Modified Opposition Based Learning to Improve Harmony Search Variants Exploration
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The research aims to identify the impact of using the electronic participatory learning strategy according to internet programs in learning some basic basketball skills for middle first graders according to the curricular course, and the sample of research was selected in the deliberate way of students The first stage of intermediate school.As for the problem of research, the researchers said that there is a weakness in the levels of school students in terms of teaching basketball skills, which prompted the researchers to create appropriate solutions by using a participatory learning strategy.The researchers imposed statistically significant differences between pre and post-test tests, in favor of the post tests individually and in favor of
... Show MoreTo develop a petrol engine so that it works under the bi-engine pattern (producer gas-petrol) without any additional engine modifications, a single-point injection method inside the intake manifold is a simple and inexpensive method. Still, it leads to poor mixing performance between the air and producer gas. This deficiency can cause unsatisfactory engine performance and high exhaust emissions. In order to improve the mixing inside the intake manifold, nine separate cases were modelled to evaluate the impact of the position and angle orientation inside the intake manifold on the uniformity and spread of the mixture under AFR=2.07. A petrol engine (1.6 L), the maximum engine speed (8000 rpm), and bi-engine mode (petrol-producer ga
... Show MoreObjectives: This study aimed to identify and analyse ATP7B variants in Iraqi adults with Wilson disease (WD) by long-read next-generation sequencing. Methods: This cross-sectional study was conducted at the Poisoning Consultation Center at Ghazy Al-Hariri Hospital for Surgical Specialties and the Gastroenterology Consultation Clinic at Baghdad Teaching Hospital, Medical City in Baghdad, Iraq. Unrelated patients with clinical and biochemical features suggestive of WD were recruited between October 2022 and October 2023. DNA was extracted from peripheral blood samples. Variants in the ATP7B gene were identified using long-read next-generation sequencing and then analysed by in-silico tools. Results: A total of 45 patients were recruited in
... Show MoreBackground: Several studies linked the development of steroid-resistant nephrotic syndrome (SRNS) to genetic variations in the multidrug resistance 1 (MDR1) gene, though a disparity in findings was underlined among children with different ethnic origins. Objective: This study examined the relationship between MDR1 variants (rs2032582 and rs2032583) and the risk of developing SRNS in Iraqi patients with idiopathic nephrotic syndrome (INS). Methods: This case-control study included children with steroid-sensitive INS (SSNS; n=30) and SRNS (n=30) from the Babylon Hospital for Maternity and Pediatrics. Sanger sequencing was used to determine the participants’ genotypes. Results: The rs2032582 genotypes and alleles were not associated
... Show MoreGlutathione-S-transferases (GSTs) play a role in the detoxification of environmental chemicals and mutagens, such as those inhaled during tobacco smoking. There have been conflicting reports concerning GST polymorphisms as risk factors in the development of lung cancer. No studies focused on Arab populations exposed to Waterpipe (WP) tobacco smoke have been undertaken. Here Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) and gene sequenc- ing were applied to analyze allelic variations in GSTP1-rs1695 and -rs1138272 amongst 123 lung cancer patients and 129 controls. The data suggest that WP smoking raised the risk of lung cancer more than three-fold (OR 3.6; 95% CI 2.1–6.0; p < 0.0001). However, there was no s
... Show MoreA 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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