• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.185-185
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• 2023

Agricultural water demand is considered as the major sector of water withdrawal due to irrigation. The majority part of the global agricultural field depends on various irrigation techniques. Therefore, a timely and sufficient supply of water is the most important requirement for agriculture. Irrigation is implemented in different ways in various land surface models, it can be modeled empirically based on observed irrigation rates or by calculating water supply and demand. Certain models can also calculate the irrigation demand as per the soil water deficit. In these implementations, irrigation is typically applied uniformly over the irrigated land regardless of crop types or irrigation techniques. Whereas, the latest version of Community Land Model (CLM) in the Community Terrestrial Systems Model (CTSM) uses a global distribution map of irrigation with 64 crop functional types (CFTs) to simulate the irrigation water demand. It can estimate irrigation water withdrawal from different sources and the amount or the areas irrigated with different irrigation techniques. Hence, we set up the model for the simulation period of 16 years from 2000 to 2015 to analyze the global irrigation demand at a spatial resolution of 1.9° × 2.5°. The simulated irrigation water demand is evaluated with the available observation data from FAO AQUASTAT database at the country scale. With the evaluated model, this study aims to suggest new sustainable scenarios for the ratios of irrigation water withdrawal, high depending on the withdrawal sources e.g. surface water and groundwater. With such scenarios, the CFT maps are considered as the determining factor for selecting the areas where the crop pattern can be altered for a sustainable irrigation water management depending on the available withdrawal sources. Overall, our study demonstrate that the scenarios for the future sustainable water resources management in terms of irrigation water withdrawal from the both the surface water and groundwater sources may overcome the excessive stress on exploiting the groundwater in major river basins globally.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.27-80
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• 2023

Nowadays usage of different applications of identity management IDM demands prime attention to clarify which is more efficient regarding preserve privacy as well as security to perform different operations concerning digital identity. Those operations represent the available interactions with identity during its lifecycle in the digital world e.g., create, update, delete, verify and so on. With the rapid growth in technology, this field has been evolving with a number of IDM models being proposed to ensure that identity lifecycle and face some significant issues. However, the control and ownership of data remines in the hand of identity service providers for central and federated approaches unlike in the self-sovereign identity management SSIM approach. SSIM is the recent IDM model were introduced to solve the issue regarding ownership of identity and storing the associated data of it. Thus, SSIM aims to grant the individual's ability to govern their identities without intervening administrative authorities or approval of any authority. Recently, we noticed that numerous IDM solutions enable individuals to own and control their identities in order to adapt with SSIM model. Therefore, we intend to make comparative study as much of these solutions that have proper technical documentation, reports, or whitepapers as well as provide an overview of IDM models. We will point out the existing research gaps and how this study will bridge it. Finally, the study will propose a technical enhancement, everKEY solution, to address some significant drawbacks in current SSIM solutions.

• KSBB Journal
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• v.20 no.4
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• pp.299-304
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• 2005

A two-dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E. coli for the production of 5-aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyed using artificial neural networks, i.e. self-organizing map (SOM) and feedforward backpropagation neural network (BPNN).Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process performance, e.g. forecasting the process performance.

• The Korea Journal of Herbology
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• v.35 no.5
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• pp.13-21
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• 2020

Objective : The genus Glycyrrhiza has been used in food and traditional herbal medicine. Many experimental studies reported that Glycyrrhiza species possess several pharmacological properties. Glycyrrhiza new varieties WONGAM and SINWONGAM have been developed by Korea Rural Development Administration doing research for registration on Ministry of Food and Drug Safety. During the evaluations about pharmacological effect of Glycyrrhiza new varieties WONGAM and SINWONGAM, we focused the anti-allergic effect in this study. Methods : We investigated the anti-allergic effect of WONGAM and SINWONGAM compared with Glycyrrhiza uralensis Fischer and G. glabra L. using anti-dinitrophenyl-immunoglobulin E (IgE)/human serum albumin-stimulated RBL-2H3 cells, phorbol 12-myristate 13-acetate plus calcium ionophore A23187-stimulated HMC-1 cells and compound 48/80-induced anaphylaxis mice model. We analyzed the effect on the expression of various cytokines, and IgE from mast cells and the underlying molecular mechanisms of WONGAM and SINWONGAM in presented models. Results : WONGAM and SINWONGAM showed the inhibitory effect on the histamine release from rat peritoneal mast cells or human mast cells without cytotoxicity. WONGAM and SINWONGAM blocked anaphylactic shock and decreased the IgE production. Furthermore, WONGAM and SINWONGAM inhibited the productions of TNF-α and IL-6 in compound 48/80-induced anaphylaxis mice model. Conclusion : These results indicated that WONGAM and SINWONGAM would have protect effect on allergic responses through the inhibition of allergic mediators and pro-inflammatory cytokines. This study may facilitate the development on Glycyrrhiza new varieties for allergy.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.53-61
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• 2022

