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.

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.

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.

Recently, with the development of industrial technology and the increase of young consumers, engine monitoring devices for small ships are rapidly changing from analog devices to LCD-based digital devices. In addition, consumers’ product selection criteria are gradually increasing in favor of luxurious and emotional products rather than price attractiveness. Therefore, in order to develop differentiated products in marketing, it is necessary to find and improve emotionally attractive quality elements. The purpose of this study is to collect 11 customer requirements related to the emotional quality of DGP (Digital Gauge Panel) for small ships through customer interviews and to find attractive quality elements among the emotional qualities of DGP for small ships. 17 design elements were derived by applying QFD to the collected customer requirements, and they were classified into one-dimensional quality, must be quality, and attractive quality through Kano model analysis, and 6 attractive quality elements were confirmed using Timko customer satisfaction index.

This study analyzed inductive and deductive instructional approaches for teaching grammar within a Presentation-Practice-Production grammar lesson. The participants of this study included 119 Korean university students enrolled in an English as a foreign language class, with approximately half receiving deductive instruction and the other half receiving inductive grammar instruction. The analysis involved comparing learning gains as well as student perceptions of the two approaches via Mann Whitney U tests. The results showed no statistical difference in terms of the immediate or delayed learning gains for each grammatical topic, nor when all grammatical topics were aggregated. However, the analysis of student perceptions indicated that students found inductive instruction to be moderately more effective, interesting, and easier than deductive instruction. The study concludes with a discussion of the implications of these findings related to instructional practices in foreign language classes that utilize the Presentation- Practice-Production model as well as suggestions for future research concerning deductive and inductive instructional approaches.

This study aimed to investigate the effects of various washing pre-treatments of native Codium fragile as a feed additive on in vitro ruminal fermentation and CH4 production in ruminants. Seaweed was included at 0.5% dry matter (DM) based on the experimental feed (forage : concentrate = 3:7). Treatment groups were classified as follows: experimental feed (C), no washing (T1), washing at 0°C (T2), washing at 22°C (T3) and washing at 70°C (T4) each immersed for 6 minutes in distilled water. The pH consistently fell within the ruminal stability range. In vitro dry matter digestibility was significantly highest in T2, T3, T4 and C, T4 was the lowest at 48 h (p<0.05). NH3-N concentration was significantly highest in T4 at 48 h (p<0.05). Total gas production at 48 h was 19% lower in T4 compared to C (p<0.01). CH4 production (mL/g DM) at 48 h was lower in all treatment groups compared to C, with T3 showing a 31% reduction (p<0.01). Similarly, CH4 production (mL/g dry matter degradability, DMD) at 48 h was 39% lower for T3 compared to C (p<0.01). At 24 h, total VFA was significantly highest in T1 and T4 (p<0.05). The proportions of acetate was significantly highest in C and T3 was the lowest at 48 h (p<0.01). The proportions of propionate was significantly highest in T3 and C was the lowest at 48 h (p<0.01). The acetate to propionate ratio was singnificantly highest in C at 48 h (p<0.01). The proportions of butyrate at 24 h was lower for T3 compared to C (p<0.05). Therefore, this study confirms that Codium fragile can reduce CH4 production when used as a feed additive for ruminants and this effect is not significantly influenced by the washing pre-treatment. However, if washing process is necessary, washing at 22°C is the most appropriate method to remove foreign objects.

In 2024, the South Korean government’s research and development budget cuts sparked significant concerns in the scientific community, prompting increased interest in international research funding opportunities. In this regard, South Korea’s upcoming participation as an Associated Country in the European Union’s (EU’s) Horizon Europe offers a timely opportunity. Horizon Europe is the EU’s flagship research and innovation program, running from 2021 to 2027 with a budget of €95.5 billion. It is structured on three key pillars: 1) excellent science; 2) global challenges and European industrial competitiveness; and 3) innovative Europe. South Korea’s direct benefits will focus on Pillar II, which emphasizes global challenges across six clusters, including health, climate, and digital innovation. It should be noted that participation in the program mandates international collaborations, typically involving consortia with diverse expertise. Meanwhile, the National Contact Points network has been expanded to support Korean researchers, offering the necessary resources to facilitate engagement with EU counterparts. By leveraging these opportunities, South Korean researchers aim to collaboratively address global challenges, thus enhancing the nation’s scientific standing.

