• Horticultural Science & Technology
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• v.32 no.3
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• pp.318-329
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• 2014

In order to gain insight into the physiological responses of plants to high temperature stress, the effects of temperature on Chinese cabbage (Brassica campestris subsp. napus var. pekinensis cv. Detong) were investigated through analyses of photosynthesis and chlorophyll fluorescence under 3 different temperatures in the temperature gradient tunnel. Growth (leaf length and number of leaves) during the rosette stage was greater at ambient $+4^{\circ}C$ and ambient $+7^{\circ}C$ temperatures than at ambient temperature. Photosynthetic $CO_2$ fixation rates of Chinese cabbage grown under the different temperatures did not differ significantly. However, dark respiration rate was significantly higher in the cabbage that developed under ambient temperature relative to elevated temperature. Furthermore, elevated growth temperature increased transpiration rate and stomatal conductance resulting in an overall decrease of water use efficiency. The chlorophyll a fluorescence transient was also considerably affected by high temperature stress; the fluorescence yield $F_J$, $F_I$, and $F_P$ decreased considerably at ambient $+4^{\circ}C$ and ambient $+7^{\circ}C$ temperatures, with induction of $F_K$ and decrease of $F_V/F_O$. The values of RC/CS, ABS/CS, TRo/CS, and ETo/CS decreased considerably, while DIo/CS increased with increased growth temperature. The symptoms of soft-rot disease were observed in the inner part of the cabbage heads after 7, 9, and/or 10 weeks of cultivation at ambient $+4^{\circ}C$ and ambient $+7^{\circ}C$ temperatures, but not in the cabbage heads growing at ambient temperature. These results show that Chinese cabbage could be negatively affected by high temperature under a future climate change scenario. Therefore, to maintain the high productivity and quality of Chinese cabbage, it may be necessary to develop new high temperature tolerant cultivars or to markedly improve cropping systems. In addition, it would be possible to use the non-invasive fluorescence parameters $F_O$, $F_V/F_M$, and $F_V/F_O$, as well as $F_K$, $M_O$, $S_M$, RC/CS, ETo/CS, $PI_{abs}$, and $SFI_{abs}$ (which were selected in this study), to quantitatively determine the physiological status of plants in response to high temperature stresses.

• Proceedings of the Korean Geotechical Society Conference
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• 1998.05a
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• pp.35-81
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• 1998

Distinct Element Method(DEM) has a great advantage to model the discontinuous behaviour of jointed rock masses such as rotation, sliding, and separation of rock blocks. Geometrical data of joints by a field monitoring is not enough to model the jointed rock mass though the results of DE analysis for the jointed rock mass is most sensitive to the distributional properties of joints. Also, it is important to use a properly joint law in evaluating the stability of a jointed rock mass because the joint is considered as the contact between blocks in DEM. In this study, a stochastic modelling technique is developed and the dilatant rock joint is numerically modelled in order to consider th geometrical and mechanical properties of joints in DE analysis. The stochastic modelling technique provides a assemblage of rock blocks by reproducing the joint distribution from insufficient joint data. Numerical Modelling of joint dilatancy in a edge-edge contact of DEM enable to consider not only mechanical properties but also various boundary conditions of joint. Preprocess Procedure for a stochastic DE model is composed of a statistical process of raw data of joints, a joint generation, and a block boundary generation. This stochastic DE model is used to analyze the effect of deviations of geometrical joint parameters on .the behaviour of jointed rock masses. This modelling method may be one tool for the consistency of DE analysis because it keeps the objectivity of the numerical model. In the joint constitutive law with a dilatancy, the normal and shear behaviour of a joint are fully coupled due to dilatation. It is easy to quantify the input Parameters used in the joint law from laboratory tests. The boundary effect on the behaviour of a joint is verified from shear tests under CNL and CNS using the numerical model of a single joint. The numerical model developed is applied to jointed rock masses to evaluate the effect of joint dilation on tunnel stability.

