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The purpose of this study was to determine faculty members' attitudes toward online learning in Kurdistan region universities. The study examined the biographic and personal characteristics of the lecturer towards e-learning. The data was collected among faculty members at Cihan University-Sulaimaniya, and to analyze the data, a full factorial design with five main factors at two levels and no central points was applied for this specific purpose. The study's findings indicated that there was no significant relationship between gender and lecturer attitude towards e-learning. In comparison to teachers with an MSc degree, those with a PhD have a more negative attitude toward e-learning. Furthermore, full-time faculty members have a greater positive effect on teachers' attitudes than part-time lecturers. Likewise, the results indicate that lecturers who earned their most recent education degrees outside of Iraq have a more favorable attitude. Similarly, lecturers in the sciences are more favorable to e-learning than those in the arts and social sciences. In addition, the findings demonstrated that the interaction factors (Gender) and (Education Degree) have a negative effect on lecturers' attitudes when they are combined. Besides that, the interaction of factors (Country of Last Education Degree) and (Faculty Member Types) improves attitudes toward e-learning. Based on the results, it is suggested that academic staff receive e-learning training to deepen their knowledge and understanding of such a modern teaching system. There is also a need to enhance factors related to positive attitudes towards e-learning among university lecturers. The findings of this study are necessarily significant to both teachers and educational organizations in Kurdistan Region universities.

In the medical world, predictive models for assessing operative risk using patient risk factors have gained appeal as a useful tool for adjusting surgical outcomes. The goal of this study was to see if there was a link between the severity of atherosclerosis as determined by angiography and changes in several key biochemical, hormonal, and hematological variables in patients who had Coronary Artery Bypass Graft (CABG) surgery. This study included 100 adult patients who had coronary angiography, as well as a standardized case-control study of acute myocardial infarction that included 60 healthy people. In addition, not all investigations of heart attack disorders were concerned with modeling; rather, they were all concerned with classification. A family of Generalized Linear Models called Binary Logistic Regression was used. Because most phenomena' outcomes have only two values (alive/dead, exposed/not exposed, presence/absence and etc.), logistic regression analysis is a common method and plays an important role in health science. Overall, 62.5% of individuals were grouped into surgical bypass grafts, while 37.5% were healthy people. Hemoglobin A1c (HBA1C) was wisely significant, and the odds of one unit increase led to roughly 7.488 times higher. Age and Body Mass Index (BMI) had quite high and substantial effect parameters with a 1.2 times higher likelihood than those who have smaller BMI and younger. According to the study, smokers were more likely to be at risk of undergoing bypass surgery by 4.18 times. However, there was no significant link between gender, screening creatinine, Cholesterol (CHO), Triglycerides (TG), High-density lipoprotein levels (HDL), Lower density level (LDL), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) with the outcome variable.

The Bayesian sampling plans for production inspection are considered a technique of sampling inspection techniques for determining the characteristics of the sampling plan based on the assumption that the rate of defectives is a random variable that varies from one production batch to the next, resulting in a probability distribution f(p) that could be determined based on experience and the available quality information available. As part of this study, the parameters of a single Bayesian sampling plan (n,c) were derived by using the Beta-Binomial distribution and compared with those of other single sampling plans. Researchers have identified (ALA company for soft drinks), which handles product quality control. 120 production batches were selected, and the size of the batch and the number of defective items were used to determine the proportion of defective items, given that the variable varies randomly from one production batch to the next. Bayesian and decision-making models can be implemented to create a single sampling inspection process that is close to the actual quality level. The researchers discovered that when the decision-making model was used, the sample size was minimal compared to other inspection plans, leading to a low inspection cost.