This study analyzes the impact of ESG (Environmental, Social, and Governance) activities on Corporate Financial Performance(CFP) using machine learning techniques. To address the linear limitations of traditional multiple regression analysis, the study employs AutoML (Automated Machine Learning) to capture the nonlinear relationships between ESG activities and CFP. The dataset consists of 635 companies listed on KOSPI and KOSDAQ from 2013 to 2021, with Tobin's Q used as the dependent variable representing CFP. The results show that machine learning models outperformed traditional regression models in predicting firm value. In particular, the Extreme Gradient Boosting (XGBoost) model exhibited the best predictive performance. Among ESG activities, the Social (S) indicator had a positive effect on CFP, suggesting that corporate social responsibility enhances corporate reputation and trust, leading to long-term positive outcomes. In contrast, the Environmental (E) and Governance (G) indicators had negative effects in the short term, likely due to factors such as the initial costs associated with environmental investments or governance improvements. Using the SHAP (Shapley Additive exPlanations) technique to evaluate the importance of each variable, it was found that Return on Assets (ROA), firm size (SIZE), and foreign ownership (FOR) were key factors influencing CFP. ROA and foreign ownership had positive effects on firm value, while major shareholder ownership (MASR) showed a negative impact. This study differentiates itself from previous research by analyzing the nonlinear effects of ESG activities on CFP and presents a more accurate and interpretable prediction model by incorporating machine learning and XAI (Explainable AI) techniques.

본 논문에서는 히니가 어떻게 저장소로서의 의미와 아일랜드의 집단적 기억이 퇴적된 공간으로서의 의미를 가지고 있는 늪의 이미지를 통해 북아일랜드에서 일어나는 폭력에 대해 사유했는지 보여주고 있다. 늪 시편에서 시인은 고고학자가 되 어 늪을 발굴하면서 늪에 있는 희생자를 다시 끄집어내어 이들이 자신의 행위에 대한 심판을 받게 한다. 이러한 발견의 과정은 또한 히니가 1974년 에세이 “Feeling into Words”에서 썼듯 시적 창작 과정에 대한 은유이기도 하다. “The Tollund Man,” “Punishment,”와 “Kinship”이라는 시에서 히니는 과거와 현재의 폭력을 나란히 비교하 면서 어떻게 두 시대에서 (더 이상 유효하지 않은) 믿음에 기반한 폭력이 자행되는지 보여준다.

As the Fourth Industrial Revolution advances, smart factories have become a new manufacturing paradigm, integrating technologies such as Information and Communication Technology (ICT), the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics to overcome traditional manufacturing limitations and enhance global competitiveness. This study offers a comprehensive approach by evaluating both technological and economic performance of smart factory Research and Development (R&D) projects, addressing gaps in previous studies that focused narrowly on either aspect. The research combines Latent Dirichlet Allocation (LDA) topic modeling and Data Envelopment Analysis (DEA) to quantitatively compare the efficiency of various topics. This integrated approach not only identifies key research themes but also evaluates how effectively resources are utilized within each theme, supporting strategic decision-making for optimal resource allocation. Additionally, non-parametric statistical tests are applied to detect performance differences between topics, providing insights into areas of comparative advantage. Unlike traditional DEA methods, which face limitations in generalizing results, this study offers a more nuanced analysis by benchmarking efficiency across thematic areas. The findings highlight the superior performance of projects incorporating AI, IoT, and big data, as well as those led by the Ministry of Trade, Industry, and Energy (MOTIE) and small and medium-sized enterprises (SMEs). The regional analysis reveals significant contributions from non-metropolitan areas, emphasizing the need for balanced development. This research provides policymakers and industry leaders with strategic insights, guiding the efficient allocation of R&D resources and fostering the development of smart factories aligned with global trends and national goals.

The green supply chain has become a central concern for global businesses, particularly in maritime industries, where sustainable development is pursued as both an economic growth strategy and a means of environmental preservation. This study seeks to identify the key challenges to implementing green supply chain in Vietnam. The Analytical Hierarchy Process (AHP) is employed to assess the significance of various factors, while Fuzzy Structural Modeling (FSM) is used to explore their interrelationships. Five major factors - economic, technological, organizational, governmental, and social - are identified as critical to the implementation of green supply chain. The study highlights that the organizational factor is the most crucial, with customer pressure, particularly regarding environmental standards from export countries, being the most influential sub-factor. The findings provide important insights for developing government policies, offering support to businesses, and guiding investment decisions in green supply chain.

