The purpose of this paper is to identify environmental awareness under the Corona pandemic among students of the Faculty of Physical Education and Sports Sciences at the University of Kufa. and comparison of environmental awareness under the Corona pandemic between students of the Faculty of Physical Education and Sports Sciences at the University of Kufa. The two researchers used the descriptive approach in the style of the survey and comparisons to identify the research community in the students of the College of Physical Education and Sports Sciences at the University of Kufa for the academic year 2020-2021, who numbered (210) students, then a sample of (80) students was chosen randomly, with a percentage of (38.09%) from the research community, with (20) students from each stage, and (10) students for the exploratory experiment at a rate of (9.52%) from the research community, then the two researchers chose And the application of the environmental awareness scale, which consists of (32) items on the research sample, and the results were extracted and the appropriate statistical treatments were used to reach the results. Then the results were presented, analyzed and discussed. The two researchers reached the most important conclusions: Students of the College of Physical Education and Sports Science have a varying level of environmental awareness. And there are real differences among students of the Faculty of Physical Education and Sports Sciences in environmental awareness and in favor of the fourth stage. Based on the findings of the research, the researchers recommend the most important recommendations: Take advantage of the environmental awareness scale that the two researchers used and applied to detect environmental awareness. And Using other psychological variables to know the psychological states of students and players in order to take into consideration how to give directions and instructions to them.
Photodetector based on Rutile and Anatase TiO2 nanostructures/n-Si Heterojunction
Abstract Background: Timely diagnosis of periodontal disease is crucial for restoring healthy periodontal tissue and improving patients’ prognosis. There is a growing interest in using salivary biomarkers as a noninvasive screening tool for periodontal disease. This study aimed to investigate the diagnostic efficacy of two salivary biomarkers, lactate dehydrogenase (LDH) and total protein, for periodontal disease by assessing their sensitivity in relation to clinical periodontal parameters. Furthermore, the study aimed to explore the impact of systemic disease, age, and sex on the accuracy of these biomarkers in the diagnosis of periodontal health. Materials and methods: A total of 145 participants were categorized into three groups based
... Show MoreRA Ali, LK Abood, Int J Sci Res, 2017 - Cited by 2
Abiotic stress-induced genes may lead to understand the response of plants and adaptability to salinity and drought stresses. Differential display reverse transcriptase – polymerase chain reaction (DDRT-PCR) was used to investigate the differences in gene expression between drought- and salinity-stressed plantlets of Ruta graveolens. Direct and stepwise exposures to drought- or salt-responsive genes were screened in R. graveolens plantlets using the DDRT technique. Gene expression was investigated both in the control and in the salt or drought-stressed plantlets and differential banding patterns with different molecular sizes were observed using the primers OPA-01 (646,770 and 983 pb), OPA-08 (593 and 988 pb), OPA-11 (674 and 831 pb
... Show MoreThe drones have become the focus of researchers’ attention because they enter into many details of life. The Tri-copter was chosen because it combines the advantages of the quadcopter in stability and manoeuvrability quickly. In this paper, the nonlinear Tri-copter model is entirely derived and applied three controllers; Proportional-Integral-Derivative (PID), Fractional Order PID (FOPID), and Nonlinear PID (NLPID). The tuning process for the controllers’ parameters had been tuned by using the Grey Wolf Optimization (GWO) algorithm. Then the results obtained had been compared. Where the improvement rate for the Tri-copter model of the nonlinear controller (NLPID) if compared with