• Korean Journal of Environment and Ecology
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• v.38 no.3
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• pp.263-276
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• 2024

Fish community characteristics and the influence of sampling gear were investigated in Lake Singal, South Korea, from August 2020 to October 2021. The employed sampling gears included a kick net, cast net, gill net, and fyke net, which are commonly utilized within the lake. Across three survey stations, a total of 18 fish species from seven families, comprising 3,501 individuals and contributing to a total biomass of 117,670 grams, were identified. Dominance among species was assessed based on individual count and biomass. Pseudorasbora parva was the most abundant, constituting 29.9% of the total catch, followed by Zacco platypus (25.1%) and Micropterus salmoides (19.3%). In terms of biomass, Carassius auratus was predominant, accounting for 45.1%, followed by Cyprinus carpio (17.4%) and M. salmoides (14.3%). Among the sampled species, three were identified as endemic to Korea: Squalidus japonicus coreanus, Cobitis nalbanti, and Odontobutis interrupta. Additionally, four exotic species were recorded, including M. salmoides and Lepomis macrochirus, both classified as invasive alien species, along with C. cuvieri and a variant of Cyprinus carpio (nudus type). Analysis of the average standard length (SL) and body weight (BW) revealed significant size variations among species. P. parva, the dominant species, measured 60 ± 24.1 mm (SL) and weighed 4.4 ± 3.42 g (BW). The subdominant species, Z. platypus, exhibited an SL of 82 ± 17.6 mm and a BW of 10.4 ± 7.27 g. M. salmoides, another dominant species, registered 96 ± 25.1 mm (SL) and 24.9 ± 96.02 g (BW), while C. auratus measured 125 ± 77.3 mm (SL) and weighed 168 ± 336.5 g (BW). In terms of gear-specific performance, the kick net captured eight species from three families, totaling 302 individuals with a biomass of 1,269 g. The cast net was more effective in coastal zones, collecting 11 species from four families, amounting to 948 individuals and 31,343 g of biomass. The gill net yielded the highest biomass, capturing 13 species from four families with 682 individuals weighing 69,695 g. The fyke net recorded the highest species diversity and number of individuals, capturing 15 species from seven families, totaling 1,569 individuals and 15,362 g of biomass. The fyke net proved most efficient in species and individual counts, whereas the gill net was superior for biomass collection. Conversely, the kick net demonstrated effectiveness in collecting small benthic species in coastal areas.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.260-276
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• 2024

The purpose of this study is to explore whether pre-service teachers majoring in earth science improve their perception of computational thinking through STEAM classes focused on engineering-based wave power plants. The STEAM class involved designing the most efficient wave power plant model. The survey on computational thinking practices, developed from previous research, was administered to 15 Earth science pre-service teachers to gauge their understanding of computational thinking. Each group developed an efficient wave power plant model based on the scientific principal of turbine operation using waves. The activities included problem recognition (problem solving), coding (coding and programming), creating a wave power plant model using a 3D printer (design and create model), and evaluating the output to correct errors (debugging). The pre-service teachers showed a high level of recognition of computational thinking practices, particularly in "logical thinking," with the top five practices out of 14 averaging five points each. However, participants lacked a clear understanding of certain computational thinking practices such as abstraction, problem decomposition, and using bid data, with their comprehension of these decreasing after the STEAM lesson. Although there was a significant reduction in the misconception that computational thinking is "playing online games" (from 4.06 to 0.86), some participants still equated it with "thinking like a computer" and "using a computer to do calculations". The study found slight improvements in "problem solving" (3.73 to 4.33), "pattern recognition" (3.53 to 3.66), and "best tool selection" (4.26 to 4.66). To enhance computational thinking skills, a practice-oriented curriculum should be offered. Additional STEAM classes on diverse topics could lead to a significant improvement in computational thinking practices. Therefore, establishing an educational curriculum for multisituational learning is essential.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.505-518
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• 2023

