Abstract

Investment in the human resource has become an important part of the strategies of educational organizations so that they can be competitive, so that the access of academics in universities to the international awards as a result of their research excellence indicates the effectiveness of this human talent so the interest has emerged in the implementation of a strategy to manage these human talent in organizations in general and educational ones In particular as one of the power sources  that can not be imitated. The focus of the organization on the interaction between the working people has increased attention to the human element in order to achieve the desired quality levels, which called for organi

Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfakedetection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection

This study is concerned with the recent changes that occurred in the last three years (2017-2019) in the marshes region in southern Iraq as a result of the changes in the global climate, the study included all the water bodies in the five governorates that are located in the southern regions of Iraq (Wasit, Maysan, Dhi-Qar, Qadisiyah and Basrah), which represent the marshes lands in Iraq. Scenes of the Landsat 8 satellite are used to create a mosaic to cover the five governorates within a time window with the slightest difference between the date of the scene capture, not to exceed 8 days. The results of calculating the changes in water areas were obtained using the classifier support vector machine, where high accuracy ratios were recorded

The members of the family of Eentrobacteriaceae harbour a gene cluster called polyketide synthase (pks) island. This cluster is responsible for the synthesis of the genotoxin colibactin that might have an important role in the induction of double-strand DNA breaks, leading to promote human colorectal cancer (CRC). Eleven out of the eighty eight isolates (12.5%) were pks+, distributed as 7 (8%) isolates of E. coli, 2 (2.25%) of K. pneumoniae and 2 (2.25%) of E. aerogenes. The cytotoxic effects of selected pks+ isolates (E. coli and E. aerogenes) on HeLa cells were represented by decreasing cell numbers and enlarged cell nuclei in comparison to the untreated cells. Cyt

The members of the family of Eentrobacteriaceae harbour a gene cluster called polyketide synthase (pks) island. This cluster is responsible for the synthesis of the genotoxin colibactin that might have an important role in the induction of double-strand DNA breaks, leading to promote human colorectal cancer (CRC). Eleven out of the eighty eight isolates (12.5%) were pks+, distributed as 7 (8%) isolates of E. coli, 2 (2.25%) of K. pneumoniae and 2 (2.25%) of E. aerogenes. The cytotoxic effects of selected pks+ isolates (E. coli and E. aerogenes) on HeLa cells were represented by decreasing cell numbers and enlarged cell nuclei in comparison to the untreated cells. Cytological changes were observed when the infected HeLa cells culture

Statistical methods of forecasting have applied with the intention of constructing a model to predict the number of the old aged people in retirement homes in Iraq. They were based on the monthly data of old aged people in Baghdad and the governorates except for the Kurdistan region from 2016 to 2019. Using Box-Jenkins methodology, the stationarity of the series was examined. The appropriate model order was determined, the parameters were estimated, the significance was tested, adequacy of the model was checked, and then the best model of prediction was used. The best model for forecasting according to criteria of (Normalized BIC, MAPE, RMSE) is ARIMA (0, 1, 2).

       The present study was performed to spotlight the potential role of soil bacteria in the Al-Rumaila oil field as a bioindicator of heavy metals pollution. For this purpose, nine soil samples were collected from different sites, with 20cm depth, to assess the pollution status depending on the total and available concentrations of heavy metals.  The result indicates pollution of the studied soils with the following metals: Cd, Cu, Fe, Zn, and Pb. The mean of total concentration for all studied metals was higher than the allowed maximum limit based on the international limit:(3.394, 3.994, 39.993, 8844.979,150.372, and 103.347 µg/g), respectively. While measuring the total Metal concentration is important in determining the de