• Korean Journal of Environment and Ecology
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• v.37 no.6
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• pp.473-483
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• 2023

This study was conducted to find out the psychological or physiological healing effects of real natural forests and virtual forest experiences using virtual reality (VR) in an artificially-controlled digital environment. To find out this, 81 healthy undergraduate students from C University were experimented on visual forest stimulation effects in the digital environment from September 5 to December 9 in 2022. The experiment evaluated the psychological and physiological healing effects of visual forest stimulation in the digital forest environment (2D, 3D). The SRI (stress response inventory) experiment for analyzing psychological effect showed statistically significant differences among groups. As for the SRI experiment for measuring psychological stress, except Control group, 2D group in the digital environment showed little difference before and after the experiment. But 3D group showed less stress than before. As a result, it was proved that visual forest stimulation in a forest-based digital environment (2D, 3D) reduces psychological stress significantly. And when analyzing how visual forest stimulation changes EEG (electroencephalogram) in the digital environment, alpha waves (RA), which are activated during relaxation or stabilization, were more active than beta waves (RB), which are activated during tension or awakening. This study is expected to be used to create a psychological and physiological healing environment for those who cannot go to a natural forest due to mobility difficulties by providing them visual forest stimulation experiences in a digital environment. It is also expected that the results will be the basis for forest healing in the digital environment and virtual reality programs will help forest healing activities.

• Information Systems Review
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• v.21 no.4
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• pp.125-141
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• 2019

With the advent of deep learning technology, which is represented by AlphaGo, artificial intelligence (A.I.) has quickly emerged as a key theme of digital transformation to secure competitive advantage for businesses. In order to understand the trends of A.I. based digital transformation, a clear comprehension of the A.I. business ecosystem should precede. Therefore, this study analyzed the A.I. business ecosystem from the multi-actor network perspective and identified the A.I. platform strategy type. Within internal three layers of A.I. business ecosystem (infrastructure & hardware, software & application, service & data layers), this study identified four types of A.I. platform strategy (Tech. vertical × Biz. horizontal, Tech. vertical × Biz. vertical, Tech. horizontal × Biz. horizontal, Tech. horizontal × Biz. vertical). Then, outside of A.I. platform, this study presented five actors (users, investors, policy makers, consortiums & innovators, CSOs/NGOs) and their roles to support sustainable A.I. business ecosystem in symbiosis with human. This study identified A.I. business ecosystem framework and platform strategy type. The roles of government and academia to create a sustainable A.I. business ecosystem were also suggested. These results will help to find proper strategy direction of A.I. business ecosystem and digital transformation.

• Applied Biological Chemistry
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• v.43 no.3
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• pp.218-224
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• 2000

The efforts were made to optimite ethanol extraction from persimmon leaf with the time of extraction$(1.5{\sim}2.5\;hrs)$, the temperature of extraction$(70{\sim}90^{\circ}C)$, and the concentration of ethanol$(0{\sim}40%)$ as three primary variables together with several functional characteristics of persimmon leaf as reaction variables. The conditions of extraction was best fitted by using response surface methodology through the center synthesis plan, and the optimal conditions of extraction were established. The contents of soluble solid and soluble tannin went up as the concentration of ethanol went up and the temperature of extraction went down, and the turbidity went down as the concentration of ethanol went down. Electron donation ability was hardly affected by the extraction temperature and had the tendency to go up as the concentration of ethanol went up. The inhibitory activity of xanthine oxidase(XOase) had the tendency to go up as both the concentration of ethanol and the temperature of extraction went up. The inhibitory activity of angiotensin converting enzyme(ACE), the significance of which still was not recognized, showed the maximum when the concentration of ethanol was 27%. In result, the optimal conditions of extraction was the extraction time of two hours, the extraction temperature of $75{\sim}81^{\circ}C$, and the ethanol concentration of $33{\sim}35%$.

