• Journal of Nutrition and Health
• /
• v.50 no.3
• /
• pp.294-301
• /
• 2017

Purpose: The purposes of this study were to investigate food consumption practices and analyze factors that influence customer satisfaction of an HACCP applied restaurant in a highway service area. Methods: A total of 207 customer responses were used for data analysis. Statistical analyses were conducted using the SPSS program (ver. 22.0) for $x^2$-test, Pearson correlation analysis, and multiple regression analysis. Results: Reasons for visiting the highway area were using the restroom (86.0%), purchasing of meals or snacks (70.1%), taking a rest (58.5%), and shopping (3.4%) and selection attributes of food sold in the highway service area were food taste (48.8%), food safety (33.3%), and waiting time for meal (10.7%). According to the results of the survey, udon (66.2%) was the most preferred meal, followed by instant noodles (56.0%), kimbap (50.7%), pork cutlet (38.2%), and bibimbap (29.0%). In addition, coffee (73.4%) was the most preferred among snacks and beverages, followed by beverages (58.9%), walnut cake (53.1%), mineral water (52.2%), and hotbar (52.2%). Satisfaction evaluation scores of foods sold in the highway service area were highest for appropriate portion size, followed by food safety, menu variety, food taste, and reasonable price. Overall customer satisfaction scores regarding the restaurant in the highway service area was 3.24 out of 5 points on average. According to the results of the multiple regressing analysis, food taste (p < 0.001) and reasonable price (p < 0.01) had significant positive effects on overall customer satisfaction. Conclusion: To enhance customer satisfaction, restaurant managers in the highway service area should implement HACCP, improve food taste, and set up a proper price for food sold at the restaurant in the highway service area.

• Economic and Environmental Geology
• /
• v.54 no.2
• /
• pp.161-176
• /
• 2021

In this study, one of the distributed hydrologic models, VELAS, was used to analyze the variation of hydrologic elements based on water balance analysis to evaluate the groundwater recharge in more detail than the annual time scale for the past and future. The study area is located in Yanggok-ri, Seobu-myeon, Hongseong-gun, Chungnam-do, which is very vulnerable to drought. To implement the VELAS model, spatial characteristic data such as digital elevation model (DEM), vegetation, and slope were established, and GIS data were constructed through spatial interpolation on the daily air temperature, precipitation, average wind speed, and relative humidity of the Korea Meteorological Stations. The results of the analysis showed that annual precipitation was 799.1-1750.8 mm, average 1210.7 mm, groundwater recharge of 28.8-492.9 mm, and average 196.9 mm over the past 18 years from 2001 to 2018 in the study area. Annual groundwater recharge rate compared to annual precipitation was from 3.6 to 28.2% with a very large variation and average 14.9%. By the climate change RCP 8.5 scenario, the annual precipitation from 2019 to 2100 was 572.8-1996.5 mm (average 1078.4 mm) and groundwater recharge of 26.7-432.5 mm (average precipitation 16.2%). The annual groundwater recharge rates in the future were projected from 2.8% to 45.1%, 18.2% on average. The components that make up the water balance were well correlated with precipitation, especially in the annual data rather than the daily data. However, the amount of evapotranspiration seems to be more affected by other climatic factors such as temperature. Groundwater recharge in more detailed time scale rather than annual scale is expected to provide basic data that can be used for groundwater development and management if precipitation are severely varied by time, such as droughts or floods.

• Korean Journal of Remote Sensing
• /
• v.37 no.2
• /
• pp.321-335
• /
• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

• Korean Journal of Remote Sensing
• /
• v.37 no.2
• /
• pp.275-290
• /
• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Korean Chemical Engineering Research
• /
• v.57 no.1
• /
• pp.90-104
• /
• 2019

In this study, CALPUFF modeling was performed, using a real surface and upper air meterological data to predict trustworthy modeling-results. Pollutant-releases from windscreen chambers of enclosed poultry farms, P1 and P2, and from a open poultry farm, P3, and their diffusing behavior were modeled by CALPUFF modeling with volume sources as well as by finally-adjusted CALPUFF modeling where a linear velocity of upward-exit gas averaged with the weight of each directional-emitting area was applied as a model-linear velocity ($u^M_y$) at a stack, with point sources. In addition, based upon the scenario of poultry farm-releasing odor and particulate matter (PM) removal efficiencies of 0, 20, 50 and 80% or their corresponding emission rates of 100, 80, 50 and 20%, respectively, CALPUFF modeling was performed and concentrations of odor and PM were predicted at the region as a discrete receptor where civil complaints had been frequently filed. The predicted concentrations of ammonia, hydrogen sulfide, $PM_{2.5}$ and $PM_{10}$ were compared with those required to meet according to the offensive odor control law or the atmospheric environmental law. Subsequently their required removal efficiencies at poultry farms of P1, P2 and P3 were estimated. As a result, a priori assumption that pollutant concentrations at their discrete receptors are reduced by the same fraction as pollutant concentrations at P1, P2 and P3 as volume source or point source, were controlled and reduced, was proven applicable in this study. In case of volume source-adopted CALPUFF modeling, its required removal efficiencies of P1 compared with those of point source-adopted CALPUFF modeling, were predicted similar each other. However, In case of volume source-adopted CALPUFF modeling, its required removal efficiencies of both ammonia and $PM_{10}$ at not only P2 but also P3 were predicted higher than those of point source-adopted CALPUFF modeling. Nonetheless, the volume source-adopted CALPUFF modeling was preferred as a safe approach to resolve civil complaints. Accordingly, the required degrees of pollution prevention against ammonia, hydrogen sulfide, $PM_{2.5}$ and $PM_{10}$ at P1 and P2, were estimated in a proper manner.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.1
• /
• pp.29-41
• /
• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Bio-Environment Control
• /
• v.30 no.1
• /
• pp.85-93
• /
• 2021

In hydroponic cultivation, in order to investigate the change of lettuce growth and physiologically active substances through CO2 tablet treatment in nutrient solution, we used a solid carbonated tablets commercially available in the Netherlands. The experiment consisted of 0.5-fold, 1-fold, and 2-fold treatment groups with no treatment as a control. As a result, the atmospheric CO2 concentration in the chamber after CO2 tablet treatment showed the highest value at 472.2 µL·L-1 in the 2-fold treatment zone immediately after treatment, and the pH in the nutrient solution decreased the most to pH 6.03 in the 2-fold treatment zone. After that, over time, the CO2 concentration and pH recovered to the level before treatment. Leaf width and leaf area of lettuce showed the highest values of 17.1cm and 1067.14 ㎠ when treated 2-fold with CO2 tablet, while fresh weight and dry weight of the above-ground part were highest at 63.87 g and 3.08 g in 0.5-fold treatment. The root length of lettuce was the longest (28.4 cm) in the control, but there was no significant difference in the fresh weight and the dry weight among the treatments. Apparently, it was observed that the root length of the lettuce was shortened by CO2 tablet treatment and a lot of side roots occurred. In addition, there was a growth disorder in which the roots turned black, but it was found that there was no negative effect on the growth of the above-ground part. As a result of analyzing the bioactive compounds of lettuce by CO2 tablet treatment, chlorogenic acid and quercetin were detected. As a result of quantitative analysis, chlorogenic acid increased by 249% compared to the control in 1-fold treatment, but quercetin decreased by 37%. As a result of comparing the DPPH radical scavenging ability showing antioxidant activity, the control and 0.5-fold treatment showed significantly higher values than the 1-fold and 2-fold treatments. This suggests that carbonated water treatment is effective in increasing the growth and bioactive compounds of hydroponic lettuce.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.1
• /
• pp.48-61
• /
• 2022

