Polycystic ovary syndrome (PCOS) is the most endocrine problem in women of regenerative age. PCOS women typically belong to an age and sex group which is at higher risk for severe coronavirus disease (COVID-19). COVID-19 targets cells through angiotensin-converting enzyme 2 (ACE2) receptor presents on cells in veins, lungs, heart, digestion tracts, and kidneys. Renin-Angiotensin System (RAS) over activity has likewise been described in metabolic disorders; type 2 diabetes mellitus (T2DM), and conditions shared by women with polycystic ovary condition. The point of this study is to know the job of renin and ACE2 in PCOS and coronavirus and its relationship with hormones and other metabolic parameters related. The study groups consist of 1

This study measures the indicators of social and environmental performance of the contents of the administration's prepared reports on its social and environmental performance by comparing the actual performance with the indicators set within the standards of the Global Reports Initiative (GRI), In preparing this research, the researchers relied on studying the criteria of the Global Reporting Initiative, which aims to achieve a high level of performance disclosure under sustainability, In light of contemporary global trends towards achieving sustainable development and its disclosure and the orientations of economic institutions and units in different countries towards emphasizing the extent of commitment during practicing its a

The purpose of this study is to explore whether the adoption of Beyond Budgeting (BB) as a management accounting practice (MAP) contributes to developing intellectual capital (IC) and creating value in Iraqi companies. This requires an understanding of the views of the Iraqi managers about the nature of the information provided by this practice, which may be used to determine whether this information is relevant in the management of IC in the context of Iraq. This research aims also to explore the challenges of the adoption of the BB in planning and controlling IC in Iraq. The study adopts a qualitative approach and an interpretive paradigm. It also adopts a semi-structured interview method of collecting data from executive managers

Background: Enterococcus faecalis (E. faecalis) is a prototypical resistant bacterium in root canal infections and a leading cause of endodontic treatment failure. German chamomile (Matricaria chamomilla) flower extract has been used as a traditional medicine to treat infections. The aim of this study was to investigate the antimicrobial efficacy of chamomile extract on the removal of E. faecalis root canal biofilm. Materials and Methods: Chamomile flower extract was prepared and subjected to detailed chemical analysis. For the in vitro biofilm model, human mandibular premolars (n=48) with 18-20mm working length were used. Root canal preparation was performed using the ProTaper® Next system. Each sample was split longitudinally and reassem

The sunflower plants are attacked by serious seed and soil-borne pathogens including charcoal rot disease that caused by Macrophomina phaseolina. This disease has serious damages to sunflower crop. This study aimed to assess the efficacy of Arbuscular mycorrhizal fungus against charcoal rot disease as fungicide alternative.

Objective : Alzheimer’s disease (AD) continues to be a major challenge because handling high-dimensional data is time-consuming and expensive due to its complexity. A large feature space often increases computational costs and reduces model interpretability. This study addresses this problem by evaluating and comparing multiple feature selection techniques to identify the most informative biomarkers for AD diagnosis.

Methods : Our study used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to implement and test three feature selection a

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a co

The research aims to determine the role of knowledge management to measure performance on a sample of faculties of Administration and Economics the University of Kufa, and to achieve this goal through a researcher from the theoretical literature and research and studies related to the construction of the scheme shows the hypothetical relationship between the variables, which was adopted by the independent variable knowledge management, distributed four variables are: (knowledge generation, knowledge storage, knowledge dissemination, the application of knowledge), as well as four variables representing the variables adopted for the performance of the university are:
(to reduce costs and increase profits, improve quality, scie