On pig farms, the highest mortality rate is observed among nursing piglets. To reduce this mortality rate, farmers need to carefully observe the piglets to prevent accidents such as being crushed and to maintain a proper body temperature. However, observing a large number of pigs individually can be challenging for farmers. Therefore, our aim was to detect the behavior of piglets and sows in real-time using deep learning models, such as YOLOv4-CSP and YOLOv7-E6E, that allow for real-time object detection. YOLOv4-CSP reduces computational cost by partitioning feature maps and utilizing Cross-stage Hierarchy to remove redundant gradient calculation. YOLOv7-E6E analyzes and controls gradient paths such that the weights of each layer learn diverse features. We detected standing, sitting, and lying behaviors in sows and lactating and starving behaviors in piglets, which indicate nursing behavior and movement to colder areas away from the group. We optimized the model parameters for the best object detection and improved reliability by acquiring data through experts. We conducted object detection for the five different behaviors. The YOLOv4-CSP model achieved an accuracy of 0.63 and mAP of 0.662, whereas the YOLOv7-E6E model showed an accuracy of 0.65 and mAP of 0.637. Therefore, based on mAP, which includes both class and localization performance, YOLOv4-CSP showed the superior performance. Such research is anticipated to be effectively utilized for the behavioral analysis of fattening pigs and in preventing piglet crushing in the future.

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure’s safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

구한말의 개혁가들은 국가 만들기에 먼저 관심을 가졌지만, 이러한 시 도가 실패로 돌아가고 대한제국이 국권을 상실해감에 따라 국가에 대한 기대를 접고 ‘아래로부터’ 네이션(nation)을 우선 만들고 이를 통해 궁극 적으로 ‘네이션 스테이트’(국민국가 또는 민족국가)를 만들어내려 했다. 그들은 서양이 성공한 이유를 연구하면서 마르틴 루터 등이 이뤄낸 ‘종 교개혁’이 중세적 마비를 이기고 개인의 주체성을 해방시켜 이를 통해 기개 넘치는 ‘네이션’을 만들어낼 수 있었다고 생각했다. 그래서, 개신교 를 배우기도 하고 박은식이나 한용운처럼 전통적인 유학이나 불교를 안 으로부터 개혁하려고도 했다. 박은식이 결정론적 경향이 강해 사람들을 수동적으로 만드는 경향이 있는 주자학의 대안으로 관심을 가졌던 것은 양명학, 정확히는 일본에 기원을 둔 ‘근대 양명학’이었다. 하지만, 박은식 의 경우 양명학을 철학적으로 깊게 천착했던 것은 아니고 개인적 주체성 이나 기개를 강조한다는 정도로 이해하고 있었으며, 이는 그의 왕양명 선생실기에서 잘 드러난다. 나라를 잃고 만주에서 망명생활을 하며 저 술한 몽배금태조는 좀 더 정치사상적 저작인데, 상무(尙武)을 강조하고 문약(文弱)의 폐단을 지적하는 것과 함께 공화주의적 요소와 혈연민족주 의 요소가 강조되고 있는 점이 주목된다. 그는 국내 통치자에 맞서는 기 개가 없이 어떻게 국외의 침략자들에게 맞설 수 있느냐면서 공화주의 내 지 민주주의가 독립의 필수요소임을 강조한다.

이 연구는 대학별로 대면수업으로 전환되면서 대면수업 학습 환경에서 메타버스를 활용한 진로교과목 수업이 학습실재감과 학습지속의향에 어 떤 영향을 미치는지 확인하였다. 연구결과 인지적, 감성적, 사회적 학습 실재감과 학습지속의향과의 상관분석결과 모두 정(+)의 상관을 보였고, 감성적 학습실재감과 사회적 학습실재감, 학습실재감 전체가 학습지속 의향에 정(+)의 영향을 미치는 것으로 나타났다. 비대면 수업에 적응해 왔던 대학교 1학년 학생들이 대면수업에서 메타버스를 활용해 팀원들 과 함께 같은 가상공간에서 느끼는 편안함과 소통방식이 학습 실재감 을 느끼게 하고 학습지속의향에 미치는 중요한 요인임을 확인하였다. 향후 개인특성을 반영한 후속연구의 필요성과 대면수업에서의 활용 가 능한 함의를 제시하여 본 연구결과가 수업현장에서 유용한 자료를 제공 한다는 점에서 의의를 갖는다.

