• Korean Journal of Breeding Science
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• v.43 no.1
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• pp.68-80
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• 2011

This study was conducted to investigate characteristics and variations of rice panicle traits for breeding temperate japonica varieties adapted to tropical environment. Eleven panicle traits were investigated from nine Korean rice varieties cultivated in Korea and wet season of the Philippines. Tested cultivars were composed of six temperate japonica varieties, three Tongil-type varieties, and one indica variety bred in the Philippines. The number of spikelets on secondary rachis branches (SRBs) was the most variable trait in both environments, while the mean number of spikelets on a primary rachis branch (PRB) was the least variable. Compared with PRB-related traits, SRB traits showed higher correlation with the number of spikelets per panicle. Compared with the plants grown in Korea, the number of spikelets on SRBs, the number of SRBs, spikelets, and rachis branches per panicle were decreased more than other traits in the Philippines. According to path analysis, the number of spikelets on SRBs per panicle affects the number of spikelets per panicle more than the number of spikelets on PRBs per panicle. Climatic factors such as growth duration, cumulative mean temperature, and integrated solar radiation were highly correlated with the relative rate of number of spikelets per panicle. To breed temperate japonica rice varieties adapted to tropical environment, it would be important to select lines which maintain proper growth duration and spikelets on SRBs in target region.

• Journal of Wetlands Research
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• v.20 no.4
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• pp.295-303
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• 2018

Inland wetlands in the Republic of Korea provide key breeding and wintering habitats, while coastal wetlands provide nutrient-rich habitats for stopover sites for East Asia/Australasia Flyway(EAAF) migrants. However, since the 1960's, Korea has reclaimed these coastal wetlands gradually for agriculture and urban expansion. The habitat loss has rippled across global populations of migrant shorebirds in EAAF. To protect a similar loss, the United States, specifically Missouri, developed the moist-soil management technique. Wetland impoundments are constructed from levees with water-flow control gates with specific soils, topography, available water sources, and target goals. The impoundments are subjected to a combination of carefully timed and regulated flooding and drawdown regimes with occasional soil disturbance. This serves a dual purpose of removing undesirable vegetation, while maximizing habitat and forage for wildlife. Flooding and drawdown schedules must be dynamic with constantly shifting climate conditions. Korea's latitude ($N33^{\circ}25^{\prime}{\sim}N38^{\circ}37^{\prime}$) is comparable to Missouri ($N36^{\circ}69^{\prime}{\sim}N40^{\circ}41^{\prime}$); as such, moist-soil management could prove to be an effective wetland restoration technique for Korea. In order to meet specific conservation goals (i.e. shorebird staging site restoration), it is necessary to test the proposed methodology on a site that can meet the required specifications for moist-soil management. Moist-soil management has the potential to not only create key habitat for endangered wildlife, but also provide valuable ecosystem services, including water filtration.

• Journal of the Korean Applied Science and Technology
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• v.35 no.4
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• pp.975-984
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• 2018

Fast pyrolysis bio-oil has unfavorable properties that restrict its use in many applications. Among the main issues are high acidity, instability, and water and oxygen content, which give rise to corrosiveness, polymerization during storage, and a low heating value. Esterification and azeotropic water removal can improve all of these properties. A 500 g of Quercus mongollica which grounded 0.8~1.4 mm was processed into bio-oil via fast pyrolysis for 2 seconds at $550^{\circ}C$. The esterification consists of treating pyrolysis oil with a high boiling alcohol like n-butanol at $70^{\circ}C$ under reduced pressure (100 hPa). All products are analyzed for water mass fraction, viscosity, higher heating value, pH, FT-IR and GC/MS. The water mass fraction can be reduced by 91.4 % (from 31.5 % to below 2.7 %), the viscosity by 65.8 % (from 36.5 to 12.5 cP) and the higher heating value can be increased by 96.8 % (from 3,918 to 7,712 kcal/kg), the pH by 1.3 (from 2.7 to 4.0). FT-IR and GC/MS analysis indicated that labile acids, aldehydes, ketones and lower alcohols were transformed to stable target products. Using this approach, the water content of the pyrolysis oil is reduced significantly. These improvements should allow the utilization of upgraded pyrolysis liquids in standard boilers and as fuel in CHP (Combined heat and power) plants.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.57-75
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• 2019

