Prince Sultan University

About: Prince Sultan University is a education organization based out in Riyadh, Saudi Arabia. It is known for research contribution in the topics: Fractional calculus & Fixed-point theorem. The organization has 523 authors who have published 2241 publications receiving 23432 citations.

Psychological Impact of COVID-19 and Lockdown among University Students in Malaysia: Implications and Policy Recommendations.

Abstract: The COVID-19 pandemic and the lockdown has taken the world by storm. This study examines its impact on the anxiety level of university students in Malaysia during the peak of the crisis and the pertinent characteristics affecting their anxiety. A cross-sectional online survey, using Zung’s self-rating anxiety questionnaire was conducted during the COVID-19 pandemic and lockdown. Out of the 983 respondents, 20.4%, 6.6%, and 2.8% experienced minimal to moderate, marked to severe, and most extreme levels of anxiety. Female gender (OR = 21.456, 95% CI = 1.061, 1.998, p = 0.020), age below 18 years (OR = 4.147, 95% CI = 1.331, 12.918, p = 0.014), age 19 to 25 (OR = 3.398, 95% CI = 1.431, 8.066, p = 0.006), pre-university level of education (OR = 2.882, 95% CI = 1.212, 6.854, p = 0.017), management studies (OR = 2.278, 95% CI = 1.526, 3.399, p < 0.001), and staying alone (OR = 2.208, 95% CI = 1.127, 4.325, p = 0.021) were significantly associated with higher levels of anxiety. The main stressors include financial constraints, remote online teaching and uncertainty about the future with regard to academics and career. Stressors are predominantly financial constraints, remote online learning, and uncertainty related to their academic performance, and future career prospects.

On a new class of fractional operators

Abstract: This manuscript is based on the standard fractional calculus iteration procedure on conformable derivatives. We introduce new fractional integration and differentiation operators. We define spaces and present some theorems related to these operators.

Medical Image Segmentation Methods, Algorithms, and Applications

Abstract: Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods for medical image analysis have been presented in this paper. In this paper, we have described the latest segmentation methods applied in medical image analysis. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements are presented in the final section.