This study aimed to assess the effectiveness of metacognitive methods in college general English classes to improve students’ communication skills and metacognitive awareness. The approach involved structuring learning activities where students adapted movie dialogues, practiced roles, collaborated with peers, and received feedback on problemsolving. The results showed significant improvements in speaking skills, with preassessment scores of 24.79 increasing to 27.17 in the post-assessment (p < .01). These findings indicate notable gains in fluency and accuracy in English communication, as well as improved attitudes towards the language. Although there were improvements in post-assessment scores for metacognitive processes, these changes were not statistically significant. This study highlights the importance of incorporating diverse learning methods into college general English classes, particularly emphasizing the value of metacognitive techniques. These methods help students apply classroom learning to realworld social situations and enhance their communication abilities.

This study aims to develop a comprehensive predictive model for Digital Quality Management (DQM) and to analyze the impact of various quality activities on different levels of DQM. By employing the Classification And Regression Tree (CART) methodology, we are able to present predictive scenarios that elucidate how varying quantitative levels of quality activities influence the five major categories of DQM. The findings reveal that the operation level of quality circles and the promotion level of suggestion systems are pivotal in enhancing DQM levels. Furthermore, the study emphasizes that an effective reward system is crucial to maximizing the effectiveness of these quality activities. Through a quantitative approach, this study demonstrates that for ventures and small-medium enterprises, expanding suggestion systems and implementing robust reward mechanisms can significantly improve DQM levels, particularly when the operation of quality circles is challenging. The research provides valuable insights, indicating that even in the absence of fully operational quality circles, other mechanisms can still drive substantial improvements in DQM. These results are particularly relevant in the context of digital transformation, offering practical guidelines for enterprises to establish and refine their quality management strategies. By focusing on suggestion systems and rewards, businesses can effectively navigate the complexities of digital transformation and achieve higher levels of quality management.

본 연구는 예이츠의 시에 나타난 프리드리히 니체의 주제를 조사하여 니체가 예이츠의 문학 작품에 끼친 상당한 영향을 강조한다. 두 사람의 서로 다른 배경에도 불구하고, 비교 분석을 통해 예이츠 시의 실존적, 철학적 주제를 조명하면 그들 간의 유사점이 발견된다. 본 연구는 “권력에의 의지,” “영원회귀” 등 니체의 주요 개념에 대한 개관을 시작으로, 예이츠의 시에서 이러한 주제가 나타나는 사례를 살펴본다. 이 분석의 핵심은 역경과 죽음에도 불구하고 초연함을 유지하는 슈퍼맨의 묘사를 통해 니체가 강조한 자기 극복을 반영하는 실존적 투쟁에 대한 탐구이다. 예이츠의 시에서 니체적 개념을 조사해 보면 철학적, 실존적 탐구에 대한 니체의 중요한 영향이 드러난다.

The purpose of this study was to verify the effect of dietary treatment of Hovenia dulcis thunb extract and Rubus coreanus Miquel extract combined with full-body nerve massage on fatigue recovery. The results are as follows. Homogeneity between groups was secured due to the general characteristics of the study subjects, and the abdominal group showed a significant decrease in fatigue and pain changes after full nerve massage. Changes in fatigue substances in the blood showed a significant decrease in the Hovenia dulcis thunb extract group, and changes in blood levels before and after the stress hormone experiment showed a significant decrease in cordicol in the Hovenia dulcis thunb extract group. Through the results of this study, we were able to confirm that oxidative stress was reduced in adult women in their 20s to 50s through a full-body nerve massage after eating Rubus coreanus Miquel extract and Hovenia dulcis thunb extract, using an objective and systematic experimental method for changes in oxidative stress.

This study aims to investigate how consumers perceive the attributes (ubiquity, continuity) of metaverse fashion brands in a virtual space. It also empirically verifies the impact on consumers’ perceived values (hedonic value, social value) and consumer behavioral intentions (intention to use the platform, intention to purchase virtual products). The results verified in this study are as follows: First, we confirmed that the metaverse attributes perceived by consumers in the virtual space of the fashion brand, ubiquity and daily extension, positively affect customers’ perceived hedonic and social values. Second, we found that consumers’ perceived hedonic and social values have a significant positive effect on their intention to use the platform. Finally, we found that consumers’ intention to use the platform had a significant positive effect on their intention to purchase virtual products. The results of this study will have academic significance by expanding the scope of research related to identifying metaverse attributes and values by identifying metaverse attributes and consumer values that perceived by consumers in fashion brands’ virtual spaces in the metaverse. This study suggests a direction and strategy for fashion brands to move forward in building virtual spaces on the metaverse platform. In this way, they can create perceived value for consumers that elicits positive consumer behavioral intentions.

In the manufacturing industry, dispatching systems play a crucial role in enhancing production efficiency and optimizing production volume. However, in dynamic production environments, conventional static dispatching methods struggle to adapt to various environmental conditions and constraints, leading to problems such as reduced production volume, delays, and resource wastage. Therefore, there is a need for dynamic dispatching methods that can quickly adapt to changes in the environment. In this study, we aim to develop an agent-based model that considers dynamic situations through interaction between agents. Additionally, we intend to utilize the Q-learning algorithm, which possesses the characteristics of temporal difference (TD) learning, to automatically update and adapt to dynamic situations. This means that Q-learning can effectively consider dynamic environments by sensitively responding to changes in the state space and selecting optimal dispatching rules accordingly. The state space includes information such as inventory and work-in-process levels, order fulfilment status, and machine status, which are used to select the optimal dispatching rules. Furthermore, we aim to minimize total tardiness and the number of setup changes using reinforcement learning. Finally, we will develop a dynamic dispatching system using Q-learning and compare its performance with conventional static dispatching methods.