• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.48-59
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• 2023

Ground-level ozone affects human health and plant growth. Ozone is produced by chemical reactions between oxides of nitrogen (NOx) and volatile organic compounds (VOCs) from anthropogenic and biogenic sources. In this study, two different land cover and emission factor datasets were input to the MEGAN v2.1 emission model to examine how these parameters contribute to the biogenic emissions and ozone production. Four input sensitivity scenarios (A, B, C and D) were generated from land cover and vegetation emission factors combination. The effects of BVOCs emissions by scenario were also investigated. From air quality modeling result using CAMx, maximum 1 hour ozone concentrations were estimated 62 ppb, 60 ppb, 68 ppb, 65 ppb, 55 ppb for scenarios A, B, C, D and E, respectively. For maximum 8 hour ozone concentration, 57 ppb, 56 ppb, 63 ppb, 60 ppb, and 53 ppb were estimated by scenario. The minimum difference by land cover was up to 25 ppb and by emission factor that was up to 35 ppb. From the modeling performance evaluation using ground ozone measurement over the six regions (East Seoul, West Seoul, Incheon, Namyangju, Wonju, and Daegu), the model performed well in terms of the correlation coefficient (0.6 to 0.82). For the 4 urban regions (East Seoul, West Seoul, Incheon, and Namyangju), ozone simulations were not quite sensitive to the change of BVOC emissions. For rural regions (Wonju and Daegu) , however, BVOC emission affected ozone concentration much more than previously mentioned regions, especially in case of scenario C. This implies the importance of biogenic emissions on ozone production over the sub-urban to rural regions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.28-36
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• 2023

Solar radiation that is measured at relatively small number of weather stations is one of key inputs to crop models for estimation of crop productivity. Solar radiation products derived from GK-2A and Himawari 8 satellite data have become available, which would allow for preparation of input data to crop models, especially for assessment of crop productivity under an agrivoltaic system where crop and power can be produced at the same time. The objective of this study was to compare the degree of agreement between the solar radiation products obtained from those satellite data. The sub hourly products for solar radiation were collected to prepare their daily summary for the period from May to October in 2020 during which both satellite products for solar radiation were available. Root mean square error (RMSE) and its normalized error (NRMSE) were determined for daily sum of solar radiation. The cumulative values of solar radiation for the study period were also compared to represent the impact of the errors for those products on crop growth simulations. It was found that the data product from the Himawari 8 satellite tended to have smaller values of RMSE and NRMSE than that from the GK-2A satellite. The Himawari 8 satellite product had smaller errors at a large number of weather stations when the cumulative solar radiation was compared with the measurements. This suggests that the use of Himawari 8 satellite products would cause less uncertainty than that of GK2-A products for estimation of crop yield. This merits further studies to apply the Himawari 8 satellites to estimation of solar power generation as well as crop yield under an agrivoltaic system.

• Journal of Wetlands Research
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• v.25 no.2
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• pp.75-82
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• 2023

Data for measuring the flow rate of streams are used as important basic data for the development and maintenance of water resources, and many experts are conducting research to make more accurate measurements. Especially, in Korea, monsoon rains and heavy rains are concentrated in summer due to the nature of the climate, so floods occur frequently. Therefore, it is necessary to measure the flow rate most accurately during a flood to predict and prevent flooding. Thus, the U.S. Geological Survey (USGS) introduces 1, 2, 3 point method using a flow meter as one way to measure the average flow rate. However, it is difficult to calculate the average flow rate with the existing 1, 2, 3 point method alone.This paper proposes a new 1, 2, 3 point method formula, which is more accurate, utilizing one probabilistic entropy concept. This is considered to be a highly empirical study that can supplement the limitations of existing measurement methods. Data and Flume data were used in the number of holesman to demonstrate the utility of the proposed formula. As a result of the analysis, in the case of Flume Data, the existing USGS 1 point method compared to the measured value was 7.6% on average, 8.6% on the 2 point method, and 8.1% on the 3 point method. In the case of Coleman Data, the 1 point method showed an average error rate of 5%, the 2 point method 5.6% and the 3 point method 5.3%. On the other hand, the proposed formula using the concept of entropy reduced the error rate by about 60% compared to the existing method, with the Flume Data averaging 4.7% for the 1 point method, 5.7% for the 2 point method, and 5.2% for the 3 point method. In addition, Coleman Data showed an average error of 2.5% in the 1 point method, 3.1% in the 2 point method, and 2.8% in the 3 point method, reducing the error rate by about 50% compared to the existing method.This study can calculate the average flow rate more accurately than the existing 1, 2, 3 point method, which can be useful in many ways, including future river disaster management, design and administration.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.4
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• pp.1-15
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• 2023

