• Food Science of Animal Resources
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• v.39 no.3
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• pp.371-378
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• 2019

The objective of this study was to develop mathematical models for describing the kinetic behavior of Staphylococcus aureus (S. aureus) in seasoned beef jerky. Seasoned beef jerky was cut into 10-g pieces. Next, 0.1 mL of S. aureus ATCC13565 was inoculated into the samples to obtain 3 Log CFU/g, and the samples were stored aerobically at $10^{\circ}C$, $20^{\circ}C$, $25^{\circ}C$, $30^{\circ}C$, and $35^{\circ}C$ for 600 h. S. aureus cell counts were enumerated on Baird Parker agar during storage. To develop a primary model, the Weibull model was fitted to the cell count data to calculate Delta (required time for the first decimal reduction) and ${\rho}$ (shape of curves). For secondary modeling, a polynomial model was fitted to the Delta values as a function of storage temperature. To evaluate the accuracy of the model prediction, the root mean square error (RMSE) was calculated by comparing the predicted data with the observed data. The surviving S. aureus cell counts were decreased at all storage temperatures. The Delta values were longer at $10^{\circ}C$, $20^{\circ}C$, and $25^{\circ}C$ than at $30^{\circ}C$ and $35^{\circ}C$. The secondary model well-described the temperature effect on Delta with an $R^2$ value of 0.920. In validation analysis, RMSE values of 0.325 suggested that the model performance was appropriate. S. aureus in beef jerky survives for a long period at low storage temperatures and that the model developed in this study is useful for describing the kinetic behavior of S. aureus in seasoned beef jerky.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.308-323
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• 2021

In this study, we aimed to evaluate the environmental changes in Jinhae Bay caused by cooling water using numerical modeling. Cooling water discharge volume from the results of Case 1 (10 m³ sec^-1) showed that the environmental changes in Jinhae Bay were extremely insignificant throughout the study period. In the simulation conditions of Case 2 (100 m³ sec^-1), there was a decrease in water temperature of approximately 1 - 3℃ within a 5 km radius from the discharge outlet. In Case 3 (1000 m³ sec^-1), a decrease in water temperature of up to 4 - 5℃ was observed within a radius of 8 km from the discharge outlet and cooling water discharge spread throughout the Bay. Growth rate of microalgae decreased by up to 15 % in November, whereas it increased by up to 6 % near the Hangam Bay in Case 3. From the above results, we confirmed that the environmental changes in Jinhae Bay due to cooling water discharged from Tongyeong LNG station are extremely insignificant. Moreover, it is expected that cooling water discharge could be utilized as a counter measure for 'red tide bloom' or 'macroalgae growth'.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.1
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• pp.1-10
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• 2023

This study aimed to confirm the importance ratio of climate and management variables on production of orchardgrass in Korea (1982-2014). For the climate, the mean temperature in January (MTJ, ℃), lowest temperature in January (LTJ, ℃), growing days 0 to 5 (GD 1, day), growing days 5 to 25 (GD 2, day), Summer depression days (SSD, day), rainfall days (RD, day), accumulated rainfall (AR, mm), and sunshine duration (SD, hr) were considered. For the management, the establishment period (EP, 0-6 years) and number of cutting (NC, 2nd-5th) were measured. The importance ratio on production of orchardgrass was estimated using the neural network model with the perceptron method. It was performed by SPSS 26.0 (IBM Corp., Chicago). As a result, EP was the most important variable (100%), followed by RD (82.0%), AR (79.1%), NC (69.2%), LTJ (66.2%), GD 2 (63.3%), GD 1 (61.6%), SD (58.1%), SSD (50.8%) and MTJ (41.8%). It implies that EP, RD, AR, and NC were more important than others. Since the annual rainfall in Korea is exceed the required amount for the growth and development of orchardgrass, the damage caused by heavy rainfall exceeding the appropriate level could be reduced through drainage management. It means that, when cultivating orchardgrass, factors that can be controlled were relatively important. Although it is difficult to interpret the specific effect of climates on production due to neural networking modeling, in the future, this study is expected to be useful in production prediction and damage estimation by climate change by selecting major factors.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.161-170
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• 2023

We attempted to estimate the carbon accumulation of Hibiscus hamabo and Paliurus ramosissimus, semimangroves native to Jeju Island, by remote sensing and to build an artificial intelligence model that predicts its spatial variation with climatic factors. The aboveground carbon accumulation of semi-mangroves was estimated from the aboveground biomass density (AGBD) provided by the Global Ecosystem Dynamics Investigation (GEDI) lidar upscaled using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images. In Jeju Island, carbon accumulation per unit area was 16.6 t C/ha for H. hamabo and 21.1 t C/ha for P. ramosissimus. Total carbon accumulation of semi-mangroves was estimated at 11.5 t C on the entire coast of Jeju Island. Random forest analysis was applied to predict carbon accumulation in semi-mangroves according to environmental factors. The deviation of aboveground biomass compared to the distribution area of semi-mangrove forests in Jeju Island was calculated to analyze spatial variation of biomass. The main environmental factors affecting this deviation were the precipitation of the wettest month, the maximum temperature of the warmest month, isothermality, and the mean temperature of the wettest quarter. The carbon accumulation of semi-mangroves predicted by random forest analysis in Jeju Island showed spatial variation in the range of 12.0 t C/ha - 27.6 t C/ha. The remote sensing estimation method and the artificial intelligence prediction method of carbon accumulation in this study can be used as basic data and techniques needed for the conservation and creation of mangroves as carbon sink on the Korean Peninsula.

