Gender mainstreaming is a goal for building a society characterized by justice and equality. Gender mainstreaming requires a clear understanding of the concept. Therefore, the research focuses on the concept of gender and how it was addressed in the literature through which the concepts related to gender were extracted which is represented by the role and social status through which the relationship of gender can be measured. In order to find out the basis for the different status and roles of both sexes in societies, the interpreted theories of gender were addressed, through which the most important factors affecting gender relations, such as biological, social and economic factors, as well as political systems, were discussed. Due to the changing factors temporally and spatially, gender relations are also changing. As a result of these changes, the concept of gender has become a prominent concept in various aspects of life and has been the focus of many writers, researchers and conferences on how to contribute to gender and what mechanisms contribute to the uncles. The research focused on the concept of social innovation and the role of community initiatives as a means of mainstreaming gender into development issues.
Background: Cancer is a lethal disease that results from a multifactorial process. Progression into carcinogenesis and an abnormal cell proliferation can occur due to the micro and macro environment as well as genetic mutations and modifications. In this review, cancer and the microbiota – mainly bacteria that inhabit the tumour tissue – have been discussed. The positive and negative impacts of the commensal bacteria on tumours being protective or carcinogenic agents, respectively, and their strategies have also been described. Methods: Related published articles written in English language were searched from Google Scholar, PubMed, Mendeley suggestions, as well as Google search using a combination of the keywords ‘Microbiota, commens
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In this work, zinc oxide nanoparticles (ZnONPs) and sawdust/epoxy composite (20:80) were mixed using a simple molding method with different ZnONPs concentrations of (0.1, 0.3, 0.5, 0.7, and 1.0 %). The samples of the nanocomposites were characterized by the Scanning Electron Microscopy (SEM) technique to demonstrate the homogeneity of the prepared ZnONPs/nanocomposites. The photocatalytic activity of the samples was examined using the methylene blue (MB) dye as a pollutant solution, through evaluation of the efficiency of the prepared compound in the treatment of organic pollutants under illumination by sunlight. The photocatalytic results showed that after 240 minutes of exposure to sunlight, the sample prepared using (0.5 vol.% of ZnON
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General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k
... Show MoreThe present study deals with the optimum design of self supporting steel communication towers. A special technique is used to represent the tower as an equivalent hollow tapered beam with variable cross section. Then this method is employed to find the best layout of the tower among prespecified configurations. The formulation of the problem is applied to four types of tower layout
with K and X brace, with equal and unequal panels. The objective function is the total weight of the tower. The variables are the base and the top dimensions, the number of panels for the tower and member's cross section areas. The formulations of design constraints are based on the requirements of EIA and ANSI codes for allowable stresses in the members
Cephalexin and its derivatives are commonly utilized in the pharmaceutical and medicinal industry due to their biological and pharmaceutical activities, including anti-microbial, anti-cancer, anti-bacterial, and herbicidal activities as well as possessing high palatability and being useful for skin and joint infections. Interestingly, some organic drugs, including cephalexin, which exhibit toxicological and pharmacological properties, can be administered in forms of metal complexes. Many researchers have synthesized organic ligands derived from cephalexin in forms of Schiff bases and azo compounds which exhibited higher biological and medicinal properties when compared to cephalexin alone. One of the important features that make Schiff base
... Show MoreThree strain of Bacillus cereus were obtained from soil sours Laboratories of Biology Department/ College of Science/ University of Baghdad. The bacteria secreted extracellular xylanase in liquid cultur the test ability of xylanase production from these isolates was studied semi quantitative and quantitative screening appeared that Bacillus cereus X3 was the highest xylanase producer. The enzyme was partial purification 191 fold from cultur by reached step by 4 U/mg proteins by ammonium sulfat precipitation 80%, Ion exchang DEAE-cellulos chromatography Characterization study of the partial purifation enzyme revealed that the enzyme had a optimum activity pH8 and activity was stable in the pH rang (8-10) for 30min. maximal activity was attai
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Aspect-based sentiment analysis is the most important research topic conducted to extract and categorize aspect-terms from online reviews. Recent efforts have shown that topic modelling is vigorously used for this task. In this paper, we integrated word embedding into collapsed Gibbs sampling in Latent Dirichlet Allocation (LDA). Specifically, the conditional distribution in the topic model is improved using the word embedding model that was trained against (customer review) training dataset. Semantic similarity (cosine measure) was leveraged to distribute the aspect-terms to their related aspect-category cognitively. The experiment was conducted to extract and categorize the aspect terms from SemEval 2014 dataset.
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