Background: An important factor influencing duration of breastfeeding is mother’s employment status. The nutritional, immunological, psychological and economic benefits of breastfeeding are well documented. Both UNICEF and the World Health Organization recommend mothers should breastfeed exclusively for at least 6 months.
Objectives: To determine how the employment and the employment variables (type of work, time of return to work and hours of work) influence the breast feeding practices.
Methods: A cross- sectional study was carried out on a sample of 200 employed mothers who had their last child been completed at least tow years. Data were collected using a questionnaire form. It was carried out during the period from 1st of April to 1st of September, 2000, in Baghdad city/ AL-Risafa in 6 different places where employed mothers of young children were expected to be found.
Results: The study revealed that the impact of employment status was noted on the breast feeding initiation time, breast feeding duration and complementary food initiation time. The majority of employed mothers (56.1%) who worked shorter hours breast-feed for longer durations and starts weaning after the fourth months of the infant’s age. The highest percentage of employed mothers (75.9%) with maternity leave of six months or more breast feed for a year or more and (66.1%) start weaning later than the fourth month of the infant’s life.
Conclusions: The study recognizes that employed mothers who worked shorter hours and mothers with longer maternity leaves breast feed for longer durations and start weaning later than mothers who worked long hours and mothers with shorter maternity leaves. It was concluded that most work-places lack accommodations to support breast-feeding.
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Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.
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Genus Salix is among family Salicaceae, distributing in the northern hemisphere. It is represented in Egypt by two species (Salix mucronata and Salix tetrasperma). The classification of Salix at the generic and infra-generic levels is still outstanding. We have agreed to list the Egyptian species of this genus. We collected them during field trips to most Egyptian habitats; fresh and herbarium specimens were subjected to taxonomic revision based on morphological characters; scanning electron microscope (SEM) for pollen grains; isozyme analysis using esterase and peroxidase enzymes and genetic diversity using random amplified polymorphic DNA (RAPD). We recorded that both sexes of S.
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The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med
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