• Journal of Wetlands Research
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• v.25 no.4
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• pp.297-305
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• 2023

The recycled irrigation is a type of irrigation that uses downstream water to fulfill irrigation demand in the upstream agricultural areas; the used irrigation water returns back to the downstream. The recycled irrigation is advantageous for securing irrigation water for plant growth, but the returned water typically contains high levels of nutrients due to excess nutrients inputs during the agricultural activities, potentially deteriorating stream water quality. Therefore, quantitative assessment on the effect of the recycled irrigation on the stream water quality is required to establish strategies for effective irrigation water supply and water quality management. For this purpose, a watershed model is generally used; however no functions to simulate the effects of the recycled irrigation are provided in the existing watershed models. In this study, we used multi-reservoir model coupled with the Hydrological Simulation Program-Fortran (HSPF) to estimate the effect of the recycled irrigation on the stream water quality. The study area was the Gwangok stream watershed, a subwatershed of Gyeseong stream watershed in Changnyeong county, Gyeongsangnam-do. The HSPF model was built, calibrated, and used to produce time series data of flow and water quality, which were used as hypothetical observation data to calibrate the multi-reservoir model. The calibrated multi-reservoir model was used for simulating the recycled irrigation. In the multi-reservoir model, the Gwangok watershed consisted of two subsystems, irrigation and the Gwangok stream, and the reactions (plant uptake, adsorption, desorption, and decay) within each subsystem, and fluxes of water and materials between the subsystems, were modeled. Using the developed model, three scenarios with different combinations of the operating conditions of the recycled irrigation were evaluated for their effects on the stream water quality.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.915-929
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• 2023

Improvements in people's quality of life, diversification of leisure activities, and changes in population structure have led to an increase in the demand for tourism and an expansion of the diversification of tourism activities. In particular, for coastal cities where land and marine tourism elements coexist, various factors influence their tourism demands. Tourism requires the construction of infrastructure and content development according to the demand at the tourist destination. This study aims to improve the prediction accuracy and explore influencing factors through time series analysis of tourism scale using agent-based data. Basic local governments in the Busan area were examined, and the data used were the number of tourists and the amount of tourism consumption on a monthly basis. The univariate time series analysis, which is a deterministic model, was used along with the SARIMAX analysis to identify the influencing factor. The tourism consumption propensity, focusing on the consumption amount according to business types and the amount of mentions on SNS, was set as the influencing factor. The difference in accuracy (RMSE standard) between the time series models that did and did not consider COVID-19 was found to be very wide, ranging from 1.8 times to 32.7 times by region. Additionally, considering the influencing factor, the tourism consumption business type and SNS trends were found to significantly impact the number of tourists and the amount of tourism consumption. Therefore, to predict future demand, external influences as well as the tourists' consumption tendencies and interests in terms of local tourism must be considered. This study aimed to predict future tourism demand in a coastal city such as Busan and identify factors affecting tourism scale, thereby contributing to policy decision-making to prepare tourism demand in consideration of government tourism policies and tourism trends.

• Journal of Nutrition and Health
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• v.56 no.6
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• pp.602-614
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• 2023

