There can be three objectives why we apply 3D graphics in TV interface. First, inevitable usability. 3D space is the most inevitable way for product-user interaction deviating from the current aesthetic or technical purpose of use. Second, enhance emotional satisfaction by merging interface into real life. Beyond digilog(analog+digital), we wanted users to naturally use the interface as if they are using everyday objects. And in order to do this properly, we analyzed the behaviors of users how they use their everyday objects and then analyzed the attributes beyond each behavior. Third, capaticy for enourmous contents of the incorporated TV. The most efficient way to display enormous amounts of contents in a limited screen is 3D. And that 3D space gives us almost unlimited capacity in accepting contents. Now to conclude, using 3D graphics for TV interface has significance in many ways but the issue is how we can maintain the 3D effects while customizing them into real products.
This study aims at finding impact of cultural and sports characteristics on urban growth. For the empirical analysis, this study uses unique urban resources associated with four categories (Social, Traffic, Cultural and Sports, and Spatial characteristics) among 228 cities and towns using a semi-log multiple regression analysis. The result shows that various local resources have significant impacts on urban growth in terms of population, industry, and fiscal self-reliance ratio. Impressively, cultural and sports resources are relatively beneficial for the urban growth. For the population and industrial growth, expanding public sports facilities and professional sports facilities can be more efficient. For solid local finance, typically focusing on professional sports facilities that have far-reaching ripple effects are useful. The conclusion for this study shows that urban growth policy should prioritize the cultural and sports resources because it can be more effective way to enhance not only for urban growth but also for quality of citizens' life.
This study is identifying early takeoff phenomenon in relational network like SNS and investigates influence of innovator or hub at early takeoff and diffusion size at takeoff point. The major finding of this study are as follows. First, multinominal-logit analysis conducted to explore the impact of innovator and hub at early stage of information diffusion and early takeoff. Hub in early stage of information diffusion impact early takeoff. Second, our study shows that innovator and hub at early stage of information diffusion impact diffusion size at takeoff point. Third, hub is more impact early takeoff and diffusion size at takeoff point then innovator. This study provides implication for professionals by confirming hub's influence in study of successful information diffusion by early takeoff. The limitation and future direction are discussed.
Park, Yu-Mi;Lee, Eui-Haeng;Lee, Sang-Jae;An, Kwang-Guk
Korean Journal of Ecology and Environment
/
v.42
no.3
/
pp.382-393
/
2009
The objectives of this study were to characterize long-term annual and seasonal trophic state of Topjeong Reservoir using conventional variables of Trophic State Index (TSI) and to determine the empirical relations between the trophic parameters. For the analysis, we used water quality dataset of 1995$\sim$2007, which is obtained from the Ministry of Environment, Korea and the number of parameters was 9. Annual ambient mean values of TN and TP were 1.78 mg $L^{-1}$ and 0.03 mg $L^{-1}$, respectively and TN : TP ratios averaged 76, indicating that this system was nitrogen-rich hypertrophic, and was probably phosphorus-limitation for algal growth. Therefore, nitrogen varied little with seasons and years, and total phosphorus (TP) varied depending on season and year. Monsoon dilutions of TP occurred in August and monthly fluctuations of suspended solid (SS) was similar to those of chlorophyll-$\alpha$ (CHL). Annual mean values of BOD and $COD_{Mn}$ were 1.61 mg $L^{-1}$ and 4.23 mg $L^{-1}$, respectively and the interannual values were directly influenced by the intensity of annual rainfall. There were no significant differences in the trophic variables between the two sampling sites. Mean values of Trophic State Index (TSI, Carlson, 1977), based on TN, TP, CHL, and SD (Secchi depth), turned out as eutrophic state, except for the TN (hypertrophic). Regression analyses of log-transformed seasonal CHL against TP and TN showed that variation of the CHL was explained 37% by the variation of TP ($R^2$=0.37, p<0.001, r=0.616), but not by TN ($R^2$=0.03, p>0.05). Regression coefficient of $Log_{10}$CHL vs $Log_{10}SD$ was 0.330 (p<0.003, r=0.580), indicating that transparency is regulated by the organic matter in the system. Results, data suggest that one of the ways controlling the eutrophication would be a reduction of phosphorus from the watershed.
Despite the growing importance of video-based social media content, such as vlogs, as a marketing tool in the travel industry, there is limited research on the characteristics that enhance engagement among potential travelers. This study explores the influence of emotional valence in YouTube travel content on viewer engagement, specifically likes and comments. We analyzed 4,619 travel-related YouTube videos from eight popular tourist cities. Using negative binomial regression analysis, we found that both positive and negative emotions significantly influence the number of likes received. Videos with higher positive emotions as well as negative emotions receive more likes. However, when it comes to the number of comments, only negative emotions showed a significant positive influence, while positive emotions had no significant impact. These findings offer valuable insights for marketers seeking to optimize engagement strategies on YouTube, considering the unique nature of travel products. Further research into the effects of specific emotions on engagement is warranted to improve marketing strategies. This study highlights the powerful impact of emotions on viewer engagement in the context of social media, particularly on YouTube.
