The research aimed mainly to discover the effectiveness of the (PEOE) model in teaching science to develop the skills of generating and evaluating information and the emotional side of the scientific sense of the intermediate first grade students. An experimental approach with a quasi-experimental design called pre-test and post-test control design was used. The research sample consisted of (60) students, who were selected in a random cluster method, (30) students in the experimental group studied the unit "The Nature of Material" using the (PEOE) model, and (30) students in the control group studied according to the prevailing method of teaching. The research materials and tools were represented in: a teacher's guide for teaching the unit "The Nature of Material" according to the (PEOE) model, a student's activity guide, a test of generating and evaluating information skills, and a measure of the affective side of the scientific sense. The results showed that there are statistically significant differences at the level (0.05 ≥ α) in the post application of each of the tests for generating and evaluating information, and measuring the emotional aspect of the scientific sense in favor of the experimental group. The results also showed the existence of a positive statistically significant correlation relationship at the level (0.05 ≥ α) between the experimental group’s students' scores in the post application of each of the tests of information generation and evaluation skills, and the affective aspect scale. In light of these results, the research presented a number of recommendations, the most important are: Training intermediate stage science teachers to use the (PEOE) model in teaching science. Reorganizing the content of science curricula at the intermediate stage in light of this model, especially the science content of the intermediate first grade; to develop the skills of generating and evaluating information and the dimensions of the emotional side of the scientific sense of the intermediate first grade students.
The objective of the current research is to find an optimum design of hybrid laminated moderate thick composite plates with static constraint. The stacking sequence and ply angle is required for optimization to achieve minimum deflection for hybrid laminated composite plates consist of glass and carbon long fibers reinforcements that impeded in epoxy matrix with known plates dimension and loading. The analysis of plate is by adopting the first-order shear deformation theory and using Navier's solution with Genetic Algorithm to approach the current objective. A program written with MATLAB to find best stacking sequence and ply angles that give minimum deflection, and the results comparing with ANSYS.
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Background: Adipose derived-mesenchymal stem cells have been used as an alternative to bone marrow cells in this study. Objective: We investigated the in vitro isolation, identification, and differentiation of stem cells into neuron cells, in order to produce neuron cells via cell culture, which would be useful in nerve injury treatment. Method: Mouse adipose mesenchymal stem cells were dissected from the abdominal subcutaneous region. Neural differentiation was induced using β-mercaptoethanol. This study included two different neural stage markers, i.e. nestin and neurofilament light-chain, to detect immature and mature neurons, respectively. Results: The immunocytochemistry results showed that the use of β-mercaptoethanol resulted in
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The temperature control process of electric heating furnace (EHF) systems is a quite difficult and changeable task owing to non-linearity, time delay, time-varying parameters, and the harsh environment of the furnace. In this paper, a robust temperature control scheme for an EHF system is developed using an adaptive active disturbance rejection control (AADRC) technique with a continuous sliding-mode based component. First, a comprehensive dynamic model is established by using convection laws, in which the EHF systems can be characterized as an uncertain second order system. Second, an adaptive extended state observer (AESO) is utilized to estimate the states of the EHF system and total disturbances, in which the observer gains are updated
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Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s
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The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of
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