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.

With the increasing integration of generative Artificial Intelligence (AI) in education, there is a growing demand for accessible AI-powered platforms for teachers and students. This study examines the efficacy of an online question-making platform using GPT-3.5 generated content to develop Korean students’ question-asking skills in English. Employing a mixed-methods approach, we assessed the platform’s impact on students’ question making abilities and its feasibility in classroom settings. Participants included thirty students from a 4th grade elementary school English classroom in Korea and seven elementary school teachers. Quantitative data were collected through pre- and post-tests measuring students’ question-making skills, while qualitative insights were gathered from semi-structured teacher interviews. Results indicated that the application was able to significantly improve students’ question-making abilities. Thematic analysis of interviews indicated positive perceptions of the platform’s usability and p otential f or l anguage learning. Findings m ay contribute to the growing body of research on using artificial intelligence in L2 education and offer practical insights for educators seeking to leverage advance language models in their instructions.

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.

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.

이 논문은 W. B. 예이츠의 유산과 멜리스 베스리의 소설 􋺷흩어진 사랑􋺸에 서 나타나는 뼈의 상징적 공명을 탐구한다. 소설에서 예이츠의 유령은 그의 삶, 예술, 그 리고 자신의 유해라는 수수께끼 같은 운명을 되새기며 지속적인 주제들을 반추한다. 뼈의 모티프는 사랑, 죽음, 그리고 기억의 교차점을 포괄하는 서사적이고 은유적인 축으로 기능한다. 이러한 관점에서 이 논문은 베스리가 예이츠의 미완의 열정, 특히 모드 곤에 대한 사랑을 어떻게 재구성하며 그의 정체성의 더 넓은 문화적, 역사적 차원을 어떻게 탐구하는지를 살펴본다. 예이츠의 유해 발굴과 재매장에 얽힌 논란은 예술적 불멸성과 신체의 덧없음 사이의 긴장을 심화시키며, 이는 아일랜드의 복잡하고 종종 단절된 역사 적 서사를 반영한다. 예이츠를 현대 문학적 맥락에 배치함으로써 베스리는 그의 비전을 확장하면서도 변모시키며, 사랑, 상실, 유산에 대한 예이츠의 사색을 현대적 맥락에 녹여 낸다. 이 논문은 베스리의 소설이 뼈를 기억의 매개체로 부각시키며 예이츠의 시적 상상 력과 그녀 자신의 재해석 사이의 다리를 놓는다고 주장한다. 궁극적으로, 이 연구는 예 이츠와 베스리가 공유하는 죽음, 역사, 그리고 예술과 감정의 초월적 힘에 대한 탐구가 여전히 중요한 의미를 가진다는 점을 강조한다. 􋺷흩어진 사랑􋺸 은 예이츠의 사랑의 유산 을 재구성하며, 그 자체로 그 유산의 본질적 일부가 된다.

Seeds of some barberry species have embryo with physiological dormancy that requires a cold stratification for germination. Berberis amurensis Rupr. is a native species of Japan, Korea, the Russian far east, and parts of China. This specific plant is important for its edible fruits and rhizomes with high medicine value. This study aimed to determine the effect of stratification on germination and physiological change of B. amurensis. Seeds were placed on sterilized sand medium moistened with distilled water in 9 cm diameter petri dishes and stored at 4 and 25˚C for 0, 15, 30, 45 and 60 days. Each treatment had 40 seeds per replica, and three repetitions per treatment. Immediately after stratification, total phenolics contents (TPC) was analyzed and seeds incubated at 15/6˚C for 12 weeks. Warm stratification had a significant effect on seed forcing for germination than cold stratification treatment. At 25˚C for 60 days, stratified seed showed highest germination percentage (25.7±4.3%) and germination started in 14 days of the stored period. Whereas TPC was significantly decreased with increasing stored period. Contrastively, cold stratification had no effect on the germination ability. In the same way germination percentage of non-stratified seeds were also zero. The results confirmed that B. amurensis seeds were in a dormant state and warm stratification increased the germination ability by breaking of dormancy.