본 연구는 전 세계가 하나가 되는 초연결주의와 다문화로 대변되는 현 대 사회의 흐름에 맞추어 글로벌 문화에 대한 관심과 이해를 넓힐 필요 성이 있다는 자각에서 시작되었다. 본 연구의 목적은 글로벌 문화 교육 을 위한 구체적인 수업 절차와 수업 활동을 제시하고 이에 대한 학습자 반응과 인식을 고찰하고자 하는 것이다. 이를 위해 다양한 미디어에 반 영되어 있는 글로벌 문화 관련 주제를 8개 선정하였고 각 주제별로 학습 자들이 비판적으로 사고하고 이해할 수 있도록 학습자 참여 중심의 수업 을 진행하였다. 글로벌 문화 내용과 수업 활동, 문화 수업을 위한 수업 방식에 대한 학습자들의 반응을 파악하기 위해 설문 조사와 의견 메모 활동을 실시하였다. 그 결과 학습자들은 다양한 미디어를 활용한 수업 방식에 만족도가 높았으며(76%) 이에 대한 이유는 학습자 참여 수업, 다 양한 매체 활용, 체계적인 수업 구성으로 나타났다. 글로벌 문화 수업 내 용과 수업 활동에 대한 만족도는 매우 높게 나타났으며(92%) 이에 대한 이유는 문화 학습에 유익함, 흥미롭고 재미있음, 영어 학습에도 유익함으 로 나타났다. 교수자들은 학습자들의 글로벌 역량을 증진시키기 위해 글 로벌 문화 교육을 위한 다양한 수업 활동의 구상을 위해 정진할 필요가 있다.

본 연구의 목적은 유아의 마음이론과 자기조절학습능력, 사회적 유능감의 상 관관계를 살펴보고, 유아의 마음이론이 자기조절학습능력에 미치는 영향에서 사회적 유능감의 매개효과에 대해 규명하는 것이다. 연구대상은 경기도에 위치 한 유치원에 재원 중인 만 3～5세 유아 307명이고, 설문지를 통해 자료를 수집 하였으며, 수집된 자료를 분석하기 위해 기술통계, 상관관계분석, 회귀분석, Sobel Test를 실시하였다. 연구결과 첫째, 유아의 마음이론과 자기조절학습능 력, 사회적 유능감은 유의미한 정적 상관관계가 있는 것으로 나타나, 유아의 마 음이론이 높을수록 자기조절학습능력과 사회적 유능감이 높고, 사회적 유능감 이 높을수록 자기조절학습능력이 높음을 알 수 있었다. 둘째, 유아의 마음이론 은 자기조절학습능력을 의미 있게 예측하는 변인으로 나타났고, 이 과정에서 사 회적 유능감이 매개효과를 갖는 것으로 나타났다. 즉, 유아의 마음이론이 자기 조절학습능력에 영향을 미치는 과정에서 사회적 유능감이 자기조절학습능력을 높일 수 있는 변인으로 작용하고 있음을 확인할 수 있었다. 이러한 결과는 유아 의 자기조절학습능력을 높이는 데 마음이론과 사회적 유능감을 균형 있게 지원 해야 함을 시사한다.

Previous research on green product innovation (GPI) has primarily focused on the performance of new product development, neglecting the critical, early fuzzy front-end (FFE) phase. Drawing upon the firm's resource-based view (RBV) and dynamic capabilities perspective, this study explores how co-production affects FFE outcomes (creative idea and product definition proficiency) of GPI and whether joint learning ambidexterity is a mediating mechanism in international buyer-supplier relationships. Additionally, we provide a contingency perspective by considering the moderating role of the innovation ecosystem on the relationship between co-production and joint learning ambidexterity. Finally, this study contributes to a theoretical understanding, analyzing co-production as a predictor, joint learning ambidexterity as a mediator, and innovation ecosystem as a moderator.

