본 연구는 복잡한 도심주변의 지형과 다양한 배출원이 존재하고 있는 세종시 부강면과 조치원읍 일원을 대상으 로 대기오염 확산의 물리적인 특성을 분석하여 오염 물질의 확산을 가정했을 때의 특징을 분석하였다. 부강면의 경우 토지의 기복이 심한 전형적인 분지형지역으로서, 대기오염물질의 확산이 불량하여 대기 오염이 심화되는 지형상의 특성을 내포하고 있다. 지형조건의 특성상 강한 풍속으로 이류된 대기오염물질의 확산이 용이하지 않아 국지적으로 정체되 어 대기오염물질의 공간적 확산 보다는 정체에 의한 대기 환경 악화의 간접 요인이 될 수 있음을 나타낸다. 또한 이러 한 경우 지형적 가열율의 차이 등에 의한 급속한 온도 상승 경향이 나타날 가능성이 있어 향후 먼지 돔현상(dust dome) 등과 관련하여 오염물질의 확산과 관련한 정책제안에 있어서 세심한 논의가 필요하다. 고농도와 비고농도 사례 일을 분석했을 때 조치원읍의 경우 부강면에 비하여 주변 배출원의 영향이 다소 높은 상태이다. 또한 고농도과 비고농 도의 사례에 대한 분석에서 특정한 풍계에 따라 고농도일의 사례가 됨과 동시에 지형적으로 경사지에 속해 서쪽에서 이류된 오염물질이 쉽게 유입되는 상황으로 분석된다. 이러한 국지 지형에 따른 난류특성 변수들인 마찰속도(friction velocity), 확산속도(convective velocity), 현열 속(sensible heat flux) 등에 의한 연직 혼합고는 대기오염물질의 확산강도 에 중요한 요인임을 알 수 있었다. 이와 관련하여 배출원관리에 따른 논의가 부강면 사례에서 보다는 조치원읍에서 상 대적으로 필요함을 분석하였다.

The advent of big data has brought about the need for analytics. Natural language processing (NLP), a field of big data, has received a lot of attention. Topic modeling among NLP is widely applied to identify key topics in various academic journals. The Korean Society of Industrial and Systems Engineering (KSIE) has published academic journals since 1978. To enhance its status, it is imperative to recognize the diversity of research domains. We have already discovered eight major research topics for papers published by KSIE from 1978 to 1999. As a follow-up study, we aim to identify major topics of research papers published in KSIE from 2000 to 2022. We performed topic modeling on 1,742 research papers during this period by using LDA and BERTopic which has recently attracted attention. BERTopic outperformed LDA by providing a set of coherent topic keywords that can effectively distinguish 36 topics found out this study. In terms of visualization techniques, pyLDAvis presented better two-dimensional scatter plots for the intertopic distance map than BERTopic. However, BERTopic provided much more diverse visualization methods to explore the relevance of 36 topics. BERTopic was also able to classify hot and cold topics by presenting ‘topic over time’ graphs that can identify topic trends over time.

This study compared research trends in universities general English program before and after the COVID-19 pandemic. After analyzing 248 articles from KCI using frequency analysis, centrality analysis, and topic modeling, this study found consistent keywords indicating a focus on learning objectives, effectiveness analysis, satisfaction surveys, and level-based learning before and after the COVID-19 pandemic. Centrality analysis revealed keywords like “teaching, research, analysis” before COVID-19 and “satisfaction, study, level, activity, effect” after COVID-19, indicating a shift towards learner satisfaction, level-based learning, and effectiveness analysis due to the transition to online learning. Topic modeling revealed shifts in research trends: Pre-COVID-19 focused on effective teaching methods, evaluation techniques, and cultural content, while Post-COVID-19 prioritized online teaching methods, web-based platforms, and selfdirected learning. Future research should address self-directed learning, attitudes and goal setting, closing learning gaps in online/blended learning, and developing effective online assessment tools and evaluation strategies. This study provides valuable insights and directions for further research in general English programs.

