This study focuses on the effectiveness of regional business support programs funded by South Korea's Balanced National Development Special Account, one of the policies designed to address regional imbalances and promote local autonomy. Using the analytical approach including DEA (Data Envelopment Analysis) methodology, This study analyzed the efficiency of 76 star companies in the Jeonbuk region based on their performance from 2018 to 2023. This study was designed to improve previous studies limitations, which only analyzed simple input-output efficiency in the short term, by using six years of mid-term data to comprehensively evaluate input variables in both R&D and Non-R&D sectors. The main purpose of this study is to analyze the effectiveness of the expiring Star Company Development Program by evaluating efficiency of supported company groups using DEA and to propose support models and policy suggestions for upcoming regional specialized industries support program by identifying the features of both optimal and inefficiency models. For this, employments along with financial indicators such as sales revenue, operating profit, and total assets were set as output variables, with R&D and non-R&D support amounts were set as input variables for analysis. According to the results, the optimal efficiency model group has strong intellectual property acquisition capabilities, and continuous R&D investment. It shows that continuous innovation activities are a key factor for improving the effectiveness of support. This study found that, from a mid․long term perspective, policy support programs should be customized by unique characteristics of each industry field, Based on this, it was suggested that upcoming regional specialized industry support programs in the Jeonbuk region should include policy planning and support program design to complement the weaknesses of each industry field.

Fetal Bovine Serum (FBS) plays a crucial role in animal cell culture; however, the increasing number of bovine fetuses used and sacrificed solely for FBS collection has raised ethical concerns globally. The welfare of fetuses during FBS blood collection has become a key focus of debate among animal welfare and ethics organizations worldwide. Previous studies indicate that heat-inactivated coelomic fluid (HI-CF) from the earthworm Perionyx excavatus may serve as a viable FBS alternative in adherent cell cultures. This study evaluates the potential of HI-CF as an FBS substitute during the in vitro maturation (IVM) stage of bovine embryo culture, with a focus on improving developmental rate through antioxidation effects. In this study, 2% HI-CF was incorporated into IVM media, assessing its impact on cell growth, differentiation, and the expression of genes related to antioxidation. The group of 2% of HI-CF exhibited a trend toward increased cleavage and blastocyst development rates compared to the control group. Although antioxidant genes such as NRF2 and GSR showed no statistically significant differences between the control and treatment groups, a trend toward increased expression was observed. Conversely, GPX1 displayed a trend of decreased expression. Notably, IGF1 and NQO1 were significant upregulated (p < 0.05) in the 2% HI-CF group. Additionally, oocytes stained with H2DCFDA showed a significantly reduced ROS levels (p < 0.05) in the 2% HI-CF group compared with controls. These findings suggest that HI-CF's antioxidative effects support enhanced cell growth and blastocyst development rate, surpassing those observed with FBS. Consequently, HI-CF shows promise as an effective alternative to FBS in vitro maturation of bovine oocytes.

Non-human primates, due to their high genetic similarity to humans, are used as laboratory animals in biotechnology researches. The growing demand has recently led to a shortage of primate resources, which has become a significant issue both domestically and internationally. This shortage has been further exacerbated by the COVID-19 pandemic. Consequently, the importance of resource conservation through effective primate management is increasing. This requires the establishment of proper quarantine procedures and infectious disease control. Quarantine is an important process that protects not only animal health but also public health significance. Non-human primate quarantine procedures were organized in order. We compared the differences in quarantine procedures not only in Korea but also in various countries such as the US, EU, and Australia. In addition, the etiology, clinical symptoms, diagnosis, and treatment methods of representative infectious diseases of quarantine concern (tuberculosis, monkeypox, monkey immunodeficiency virus, salmonellosis, and shigellosis) were summarized. A literature review of nonhuman primate quarantine procedures in other countries revealed minimal differences in the basic structure. The quarantine periods were similar around 30 days, but we found some differences in details such as legal requirements, documentation forms, and quarantine authorities. These findings are expected to contribute to the development of strategies for improving methods to prevent the spread of infectious diseases and enhancing quarantine management methods.

