• Journal of Marine Life Science
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• v.8 no.2
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• pp.136-149
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• 2023

Basic bioactivities (antioxidant, anti-inflammatory, antibacterial, anticancer, antiviral) were investigated from 370 strains of marine bacteria, fungi, and microalgae obtained from various marine environmental regions in Korea, and the activity results were obtained at the collection site, isolation source, and species level was compared. In the case of marine bacteria, strains belonging to the generally useful genera Streptomyces and Bacillus were observed to have particularly strong efficacy and useful resources were mainly isolated from marine sediments. In the case of marine fungi and microalgae, results showing strong species-specific activity were confirmed, and results showing efficacy-specific activity were also obtained. Based on these results, it is a research result that can facilitate priority access as a strategic material for industrial revitalization and the establishment of a strategy to secure resources based on usefulness when conducting research on chemicals that are selectively effective against specific diseases or when conducting resource-based research. In addition, we believe that by using these results as material for sale through the Marine BioBank (MBB), academia and industry can use them to help accelerate the revitalization of the marine bio industry.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.417-425
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• 2023

The aim of this study was to derive stem taper equations for Quercus acuta, one of main evergreen broad-leaved tree species found in warm temperate regions, and to prepare a stem volume table using those stem taper equations. A total of 688 individual trees were used in the analysis, which were collected from Jeonnam-do, Gyeongnam-do, and Jeju-do. The stem taper models applied to derive the stem curve pattern were the Max and Burkhart, Kozak, and Lee models. Among the three stem taper models, the best explanation of the stem curve shape of Q. acuta was found to be given by the Kozak model, which showed a fitness index of 0.9583, bias of 0.0352, percentage of estimated standard error of 1.1439, and mean absolute deviation of 0.6751. Thus, the stem taper of Q. acuta was estimated using the Kozak model. Moreover,thestemvolumecalculationwasperforme d by applying the Smalian formula to the diameter and height of each stem interval. In addition, an analysis of variance (ANOVA) was conducted to compare the two existing Q. acuta stem volume tables (2007 and 2010) and the newly created stem volume table (2023). This analysis revealed that the stem volume table constructed in the Wando region in 2007 included about twice as much as the stem volume tables constructed in 2010 and 2023. The stem volume table (2023) developed in this study is not only based on the regional collection range and number of utilized trees but also on a sound scientific basis. Therefore, it can be used at the national level as an official stem volume table for Q. acuta.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• 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.

• Journal of Venture Innovation
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• v.5 no.3
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• pp.71-80
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• 2022

The real estate market is an important part of a country's economy and plays a major role in economic growth through the growth of many related industries. Changes in interest rates affect asset prices and have a significant impact on housing prices. This study analyzed housing prices by dividing them into nationwide, local, and Seoul housing prices in order to analyze whether the effect of changes in interest rates on housing prices shows regional differences. The analysis was conducted from the first quarter of 2011 to the fourth quarter of 2021, and was analyzed using the DOLS model. The main analysis results are as follows. First, interest rates were found to have a significant negative effect on national housing prices, and a drop in interest rates significantly increased national housing prices and an increase in interest rates significantly lowered national housing prices. The consumer price index and loan growth rate also had a positive effect on housing prices nationwide, but statistical significance was not high. Second, interest rates had a negative effect on local housing prices, unlike national housing prices, but were not statistically significant. On the other hand, it was found that the consumer price index and loan growth rate had a larger and significant positive effect on local housing prices compared to national housing prices. Finally, it was found that the interest rate had the only significant negative effect on housing prices in Seoul. And this effect was greater and more significant than the effect on national and local housing prices. In the end, it was found that the effect of interest rates on Korean housing prices differs locally. Interest rates have a significant negative effect on national housing prices, and local housing prices, but they are not statistically significant. In addition, the interest rate was found to have the largest and most significant negative effect on housing prices in Seoul. In addition, it was found that there was a difference in the effect of macroeconomic variables on housing prices. This means that there are differences between regions with different factors influencing local and Seoul housing prices, and this point should be considered when drafting and implementing real estate policies.

