Background: The excess mortality associated with
rheumatoid arthritis is largely due to
cardiovascular disease. Rheumatoid arthritis is of
primary importance in atherogenesis. The intimamedia thickness of extracranial carotid arteries is a
measurable index of the presence of
atherosclerosis.
Objective: To assess carotid intima-media
thickness in rheumatoid arthritis by Doppler
ultrasound.
Methods: Fifty two Iraqi patients with rheumatoid
arthritis were studied and compared with another
fifty two, healthy individuals matched for age and
sex as a control group. Full history was taken and
complete clinical examination was done for all
individuals in both groups. Rheumatoid arthritis
diagnosis was

Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization

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In this work, we first construct Hermite wavelets on the interval [0,1) with it’s product, Operational matrix of integration 2^k M×2^k M is derived, and used it for solving nonlinear Variational problems with reduced it to a system of algebric equations and aid of direct method. Finally, some examples are given to illustrate the efficiency and performance of presented method.

With the continuous downscaling of semiconductor processes, the growing power density and thermal issues in multicore processors become more and more challenging, thus reliable dynamic thermal management (DTM) is required to prevent severe challenges in system performance. The accuracy of the thermal profile, delivered to the DTM manager, plays a critical role in the efficiency and reliability of DTM, different sources of noise and variations in deep submicron (DSM) technologies severely affecting the thermal data that can lead to significant degradation of DTM performance. In this article, we propose a novel fault-tolerance scheme exploiting approximate computing to mitigate the DSM effects on DTM efficiency. Approximate computing in hardw