본 연구는 사주 서비스의 디지털 트랜스포메이션 과정에서 나타나는 특징을 도출하고, MZ세 대에 적합한 게임 요소를 제안하는 것을 목적으로 한다. COVID-19 팬데믹 이후 MZ세대의 디지털 소비 증가에 따라, 오프라인 사주 서비스의 온라인화 과정에서 발생한 변화를 분석하 였다. 요소 분석과 사례 연구를 통해 디지털 전환 과정을 정리하고, MZ세대의 참여를 유도할 핵심 게임 요소를 구체적으로 제안하였다. 연구에서 제안한 게임 요소는 장소성, 캐릭터성, 기 술성의 세 가지 축을 중심으로 정의된다. 장소성은 팝업스토어와 같은 특정 시간과 장소에서 의 이벤트로 몰입을 강화한다. 캐릭터성은 매력적이고 신뢰를 주는 디지털 캐릭터로 사용자와 의 상호작용을 풍부하게 한다. 기술성은 AI, 버추얼 휴먼, 예약 시스템 등 개인화된 기술을 통 해 사용자 경험을 극대화한다.이 연구는 사주 서비스가 단순 운세 제공을 넘어, 사용자가 스스 로를 탐구하고 안정감을 찾는 경험으로 자리 잡을 가능성을 보여준다.

The study explores the relationship among teacher identity, teacher transparency, teacher self-efficacy, and teachers’ adaptation to digital change. Eighty-four English teachers participated in the study. For comparison between English and other subject teachers, 38 survey results of different subject teachers were included in the analysis. The results showed that English teachers’ scores were lower across all the constructs in terms of both transparency and self-efficacy compared to the scores of teachers in other subjects. For further analysis, the Structural Equation Modeling was run, and the results revealed that teacher transparency influences teacher self-efficacy, facilitating digital adaptation. Instructional Transparency and Peer Transparency were significant predictors of selfefficacy, directly influencing digital adaptation. This result illustrates the dynamic interplay between evolving teacher identity and self-efficacy in relation to digital adaptation through the relationship between teacher transparency and teacher selfefficacy. The findings indicate the need for targeted programs to enhance English teachers’ transparency and self-efficacy as a pathway to their digital adaptation.

This study proposes a weight optimization technique based on Mixture Design of Experiments (MD) to overcome the limitations of traditional ensemble learning and achieve optimal predictive performance with minimal experimentation. Traditional ensemble learning combines the predictions of multiple base models through a meta-model to generate a final prediction but has limitations in systematically optimizing the combination of base model performances. In this research, MD is applied to efficiently adjust the weights of each base model, constructing an optimized ensemble model tailored to the characteristics of the data. An evaluation of this technique across various industrial datasets confirms that the optimized ensemble model proposed in this study achieves higher predictive performance than traditional models in terms of F1-Score and accuracy. This method provides a foundation for enhancing real-time analysis and prediction reliability in data-driven decision-making systems across diverse fields such as manufacturing, fraud detection, and medical diagnostics.

본 연구의 목표는 보드게임에서 인간의 행동을 모방하고 예측하여 인간 수준의 성능을 달성하 는 AI를 만드는 것이다. 그 사례로 Dixit이라고 하는 보드게임을 사용하였는데 이는 창의력과 상상력을 자극하는 카드 게임으로, 주로 3명 이상의 플레이어들이 자신이 가진 카드를 다른 사 람들에게 설명하여, 다른 사람들이 그 카드를 맞추도록 유도하는 게임이다. 우리는 디지털 카 드 예측을 위한 AI 모델을 설계하고, 다양한 데이터 전처리 기법과 모델링 방법을 비교 분석하 여 최적의 성능을 도출하는 것을 목표로 하였다. 본 연구에서는 출제자가 제시한 카드를 예측 하는 문제를 다루며, 다양한 임베딩 기법(Word2Vec, GloVe)과 LSTM 모델을 활용하여 예측 정 확도를 개선하였다. 실험 결과, 데이터 전처리 방식과 모델링 방법에 따른 정확도 차이를 관찰 할 수 있었으며, 카드 번호를 단어로 취급하여 모델에 입력하는 방식이 성능 향상에 기여했다. 또한, 모델에 LSTM층과 Bidirectional 층을 추가한 실험에서 인간 성능을 능가하는 높은 정확 도를 기록했다.

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.