A typical consumer is exposed to more than 5,000 advertisements per day (Story 2015) by exposure to around 500 advertising messages before ending breakfast (Marshall, 2015), and it is impossible for consumers to remember most of the advertisement images and messages. Thus, one consistent yet not thoroughly investigated question for advertisers is how advertisers draw consumers' attention by differentiating their brand from competitors' brands. One suggestion from academia is making more "creative" advertising (Dahlén et al., 2008; Lehnert et al., 2014; Rosengren et al., 2013; Smith et al., 2008). However, it is still questionable the exact meaning of "advertising creativity," and the effects of creative advertising on consumer evaluation have not been fully investigated the effects of creativity in advertising evaluation by considering various boundary conditions. The objective of this research is to redefine advertising creativity, to understand how advertising creativity shapes consumers' evaluation, and how these effects are moderated by the different types of boundary conditions, such as industry category, by analyzing more than 100,000 advertising images and copies using a cutting-edge transfer learning technique. The results of the transfer learning algorithm indicate that both cognitive dimensions (e.g., novelty of image) and affective dimensions (e.g., awe and coolness) simultaneously affect the consumers' perception of the advertising creativity, and the current algorithm enables to detect of creative advertising image with 92% accuracy rate.

The popularity of live streaming is driving the emergence of a new business model, known as live-streaming commerce (LSC). While there are more and more broadcasters in LSC, their behaviors and performance of them are significantly different. To have a better understanding of broadcasters, we employ different machine learning models to identify different portraits in both static and dynamic dimensions. We collect a rich live-streaming dataset from one leading platform in China. Our dataset features information for both broadcasters and viewers, including viewers’ purchasing behaviors, viewers’ records of posting words, broadcasters’ gender, the number of followers for broadcasters, and the live streaming show information, including the start and end time, and the viewers in each live streaming show. The rich textual information in broadcasters’ profile induction provides us a good opportunity to uncover different static portraits and the records in live streaming shows give us a chance to identify different dynamic behavioral portraits for broadcasters.

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.

The concept of Social Marketing has existed in literature for a long time and is a widely discussed topic. Many scholars have discussed the importance of health related social marketing to bring about positive lifestyle changes and improve health of populations. Marketing efforts and their effectiveness in creating positive changes is quite complex to assess.Social marketing for HIV/AIDS aims to generate awareness, teach people about the disease and influence people’s behavior towards a healthier and better lifestyle. To evaluate how aware people are, what they’ve learnt and how their behavior is influenced by social marketing communications, the authors use a modified version of Kirkpatrick model. This paper aims to ascertain the perceptions and knowledge about HIV/AIDS information disseminated by social marketers using traditional and new media such as Television, hoardings, pole kiosks, bus panels, information panels, radio, social media and celebrity endorsement. The study followed exploratory research design and Survey technique was used.

Social media have emerged as one of the most important tools for firms to engage customers (e.g., Chandrasekaran et al., 2022; Cheng & Edwards, 2015; Lee et al., 2018; Wedel & Kannan, 2016). Within the tourism industry, scholars have investigated the role of social media communication in various contexts, such as online travel information search (Xiang & Gretzel, 2010), sharing travel experiences (So et al., 2018; Wang et al., 2022) and establishing positive customer relationships (Jamshidi et al., 2021). Insights into which social media content makes for generating positive engagement are, however, still largely based on marketers’ intuitions or focusing on message factors of social media posts such as message appeals (e.g., Wang & Lehto, 2020). It also often neglects the importance of the visual component of social media posts, and only a few research have investigated the effects of the image in social media on the travel industry (e.g., Fusté-Forné, 2022). The objective of this research is, therefore, to understand how textual features and image features generate user engagement in social media utilizing cutting-edge transfer learning techniques and to propose how these features should be customized to maximize user engagement for online travel shopping companies. We collect and analyze more than 10,000 Instagram posts from three online travel shopping companies, including Expedia, Priceline, and Kayak. The results from transfer learning algorithms utilizing 24 features, such as the number of people in the image, emotions expressed in the people in the image, hue, and RGB value, successfully predict the level of engagement measured by the number of likes and comments.

