of the corporate governance in its financial reports. In addition to its commitment to transparency and clarity in dealing with the shareholders and stockholders to protect their interests and to determine the impact of the commitment of the corporate governance on assessing the financial performance of the conventional and Islamic banks that participate in Bahrain Stock Exchange.

Summary:

This research revolves around the probing of those whom Ibn Hajar said, "He has a vision", its significance, and the ruling on the connection and transmission to it.  The number of narrators reached fifty-one (51) narrators, among whom it was said, “He has a vision, whether it is definite or possibly. Some of them had a vision and companionship.”They are eleven (11) narrators, And among them were those who had visions and had no company, and their number was twenty-one (21) narrators, and among them were those who had no vision and nor company, and their number is nineteen (19) narrators.
As a result , whoever said about him “has a vision” and has companions, his hadith is connected, even i

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

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

New biscarboxylato zwitterionic ligands, namely bis(N-carboxylatoethyl)-4,4 -dipyridinum) L1 and bis(Ncarboxylatopropyl)-)-4,4 -dipyridinum) L2, were synthesized from the reaction of 4,4 -dipyridine with 3-bromopropanoic acid and 4-bromobutanoic acid, respectively. The reaction of these ligands and the azido coligand with some metal ions resulted in the formation of polymeric complexes of general formulae [Cr2(Ln)(N3)4]Cl2·H2O and [M2(Ln)(N3)4] xH2O, where (M = Mn(II), Fe(II), Co(II), Ni(II), Cu(II), Zn(II) and Cd(II); n = 1, 2; x = 1, 1, 1, 1, 0, 1, and 1, respectively.) The oxygens of the carboxylato group are coordinated to the metal ion in a bidentate fashion. The mode of bonding and overall geometry of the complexes were determin

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att