• Journal of Oral Medicine and Pain
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• v.30 no.2
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• pp.269-276
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• 2005

This in vitro study evaluated the influence of a flowable composite resin on the tensile bond strength of resin to enamel and dentin treated with Er:YAG laser and diamond bur. 96 Buccal enamel and mid-coronal dentin were laser-irradiated using an Er:YAG laser and treated with diamond bur. Each groups(48) were divided two small groups depends on acid-etching procedure. Light-cure flowable resin(Metafil Flo) and self-cure resin(Clearfil FII New Bond) were used in this study. After surface etching with 37% phosphoric acid and the application of an adhesive system, specimens were prepared with a hybrid composite resin. After 24hours storage in distilled water at 37$^{\circ}C$, all samples were submitted to the tensile bond strength evaluation, using a universal testing machine(Z020, Zwick, Germany). The obtained results were as follows: 1. TBS of acid-etching group were higher than those of non-etching group in both enamel and dentin treated with Er:YAG laser and diamond bur. Laser 'conditioning' was clearly less effective than acid-etching. Moreover, acid etching lased enamel and dentin significantly improved the microTBS of M-Flo. 2. In enamel, TBS of laser-irradiated group were lower than those of bur-prepared group. However, in flowable resin subgroup, there were not differed those between two groups in dentin. 3. In laser-treated group, TBS of flowable composite resin were higher than those of self-curing resin in dentin, however, there was no difference in enamel. From this study, we can conclude that the self- and light-cure composite resin bonded significantly less effective to lased than to bur-cut enamel and dentin, and that acid-etch procedure remains mandatory even after laser ablation. We suggest that Er:YAG laser was useful for preparing dentin cavity with flowable resin filling.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.11
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• pp.1535-1542
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• 2009

To find the new use of Korean ginseng and mushroom, crude polysaccharides were prepared from submerged cultures of Hericium erinaceum in the medium supplemented with Korean ginseng extracts. When we fractionated crude polysaccharides (HE-GE-CP-1, 3, and 5) from hot-water extracts of submerged cultures of H. erinaceum with ginseng extracts (1%, 3%, and 5% addition of total medium), the yields of HE-GE-CP-1, 3, and 5 were identified at 5.7, 5.1, and 4.8%, respectively. Among crude polysaccharide fractions, HE-GE-CP-5 was significantly higher (1.89-fold of the saline control) than those of HE-GE-CP-1 (1.64-fold) or HE-GE-CP-3 (1.76-fold) on mitogenic activity of splenocytes. HE-GE-CP-5 also had the more potent bone marrow cell proliferation (1.83-fold) rather than HE-CP or HE-GE-CP-1 or HE-GE-CP-3 (1.59- or 1.44- or 1.69-fold, respectively), and anti-metastatic activity as anti-cancer effect showed the highest prophylactic value (72.4% inhibition of tumor control) in 5% supplementation of ginseng extract. However, the lysosomal phosphatase of macrophage was significantly stimulated after HE-GE-CP-3 treatment (2.03-fold). In addition, the immunostimulating and anti-metastatic crude polysaccharide, HE-GE-CP-5, contained mainly neutral sugars (63.2%) with considerable amounts of uronic acid (19.3%) and a small amount of proteins (8.8%). HE-GE-CP-5 can stimulate immune system to inhibit tumor metastasis, and its anti-tumor metastasis may be associated with macrophages, splenocytes and Peyer's patch cells activation.

• Journal of the korean academy of Pediatric Dentistry
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• v.29 no.3
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• pp.376-381
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• 2002

The purpose of this study was to analyse the color of natural deciduous teeth in Korean children and to compare with that of composite resin specimens. The subjects were 148 children (80 boys and 68 girls) with good general condition and normal teeth color, aged between 3 and 6 years. The color of middle third of maxillary central incisor in deciduous teeth was examined with shade guide and then measured by means of the colorimeter CV300 which can be measured by CIELAB system. The data were analyzed statistically by SPSS program. The results were summerized as follows; 1. Over 90% of the color for the deciduous anterior teeth was in A1, A2, B1, B2 and P shade. 2. The means of deciduous teeth color were $L^*=58.72,\;a^*=-1.18,\;b^*=-0.63$ by colorimeter CV300. 3. $L^*,\;a^*\;and\;b^*$ prices for A1, A2, B1, B2, P were $L^*=52.52,\;a^*=-1.90,\;b^*=1.18$ in A1 specimen, $L^*=54.90,\;a^*=-1.87,\;b^*=1.60$ in A2 specimen, $L^*=59.80,\;a^*=-2.70,\;b^*=-0.63$ in B1 specimen, $L^*=56.90,\;a^*=-1.70,\;b^*=1.63$ in B2 specimen, $L^*=52.93,\;a^*=-2.33,\;b^*=1.10$ in P specimen. The means of B1 color specimen were most similar to those of deciduous teeth color. The A1 color values were similar to the P color values. 4. The standard deviation of $L^*,\;a^*$ was small among colors, but that of $b^*$, in the yellowish color, was large.