South Korea is quite vegetation rich country which has 63% forests and 16% cropland area. Massive NOx emissions from megacities, therefore, are easily combined with BVOCs emitted from the forest and cropland area, then produce high ozone concentration. BVOCs emissions have been estimated using well-known emission models, such as BEIS (Biogenic Emission Inventory System) or MEGAN (Model of Emission of Gases and Aerosol from Nature) which were developed using non-Korean emission factors. In this study, we ran MEGAN v2.1 model to estimate BVO Cs emissions in Korea. The MO DIS Land Cover and LAI (Leaf Area Index) products over Korea were used to run the MEGAN model for June 2012. Isoprene and Monoterpenes emissions from the model were inter-compared against the enclosure chamber measurements from Taehwa research forest in Korea, during June 11 and 12, 2012. For estimating emission from the enclosed chamber measurement data. The initial results show that isoprene emissions from the MEGAN model were up to 6.4 times higher than those from the enclosure chamber measurement. Monoterpenes from enclosure chamber measurement were up to 5.6 times higher than MEGAN emission. The differences between two datasets, however, were much smaller during the time of high emissions. More inter-comparison results and the possibilities of improving the MEGAN modeling performance using local measurement data over Korea will be presented and discussed.

• Journal of the Society of Cosmetic Scientists of Korea
• /
• v.48 no.3
• /
• pp.189-196
• /
• 2022

Plasma refers to an ionized gas that is often referred to as "the fourth phase of matter", following solid, liquid, and gas. Plasma has traditionally been utilized for industrial applications such as welding and neon signs, but its promise in biomedical fields such as cancer treatment and dermatology has lately been recognized. Indeed, due to its beneficial effects in promoting collagen production, improving skin tone, and eliminating harmful bacteria in the skin, plasma treatment constitutes an important target for dermatological research. In this study, a plasma device for cosmetic manufacturing based on nitrogen, the main component of the atmosphere, was designed and assembled. Moreover, nitric oxide (NO) was selected since is easier to follow and evaluate than other nitrogen plasma active species, and its contents were measured to perform a quantitative and qualitative evaluation of plasma. First, an injection method, using different proximities labeled "sinking" and "non sinking" treatments, was performed to test the most efficient plasma treatment method. As a result, it was observed that the formulation obtained by a non sinking treatment was more effective. Furthermore, toner and ampoule were selected as cosmetics formulations, and the characteristics of the formulation and changes in the injected plasma state were observed. In both formulations, the successful injection of NO plasma was 2 times higher in toner formulation than ampoule formulation, and it gradually decreased with time, having dissipated after a week. It was confirmed that the nitrogen plasma used did not affect the stability of the toner and ampoule formulations at low temperature (4 ℃), room temperature (25 ℃), and high temperature (37 ℃ and 50 ℃) conditions. The results of this study demonstrate the potential of plasma cosmetics and highlight the importance of securing the stability of the injected plasma.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.6
• /
• pp.35-54
• /
• 2021

This study examines the characteristics of CBS's Kim Hyun-Jung's News Show(hearafter News Show) and the change to transmedia. The distinguishing characteristics of News Show compared to radio-based current affairs programs are as follows. First, despite its time limit, it adopts the form of investigative reports or investigative documentaries to uncover the truth of the events through 'Detective Son Su-Ho', etc. Second, News show's interviews have avoided biased stance in the reporting of minority issues by interviewing the affected parties anonymously including people involved in incidents and accidents, bereaved families, and other victims. News Show has been producing a transmedia content called 'That Honey Show' (a show that reads comments as fun as honey) since November 2018. 'That Honey Show' is broadcast in real time on YouTube right after the News Show radio broadcast ends. As a form of spin-off content, 'That Honey Show' breaks down the boundaries among staff, MCs, and guests, as well as shifting roles by using 'vice characters'. The female host, Kim conducts interviews with the main characters related to various issues and extends the fixed identity of current affairs shows to the everyday politics and cultural realms. Thus she draws active participation and responses audience. This paper analyzes two representative broadcast cases of 'That Honey Show'-first, the case of resistance and activity of the BTS fandom ARMY in the US presidential election, and, second, the case of reporting on the Nth room incident. This analysis considers the critical participation of digital citizens and the effect of fostering a sense of community in the current affairs show in the transmedia era.