• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.1
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• pp.42-55
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• 2023

A methodology to predict the carbon performance of newly created urban greening plans is required as policies based on quantifying carbon performance are rapidly being introduced in the face of the climate crisis caused by global warming. This study developed a tree carbon calculator that can be used for carbon reduction designs in landscaping and attempted to verify its effectiveness in landscape design. For practical operability, MS Excel was selected as a format, and carbon absorption and storage by tree type and size were extracted from 93 representative species to reflect plant design characteristics. The database, including tree unit prices, was established to reflect cost limitations. A plantation experimental design to verify the performance of the tree carbon calculator was conducted by simulating the design of parks in the central region for four landscape design, and the causal relationship was analyzed by conducting semi-structured interviews before and after. As a result, carbon absorption and carbon storage in the design using the tree carbon calculator were about 17-82% and about 14-85% higher, respectively, compared to not using it. It was confirmed that the reason for the increase in carbon performance efficiency was that additional planting was actively carried out within a given budget, along with the replacement of excellent carbon performance species. Pre-interviews revealed that designers distrusted data and the burdens caused by new programs before using the arboreal carbon calculator but tended to change positively because of its usefulness and ease of use. In order to implement carbon reduction design in the landscaping field, it is necessary to develop it into a carbon calculator for trees and landscaping performance. This study is expected to present a useful direction for ntroducing carbon reduction designs based on quantitative data in landscape design.

• Korean Journal of Culture and Social Issue
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• v.29 no.2
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• pp.171-197
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• 2023

The traditional Korean society has been classified as an Eastern collectivist culture, but in the flow of globalization and digitalization along with the post-Cold War era of the 1970s, Western individualistic culture and values quickly permeated the Korean younger generation. Since rapid changes occurred within a short period of time, there may be differences in cultural self-construal between generations living in the same era. Due to this, psychological problems related to emotional expression and suppression may appear differently depending on generations. Therefore, in the current study, 1,000 Korean adult men and women from their 20s to 60s were investigated for their level of independent and interdependent self-construal, alexithymia, ambivalence over emotional expression(AEE) and emotional suppression(ES). Then the relationship between the variables(self-construal and alexithymia,) and the mediating process of AEE and ES were examined. The generation of participants were divided into the industrialization cohort (birth year < 1970) and the digitalization cohort (birth year starting from 1970). Using the PROCESS macro(Hayes, 2022), we tested a serial mediation model of AEE and ES between the relative independent self-construal(RIS) and alexithymia. The results indicate that the level of alexithymia increases by the serial increase of AEE and ES when RIS decreases. Next, we examined a moderation effect of generatione on the mediation process of AEE and ES, and found that generation moderates the relationship between ES and alexithymia. That is, the effect of ES on alexithymia is significant for the digitalization cohort, while it is not significant for the industrialization cohort. The current results imply that emotion regulation strategies of Koreans have been differently developed according to prevailing cultural values in each generation, and that the negative influence of emotion suppression could be different according to the cultural background of each generation.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• Journal of agriculture & life science
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• v.50 no.1
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• pp.167-178
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• 2016

This study was conducted to estimate genetic parameters for milk production and linear type traits in Holstein dairy cattle in Korea. The data including milk yields, fat yields, protein yields, fat percent, protein percent, somatic score and 15 linear type traits for 10,218 first parity cows collected by Dairy Cattle Improvement Center, National Agricultural Cooperative, Korea, which were calving from January 2009 to April 2013. Genetic and error (co)variances between two traits selected form 19 traits were estimated using bi-trait pairwise analyses with WOMBAT package. The estimated heritabilities for milk yield(MY), fat yield(FY), protein yield(PY), fat percent(FP), protein percent(PP), somatic cell score(SCS), udder depth(UD), udder texture(UT), median suspensory(MS), fore udder attachment(FUA), front teat placement (FTP), rear attachment height(RAH), rear attachment width(RAW), rear teat placement(RTP), front teat length(FTL), foot angle(FA), heel depth(HD), bone quality(BQ), rear legs side view(RLSV), rear legs rear view(RLRV) and locomotion(LC) were 0.128, 0.144, 0.100, 0.273, 0.333, 0.090, 0.179, 0.066, 0.104, 0.109, 0.127, 0.099, 0.059, 0.069, 0.154, 0.014, 0.010, 0.052, 0.065, 0.175 and 0.031, respectively. Among the genetic correlations, UD, UT, FTP, RAW, FTL, FA and RLSV with MY were -0.334, 0.271, 0.445, 0.544, 0.076, -0.281 and -0.228, respectively, and MS, FTP, RTP, FTL, FA, BQ, RLSV, RLRV and LC with PP were -0.147, -0.182, -0.262, -0.136, 0.355, 0.311, 0.135, 0.233 and 0.143, respectively. Especially, MY had the highest positive genetic correlation with RAW (0.544), while SCS had the highest negative genetic correlation with LC (-0.603). FP had negative genetic correlation with most udder traits, whereas, FP had positive genetic correlation with leg and hoof traits (0.056 - 0.355).

