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People's lives and jobs have changed drastically because of the COVID-19 crisis. COVID-19 has resulted in burnout brought on by ongoing work stress, which negatively affects both employees and their businesses. It may result in despair, substance abuse, interpersonal issues, personal and professional frustration, social isolation, relationship issues, and, in severe cases, suicide. The experience of burnout has been the focus of much research during the past few decades. However, there is a lack of research that covers scope on work burnout among the government and private sectors and focusing more on medical health practices. Therefore, this study was conducted to investigate the level of burnout among workers in the government and private sectors in Malaysia in terms of personal-related burnout, workrelated burnout, and colleagues-related burnout. For this study, a quantitative and crosssectional study was employed and a total of 108 respondents were purposely chosen across age groups from the private and government sector in Malaysia. The respondents were given an online questionnaire that was distributed using a google form that consists of 18 questions that measures the domains of personal burnout, work related burnout and colleague related burnout. The findings showed that on average, respondents sometimes have personal and work burnout. On the other hand, respondents rarely perceived colleagues related burnout.In conclusion, all employees and employers must be willing to work together to minimize this problem especially in organization and it is crucial to learn how to prevent burnout and to seek professional attention if it happens.

Organic food is said to have a positive impact on people's health and wholesome to the environment due to its ecological nature. However, there are challenges for organic food retailers in Malaysia to create a marketing strategy since the number of consumers of organic foods is quite low compared to other countries. This aims of this research is to study factors and sub-factors influencing consumer buying behaviour for organic food in Kuantan, Pahang to firmly grasp the nature of organic food business industry. To achieve such objective, Fuzzy Delphi Method has been utilised to analyse the five factors considered which are price, behavioural intention, subjective norm, attitude, and consumer knowledge. The findings shown that the most influential sub-factor affecting consumer purchasing behaviour towards organic food under behavioural intention was that people usually consider purchasing organic food that meets their taste, while for subjective norms, the concept of organically grown food is the most influential sub-factor. Furthermore, the reasonable price, familiarity with products and health and fitness reasons are the leading sub-factors for price, consumer knowledge and attitude factors, respectively.

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Handling flood quantile with little data is essential in managing water resources. In this paper, we propose a potential model called Modified Group Method of Data Handling (MGMDH) to predict the flood quantile at ungauged sites in Malaysia. In this proposed MGMDH model, the principal component analysis (PCA) method is matched to the group method of data handling (GMDH) with various transfer functions. The MGMDH model consists of four transfer functions: polynomial, sigmoid, radial basis function, and hyperbolic tangent sigmoid transfer functions. The prediction performance of MGMDH models is compared to the conventional GMDH model. The appropriateness and effectiveness of the proposed models are demonstrated with a simulation study. Cauchy distribution is used in the simulation study as a disturbance error. The implementation of Cauchy Distribution as an error disturbance in artificial data illustrates the performance of the proposed models if the extreme value or extreme event occurs in the data set. The simulation study may say that the MGMDH model is superior to other comparison models, namely LR, NLR, GMDH and ANN models. Another beauty of this proposed model is that it shows a strong prediction performance when multicollinearity is absent in the data set.