أجري هذا البحث في العراق/محافظة ديالى، وهدف إلى تعرف أثر إستراتيجية شكل البيت الدائري في الاداء التعبيري لدى تلاميذ الصف الخامس الابتدائي,وبلغت عينة البحث(75) تلميذا بواقع (40) تلميذا في المجموعة التجريبية و(35)تلميذا في المجموعة الضابطة ، وأجرى الباحث بينهما تكافؤا في المتغيرات الآتية (العمر الزمني  محسوبا بالشهور، والتحصيل الدراسي للأبوين ، ودرجات اللغة العربية للعام الدراسي السابق واستعمل الباحث أداة م

The focus of this research lies in the definition of an important aspect of financial development, which is reflected on the alleviation of poverty in Iraq, namely financial inclusion and then taking the path of achieving a sustainable economy, certainly after reviewing one of the important international experiences in this regard and finally measuring the level of financial inclusion in Iraq and its impact on poverty reduction through the absolute poverty line indicator.

توظيف القوة الناعمة في تنفيذ السياسة الخارجية الفرنسية اتجاه المنطقة العربية بعد 2017م

Diabetes mellitus type 2 (T2DM) is a chronic and progressive condition, which affects people all around the world. The risk of complications increases with age if the disease is not managed properly. Diabetic neuropathy is caused by excessive blood glucose and lipid levels, resulting in nerve damage. Apelin is a peptide hormone that is found in different human organs, including the central nervous system and adipose tissue. The aim of this study is to estimate Apelin levels in diabetes type 2 and Diabetic peripheral Neuropathy (DPN) Iraqi patients and show the extent of peripheral nerve damage. The current study included 120 participants: 40 patients with Diabetes Mellitus, 40 patients with Diabetic peripheral Neuropathy, and 40 healthy

تعد ظاهرة الصراعات ظاهرة قديمة كقدم الوجود البشري وهذا يطبق على المستوى الدولي مما يجعل الدول الصغرى هي ساحة تصفيات كما هو الحال في سوريا مما يجعل الامن الانساني فيها معدوم

Modern civilization increasingly relies on sustainable and eco-friendly data centers as the core hubs of intelligent computing. However, these data centers, while vital, also face heightened vulnerability to hacking due to their role as the convergence points of numerous network connection nodes. Recognizing and addressing this vulnerability, particularly within the confines of green data centers, is a pressing concern. This paper proposes a novel approach to mitigate this threat by leveraging swarm intelligence techniques to detect prospective and hidden compromised devices within the data center environment. The core objective is to ensure sustainable intelligent computing through a colony strategy. The research primarily focusses on the

This work aims to see the positive association rules and negative association rules in the Apriori algorithm by using cosine correlation analysis. The default and the modified Association Rule Mining algorithm are implemented against the mushroom database to find out the difference of the results. The experimental results showed that the modified Association Rule Mining algorithm could generate negative association rules. The addition of cosine correlation analysis returns a smaller amount of association rules than the amounts of the default Association Rule Mining algorithm. From the top ten association rules, it can be seen that there are different rules between the default and the modified Apriori algorithm. The difference of the obta

Assessing the accuracy of classification algorithms is paramount as it provides insights into reliability and effectiveness in solving real-world problems. Accuracy examination is essential in any remote sensing-based classification practice, given that classification maps consistently include misclassified pixels and classification misconceptions. In this study, two imaginary satellites for Duhok province, Iraq, were captured at regular intervals, and the photos were analyzed using spatial analysis tools to provide supervised classifications. Some processes were conducted to enhance the categorization, like smoothing. The classification results indicate that Duhok province is divided into four classes: vegetation cover, buildings,

   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be