• Journal of Korea Foundry Society
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• v.43 no.6
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• pp.271-278
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• 2023

In the present study, A356 melt degassing experiments were conducted under various impeller rotation speed and inert gas flow rate conditions to determine changes in the melt temperature, composition and density during a degassing treatment. The melt temperature was found to decrease gradually as the degassing time increased, but a clear correlation between the impeller rotation speed or inert gas flow rate and the melt heat loss could not be confirmed. Regardless of the impeller rotation speed or inert gas flow rate, the Mg and Ti contents in the A356 melt scarcely changed, even after degassing for more than 10 minutes, while Sr contents decreased at the maximum degassing rate of 70 ppm. From a quantitative analysis of the degassing rate under each experimental condition based on the hydrogen concentration in the melt derived from the melt density and the degassing model equation, the inert gas flow rate was found to affect the degassing rate rather than the impeller rotation speed under the degassing operation condition employed in the present study.

• Proceedings of the Korea Society of Poultry Science Conference
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• 2004.11a
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• pp.80-90
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• 2004

Three experiments were performed to test the assumption that imbalanced dietary amino acid mixtures must lead to increased heat production (HP). The first experiment was based on diets formulated to have a wide range of crude protein (CP) concentrations but a fixed concentration of lysine, formulated to be the first-limiting amino acid. In the second (converse) experiment, lysine concentration was varied over a wide range while CP content was kept constant. To prevent the masking of dietary effects by thermoregulatory demands, the third experiment was performed at 30 $^{\circ}C$ with the diets similar to the diets used in the second experiment. The detailed relationships among amino acid balance, nitrogen (N) metabolism and energy (E) metabolism were investigated in a computer-controlled chamber calorimetry system. The results of experiments were compared with the predictions of a computerised simulation model of E metabolism. In experiment 1. with constant lysine and varying CP, there was a 75 % increase in N intake as CP concentration increased. This led to a 150 % increase in N excretion. with no significant change in HP. Simulated HP agreed with the empirically determined results in not showing a trend with dietary CP. In experiment 2, with varying lysine but constant CP, there was a 3-fold difference in daily weight gain between the lowest and highest lysine diets. HP per bird increased significantly with dietary lysine concentration. There was still an effect when HP was adjusted for body weight differences, but it failed to maintain statistical significance. Simulated HP results agreed in showing little effect of varying lysine concentration and growth rate on HP. Based on the results of these two experiments, the third experiment was designed to test the response of birds to dietary lysine in high ambient temperature. In experiment 3 which performed at high ambient temperature (30 $^{\circ}C$), HP per bird increased significantly with dietary lysine content, whether or not adjusted for body-weight. The trend was greater than in the previous experiment (20 $^{\circ}C$).

• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.2
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• pp.145-153
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• 2017

This study was aimed to find yield prediction model of Italian ryegrass using climate big data and geographic information. After that, mapping the predicted yield results using Geographic Information System (GIS) as follows; First, forage data were collected; second, the climate information, which was matched with forage data according to year and location, was gathered from the Korean Metrology Administration (KMA) as big data; third, the climate layers used for GIS were constructed; fourth, the yield prediction equation was estimated for the climate layers. Finally, the prediction model was evaluated in aspect of fitness and accuracy. As a result, the fitness of the model ($R^2$) was between 27% to 95% in relation to cultivated locations. In Suwon (n=321), the model was; DMY = 158.63AGD -8.82AAT +169.09SGD - 8.03SAT +184.59SRD -13,352.24 (DMY: Dry Matter Yield, AGD: Autumnal Growing Days, SGD: Spring Growing Days, SAT: Spring Accumulated Temperature, SRD: Spring Rainfall Days). Furthermore, DMY was predicted as $9,790{\pm}120$ (kg/ha) for the mean DMY(9,790 kg/ha). During mapping, the yield of inland areas were relatively greater than that of coastal areas except of Jeju Island, furthermore, northeastern areas, which was mountainous, had lain no cultivations due to weak cold tolerance. In this study, even though the yield prediction modeling and mapping were only performed in several particular locations limited to the data situation as a startup research in the Republic of Korea.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.