Purpose: The balance between synthesis and degradation of proteins plays a critical role in the maintenance of skeletal muscle mass. Mitochondrial dysfunction has been closely associated with skeletal muscle atrophy caused by aging, cancer, and chemotherapy. Polygalacin D is a saponin derivative isolated from Platycodon grandiflorum (Jacq.) A. DC. This study aimed to investigate the effects of polygalacin D on myoblast differentiation and muscle atrophy in association with mitochondrial function in in vitro and in zebrafish models in vivo. Methods: C2C12 myoblasts were cultured in differentiation media containing different concentrations of polygalacin D, followed by the immunostaining of the myotubes with myosin heavy chain (MHC). The mRNA expression of markers related to myogenesis, muscle atrophy, and mitochondrial function was determined by real-time quantitative reverse transcription polymerase chain reaction. Wild type AB^* zebrafish (Danio rerio) embryos were treated with 5-fluorouracil, leucovorin, and irinotecan (FOLFIRI) with or without polygalacin D, and immunostained to detect slow and fast types of muscle fibers. The Tg(Xla.Eef1a1:mitoEGFP) zebrafish expressing mitochondria-targeted green fluorescent protein was used to monitor mitochondrial morphology. Results: The exposure of C2C12 myotubes to 0.1 ng/mL of polygalacin D increased the formation of MHC-positive multinucleated myotubes (≥ 8 nuclei) compared with the control. Polygalacin D significantly increased the expression of MHC isoforms (Myh1, Myh2, Myh4, and Myh7) involved in myoblast differentiation while it decreased the expression of atrophic markers including muscle RING-finger protein-1 (MuRF1), mothers against decapentaplegic homolog (Smad)2, and Smad3. In addition, polygalacin D promoted peroxisome proliferator-activated receptor-gamma coactivator (Pgc1α) expression and reduced the level of mitochondrial fission regulators such as dynamin-1-like protein (Drp1) and mitochondrial fission 1 (Fis1). In a zebrafish model of FOLFIRI-induced muscle atrophy, polygalacin D improved not only mitochondrial dysfunction but also slow and fast muscle fiber atrophy. Conclusion: These results demonstrated that polygalacin D promotes myogenesis and alleviates chemotherapy-induced muscle atrophy by improving mitochondrial function. Thus, polygalacin D could be useful as nutrition support to prevent and ameliorate muscle wasting and weakness.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.802-811
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• 2023

Marine spatial planning (MSP) is a crucial element for rational allocation and sustainable use of marine areas. Particularly, Fishing Activity Protected Areas constitute essential zones accounting for 45.6% designated for sustainable fishing activities. However, the current assessment of these zones does not adequately consider future demands and potential values, necessitating appropriate evaluation methods and predictive tools for long-term planning. In this study, we selected key fish species (Scomber japonicus, Trichiurus lepturus, Engraulis japonicus, and Larimichthys polyactis) within the Fishing Activity Protected Area to predict their distribution and compare it with the current designated zones for evaluating the ability of the prediction tool. Employing the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report scenarios (SSP1-2.6 and SSP5-8.5), we used species distribution models (such as MaxEnt) to assess the movement and distribution changes of these species owing to future variations. The results indicated a 30-50% increase in the distribution area of S. japonicus, T. lepturus, and L. polyactis, whereas the distribution area of E. japonicus decreased by approximately 6-11%. Based on these results, a species richness map for the four key species was created. Within the marine spatial planning boundaries, the overlap between areas rated "high" in species richness and the Fishing Activity Protected Area was approximately 15%, increasing to 21% under the RCP 2.6 scenario and 34% under the RCP 8.5 scenario. These findings can serve as scientific evidence for future evaluations of use zones or changes in reserve areas. The current and predicted distributions of species owing to climate change can address the limitations of current use zone evaluations and contribute to the development of plans for sustainable and beneficial use of marine resources.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• Journal of Life Science
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• v.33 no.11
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• pp.923-935
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• 2023

Rubus crataegifolius (RC), Ulmus macrocarpa (UM), and Gardenia jasminoides (GJ) are well-known folk medicines in Asia used to treat various gastrointestinal disturbances. The present study evaluated the gastroprotective effect of LS-RUG-com, a mixture of commercially prepared powders of RC, UM, and GJ with a ratio of 3:1:2(w/w/w) against HCl/ethanol-induced gastritis, indomethacin-induced ulcers, and esophageal reflux-induced esophageal mucosal damage and Helicobacter pylori infections. In addition, TNF-α and IL-1β expressions were also determined and measured in esophageal tissue. As to HCl/ethanol-induced gastritis, the LS-RUG-com treatment at a dose of 150 mg/kg showed a remarkable anti-gastritis effect. Regarding indomethacin-induced gastric ulcers, the LS-RUG-com treatment had a significant anti-gastric ulcer effect. Furthermore, in the gastroesophageal reflux disease (GERD) model experiment, the LS-RUG-com treatment resulted in the histological recovery of stomach damage and mucosal injuries. Furthermore, the LS-RUG-com treatment led to an increase in gastric content pH, an increase in mucus protection, and a decrease in gastric pepsin output with a significant decrease in TNF-α and IL-1β. As to the Helicobacter pylori infected animal model, LS-RUG-com had a notable inhibitory effect on Helicobacter growth. The use of RC, UM, or GJ in isolation or the LS-RUG-com treatment as whole had good effects in terms of anti-oxidation, anti-neutralization, gastric acid secretion inhibition, and anti-lipid peroxidation, which supported the use of natural products as systemic gastric protective agents. Our results suggest that the LS-RUG-com might be a significant systemic gastroprotective agent that could be utilized for the treatment and/or protection from gastric disturbances and related damage.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Journal of Venture Innovation
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• v.4 no.1
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• pp.51-65
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• 2021

