Abstract 

For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS, 

In this research a new system identification algorithm is presented for obtaining an optimal set of mathematical models for system with perturbed coefficients, then this algorithm is applied practically by an “On Line System Identification Circuit”, based on real time speed response data of a permanent magnet DC motor. Such set of mathematical models represents the physical plant against all variation which may exist in its parameters, and forms a strong mathematical foundation for stability and performance analysis in control theory problems.

This Study aimed to studying the effect of Volatile oil extracted from the leaves of Myrtus communis on the growth and activities of the following types of bacteria: Staphylococcus aureus, Streptococcus pyogenes, Klebsilla pneumoniae, Pseudomonas aeruginosa, and the yeast Candida albicans. The results showed an inhibitory effect of the oil on both the growth and activity of the tested microbes. This was reflected by the minimum inhibitory concentration (MIC) of Staphylococcus aureus, Streptococcus pyogenes, Klebsilla pneumoniae, Pseudomonas aeruginosa which was: (2.5, 1.25, and 2.5,5 % respectively), and the yeast (5) %. Also, the Minimum bactericidal concentration (MBC) to the bacteria mentioned above was (5, 2.5,5,10 % respectivel

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

   The di-(2-ethylhexyl) phthalate (DEHP) was extracted using different solvents from plastic blood bag. The extracted product was identified using FT-IR, NMR (1H and 13C), DEPT, COSY, HMBC and HSQC_TOCSY spectrometry. The extracted plasticizer was tested in complex formation with Fe2+ and Cr3+ using UV-visible spectrophotometric method. The migration of the plasticizer from the blood bags to the blood was studied and determined during different storage times depending upon the formation of complexes with Fe2+ and Cr3+, and the change in the concentration of Fe2+ and Cr3+.