• Journal of Embryo Transfer
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• v.17 no.1
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• pp.79-85
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• 2002

The purpose of this study was to determine the effect of bST treatment on embryo recovery and pregnancy rate following embryo transfer. Donor cows were superovulated with Folltropin-V and PGF$_2$$\alpha$ combination method and then inseminated with frozen semen 3 times 12 hrs interval. Donor and recipient cows were assigned to control and bST group, of which was given a single injection of bST (500 mg, im) at insemination or estrus detection. Embryo collection of superovulated cows were flushed nonsurgical method at 7 to 8 days after artificial insemination. The percentare and Mean$\pm$S.E. of transferable embryo was not significantly different between control and bST treatment (72.8%/5.9$\pm$4.5 vs. 83.7%15.1 $\pm$ 1.6). The percentage and Mean$\pm$S.E. of transferable embryo in non-summer season was significantly higher than in summer (81.8%/5.4$\pm$2.1 vs. 68.7%14.774.6; P<0.05). The pregnancy rate after embryo transfer in bST treatment was significantly higher than in control (64.0 vs. 47.1%; P<0.05). There was no significant difference in pregnancy rate between summer and non-summer (51.6 vs. 61.5%; P>0.05). The results indicated that InST treatment in recipient cows could improve the efficiency of transferable embryo production and pregnancy rate after embryo transfer, and non-summer season may be better far superovulation treatment and embryo transfer.

• Child Health Nursing Research
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• v.8 no.1
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• pp.32-43
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• 2002

The purpose of this study was to provide basic data on elementary school children's lifestyle and to contribute to developing on the health education program in elementary schools. The subjects were 1,412 children in 4 elementary schools in Gangwon-Do and Chonrabuk-Do area. Data collection was done from September to November 2001 by questionnaire and school health documents. The questionnaire corrected for the purpose of this study which had been developed by Bronson School of Nursing(1991), 'Lifestyle Questionnaire for School-age Children'. The questionnaire consists of 3 categories; 'Activities that promote health', 'Injury prevention', 'Feeling'. Cronbach coefficient alpha for the 29 items was .68. The data analyzed to obtain frequency, mean, percentage, t-test, ANOVA and Pearson correlation coefficient by SPSS Win program. The results of this study were as follows. 1. Females(50.2%) of gender, 6th grade(24.2%) of grade, nuclear family(82.0%) of family type, beyond college graduate(54.5%) of father's school career, under high school graduate(58.1%) of mother's school career, first of birth order(47.1%) were majority. Mean of father's age was 41.2 and mother's age was 38.1. 2. The mean of lifestyle was 66.4, feeling was 73.3, activities that promote health was 60.3 and injury prevention was 64.0. The highest degree of activities that promote health was 「I eat fruits」and injury prevention was 「I look both ways when crossing streets」and feeling was 「I enjoy my family」. The lowest degree of activities that promote health was 「I visit the dentist every tear」 and injury prevention was 「I wear a helmet when I go on bike trips」 and feeling was 「I think it is okay to cry」. 3. There were significant differences in lifestyle of gender(t=4.309, p=.000), grade(F=6.299, p=.000), father's age(t=2.534, p=.011), father's education(t=-4.933, p=.000), mother's education(t=-3.360, p=.001), birth order (t=5.363, p=.000). There were significant differences in activities that promote health of gender(t=-2.462, P=.014), grade(F=4.893, p=.000), father's education(t=-4.480, p=.000), birth order(t=4.343, p=.000), in injury prevention of gender(t=-4.452, p=.000), grade(F=8.636, p=.000), father's age(t=3.386, p=.001), mother's age(t=2.059, p=.040), father's education(t=-6.051, p=.000), mother's education(t=-5.173, p=.000), birth order(t=4.417, p=.000) and in feeling of gender (t=-3.285, p=.001), grade(F=7.526, p=.000), mother's age(t=-3.268, p=.001). 4. Activities that promote health was positively correlated with injury prevention(r=.432, p=.000), feeling(r=.210, p=.000), lifestyle (r=.785, p=.000). Injury prevention was positively correlated with feeling(r=.256, p=.000), lifestyle(r=.854, p=.000) also feeling was positively correlated with lifestyle(r=.504, p=.000). These findings suggest the need to develop nursing strategy to promote elementary school children's health. Because helmet use score in injury prevention marked the lowest score, it is necessary to encourage helmet use when planning injury prevention and health promotion.

