Background and objective:The purpose of this study aims to understand and explore the relationships of stress, job satisfaction, and the career decisions of taxi drivers in South Korea, particularly during the coronavirus disease 2019 (COVID-19) pandemic. Based on the self-efficacy approach and social cognitive career and motivation theory, this study was guided by two research questions, as follows: (1) What are the stress factors for senior taxi drivers? How do senior taxi drivers describe the relationship between stress and job satisfaction, particularly during the COVID-19 pandemic in South Korea? Does age play a role? (2) Did senior taxi drivers leave or retire from the taxi driving profession due to stress and job satisfaction during or after the COVID-19 pandemic in South Korea? Why? Does age play a role?
Materials and methods:The purposive and snowball sampling strategies were employed to recruit 62 male senior taxi drivers all across South Korea. The in-depth, semi-structured, and private interview session was employed.
Results:The results indicated that the surrounding environment and individuals, personal considerations with health and body condition, and financial consideration with insufficient pension from the government policy were three key elements for motivations and career decisions.
Contributions and conclusion:Government leaders and policymakers should take the results from this study as a blueprint to reform and polish human resources planning and working rights for senior citizens who continue to work in the professions. Although the government has encouraged senior citizens to be part of the workforce until the age of 65, many senior citizens in late adulthood still suffer from negative workplace conditions and stress. Immediate solutions are needed as senior citizens deserve a manageable retirement.
Predicting the insulating thermal behavior of a multi-component refractory ceramic system could be a difficult task, which can be tackled using the finite element (FE) method to solve the partial differential equations of the heat transfer problem, thus calculating the temperature profiles throughout the system in any given period. Nevertheless, using FE can still be very time-consuming when analyzing the thermal performance of insulating systems in some scenarios. This paper proposes a framework based on a machine learning surrogate model to significantly reduce the required computation time for estimating the thermal performance of several multi-component insulating systems. Based on an electric resistance furnace case study, the framework estimated the feasibility and the final temperature of nearly 1.9×105 insulating candidates’ arrangements with reasonable accuracy by simulating only an initial sample of 2.8% of them via FE. The framework accuracy was evaluated by varying the initial sample size from ≈0.9% to 8% of total combinations, indicating that 3%–5% is the optimal range in the case study. Finally, the proposed framework was compared to the evolutionary screening procedure, a previously proposed method for selecting insulating materials for furnace linings, from which it was concluded that the machine learning framework provides better control over the number of required FE simulations, provides faster optimization of its hyperparameters, and enables the designers to estimate the thermal performance of the entire search space with small errors on temperature prediction.
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