• Journal of the Korean Geographical Society
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• v.40 no.3 s.108
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• pp.296-309
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• 2005

In this study, we investigated the change of future land-surface and relationships of land-surface change with geo-spatial information, using a Bayesian prediction model based on a likelihood ratio function, for analysing the land-surface change of the Gongju area. We classified the land-surface satellite images, and then extracted the changing area using a way of post classification comparison. land-surface information related to the land-surface change is constructed in a GIS environment, and the map of land-surface change prediction is made using the likelihood ratio function. As the results of this study, the thematic maps which definitely influence land-surface change of rural or urban areas are elevation, water system, population density, roads, population moving, the number of establishments, land price, etc. Also, thematic maps which definitely influence the land-surface change of forests areas are elevation, slope, population density, population moving, land price, etc. As a result of land-surface change analysis, center proliferation of old and new downtown is composed near Gum-river, and the downtown area will spread around the local roads and interchange areas in the urban area. In case of agricultural areas, a small tributary of Gum-river or an area of local roads which are attached with adjacent areas showed the high probability of change. Most of the forest areas are located in southeast and from this result we can guess why the wide chestnut-tree cultivation complex is located in these areas and the capability of forest damage is very high. As a result of validation using a prediction rate curve, a capability of prediction of urban area is $80\%$, agriculture area is $55\%$, forest area is $40\%$ in higher $10\%$ of possibility which the land-surface change would occur. This integration model is unsatisfactory to Predict the forest area in the study area and thus as a future work, it is necessary to apply new thematic maps or prediction models In conclusion, we can expect that this way can be one of the most essential land-surface change studies in a few years.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.85-97
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• 2010

Internet communities are a typical space for knowledge creation and use on the Internet as people discuss their common interests within the internet communities. When we define 'Knowledge Communities' as internet communities that are related to knowledge creation and use, they are categorized into 4 different types such as 'Search Engine,' 'Open Communities,' 'Specialty Communities,' and 'Activity Communities.' Each type of knowledge community does not remain the same, for example. Rather, it changes with time and is also affected by the external business environment. Therefore, it is critical to develop processes for practical use of such changeable knowledge communities. Yet there is little research regarding a strategic framework for knowledge communities as a source of knowledge creation and use. The purposes of this study are (1) to find factors that can affect knowledge creation and use for each type of knowledge community and (2) to develop a strategic framework for practical use of the knowledge communities. Based on previous research, we found 7 factors that have considerable impacts on knowledge creation and use. They were 'Fitness,' 'Reliability,' 'Systemicity,' 'Richness,' 'Similarity,' 'Feedback,' and 'Understanding.' We created 30 different questions from each type of knowledge community. The questions included common sense, IT, business and hobbies, and were uniformly selected from various knowledge communities. Instead of using survey, we used these questions to ask users of the 4 representative web sites such as Google from Search Engine, NAVER Knowledge iN from Open Communities, SLRClub from Specialty Communities, and Wikipedia from Activity Communities. These 4 representative web sites were selected based on popularity (i.e., the 4 most popular sites in Korea). They were also among the 4 most frequently mentioned sitesin previous research. The answers of the 30 knowledge questions were collected and evaluated by the 11 IT experts who have been working for IT companies more than 3 years. When evaluating, the 11 experts used the above 7 knowledge factors as criteria. Using a stepwise linear regression for the evaluation of the 7 knowledge factors, we found that each factors affects differently knowledge creation and use for each type of knowledge community. The results of the stepwise linear regression analysis showed the relationship between 'Understanding' and other knowledge factors. The relationship was different regarding the type of knowledge community. The results indicated that 'Understanding' was significantly related to 'Reliability' at 'Search Engine type', to 'Fitness' at 'Open Community type', to 'Reliability' and 'Similarity' at 'Specialty Community type', and to 'Richness' and 'Similarity' at 'Activity Community type'. A strategic framework was created from the results of this study and such framework can be useful for knowledge communities that are not stable with time. For the success of knowledge community, the results of this study suggest that it is essential to ensure there are factors that can influence knowledge communities. It is also vital to reinforce each factor has its unique influence on related knowledge community. Thus, these changeable knowledge communities should be transformed into an adequate type with proper business strategies and objectives. They also should be progressed into a type that covers varioustypes of knowledge communities. For example, DCInside started from a small specialty community focusing on digital camera hardware and camerawork and then was transformed to an open community focusing on social issues through well-known photo galleries. NAVER started from a typical search engine and now covers an open community and a special community through additional web services such as NAVER knowledge iN, NAVER Cafe, and NAVER Blog. NAVER is currently competing withan activity community such as Wikipedia through the NAVER encyclopedia that provides similar services with NAVER encyclopedia's users as Wikipedia does. Finally, the results of this study provide meaningfully practical guidance for practitioners in that which type of knowledge community is most appropriate to the fluctuated business environment as knowledge community itself evolves with time.