In this paper, we conducted a study focusing on the innovation direction of the documentary model on the Internet of Things industry, which is the most actively industrialized among the core technologies of the 4th Industrial Revolution. Policy, economic, social, and technical issues were derived using PEST analysis for global trend analysis. It also presented future prospects for the Internet of Things industry of ICT-related global research institutes such as Gartner and International Data Corporation. Global research institutes predicted that competition in network technologies will be an issue for industrial Internet (IIoST) and IoT (Internet of Things) based on infrastructure and platforms. As a result of the PEST analysis, developed countries are pushing policies to respond to the fourth industrial revolution through cooperation of private (business/ research institutes) led by the government. It was also in the process of expanding related R&D budgets and establishing related policies in South Korea. On the economic side, the growth tax of the related industries (based on the aggregate value of the market) and the performance of the entity were reviewed. The growth of industries related to the fourth industrial revolution in advanced countries overseas was found to be faster than other industries, while in Korea, the growth of the "technical hardware and equipment" and "communication service" sectors was relatively low among industries related to the fourth industrial revolution. On the social side, it is expected to cause enormous ripple effects across society, largely due to changes in technology and industrial structure, changes in employment structure, changes in job volume, etc. On the technical side, changes were taking place in each industry, representing the health and medical sectors and manufacturing sectors, which were rapidly changing as they merged with the technology of the Fourth Industrial Revolution. In this paper, various management methodologies for innovation of existing business model were reviewed to cope with rapidly changing industrial environment due to the fourth industrial revolution. In addition, four criteria were established to select a management model to cope with the new business environment: 'Applicability', 'Agility', 'Diversity' and 'Connectivity'. The expert survey results in an AHP analysis showing that Business Model Canvas is best suited for business model innovation methodology. The results showed very high importance, 42.5 percent in terms of "Applicability", 48.1 percent in terms of "Agility", 47.6 percent in terms of "diversity" and 42.9 percent in terms of "connectivity." Thus, it was selected as a model that could be diversely applied according to the industrial ecology and paradigm shift. Business Model Canvas is a relatively recent management strategy that identifies the value of a business model through a nine-block approach as a methodology for business model innovation. It identifies the value of a business model through nine block approaches and covers the four key areas of business: customer, order, infrastructure, and business feasibility analysis. In the paper, the expansion and application direction of the nine blocks were presented from the perspective of the IoT company (ICT). In conclusion, the discussion of which Business Model Canvas models will be applied in the ICT convergence industry is described. Based on the nine blocks, if appropriate applications are carried out to suit the characteristics of the target company, various applications are possible, such as integration and removal of five blocks, seven blocks and so on, and segmentation of blocks that fit the characteristics. Future research needs to develop customized business innovation methodologies for Internet of Things companies, or those that are performing Internet-based services. In addition, in this study, the Business Model Canvas model was derived from expert opinion as a useful tool for innovation. For the expansion and demonstration of the research, a study on the usability of presenting detailed implementation strategies, such as various model application cases and application models for actual companies, is needed.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Korean Journal of Psychosomatic Medicine
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• v.27 no.1
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• pp.35-41
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• 2019

