• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.28 no.3
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• pp.95-120
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• 2023

Methane (CH₄) is a key greenhouse gas in the atmosphere with 85 times greater greenhouse potent relative to carbon dioxide (CO₂). The atmospheric concentration of CH₄ is rapidly increasing due to the intensive usage of CH₄ and the thawing of the cryosphere. Additionally, with the current warming of ocean water, the dissociation of gas hydrates, an ice-like compound and the largest reservoir of CH₄ on Earth, is expected to occur, resulting in the release of CH₄ from the seafloor into the overlying water and atmosphere. Moreover, bottom water hypoxia is another concern that potentially introduces greenhouse gases into the atmosphere. With ongoing global warming and eutrophication, the size and duration of bottom water hypoxia are rapidly increasing. These low-oxygen conditions would relocate the redox zone shallower in sediment or in the water column, causing the release of CH₄ into the atmosphere and thereby intensifying global warming. However, there exists a gap in the understanding of CH₄ dynamics including its generation in relation to bottom water hypoxia. Therefore, this review article aims to understand the relationship between CH₄ and bottom water hypoxia and to draw attention to CH₄ investigation in Korea.

• Journal of Life Science
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• v.33 no.8
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• pp.663-679
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• 2023

This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis revealed a similar word frequency trend between 2010 and 2019 to that between 2020 and 2022. In terms of the research method, many studies using machine learning were conducted from 2010 to 2020, and since 2021, research using deep learning has been increasing. Through these studies, we investigated the trends in research on artificial intelligence utilization by field and the strengths, problems, and challenges of related research. We found that since 2021, the application of artificial intelligence has been expanding, such as research using artificial intelligence for drug rearrangement, using computers to develop anticancer drugs, and applying artificial intelligence to clinical trials. This article briefly presents the prospects of new drug development research using artificial intelligence. If the reliability and safety of bio and medical data are ensured, and the development of the above artificial intelligence technology continues, it is judged that the direction of new drug development using artificial intelligence will proceed to personalized medicine and precision medicine, so we encourage efforts in that field.

• Journal of Technology Innovation
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• v.30 no.1
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• pp.57-104
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• 2022

The government has supported the innovation of private firms by intervening the market for various purposes, such as preventing market failure, alleviating information asymmetry, and allocating resources efficiently. Although the government's R&D budget increased rapidly in the 2000s, it is not clear whether the government intervention has made desirable impact on the market. To address this, the current study attempts to explore this issue by doing a systematic literature review on foreign and domestic papers in an integrated way. In total, 168 studies are analyzed using contents analysis approach and various lens, such as policy additionality, policy tools, firm size, unit of analysis, data and method, are adopted for analysis. Overlapping policy target, time lag between government intervention and policy effects, non-linearity of financial supports, interference between different polices, and out-dated R&D tax incentive system are reported as factors hampering the effect of the government intervention. Many policy prescriptions, such as program evaluation indices reflecting behavioral additionality, an introduction of policy mix and evidence-based policy using machine learning, are suggested to improve these hurdles.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.27-40
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• 2022

Every year landslides cause serious casualties and property damages around the world. As the accurate prediction of landslides is important to reduce the fatalities and economic losses, various approaches have been developed to predict them. Prediction methods can be divided into landslide susceptibility analysis, landslide hazard analysis and landslide risk analysis according to the type of the conditioning factors, the predicted level of the landslide dangers, and whether the expected consequence cased by landslides were considered. Landslide susceptibility analyses are mainly based on the available landslide data and consequently, they predict the likelihood of landslide occurrence by considering factors that can induce landslides and analyzing the spatial distribution of these factors. Various qualitative and quantitative analysis techniques have been applied to landslide susceptibility analysis. Recently, quantitative susceptibility analyses have predominantly employed the physically based model due to high predictive capacity. This is because the physically based approaches use physical slope model to analyze slope stability regardless of prior landslide occurrence. This approach can also reproduce the physical processes governing landslide occurrence. This review examines physically based landslide susceptibility analysis approaches.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.2
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• pp.33-42
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• 2023

Throughout all industries, eco-friendliness is being promoted worldwide with focus on suppressing the environmental impact. With recent international environment policies and regulations supported by government, the electric vehicles demand is expected to increase rapidly. Battery system itself perform an essential role in EVs technology that is arranged in cells, modules, and packs, and each of them are connected mechanically and electrically. A multifaceted approach is necessary for battery pack bonding technologies. In this paper, pros and cons of applicable bonding technologies, such as resistance welding, laser and ultrasonic bonding used in constructing electric vehicle battery packs were compared. Each bonding technique has different advantages and limitations. Therefore, several criteria must be considered when determining which bonding technology is suitable for a battery cell. In particular, the shape and production scale of battery cells are seen as important factors in selecting a bonding method. While dealing with the types and components of battery cells, package bonding technologies and general issues, we will review suitable bonding technologies and suggest future directions.