• 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.808-819
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• 2023

This study was conducted to develop a selection marker for the identification of the Bacillus licheniformis K12 strain in microbial communities. The strain not only demonstrates good growth at moderate temperatures but also contains enzymes that catalyze the decomposition of various polymer materials, such as proteases, amylases, cellulases, lipases, and xylanases. To identify molecular markers appropriate for use in a microbial community, a search was conducted to identify variable gene regions that show considerable genetic mutations, such as recombinase, integration, and transposase sites, as well as phase-related genes. As a result, five areas were identified that have potential as selection markers. The candidate markers were two recombinase sites (BLK1 and BLK2), two integration sites (BLK3 and BLK4), and one phase-related site (BLK5). A PCR analysis performed with different Bacillus species (e.g., B. licheniformis, Bacillus velezensis, Bacillus subtilis, and Bacillus cereus) confirmed that PCR products appeared at specific locations in B. licheniformis: BLK1 in recombinase, BLK2 in recombinase family protein, and BLK3 and BLK4 as site-specific integrations. In addition, BLK1 and BLK3 were identified as good candidate markers via a PCR analysis performed on subspecies of standard B. licheniformis strains. Therefore, the findings suggest that BLK1 can be used as a selection marker for B. licheniformis species and subspecies in the microbiome.

• Korean Journal of Poultry Science
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• v.50 no.4
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• pp.231-240
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• 2023

Infectious bursal disease (IBD) is an acute, highly contagious, and immunosuppressive disease in young chickens, and causes considerable economic losses to the poultry industry. More than 30 years ago, an antigenic variant IBDV (avIBDV) was reported in chicken farms in the United States. Recently, a novel avIBDV exhibited clear differences in molecular characteristics compared with previous variant strains. This study investigated the molecular characteristics of recently isolated avIBDV strains in Korea. Strains of avIBDV were confirmed by reverse transcription PCR (RT-PCR) and were propagated in 10-day-old specific-pathogen-free (SPF) embryonated chicken eggs through chorioallantoic membrane (CAM) inoculation. Multiple sequence alignment and phylogenetic analyses of hypervariable regions VP2 gene revealed that the strains originated from two different avIBDV lineages (G2a and G2d). In our results, we confirmed the co-existence and prevalence of avIBDV genogroup G2a and G2d in chicken farms. It is necessary to study the protective efficacy of current vaccines against avIBDVs.

• Conservation Science in Museum
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• v.30
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• pp.71-88
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• 2023

This paper examined the visible characteristics and chemical composition of glass beads from the Joseon Dynasty as well as the associations thereof. It also explored the characteristics and uses of glass beads by region. This study covered a total of 1,819 pieces excavated from 25 locations in the Gyeonggi, Chungcheong, and Gyeongsang regions, of which 537 pieces were analyzed for their chemical composition. Glass beads of the Joseon Dynasty take a variety of shapes such as a Round, Coil, Floral, Segmented, Flat, Oval, and Calabash. Colors vary from shades of brown (brown, lemon yellow) and shades of blue (Bluish-Green, greenish-Blue, Purple-Blue) to shades of white (colorless, white) and shades of green (Green, Greenish-Blue, Greenish-Brown). Brown accounts for the largest percentage, followed by Bluish-Green, greenish-Blue. It was identified that Drawing technique was the most common glass bead production technique of the Joseon Dynasty. Potassium oxide (K₂O) was the most common flux agent for glass beads, while the potash glass and mixed alkali glass groups account for the largest quantity. The choice of stabilizers depended on the type of flux agents used, but the most common were calcium oxide (CaO) and aluminum oxide (Al₂O₃). The potash glass and potash lead glass groups are high in CaO and low in Al₂O₃, the mixed alkali glass group is high in CaO, and the lead glass group is low in CaO. In terms of the association between color and shape, most of the beads with shade of brown and blue have round shapes of brown and blue have spherical shapes, while the coil shape is prominent in blue beads. A high percentage of green and colorless beads also take the shape of a coil, while white beads in general have a floral shape. In terms of the association between shape and chemical composition, round, floral and segmented shapes account for a high percentage of the potash glass group, while coil and flat shapes are common in the mixed alkali glass group. This paper also analyzed the colorants for each color based on the association between color and chemical composition. Iron (Fe) was used as the colorant for brown and white, and titanium (Ti) and iron were used for light yellow. Purple-Blue was produced by by cobalt (Co), and greenish-Blue, Bluish-Green, green, Greenish-Blue were produced by iron and copper (Cu). Colorless beads had a generally low colorant content.