본 연구에서는 코로나 이후 색조화장품 시장의 소비자들의 온라인 관심 정보에 대한 자료 수집 을 통하여 색조화장품 정보 검색의 특성과 텍스트 마이닝 분석 결과에 나타난 코로나 이후 색조화장품 시 장의 주요 관심정보들을 분석하고자 하였다. 실증분석에서는 “색조화장품” 이라는 단어를 포함하는 뉴스, 블로그, 카페, 웹페이지 등의 모든 문서들을 분석 대상으로 텍스트 마이닝을 수행하였다. 분석 결과 코로나 이후 색조화장품에 대한 온라인 정보 검색은 주로 구매 정보와 피부와 마스크 관련 화장법 등에 관한 정보 와 관심 브랜드와 행사 정보 등의 주요 토픽이 주를 이루고 있었다. 결과적으로 코로나 이후 색조화장품 구매자들은 적극적인 온라인 정보 검색을 통하여 제품 가치와 안전성, 가격 혜택, 매장 정보 등의 구매 정 보에 더욱 민감하게 될 것이므로 이에 대한 대응전략이 요구된다.

While machine learning has gained popularity in choice behavior modeling, most machine learning models are often complex, difficult to interpret, and even considered as black box. This study investigates machine learning methods for choice behavior modeling that provide interpretability of models’ output. We explore various approaches including (1) explicitly descriptive models such as tree-based models, (2) interpretation of predictive models through feature importance measures, and (3) recent advancements in prediction explanation methods such as LIME and SHAP (Shapley Additive exPlanations). We demonstrate the methods on consumers’ airport choice behavior in Seoul metropolitan area. Through the comparative analysis with traditional discrete choice models, we discuss advantages as well as limitations of machine learning models in consumer choice behavior modeling.

Non-fungible tokens (NFTs) exploded onto the global digital landscape in 2020, spurred by pandemic-related lockdowns and government stimulus (Ossinger, 2021). An NFT is a unit of data stored on a blockchain that represents or authenticates digital or physical items (Nadini, 2021). Since it resides on a blockchain, NFTs carry the benefits of decentralization, anti-tampering, and traceability (Joy et al., 2022). Fashion brands quickly capitalized on these features, launching fashion NFT collections and garnering significant profits from the sale of fashion NFTs in 2021 (Zhao, 2021). For example, Nike’s December 2021 acquisition of RTFKT (pronounced “artifact”) resulted in USD 185 million in sales less than a year after their acquisition (Marr, 2022).

With the development of information technology, online food delivery services have significantly changed people’s food consumption patterns. The outbreak of COVID-19 has further facilitated the development due to restaurant restrictions and social distancing measures. Due to the emergence of online food delivery services, scholars have paid increasing attention to this popular topic, and a large number of studies have examined which factors are related to consumers’ intention to use online food delivery services. However, previous studies showed inconsistent findings, and relationships between variables lack a comprehensive understanding. Therefore, this study aimed to develop a research model and examine the factors influencing consumers’ intention to use online food delivery services. By reviewing and analyzing 52 studies with 53 independent samples (N = 22,937), the meta-analytic structural equation modeling approach was carried out to assess the research model. The findings indicate that two attributes of online food delivery services (i.e., convenience and price-saving orientation) play different roles in the research model. Specifically, convenience significantly impacted perceived ease of use but had no direct impact on perceived usefulness. Price-saving orientation significantly influenced both perceived ease of use and perceived usefulness. Moreover, relationships between perceived ease of use, perceived usefulness, and perceived trust were significant, and these factors ultimately significantly led to the intention to use food delivery services. The findings contribute to theoretical advancement in the extant literature on food delivery services and help firms develop to provide better food delivery services for consumers’ continuous usage.

In this study, numerical modeling on the gas flow and off-gases in the low temperature carbonization furnace for carbon fiber was analyzed. The furnace was designed for testing carbonization process of carbon fibers made from various precursors. Nitrogen gas was used as a working gas and it was treated as an incompressible ideal gas. Three-dimensional computational fluid dynamics for steady state turbulent flow was used to analyze flow pattern and temperature field in the furnace. The off-gas mass fraction and cumulative emission gas of species were incorporated into the CFD analyses by using the user defined function(UDF). As a results, during the carbonization process, the emission of CO2 was the dominant among the off-gases, and tow moving made the flow in the furnace be uniform.