The research aimed to identify “The impact of an instructional-learning design based on the brain- compatible model in systemic thinking among first intermediate grade female students in Mathematics”, in the day schools of the second Karkh Educational directorate.In order to achieve the research objective, the following null hypothesis was formulated:There is no statistically significant difference at the significance level (0.05) among the average scores of the experimental group students who will be taught by applying an (instructional- learning) design based to on the brain–compatible model and the average scores of the control group students who will be taught through the traditional method in the systemic thinking test.The research community was determined and represented by the intermediate and secondary day schools for girls within the Karkh II general educational directorate, and Al-Jana in Intermediate School for girls was selected, the research sample consisted of (75) female students from the first intermediate grade that were distributed as (37) students female in the first experimental group, and (38) in the control group.For the purpose of the research hypothesis testing, a systemic thinking test was built, as the test in its final form consisted of (24) essay items with restricted answers, and the test was divided into ( (2) items for each sub-skill).The appropriate statistical analysis were carried out (the difficulty coefficient and coefficient of ease, the discrimination coefficient, and the effect of wrong alternatives), and their psychometric properties were ascertained,after the statistical tools were chosen to analyze the results of the application of the test, such as using the (t-test), and the results indicated:The experimental group students who studied according to the brain concordance model out performed in the systemic thinking test on the control group students who studied according to the traditional method.
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The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com
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The aim of the research was to identify On the effect of the learning-by-play strategy on learning some handball skills among second-stage female students,Comparing the results of students' performance before and after implementing the learning by playing strategy,Providing development proposals for teaching handball skills in light of the research results.,I dependresearcherCurriculumexperimentalIn research procedures as an appropriate approach to achieving research objectives,For two groups, one experimental and the other control,Community of female studentsSecondFor the academic year 2024-2025, consisting of (4Female people (numbering)151) StudentChosen.Research sample30 female students from Section (B) were selected and (4) fe
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In this paper, we proved coincidence points theorems for two pairs mappings which are defined on nonempty subset in metric spaces by using condition (1.1). As application, we established a unique common fixed points theorems for these mappings by using the concept weakly compatible (R-weakly commuting) between these mappings.
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The aim of this study is to uncover the views of female students of Shaqra University towards midterm tests. A survey of (42) items distributed in three fields. The study has been applied to a random sample of (421) students from two faculties. The results showed that all three fields have achieved an intermediate level. The results also indicated that there were no statistically significant differences in students’ views of midterm tests due to the specialization or academic level variables.
Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). Simulation results
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The present study aims to identify wisdom-based thinking and its relationship to psychological capital. It further aims to find out the differences in the level of wisdom-based thinking and psychological capital according to the variables of gender and specialization (scientific, humanities). To achieve this, the study has been conducted on a sample of (380) male and female students. The two scales, wisdom-based thinking and psychological capital are implemented to the sample after being constructed by the researcher and after ensuring their psychometric characteristics' suitability for the study's aims. Results concerning the first aim have shown that there is a significant relationship among students. The second aim has revealed that t
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Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings
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