무궁화(Hibiscus syriacus L.)는 여름에 크고 화려한 꽃을 피 우는 수종으로 전 세계적으로 주요한 관상수 중 하나로 재배되 고 있다. 분화 재배에 적합한 신품종 육성을 위해 꽃이 크고 개화 기간이 긴 품종 ‘근형’을 모수(母樹)로, 생육이 느리고 가지 정단부에 꽃눈이 집중적으로 형성되는 품종 ‘임진홍’을 수분수 (授粉樹)로 교배하여 단심이 크고 뚜렷한 신품종 ‘라온’을 개발 하였다. 육성과정은 2012년에 양친을 인공교배하여 총 36립의 종자를 수확하여 파종하였고, 2015년에 주요 형질을 평가하여 차대묘 9개체 중 2개체를 1차적으로 선발하였다. 2016년부터 2019년까지 1차선발된 2 개체에 대한 삽목증식과 안정성 평가 를 거쳐 최종적으로 ‘라온’을 선발하였다. ‘라온’은 중앙에 크고 뚜렷한 단심이 있는 백단심계 홑꽃이다. 꽃 직경은 84.2mm로 모수보다 작고, 가지의 길이생장이 연간 평균 13.8cm으로 모수 (연간 평균 33.0cm)보다 느리다. ‘라온’의 개화기간은 68일 정 도이며, 모수에 비해 꽃과 잎이 작고 수고가 낮아져 분화용으로 재배하기에 적합한 것으로 보인다. ‘라온’은 2020년에 제8378 호로 품종보호 등록되었다.

The demand for automated diagnostic facilities has increased due to the rise in high-risk infectious diseases. However, small and medium-sized centers struggle to implement full automation because of limited resources. An integrated molecular diagnostics automation system addresses this issue by integrating small-scale automated facilities for each diagnostic process. Nonetheless, determining the optimal number of facilities and human resources remains challenging. This study proposes a methodology combining discrete event simulation and a genetic algorithm to optimize job-shop facility layout in the integrated molecular diagnostics automation system. A discrete event simulation model incorporates the number of facilities, processing times, and batch sizes for each step of the molecular diagnostics process. Genetic algorithm operations, such as tournament, crossover, and mutation, are applied to derive the optimal strategy for facility layout. The proposed methodology derives optimal facility layouts for various scenarios, minimizing costs while achieving the target production volume. This methodology can serve as a decision support tool when introducing job-shop production in the integrated molecular diagnostics automation system

This study aims to examine how the marketing characteristics of Wanghong (Chinese internet influencers) influence Chinese consumer attitudes and purchase intentions toward fashion products. Specifically, it investigates how the factors of expertise, trustworthiness, and attractiveness play a critical role in shaping consumer behavior in the digital marketing landscape. The study employed a survey-based quantitative approach, collecting data from 272 Chinese consumers engaging regularly with Wanghong marketing content. A structured questionnaire using a 5-point Likert scale was developed, incorporating validated measurement items from previous studies. The collected data were then analyzed using exploratory factor analysis, correlation analysis, and multiple regression analysis. The findings indicate that while expertise negatively influences consumer attitudes, trustworthiness and attractiveness have a positive impact. Furthermore, trustworthiness and attractiveness were found to significantly enhance purchase intention, while expertise was found to have no significant effect. Additionally, the findings showed that consumer attitudes toward Wanghong-mediated marketing play a mediating role in the relationship between Wanghong characteristics and purchase intention, with the results suggesting that consumers perceive highly knowledgeable Wanghong as distant or overly commercial, reducing their willingness to engage. In contrast, Wanghong who exhibit trustworthiness and personal appeal are more effective in driving consumer interest and purchases. These findings provide valuable insights for fashion brands aiming to optimize influencer marketing strategies in China, emphasizing the importance of relationship-driven and authenticity-focused marketing.

The transportation of spent nuclear fuel between management stages is expected, and the transportation workers may be exposed to radiation. When transporting spent nuclear fuel, the ALARA principle must be observed for the workers. The objective of this study is to assess a radiation dose for workers transporting spent nuclear fuel using metal overpacks. For this objective, the cask to be handled was selected and the radiation source term was set. Then, the radiation exposure scenario for the transportation workers was defined. Finally, the dose rates for each location of operation were assessed using Monte Carlo simulations, and collective doses were derived for each operation considering the radiation exposure scenario. Each worker performed 11 operations to transport spent nuclear fuel to other facilities and was exposed to a total of 1.138 man-mSv. The operation of removing the bottom shield ring resulted in the highest radiation exposure at 0.503 man-mSv. In contrast, the operation of installing the impact limiter resulted in the lowest radiation exposure at 0.0009 man-mSv. The results of this study can be used to strengthen radiation protection measures for workers transporting spent nuclear fuel in dry storage facilities using metal overpacks.

Morphological analyses of two newly recorded sea star species, Henricia exigua Hayashi, 1940 and Henricia tumida Verrill, 1909, of genus Henricia collected from Dongsan Harbor in the East Sea and Chuja-do, Jeju, Korea, respectively, were carried out. Henricia exigua could be characterized by an irregular arrangement of abactinal plates with adambulacral armature comprised of 10-13 spinelets. Henricia tumida has a short arm (R/r=4.0-4.2) with abactinal paxillae scattered and 4-8 abactinal spinelets with adambulacral spines having 4-6 spinelets. This study provides the first report of H. exigua and H. tumida in the Korean fauna along with detailed morphological description with their key characteristics.