본 연구는 보드게임 플레이를 주도하는 메커니즘을 태그 데이터로 구조화하는 것을 목표로 한 다. 이를 통해 보드게임 디자이너의 디자인 격차를 해소하고 기획 역량을 강화하며 경쟁력 있 는 보드게임 개발을 지원하는 것이 목표이다. 이를 위해 BGG 데이터 베이스에 등록된 136,652개의 보드게임과 이 게임에 채택된 192개의 메커니즘 데이터 세트를 수집하고 분석하 였다. 연구 분석을 통해 보드게임 메커니즘의 태그 데이터 7개를 추출하고 구조화하였다.

As digital transformation accelerates, platform business has become a core business model in modern society. Platform business has a network effect where the winner takes all. For this reason, it is crucial for a company's pricing policy to attract as many customers as possible in the early stages of business. Telecommunication service companies are experiencing stagnant growth due to the saturation of the smartphone market and intensifying competition in rates, but the burden of maintaining communication networks is increasing due to the rapid increase in traffic caused by domestic and foreign CSPs. This study aims to understand the dynamic characteristics of the telecommunications market by focusing on pricing policy. To this end, we analyzed how ISPs, CSPs, and consumers react to changes in pricing policy based on the prisoner's dilemma theory. The analysis of the dynamic characteristics of the market was conducted through simulation using the Agent-Based Model.

The current study examines the digital behaviors of 124 university students enrolled in a blended learning class in Korea. The students were divided into two groups (upper and lower) based on their scores on a self-regulated learning questionnaire. Their digital behaviors were compared across four areas: (a) task completion scores; (b) strategy use; (c) the days on which tasks were completed; and (d) learning gains. The results revealed a significant difference in task completion scores between the upper and lower groups. However, no meaningful difference was observed in strategy use between the two groups. Students in the upper group were more proactive in completing videos, homework, and TOEIC tests, often finishing these tasks ahead of deadlines. In contrast, students in the lower group tended to complete their assignments on the due date. Finally, there were no significant differences in learning gains between the two groups. These findings may inform the design and management of online L2 learning programs.

이 연구는 여고생을 대상으로 외모관심도와 자기표현 욕구가 자아존중감 및 외모관리행동에 미치는 영향을 알아보기 위해 자 기기입식 설문조사 방법을 통해 이루어졌다. 조사 기간은 2024년 8월 1일부터 2024년 9월 20일까지 설문조사를 실시하였으며, 유효표본 200부를 최종 분석자료로 활용하였고 리커트 5점 척도 를 사용하였다. 첫째, 여고생의 외모 관심도와 자기표현 욕구는 모두 자아존중 감에 유의미한 정(+)의 영향을 미치는 것으로 나타나 외모관심도 와 자기표현 욕구가 높을수록 자아존중감도 높아지는 것으로 볼 수 있었다. 둘째, 여고생의 자기표현 욕구가 외모관리행동에 유의미한 정 (+)의 영향을 미치는 것으로 나타나 자기표현 욕구가 높을수록 외모관리행동도 높아지는 것으로 볼 수 있었다. 셋째, 여고생의 자아존중감이 외모관리행동에 유의미한 정(+)의 영향을 미치는 것으로 나타나 자아존중감이 높을수록 외모관리행 동도 높아지는 것으로 볼 수 있었다. 이상의 결과를 볼 때, 부모나 교육자는 여고생들이 외모관리행 동에 대한 올바른 판단하에 외모관심과 자기표현을 과하거나 지 나치지 않게 표출하여 자아존중감 및 외모관리를 할 수 있도록 지도하여야만 할 것이다. 결론적으로 여고생들은 질풍노도의 사춘기 단계에 있고 감정조 절이 미숙하기 때문에 자기표현 단계에서 부정적인 감정에 휘말 릴 수 있으며, 따라서 부모나 교육자는 솔직한 감정을 표출할 수 있는 열린 자세로 외모에 대한 건강한 인식과 올바른 개념을 정 립할 수 있도록 도와야 할 것이다.

