The current study aims to examine the level of problems faced by university students in distance learning, in addition to identify the differences in these problems in terms of the availability of internet services, gender, college, GPA, interactions, academic cohort, and family economic status. The study sample consisted of (3172) students (57.3% females). The researchers developed a questionnaire with (32) items to measure distance learning problems in four areas: Psychological (9 items), academic (10 items), technological (7 items), and study environment (6 items). The responses are scored on a (5) point Likert Scale ranging from 1 (strongly disagree) to 5 (strongly agree). Means, standard deviations, and Multivariate Analysis of Variance (MANOVA) were used to analyze the data. The findings showed that students faced high levels of psychological and academic problems and medium levels of technological and study environmental problems. The findings also indicated statistically significant differences in the levels of all problems based on the availability of internet services. In addition, the sample in scientific colleges manifested higher levels of academic problems, and females showed higher levels of study environmental problems. Statistically significant differences also appeared in all types of problems based on study cohort and family economic status.
Background: Dental anomalies might occur due to abnormal events during teeth development caused by environmental or genetic factors during histo differentiation or morph differentiation stages of embryological development. Aims of the study: To evaluate the distribution of developmental dental anomalies according to age and gender in relation to nutritional status in children attending College of Dentistry /University of Baghdad. Materials and method: After examination 5760 children aged 5-12 years of both genders only 147child with dental anomalies were found, all developmental dental anomalies that were clinically observable were recorded. The developmental dental anomalies which diagnosed in this study were supernumerary, missing teeth,
... Show MoreIn the present work, a set of indoor Radon concentration measurements was carried out in a number of rooms and buildings of Science College in the University of Mustansiriyah for the first time in Iraq using RAD-7 detector which is an active method for short time measuring compared with the passive method in solid state nuclear track detectors (SSNTD's). The results show that, the Radon concentrations values vary from 9.85±1.7 Bq.m-3 to 94.21±34.7 Bq.m-3 with an average value 53.64±26 Bq.m-3 which is lower than the recommended action level 200-300 Bq/m3 [ICRP, 2009].
The values of the annual effective dose (A.E.D) vary from 0.25 mSv/y to 2.38 mSv/y, with an average value 1.46±0.67 mSv/y which is lower than the recommended the rang
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he research aims to determine the competencies that must be met in the digital media literacy curriculum, which contributes to a great extent in developing the skills of criticism and analysis of the media contents of the students. The study of the two researchers according to the methodology of the media survey. The research tools were: the questionnaire tool, which distributed on 86 . The main objectives of the research were:
1. Knowing the best strategy in teaching the digital media literacy curriculum.
2. Knowing which education fits the digital media literacy curriculum.
3. Identifying the cognitive, educational, media, technical skills, and emotional competencies required for the digital media literacy curriculum from the
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In the pandemic era of COVID19, software engineering and artificial intelligence tools played a major role in monitoring, managing, and predicting the spread of the virus. According to reports released by the World Health Organization, all attempts to prevent any form of infection are highly recommended among people. One side of avoiding infection is requiring people to wear face masks. The problem is that some people do not incline to wear a face mask, and guiding them manually by police is not easy especially in a large or public area to avoid this infection. The purpose of this paper is to construct a software tool called Face Mask Detection (FMD) to detect any face that does not wear a mask in a specific
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Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with
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Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas
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