Undesirable behaviors among students  are consider one of the danger problems threating societies and educational and scientific institutions of countries  because its one of the way to express aggression , so the instructors consider one of the most important people could be trust their evaluation and logical view , therefore the present research aimed to :

1. Recognize undesirable behavior in students of University by the view of their instructors
2. Measure the level of undesirable behavior in students of university by the view of their instructors
3. Recognize more common  undesirable behavior in students of University by the view o

Situational regret and its relation to the vitality of conscience among university students The research seeks to identify situational regret and its relation to the vitality of conscience among university students, identify the significant differences in regard with students’ gender, and identify if there is a correlation between situational regret and the vitality of conscience. To do this, two scales were adopted; one to measure situational regret consisted of (31) items, which was designed by (Al-badrani, 2006), besides, costa and macrys’ (1992) scale that translated in Arabic language by (Al-qaisy, 2013). It composed of (35) items. Total of (120) male and female students were collected from three-different colleges (science, art

The research aimed to identifying :

1. Egocentrism of the university students .
2. Academic adaptation of the university student.
3. Differences in Egocentrism according to the both variable ( Gender and Specialization ) of the university student.
4. Differences in academic adaptation according to the both variable ( Gender and Specialization ) of the university student .
5. The Contribution of academic adaptation to Egocentrism of the university student .

The sample of the research consisted of ( 400 ) students.      
Tow instruments have been us

The study aims to identify the relationship between forgiveness and social intelligence among elementary school students. The study employed a descriptive analytical approach, whereby a total of (500) elementary school student were selected randomly regarding the variable of gender and economical status. Two scales were prepared: one to measure the forgiveness depending on Albort’s theory that consist of (20) item, and the other to measure the social intelligence according to Tony’s theory which composed of (20) item as well. The result revealed that 6^th grade students have interested level of the forgiveness and social intelligence, the girl showed significant differences according to the forgiveness variable, the sample

This study aimed to assess orthodontic postgraduate students’ use of social media during the COVID-19 lockdown. Ninety-four postgraduate students (67 master’s students and 27 doctoral students) were enrolled in the study and asked to fill in an online questionnaire by answering questions regarding their use of social media during the COVID-19 lockdown. The frequency distributions and percentages were calculated using SPSS software. The results showed that 99% of the students used social media. The most frequently used type of social media was Facebook, 94%, followed by YouTube, 78%, and Instagram, 65%, while Twitter and Linkedin were used less, and no one used Blogger. About 63% of the students used elements of social media to l

The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy