• Journal of Food Hygiene and Safety
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• v.36 no.1
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• pp.68-76
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• 2021

Foodborne outbreaks frequently occur worldwide and result in huge economic losses. It is the therefore important to estimate the costs associated with foodborne diseases to minimize the economic damage. At the same time, it is difficult to accurately estimate the economic loss from foodborne disease due to a wide variety of cost components. In Korea, there are a limited number of analytical studies attempting to estimate such costs. In this study we investigated the components of economic cost used in foreign countries to better estimate the cost of foodborne disease in Korea. Seven recent studies investigated the cost components used to estimate the cost of foodborne disease in humans. This study categorized the economic loss into four types of cost: direct costs, indirect costs, food business costs, and government administration costs. The healthcare costs most often included were medical (outpatient) and hospital costs (inpatient). However, these cost components should be selected according to the systems and budgets of medical services by country. For non-healthcare costs, several other studies considered transportation costs to the hospital as an exception to the cost of inpatient care. So, further discussion is needed on whether to consider inpatient care costs. Among the indirect costs, premature mortality, lost productivity, lost leisure time, and lost quality of life/pain, grief and suffering costs were considered, but the opportunity costs for hospital visits were not considered in any of the above studies. As with healthcare costs, government administration costs should also be considered appropriate cost components due to the difference in government budget systems, for example. Our findings will provide fundamental information for economic analysis associated with foodborne diseases to improve food safety policy in Korea.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.235-250
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• 2021

The overarching question of this study is how a typical rice cultivation system in Gimje, Korea was keeping up with the triple-win challenge of climate-smart agriculture (CSA). To answer this question, we have employed (1) quantitative data from direct measurement of energy, water, carbon and information flows in and out of a rice cultivation system and (2) appropriate metrics to assess production, efficiency, GHG fluxes, and resilience. The study site was one of the Korean Network of Flux measurement (KoFlux) sites (i.e., GRK) located at Gimje, Korea, managed by National Academy of Agricultural Science, Rural Development Administration. Fluxes of energy, water, carbon dioxide (CO₂) and methane (CH₄) were directly measured using eddy-covariance technique during the growing seasons of 2011, 2012 and 2014. The production indicators include gross primary productivity (GPP), grain yield, light use efficiency (LUE), water use efficiency (WUE), and carbon uptake efficiency (CUE). The GHG mitigation was assessed with indicators such as fluxes of carbon dioxide (F_CO2), methane (F_CH4), and nitrous oxide (F_N2O). Resilience was assessed in terms of self-organization (S), using information-theoretic approach. Overall, the results demonstrated that the rice cultivation system at GRK was climate-smart in 2011 in a relative sense but failed to maintain in the following years. Resilience was high and changed little for three year. However, the apparent competing goals or trade-offs between productivity and GHG mitigation were found within individual years as well as between the years, causing difficulties in achieving the triple-win scenario. The pursuit of CSA requires for stakeholders to prioritize their goals (i.e., governance) and to practice opportune interventions (i.e., management) based on the feedback from real-time assessment of the CSA indicators (i.e., monitoring) - i.e., a purpose-driven visioneering.

• Journal of the Korean earth science society
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• v.43 no.2
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• pp.239-252
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• 2022

Quality control methods for the first G-band vapor radiometer (GVR) mounted on a weather aircraft in Korea were developed using the GVR Precipitable Water Vapor (PWV). The aircraft attitude information (degree of pitch and roll) was applied to quality control to select the shortest vertical path of the GVR beam. In addition, quality control was applied to remove a GVR PWV ≥20 mm. It was found that the difference between the warm load average power and sky load average power converged to near 0 when the GVR PWV increased to 20 mm or higher. This could be due to the high brightness temperature of the substratus and mesoclouds, which was confirmed by the Communication, Ocean and Meteorological Satellite (COMS) data (cloud type, cloud top height, and cloud amount), cloud combination probe (CCP), and precipitation imaging probe (PIP). The GVR PWV before and after the application of quality control on a cloudy day was quantitatively compared with that of a local data assimilation and prediction system (LDAPS). The Root Mean Square Difference (RMSD) decreased from 2.9 to 1.8 mm and the RMSD with Korea Local Analysis and Precipitation System (KLAPS) decreased from 5.4 to 4.3 mm, showing improved accuracy. In addition, the quality control effectiveness of GVR PWV suggested in this study was verified through comparison with the COMS PWV by using the GVR PWV applied with quality control and the dropsonde PWV.

• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.303-314
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• 2021

To understand how to efficiently observe the biomass and community of phytoplankton, phytoplankton sampling was carried out from June to October 2019 at the Yeongju dam sediment control reservoir(YJ) and Bohyeonsan dam reservoir(BH1 and BH2). The results derived from microscopic observation, such as the conventional phytoplankton qualitative/quantitative analysis, and from the CHEMTAX method based on the pigments, were compared. The relative contribution of phytoplankton, calculated by the microscopy and CHEMTAX methods, showed a significant difference in all four classes: cryptophyta, chlorophyta, cyanobacteria, and diatoms. In addition, the correlation between the two observation methods was poor. This might be caused by methodological differences in microscopy that do not consider the varying cell sizes among phytoplankton species. In this study, by converting the cells into carbon, the slope between both carbon biomasses based on microscopy and CHEMTAX was improved close to the 1 : 1 line, and the y-intercept was closer to 0 for cryptophyta and diatoms. For cyanobacteria, the slope increased, the y-intercept decreased, and the plot approached 1 : 1 although the correlation coefficients were not improved in all classes. The present study suggests that application of CHEMTAX based on pigment analysis could be a possible approach to efficiently determine the relative carbon proportions of individual classes of phytoplankton community composition.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.468-475
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• 2022

