• Nutrition Research and Practice
• /
• v.18 no.5
• /
• pp.663-673
• /
• 2024

BACKGROUND/OBJECTIVES: Poly-γ-glutamic acid (γ-PGA), a natural polymer found in fermented soybean products, has been reported to play a prebiotic role in the gut. This intervention study investigated the effects of γ-PGA-containing supplement consumption on urinary microbiota in healthy adults because of limited data on such investigation. SUBJECTS/METHODS: A 4-week parallel trial including 39 male and female Korean adults, who were free of chronic diseases and infection, was designed as a randomized, double-blinded, placebo-controlled study. A total of 30 participants completed the study wherein the intervention group (n = 17) received a mixture supplement containing 600 mg/day of γ-PGA and 100 mg/day of vitamin B6, while the control group (n = 13) received a placebo. Paired datasets (baseline and endpoint data) of microbiota profiles, which were constructed via urinary assays of microbe-derived extracellular vesicles, were analyzed and compared between the two groups. RESULTS: Only the intervention group yielded significant results for the Bray-Curtis and Jaccard dissimilarity indices between baseline and endpoint data (P < 0.05). In the phylum-level analysis of microbial composition, the Firmicutes to Bacteroidetes ratio (FB ratio) tended to decrease from baseline in the intervention group; however, it increased in the control group. Differences between the baseline and endpoint FB ratios were significant between the two groups (P < 0.05). CONCLUSION: This study's findings suggest that γ-PGA-vitamin B6 supplementation potentially alters the microbial community composition of a host. Further investigation into the biological consequences of commensal microbiota alteration by γ-PGA-containing supplement consumption is warranted.

• Journal of the Korean earth science society
• /
• v.41 no.2
• /
• pp.95-110
• /
• 2020

Comparisons between two different surface temperatures from high-resolution ECMWF ReAnalysis 5 (ERA5) and Automated Synoptic Observing System (ASOS) observations were performed to investigate the reliability of the new reanalysis data over South Korea. As ERA5 has been recently produced and provided to the public, it will be highly used in various research fields. The analysis period in this study is limited to 1999-2018 because regularly recorded hourly data have been provided for 61 ASOS stations since 1999. Topographic characteristics of the 61 ASOS locations are classified as inland, coastal, and mountain based on Digital Elevation Model (DEM) data. The spatial distributions of whole period time-averaged temperatures for ASOS and ERA5 were similar without significant differences in their values. Scatter plots between ASOS and ERA5 for three different periods of yearlong, summer, and winter confirmed the characteristics of seasonal variability, also shown in the time-series of monthly error probability density functions (PDFs). Statistical indices NMB, RMSE, R, and IOA were adopted to quantify the temperature differences, which showed no significant differences in all indices, as R and IOA were all close to 0.99. In particular, the daily mean temperature differences based on 1-hour-averaged temperature had a smaller error than the classical daily mean temperature differences, showing a higher correlation between the two data. To check if the complex topography inside one ERA5 grid cell is related to the temperature differences, the kurtosis and skewness values of 90-m DEM PDFs in a ERA5 grid cell were compared to the one-year period amplitude among those of the power spectrum in the time-series of monthly temperature error PDFs at each station, showing positive correlations. The results account for the topographic effect as one of the largest possible drivers of the difference between ASOS and ERA5.

• Journal of Digital Convergence
• /
• v.16 no.4
• /
• pp.95-105
• /
• 2018

This study empirically test the roles of network closure and structural hole on radical innovation in technological knowledge exchange network in Gumi cluster. In doing so, we build 2,550 firm network, transforming association*firm(2-mode) to firm*firm(1-mode) network data. In addition, in order to investigate firms' attributes, we conduct survey for 101 firms in Gumi cluster using random sampling, and finally collect 86 firm samples. For analysis, we use ridge regression since network density and efficiency, indices of network closure and structural hole respectively, has a high level of multicollinearity. The findings show that structural hole has a significant and positive impact on radical innovation, but network closure has a significant and negative impact on radical innovation. This study contributes to present an empirical evidence of debate on network closure and structural hole based on past conceptual discussions and literature review and further goes a long way towards strategy formulation to establish social capital in accomplishing radical innovation. Further research is required that pays closer attention to features of technological knowledge, innovation types and interaction between network closure and structural hole, directing efforts to structural characteristics of various networks.