This research intended to reveal the special characteristics of the vegetation structure and the tendency of change of -landfill slope districts, which are reclaimed land, through an investigationsinto the presently existent vegetation and plant community structure of the World Cup Park landfill slope district. For the analysis of changes in vegetation, this study compared the results of field surveys in 1999, 2003, 2005, 2007, 2008, 2012, 2016, and 2021. For the investigation into the plant community structure, a field investigation was carried out in 2021 with six fixed investigation districts designated in 1999 as subjects. To analyze the change in the plant community structure, the past data on the population, the number of the species, and the species diversity by the layer in 2021 were compared and analyzed in the landfill slope district, which is reclaimed land. The changes of the vegetation distribution and the power had been affected by typhoons (Kompasu). Above the plantation foundation, which had been dry and poor, Salix koreensis, marsh woody plants that had formed the community, decreased greatly. The Robinia pseudoacacia community, after the typhoon in 2010, decreased in the number of species and population. Afterward, it showed a tendency to rebound. Regarding the Ailanthus altissima-Robinia pseudoacacia-Paulownia tomentosa community, the number of the species and the population had shown a change similar to the Robinia pseudoacacia community. The Paulownia tomentosa and the Ailanthus altissima have been culled. The slope was predicted as a Future Robinia pseudoacacia forest. The Salix pseudolasiogyne community has been transitioning to a Robinia pseudoacacia forest. Only some enumeration districts, the Robinia pseudoacacia forests and the Salix pseudolasiogyne, had been growing. However, most had been in been declining. It was predicted that this community will be maintained as a Robinia pseudoacacia forest in the future. As these vegetation communities are the representative vegetation of the landfill slope districts, which is reclaimed land, there is a need to understand the ecosystem changes of the community through continuous monitoring. The results of this research can be utilized as a basic material for the vegetation restoration of reclaimed land.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.68-81
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• 2023

This study aims to establish a scenario based on the spacing and arrangement of the roadside trees to reduce heat waves and fine dust in cities that occurred during the urbanization process and to quantitatively analyze the degree of reduction. The ENVI-met 5.0.2v model, a micro-climate simulation program, was used to analyze the degree of improvement in the thermal environment and fine dust according to the roadside tree scenario. As a result of temperature analysis according to street tree spacing, the narrower the distance between roadside trees, the lower the temperature during the day as the number of planted trees increased, and a similar pattern was shown regardless of the distance between roadside trees in the morning and evening. In the case of fine dust emitted from the road, the concentration of fine dust increased slightly due to the increase in roadside trees, but the concentration of sidewalks where people walk increased slightly or there was no difference because of blocking fine dust on trees. The temperature according to the arrangement of street trees tended to decrease as the number of planted trees increased as the arrangement increased. However, not only the amount of trees but also the crown projected area was judged to have a significant impact on the temperature reduction because the temperature reduction was greater in the scenario of planting the same amount of trees and widening the interval of arrangement. In terms of the arrangement, the fine dust concentration showed a difference from the results according to the interval, suggesting that the fine dust concentration may change depending on the relationship between the main wind direction and the tree planting direction. By quantitatively analyzing the degree of thermal environment and fine dust improvement caused by roadside trees, this study is expected to promote policies and projects to improve the roadside environment efficiently, such as a basic plan for roadside trees and a project for wind corridor forests.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.172-189
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• 2023