• The Journal of Bigdata
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• v.7 no.2
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• pp.153-172
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• 2022

Online shopping has transformed and rapidly grown the entire market at the forefront of wholesale and retail services as an effective solution to issues such as digital transformation and social distancing policy (COVID-19 pandemic). Small business owners, who form the majority at the center of the online shopping industry, are constantly collecting policy changes and market trend information to overcome these problems and use them for marketing and other sales activities in order to overcome these problems and continue to grow. Objective and refined information that is more closely related to the business is also needed. Therefore, in this paper, through the collection and analysis of big data information, which is the core technology of digital transformation, key variables are set in product classification, sales trends, consumer preferences, and review information of online shopping malls, and a method of using them for competitor comparison analysis and business sustainability evaluation has been prepared and we would like to propose it as a service. If small and medium-sized businesses can benchmark competitors or excellent businesses based on big data and identify market trends and consumer tendencies, they will clearly recognize their level and position in business and voluntarily strive to secure higher competitiveness. In addition, if the sustainable growth of the online shopping mall operator can be confirmed as an indicator, more efficient policy establishment and risk management can be expected because it has an improved measurement method.

• Research in Plant Disease
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• v.29 no.2
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• pp.108-117
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• 2023

With the development of advanced virus diagnostic technologies, numerous viruses, including novel viruses, have been identified explosively from various biological samples around the world over the last decade. For plant viruses, approximately 376 novel viruses have been reported in the last three years. Information on the occurrence and host ranges of plant viral diseases in a particular country or region is very important for diagnosis, quarantine, and control of the viral diseases. Recently, based on active research on the diagnosis and identification of plant viruses, a significant number of newly occurring viruses and new viral host plants have been reported in South Korea. This review paper provides integrated information on plant viruses and viral host plants newly reported in South Korea between 2020 and 2022 to help diagnose, control, and quarantine plant virus diseases in crop fields.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.8-16
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• 2023

The importance of next-generation packaging technologies is being emphasized as a solution as the miniaturization of devices reaches its limits. To address the bottleneck issue, there is an increasing need for 2.5D and 3D interconnect pitches. This aims to minimize signal delays while meeting requirements such as small size, low power consumption, and a high number of I/Os. Hybrid bonding technology is gaining attention as an alternative to conventional solder bumps due to their limitations such as miniaturization constraints and reliability issues in high-temperature processes. Recently, there has been active research conducted on SiCN to address and enhance the limitations of the Cu/SiO₂ structure. This paper introduces the advantages of Cu/SiCN over the Cu/SiO₂ structure, taking into account various deposition conditions including precursor, deposition temperature, and substrate temperature. Additionally, it provides insights into the core mechanisms of SiCN, such as the role of Dangling bonds and OH groups, and the effects of plasma surface treatment, which explain the differences from SiO₂. Through this discussion, we aim to ultimately present the achievable advantages of applying the Cu/SiCN hybrid bonding structure.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.160-165
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• 2023

In this study, we propose an SNS sentence writing assistance system that utilizes YOLO and GPT to assist users in writing texts with images, such as SNS. We utilize the YOLO model to extract objects from images inserted during writing, and also extract meta-information such as GPS information and creation time information, and use them as prompt values for GPT. To use the YOLO model, we trained it on form image data, and the mAP score of the model is about 0.25 on average. GPT was trained on 1,000 blog text data with the topic of 'restaurant reviews', and the model trained in this study was used to generate sentences with two types of keywords extracted from the images. A survey was conducted to evaluate the practicality of the generated sentences, and a closed-ended survey was conducted to clearly analyze the survey results. There were three evaluation items for the questionnaire by providing the inserted image and keyword sentences. The results showed that the keywords in the images generated meaningful sentences. Through this study, we found that the accuracy of image-based sentence generation depends on the relationship between image keywords and GPT learning contents.

• Journal of Korea Water Resources Association
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• v.57 no.4
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• pp.263-276
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• 2024

Since the first introduction of one-dimensional transient storage modeling in the field of solute transport analysis in rivers, its application has notably expanded for various purposes, including for hydrology and geobiology over the past few decades. Despite strides in refining transient storage models, there remain unresolved challenges in simplifying complex river transport dynamics into concise formulas and a limited set of parameters. This review paper is dedicated to cataloging and assessing existing transient storage models, outlining the difficulties associated with model structures, parameters, and data, and suggesting directions for future research. We seek to enhance understanding of transient storage by highlighting the importance of continuously evaluating residence time distribution modeling, integrating hydrodynamic models, and using data with minimal assumptions. This paper would contribute to advance our comprehension of the transient storage process, offering insights into sophisticated modeling techniques, pinpointing uncertainty in parameters, and suggesting the necessary avenues for further study.