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.

Drought is a natural phenomenon in many arid, semi-arid, or wet regions. This showed that no region worldwide is excluded from the occurrence of drought. Extreme droughts were caused by global weather warming and climate change. Therefore, it is essential to review the studies conducted on drought to use the recommendations made by the researchers on drought.  The drought was classified into meteorological, agricultural, hydrological, and economic-social. In addition, researchers described the severity of the drought by using various indices which required different input data.  The indices used by various researchers were the Joint Deficit Index (JDI), Effective Drought Index (EDI), Streamflow Drought Index (SDI), Sta

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, 

Consumer protection requires seeking one official entity to accomplish its tasks. The large number of devices control and implementation in it, return to several ministries, and executive bodies lose coordination and cooperation, and unite efforts to eliminate the types of fraud and control the quality of production within the approved standards. This is in addition to the seriousness of the existence of administrative and legal corruption which hinders the role of inspection committees and representatives of official bodies with the relationship and prevents them from carrying out their duties entrusted to them or presenting them with administrative complications to thwart their duties in consumer protection. This is despite the

The study included the investigation of fungi which associated with heavy animal's leather (Cows and Buffalos) and light (Sheep’s and Goats )through different processing stages (raw hides ,dehairing ,pickling,chrome tanned and stainning or finished stages)there were 10 genera and 25 species in addition to sterile fungi associated with animal leathers which included Alternaria ,Aspergillus,Cladosporium,Fusarium, Mucor , Penicillium , Rhizopus , and Trichoderma .Aspergillus and Penicillium have observed in all leather samples and different processing stages, and that the first time isolate two genera Helminthosporium , Stemphylium form leather for staining stage.

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

Gram-positive enterococciare opportunistic and resistant to many antibiotics. This study aimed to investigate the presence of Enterococcus spp. in our community and whether these isolates are resistant to the macrolides class of antibiotics. Fifty isolates from 112 clinical samples were recognized as Enterococcus spp. and confirmed using Vitek-2 system. The current study found that 50/112 (44.6%) represented the total isolates, 38/50 (76%) of which were Enterococcus faecalis, while 12/50 (24%) were Enterococcus faecium, twenty (40%) isolates from root canals and 30 (60%) isolates from urine were isolated. The sensitivity of the enterococcal isolates to various macrolides (erythromycin, azithromycin and clarithromycin) antibiotics wa

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

To identify the fungi associated with water hyacinth (Eichhornia crassipes [Mart.] Solms), an aquatic weed, which presents in Tigris river from Baghdad south ward. Five regions from middle and south of Iraq (Al-Noumanya, Saeid Bin-Jubier, Al-Azizia, Al-Reyfay and Al-Hay) were selected for this study. Twelve fungal species were isolated. Alternaria alternata, Acremonium sp and Cladsporium herbarum, were the most frequently species (91.66 % ,50 % and 25 %) respectively. The fungi Alternaria alternata, Acremonium sp. and Phoma eupyrena were more aggressive to water hyacinth as (91.66%,83,33%, and 75%) in pathogenicity test.