• Science of Emotion and Sensibility
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• v.25 no.4
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• pp.139-156
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• 2022

This study aimed to examine the cause and consequence of emotional labor strategies based on the emotional labor framework. To investigate the boundary condition of the current research model, the study proposed that job autonomy would moderate the effects of emotional labor on employees' well-being. To achieve the purpose of the study, it was first tested whether neuroticism and extroversion of employees predicted the focal outcomes (i.e., burnout and work engagement) via distinct emotional labor strategies. Second, the moderation effects of job autonomy were tested for each emotional labor strategy in predicting the focal outcomes. Third, the conditional indirect effects of job autonomy on the mediation process were examined. The results revealed that surface acting partially mediated the relationship between neuroticism and burnout, whereas deep acting fully mediated the relationship between extraversion and work engagement. Regarding the moderating effects of job autonomy, it significantly moderated the relationship between surface acting and burnout and between deep acting and work engagement. In addition, from the moderated mediation effects, the conditional indirect effects of job autonomy were significant. Finally, theoretical and practical implications are discussed and limitations and future research directions were suggested.

• Korean Parent-Child Health Journal
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• v.8 no.1
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• pp.37-48
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• 2005

Purpose: The purpose of this study was to develop a play education program to reduce children's fear of needle insertion to the implanted port, and to assess the effect of this program. Method: The play education program was composed of play education before needle insertion, encouragement during needle insertion, and a present to reward then after needle insertion. Measurement instruments were the Procedure Behavior Check List(PBCL) and Faces Rating Scale(FRS). Results: The first hypothesis, "the PBCL point of children with malignant disease would decrease after play education program", was rejected(before insertion : Z=-0.189, p= .850, during insertion : Z=-0.350. p= .727, after insertion : Z=-0.590, p= .555). The second hypothesis, "the FRS point of children with malignant disease would decrease after play education program education", was rejected(observer 1 : Z=-0.245, p= .806, observer 2 : Z=-0.912, p= .362, self-report : Z=-0.181, p= .856). The third hypothesis, "the Time of needle insertion would decrease after play education program", was rejected(Z=-0.464, p= .642). Conclusion: The effect on fear-reduction of play education program for children with malignant disease inserted implanted port was not significant but continuous education is needed for parents and children.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.73-78
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• 2022

The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.595-600
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• 2024

As the use of artificial intelligence technologies such as machine learning increases in research fields that predict learning outcomes or optimize learning pathways, the use of artificial intelligence in education is gradually making progress. This research is gradually evolving into more advanced artificial intelligence methods such as deep learning and reinforcement learning. This study aims to improve the method of predicting future learning performance based on the learner's past learning performance-history data. Therefore, to improve prediction performance, we propose conditional probability applying the Markov Chain method. This method is used to improve the prediction performance of the classifier by allowing the learner to add learning history data to the classification prediction in addition to classification prediction by machine learning. In order to confirm the effectiveness of the proposed method, a total of more than 30 experiments were conducted per algorithm and indicator using empirical data, 'Teaching aid-based early childhood education learning performance data'. As a result of the experiment, higher performance indicators were confirmed in cases using the proposed method than in cases where only the classification algorithm was used in all cases.