• Radiation Oncology Journal
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• v.17 no.3
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• pp.223-229
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• 1999

Purpose : The expression pattern of c-jun by ionizing radiation according to cell growth state (exponential growth vs. stationary phase) and its relationship with cell cycle redistribution were investigated. Materials and Methods : The exponential growth phase (day 4) and stationary phase (day 9) cells were determined from cell growth curve according to the elapse of days in CaSki. The cells were irradiated using 6 MV X-ray with a dose of 2 Gy at a fixed dose rate of 3 Gy/min. Northern blot analysis was peformed with total cellular RNA and cell cycle distribution was analyzed using flow cytometry according to time-course after irradiation. Results : The maximum expression of c-jun occurred 1 hour after irradiation in both exponential growth and stationary phase cells. After then c-jun expression was elevated upto 6 hours in exponential growth phase cells, but the level decreased in stationary phase cells. Movements of cells from G0-G1 to S, G2-M phase after irradiation were higher in exponential growth phase than stationary phase. Conclusion : c-jun may be involved in the regulation of cellular proliferation according to the growth states after irradiation.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.155-162
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• 2003

A tandem network in which all nodes have the same load is considered. We derive bounds on the probability that the total population of the tandem network exceeds a large value by using its relation to the stationary distribution. These bounds imply a stronger asymptotic limit than that in the large deviation theory.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.109-113
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• 2000

We consider the probability that the total population of a Jackson network exceeds a given large value. By using the relation to the stationary distribution, we derive upper and lower bounds on this probability. These bounds imply the stronger logarithmic limit than that in Glasserman and Kou(1995) when several nodes have the same maximal load.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2010.04a
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• pp.291-292
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• 2010

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.715-723
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• 2003

A tandem network in which all nodes have the same load is considered. We derive bounds on the probability that the total population of the tandem network exceeds a large value by using its relation to the stationary distribution. These bounds imply a stronger asymptotic limit than that in the large deviation theory.

• Journal of Global Scholars of Marketing Science
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• v.16 no.1
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• pp.13-26
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• 2006

In this research, we develope a procedure for detecting a random non-stationarity to the individual's purchasing rate in a stationary NED model. On this purpose, we derive the likelihood ratio statistic for a testing null and alternative hypotheses defined as whether there is no significant structural change in a stationary NED model or any. Where the structural change comes from a random non-stationarity(marketing mix activities or seasonality, for example) to the individual's purchasing rate. We also apply the developed method to a panel data for a frequently purchased good. This research could be a solution to include the non-stationarity in a stationary NED model. We also expect that the developed model could give a signal for an early detection of significant changes in marketing environment, and a mean for a measurement of the effects of marketing mix activities.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.677-682
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• 2020

To investigate the nexus between urban population, gross capital formation, and economic growth in the Kingdom of Saudi Arabia, yearly data was collected from the World Bank for the period 1974- 2018. Basic statistics test and correlation matrix was used to investigate the causal effect among the tested parameters, followed by Augmented Dickey-Fuller (ADF) stationary test, co-integration analysis by Johansen test after that Vector Auto-Correction Model for both short-run and long-run and finally the Granger-Causality tests. Result of unit root test analysis shows that the urban population became stationary at I (0) level while economic growth and gross capital formation became stationary at I (1). Johansen co-integration analysis indicates that there is presence of both long-run and short-run relationship between the three variables in the Kingdom of Saudi Arabia. The result of the VECM Model reflects that both economic growth and gross capital formation have a negative impact on urban population in the short run. According to the Granger-Causality tests, there is unidirectional causality with the urban population by both gross capital formation and economic growth. Also, the result of the Granger Causality tests show that there is unidirectional causality between economic growth and gross capital formations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3769-3789
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• 2018

Social pedestrian groups are the basic elements that constitute a crowd; therefore, detection of such groups is scientifically important for modeling social behavior, as well as practically useful for crowd video understanding. A social group refers to a cluster of members who tend to keep similar motion state for a sustained period of time. One of the main challenges of social group detection arises from the complex dynamic variations of crowd patterns. Therefore, most works model dynamic groups to analysis the crowd behavior, ignoring the existence of stationary groups in crowd scene. However, in this paper, we propose a novel unified framework for detecting social pedestrian groups in crowd videos, including dynamic and stationary pedestrian groups, based on spatiotemporal-oriented energy measurements. Dynamic pedestrian groups are hierarchically clustered based on energy flow similarities and trajectory motion correlations between the atomic groups extracted from principal spatiotemporal-oriented energies. Furthermore, the probability distribution of static spatiotemporal-oriented energies is modeled to detect stationary pedestrian groups. Extensive experiments on challenging datasets demonstrate that our method can achieve superior results for social pedestrian group detection and crowd video classification.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.2
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• pp.112-120
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• 2010

In this paper time series wave data measured continuously for 24 hours during a storm in Yura Sea Area are used to investigate statistical characteristics of nonlinear waves. The exceedance probability of wave height is compared using the Rayleigh distribution and the Edgeworth-Rayleigh (ER) distribution. Wave data which show stationary state for 10 hours contain 4600 waves approximately. The Gram-Chalier distribution fits the probability of wave elevation better than the Gaussian distribution. The Rayleigh ($H_{rms}$) distribution follows the exceedance probability of wave height in general and predicts the probability of freak waves well. The ER distribution overpredicts the exceedance probability of wave heights and the occurrence of freak waves. If wave data measured for 30 minute period which contains 250 waves are used, the ER distribution can predict the occurrence probability of freak waves well. But it overpredicts the probability of overall wave height If no freak wave occurs, the Rayleigh ($H_{rms}$) distribution agrees well with wave height distribution for the most of wave height ranges. The wave height distribution of freak waves of which height are less than 10 m shows similar tendency compared with freak waves greater than 10 m. The value of $H_{max}/H_{1/3}$ is related to the kurtosis of wave elevation. It seems that there exists threshold value of the kurtosis for the occurrence of freak waves.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.511-527
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• 2015

For time series analysis, power transformation (especially log-transformation) is widely used for variance stabilization or normalization for stationary ARMA(p, q) model. A simple and naive back transformed forecast is obtained by taking the inverse function of expectation. However, this back transformed forecast has a bias. Under the assumption that the log-transformed data is normally distributed. The unbiased back transformed forecast can be obtained by the expectation of log-normal distribution; consequently, the property of this back transformation was studied by Granger and Newbold (1976). We investigate the sensitivity of back transformed forecasts under several different underlying distributions using simulation studies.