This study evaluated the impacts of subsurface drainage design, i.e., spacing and depth, on agricultural productivity and environmental sustainability in two tile-drained fields (Sites A and E) under a corn-soybean rotation in the Midwestern United States. A calibrated and validated Root Zone Water Quality Model (RZWQM) was used to simulate Nitrate-N (nitrogen) losses to tile drainage and crop yields of 30 tile spacing and depth scenarios over 24 years (1992-2015). Our results presented that the narrower and deeper the tile drains are placed, the greater corn yield and Nitrate-N losses, indicating that the subsurface drainage design may cause a trade-off between agricultural productivity and environmental sustainability. The simulation results also presented that up to about 255.7% and 628.0% increase in Nitrate-N losses in Sites A and E, respectively, far outweigh the rate of increase in corn yield up to about 1.1% and 1.6% from the adjustment of tile spacing and depth. Meanwhile, the crop yield and Nitrate-N losses according to the tile configuration differed depending on the field, and the soybean yield presented inconsistent simulation results, unlike the corn yield, which together demonstrate the heterogeneous characteristic of agro-environmental systems to a subsurface drainage practice. This study demonstrates the applicability of agricultural systems models in exploring agro-environmental responses to subsurface drainage practices, which can help guide the introduction and installation of tile systems into farmlands, e.g., orchards and paddy fields, in our country.

• Food Science of Animal Resources
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• v.40 no.5
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• pp.813-830
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• 2020

In this study, the potential of infrared assisted freeze-drying (IRAFD) was tested for the production of shelf-stable edible insects: Protaetia brevitarsis larva (larva of white-spotted flower chafer). The IRAFD system was customized using an infrared lamp, K-type thermocouple, controller, and data acquisition system. The infrared lamp provided the sublimation energy for rapid freeze-drying (FD). The IRAFD conditions were continuous IRAFD-5.0 kW/㎡ and IRAFD-5.0 kW/㎡ at different weight reduction (WR) (10%, 20%, and 30%). The continuous IRAFD reduced the drying time to 247 min compared to the 2,833 min duration of FD (p<0.05). The electrical energy could be reduced by more than 90% through infrared radiation during FD (p<0.05). The Page model resulted in the best prediction among the tested drying kinetic models. In terms of quality, IRAFD showed significantly lower hardness, chewiness, and higher protein levels than hot air drying and FD (p<0.05). IRAFD better preserved the glutamic acid (6.30-7.29 g/100 g) and proline (3.84-5.54 g/100 g). The external product appearance after IRAFD exhibited more air pockets and volume expansion, which might result in a good consumer appeal. In conclusion, this study reports the potential of IRAFD in producing shelf-stable and value-added edible insects.

