This research sought to present a concept of cross-sectional data models, A crucial double data to take the impact of the change in time and obtained from the measured phenomenon of repeated observations in different time periods, Where the models of the panel data were defined by different types of fixed , random and mixed, and Comparing them by studying and analyzing the mathematical relationship between the influence of time with a set of basic variables Which are the main axes on which the research is based and is represented by the monthly revenue of the working individual and the profits it generates, which represents the variable response And its relationship to a set of explanatory variables represented by the years of service of the working individual and the academic achievement of him and the classifier starting with (graduates of the intermediate school or below, graduates of the preparatory school, graduates of institutes, graduates of colleges and universities) Finally sex was divided into two categories: male and female The sample of the research is a group of individuals (100) individuals working in private sector companies with different commercial agencies in Baghdad with financial, administrative, technical, sales and services sectors, namely, the research community And then to extract the estimates of the parameters of the models and the Variations of errors as well as their testing and analysis where it was observed how to control the heterogeneity of variance and increase in degrees of freedom and less multicollinearity among the variables, which illustrates the efficiency and importance and accuracy of this type of models and their importance in making sound decisions and reliable results The results also showed the importance of the years of service on the performance of the working individual and the increase in productivity and efficiency, adding to the practical experience of utmost importance, as well as distinguished individuals working in the achievement of university degree on the rest of the other educational collections.
Hypothyroidism has been associated with disorders of glucose and insulin metabolism..The present study was designed to evaluate the possible change in some hormones (free testosterone, estradiol, prolactin, insulin), glucose and homeostasis model assessment of insulin resistance (HOMA-IR) in women with primary hypothyroidism under thyroid hormone replacement therapy .This cross-sectional study was carried on 62 hypothyroid patients׳ women and 22 healthy women as control group at the specialized center for endocrinology and diabetes, AL-Rasafa Directorate of Health Baghdad, with age range(15-60 years), diagnosed as having primary hypothyroidism on thyroxine replacement therapy with duration not less than four months.
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Arabian killifish,
In this study, the staging of normal embryonic development of
An innovative desalination method called electrosorption or capacitive deionization (CDI) has significant benefits for wastewater treatment. This process is performed by using a carbon fiber electrode as a working electrode to remove hexavalent chromium ions from an aqueous solution. The pH, NaCl concentration, and cell voltage were optimized using the Box-Behnken experimental design (BDD) in response surface methodology (RSM) to study the effects and interactions of selected variables. To attain the relationship between the process variables and chromium removal, the experimental data were subjected to an analysis of variance and fitted with a quadratic model. The optimum conditions to remove Cr(VI) ions were: pH of 2, a cell voltage of 4.
... Show MoreAutomated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health
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