본 연구는 인공지능(AI) 기술이 미디어 및 엔터테인먼트 산업의 콘텐츠 제작 과정에 미치는 영 향을 포괄적으로 분석한다. AI 기술의 급속한 발전은 콘텐츠 제작 방식을 획기적으로 변화시 키며, 업계 전반에 걸쳐 새로운 기회와 복합적인 도전 과제를 제시하고 있다. 본 논문은 머신 러닝, 자연어 처리, 컴퓨터 비전 등 다양한 AI 기술이 영화, 방송, 음악, 게임과 같은 주요 엔 터테인먼트 분야의 콘텐츠 제작에 어떻게 적용되고 있는지를 조사한다. AI 도입의 긍정적 효 과로는 제작 효율성 향상, 창의성 증진, 개인화된 콘텐츠 제공 등을 들 수 있으며, 동시에 저 작권 문제, 윤리적 쟁점, 인력 대체에 대한 우려와 같은 잠재적 문제점들도 함께 다룬다. 이 연구는 AI가 엔터테인먼트 산업에 가져오는 변혁적 영향을 이해하고, 기술 발전이 제시하는 기회와 직면한 도전 과제를 파악하는 데 중요한 기여를 할 것이다. 이러한 분석을 통해 업계 및 학계는 AI 기술 활용에 대한 보다 구체적이고 실질적인 통찰을 얻을 수 있을 것이다.

Bearing-shaft systems are essential components in various automated manufacturing processes, primarily designed for the efficient rotation of a main shaft by a motor. Accurate fault detection is critical for operating manufacturing processes, yet challenges remain in sensor selection and optimization regarding types, locations, and positioning. Sound signals present a viable solution for fault detection, as microphones can capture mechanical sounds from remote locations and have been traditionally employed for monitoring machine health. However, recordings in real industrial environments always contain non-negligible ambient noise, which hampers effective fault detection. Utilizing a high-performance microphone for noise cancellation can be cost-prohibitive and impractical in actual manufacturing sites, therefore to address these challenges, we proposed a convolution neural network-based methodology for fault detection that analyzes the mechanical sounds generated from the bearing-shaft system in the form of Log-mel spectrograms. To mitigate the impact of environmental noise in recordings made with commercial microphones, we also developed a denoising autoencoder that operates without requiring any expert knowledge of the system. The proposed DAE-CNN model demonstrates high performance in fault detection regardless of whether environmental noise is included(98.1%) or not(100%). It indicates that the proposed methodology effectively preserves significant signal features while overcoming the negative influence of ambient noise present in the collected datasets in both fault detection and fault type classification.

정보 기술이 교육에서 지속적으로 응용됨에 따라 ‘혼합형’ 수업 교육 모델이 대학 교의 개방 교육에 점차 융합되고 있다. 중국의 많은 대학교들은 시대의 조류를 따라 현대 정보 기술과 원격 개방 교육의 결합을 통해 온라인 핵심 과정, 온라인 학습 공 간, 온라인 교수진, 온라인 학습 서비스, 온라인 학습 평가, 온라인 교수 관리 등을 중심으로 한 ‘혼합형’ 교수 모델을 구축하고 있다. 본 연구는 중국 개방대학의 ‘응용 한국어’ 전공 교재 편찬에서 나타나는 특징을 분석하였다. 특히, 대학생을 중심으로 한 인터넷 플랫폼을 활용한 자율 학습을 강조하는 하는 교재 편찬 사고방식을 바탕으로, 온라인 교육 시대에 적합한 교재의 내용 및 디자인 방안을 논의하였다. 연구는 한국어 발음, 전문 용어, 한국어 문법 및 문법 프로젝트, 평가 방안 등 교재 편찬에 있어 중요한 요소들을 살펴보고, 각 항목 별로 제시되는 편찬 순서를 구체적으로 소 개하였다.

본 연구는 메타버스 플랫폼의 교육적 활용 가능성을 심층적으로 평가하기 위해 진행되었다. 학생과 교수들을 대상으로 질적 포커스 그룹 인터뷰를 수행하여, 상호작용적 학습 환경 조성 측면에서 플랫폼의 잠재적 장점을 확인하는 한편, 기술적 제약과 콘텐츠 통합 필요성도 연구 했다. 연구 결과, 메타버스 플랫폼은 학습 참여를 촉진하는 데 긍정적인 영향을 미치는 것으로 나타났으나, 학습 유지력 및 학업 성과에 미치는 영향은 일관되지 않은 양상을 보였다. 이를 토대로 본 연구는 지역 특화 교육 기술의 도입 전략을 제안하며, 향후 효과적인 적용을 위한 구체적인 방향성을 제시한다.

