DEPRESSION AND PSYCHOLOGICAL WELL-BEING AMONG PEOPLE HAVING COVID-19 UNDERGONE QUARANTINE

Aims Of Study: Coronavirus is a potentially deadly disease that mostly affects human lung tissue. Multiple cases of unexplained respiratory tract infections were reported to the World Health Organization China in December 2019. The focus of this research was to assess association between depression and PWB in patients who had been isolated due to covid-19. Methodology: A sample of 250 people with covid-19 post quarantine, with age ranges from 25 to 45 were selected by using purposive sampling methods. Depression, anxiety, stress scale and psychological well-being scales were administered. Regression analysis and t-test were employed for statistical analysis. Results: Obtained results evidenced that depression is significantly predict psychological health in the covid-19 population [B= -.015, -.019, F (7.915) sig= .000]. Gender difference is also observed in variable of depression also [(4.78) =.030, p.000]. Limitations and future implications: Future research may base on longitudinal analyses focusing on familial and social factors that may influence the psychological well-being of people living in quarantine. Originality: I certify that the intellectual substance of this article is the result of my own effort and that all assistance and sources used in the preparation of this article have been acknowledged. Conclusion: It was concluded that Depression affects people with COVID-19 and PWB after quarantine. future research may focus more on the influence of the interaction between quarantine and adults’ mental health to fully comprehend the link. 

Development of Model Order Reduction Techniques

Mathematical modeling is an essential feature for the analysis and design of a dynamical system. Generally, large and complex models are obtained from physical systems. Some ex- amples are robotic, telecommunications, network, mechanical and many other complex sys- tems. These systems are governed by the partial differential, Laplace and integro-differential equations etc. For the analysis and design of such systems, reduced order models are desir- able that provide a good approximation of the original systems. In last few decades, notable research work has been done on different aspects of model reduction. Existing techniques of model order reduction mostly suffer from the limitation of absence of original system key properties in reduced order system like passivity, stability, large approximation error and lack of a priori error bounds etc. This thesis investigates frequency weighted balanced model order reduction problem for standard and generalized (singular and non-singular), continuous and discrete linear time invariant systems. Firstly the frequency weighted model reduction problem is formulated. New frequency weighted model order reduction techniques are proposed for standard continuous and dis- crete time systems. Frequency interval Gramians based model order reduction techniques (where weights are not explicitly predefined) are also presented for standard continuous and discrete time systems. The proposed techniques guarantee stability even for the case where double sided weightings are employed. A priori frequency response error bounds are also de- rived. The proposed techniques yield mostly low frequency response error when compared to well known existing frequency weighted model reduction techniques. A generalization of existing frequency interval Gramians based model reduction tech- niques for Generalized non-singular discrete and continuous time systems is also presented. Moreover, a frequency limited model reduction technique for Generalized descriptor system is also presented. Simple algorithms are also given for preserving the stability of reduced- order models. The work also extends Poor Man’s truncated balanced realization for fre- quency limited case. Numerical examples are also presented for comparison of generalized techniques. Finally, new techniques to address time interval Gramians based model reduction are also presented for standard continuous time systems. The proposed techniques yield easily com- putable error bounds and comparable frequency response error.