This study investigated the morphological characteristics and regional variations of leaves, flowers, and seeds of Quercus myrsinifolia Blume to understand its ecological adaptation and the effects of environmental factors. Samples were collected from Jinju, Hapcheon, and Sancheong, and nine leaf traits, six flower traits, and five seed traits were analyzed. Significant regional variations were observed, with Hapcheon exhibiting the largest leaf and flower sizes, while Sancheong showed the largest and heaviest seeds. Jinju recorded the smallest values for most traits. Principal Component Analysis (PCA) revealed distinct regional groupings, with Hapcheon displaying intermediate traits, Sancheong larger traits, and Jinju smaller traits. Correlation analysis identified strong positive relationships between leaf length and width, seed length and weight, and the number of staminate flowers and catkin width, highlighting key indicators for growth. Climate factors such as temperature and precipitation significantly influenced morphological traits, with higher temperatures negatively affecting leaf and seed sizes, while precipitation showed a weak positive correlation with seed weight. Among soil factors, pH and magnesium content were closely related to morphological traits. pH exhibited a negative correlation with leaf length and petiole length, while magnesium showed a positive correlation with seed weight and leaf width. These findings underscore the significant role of environmental factors in morphological variation and provide valuable insights for developing regionally adaptive breeding strategies. These findings provide foundational data for developing region-specific breeding strategies and cultivars for Q. myrsinifolia, contributing to ecological management and climate change adaptation strategies.

자기공명영상 평가 시 정확한 영상평가를 방해하는 요인에는 여러 가지가 있다. 그중 측정자로 인한 관심 영역의 크기 설정도 여러 요인 중 하나인데, 아직 다른 요인에 비해 관심 영역의 크기 설정은 연구가 부족한 실정이다. 이에 본 연구에서는 관심 영역의 크기 변화에 따른 SNR의 변화를 통계적으로 비교·분석하여 설정하는 방법을 제시하고 그 유용성을 증명하고자 하였다. 연구 방법은 팬텀의 T1, T2 강조영상을 획득한 다음 획득한 영상에 관심 영역의 크기를 변화시켜 신호강도를 측정한 후 관심 영역의 크기 변화에 따른 SNR 산출하여 비교평가 하였다. 연구 결과 T1 강조영상은 관심 영역의 크기 설정 시 20% 이하, T2 강조영상은 40% 이하로 설정할 때 기준 관심 영역 크기와 SNR이 통계적으로 차이가 없었다. 결론적으로 관심 영역의 크기 설정 시 본 연구의 통계를 이용한 설정 방법을 적절히 활용하여 측정을 시행한다면 관심 영역 크기 설정의 합리적 근거를 마련할 수 있어 유용하리라 판단된다.

In the integrated ancient East Asian sphere, literature is an explicit expression of unity. However, due to differences in perspectives, there are huge contrasts and disharmony in contemporary East Asia surrounding historical issues. Using artificial intelligence, specifically the retrieval-enhanced generative model, to build an intelligent research platform for Yanxinglu, and completing research auxiliary work that includes named entity recognition, relationship extraction, and knowledge graph construction, the study of East Asian history can be enhanced. This paper focuses on the construction of the LLM-RAG model and rules in the construction of the Yanxinglu knowledge base, and discusses the time process and precautions for the refined processing of the Yanxinglu text data.

Recently, there have been studies on space and time priority queues, where space priorities are given to a class of packets that are sensitive to loss, and time priorities to another class of packets that are sensitive to delay. However, these studies have been restricted to such models with push-out space priorities. In this paper, we extend the studies to the space and time priority M/G/1 model with partial-buffer-sharing (PBS) space priorities, where the whole buffer is divided into two regions: one is shared by packets of all classes and the other is dedicated only for packets of the higher space-priority class. Since the PBS space-priority mechanism can be implemented more readily in communication systems than the push-out one, there have been a lot of contributions on PBS space-priority queues. However, there are no contributions on space and time priority queues with PBS space priorities. To analyze the proposed queueing model, we first study the probabilistic structure of the service time of a packet, which is more involved to analyze than the push-out alternative because it may be divided into three different regimes: a regime (S-period) from the beginning of the service until the shared buffer region becomes full, a second one (P-period) from the end of the S-period until the whole buffer becomes full, a third one (F-period) from the end of the P-period until the end of the service. Using the distributions of the S-, P-, F-periods, we then construct and analyze the embedded Markov chain and the corresponding semi-Markov process governing the system state, and also derive system performance measures such as expected sojourn times and loss probabilities of different priority classes of packets. In numerical examples, we finally explore the effect of the shared buffer size, which is a major system control parameter of PBS priority queues, and the distributions of the service times of packets of different classes on the system performance measures.

Sustainable development is a critical global priority, as showed by United Nations' Sustainable Development Goals (SDGs). Effective logistics are crucial for achieving several SDGs so that improvements in Logistics Performance Index (LPI) often align with progress in SDG scores. For ASEAN countries, they may fall short of achieving 90% of their targeted SDGs and struggle to challenges of LPI fluctuations. By calculating the correlation between LPI and SDG scores in R software, this study seeks to explore the relationship between logistics performance and progress toward the SDGs in ASEAN countries from 64 secondary observations. As a result, the increasing logistics performance can greatly impact on the population well-being, accessibility, new energy approach, infrastructure formation, and sustainable production and consumption (G1, G3, G7, G9, G12) in ASEAN countries. The study contributes a background for national policymakers in the region to develop the sustainable logistics.