This study was conducted to find out the efficiency of heating initiative temperature and carbon dioxide fertilization in summer squash (Cucurbita moschata D.). The heating start temperature experiment was performed at 9℃, 12℃, and 15℃ using an electric heater and operated when the temperature was lower than the target temperature. The CO₂ fertilization concentration experiment was performed from 7 to 12 with the control, 500 µmol·mol^-1, and 800 µmol·mol^-1 using liquefied carbon dioxide. Investigation items were plant height, stem diameter, number of leaves, leaf area, fresh weight, dry weight, also economic analysis was conducted by surveying only fruits exceeding 100 g. Photosynthesis was measured for the upper leaf position to calculate the saturation point according to the control. The photo saturation point was 587 µmol·m^-2·s^-1, and the CO₂ saturation point was 702 µmol·mol^-1. A_max values by carbon dioxide were 13.4, 17.8, 17.2, 19.6, and 17.5 µmolCO₂·m^-2·s^-1 in the order of 9℃, 12℃, 15℃, 500 µmol·mol^-1, and 800 µmol·mol^-1. In the temperature experiment, 9℃ in growth did not grow normally and no fruiting was performed. 12℃ and 15℃ were higher than 9℃, but there was no significant difference in growth and production. The CO₂ fertilization experiment showed no significant difference between the treatment in growth, but the productivity of 800 µmol·mol^-1 was the best. Comprehensively, the heating initiative temperature of 15℃ was good for crop growth and production, but there is no significant difference from 12℃, so it is good to set the heating start temperature to 12℃ economically, and maintaining of 800 µmol·mol^-1 is effective in increasing production.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.393-404
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• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• Journal of The Korean Association For Science Education
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• v.43 no.1
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• pp.17-28
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• 2023

In line with the entry into the digital-based intelligent information society, the science curriculum emphasizes the cultivation of scientific competencies, and computer-based test (CBT) is drawing attention for assessment of competencies. CBT has advantages to develop items that have high fidelity, and to establish a feedback system by accumulating results into the database. However, it is necessary to solve the problems of improving validity of assessment results, lowering measurement efficiency, and increasing management factors. To examine students' responses to the introduction of the new assessment tools in the process of transitioning from paper-based test (PBT) to CBT, in this study, we analyzed the results of the PBT and the CBT conducted in 2021 National Assessment of Educational Achievement (NAEA). In particular, we sought to find the effects on student achievement when only the mode of assessment was changed without change of items, and the effect on student achievement when the items were composed including technology enhanced features that take advantage of CBT. This study is derived through the analysis of the results of 7,137 third-grade middle school students taking one among the three kinds of assessments, which were the PBT or two kinds of CBT. After the assessment, the percentage of correct answers and the item discriminations were collected for each group, and expert opinions on characteristics of response were collected through the expert council involving 8 science teachers with experience in NAEA. According to the results, there was no significant difference between students' achievement results in the PBT and the CBT-M, which means simple mode conversion type of CBT, so it could be explained that the mode effect did not appear. However, it was confirmed that the percentage of correct answers for the construct response items was somewhat high in the CBT, and this result was analyzed to be related to the convenience of the response. On the other hand, there were the items with a difference of more than 10%p from the correct answer rate of similar items, among the items to which technology enhanced functions were applied following the introduction of CBT. According to the analysis of response rate of options, these results could be explained that the students' level of understanding could be more closely grasped through the innovative items developed through the technology enhanced function. Based on the results, we discussed some guidance to be considered when introducing CBT and developing items through CBT, and presented implications.

• Ecology and Resilient Infrastructure
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• v.10 no.3
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• pp.85-96
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• 2023

The study involved the categorization of domestic lakes located in South Korea into three groups based on their salinity levels: upstream reservoirs with salinity less than 0.3 psu, estuarine reservoirs with salinity ranging from 0.3 to 2 psu, and brackish lagoons with salinity exceeding 2 psu. Subsequently, the research assessed variations in the concentrations of total nitrogen (T-N) and total phosphorus (T-P) in the sediment of these lakes using statistical analysis, specifically one-way analysis of variance (ANOVA). Additionally, a laboratory core incubation test was conducted to investigate the benthic nutrient fluxes in Songji lagoon (salinity: 11.80 psu), Ganwol reservoir (salinity: 0.73 psu), and Janggun reservoir (salinity: 0.08 psu) under both aerobic and anoxic conditions. The findings revealed statistically significant differences in the concentrations of T-N and T-P among sediments in the lakes with varying salinity levels (p<0.05). Further post-hoc analysis confirmed significant distinctions in T-N between upstream reservoirs and estuarine reservoirs (p<0.001), as well as between upstream reservoirs and brackish lagoons (p<0.01). For T-P, a significant difference was observed between upstream reservoirs and brackish lagoons (p<0.01). Regarding benthic nutrient fluxes, Ganwol Lake exhibited the highest diffusive flux of NH₄⁺-N, primarily due to its physical characteristics and the inhibition of nitrification resulting from its relatively high salinity. The flux of NO₃^--N was lower at higher salinity levels under aerobic conditions but increased under anoxic conditions, attributed to the impact of salinity on nitrification and denitrification. Additionally, the flux of PO₄^3--P was highest in Songji Lake, followed by Ganwol Lake and Janggun Reservoir, indicating that salinity promotes the diffusive flux of phosphate through anion adsorption competition. It's important to consider the influence of salinity on microbial communities, growth rates, oxidation-reduction processes, and nutrient binding forms when studying benthic diffusive nutrient fluxes from lake sediments.