This study investigated the potential contribution of ChatGPT, a massive language and information model, in the decision-making process of public design policies, focusing on the characteristics inherent to public design. Public design utilizes the principles and approaches of design to address societal issues and aims to improve public services. In order to formulate public design policies and plans, it is essential to base them on extensive data, including the general status of the area, population demographics, infrastructure, resources, safety, existing policies, legal regulations, landscape, spatial conditions, current state of public design, and regional issues. Therefore, public design is a field of design research that encompasses a vast amount of data and language. Considering the rapid advancements in artificial intelligence technology and the significance of public design, this study aims to explore how massive language and information models like ChatGPT can contribute to public design policies. Alongside, we reviewed the concepts and principles of public design, its role in policy development and implementation, and examined the overview and features of ChatGPT, including its application cases and preceding research to determine its utility in the decision-making process of public design policies. The study found that ChatGPT could offer substantial language information during the formulation of public design policies and assist in decision-making. In particular, ChatGPT proved useful in providing various perspectives and swiftly supplying information necessary for policy decisions. Additionally, the trend of utilizing artificial intelligence in government policy development was confirmed through various studies. However, the usage of ChatGPT also unveiled ethical, legal, and personal privacy issues. Notably, ethical dilemmas were raised, along with issues related to bias and fairness. To practically apply ChatGPT in the decision-making process of public design policies, first, it is necessary to enhance the capacities of policy developers and public design experts to a certain extent. Second, it is advisable to create a provisional regulation named 'Ordinance on the Use of AI in Policy' to continuously refine the utilization until legal adjustments are made. Currently, implementing these two strategies is deemed necessary. Consequently, employing massive language and information models like ChatGPT in the public design field, which harbors a vast amount of language, holds substantial value.

• Research in Plant Disease
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• v.29 no.3
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• pp.243-250
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• 2023

To explore the distribution and the resistance reaction of rice blast pathogens that may occur in North Korea, rice blast pathogens in the North Korean border regions of Dandong and Yeon-gil in China and the North Korean border region of Cheorwon in South Korea were analyzed. In addition, comparative analysis was conducted with rice blast pathogen in Suwon and Jeonju, inland regions of South Korea. Resistance reactions above average were observed in monogenic rice lines (IRBLzt-T, IRBL9-W, IRBL20-IR24, and IRBLta-CP1) in Jeonju, Suwon, and Cheorwon from 2018 to 2020. In Dandong and Yeon-gil, the monogenic lines IRBLz5-CA, IRBL12-M, and IRBL19-A consistently showed resistance reactions for three years. Notably, IRBL19-A exhibited strong resistance. Race distribution analysis in South Korea indicated a shift from KI to KJ dominance from 2018 to 2020, while in the North Korean border regions of Dandong and Yeon-gil, the KI race was dominant in 2021 and 2022. The race distribution of rice blast pathogens in China's North Korean border regions differed significantly from that in South Korea.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.167-183
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• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.1
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• pp.113-120
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• 2023

Restoring composite resins with the optimal shades for all primary teeth is a great challenge for pediatric dentists. A newly developed single-shade composite resin can exhibit a color similar to that of the surrounding tooth structure based on the structural color phenomenon. This study aims to evaluate the color adjustment potential (CAP) of a single-shade composite resin compared to conventional multi-shade composite resins in primary teeth. A single-shade composite resin and two conventional multi-shade composite resins were included in this study. Two types of specimens, a single specimen and a dual specimen, were evaluated. For single specimens, duplications of the primary second molar denture teeth were made using experimental composite resins. For dual specimens, cavities were prepared on the buccal surfaces of extracted primary second molars and restored with experimental composite resins. The L^*, a^*, and b^* values were measured using a colorimeter for the extracted teeth and specimens. The mean ΔE_ab^* values for single and dual specimens and CAP were calculated. Bonferroni post-hoc analysis was performed to confirm the statistical significance between the ΔE_ab^* and CAP values of the experimental resins. Among the single specimens, the single-shade composite resin showed significantly higher ΔE_Single compared to other composite resins (p < 0.0167). There was no significant difference between ΔE_Dual for all experimental resins. The single-shade composite resin showed highest CAP compared to other multi-shade composite resins. A single-shade composite resin exhibited the most prominent color adaptability compared to other conventional multi-shade composite resins for primary second molars. A single-shade composite resin can simplify shade matching and provide esthetic outcomes for the restoration of primary second molars.