Diabetes imposes a substantial public health burden; according to the International Diabetes Federation, there were about 3.4 million diabetes related deaths worldwide in 2024, and in Iraq, the Federation reports that one in nine adults lives with diabetes in 2024, with 14,683 adult deaths attributable to diabetes and a total diabetes related health expenditure of 2,078 million United States dollars. The dataset analyzed in this study contains 1,000 records collected in 2020 from two Iraqi teaching hospitals and includes multiple clinical and laboratory measurements with three outcome classes, namely Non diabetic, Pre diabetic, and Diabetic, with a low prevalence of the Pre diabetic class and an imbalanced overall class distribution; the data are challenging because they contain many outliers, non homogeneous covariance matrices across classes, exact duplicate rows that were removed before modelling, and linear correlations among certain variables. The study objective was to train and evaluate models that discriminate among the three classes and yield accurate, well calibrated predictions for future cases in similar clinical settings, but the diagnostic properties of the data limited the applicability of classical discriminant functions; therefore two supervised learners were employed: Classification and Regression Trees (CART) and Extreme Gradient Boosting (XGBoost), together with preprocessing that removed exact duplicate rows and excluded VLDL because it is algebraically derived from triglycerides in mmol per liter as VLDL equals triglycerides divided by 2.2, which would introduce redundancy and multicollinearity. On the heldout test set, XGBoost achieved higher Accuracy at 98.18 percent compared with 97.58 percent for CART and higher Balanced Accuracy at 93.84 percent compared with 88.16 percent for CART, indicating that XGBoost provided the strongest overall operating point for this three-class task while CART remains useful when simple and transparent rules are required.
Matching between wind site characteristics and wind turbine characteristics for three selected sites in Iraq was carried out. Site-turbine matching for potential wind power application in Iraq has not yet been well reported on. Thus, in this study, five years’ wind speed data for sites located in Baghdad (33.34N, 44.40E), Nasiriyah (31.05N, 46.25E), and Basrah (30.50N, 47.78E) were collected. A full wind energy analysis based on the measured data, Weibull distribution function, and wind turbine characteristics was made. A code developed using MATLAB software was used to analyse the wind energy and wind turbines models. The primary objective was to achieve a standard wind turbine-site matching based on the capacity factor. Another matching
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Purpose – The research aims to introduce sustainable agricultural development and the possibility of its application in the Iraqi agricultural sector by setting a proposed plan by which to overcome obstacles and then advance the reality of the agricultural sector in Iraq and the fact that the process of achieving agricultural development in the Iraqi agricultural sector today has become more sophisticated and more distant than before. The study adopted the descriptive analytical approach based on the principles of economic theory to clarify the shortcomings in the process of harmony between the three main elements of sustainable agricultural development, which are natural, social, and manufactured.
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Abstract Leishmania species are intracellular protozoan parasites that spend a portion of their life cycle in the midgut of sand flies and the remainder in the tissues of mammals. These parasites, which cause a class of human disorders known as leishmaniasis, live mostly in macrophages, where they multiply and survive by employing a variety of defense mechanisms against the oxidative stress and acidity generated by these immune cells. To help control their reaction to heat stress, they also produce heat shock proteins. Furthermore, the promastigote form has a glycocalyx that is necessary for colonizing the gut wall of the sand fly and completing its life cycle. Consequently, a variety of virulence factors contribute to the parasite's pathog
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Leishmania species are intracellular protozoan parasites that spend a portion of their life cycle in the midgut of sand flies and the remainder in the tissues of mammals. These parasites, which cause a class of human disorders known as leishmaniasis, live mostly in macrophages, where they multiply and survive by employing a variety of defense mechanisms against the oxidative stress and acidity generated by these immune cells. To help control their reaction to heat stress, they also produce heat shock proteins. Furthermore, the promastigote form has a glycocalyx that is necessary for colonizing the gut wall of the sand fly and completing its life cycle. Consequently, a variety of virulence factors contribute to the parasite's pathoge
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