• Fisheries and Aquatic Sciences
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• v.10 no.1
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• pp.8-15
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• 2007

Rhodopsin, a dim-light receptor, is a model system for the study of G protein-coupled receptors that transduce extracellular signals into cells. To study the molecular mechanisms of visual systems in fish, the rod opsin gene of olive flounder Paralichthys olivaceus was characterized. The full-length P. olivaceus opsin gene was obtained by PCR amplification of genomic DNA, as well as cDNA synthesis. A comparison of clones obtained from both methods indicated that the olive flounder rod opsin gene lacks introns. Sequence analysis of the opsin gene indicated that it contains a 1,056-bp open reading frame encoding 352 amino acids. The deduced amino acid sequence contains features of typical rod opsins, such as sites for Schiff's base formation (K296) and its counterion (E113), disulfide formation (C110 and C187), and palmitoylation (C322 and C323). An opsin sequence alignment showed the highest similarity between P. olivaceus and Solea solea (95.1%), followed by Hippoglossus hippoglossus (94.5%). An opsin phylogenetic tree revealed a close relationship between olive flounder and teleost rod opsins.

• The Korean Journal of Mycology
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• v.47 no.3
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• pp.181-186
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• 2019

A designated fungal isolate, KNU-US-1802E was isolated from the soil in Uiseong, Korea. To identify characteristics of the isolate, it was cultured on PDA media for 6 days at $35^{\circ}C$. Colonies on PDA are flat, light gray, dense, with entire margins; reverse dark gray to black, with white margins. Aerial mycelia were smooth-walled, hyaline and 40~42 mm diameter after 6 days at $35^{\circ}C$. Conidia were hyaline, one-celled, ellipsoidal to fusiform, forming long chains with average length ${\times}$ width of $5.0{\pm}0.3{\times}2.9{\pm}0.2{\mu}m$. Molecular analysis indicates that the internal transcribed spacer (ITS) region and partial beta-tubulin (tub2) gene sequence showed 100% and 99% similarities, respectively with Acrophialophora ellipsoidea CGMCC 3.15255 collected from China. Phylogenetic analysis by the neighbor-joining (NJ) method shows that the KNU-US-1802E was clustered with A. ellipsoidea CGMCC 3.15255 in a phylogenetic tree constructed using the concatenated sequences of ITS region and tub2 gene sequences with a high bootstrap value. Based on these findings, the isolate KNU-US-1802E was identified as Acrophialophora ellipsoidea, and this is the first report of this isolate in Korea.

• Parasites, Hosts and Diseases
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• v.59 no.1
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• pp.89-95
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• 2021

Microcotyle sebastis is a gill monogenean ectoparasite that causes serious problems in the mariculture of the Korean rockfish, Sebastes schlegelii. In this study, we isolated the parasite from fish farms along the coasts of Tongyeong, South Korea in 2016, and characterized its infection, morphology and molecular phylogeny. The prevalence of M. sebastis infection during the study period ranged from 46.7% to 96.7%, and the mean intensity was 2.3 to 31.4 ind./fish, indicating that the fish was constantly exposed to parasitic infections throughout the year. Morphological observations under light and scanning electron microscopes of the M. sebastis isolates in this study showed the typical characteristics of the anterior prohaptor and posterior opisthaptor of monogenean parasites. In phylogenetic trees reconstructed using the nuclear 28S ribosomal RNA gene and the mitochondrial cytochrome c oxidase I gene (cox1), they consistently clustered together with their congeneric species, and showed the closest phylogenetic relationships to M. caudata and M. kasago in the cox1 tree.

• The Korean Journal of Mycology
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• v.48 no.4
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• pp.511-518
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• 2020

This study was conducted to isolate and identify the fungal pathogen caused unreported post-harvest disease on apples (cv. Fuji) fruit in Korea. The disease symptoms on apples appeared as irregular, light to dark brown, slightly sunken spots. The three fungal strains were isolated from infected tissues of apple fruits and their cultural and morphological characteristics were completely consistent with those of Plenodomus collinsoniae. The phylogenetic analysis using the internal transcribed spacer (ITS) regions, beta-tubulin (TUB), and the second largest subunit of RNA polymerase II (RPB2) sequences revealed the closest relationship of the isolates with Plenodomus collinsoniae at the species level. The pathogenicity test showed the same dark brown spots on Fuji apple cultivar. Therefore, P. collinsoniae is a newly reported fungal agent causing post-harvest disease on apples in Korea.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.330-334
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• 2022

In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the right time. First problem is that How Farmers can predict the right crop for cultivation because famers have no knowledge about prediction of crop. Second problem is that which algorithm is best that provide the maximum accuracy for crop prediction. Therefore, in this research Author proposed a method that would help to select the most suitable crop(s) for a specific land based on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine learning. In this work, the author implemented Random Forest Classifier, Support Vector Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these algorithms with the training dataset and later these algorithms were tested with the test dataset. The author compared the performances of all the tested methods to arrive at the best outcome. In this way best algorithm from the mention above is selected for crop prediction.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.159-165
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• 2023

The entire industry is increasing the use of big data analysis using artificial intelligence technology due to the Fourth Industrial Revolution. The value of big data is increasing, and the same is true of the production technology. However, small and medium -sized manufacturers with small size are difficult to use for work due to lack of data management ability, and it is difficult to enter smart factories. Therefore, to help small and medium -sized manufacturing companies use big data, we will predict the gross production time through machine learning. In previous studies, machine learning was conducted as a time and quantity factor for production, and the excellence of the ExtraTree Algorithm was confirmed by predicting gross product time. In this study, the worker's proficiency factors were added to the time and quantity factors necessary for production, and the prediction rate of LightGBM Algorithm knowing was the highest. The results of the study will help to enhance the company's competitiveness and enhance the competitiveness of the company by identifying the possibility of data utilization of the MES system and supporting systematic production schedule management.