Korean English medium instruction (EMI) classes aim to foster active discussions and communicative interactions in English between instructors and students. However, many Korean students in these classes struggle due to their limited English proficiency. This paper examines the challenges faced by Korean EFL students in EMI environments, highlighting the necessity for support in both English and their native language to facilitate effective learning. It also identifies teaching strategies that have proven effective in helping these students navigate language barriers. The findings indicate that participants had difficulty developing their writing skills for assignments in EMI settings and encountered limited opportunities to communicate their understanding of course material with instructors. To address these challenges, it is important to assess students’ language skills and find a balance between Korean and English. Implementing flexible teaching methods can enhance the learning experience, making it more effective and supportive. By providing multiple approaches to learning, such as interactive activities or peer support, learning gaps can be bridged and overall educational outcomes enhanced.

Mesenchymal stem cells (MSCs) have emerged as a promising therapeutic resource for the peripheral nervous system (PNS) and central nervous system (CNS) that is attributable to their capacity for neuronal differentiation. Human dental pulp stem cells (hDPSCs), which exhibit MSC-like traits, can differentiate into neuron-like cells and secrete critical neurotrophic factors; however, their therapeutic potential in peripheral nerve injury remains unexplored. This study investigated the regenerative effects of hDPSC transplantation following sciatic nerve injury (SNI) in rats. Transplantation of hDPSCs, STRO-1+ hDPSCs, or CD146+ hDPSCs after sciatic nerve transection in rats upregulated the levels of β3 tubulin, a marker of immature newborn neurons. Furthermore, the levels of glial cellderived neurotrophic factor, insulin-like growth factor 2, and the neuroregenerative factor NeuroD1 were upregulated. Motor dysfunction in rats with SNI was restored, as demonstrated by significantly higher sciatic functional index scores compared with the sciatic nerve transection group without transplantation. Transplantation of hDPSCs into injured peripheral nerves results in the upregulation of neurotrophic factors, differentiation into immature neurons, and promotion of motor function recovery. This approach holds promise as a valuable therapeutic strategy for repairing injured peripheral sciatic nerves, potentially providing a solution for nerve damage in both the PNS and CNS.

Afghanistan is a producer of high-quality pistachios among the top ten producers in the World. Nowadays, these edible nuts are losing their quality and quantity due to hemipteran insect infestation and their diversity during all growth phases in the country borderline. The expansion of the pistachio pest in Iran has caused concerns for Afghanistan, especially in the provinces of Herat, Badghis, and Takhar which are close to the Iran borders. Hence, this survey was initiated to primarily detect the phytophagous insect pest presence in the pistachio open forest areas and to know what management tools were effectively used in alleviation of the pest infestation. Our findings indicated that galling aphids, aphids, thrips, and root beetles received the maximum positive responses in the Herat province, but psyllids, aphids, root beetles, leave miners, fruit borers and thrips received the highest responses in Badghis province. In addition, Takhar province depicted plant bugs, twig borer moths, galling aphids, leaf miners, and leafhoppers got the most positive responses accordingly. In the meantime, the data showed that management outcomes were not satisfactory despite the use of sanitation, cultural practices, and chemical application. The survey presented significant differences in the prevalence of pest and in the application of pest control methods among the survey regions. Finally, it is essential to develop the strategy of integrated pest management in order to stop the phytophagous insects from turning into actual pests. This will reduce the pistachio loss annually and improve the nut culture quality in Afghanistan.

본 연구에서는 source face와 target face를 합성해서 게임 캐릭터의 얼굴을 효과적으로 생성 하기 위한 방법으로 target face의 특징을 제거한 nullface를 생성한 다음 source face의 얼굴 의 특징을 적용해서 새로운 얼굴을 합성하는 기법을 제안한다. 이를 위해서 본 연구는 2 가지 모듈을 이용해서 수행된다. 첫 번째 모듈은 nullface를 생성하며, 두번째 모듈은 nullface에 source face의 특징을 적용해서 새로운 얼굴을 합성한다. 이 두 과정 모두 diffusion model에 기반한 방법론을 적용함으로써 효과적인 얼굴 합성 기법을 구현한다.