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.

Neural machine translators (NMTs), such as Google Translate, may assist second language (L2) readers with general comprehension. However, previous empirical studies show mix ed r esults r egarding their e ffectiveness. In this study, 145 Korean English learners from a girls’ high school were asked to solve three types of reading comprehension problems (grammar judgment, inferring meaning from context, inferring main idea) under three reading conditions (no aid, MT, glossary). Overall, when using MT, reading comprehension scores were higher than in either the no aid or glossary conditions individually. However, none of the reading aid conditions improved grammar judgment. Only mid-proficiency learners benefited from MT in both inferring meaning from context and inferring main idea tasks. The results suggest that the glossary may have interrupted the flow of the reading process. With the widespread availability of MT as an online reference tool, L2 teachers should consider incorporating MT as a legitimate reading aid for different proficiency levels and reading purposes.

Defective product data is often very few because it is difficult to obtain defective product data while good product data is rich in manufacturing system. One of the frequently used methods to resolve the problems caused by data imbalance is data augmentation. Data augmentation is a method of increasing data from a minor class with a small number of data to be similar to the number of data from a major class with a large number of data. BAGAN-GP uses an autoencoder in the early stage of learning to infer the distribution of the major class and minor class and initialize the weights of the GAN. To resolve the weight clipping problem where the weights are concentrated on the boundary, the gradient penalty method is applied to appropriately distribute the weights within the range. Data augmentation techniques such as SMOTE, ADASYN, and Borderline-SMOTE are linearity-based techniques that connect observations with a line segment and generate data by selecting a random point on the line segment. On the other hand, BAGAN-GP does not exhibit linearity because it generates data based on the distribution of classes. Considering the generation of data with various characteristics and rare defective data, MO1 and MO2 techniques are proposed. The data is augmented with the proposed augmentation techniques, and the performance is compared with the cases augmented with existing techniques by classifying them with MLP, SVM, and random forest. The results of MO1 is good in most cases, which is believed to be because the data was augmented more diversely by using the existing oversampling technique based on linearity and the BAGAN-GP technique based on the distribution of class data, respectively.

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.

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.

급성 뇌경색 진단에는 환자에 따라 관류강조영상과 조영증강 자기공명혈관영상이 동시에 촬영되는 경우가 있으며, 이 과정에서 MRI 조영제인 가돌리늄의 T1 단축 효과가 발생하여 동맥과 정맥의 신호 겹침이 빈번히 발생한다. 특 히, 3D T1 경사에코 기법을 사용한 CE-MRA에서 첫 번째 조영제 주입(PWI) 후 연속적으로 발생하는 T1 감소 효과로 인해 혈관의 정확한 묘사에 한계가 있었다. 본 연구에서는 이러한 문제를 해결하기 위해 마스크 영상의 획득 시점을 조정하여 새로운 방법(B)을 제안하였다. 기존 방법(A)에서는 관류강조영상 이전에 마스크 영상을 획득했지 만, 새로운 방법(B)에서는 관류강조영상 이후 마스크 영상을 획득하여 동맥과 정맥 신호 겹침을 효과적으로 줄였다. 연구 결과, S상 정맥동과 내경정맥에서 새로운 방법이 기존 방법보다 유의미하게 낮은 대조대잡음비를 보였으며, 신호 겹침이 줄어들어 정맥 신호의 억제 효과가 극대화되었다(p<0.05). 본 연구의 새로운 방법은 기술적으로 추가적 인 복잡성 없이 효율적으로 정맥 신호를 제거하여 CE-MRA 영상의 진단적 정확성을 향상하는 유용한 기법으로 평 가된다. 향후 연구에서는 더 다양한 환자군을 대상으로 본 방법의 유용성을 검증하고, 최적의 방안 모색을 할 필요가 있다.

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.

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.

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.