• Journal of Forest and Environmental Science
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• v.39 no.4
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• pp.195-202
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• 2023

In this study, we sought to compare and evaluate the accuracy and predictive performance of machine learning algorithms for estimating the growth of individual Larix kaempferi trees in Gangwon Province, Korea. We employed linear regression, random forest, XGBoost, and LightGBM algorithms to predict tree growth using monitoring data organized based on different thinning intensities. Furthermore, we compared and evaluated the goodness-of-fit of these models using metrics such as the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results revealed that XGBoost provided the highest goodness-of-fit, with an R² value of 0.62 across all thinning intensities, while also yielding the lowest values for MAE and RMSE, thereby indicating the best model fit. When predicting the growth volume of individual trees after 3 years using the XGBoost model, the agreement was exceptionally high, reaching approximately 97% for all stand sites in accordance with the different thinning intensities. Notably, in non-thinned plots, the predicted volumes were approximately 2.1 m³ lower than the actual volumes; however, the agreement remained highly accurate at approximately 99.5%. These findings will contribute to the development of growth prediction models for individual trees using machine learning algorithms.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.539-554
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• 2024

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.

• Journal of Bio-Environment Control
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• v.24 no.3
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• pp.161-166
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• 2015

To determine the adequate levels of light intensity and $CO_2$ concentration for mango grown in greenhouses, quantitative measurements of photosynthetic rates at various leaf positions in the tree are required. The objective of this study was to develop two-variable leaf photosynthetic models of Irwin mango (Mangifera indica L. cv. Irwin) using light intensity and $CO_2$ concentration at different leaf positions. Leaf photosynthetic rates at different positions (top, middle, and bottom) were measured by a leaf photosynthesis analyzer at light intensities (0, 50, 100, 200, 300, 400, 600, and $800{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$) with $CO_2$ concentrations (100, 400, 800, 1200, and $1600{\mu}mol{\cdot}mol^{-1}$). The two-variable model consisted of the two leaf photosynthetic models expressed as negative exponential functions for light intensity and $CO_2$ concentrations, respectively. The photosynthetic rates of top leaves were saturated at a light intensity of $400{\mu}mol{\cdot}^{-2}{\cdot}s^{-1}$, while those of middle and bottom leaves saturated at $200{\mu}mol{\cdot}^{-2}{\cdot}s^{-1}$. The leaf photosynthetic rates did not reach the saturation point at a $CO_2$ concentration of $1600imolmol^{-1}$. In validation of the model, the estimated photosynthetic rates at top and bottom leaves showed better agreements with the measured ones than the middle leaves. It is expected that the optimal conditions of light intensity and $CO_2$ concentration can be determined for maximizing photosynthetic rates of Irwin mango grown in greenhouses by using the two-variable model.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.3
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• pp.22-30
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• 2013

This study aims to measure the thermal comfort effects of urban street trees. As the usual dry bulb air temperature does not indicate properly how the average pedestrian feels the heat of a typical summer day under the strong sunshine, we adopted the Wet Bulb Globe Temperature(WBGT). WBGT involves black globe temperature to measure the direct radiation of sun beams on our bodies, for example our heads. We measured temperatures on very sunny and hot summer days, August 3, 4, and 7, 2012, on the urban streets of Seoul, Korea. Wet bulb, globe, and dry bulb temperatures were measured under direct sunlight from 1 O'clock to 5 O'clock pm. Globe and dry bulb temperatures were measured under street tree shades nearby during the same hours. Then the WBGTs were calculated with the formulae, one for sunny outdoor spaces, and the other for shaded outdoor spaces or indoor. The results are compared with the Korean Standards Association(KS A ISO 7243). The major findings were: 1) On very sunny and hot summer days in Seoul, street tree shades lower the WBGT about 1 to 4 degrees, 2) during the hours of 3 and 4 O'clock in the afternoon, the WBGT under the tree shades are about 3 to 4 degrees lower compared to those under sunshines(approx. 29 to 32 degrees respectively), 3) This difference makes a major thermal comfort for urban pedestrians because senior citizens or weak persons are recommended to move indoor, and even healthy people are recommended stop outdoor sports and take rests in the shades when WBGT is about 32. On the other hand, if the WBGT is around 29, or 3 degrees lower, slower walking, light works or sports are allowable, 4) On site questionnaire survey confirms the thermal comforts under the tree shades, and we even could not get survey subjects on the sunny parts of the sidewalks, 5) We strongly recommend change of guidelines for urban street trees from "one row of street trees on 6m~8m intervals" to "street trees to make continuous shades".