MES(manufacturing execution system) plays a critical role in improving production efficiency by managing operations across the entire manufacturing system. Conventional manufacturing systems employ a centralized control structure, which has limitations in terms of the flexibility, scalability and reconfigurability of the manufacturing system. Agent-based manufacturing systems, on the other hand, are better suited to dynamic environments due to their inherent high autonomy and reconfigurability. In this study, we propose an agent-based MES and present its collaboration model between agents along with a data structure. The agent-based MES consists of three types of core agents: WIPAgent, PAgent(processing agent), and MHAgent(material handling agent). The entire manufacturing execution process operates through collaboration among these core agents, and all collaboration is carried out through autonomous interactions between the agents. In particular, the order-by-order dispatching process and the WIP(work-in-process) routing process are represented as respective collaboration models to facilitate understanding and analyzing the processes. In addition, we define data specifications required for MES implementation and operation, and their respective structures and relationships. Moreover, we build a prototype system employing a simulation model of an exemplary shop-floor as a simulation test bed. The framework proposed in this study can be used as a basis for building an automated operating system in a distributed environment.

The purpose of this study is to develop a timely fall detection system aimed at improving elderly care, reducing injury risks, and promoting greater independence among older adults. Falls are a leading cause of severe complications, long-term disabilities, and even mortality in the aging population, making their detection and prevention a crucial area of public health focus. This research introduces an innovative fall detection approach by leveraging Mediapipe, a state-of-the-art computer vision tool designed for human posture tracking. By analyzing the velocity of keypoints derived from human movement data, the system is able to detect abrupt changes in motion patterns, which are indicative of potential falls. To enhance the accuracy and robustness of fall detection, this system integrates an LSTM (Long Short-Term Memory) model specifically optimized for time-series data analysis. LSTM's ability to capture critical temporal shifts in movement patterns ensures the system's reliability in distinguishing falls from other types of motion. The combination of Mediapipe and LSTM provides a highly accurate and robust monitoring system with a significantly reduced false-positive rate, making it suitable for real-world elderly care environments. Experimental results demonstrated the efficacy of the proposed system, achieving an F1 score of 0.934, with a precision of 0.935 and a recall of 0.932. These findings highlight the system's capability to handle complex motion data effectively while maintaining high accuracy and reliability. The proposed method represents a technological advancement in fall detection systems, with promising potential for implementation in elderly monitoring systems. By improving safety and quality of life for older adults, this research contributes meaningfully to advancements in elderly care technology.

아일랜드 문학의 저명한 인물인 W.B. 예이츠는 소중한 친구인 로버트 그레고리의 죽음 이후 깊은 슬픔과 실존적 의문에 직면하게 되었다. 이러한 상실은 그 의 죽음에 대한 성찰에 깊은 영향을 미쳤다. 본 논문은 로버트 그레고리 소령을 추모 하며 , 아일랜드 비행사가 그의 죽음을 예견하다 , 양치기와 염소치기 와 같이 로버 트 그레고리를 위해 쓴 예이츠의 애가를 통해 그의 복잡한 감정과 철학적 신념을 탐 구하며, 죽음과 환생이라는 주제가 이 애가들에 어떻게 반영되었는지 살펴본다. 로버 트 그레고리의 특성을 분석함으로써 숭고함과 초연함에 대한 예이츠의 인식을 엿볼 수 있으며, 이는 죽음을 바라보는 그의 독특한 관점도 제공한다. 이들 애가를 탐구하 는 것은 예이츠의 삶과 죽음에 대한 철학적 통찰을 깊이 이해하는 데 필수적이다.

Recently, in the manufacturing industry, changes in various environmental conditions and constraints appear rapidly. At this time, a dispatching system that allocates work to resources at an appropriate time plays an important role in improving the speed or quality of production. In general, a rule-based static dispatching method has been widely used. However, this static approach to a dynamic production environment with uncertainty leads to several challenges, including decreased productivity, delayed delivery, and lower operating rates, etc. Therefore, a dynamic dispatching method is needed to address these challenges. This study aims to develop a reinforcement learning-based dynamic dispatching system, in which dispatching agents learn optimal dispatching rules for given environmental states. The state space represents various information such as WIP(work-in-process) and inventory levels, order status, machine status, and process status. A dispatching agent selects an optimal dispatching rule that considers multiple objectives of minimizing total tardiness and minimizing the number of setups at the same time. In particular, this study targets a multi-area manufacturing system consisting of a flow-shop area and a cellular-shop area. Thus, in addition to the dispatching agent that manages inputs to the flow-shop, a dispatching agent that manages transfers from the flow-shop to the cellular-shop is also developed. These two agents interact closely with each other. In this study, an agent-based dispatching system is developed and the performance is verified by comparing the system proposed in this study with the existing static dispatching method.

Carbon quantum dots (CQDs) are novel nanocarbon materials and widely used nanoparticles. They have gradually gained popularity in various fields due to their abundance, inexpensive cost, small size, ease of engineering, and distinct properties. To determine the antibacterial activity of metal-doped CQDs (metal-CQDs) containing Fe, Zn, Mn, Ni, and Co, we chose Staphylococcus aureus as a representative Gram-positive strain and Escherichia coli as a representative Gram-negative bacterial strain. Paper disc diffusion tests were conducted for the qualitative results, and a cell growth curve was drawn for quantitative results. The minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and IC50 were measured from cell growth curves. As a result, all of the metal-CQDs showed toxicity against both Gram-positive and Gram-negative bacteria. Furthermore, Gram-negative bacteria was vulnerable to metal-CQDs than Gram-positive bacteria. The toxicity differed concerning the type of metal-CQDs; Mn-CQDs exhibited the highest efficacy. Hence, this study suggested that CQDs can be used as new nanoparticles for antibiotics.

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.