Objectives : Although delirium is a common complication among patients hospitalized in intensive care units(ICUs), little is known about the roles that diagnostic and therapeutic procedures play in its development. This study investigates the procedure-related risk factors of delirium in ICU patients. Methods : All the consecutive patients admitted to the ICU between June 2016 and May 2017 were routinely evaluated for delirium by psychiatrists. In total, 1156 patients met the inclusion criteria and were retrospectively analyzed. A multiple logistic regression analysis was conducted to investigate independent risk factors of delirium development while adjusting for other characteristics. Results : The age, Acute Physiology and Chronic Health Evaluation (APACHE II) score, proportion of patients who had undergone an operation, and proportion of patients who were foley catheterized, mechanically ventilated, and physically restrained were higher in the delirium group. The multiple logistic regression analysis confirmed that the use of restraint was an independent risk factor of delirium (odds ratio : 10.006 ; 95% confidence interval : 6.120-16.360 ; p<0.001). The patient factors independently associated with delirium were an advanced age and a higher APACHE II score. The incidence of delirium was 15.3%. Conclusions : There is a high prevalence of delirium influenced by potentially harmful procedures in patients in ICU settings. The use of physical restraint had the strongest association with the development of delirium. These findings advocate the need to target procedure-related risk factors such as the use of restraints as preventive intervention measures for ICU delirium.

• Korean Journal of Child Education & Care
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• v.19 no.2
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• pp.177-196
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• 2019

Objective: This study aims to analyze research trends related to software education for young children, focusing on studies published in Korea from 2016 to 2019 March. Methods: A total of 26 research publications on software education for young children, searched from Korea Citation Index and Research Information Sharing Service were identified for the analysis. The trend in these publications was classified and examined respectively by publication dates, types of publications, and the fields of study. To investigate a means of research, the analysis included key topics, types of research methods, and characteristics of the study variables. Results: The results of the analysis show that the number of publications on the topic of software education for young children has increased over the three years, of which most were published as a scholarly journal article. Among the 26 research studies analyzed, 16 (61.5%) are related to the field of early childhood education or child studies. Key topics and target subjects of the most research include the curriculum development of software education for young children or the effectiveness of software education on 4- and 5-year-old children. Most of the analyzed studies are experimental research designs or in the form of literature reviews. The most frequently studied research variable is young children's cognitive characteristics. For the studies that employ educational programs, the use of a physical computing environment is prevalent, and the most frequently used robot as a programming tool is "Albert". The duration of the program implementation varies, ranging from 5 weeks to 48 weeks. In the analyzed research studies, computational thinking is conceptualized as a problem-solving skill that can be improved by software education, and assessed by individual instruments measuring sub-factors of computational thinking. Conclusion/Implications: The present study reveals that, although the number of research publications in software education for young children has increased, the overall sufficiency of the accumulated research data and a variety of research methods are still lacking. An increased interest in software education for young children and more research activities in this area are needed to develop and implement developmentally appropriate software education programs in early childhood settings.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.10-20
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• 2019

Irregular rainfall caused by climate change, in combination with non-point pollution, can cause water systems worldwide to suffer from frequent eutrophication and algal blooms. This type of water pollution is more common in agricultural prone to water system inflow of non-point pollution. Therefore, in this study, the correlation between Unmanned Aerial Vehicle(UAV) multi-spectral images and total phosphorus, total nitrogen, and chlorophyll-a with indirect association of algal blooms, was analyzed to identify the usability of UAV image to identify water quality characteristics in agricultural streams. The analysis the vegetation index Normalized Differences Index (NDVI), the Normalized Differences Red Edge(NDRE), and the Chlorophyll Index Red Edge(CIRE) for the detection of multi-spectral images and algal blooms collected from the target regions Yang cheon and Hamyang Wicheon. The analysis of the correlation between image values and water quality analysis values for the water sampling points, total phosphorus at a significance level of 0.05 was correlated with the CIRE(0.66), and chlorophyll-a showed correlation with Blue(-0.67), Green(-0.66), NDVI(0.75), NDRE (0.67), CIRE(0.74). Total nitrogen was correlated with the Red(-0.64), Red edge (-0.64) and Near-Infrared Ray(NIR)(-0.72) wavelength at the significance level of 0.05. The results of this study confirmed a significant correlations between multi-spectral images collected through UAV and the factors responsible for water pollution, In the case of the vegetation index used for the detection of algal bloom, the possibility of identification of not only chlorophyll-a but also total phosphorus was confirmed. This data will be used as a meaningful data for counterplan such as selecting non-point pollution apprehensive area in agricultural area.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.2
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• pp.127-136
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• 2019

