In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

Doxorubicin (DOX) is a potent antineoplastic drug used to treat many types of human tumor. The long-term adverse effect is cardiomyopathy. Chromium is an essential trace element mostly used to regulate glucose levels and enhance the response to insulin, especially in diabetes. Current study aimed to evaluate the cardioprotective effect of chromium picolinate against doxorubicin-induced cardiotoxicity in 28 male rats divided into four groups. Group I (Control group): received distilled water orally for 8 days. Group II (Doxorubicin group): received distilled water orally for 7 days, followed by a single doxorubicin dose (25 mg/kg) intraperitoneally. Group III (Chromium 2 mg): received chromium picolinate at a dose

A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co

The impact of COVID-19 pandemic on education models was mainly through the expansion of technology use in the different educational programs. Earlier impact of COVID-19 was manifested in the complete and sudden transition to distance education regardless of institution preparedness status. Gradually, many institutions are moving back to on-campus face-to-face education. However, others including all higher education institutions in Iraq are adopting the hybrid education model. This report presents part of the end of semester evaluation survey conducted at the University of Baghdad College of Pharmacy for the Spring 2021 semester. The survey aims to address points of strength and weakness associated with the hybrid education model and spe

Coaches and analysts face a significant challenge of inaccurate estimation when analyzing Men's 100 Meter Sprint Performance, particularly when there is limited data available. This necessitates the use of modern technologies to address the problem of inaccurate estimation. Unfortunately, current methods used to estimate Men's 100 Meter Sprint Performance indexes in Iraq are ineffective, highlighting the need to adopt new and advanced technologies that are fast, accurate, and flexible. Therefore, the objective of this study was to utilize an advanced method known as artificial neural networks to estimate four key indexes: Accelerate First of 10 meters, Speed Rate, Time First of 10 meters, and Reaction Time. The application of artifi

In this study, the stress-strength model R = P(Y < X < Z)  is discussed as an important parts of reliability system by assuming that the random variables follow Invers Rayleigh Distribution. Some traditional estimation methods are used    to estimate the parameters  namely; Maximum Likelihood, Moment method, and Uniformly Minimum Variance Unbiased estimator and Shrinkage estimator using three types of shrinkage weight factors. As well as, Monte Carlo simulation are used to compare the estimation methods based on mean squared error criteria.  

Methotrexate is a folate antimetabolite chemical used to treat many forms of cancer and cleared mainly by kidney which result in a nephrotoxicity as a major complication of methotrexate. Trivalent chromium complex, chromium picolinate (CrPic), is mostly utilized to regulate glucose and raise insulin sensitivity—particularly in diabetes. The objective of this study was to assess the renoprotective effects of Chromium in mitigating nephrotoxicity induced by Methotrexate in rats. Study conducted on 32 male rats divided into 4 groups; Group I (Control group) in which rats received distilled water orally for 8 days, Group II (Induction group) which received a single intraperitoneal injection of Methotrexate (20 mg/kg) on

The relationship between government revenues and the fiscal balance represents a central pillar in the analysis of fiscal sustainability. However, its modeling faces a fundamental challenge in the form of structural uncertainty, which is not captured by point estimates in traditional models such as ARDL, as these models assume structural stability that is inconsistent with the nature of rentier economies. The current study aims to develop a fuzzy framework by constructing a Fuzzy Autoregressive Distributed Lag (FARDL) model. This is achieved through integrating the Autoregressive Distributed Lag (ARDL) approach with fuzzy logic theory, thereby enabling the incorporation of uncertainty into the inherent structure of the economic rela