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Feelings of stress are a part of university students’ life.  Stress is simply the body's response to changes that create taxing demands. There is a difference between eustress, which is a term for positive stress, and distress, which refers to negative stress. Coping mechanism plays a significant part to overcome or reduce the stress experienced by individuals. People use different types of coping strategies to overcome their stress. The purpose of the study was to study the relationship between stress and coping strategies among university students. Eighty- six university students participated in the study. A quantitative study utilizing a cross sectional non-probability sampling research design was used to gather data. The data was collected using a questionnaire with the addition of Perceived Stress Scale (PSS) and the Adolescent Coping Scale (ACS) were administered. The result of this study indicated that majority of the university students have moderate level of stress. There was a significant inverse relationship between stress level and coping strategies among undergraduates. In conclusion, some levels of stress can be good, as the right kind of stress encourages them towards change and growth. However, when students are unable to cope with stress, they can become a burden. It is recommended that students should be encouraged to take part in extra-curricular activities such as sports to reduce stress.  Keywords: Stress; coping strategies; university students

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This article reports the biomarkers of the Mild Cognitive Impairment (MCI) among the elderly group aged around 60 -75 years old by analyzing the EEG signals recorded by using the quantitative electroencephalograph (qEEG). There is growing evidence that EEG analysis in resting state condition are useful in early detection of neural signatures of Alzheimer’s and dementia. EEG findings and analysis shows potential of discriminating MCI, Alzheimer’s and dementia. In this research, the purpose is to 1) develop the indexes for each of the EEG bands and sub-bands such as delta (1.5 – 3.5 Hz), theta (3.3 – 7.5 Hz), alpha ( 8 – 12 Hz), beta 1 (15 – 18 Hz) and beta 2 (22 – 30 Hz); 2) provide reference for early diagnosis; 3) extract and analyze the brainwave pattern of MCI and cognitively healthy group. This study involved 19 channel resting state EEG from a total of 30 subjects, 18 diagnosed as having MCI and 12 cognitively healthy elderly with criteria for inclusion if the mini mental state examination (MMSE) score is more than or equal to 28, based on the age and educational level. Development of qEEG index started by decomposition of EEG by performing Fourier analysis, averaging and normalizing the value from the 19 channels to obtain the z-score. Findings showed promise of utility in early detection of Alzheimer’s. Notably, 1) Increases in delta/ theta posterior temporal and prefrontal area i.e., H(6.64) vs. MCI(26.29); beta temporal region, 2) Decreases in alpha at sensory motor region i.e., H(0.27) vs. MCI(0.23).