IMPACT OF LEISURE ACTIVITIES ON WELL-BEING IN COVID-19 PANDEMIC SITUATION, A CROSS SECTIONAL SURVEY

Aims Of Study: During COVID-19 people were forced to stay home and this increased the risk of limiting their PA and adaptation of sedentary behaviour hence our objective is to measure the impact of leisure activities on well-being during COVID-19. Methodology: 366 participants were selected, using Non-probability convenient sampling from UOL and PGC. PA and well-being were assessed using IPAQ-short form and WHO-5 well-being questionnaire. Results: IPAQ-short form with well-being Pearson Chi square was 638.012, Spearman correlation was 0.956 and p-value Asymptotic significance (2 sided) was .000 which means there is positive strong correlation between variables. Limitations and Future Implication: Adults having ages 18-40yrs volunteered due to short time period and since our study limited to only 2 settings so to generalize our outcomes for mass population was not feasible. During lockdown gaining past medical history in data collection was impacted, it is suggested to include the role of Physiotherapist in improving health status by PA, create awareness among common population about role of PA and their relation with well-being also including diversity in age groups, ethnicity and localities is suggested. Originality: Strong positive correlation between PA and well-being. Conclusion: This study shows that during Covid-19 pandemic, those individuals who remained physically active had good impact on their health. Being physically active not only improves an individual’s physical fitness level but also helps to cope with psychological problems degrading one’s mental health so our study found out direct relation between physical activity levels and health status.

Nonlinear Stochastic Analysis of Machinery Health Dynamics

Condition based maintenance of machinery is being much talked about in the engineering sector of defense and commercial industry. A lot of expenditure is generally incurred on condition monitoring of machinery to avoid unexpected downtimes and failures vis-à-vis optimizing machinery operation. The concept is ever evolving due to technological advancements as well as with the emergence of unique nature of defects in complex systems. The features of machinery health extracted through modern condition monitoring technologies helps in diagnostics of current health; however, utilizing the current data for prediction of future machinery state i.e Prognostics is a challenging task. Prognostic is one of the key elements of modern maintenance philosophies. Effective prognostic, from the machinery data, leads towards operational reliability, reduced machinery downtime, cost savings, secondary/catastrophic failures etc. Machinery health prognosis follows a sequential methodology inclusive of various processes ranging from data acquisition till remaining useful life estimation. Every step depicts distinct statistical features, which are helpful in estimating present and future health state of a machine. Various methodologies have been adopted by the researchers in an effort to precisely forecast/predict machinery health. Research in this area, where stochastic models have been applied, revealed encouraging results. In this thesis, we have presented three nonlinear stochastic models with their application on bearing health prognosis. These include Markov Switching Auto Regressive Model with Time Varying Regime Probabilities, Threshold Auto Regressive Model and Structural Break Point Classifier Model. The results showed that the applied models can be effectively utilized for data driven machinery health prognosis.