The experiment was carried out in the field of botanical garden belonging to the Department of Biology Sciences, College of Education for Pure Science -Ibn AL-Haitham ,Baghdad University. for the growing season. 2014 -2013 to study the effect of the electromagnetic field which included five different intensities (0,5,10,15,20) MT and three periods of time, namely, (1,2,3) an hour and their interaction on some of the morphological characteristics of the safflower plant . designed experiment by Randomized Complete Block Design (RCBD) and three replicates per treatment, compared to the average using less significant difference at the level of probability (0.05) , the results showed the following:- 1-Exposing seeds to different electromagnetic field intensities led to a significant increase except the number of vegetative branches, as it gave the highest rates in the percentage of seed germination, plant height, the size of the root, leaf area and leaf area guide. The highest increase was when the seeds which were exposed to the intensity of the electromagnetic field 10MT. 2-Exposing the seeds to different time periods of the electromagnetic field led to obtain a significant increase in most of the treatment mentioned except the character of vegetable branches number and the size of root, and the highest increase when the seeds exposing for two hours for most of the qualities mentioned and for three hours to the percentage of seed germination. 3-The interaction between the factors of the study had significant effect on all treatment with the superiority of treatment 10MT for two hours to give them the best rates for the studied traits compared with the control plants.
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Breast cancer is the most prevalent malignancy among women worldwide, in Iraq it ranks the first among the population and the leading cause of cancer related female mortality. This study is designed to investigate the correlations between serum and tissue markers in order to clarify their role in progression or regression breast cancer. Tumor Markers are groups of substances, mainly proteins, produced from cancer cell or from other cells in the body in response to tumor. The study was carried out from April 2018 to April 2019 with total number of 60 breast cancer women. The blood samples were collected from breast cancer women in postoperative and pretherapeutic who attended teaching oncology hospital of the medical city in Baghdad and
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This article aims to provide a bibliometric analysis of intellectual capital research published in the Scopus database from 1956 to 2020 to trace the development of scientific activities that can pave the way for future studies by shedding light on the gaps in the field. The analysis focuses on 638 intellectual capital-related papers published in the Scopus database over 60 years, drawing upon a bibliometric analysis using VOSviewer. This paper highlights the mainstream of the current research in the intellectual capital field, based on the Scopus database, by presenting a detailed bibliometric analysis of the trend and development of intellectual capital research in the past six decades, including journals, authors, countries, inst
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With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Lev
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Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the most effective parameter, particularly when Age<49.5. Whereas Ki67 appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu
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