Motivated by the effort of diagnostic interventions for EFL learners in Korean educational contexts, this study aims to identify heterogeneous L2 reading skill profiles among students attending the same school. Subsequently, it investigates the impact of learners’ L2 learning backgrounds on heterogeneity of these reading skills. Using Latent Profile Analysis on 234 vocational high school students, we identified four distinct profiles: Profile 1, severely weak L2 readers with very low decoding and syntactic knowledge; Profile 2, moderately weak L2 readers; Profile 3, above-average L2 readers; and Profile 4, fairly well-developed L2 readers with good vocabulary breadth. Multinomial regression analysis revealed that profile membership was significantly predicted by positive past learning experiences, extra-curricular English reading, and motivational attitudes. These findings underscore the significant heterogeneity in L2 reading skills within the seemingly homogeneous EFL group and highlight the critical association with past and current educational experiences, emphasizing the importance of tailored interventions based on individual learning histories.

자기공명영상은 인체 내부 구조와 병변을 비침습적으로 시각화하는 핵심 의료 영상 기법으로 자리 잡고 있으며, 특히 신경계 및 심혈관계 질환과 같은 복잡한 질병의 진단에서 필수적인 도구로 활용되고 있다. 기존의 자기공명영상 시스 템은 영상의 해상도와 신호대잡음비에서 한계가 있었으나, 최근의 기술 발전은 이러한 한계를 극복하고 진단 정확성 을 높이는 방향으로 나아가고 있다. 고자기장 자기공명영상 시스템의 도입은 해상도와 신호대잡음비를 개선하는 데 기여하고 있으며, 병렬 영상 기법은 촬영 속도를 향상시키면서도 영상 품질의 손실을 최소화한다. 또한, 압축 센싱 (compressed sensing) 기술은 데이터 획득 시간을 줄여 촬영 효율성을 높이는 데 중요한 역할을 하고 있다. 최근 인공지능(AI)의 발전으로, 자기공명영상 데이터에서 초해상도 복원(super-resolution) 및 노이즈 제거와 같은 영상 후처리 기술이 획기적으로 향상되었다. 인공지능 기반의 영상 향상 기술은 저해상도 데이터를 고해상도로 변환하고, 촬영 과정에서 발생할 수 있는 왜곡과 노이즈를 효과적으로 제거하여, 더 정확하고 명확한 진단 영상을 제공한다. 이러한 발전은 단순히 영상의 품질을 높이는 것을 넘어, 임상 진단의 정확성과 효율성을 크게 향상시키고 있으며, 특히 제한된 촬영 시간을 요구하는 응급 상황에서 유용성이 두드러진다. 본 논문에서는 자기공명영상 촬영 기법의 최신 발전과 인공지능 기반 영상 향상 기술의 동향을 여러모로 분석하고, 이들의 임상적 유용성을 조명함으로써 고해 상도 자기공명영상이 의료 분야에서 가지는 의미와 향후 발전 방향을 제시하고자 한다.

This study evaluated the applicability of existing overseas-developed allometric equations for estimating the biomass of domestic apple trees (Malus domestica) in South Korea. To assess their suitability, predicted biomass values derived from these equations were compared with actual measurements obtained through destructive sampling. The results indicate that some overseas equations showed no significant difference when applied to domestic apple trees, suggesting their potential applicability to local conditions. However, it was also noted that certain equations may not fully capture the unique characteristics of domestic apple trees, such as the slender spindle form associated with modern high-density planting practices. Given these findings, the study highlights the need to develop new allometric equations that are specifically optimized for the growth characteristics and cultivation environments of domestic apple orchards. Such equations would enable more accurate biomass estimation, support agricultural sustainability, and provide reliable data for the national greenhouse gas inventory. This research is expected to serve as a foundation for future Tier 2-level studies on biomass and carbon sequestration in apple orchards while also underscoring the importance of expanding research to include various fruit tree species to develop customized biomass estimation models for enhanced agricultural management and environmental conservation.