The HR policy in the public sector was closed and operated mainly on written tests, but in 2006, a new evaluation, promotion and education system based on competence was introduced in the promotion and selection system of civil servants. In particular, the seniority-oriented promotion system was evaluated based on competence by operating an Assessment Center related to promotion. Competency evaluation is known to be the most reliable and valid evaluation method among the evaluation methods used to date and is also known to have high predictive feasibility for performance. In 2001, 19 government standard competency models were designed. In 2006, the competency assessment was implemented with the implementation of the high-ranking civil service team system. In the public sector, the purpose of the competency evaluation is mainly to select third-grade civil servants, assign fourth-grade civil servants, and promotion fifth-grade civil servants. However, competency assessments in the public sector differ in terms of competency assessment objectives, assessment processes and competency assessment programmes compared to those in the private sector. For the purposes of competency assessment, the public sector is for the promotion of candidates, and the private sector focuses on career development and fostering. Therefore, it is not continuously developing capabilities than the private sector and is not used to enhance performance in performing its duties. In relation to evaluation items, the public sector generally operates a system that passes capacity assessment at 2.5 out of 5 for 6 competencies, lacks feedback on what competencies are lacking, and the private sector uses each individual's competency score. Regarding the selection and operation of evaluators, the public sector focuses on fairness in evaluation, and the private sector focuses on usability, which is inconsistent with the aspect of developing capabilities and utilizing human resources in the right place. Therefore, the public sector should also improve measures to identify outstanding people and motivate them through capacity evaluation and change the operation of the capacity evaluation system so that they can grow into better managers through accurate reports and individual feedback

• Journal of Venture Innovation
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• v.3 no.2
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• pp.147-175
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• 2020

This study analyzed the effects that digital experience factors influence on purchase intention and the purchase. The study targeted an online shopping mall with a strong digital experience value among industries. The research model was derived by adding variables to independent and mediating variables according to the industry context of online shopping which is based on the theoretical background and previous studies. Product variety, price efficiency, convenience and conversation were used by terms of digital marketing mix as independent variables. Personalization has been very important factor in online shopping malls, and therefore added as a independent variable. Flow has been added as a mediating variable. Purchase and purchase intention has been used as dependent variables. For empirical testing of established research models and generalization of research results, research was conducted on online shopping malls where digital experiences are important. To do this, a survey was conducted for existing users of online shopping malls. In hypothesis testing, the hypothesis was established that product diversity, price efficiency, convenience, conversation and personalization influenced the intention to purchase online shopping. In particular, the product diversity and conversation variable were tested as the most influential factors on purchase intention. For price efficiency and personalization there were no statistically significant effect. Flow has been shown to be a partial mediator between Product variety and purchase intention in online shopping. In particular, in the case of personalization, it was tested to have a significant influence on purchase intention only when there was a flow experience called pleasure and immersion. This is because the flow experience of pleasure and immersion has played a full mediating role and significantly has affected the purchase intention, because the consumers themselves have to carry out the overall purchase journey without human help due to the nature of online. In the digital experience economy, since consumers are mostly digital consumers, where communication and sharing are the basics, they have been conducting digital word-of-mouth communication and sharing naturally before purchasing. Based on these results, theoretical and practical implications were suggested.