The key to invasive pest management lies in preemptive action. However, most current research using species distribution models is conducted after an invasion has occurred. This study modeled the potential distribution of the globally notorious sweet potato pest, the sweet potato weevil(Cylas formicarius), that has not yet invaded Korea using MaxEnt. Using global occurrence data, bioclimatic variables, and topsoil characteristics, MaxEnt showed high explanatory power as both the training and test areas under the curve exceeded 0.9. Among the environmental variables used in this study, minimum temperature in the coldest month (BIO06), precipitation in the driest month (BIO14), mean diurnal range (BIO02), and bulk density (BDOD) were identified as key variables. The predicted global distribution showed high values in most countries where the species is currently present, with a significant potential invasion risk in most South American countries where C. formicarius is not yet present. In Korea, Jeju Island and the southwestern coasts of Jeollanam-do showed very high probabilities. The impact of climate change under shared socioeconomic pathway (SSP) scenarios indicated an expansion along coasts as climate change progresses. By applying the 10th percentile minimum training presence rule, the potential area of occurrence was estimated at 1,439 km² under current climate conditions and could expand up to 9,485 km² under the SSP585 scenario. However, the model predicted that an inland invasion would not be serious. The results of this study suggest a need to focus on the risk of invasion in islands and coastal areas.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.400-412
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• 2023

In this study, the biogeochemistry management (BGC-MAN) model was applied to North and South Korea pine and oak forest stands to evaluate the Net Primary Productivity (NPP), an indicator of forest ecosystem productivity. For meteorological information, historical records and East Asian climate scenario data of Shared Socioeconomic Pathways (SSPs) were used. For vegetation information, pine (Pinus densiflora) and oak(Quercus spp.) forest stands were selected at the Gwangneung and Seolmacheon in South Korea and Sariwon, Sohung, Haeju, Jongju, and Wonsan, which are known to have tree nurseries in North Korea. Among the biophysical information, we used the elevation model for topographic data such as longitude, altitude, and slope direction, and the global soil database for soil data. For management factors, we considered the destruction of forests in North and South Korea due to the Korean War in 1950 and the subsequent reforestation process. The overall mean value of simulated NPP from 1991 to 2100 was 5.17 Mg C ha^-1, with a range of 3.30-8.19 Mg C ha^-1. In addition, increased variability in climate scenarios resulted in variations in forest productivity, with a notable decline in the growth of pine forests. The applicability of the BGC-MAN model to the Korean Peninsula was examined at a time when the ecosystem process-based models were becoming increasingly important due to climate change. In this study, the data on the effects of climate change disturbances on forest ecosystems that was analyzed was limited; therefore, future modeling methods should be improved to simulate more precise ecosystem changes across the Korean Peninsula through process-based models.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.24-35
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• 2024

Recently, research using deep learning technologies such as artificial intelligence, convolutional neural networks, etc. has been actively conducted in various fields including healthcare, manufacturing, autonomous driving, and security, and is having a significant influence on society. In line with this trend, the present study attempted to apply deep learning to the classification of archaeological artifacts, specifically ancient Korean roof-end tiles. Using 100 images of roof-end tiles from each of the Goguryeo, Baekje, and Silla dynasties, for a total of 300 base images, a dataset was formed and expanded to 1,200 images using data augmentation techniques. After building a model using transfer learning from the pre-trained EfficientNetB0 model and conducting five-fold cross-validation, an average training accuracy of 98.06% and validation accuracy of 97.08% were achieved. Furthermore, when model performance was evaluated with a test dataset of 240 images, it could classify the roof-end tile images from the three dynasties with a minimum accuracy of 91%. In particular, with a learning rate of 0.0001, the model exhibited the highest performance, with accuracy of 92.92%, precision of 92.96%, recall of 92.92%, and F1 score of 92.93%. This optimal result was obtained by preventing overfitting and underfitting issues using various learning rate settings and finding the optimal hyperparameters. The study's findings confirm the potential for applying deep learning technologies to the classification of Korean archaeological materials, which is significant. Additionally, it was confirmed that the existing ImageNet dataset and parameters could be positively applied to the analysis of archaeological data. This approach could lead to the creation of various models for future archaeological database accumulation, the use of artifacts in museums, and classification and organization of artifacts.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.481-491
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• 2024