• Journal of Distribution Science
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• v.10 no.5
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• pp.37-44
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• 2012

In this paper, we investigate several factors that affect the locational decision of discount stores by using previous studies on the marketing area and the location of commercial facilities. We selected 21 primary variables that are expected to influence the decision of store location and, by factor analysis, grouped them into five underlying factors. Among these, the demographic factor, which shows the potential purchasing power level, had the greatest impact on the locational decision for the store. However, we found individual stores positioned according to unique locational characteristics in addition to the demographic factor. It means that we have to additionally consider if the vicinity of the market is based on any physical properties. Many previous studies proposed four decision factors for store location: the economic factor, the demographic factor, the land utilization factor, and traffic factor. However, the fivefold factors-our distinctive contribution-are more concrete and persuasive according to Korean reality. We show that location preference is based on the following criteria: (1) the area is densely populated, (2) houses stand close together, (3) residents have a high income level, (4) road traffic is developed and easy to access, and (5) public transportation is well developed. The demographic factor has the greatest impact on the location of a discount store. The number of households has a greater relevance to the demographic factor than does the individual consumer. Second, discount stores relatively prefer places where houses are located close together because such places offer easy access to the market. Third, a place whose residents have a high income level will be preferred, with its large cars and excellent traffic conditions. Fourth, a location would be highly rated if the roads around commercial facilities are well developed and their accessibility is good. Finally, discount stores must be located close to bus stops because female consumers, including housewives-the most important customers-evaluate stores based on distance. In this research, the variable of consumer attitude and preference was excluded, and the location factors of discount stores were analyzed according to a microscopic view through physical spatial data. In the future, the opening of new discount stores based on the five factors indicated above will require a comparatively shorter time from the first project feasibility analysis. In addition, the result of our study can be applied to the field of public policy for constructing and attracting large-scale distribution facilities.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of The Korean Association For Science Education
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• v.30 no.6
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• pp.693-718
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• 2010

This study investigated secondary school science teachers' experiences to explore the influencing factors in science teachers efficiency (STE). The participants, thirty three secondary school science teachers who have more than four years of teaching experience, were interviewed about describing each teacher's experience throughout one's years of teaching. The grounded theory introduced by Strauss and Corbin (1998) was used to analyze the data in this study. The results of paradigm analysis revealed that STE is influenced by 125 concepts, 38 sub-categories, and 16 categories. In a paradigm model, the central phenomenon was 'constructing STE', and the causal condition was 'want to be a teacher' as career choice motivation. The contextual conditions that have an affect on the central phenomenon were 'self awareness of the teacher' and 'social awareness of the teacher.' The mediate conditions, which facilitated or restrained the action/interaction strategies, were 'societal tendency', 'school climate', and 'personal context.' The action/interaction strategies to control the phenomenon were 'following the line,' 'identifying effective teaching strategies,' 'taking teacher education programs,' and 'contributing to school improvement.' The consequences were 'teacher's self awareness', 'challenge,' and 'stagnating in teaching.' The overall conclusion drawn from this research is that, the definition of STE is beliefs in science teachers' capabilities to set up objects in some school teaching context and, organize and execute the course of action required to attain these. Additionally, STE has three dimensions of teacher's behaviors: science instructional efficiency, efficiency in engaging students, and efficiency in managing school conditions. This study offers insight into the nature of STE and theoretical framework. These findings may give science teachers and teacher educators the practical knowledge necessary to build effective training programs and interventions that would help increase STE and facilitate effective teaching.

