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.

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, 

The Manganese doped zinc sulfide nanoparticles of the cubic zinc blende structure with the average crystallite size of about 3.56 nm were synthesized using a coprecipitation method using Thioglycolic Acid as an external capping agent for surface modification. The ZnS:Mn2+ nanoparticles of diameter 3.56 nm were manufactured through using inexpensive precursors in an efficient and eco-friendly way. X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) spectroscopy are used to examine the structure, morphology and chemical composition of the nanoparticles. The antimicrobial activity of (ZnS:Mn2+) nanocrystals was investigated by measuring the diameter of inhibition zone using well diffusion mechanism

COVID-19 is a pandemic disease that has a wide spectrum of symptoms from asymptomatic to severe fatal cases due to hyperactivation of the immune system and secretion of pro-inflammatory cytokines. This study aimed to assess the level and impact of interleukin (IL)-13, IL-33, and tumor necrosis factor (TNF)-α cytokines on immune responses in mild and moderate COVID-19-infected Iraqi patients. A prospective case-control study was conducted from January 2023 to January 2024; it included 80 patients infected with moderate COVID-19 infection who consulted in different private clinics and 40 healthy controls. The serum of both groups was tested for quantification of serum IL-13, IL-33, and TNF-α using the human enzyme-linked immunosorbe

Five novel nickel, iron, cobalt, copper, and mercury complexes were synthesized from tetraazamacrocyclic Schiff base ligand (L), which were derived from 3-(4-(dimethyl amino) benzylidene) pentane-2,4-dione and 1,2- diaminocyclohexane in a 2:2 molar ratio. Many physico-chemical and spectroscopic techniques, including melting point, 1HNMR, 13CNMR, elemental analysis, molar conductance, magnetic susceptibility, UV-Vis, FT-IR, and thermogravimetric analysis (TGA), were used to characterize the Schiff base ligand and all metal complexes. The octahedral geometry of all the complexes [MLCl2] is confirmed by spectroscopic analyses. All substances' biological properties, such as their in vitro antioxidant activity or level of free radical scavenging

Objective(s) : This study aimed at evaluating the seroprevalence of anti -HCV and studying the
correlation between hemophilia and risk factors for acquiring HCV such as age , marital status &
occupation among hemophilic patients .
Methodology : 210 hemophilic patients in children welfare teaching hospital/medical city/Baghdad–Iraq
(hemophilia center) were investigated using prepared questionnaire and tested for HCV infection, those
were measuring patient’s age, hemophilia types and severity, marital status, residency and history of
previous HCV infection .
Results : Most hemophilic patients were hemophilia A at severe , hemophilia was at age group 20 – 29
years , the majority of patients were unmarried a

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