Cihan University-Erbil Scientific Journal <p>Cihan University-Erbil Scientific journal (CUESJ) is a biannual academic journal published by the Cihan University-Erbil. CUESJ a periodical journal publishes original researches in all areas of Science, Engineering and Technology. CUESJ is a Peer-Reviewed Open Access journal with Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (CC BY-NC-ND 4.0). CUESJ provides immediate, worldwide, barrier-free access to the full text of research articles without requiring a subscription to the journal, and has neither article processing charge (APC) nor article submission charge (ASC). CUESJ applies the highest standards to everything it does and adopts IEEE citation/referencing style. CUESJ Section Policy includes three types of publications; Articles, Review Articles, and Letters. CUESJ has a print-ISSN: <a href="">2519-6979</a>. It is a member of the ROAD with e-ISSN: <a href="">2707-6377</a> and a member of the Crossref with a doi: <a href="">10.24086/issn.2519-6979</a>.</p> Cihan University-Erbil en-US Cihan University-Erbil Scientific Journal 2519-6979 <p>Authors who publish with this journal agree to the following terms:<br>1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-ND 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.<br>2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.<br>3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).</p> Association of Total Dietary Fats and Its Subtypes with Risk of Breast Cancer <p>Specific classes of dietary fatty acids may be important modifiers of breast cancer (B-Ca) risk. Aim of this study was identification risk of subtypes of dietary fat for B-Ca. This case–control study carried out in Rizgary Teaching Hospital in Erbil city. Data collected by interview questionnaire and included demographic and reproductive properties; anthropometric measurements; and medical history. Dietary data collected by food frequency questionnaire. They were analyzed by program for Mosby’s Nutritrac Nutrition Analysis Software, for calculation intake of dietary; fiber, total fat, and its subtypes, energy intake, acceptable macronutrient distribution range, and antioxidant nutrients. Statistical analysis was performed using SPSS program. Polyunsaturated fats decreased risk of B-Ca while saturated and monounsaturated fats (Cis form) increased risk among all and postmenopause obese women, respectively. Risk of cancer increased significantly in high percentage of energy intake from monounsaturated fats, cooking oil, and dietary red meats. The study concluded that total polyunsaturated fatty acids (PUFA) decrease risk of B-Ca among obese menopause woman. Increase risk of B-Ca by cooking oils and animal origin diet may due to increased intake of saturated monounsaturated and specific PUFA. These subtypes of dietary fats may promote hormones imbalance and inflammation.</p> Jwan I. Jawzali ##submission.copyrightStatement## 2020-07-10 2020-07-10 4 2 1 5 10.24086/cuesj.v4n2y2020.pp1-5 Series Solution for Single and System of Non-linear Volterra Integral Equations <p>In this paper, Taylor expansion has been used for solving non-linear Volterra integral equations (VIEs) of the second kind. This method allows us to overcome the difficulty caused by integrals and non-linearity; also, it has more precise and rapidly convergent to the exact solution. Two examples are presented for illustrate the performance of this method.</p> Narmeen N. Nadir ##submission.copyrightStatement## 2020-07-20 2020-07-20 4 2 6 8 10.24086/cuesj.v4n2y2020.pp6-8 Likelihood Approach for Bayesian Logistic Weighted Model <p>Increasing the response rate and minimizing non-response rates represent the primary challenges to researchers in performing longitudinal and cohort research. This is most obvious in the area of paediatric medicine. When there are missing data, complete case analysis makes findings biased. Inverse Probability Weighting (IPW) is one of many available approaches for reducing the bias using a complete case analysis. Here, a complete case is weighted by probability inverse of complete cases. The data of this work is collected from the neonatal intensive care unit at Erbil maternity hospital for the years 2012 to 2017. In total, 570 babies (288 male and 282 females) were born very preterm. The aim of this paper is to use inverse probability weighting on the Bayesian logistic model developmental outcome. The Mental Development Index (MDI) approach is used for assessing the cognitive development of those born very preterm. Almost half of the information for the babies was missing, meaning that we do not know whether they have cognitive development issues or they have not. We obtained greater precision in results and standard deviation of parameter estimates which are less in the posterior weighted model in comparison with frequent analysis.</p> Dler H. Kadir ##submission.copyrightStatement## 2020-08-13 2020-08-13 4 2 9 12 10.24086/cuesj.v4n2y2020.pp9-12