Sweet sorghum (Sorghum bicolor L. Moench), compared to traditional crops, has been evaluated as a useful crop with high adaptability to the environment and various uses, but cultivation has not expanded owing to a lack of related research and information in Korea. This study was conducted to estimate heading date in 'Chorong' sweet sorghum based on climate data of the last 30 years (1989 - 2018) from six regions (Jeonju, Buan, Jeongup, Imsil, Namwon, and Jangsu) in Jellabuk Province. In addition, we compared the growth and quality factors by sowing date (April 10, April 25, May 10, May 25, June 10, June 25, and July 10) in 2018. Days from sowing to heading (DSH) increased to 107, 96, 83, 70, 59, 64, and 65 days in order of the sowing dates, respectively, and the average was 77.7 days. The effective accumulated temperature for heading date was $1,120.3^{\circ}C$. The mean annual temperature was the highest in Jeonju, followed in descending order by Jeongup, Buan, Namwon, Imsil, and Jangsu. The DSH based on effective accumulated temperature gradually decreased in all sowing date treatments in the six regions during the last 30 years. DSH of the six regions showed a negative relationship with mean temperature (sowing date to heading date) and predicted DSH ($R^2=0.9987**$) calculated by mean temperature was explained with a probability of 89% of observed DSH in 2017 and 2018. At harvest, fresh stem weight and soluble solids content were higher in the April and July sowings, but sugar content was higher in the May 10 ($3.4Mg{\cdot}ha^{-1}$) and May 25 ($3.1Mg{\cdot}ha^{-1}$) sowings. Overall, the April and July sowings were of low quality and yield, and there is a risk of frost damage; thus, we found May sowings to be the most effective. Additionally, sowing dates must be considered in terms of proper harvest stage, harvesting target (juice or grain), cultivation altitude, and microclimate.

• Journal of Nutrition and Health
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• v.51 no.6
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• pp.556-566
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• 2018

Purpose: This study was designed to determine the relationship between health behaviors and high levels of low-density lipoprotein-cholesterol (LDL-cholesterol) according to cardiovascular risk factors among Korean adults. Methods: This cross-sectional study was based on the sixth Korea national health and nutrition examination survey (KNHANES VI). Participants were 13,841 adults aged 19 years and older. Cardiovascular risk factors were stroke, myocardial infarction or angina, diabetes mellitus, smoking, hypertension, aging, high density lipoprotein-cholesterol (HDL-cholesterol) under 40 mg/dL and HDL-cholesterol over 60 mg/dL. Cardiovascular risk groups were classified as very high risk (stroke, myocardial infarction or angina), high risk (diabetes mellitus), moderate risk (over 2 risk factors), and low risk (below 1 risk factor). The prevalence of high LDL-cholesterol was calculated using the LDL-cholesterol target level according to cardiovascular risk group. Results: The prevalence of high LDL-cholesterol was 25.5% in males and 21.7% in females. Complex sample cross tabulation demonstrated that the high LDL-cholesterol and normal groups differed significantly according to age, education, body mass index, percentage of energy from carbohydrate, fat, saturated fat and n-6 in males and females. These two groups were also significantly different according to smoking in males and the percentage of energy from n-3 in females. Complex sample multiple logistic regression analysis adjusted for multiple confounding factors demonstrated that the probability of high LDL-cholesterol was significantly associated with current smoking (OR: 1.66, 95% CI: 1.40-1.99), obesity (OR: 1.95, 95% CI: 1.64-2.31) in males, and current smoking (OR: 1.73, 95% CI: 1.19-2.52), obesity (OR: 1.63, 95% CI: 1.39-1.90), percentage of energy from n-3 (quartile 1 vs. quartile 2; OR: 0.77, 95% CI: 0.62-0.96; quartile 1 vs. quartile 3; OR: 0.73, 95% CI: 0.56-0.94; quartile 1 vs. quartile 4: OR: 0.67, 95% CI: 0.51-0.87) in females. Conclusion: This study reveals the impact of smoking, obesity, energy percentage of nutrient intake on LDL-cholesterol.