• 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 Intelligence and Information Systems
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• v.22 no.4
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• pp.157-176
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• 2016

For a long time, mobile phone had a sole function of communication. Recently however, abrupt innovations in technology allowed extension of the sphere in mobile phone activities. Development of technology enabled realization of almost computer-like environment even on a very small device. Such advancement yielded several forms of new high-tech devices such as smartphone and tablet PC, which quickly proliferated. Simultaneously with the diffusion of the mobile devices, mobile applications for those devices also prospered and soon became deeply penetrated in consumers' daily lives. Numerous mobile applications have been released in app stores yielding trillions of cumulative downloads. However, a big majority of the applications are disregarded from consumers. Even after the applications are purchased, they do not survive long in consumers' mobile devices and are soon abandoned. Nevertheless, it is imperative for both app developers and app-store operators to understand consumer behaviors and to develop marketing strategies aiming to make sustainable business by first increasing sales of mobile applications and by also designing surviving strategy for applications. Therefore, this research analyzes consumers' mobile application usage behavior in a frame of substitution/supplementary of application categories and several explanatory variables. Considering that consumers of mobile devices use multiple apps simultaneously, this research adopts multivariate probit models to explain mobile application usage behavior and to derive correlation between categories of applications for observing substitution/supplementary of application use. The research adopts several explanatory variables including sociodemographic data, user experiences of purchased applications that reflect future purchasing behavior of paid applications as well as consumer attitudes toward marketing efforts, variables representing consumer attitudes toward rating of the app and those representing consumer attitudes toward app-store promotion efforts (i.e., top developer badge and editor's choice badge). Results of this study can be explained in hedonic and utilitarian framework. Consumers who use hedonic applications, such as those of game and entertainment-related, are of young age with low education level. However, consumers who are old and have received higher education level prefer utilitarian application category such as life, information etc. There are disputable arguments over whether the users of SNS are hedonic or utilitarian. In our results, consumers who are younger and those with higher education level prefer using SNS category applications, which is in a middle of utilitarian and hedonic results. Also, applications that are directly related to tangible assets, such as banking, stock and mobile shopping, are only negatively related to experience of purchasing of paid app, meaning that consumers who put weights on tangible assets do not prefer buying paid application. Regarding categories, most correlations among categories are significantly positive. This is because someone who spend more time on mobile devices tends to use more applications. Game and entertainment category shows significant and positive correlation; however, there exists significantly negative correlation between game and information, as well as game and e-commerce categories of applications. Meanwhile, categories of game and SNS as well as game and finance have shown no significant correlations. This result clearly shows that mobile application usage behavior is quite clearly distinguishable - that the purpose of using mobile devices are polarized into utilitarian and hedonic purpose. This research proves several arguments that can only be explained by second-hand real data, not by survey data, and offers behavioral explanations of mobile application usage in consumers' perspectives. This research also shows substitution/supplementary patterns of consumer application usage, which then explain consumers' mobile application usage behaviors. However, this research has limitations in some points. Classification of categories itself is disputable, for classification is diverged among several studies. Therefore, there is a possibility of change in results depending on the classification. Lastly, although the data are collected in an individual application level, we reduce its observation into an individual level. Further research will be done to resolve these limitations.