Recent droughts make hydroelectric power generation (HPG) decreasing. Due to climate change in the future, the frequency and intensity of drought are expected to increase, which will increase uncertainty of HPG in multi-purpose dams. Therefore, it is necessary to estimate the amount of HPG according to climate change scenarios and analyze the effect of drought on the amount of HPG. This study analyzed the future HPG of the Soyanggang Dam and Chungju Dam according to the SSP2-4.5 and SSP5-8.5 scenarios. Regression equations for HPG were developed based on the observed data of power generation discharge and HPG in the past provided by My Water, and future HPGs were estimated according to the SSP scenarios. The effect of drought on the amount of HPG was investigated based on the drought severity calculated using the standardized precipitation index (SPI). In this study, the future SPIs were calculated using precipitation data based on four GCM models (CanESM5, ACCESS-ESM1-5, INM-CM4-8, IPSL-CM6A) provided through the environmental big data platform. Overall results show that climate change had significant effects on the amount of HPG. In the case of Soyanggang Dam, the amount of HPG decreased in the SSP2-4.5 and SSP5-8.5 scenarios. Under the SSP2-4.5 scenario the CanESM model showed a 65% reduction in 2031, and under the SSP5-8.5 scenario the ACCESS-ESM1-5 model showed a 54% reduction in 2029. In the case of Chungju Dam, under the SSP2-4.5 and SSP5-8.5 scenarios the average monthly HPG compared to the reference period showed a decreasing trend except for INM-CM4 model.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.65-72
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• 2024

To faithfully represent the intention of the work in the exhibition space, lighting that provides high color reproduction like natural light is required. Thus, many lighting technologies have been introduced to improve CRI, but most of them only evaluated the general color rendering index (CRI Ra), which considers eight pastel colors. Natural light provides excellent color rendering performance for all colors, including red and blue, expressed by color rendering index of R9 and R12, but most artificial lighting has the problem that color rendering performance such as R9 and R12 is significantly lower than that of natural light. Recently, lighting technology that provides CRI at the level of natural light is required to realistically express the colors of works including primary colors but related research is very insufficient. Therefore this paper proposes exhibition hall lighting that fulfills CRI with a focus on CRI Ra, R9, and R12 based on the characteristics of natural light. First reinforcement wavelength bands for improving R9 and R12 are selected through analysis of the actual measurement SPD of natural and artificial lighting. Afterward virtual SPDs with a peak wavelength within the reinforcement wavelength band are created and then SPD combination conditions that satisfy CRI Ra≥95, R9, and R12≥90 are derived through combination simulation with a commercial LED light source. Through this, after specifying two types of light sources with 405,630nm peak wavelength that had the greatest impact on the improvement of R9 and R12, the exhibition hall lighting applied with two W/C White LEDs is designed and a control Index DB of the lighting is constructed. Afterward experiments with the proposed method showed that it was possible to achieve high CRI at the level of natural light with average CRI Ra 96.5, R9 96.2, and R12 94.0 under the conditions of illuminance (300-1,000 Lux) and color temperature (3,000-5,000K).

• Korean Journal of Plant Resources
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• v.37 no.4
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• pp.386-391
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• 2024

