The research problem can be summarized through focusing on the environment that surrounds students and class congestion, how these factors affect directly or indirectly the academic achievement of students, how these factors affect understanding the scientific material that the student receives in this physical environment, how classroom’s components such as seats, space With which the student can move, the number of students in the same class, the lighting, whether natural or artificial, and is this lighting sufficient or not enough, the nature of the wall paint old or modern, is it comfortable for sight, the blackboard if it is Good or exhausted, In addition to air-conditioning sets in summer and winter, this is on the one hand, and on the other hand, the school environment is outside the classes in general And being appropriate and encouraging for scientific and cognitive activities. All these vocabulary and others have a great impact on the authentication of the learning process and achieving its immediate and future goals. Likewise, class congestion impedes the use of educational facilities and school workshops in an appropriate manner, such as the library, laboratory, and computer, and adversely affects the implementation of practical activities accompanying some curricula, and this affects academic achievement. Therefore, the study deals with answering the following question: What is the effect of the physical environment and overcrowded classes on academic achievement? The current research aims to identify what the physical environment is in schools, whether schools provide students with a physical environment consistent with the requirements imposed by the educational process, the effect of classroom overcrowding on the academic achievement of students.
Vol. 6, Issue 1 (2025)
Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In
... Show MoreDetermining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on
... Show MoreUnderstanding the compatibility between spider silk and conducting materials is essential to advance the use of spider silk in electronic applications. Spider silk is tough, but becomes soft when exposed to water. Here we report a strong affinity of amine-functionalised multi-walled carbon nanotubes for spider silk, with coating assisted by a water and mechanical shear method. The nanotubes adhere uniformly and bond to the silk fibre surface to produce tough, custom-shaped, flexible and electrically conducting fibres after drying and contraction. The conductivity of coated silk fibres is reversibly sensitive to strain and humidity, leading to proof-of-concept sensor and actuator demonstrations.
CuO-ZnO-Al2O3 catalyst was prepared in the ratios of 20:30:50 respectively, using the coprecipitation method of Cu, Zn and Al carbonates from their nitrate solutions dissolved in distilled water by adding sodium bicarbonate as precipitant.The catalyst was identified by XRD and quantitatively analysis to determine the percentages of its components using flame atomic absorption technique. Also the surface area was measured by BET method. The activity of this prepared catalyst was examined through the oxidation of ethanol to acetaldehyde which was evaluated by gas chromatography.
Background: spontaneous abortion constitutes one of the most important adverse pregnancy outcomes affecting human reproduction, and its risk factors are not only affected by biological, demographic factors such as age, gravidity, and previous history of miscarriage,but also by individual women’s personal social characteristics, and by the larger social environment. Objective:To identifyEnvironmental effects on Women's with Spontaneous Abortion. Methodology:Non-probability(purposive sample)of(200) women, who were suffering from spontaneous abortion in maternity unitfrom four hospitals at Baghdad City which include Al-ElwiaMaternity Teaching Hospital, and Baghdad Teaching Hospital at Al-Russafa sector. Al–karckhMaternityHospita
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