Consumers' online reviews have become more powerful in the Internet market. Consumers share reviews, post comments and constantly evaluate products online. In previous studies, the analysis of online reviews mainly focused on purchasing products based on consumers' own use experience, but in innovative products, it was difficult to find an analysis of product acceptor's response to product user reviews. In particular, there is no online review study of VR covered in this study. This study not only quantitatively analyzed online reviews of consumers who purchased VR products on Amazon, an online distribution site, but also qualitatively analyzed them through crawling. This study used Amazon's VR product user review, where purchases were confirmed, to select algorithms that are more likely to be matched by predicting a helpful review and presenting a predictive model. In addition, the online review extracted deep text associated with Helpful and conducted topical modeling. As a result, topics related to 1) experience in use, 2) post-product evaluation, 3) product composition and peripherals, 4) immersion, and 5) comfort were highly acceptable to potential inmates. To enhance the acceptability of innovative products through online reviews, it is not just highlighting the product advantages of VR, but also suggests that the link between smartphones and applications can bring in more potential users. Also, interworking with other peripheral devices (speakers or screens) can be predicted as a way to increase the acceptability of VR products. From a marketing perspective, this study has found targeted topics that help consumers in pioneering the VR market, which will help potential customers create the services they want.

Fast-paced advancements in technology demand swift adaptation and presents new opportunities and challenges for the optimization of communication, especially for advertisers. Digitalization and new developments in ICT have brought significant changes to the ways in which information, especially promotional messages, is disseminated to consumers. Additionally, with explosive interests in anticipation of fully autonomous vehicles, this study identifies and addresses the potential to optimize communication in an under examined digital media environment – in-vehicle infotainment system. Therefore, this study proposes a text-image embedding method recommender system for the personalization of multimedia contents and advertisements for in-vehicle infotainment systems. Unlike most previous research, which focuses on textual-only or image-only analyses, the current study explores the understanding, development and application of text embedding models and image feature extraction methods simultaneously in the context of target advertisement research. Overall, this study highlights the need to adapt to the ever-evolving technological landscape to optimize communication in various digital media environments. With the proposed text-image embedding method, this study offers a unique approach to personalizing multimedia content and advertisements in the under-explored digital media environment of in-vehicle infotainment systems.

To successfully expand their business activities in overseas markets, small- and medium-sized enterprises (SMEs) must first acquire a thorough knowledge and understanding of prevailing environmental and market conditions. This study examines the crucial role that a learning orientation can play in the generation of relevant foreign market knowledge. It also investigates the impact of foreign market knowledge on strengthening internationalizing SMEs’ operational adjustment agility and market capitalizing agility, which in turn enhance firms’ international venture performance. Our empirical effort is based on data collected from 209 Nigerian industrial SMEs which internationalize their efforts. To test our research model and hypotheses we collected data by means of a survey conducted among Nigerian small- and medium-sized firms (i.e., employing 250 or less people) which internationalize their efforts and launch their products in B2B markets. The positive role of learning orientation, foreign market knowledge and organizational agility is confirmed by our results on driving international venture performance.

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

Due to COVID-19, changes in consumption trends are taking place in the distribution sector, such as an increase in non-face-to-face consumption and a rapid growth in the online shopping market. However, it is difficult for small and medium-sized export sellers to obtain forecast information on the export market by country, compared to large distributors who can easily build a global sales network. This study is about the prediction of export amount and export volume by country and item for market information analysis of small and medium export sellers. A prediction model was developed using Lasso, XGBoost, and MLP models based on supervised learning and deep learning, and export trends for clothing, cosmetics, and household electronic devices were predicted for Korea's major export countries, the United States, China, and Vietnam. As a result of the prediction, the performance of MAE and RMSE for the Lasso model was excellent, and based on the development results, a market analysis system for small and medium sellers was developed.