• Resources Recycling
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• v.5 no.4
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• pp.32-41
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• 1996

The objective of this study was to investigate the facton affecting the property of CaCO, farmed from CaClz-Na,CO,-HiOsystem. The effcct of the concentmtlon of reaclants, impurity, the pH of reaction, the addition of sccd crystal, and injectingvelocity af reaclant solution an thc yield oI CaCO; polymorphs. parlide size and whiteness of CaCO, were investigated. Thcmqor resulls are ;o fallows; I The optimum concentratinn of reildilnts for forming vaterlte and aragonite is the range of 0.1-1.0 mol/l, when the yicld of vittcrite and araga~nles howed 7542% and XU-90%. respedively. 2. Among thc composition of impunticscontained h limestone, Fe' decrease the wh~tcness nf CaCO;. md Mg" increase the yield of aragonite. 3. The pHrange of vaterite and aragonite are formed with high yield is 8-11, and Calcite is famed in pH 6-8 with big particle size of 1over and in pH 11-13 with small particle size of I under. 4. The yicld of calcite and aragonite was increased by addingthc seed cryst.al nf itself.d cryst.al nf itself.

• Korean Chemical Engineering Research
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• v.43 no.2
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• pp.206-214
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• 2005

Heterogeneously-catalyzed oxidation of aqueous phase trichloroethylene (TCE) over supported metal oxides has been conducted to establish an approach to eliminate ppm levels of organic compounds in water. A continuous flow reactor system was designed to effect predominant reaction parameters in determining catalytic activity of the catalysts for wet TCE decomposition as a model reaction. 5 wt.% $CoO_x/TiO_2$ catalyst exhibited a transient period in activity vs. on-stream time behavior, suggesting that the surface structure of the $CoO_x$ might be altered with on-stream hours; regardless, it is probable to be the most promising catalyst. Not only could the bare support be inactive for the wet decomposition reaction at $36^{\circ}C$, but no TCE removal also occurred by the process of adsorption on $TiO_2$ surface. The catalytic activity was independent of all particle sizes used, thereby representing no mass transfer limitation in intraparticle diffusion. Very low TCE conversion appeared for $TiO_2$-supported $NiO_x$ and $CrO_x$ catalysts. Wet oxidation performance of supported Cu and Fe catalysts, obtained through an incipient wetness and ion exchange technique, was dependent primarily on the kinds of the metal oxides, in addition to the acidic solid supports and the preparation routes. 5 wt.% $FeO_x/TiO_2$ catalyst gave no activity in the oxidation reaction at $36^{\circ}C$, while 1.2 wt.% Fe-MFI was active for the wet decomposition depending on time on-stream. The noticeable difference in activity of the both catalysts suggests that the Fe oxidation states involved to catalytic redox cycle during the course of reaction play a significant role in catalyzing the wet decomposition as well as in maintaining the time on-stream activity. Based on the results of different $CoO_x$ loadings and reaction temperatures for the decomposition reaction at $36^{\circ}C$ with $CoO_x/TiO_2$, the catalyst possessed an optimal $CoO_x$ amount at which higher reaction temperatures facilitated the catalytic TCE conversion. Small amounts of the active ingredient could be dissolved by acidic leaching but such a process gave no appreciable activity loss of the $CoO_x$ catalyst.

• Progress in Medical Physics
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• v.25 no.4
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• pp.210-217
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• 2014

This study investigated the dosimetric effects of different dose calculation algorithm for lung stereotactic ablative radiotherapy (SABR) using flattening filter-free (FFF) beams. A total of 10 patients with lung cancer who were treated with SABR were evaluated. All treatment plans were created using an Acuros XB (AXB) of an Eclipse treatment planning system. An additional plans for comparison of different alagorithm recalcuated with anisotropic analytic algorithm (AAA) algorithm. To address both algorithms, the cumulative dose-volume histogram (DVH) was analyzed for the planning target volume (PTV) and organs at risk (OARs). Technical parameters, such as the computation times and total monitor units (MUs), were also evaluated. A comparison analysis of DVHs from these plans revealed the PTV for AXB estimated a higher maximum dose (5.2%) and lower minimum dose (4.2%) than that of the AAA. The highest dose difference observed 7.06% for the PTV $V_{105%}$. The maximum dose to the lung was also slightly larger in the AXB plans. The percentate volumes of the ipsilateral lung ($V_5$, $V_{10}$, $V_{20}$) receiving 5, 10, and 20 Gy were also larger in AXB plans than for AAA plans. However, these parameters were comparable between both AAA and AXB plans for the contralateral lung. The differences of the maximum dose for the spinal cord and heart were also small. The computation time of AXB plans was 13.7% shorter than that of AAA plans. The average MUs were 3.47% larger for AXB plans than for AAA plans. The results of this study suggest that AXB algorithm can provide advantages such as accurate dose calculations and reduced computation time in lung SABR plan using FFF beams, especially for volumetric modulated arc therapy technique.