• The Korean Journal of Mycology
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• v.13 no.3
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• pp.169-177
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• 1985

The studies were carried out to obtain the basic data for maximizing the protoplast yields from the mycelia of P. ostreatus and P. sajor-caju. Some factors affecting the regeneration of the protoplast of both species and the productivity of their reversion were also examined. The maximum yields of protoplasts were obtained from four days cultured mycelia of both species on cellophan membrane placed on the surface of PSA or MCM media in a petri dish. The optimal concentration of lytic enzyme Novozym 234 for protoplast releasing was 5 mg per ml of 0.5 M phosphate buffer solution with 0.6 M sucrose or 0.6 M $MgSO_4$ at pH 6.0. The greatest number of protoplasts was released 3 hours after incubation of the mycelia of P. ostreatus and after 4 hours for the P. sajor-caju in the lytic enzyme solution. Among the osmotic stabilizer solutions tested 0.6 M sucrose and 0.6 M KCl showed the best regeneration rates of the protoplasts of both species. When 0.75 % agar solution was over-layed on the regeneration media immediately after inoculation of the protoplast the regeneration rates were greatly enhanced. The ampicillin added to the agar solution prevented bacteria from infection. The reverted isolates produced the sporophores and basidial spores just like their parents without any mutations when they were cultivated in a broad mouth bottle with sawdust substrates.

• Restorative Dentistry and Endodontics
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• v.33 no.3
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• pp.169-176
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• 2008

The purpose of this study was to evaluate the effect of thickness, filling methods and curing methods on the polymerization of dual cured core materials by means of microhardness test. Two dual cured core materials, MultiCore Flow (Ivoclar Vivadent AG, Schaan, Liechtenstein) and Bis-Core (Bisco Inc., Schaumburg, IL, USA) were used in this study. 2 mm (bulky filled), 4 mm (bulky filled), 6 mm (bulky and incrementally filled) and 8 mm (bulky and incrementally filled)-thickness specimens were prepared with light cure or self cure mode. After storage at $37{\circ}C$ for 24 hours, the Knoop hardness values (KHN) of top and bottom surfaces were measured and the microhardness ratio of top and bottom surfaces was calculated. The data were analyzed using one-way ANOVA and Scheffe multiple comparison test, with ${\alpha}$= 0.05. The effect of thickness on the polymerization of dual cured composites showed material specific results. In 2, 4 and 6 mm groups, the KHN of two materials were not affected by thickness. However, in 8 mm group of MultiCore Flow, the KHN of the bottom surface was lower than those of other groups (p < 0.05). The effect of filling methods on the polymerization of dual cured composites was different by their thickness or materials. In 6 mm thickness, there was no significant difference between bulk and incremental filling groups. In 8 mm thickness, Bis-Core showed no significant difference between groups. However, in MultiCore Flow, the microhardness ratio of bulk filling group was lower than that of incremental filling group (p < 0.05). The effect of curing methods on the polymerization of dual cured composites showed material specific results. In Bis-Core, the KHN of dual cured group were higher than those of self cured group at both surfaces (p < 0.05). However, in MultiCore Flow, the results were not similar at both surfaces. At the top surface, dual cured group showed higher KHN than that of self cured group (p < 0.05). However, in the bottom surface, dual cured group showed lower value than that of self cured group (p < 0.05).

• Food Science and Preservation
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• v.24 no.8
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• pp.1149-1157
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• 2017

Inflammation is the first response of the immune system to infection or irritation in our body. The use of medicinal plants has been widely applied as an alternative source for drug development. One of marine natural resources, the anti-inflammatory effect of Ishige sinicola ethanol extract (ISEE), was evaluated by using LPS-induced RAW 264.7 cell and mice model. As a result, the production of nitric oxide (NO) and pro-inflammatory cytokines (IL-6, IL-$1{\beta}$, TNF-${\alpha}$) were inhibited with increasing concentration of ISEE without any cytotoxicity. Furthermore, ISEE suppressed the expression of not only inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), nuclear factor-kappa B (NF-${\kappa}B$) p65, and mitogen-activated protein kinases (MAPKs), including extracellular signal-regulated kinase (ERK) 1/2, p38, and c-Jun N-terminal kinase (JNK) in a dose-dependent manner. In mice ear edema test, the formation of edema was reduced at the highest dosage of ISEE and the reduction of the number of infiltrated mast cells was observed in histological analysis. These results indicate that ISEE has a potent anti-inflammatory activity and can be used as a pharmaceutical material for many kinds of inflammatory disease.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.