• Information Systems Review
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• v.25 no.4
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• pp.131-159
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• 2023

Recently, amidst the backdrop of the COVID-19 pandemic shifting towards an endemic phase, there has been a rise in discussions and debates about the future of the metaverse. Simultaneously, major metaverse platforms like Roblox have been launching services integrated with generative AI, and Apple's mixed reality hardware, Vision Pro, has been announced, creating new expectations for the metaverse. In this situation where the outlook for the metaverse is divided, it is crucial to diagnose the metaverse from an ecosystem perspective, examine its key ecological features, driving forces for development, and future possibilities for advancement. This study utilized Wang's (2021) Information Ecology Theory (IET) framework, which is representative of ecosystem research in the field of Information Systems (IS), to derive the Metaverse Industrial Ecosystem (MIE). The analysis revealed that the MIE consists of four main domains: Tech Landscape, Category Ecosystem, Metaverse Platform, and Product/Service Ecosystem. It was found that the MIE exhibits characteristics such as digital connectivity, the integration of real and virtual worlds, value creation capabilities, and value sharing (Web 3.0). Furthermore, the interactions among the domains within the MIE and the four characteristics of the ecosystem were identified as driving forces for the development of the MIE at an ecosystem level. Additionally, the development of the MIE at an ecosystem level was categorized into three distinct stages: Narrow Ecosystem, Expanded Ecosystem, and Everywhere Ecosystem. It is anticipated that future advancements in related technologies and industries, such as robotics, AI, and 6G, will promote the transition from the current Expanded Ecosystem level of the MIE to an Everywhere Ecosystem level, where the connection between the real and virtual worlds is pervasive. This study provides several implications. Firstly, it offers a foundational theory and analytical framework for ecosystem research, addressing a gap in previous metaverse studies. It also presents various research topics within the metaverse domain. Additionally, it establishes an academic foundation that integrates concept definition research and impact studies, which are key areas in metaverse research. Lastly, referring to the developmental stages and conditions proposed in this study, businesses and governments can explore future metaverse markets and related technologies. They can also consider diverse metaverse business strategies. These implications are expected to guide the exploration of the emerging metaverse market and facilitate the evaluation of various metaverse business strategies.

• Culinary science and hospitality research
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• v.21 no.3
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• pp.168-180
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• 2015

This study set out to make sponge cake a product of confectionery and bakery to expand the uses of rice bran and conducted a preliminary experiment to revise and supplement the addition of rice bran. The study sought to determine the level of added rice bran, 0~20%, by taking into account the taste, color, and marketability of rice bran in order to provide basic data for the possibilities of new product development and increase the usage of rice bran. As for the general composition, moisture, protein, fat, carbohydrate, and ash content comprised 9.50%, 15.51%, 18.12%, 48.17%, and 8.70% of the rice bran powder respectively. The pH of the dough decreased significantly with increased levels of rice bran. The specific gravity of the dough tended to rise significantly with the addition of rice bran. The group of 0% rice bran powder recorded the highest score of brightness, whereas the group of 20% rice bran powder scored lowest in terms of brightness. While there were significant differences between the control and experiment groups, no significant differences were found among the addition groups. Hardness also showed a tendency to significantly increase. The sensory evaluation results indicate that the group of 0% rice bran powder recorded the highest overall preference score at 5.00 and that the group of 20% rice bran powder had the lowest overall preference score at 2.87. The results also suggest that 10% rice bran powder sponge cake could be helpful in improving physical quality.

• Pediatric Infection and Vaccine
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• v.12 no.2
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• pp.186-194
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• 2005

Purpose : Hepatitis A viral infections have been continued after re-emerging since mid 1990s in Korea. The incidence of this disease has been increased in young adults younger than 30 years of age since 2000. This study was performed to evaluate the prevalence of antibody to hepatitis A in Korea(two regions; Incheon and Changwon) in 2005, and was compared with the results of similar studies in mid 1990s. Methods : The study was conducted from January 2005 to June 2005, and consisted of 1,301 enrolled subjects, neonates to 50 years old, living in Incheon and Changwon in Korea. All sera were frozen and stored at $-70^{\circ}C$ until assayed. Anti-HAV IgG antibodies were measured by microparticle enzyme immunoassay(HAVAB, Abbott Lab., IL, USA). Results : The prevalence of anti-HAV IgG was 61.1% in infants younger than 1 year old, 30.5% in 1~5 years, 14.6% in 6~10 years, 1.7% in 11~15 years, 6.5% in 16~20 years, 36.6%in 21~30 years, 77.5% in 31~40 years, and 99.8% in 41~50 years. Statistical differences were not found between male and female, but there was statistical difference in 6~10 years old age group between the two areas. Conclusion : Our study indicate that the prevalence of antihepatitis A virus antibody has shifted from children to old adolescents and young adults. This result suggests that the risk of sudden outbreaks or increasing incidence of hepatitis A viral infections in young adults may be expected in our society. The preventive strategies of hepatitis A including vaccination should be prepared.

