Abstract---The aim of the current research is to identify the level of logical reasoning skills in chemistry students at the Faculty of Education for Pure Sciences/ Ibn Al-Haytham for the academic year (2021-2022). The differences in their level of skills according to the gender variable (males and females) and the academic stages (first- second - third - fourth). The descriptive approach was adopted because it corresponds to the nature of the research objectives. The research sample consisted of (400 )students selected in a relatively random stratified way. The researcher constructed a logical reasoning test, which includes (6) sub-skills , which is (proportional - probabilistic- synthetic- deductive- logic- variable adjustment). The psychometric properties of the test were also verified from face validity, discriminatory power , item difficulty index , and the relationship between the items score and the total degree of the test. Statistical methods were used in the Cronbach equation, the Spearman-Brown equation, (SPSS+22) . Pearson correlation coefficient, one sample T-test, two independent samples T-test , mono-variance analysis- Chevy test). The results that were reached showed that the students of the Chemistry Department have an average level of reasoning skills (proportional- probabilistic- synthetic) more than other skills and that males are superior to females in all reasoning skills. The results also showed that fourth stage students are superior than students in other stages in all reasoning skills. In the placement of the results that have been reached, the current research recommended the need to develop the curricula in general for all stages of study and work to include various training in reasoning 7007 skills and the need for guidance by officials and supervisors on the use of modern teaching methods and to move away from methods of memorization and indoctrination that make the student a recipient of information without interest in stimulating thinking. Keywords---logical reasoning skills, piaget theory, cognitive development theory, chemistry students.
Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob
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The heat exchanger is a device used to transfer heat energy between two fluids, hot and cold. In this work, an output feedback adaptive sliding mode controller is designed to control the temperature of the outlet cold water for plate heat exchanger. The measurement of the outlet cold temperature is the only information required. Hence, a sliding mode differentiator was designed to estimate the time derivative of outlet hot water temperature, which it is needed for constructing a sliding variable. The discontinuous gain value of the sliding mode controller is adapted according to a certain adaptation law. Two constraints which imposed on the volumetric flow rate of outlet cold (control input) were considered within the rules of the proposed
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Amplitude variation with offset (AVO) analysis is an 1 efficient tool for hydrocarbon detection and identification of elastic rock properties and fluid types. It has been applied in the present study using reprocessed pre-stack 2D seismic data (1992, Caulerpa) from north-west of the Bonaparte Basin, Australia. The AVO response along the 2D pre-stack seismic data in the Laminaria High NW shelf of Australia was also investigated. Three hypotheses were suggested to investigate the AVO behaviour of the amplitude anomalies in which three different factors; fluid substitution, porosity and thickness (Wedge model) were tested. The AVO models with the synthetic gathers were analysed using log information to find which of these is the
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Ground Penetrating Radar (GPR) is a nondestructive geophysical technique that uses electromagnetic waves to evaluate subsurface information. A GPR unit emits a short pulse of electromagnetic energy and is able to determine the presence or absence of a target by examining the reflected energy from that pulse. GPR is geophysical approach that use band of the radio spectrum. In this research the function of GPR has been summarized as survey different buried objects such as (Iron, Plastic(PVC), Aluminum) in specified depth about (0.5m) using antenna of 250 MHZ, the response of the each object can be recognized as its shapes, this recognition have been performed using image processi |
Thirty local fungal isolates according to Aspergillus niger were screened for Inulinase production on synthetic solid medium depending on inulin hydrolysis appear as clear zone around fungal colony. Semi-quantitative screening was performed to select the most efficient isolate for inulinase production. the most efficient isolate was AN20. The optimum condition for enzyme production from A. niger isolate was determined by busing a medium composed of sugar cane moisten with corn steep liquor 5;5 (v/w) at initial pH 5.0 for 96 hours at 30 0C . Enzyme productivity was tested for each of the yeast Kluyveromyces marxianus, the fungus A. niger AN20 and for a mixed culture of A. niger and K. marxianus. The productivity of A. niger gave the highest
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Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes
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