Abstract

This study aims to identify the empathy of University Students, as well as the significant differences in sympathy in terms of gender and specialization. To achieve the aims of the study, a scale of empathy was administered to a sample of (450) students collected randomly from Baghdad university. The results showed that the study sample has a level of empathy. There is a significant difference between males and females in empathy, in favor of the female students. There is no significant difference in empathy in terms of specialization (scientific, humanities), and the interaction between males and females. The study came out with a number of recommendations and suggestions. 

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

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

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

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