• Korean Journal of Ecology and Environment
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• v.39 no.1 s.115
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• pp.110-118
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• 2006

The Namyang Stream in Hwaong polder was planned for several water uses including recreation, where people can contact the water and consume some amount during the recreational activity. A human health risk was assessed from exposure to E. coli in the Namyang Stream, which receives partially treated wastewater from watershed. The QUAL2E model was applied to simulate stream water quality, and this model was calibrated and verified with field monitoring data. The calibration result showed a high correlation coefficient of greater than 0.9. The mean concentration of E. coli in the Namyang Stream from the QUAL2E output was in the range of 5,000 ${\sim}$ 8,000 MPN 100 mL^{-1}$, which exceeded national and international guidelines. The Beta-Poisson was used to estimate the microbial risk of pathogens ingestion and the Monte-Carlo analysis (10,000 trials) was used to estimate the risk characterization of uncertainty. The Microbial risk assessment showed that the risk ranged from 7.9 ${\times}\;10^{-6}\;to\;9.4\;{\times}10^{-6}$. Based on USEPA guidelines, the range of $10^{-6}\;to\;10^{-8}$ was considered reasonable levels of risk for communicable disease transmission from environmental exposure, and the risk above $10^{-4}$ was considered to be in the danger of infection. Therefore, water quality of the Namyang Stream might not be in the danger of infection although it exceeded national and international guidelines. However, it was in the range of communicable disease transmission, and thorough wastewater collection and treatment at the source is recommended to secure safe recreation water quality.

• Journal of the Korea Organic Resources Recycling Association
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• v.28 no.3
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• pp.27-36
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• 2020

This study investigates the changes in inorganic composition and the microbial counts during the process of fermentation of mixed domestic organic resources for the development of alternatives for imported oil cake, and examines the characteristics of mixed fermentation organic fertilizer (MFOF). The effect of the MFOF on the lettuce growth is investigated in order to evaluate the possibility of replacing the existing mixed oil cake with the MFOF. Six kinds of domestic by-product resources, which are rice bran, distiller's dried grains, sesame meal, fish meal, and spent mushroom substrate, are mixed by mixing ratio and the composition was analyzed during the fermentation process for 90 days under moisture content 30% and sealed condition. During the 90 days of fermentation, the pH change of the MFOF was little, and the moisture content was maintained at 34-35% until the 60^th day of fermentation, and then decreased to 30-31% on the 90^th day. Total nitrogen content remained unchanged during the fermentation period, but total carbon content showed a significant difference on the 21^st day of fermentation. It was confirmed that the content of fertilizer composition (nitrogen, phosphate, and potash) of the MFOF was 8.7% or more, which is suitable for the minimum amount standard of the main nutrients to be contained in the organic fertilizer. During the fermentation process of organic fertilizer, the density of bacteria and actinomycetes increased until 60 days and 30 days, respectively, and thereafter little changes were shown, and fungal population showed an increasing trend. As a result of lettuce cultivation test in the greenhouse by applying the MFOF, the growth and yield were comparable to that of using the existing mixed oil cake fertilizer when 100% was applied based on crop standard nitrogen fertilizer level. The use of mixed fermentation organic fertilizer made with domestic by-product resources can be used for use in farms in the future and is expected to contribute to the stable production of environment friendly agricultural products.