Background: Preoperative risk analysis for Fontan candidates is still less than optimal in that patients with apparently low risks may have poor surgical outcome; prolonged pleural drainage, protein losing enteropathy, pulmonary thromboembolism and death. We hypothesized that low pulmonary vascular compliance (PVC) is a risk factor for prolonged pleural effusion drainage after the Fontan operation. Material and Method: A retrospective review of 96 consecutive patients who underwent the Extracardiac Fontan procedures (median age: 3.9 years) was performed. Fontan risk score (FRS) was calculated from 12 categorized preoperative anatomic and physiologic variables. PVC $(mm^2/m^2{\cdot}mmHg)$ was defined as pulmonary artery index $(mm^2/m^2)$ divided by total pulmonary resistance $(W.U{\cdot}/m^2)$ and pulmonary blood flow $(L/min/m^2)$ based on the electrical circuit analogue of the pulmonary circulation. Chest tube indwelling time was log-transformed (log indwelling time, LIT) to fit normal distribution, and the relationship between preoperative predictors and LIT was analyzed by multiple linear regression. Result: Preoperative PVC, chest tube indwelling time and LIT ranged from 6 to 94.8 $mm^2/mmHg/m^2$ (median: 24.8), 3 to 268 days (median: 20 days), and 1.1 to 5.6 (mean: 2.9, standard deviation: 0.8), respectively. FRS, PVC, cardiopulmonary bypass time (CPB) and central venous pressure at postoperative 12 hours were correlated with LIT by univariable analyses. By multiple linear regression, PVC (p=0.0018) and CPB (p=0.0024) independently predicted LIT, explaining 21.7% of the variation. The regression equation was LIT=2.74-0.0158 PVC+0.00658 CPB. Conclusion: Low pulmonary vascular compliance is an important risk factor for prolonged pleural effusion drainage after the extracardiac Fontan procedure.
Journal of Korean Society for Geospatial Information Science
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v.21
no.2
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pp.53-62
/
2013
Land-use/cover changes not only amplify or alleviate influence of climate changes but also they are representative factors to affect environmental change along with climate changes. Thus, the use of land-use/cover changes scenario, consistent climate change scenario is very important to evaluate reliable influences by climate change. The purpose for this study is to predict and analyze the future urban growth considering social and economic scenario from RCP scenario suggested by the 5th evaluation report of IPCC. This study sets land-use/cover changes scenario based on storyline from RCP 4.5 and 8.5 scenario. Urban growth rate for each scenario is calculated by urban area per person and GDP for the last 25 years and regression formula based on double logarithmic model. In addition, the urban demand is predicted by the future population and GDP suggested by the government. This predicted demand is spatially distributed by the urban growth probability map made by logistic regression. As a result, the accuracy of urban growth probability map is appeared to be 89.3~90.3% high and the prediction accuracy for RCP 4.5 showed higher value than that of RCP 8.5. Urban areas from 2020 to 2050 showed consistent growth while the rate of increasing urban areas for RCP 8.5 scenario showed higher value than that of RCP 4.5 scenario. Increase of urban areas is predicted by the fact that famlands are damaged. Especially RCP 8.5 scenario indicated more increase not only farmland but also forest than RCP 4.5 scenario. In addition, the decrease of farmland and forest showed higher level from metropolitan cities than province cities. The results of this study is believed to be used for basic data to clarify complex two-way effects quantitatively for future climate change, land-use/cover changes.
This study intended to evaluate hydrogeological characteristics in relation to subsurface geology data obtained from borehole, groundwater level, borehole flowmeter test, and field hydraulic tests. The regression equation of groundwater level (Y) versus ground elevation (X) is expressed by Y=0.75X-7.00 with quite high correlation coefficient of 0.78. Relationship between groundwater level and thickness of landfill, alluvium, and weathered zone results in higher correlation of groundwater level (Y) versus natural log value of weathered tone (A) than other correlations, with the regression equation Y= exp(9.974A)-14.155. The result of groundwater flow measurement in the boreholes indicates that groundwater flows towards between south and southwest, and this approximately agree with regional distribution of groundwater levels.
Performances of retroreflectivity vary place to place, according to traffic volumes and time lengths after striping, depending on pavement marking materials and colors. The present paper uses the nation wide data of retroreflectivity, which has been collected from freeways and then tries to develop the regression curve setting traffic volume and service life as independent variables and retroreflectivities as dependent variables. The DB system includes two year's measurement in $2005{\sim}2006$ over Korean freeway pavement marking at an interval of three months for the period. The mobile measurement system, a laserlux, was employed for the purpose. The DB has provided a lot of information about materials and performance of the specific pavement marking such as geometric features, traffic volumes, material characteristics and the installation date. This study provides the comparison of pavement marking performances under diversified conditions. Based on accumulated pavement marking performances, this study provides performance curves based on the diversified factors. The goal of the retroreflectivity modeling is to develop equations that can be used to estimate an average retroreflectivity of pavement markings as a function time since application and traffic volume. After representing the variation of retroreflectivities and estimating regression curves by linear, exponential, logarithmic and power function, the regression curve which had the highest coefficient of determination and the value similar to the last field measurement was regarded as the retroreflectivity decay model. As a result of verification, the decay model showed the signification within the 90% confidence level and especially showed the clear relation with field data according to increase of cumulative vehicle exposure. Accordingly, these models can be used to determine service lives, retroreflectivity degradation rates, and retroreflectivity of new markings.
Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.
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