• Journal of Nutrition and Health
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• v.56 no.3
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• pp.264-276
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• 2023

Purpose: Food environmental factors related to food insecurity affect household food intake in several socio-ecological aspects. This study explores the relationship between food environment factors and food insecurity in households with married immigrant women. Methods: From November 2018 to February 2020, a survey was conducted enrolling 249 married immigrant women residing in the metropolitan areas of South Korea. In the final analysis, 229 subjects were divided into 2 groups classified as food security (n = 154) and food insecurity (n = 75), as assessed by the score of food security. Three aspects of food environments were measured: built·natural, political·economic, and socio-cultural Results: Food environments were significantly different between food security and food insecurity groups, as follows: the number of foods market and their distance from the home and food status for the last week at home in the built·natural domain; monthly cost of food purchase and experience for food assistance in the political·economic domain; total score of social support, parenting, and cooking skills in the socio-cultural domain. A stepwise multivariate linear regression model showed a negative association between the food insecurity score with social support from family and food inventory status in the last week. After adjusting for confounders, a positive association was obtained between the experience of a food support program. The final regression model explains about 30% of the relationship obtained in the three food environment domains and food insecurity (p < 0.001). Conclusion: Not only economic factors, which are common determinants of household food insecurity, but socio-cultural factors such as social support also affect household food insecurity. Therefore, plans for implementing a food assistance program to improve food insecurity for households with immigrant women should consider financial support as well as other comprehensive aspects, including socio-cultural domain such as social support from family and community.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1605-1613
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• 2023

The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R² and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Life Science
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• v.33 no.10
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• pp.828-834
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• 2023

The cDNA that encodes transmembrane protein 258 (Tmem258) was cloned from Gryllus bimaculatus and named GbTmem258. This protein comprises 80 amino acids, has no N-glycosylation site, and contains five potential phosphorylation sites at two serines, two threonines, and one tyrosine. The predicted molecular mass of GbTmem258 is 9.06 kDa, and its theoretical isoelectric point is 5.5. The tertiary structure of GbTmem258 was predicted using the available secondary structure information, which suggests the presence of alpha helices (52.5%), random coils (22.5%), extended strands (16.25%), and beta turns (8.75%). Homology analysis revealed that GbTmem258 exhibits high similarity at the amino-acid level to Tmem258 found in other species. The effect of starvation and refeeding on GbTmem258 mRNA expression was also examined in this study. It was found that GbTmem258 mRNA expression in the hindgut progressively increased throughout the starvation period, peaking at almost 1.5 times the control level after six days of starvation. However, refeeding for one to two days after the six-day starvation period restored GbTmem258 mRNA expression to the control level. In fat body, GbTmem258 mRNA expression was almost 3-fold higher during starvation compared to the control level. Refeeding for one to two days after the six-day fast resulted in a decline in the expression to about a 2.5-fold increase over the control level. Throughout the starving and refeeding periods, no other tissues showed any discernible alterations in GbTmem258 mRNA expression.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.77-100
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• 2024

Circulation, tides, currents, harmful algal blooms, water quality, and hypoxic conditions in Jinhae-Masan Bay have been extensively studied. However, these previous studies primarily focused on short-term variations, and there was limited detailed investigation into the physical mechanisms responsible for ocean circulation in the bays. Oceanic processes in the bays, such as pollutant dispersal, changes on a seasonal time scale. Therefore, this study aimed to understand how the circulation in Jinhae-Masan Bay varies seasonally and to examine the effects of tides, winds, and river discharges on regional ocean circulation. To achieve this, a three-dimensional ocean circulation model was used to simulate circulation patterns from 2016 to 2018, and sensitivity experiments were conducted. This study reveals that convective estuarine circulation develops in Jinhae and Masan Bays, characterized by the inflow of deep oceanic water from the Korea Strait through Gadeoksudo, while surface water flows outward. This deep water intrusion divides into northward and westward branches. In this study, the volume transport was calculated along the direction of bottom channels in each region. The meridional water exchange in the eastern region of Jinhae Bay is 2.3 times greater in winter and 1.4 times greater in summer compared to that of zonal exchange in the western region. In the western region of Jinhae Bay, the circulation pattern varies significantly by season due to changes in the balance of forces. During winter, surface currents flow southward and bottom currents flow northward, strengthening the north-south convective circulation due to the combined effects of northwesterly winds and the slope of the sea surface. In contrast, during summer, southwesterly winds cause surface seawater to flow eastward, and the elevated sea surface in the southeastern part enhances northward barotropic pressure gradient intensifying the eastward surface flow. The density gradient and southward baroclinic pressure gradient increase in the lower layer, causing a strong westward inflow of seawater from Gadeoksudo, enhancing the zonal convective circulation by 26% compared to winter. The convective circulation in the western Jinhae Bay is significantly influenced by both tidal current and wind during both winter and summer. In the eastern Jinhae Bay and Masan Bay, surface water flows outward to the open sea in all seasons, while bottom water flows inward, demonstrating a typical convective estuarine circulation. In winter, the contributions of wind and freshwater influx are significant, while in summer, the influence of mixing by tidal currents plays a major role in the north-south convective circulation. In the eastern Jinhae Bay, tidally driven residual circulation patterns, influenced by the local topography, are distinct. The study results are expected to enhance our understanding of pollutant dispersion, summer hypoxic events, and the abundance of red tide organisms in these bays.