• Journal of Animal Science and Technology
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• v.65 no.5
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• pp.1024-1039
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• 2023

In this study, we performed a quantitative microbial risk assessment (QMRA) of Salmonella through intake of egg consumption after cooking (dry-heat, moist-heat, and raw consumption). Egg samples (n = 201) from retail markets were analyzed for the presence of Salmonella. In addition, temperature and time were investigated during egg transit, storage, and display. A predictive model was developed to characterize the kinetic behavior of Salmonella in eggs, and data on egg consumption and frequency were collected. Eventually, the data was simulated to estimate egg-related foodborne illnesses. Salmonella was not found in any of the 201 egg samples. Thus, the estimated initial contamination level was -4.0 Log CFU/g. With R² values of 0.898 and 0.922, the constructed predictive models were adequate for describing the fate of Salmonella in eggs throughout distribution and storage. Eggs were consumed raw (1.5%, 39.2 g), dry-heated (57.5%, 43.0 g), and moist-heated (41%, 36.1 g). The probability of foodborne Salmonella illness from the consumption of cooked eggs was evaluated to be 6.8×10^-10. Additionally, the probability of foodborne illness not applied cooking methods was 1.9×10^-7, indicating that Salmonella can be reduced by cooking. Therefore, the risk of Salmonella infection through consumption of eggs after cooking might be low in S. Korea.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.709-720
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• 2018

In order to optimize the evaluation of biomass in crop monitoring, accurate and timely data of the crop-field are required. Evaluating above-ground biomass helps to monitor crop vitality and to predict yield. Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study reports on the development of remote sensing techniques for evaluating the biomass of winter crop. Specific objective was to develop statistical models for estimating the dry weight of barley and wheat using a Excess Green index ($E{\times}G$) based Vegetation Fraction (VF) and a Crop Surface Model (CSM) based Plant Height (PH) value. As a result, the multiple linear regression equations consisting of three independent variables (VF, PH, and $VF{\times}PH$) and above-ground dry weight provided good fits with coefficients of determination ($R^2$) ranging from 0.86 to 0.99 with 5 cultivars. In the case of the barley, the coefficient of determination was 0.91 and the root mean squared error of measurement was $102.09g/m^2$. And for the wheat, the coefficient of determination was 0.90 and the root mean squared error of measurement was $110.87g/m^2$. Therefore, it will be possible to evaluate the biomass of winter crop through the UAV image for the crop growth monitoring.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.1
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• pp.1-14
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• 2015

Climate change caused by elevated greenhouse gases would affect crop production through different pathways in agricultural ecosystems. Because an agricultural ecosystem has complex interactions between societal and economical environment as well as organisms, climate, and soil, adaptation measures in response to climate change on a specific sector could cause undesirable impacts on other sectors inadvertently. An integrated system, which links individual models for components of agricultural ecosystems, would allow to take into account complex interactions existing in a given agricultural ecosystem under climate change and to derive proper adaptation measures in order to improve crop productivity. Most of models for agricultural ecosystems have been used in a separate sector, e.g., prediction of water resources or crop growth. Few of those models have been desiged to be connected to other models as a module of an integrated system. Threfore, it would be crucial to redesign and to refine individual models that have been used for simulation of individual sectors. To improve models for each sector in terms of accuracy and algorithm, it would also be needed to obtain crop growth data through construction of super-sites and satellite sites for long-term monitoring of agricultural ecosystems. It would be advantageous to design a model in a sector from abstraction and inheritance of a simple model, which would facilitate development of modules compatible to the integrated prediction system. Because agricultural production is influenced by social and economical sectors considerably, construction of an integreated system that simulates agricultural production as well as economical activities including trade and demand is merited for prediction of crop production under climate change.

• Ecology and Resilient Infrastructure
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• v.2 no.2
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• pp.185-190
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• 2015

Greenhouse gas (GHG) emission from agricultural lands is recognized as one of important factors of global warming. The objective of this short communication was to evaluate the applicability of different soil GHG emission prediction models on agricultural systems in Korea. Four models, namely, DNDC, DAYCENT, EXPERT-N and COUP, were selected and the basic structure (e.g., components and sub-model), input variables, and output variables were compared. In particular, the availability and compilation of essential input variables were assessed. Major input variables needed for operating these predictive models were found to be available through database systems established by national organizations such as the Korea Meteorological Administration, the Korean Soil Information System, and the Rural Development Administration. However, in order to apply these models in Korea, it was necessary to calibrate and validate each of the models for the domestic landscape settings and climate conditions. In addition, field data of long-term monitoring of GHG emission from agricultural lands are limited and therefore should be measured.