Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

CNC machine is used to machine complex or simple shapes at higher speed with maximum accuracy and minimum error. In this paper a previously designed CNC control system is used to machine ellipses and polylines. The sample needs to be machined is drawn by using one of the drawing software like AUTOCAD® or 3D MAX and is saved in a well-known file format (DXF) then that file is fed to the CNC machine controller by the CNC operator then that part will be machined by the CNC machine. The CNC controller using developed algorithms that reads the DXF file feeds to the machine, extracts the shapes from the file and generates commands to move the CNC machine axes so that these shapes can be machined.

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

     The research involved a rapid, automated and highly accurate developed CFIA/MZ technique for estimation of phenylephrine hydrochloride (PHE) in pure, dosage forms and biological sample. This method is based on oxidative coupling reaction of 2,4-dinitrophenylhydrazine (DNPH) with PHE in existence of sodium periodate as oxidizing agent in alkaline medium to form a red colored product at ʎ_max )520 nm (. A flow rate of 4.3 mL.min^-1 using distilled water as a carrier, the method of FIA proved to be as a sensitive and economic analytical tool for estimation of PHE.

Within the concentration range of 5-300 μg.mL^-1, a calibration curve was rectilinear, where the detection limit was 3.252 μg.mL

The main aim of this paper are the design and implementation of a pharmaceutical inventory database management system. The system was implemented by creating a database containing information about the stored medicines in the inventory, customers making transactions with the pharmaceutical trading company (which owns the inventory), medical suppliers, employees, payments, etc. The database was connected to the main application using C sharp. The proposed system should help in manag inginventory operations which include adding/updating employees’ information, preparing sale and purchase invoices, generating reports, adding/updating customers and suppliers, tracking customer payments and checking expired medicines in order to be disposed

Background: Type two diabetic patients have higher risk of cardiovascular and periodontal disease. Furthermore, patients with more severe periodontal disease have higher incidence of cardiovascular disease. This study aimed to assess the association between periodontal health status and the risk of vascular disease in type 2 diabetic patients. Materials and Methods: One hundred type 2 diabetes mellitus patients and fifty apparently healthy males were enrolled in this study. Oral examinations conducted were; plaque Index, calculus index, gingival index, probing pocket depth, and clinical attachment level. For the assessment of vascular risk, arterial stiffness index was used. Results: According to arterial stiffness index, type 2 diabetic p

Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-ba

In this research we study a variance component model, Which is the one of the most important models widely used in the analysis of the data, this model is one type of a multilevel models, and it is considered as linear models , there are three types of linear variance component models ,Fixed effect of linear variance component model, Random effect of linear variance component model and Mixed effect of linear variance component model . In this paper we will examine the model of mixed effect of linear variance component model with one –way random effect ,and the mixed model is a mixture of fixed effect and random effect in the same model, where it contains the parameter (μ) and treatment effect (τ_i ) which  has