The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the  classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique an

Background: Patients requiring renal biopsies have various glomerular diseases according to their demographic characteristics.
Objective: To study types of glomerular disease among adult Iraqi patients in a single center in Baghdad/Iraq
Material and Methods: A total of 120 native kidney biopsies were studied. All biopsies were adequate and were processed for Light Microscopy.
The age range of the study patients was 17-67 years, with a mean of 38.5 years. The mean follow up period was 28 weeks (4-52 weeks)
Indication for biopsy included: Nephrotic syndrome (N=72; 60%), Asymptomatic proteinuria (N=21; 17.5%), acute nephritic presentation (N=17; 14.16%), asymptomatic haematuria (N=10; 8.33%).
Results: Primary glomerulonephrit

In the light of the globalization Which surrounds the business environment and whose impact has been reflected on industrial economic units  the whole world has become a single market that affects its variables on all units and is affected by the economic contribution of each economic unit as much as its share. The problem of this research is that the use of Pareto analysis enables industrial economic units to diagnose the risks surrounding them , so the main objective of the research was to classify risks into both internal and external types and identify any risks that require more attention.

The research was based on the hypothesis that Pareto analysis used, risks can be identified and addressed before they occur.

KE Sharquie, AA Noaimi, HA Al-Mudaris, Journal of Drugs in Dermatology: JDD, 2013 - Cited by 22

Linear programming currently occupies a prominent position in various fields and has wide applications, as its importance lies in being a means of studying the behavior of a large number of systems as well. It is also the simplest and easiest type of models that can be created to address industrial, commercial, military and other dilemmas. Through which to obtain the optimal quantitative value. In this research, we dealt with the post optimality solution, or what is known as sensitivity analysis, using the principle of shadow prices. The scientific solution to any problem is not a complete solution once the optimal solution is reached. Any change in the values of the model constants or what is known as the inputs of the model that will chan

This study aims to identify changes in vegetation cover and its impact on the climate of Mosul City. The analytical method of the study relies on changes in Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST); GIS technology was used to measure these statistics. Landsat (5,8) imagery was used to detect the change in vegetation cover change and land surface temperature during the study period from 2010 to 2022, where the unsupervised classification technique was used to determine LU variations. The results revealed significant changes among the LU classes during the study period, primarily due to human activities. The most prominent change in LU was the urban expansion of agricultural

Time series have gained great importance and have been applied in a manner in the economic, financial, health and social fields and used in the analysis through studying the changes and forecasting the future of the phenomenon. One of the most important models of the black box is the "ARMAX" model, which is a mixed model consisting of self-regression with moving averages with external inputs. It consists of several stages, namely determining the rank of the model and the process of estimating the parameters of the model and then the prediction process to know the amount of compensation granted to workers in the future in order to fulfil the future obligations of the Fund. , And using the regular least squares method and the frequ

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