In this paper, we review the extended halo material and the circumgalactic medium (CGM), including both dust and gas, and discuss promising science cases that could be realized using the KASI Deep Rolling Imaging Fast Telescope (K-DRIFT). Scattered starlight from cirrus clouds in our Galaxy poses one of the major challenges to studying the low surface brightness features of extragalactic sources. Therefore, it is essential to investigate how to discriminate extragalactic sources from cirrus cloud features. At the same time, interstellar dust clouds themselves are fundamental to understanding dust properties and the interstellar radiation field, both of which are essential for studies of chemical evolution and star formation in our Galaxy. Measuring the reddening of background sources, such as quasars, with K-DRIFT, which benefits from its broad field of view and accurate background subtraction, allows for the effective detection of extended dust in galactic halos, the CGM, and intracluster space. Observations of the Hα emission lines can be used to identify signatures of star formation activity within galaxies, as well as the environmental effects acting on them. Galactic winds driven by active galactic nuclei and starbursts can be traced through Hα emission. Strong ram pressure stripping effectively removes the interstellar medium (ISM) from galaxies. The stripped ISM becomes ionized or dissociated through mixing with the hot intracluster medium (ICM), forming Hα tails. The surface brightness of these Hα tails correlates not only with the presence of star formation in the tails but also the mixing stage of the stripped ISM and ICM. The Hα survey with K-DRIFT will enable the investigation of the evolutionary stages of ram pressure stripped galaxies in cluster environments, as well as the multiphase gas reservoir around galaxies and in the CGM.

Loneliness, a well-established risk factor for mental health, has been strongly associated with low subjective well-being (SWB). However, less is known about potential boundary conditions that may ameliorate this ‘dark side’ of loneliness. Social connections are critical for SWB based on innate evolutionary traits; a lack of belonging was directly harmful to human survival in the past. In this study, we hypothesized that loneliness would exert a more pronounced influence on SWB when an individual’s need for others (i.e., a social resource) is perceived as high while simultaneously existing in a harsh environment. With a particular focus on urban residents in Seoul, who are presumed to be more vulnerable to loneliness, we examined whether feeling lonely matters less to SWB under favorable environmental conditions. As expected, a pilot study indicated that loneliness was less harmful to the SWB of individuals who perceived their surroundings as relatively secure and favorable. We then replicated the results in an experimental study by exposing people to cues of either a harsh (e.g., via scarcity cues) or a favorable environment.

해상에서의 안전한 의사소통은 선박 운항의 핵심 요소로, 국제해사기구(IMO)는 SMCP(Standard Marine Communication Phrases)를 제정하여 선내외 교신에서 활용할 수 있도록 하였다. SMCP를 포함한 해사영어는 효과적이고 정확한 의사소통을 위해 일반 영어와는 다 른 문법적, 어휘적, 구조적 특성을 반영하고 있으며, 간결성과 명확성에 초점이 맞추어져 표준화되어 있다. 이러한 맥락에서 본 연구는 상 용 LLM 모델의 해사영어 활용 능력을 PHP Text Similarity 알고리즘과 BERT 기반 모델을 활용하여 평가하였다. 먼저 ChatGPT, Google Gemini, Meta LLaMA 3 70B Instruct 모델을 대상으로 SMCP 기반 문장 구성, 용어 정의, 빈칸 채우기 문제를 포함한 총 60문항을 활용하여 성능을 비교 분석하였다. 이후 해사고등학교 학생들의 시험 결과와 LLM 모델의 결과를 비교하여, LLM이 실제 해기사 교육 수준과 비교 했을 때 어느 정도의 해사영어 이해 및 문장 구성 능력을 갖추었는지 평가하였다. 대체적으로 LLM 모델들은 높은 정답률을 보였으나, 표 준화된 문구를 정확하게 활용하거나 관용적으로 사용되는 해사영어 표현을 이해하고 적용하는 데 한계점이 있음을 확인하였다. 본 연구 는 해기교육기관 및 실무 현장에서 상용 LLM 모델의 해사영어 활용 가능성을 평가하는 기초 자료로 활용될 수 있을 것으로 기대되며, 향 후 보다 정교한 모델을 대상으로 추가연구가 필요하다.

This study develops a machine learning-based tool life prediction model using spindle power data collected from real manufacturing environments. The primary objective is to monitor tool wear and predict optimal replacement times, thereby enhancing manufacturing efficiency and product quality in smart factory settings. Accurate tool life prediction is critical for reducing downtime, minimizing costs, and maintaining consistent product standards. Six machine learning models, including Random Forest, Decision Tree, Support Vector Regressor, Linear Regression, XGBoost, and LightGBM, were evaluated for their predictive performance. Among these, the Random Forest Regressor demonstrated the highest accuracy with R2 value of 0.92, making it the most suitable for tool wear prediction. Linear Regression also provided detailed insights into the relationship between tool usage and spindle power, offering a practical alternative for precise predictions in scenarios with consistent data patterns. The results highlight the potential for real-time monitoring and predictive maintenance, significantly reducing downtime, optimizing tool usage, and improving operational efficiency. Challenges such as data variability, real-world noise, and model generalizability across diverse processes remain areas for future exploration. This work contributes to advancing smart manufacturing by integrating data-driven approaches into operational workflows and enabling sustainable, cost-effective production environments.

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.

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.

Aphis gossypii is a representative pest that transmits plant viral diseases. It is difficult to control with chemical pesticides alone due to their high pesticide resistance. Entomopathogenic fungi are biological control agents that can replace chemical pesticides and have characteristics of high host specificity and safety to humans. Therefore, we investigated the immune pathways of aphids against initial infection by entomopathogenic fungus. We treated aphids with the Beauveria bassiana JEF 544 strain and examined the immune response in early infection by qPCR. furthermore, we also studied changes the molting time of nymphs and changes in adult nymphal production caused by entomopathogenic fungi.

The biggest jewel beetle in Korea, Chrysochroa coreana, has been nominated as the Natural Monument No. 496 and also classified as Category I of Endangered Species by the Red Data Book. Due to the invisible feature of a saproxylic larval hood inside the host tree for years, the whole life history was hitherto been unknown to the academic world. In order to clarify the period of larval-hood and record images of the process of the final stage of emergence, we obtained sample eggs from two mated couples of adults that emerged from a dead tree of Celtis sinensis on Wando Island, which is well-known as the habitat of C. coreana. Larvae were hatched on four pieces of timber (Celtis aurantiaca) in July 2018 and kept in a growth chamber under the conditions of 25°C, 65% humidity, and in a 12-hour light/dark cycle. The development of larvae was monitored via the non-destructive C/T method every month. Six adults were emerged between February and March 2024. As a result, we obtained the fact that the larval period of C. coreana is minimum 66 months (5.5 years) under lab conditions.