The aim of this study is to compare the effects of three methods: problem-based learning (PBL), PBL with lecture method, and conventional teaching on the understanding of thermodynamics, group work and self-directed learning skills among physics undergraduates. The actual sample size comprises of 122 students, who were selected randomly from the Physics Department, College of Education in Iraq, for academic year 2011-2012. In this study, the pre and posttest were done and the instruments were administered to the students for data collection. Inferential statistics were employed to analyze data. The independent variables were the PBL, the PBL with lecture method, and the conventional teaching. Dependent variables of statistical analysis were posttest scores on the understanding of thermodynamics, group work skills, and self-directed learning skills. Covariates of statistical analysis were pretest scores of the understanding of thermodynamics, group work skills, and self-directed learning skills. The data were analyzed using statistical package social sciences (SPSS) version 19. Overall, the statistical results rejected all null hypotheses of this study. Thus, the use of PBL with lecture method enhances the understanding of thermodynamics better than using the PBL alone or using conventional teaching method. Using the PBL without or with lecture method promotes the skills of group work, and self-directed learning better than using the conventional teaching, among physics undergraduate.
Intravaginal progesterone sponges have been widely utilized to synchronize estrus in goats; however, extended use may be accompanied by vaginal microbial imbalance, vaginal discharge abnormalities, and diminished reproductive performance. Intravaginal probiotics have been suggested as an alternative to antibiotics for addressing vaginal dysbiosis, but their effects across varying synchronization durations have not been thoroughly studied in goats. The current study was conducted to assess the effect of intravaginal probiotic use in association with various progesterone sponge durations on vaginal microbiota and reproductive effciency in Black Iraqi goats. Forty-four adult goats (2–5 years) were randomly assigned to four groups (n = 11) fo
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This study was conducted to determine the relationship between test anxiety and cognitive representation among university students. To this end, 152 student (male, female) were chosen randomly from scientific and social departments to fill out the questionnaires of test anxiety and cognitive representation. The researcher utilized Independent Samples T-Test, Pearson product-moment correlation coefficient, Cronbach's alpha and T-Test in his study. The result revealed that there were negative and a weak correlation between test anxiety and cognitive representation among university students.
Background: Undergraduate dental students are more susceptible to situations of stress that affected quality of sleep, such profiles of stress may result in sleep bruxism and/or awake bruxism, parafunctions that can affect oral and general health. The aim of the study was to evaluate the association of sleep bruxism, awake bruxism and sleep quality among dental students. Materials and methods: A cross-sectional study was performed including 260 Iraqi dental students from university of Baghdad aged from 20to25years old. Students enrolled in the third and fifth class participated in the study. The Pittsburgh Sleep Questionnaire Index (PSQI) was used for data collection. The PSQI was distributed during lecture classes. Sleep bruxism and awake
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This study seeks to answer urgent questions about the role of new media that have emerged recently, such as the Internet and social networking sites and information services through mobile and others to increase awareness among young people, especially university students. during a field study that took place on a sample of students in the Department of Media at the University of Petra Kingdom of Jordan during the second semester in 2013 in the study the researcher finds increased attention and follow-up by young people for many of the world events and regional and local communities through exposure to a number of new media; in the forefront of the Internet and increase the level of awareness and interests young people do many of the eve
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Spinach, Spinacia oleracea L is a popular vegetable belonging to the family Chenopodiaceae. This study was concerned with extraction of compounds in Iraqi spinach leaves, preliminary phytochemical evaluation, identification of two biological important flavonols, quercetin and kaempferol in spinach leaves and evaluation of the protective effect of aqueous spinach extract on methotrexate (MTX) induced hepatotoxicity in rats. The percentage yield of extraction procedure, identification of spinach by chemical tests and identification of flavonols by thin layer chromatography (TLC) and High performance liquid chromatography (HPLC) were fully described in this study. The results indicate that the percentage of quarce
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A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an
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Natural settings make it challenging to identify facial expressions since head position, illumination level, and occlusion vary. Thus, developing a more generic model without front-facing images alone is quite crucial. This research proposes a facial expression recognition model based on pre-trained deep convolutional neural networks with transfer learning. The model was trained on several cases to classify face expressions into seven classifications efficiently. The proposed system used the EfficientNetB0 model that has one dense dropout layer. The model first rescales and norms the input dataset in the input layer that takes images of a larger resolution to get better results. After entering 7 blocks sequential
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The increasing demand for continual learning in sequential data processing has led to progressively complex training methodologies and larger recurrent network architectures. Consequently, this has widened the knowledge gap between continual learning with recurrent neural networks (RNNs) and their ability to operate on devices with limited memory and compute. To address this challenge, we investigate the effectiveness of simplifying RNN architectures, particularly gated recurrent unit (GRU), and its impact on both single-task and multitask sequential learning. We propose a new variant of GRU, namely the minion recurrent unit (MiRU). MiRU replaces conventional gating mechanisms with scaling coefficients to regulate dynamic updates of hidden
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