안면 부 MRI 검사는 주변 조직에 대한 높은 대조도 와 해상력으로 해부학적 구조 파악과 질환 진단에 이용되고 있다. 최근 검사 시간을 줄이는 동시에 영상의 질을 향상하는 딥러닝이 주목받고 있다. 본 연구는 안면 부 MRI 검사에서 딥러닝 의 유용성을 알아보기 위해 34명의 환자를 대상으로 딥러닝 T2 강조 영상과 고식적인 T2 강조 영상의 축상면, 관상면 영상을 각각 획득하여, 무참조 영상 품질평가 기법인 NIQE와 NIMA를 통하여 정량적 평가하였고, 리커트 4점 척도를 통해 정성적 평가하였다. NIQE 결과에서 딥러닝 T2 강조 영상은 고식적인 T2 강조 영상보다 영상 품질이 우수하였고, NIMA 결과에서는 딥러닝 T2 강조 영상의 축상면은 통계적으로 유의한 차이가 없었고, 딥러닝 T2 강조 영상의 관상면에서는 통계 적으로 유의한 차이가 있었다. 정성적 평가지표에서는 입 인두, 후두 인두에서 질적인 이득이 있었다. 연구 결과를 통해 안면 부 영역 중 무의식적인 움직임이 많은 영역에서 딥러닝을 적용함으로써 고식적인 T2 강조 영상보다 높은 영상의 품질 을 제공하고, 상대적으로 움직임이 덜한 구조물에서도 품질을 유지하며 검사 시간을 2분 이상 단축하여 움직임에 의한 인공 물을 감소시킴으로써 응급 환자 및 비협조 환자의 진단에 유용하게 활용될 것으로 사료 된다.

This study aimed to investigate the effect of visual input enhancement (VIE) on the comprehension of reading texts and the learning of two grammatical forms: English relative clauses and articles. Individual learners’ working memory (WM) capacity was also tested to explore its impact on the effectiveness of VIE. A total of 48 Korean college learners of English were assigned into three groups: (a) relative group receiving VIE on relative clauses (b) article group receiving VIE on articles, and (c) a control group receiving no VIE. Results showed that VIE did not have any negative effect on the learners’ reading comprehension. Rather, it had positive effects on the learning of the two grammatical forms. According to the findings, VIE on relative clauses enhanced the learners’ receptive knowledge of the grammatical form, whereas VIE on articles enhanced the learners’ productive knowledge of the form. There was a potential link between the effectiveness of VIE and the learners’ working memory processing ability. Pedagogical implications are also discussed based on these findings.

본 연구는 간 담도기 이미지에서 CAIPIRINHA, 압축 센싱(CS), 딥러닝(DL) 기법을 비교하여 주관적 영상의 질과 국소병변 을 평가하였다. 후향적 연구로 간 담도기 이미지를(획득 시간, CAIPIRINHA 16초, DL 11초, CS 15초; 절편두께, 3mm, 3mm, 1.5mm) 포함한 가도세틱산 조영증강 자기공명영상을 시행한 51명의 환자에서 3개의 이미지와 국소 간 병변은 주관적 이미지 질 평가를 분석하였다. 간 가장자리 선명도는 CAIPIRINHA(3.9±0.8), DL(4.5±0.6), CS(4.5±0.8), 호흡에 의한 운동 허상은 CAIPIRINHA(4.3±0.9), DL(4.7±0.6), CS(4.5±0.8)를 보였다. 21명 환자의 48개 병변에서, 가장자리 선예 도는 CAIPIRINHA(4.3±0.7), DL(4.5±0.6), CS(4.6±0.5), 선명도는 CAIPIRINHA(4.4±0.7), DL(4.7±0.5), CS (4.7±0.5)을 보였다. DL은 검사 시간을 줄이면서 CAIPIRINHA와 비슷한 질을 보이고 호흡 허상을 줄일 수 있다. CS는 얇은 절편 영상의 획득이 가능하여 비슷한 영상의 질을 보여 선택적으로 유용하게 사용할 수 있다.