• Journal of Digital Convergence
• /
• v.15 no.8
• /
• pp.501-509
• /
• 2017

The purpose of this study was to review the effects of the physical fatigue induced by overtraining on cognitive information processing and emotional change of athletes by neurophysiologic study. And to provide basic data for the athletes and coaches in the sports field to improve understanding of the overtraining symptoms from the brain physiological point of view. We reviewed the domestic and international academic materials and research reports published in the last 20 years using electronic databases and examined the literature that is consistent with the purpose of this study among the retrieved documents. Based on the reviewed literature, we examined the cognitive information processing and emotional change of the athlete according to the training, and overtraining can accumulate the fatigue of the body, which can cause severe side effects in terms of cognitive ability and emotional ability of athletes as well as deterioration of performance. The study using P300 and EEG cerebral hemispheric asymmetry difference indices also confirmed the possibility of a new approach to understand psychological and physiological effects of overtraining on information processing and emotional changes.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.2 no.1
• /
• pp.9-15
• /
• 2000

The objective of this study is to evaluate the use of RADARSAT and Landsat TM data for the monitoring of rice growth. The relationships between backscatter coefficients($\sigma$$^{0}$ ) of RADARSAT data and digital numbers (DN) of Landsat TM and rice growth parameters were investigated. Radar backscatter coefficients were calculated by calibration process and then compared with rice growth parameters; plant height, leaf area index (LAI), and fresh and dry biomass. When radar backscatter coefficient ($\sigma$$^{0}$ ) of rice was expressed as a function of time, it is shown that the increasing trend ranged from -22--20dB to -9--8dB as growth advances. The temporal variation of backscatter coefficient was significant to interpret rice growth. According to the relationship between leaf area index and backscatter coefficient, backscatter coefficient underestimated leaf area index at the beginning of life history and overestimated, at the reproductive stage. The same increasing trend between biomass and backscatter coefficient was shown. From these results, RADARSAT data appear positive to the monitoring of rice growth. Each band of time-series Landsat TM data had a significant trend as a rice crop grows during its life cycle. Spectral indices, NDVI[(TM4-TM3)/(TM4+TM3)] and RVI(TM4/TM2), derived from Landsat TM equivalent bands had the same trend as leaf area index.

• Journal of Digital Convergence
• /
• v.14 no.6
• /
• pp.157-165
• /
• 2016

The purpose of this study was to analyze the effect of the shipping industry real economy index on the stock prices of domestic shipping companies. The parameters used in this analysis were the stock price of H Company in South Korea and shipping industry real economy indices including BDI, CCFI and HRCI. The period analysis was from 2012 to 2015. The weekly data for four years of the stock price index of shipping companies, BDI, CCFI, and HRCI were used. The effects of CCFI and HRCI on the stock price index of domestic shipping companies were analyzed using the VAR model, and the effects of BDI on the stock price index of domestic shipping companies were analyzed using the VECM model. The VAR model analysis results showed that CCFI and HRCI had negative effects on the stock price index, and the VECM model analysis results showed that BDI also had a negative effect on the stock price index.