Despite the widespread recognition of the prominent contribution of key language subskills, such as grammar and vocabulary knowledge, to reading comprehension, a research consensus on their relative significance has not been reached. Moreover, the extent of the contribution vocabulary depth makes to reading comprehension has received little research attention. The present study assessed the relative potential contribution of vocabulary depth and grammar knowledge to advanced Korean EFL college students’ reading comprehension abilities, while controlling for their language proficiency and vocabulary breadth, through hierarchical regression analyses. 56 advanced EFL Korean college students were tested on reading comprehension abilities and a range of reading-related subskills including vocabulary breadth, vocabulary depth, grammar, and listening comprehension in English. The findings revealed the unique contribution of vocabulary depth to reading comprehension abilities beyond the effects of both vocabulary breadth and grammar knowledge when English proficiency was controlled for. The findings further underscore the need for balanced approaches in developing L2 learners’ language skills to enhance their reading comprehension abilities.

This study explores the innovative utilization of a biomimetic electric ray friction nanogenerator (ER-TENG) in combination with electrolysis technology for the remediation of maritime effluent. The ER-TENG is ingeniously crafted with a flexible, planar structure, enabling seamless adaptation to various curved and irregular substrates such as rocks, corals, and shipwrecks on the ocean floor, obviating the necessity for specialized mounting or securing devices. Simulation results regarding the potential distribution between the copper electrode and the PDMS film under different inter-electrode distances indicate that an increase in separation distance is correlated with an enhanced potential difference on the material's surface, exhibiting a linear upward trend, with the maximum potential difference reaching 120 V. When TiO2 nanoparticles are incorporated at a doping mass fraction of 4.65 wt%, the friction nanogenerator attains its peak electrical performance, boasting a peak opencircuit voltage of 123.25 V and a maximum short-circuit current of 13.52 μA, representing increases of 2.73-fold and 2.56-fold in open-circuit voltage and short-circuit current, respectively. At operational frequencies of 1.2 Hz and 1.0 Hz, the initial stage of sterilization rate enhancement proceeds at a moderate pace. However, after 60 minutes of electrolysis, sterilization rates reach 88.12% and 46.36%, respectively. The electrical energy harvested by the ER-TENG facilitates the generation of potent oxidizing chlorine through electrolysis, which effectively eliminates harmful aquatic organisms and pathogens present in ship ballast water.

본 연구는 금속성 물질로 인해 발생하는 자화율 인공물(susceptibility artifact)의 정확한 길이 측정을 위해 자체 제작한 팬텀을 이용하여 분석하였다. 치과용 임플란트 고정체를 자체 제작한 아크릴 팬텀에 위치시키고, T2 강조 영상을 통해 자화율 인공물을 검사하였다. 자화율 인공물의 길이를 팬텀 기반 측정법과 선 프로파일 기반 측정법을 사용하여 분석하였고 팬텀 기반 측정법을 기준값으로 선 프로파일 기반 측정법으로 도출된 결과와 비교하였다. 결과 적으로 선 프로파일 기반 측정법에서 배경 신호 기준 25% 허용 범위를 설정했을 때 팬텀 기반 측정법과 가장 유사한 데이터를 얻을 수 있었다. 이를 통해 선 프로파일 기반 측정법이 자화율 인공물 길이의 정량적 측정에 적합할 수 있는 가능성을 확인하였다. 추후 미흡한 부분을 보완한 추가 연구를 통해 자화율 인공물 연구에서 객관적인 데이터 제공이 가능할 수 있게 되기를 기대한다.

In response to the escalating demands of global trade and the pressing imperative for environmental preservation, the shipping industry is confronted with the dual challenges of augmenting energy efficiency and significantly curtailing carbon emissions. Ship drag reduction technology emerges as a promising solution to address these critical issues. Over the recent years, a spectrum of diverse drag reduction technologies has been developed, each precisely targeting distinct components of ship resistance and influenced by a multitude of factors. We provide a comprehensive synthesis and critical evaluation of the existing literature on ship drag reduction technologies. It categorizes these technologies into four primary domains: body-attached drag reduction, surface drag reduction, air lubrication drag reduction, and other specialized drag reduction techniques. By presenting detailed and extensive experimental data, coupled with real-world application cases, we underscore the practical implementation and proven efficacy of these technologies in reducing ship drag. We delve into the current limitations and challenges encountered by these technologies. We also offer strategic recommendations for future research endeavors and practical applications, aiming to overcome these limitations and enhance the overall performance of drag reduction technologies. The insights provided in this paper aim to serve as a guide for ongoing efforts in developing innovative and effective utilization of ship drag reduction technologies, ultimately contributing to the sustainability and efficiency of the shipping industry.