'Jinha' is a new strawberry (Fragaria × ananassa Duch.) cultivar, which was released by the Highland Agriculture Research Institute in 2019. The 'Jinha' cultivar originates from a 2011 cross between 'Albion' and 'Goha,' both of which exhibited excellent ever-bearing characteristics, including continuous flowering and large fruits under long-day and high temperature conditions. This new cultivar was initially named 'Saebong No. 11' after examining its characteristics and productivity during summer cultivation between 2012 and 2016. After regional adaptability tests, 'Jinha' was selected from 'Saebong No. 11' as an elite cultivar. The general characteristics of 'Jinha' include intermediate, elliptic leaves, and medium growth. The fruits are conical and of a red color. The plant height of 'Jinha' is simiar to that of the control variety, 'Flamenco', but it has a lot of number of leaves. The cluster length of 'Jinha' was 35.5 cm, 10.8 cm longer than 24.7 cm of the control variety. The number of flower clusters of 'Jinha' appeared 14.4, which was 4.1 more than that of 'Flamenco'. The average fruit weight of 'Jinha' was 10.1 g, which was 0.8 g heavier than that of 'Flamenco'. The soluble solid content of 'Jinha' was 10.2 °Brix, which was 2.0 °Brix higher than that of 'Flamenco'. The marketable yield of 'Jinha' was 25,931 kg·ha^-1, 440% more than that of 'Flamenco' with 5,900 kg·ha^-1. Therefore, the new cultivar of ever-bearing strawberry 'Jinha' is expected to be very popular in the export or bakery market because it is high soluble solid content and good shape.

• Journal of Nutrition and Health
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• v.57 no.4
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• pp.418-434
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• 2024

Purpose: The nutritional status of folate in Korean adults was evaluated using the newly established folate database (DB) and data from the 7th Korea National Health and Nutrition Examination Survey. Methods: This study analyzed the folate intake of 15,054 people (6,278 men and 8,776 women) and the relationship with serum folate concentration of 5,260 people (2,272 men and 2,988 women). Results: The average daily folate intake among Korean adults was lowest in the 19 to 29-year age group and highest in those in their 50s. Folate intake was higher in groups with higher education and household income, non-smokers, participants in aerobic physical activity, and dietary supplement users regardless of sex. Among men, office workers consumed more folate than physical workers. Vegetables and grains were the first and second most contributing food groups to folate intake. The serum folate levels were higher in women than men and lowest in the 19-29 year age group for both sexes. After adjusting for energy intake, age, income, smoking, physical activity, and dietary supplement intake, serum folate concentration increased significantly as intake increased (p < 0.001). The explanatory power (R²) of folate intake on the blood folate concentration was 0.183 and 0.141 in men and women, respectively. Conclusion: The proportion of participants consuming less than the estimated average requirement was 48.1% and 65.3% in men and women, respectively. In particular, the folate intake and serum levels of young men aged 19-29 years were the lowest. Therefore, it is necessary to improve their folate nutritional status through a balanced diet. In addition, the newly established folate DB may be useful for evaluating the folate nutritional status of Koreans.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.125-136
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• 2024

This study presents an automated Python algorithm for analyzing rainfall characteristics to establish critical rainfall thresholds as part of a landslide early warning system. Rainfall data were sourced from the Korea Meteorological Administration's Automatic Weather System (AWS) and the Korea Forest Service's Automatic Mountain Observation System (AMOS), while landslide data from 2020 to 2023 were gathered via the Life Safety Map. The algorithm involves three main steps: 1) processing rainfall data to correct inconsistencies and fill data gaps, 2) identifying the nearest observation station to each landslide location, and 3) conducting statistical analysis of rainfall characteristics. The analysis utilized power law and nonlinear regression, yielding an average R² of 0.45 for the relationships between rainfall intensity-duration, effective rainfall-duration, antecedent rainfall-duration, and maximum hourly rainfall-duration. The critical thresholds identified were 0.9-1.4 mm/hr for rainfall intensity, 68.5-132.5 mm for effective rainfall, 81.6-151.1 mm for antecedent rainfall, and 17.5-26.5 mm for maximum hourly rainfall. Validation using AUC-ROC analysis showed a low AUC value of 0.5, highlighting the limitations of using rainfall data alone to predict landslides. Additionally, the algorithm's speed performance evaluation revealed a total processing time of 30 minutes, further emphasizing the limitations of relying solely on rainfall data for disaster prediction. However, to mitigate loss of life and property damage due to disasters, it is crucial to establish criteria using quantitative and easily interpretable methods. Thus, the algorithm developed in this study is expected to contribute to reducing damage by providing a quantitative evaluation of critical rainfall thresholds that trigger landslides.