• Journal of Digital Convergence
• /
• v.12 no.5
• /
• pp.89-99
• /
• 2014

Correlation of KOSPI from stock market and Apartment Prices in Seoul HPPCI from real estate market has been found from this research. Furthermore, from the comparison of those indicators' flows, certain precedence was found as well. The purpose of this research is to analyze correlation and precedence among KOSPI, Apartment price in Seoul, HPPCI and CLI. As for predicting KOSPI of stock market and real estate market, it is necessary to find out preceding indices and analyzing their progresses first. For 27 years from the January 1987 to December 2013, KOSPI has been grown by 687%, while CLI showed 443%, Apartment of Seoul showed 391%, HPPCI showed 263% of growth rate in order. As the result of correlation analysis among Apartment of Seoul, CLI, KOSPI and HPPCI, KOSPI and HPPCI showed high correlation coefficient of 0.877, and Apartment of Seoul and CLI showed that of 0.956 which is even higher. Result from the analysis, CLI shows high correlation with stock and real estate market, it is a good option to watch how CLI flows to predict stock and real estate market.

• Journal of Digital Convergence
• /
• v.19 no.11
• /
• pp.351-357
• /
• 2021

IoT devices, which are used in various ways in distributed environments, are becoming more important in data transmitted and received from IoT devices as fields of use such as medical, environment, transportation, bio, and public places are diversified. In this paper, as a method to ensure the reliability of IoT data, an autoencoder-based IoT-linked processing technique is proposed to classify and process numerous data by various important attributes. The proposed technique uses correlation indices for each IoT data so that IoT data is grouped and processed by blockchain by characteristics for IoT linkage processing based on autoencoder. The proposed technique expands and operates into a blockchain-based n-layer structure applied to the correlation index to ensure the reliability of IoT data. In addition, the proposed technique can not only select IoT data by applying weights to IoT collection data according to the correlation index of IoT data, but also reduce the cost of verifying the integrity of IoT data in real time. The proposed technique maintains the processing cost of IoT data so that IoT data can be expanded to an n-layer structure.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.4
• /
• pp.383-388
• /
• 2024

This study conducted voluntary focus group user observations to empirically validate the efficacy of the self-developed psychological support mobile application, "Mindful Now". The app is structured as an interactive game format, enabling individuals to activate self-awareness of mindfulness states anytime, anywhere. It consists of a 3-step process of sensory/emotional/consciousness awareness, facilitating the expression of non-judgmental awareness. To demonstrate the effectiveness of this mindful activation in enhancing psychological well-being such as happiness and stress reduction, voluntary mindfulness mobile app usage was tracked among 49 university students. The results revealed significant improvements, with a 14.4% increase in SWLS happiness index and a 17.1% decrease in PSS-10 stress levels among 12 users who used the app continuously for over 60 days to practice mindfulness awareness. Particularly, higher app engagement was observed among students who initially reported relatively lower indices before using the app. The utilization of mobile apps that promote mindful activation aligns with various therapeutic paradigms based on mindfulness and meditation, contributing to advancements in digital therapeutic interventions for psychological support.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.398-403
• /
• 2023

Process-based K-cabbage model is based on physiological processes such as photosynthesis and phenology, making it possible to predict crop growth under different climate conditions that have never been experienced before. Current first-stage process-based models can be used to assess climate impact through yield prediction based on climate change scenarios, but no comparison has been performed between big data obtained from the main production area and model prediction so far. The aim of this study was to find out the direction of model improvement when using the current model for yield prediction. For this purpose, model performance evaluation was conducted based on data collected from farmers growing 'Chungwang' cabbage in Taebaek and Samcheok, the main producing areas of Chinese cabbage in highland region. The farms surveyed in this study had different cultivation methods in terms of planting date and soil water and nutrient management. The results showed that the potential biomass estimated using the K-cabbage model exceeded the observed values in all cases. Although predictions and observations at the time of harvest did not show a complete positive correlation due to limitations caused by the use of fresh weight in the model evaluation process (R²=0.74, RMSE=866.4), when fitting the model based on the values 2 weeks before harvest, the growth suitability index was different for each farm. These results are suggested to be due to differences in soil properties and management practices between farms. Therefore, to predict attainable yields taking into account differences in soil and management practices between farms, it is necessary to integrate dynamic soil nutrient and moisture modules into crop models, rather than using arbitrary growth suitability indices in current K-cabbage model.