• Sleep Medicine and Psychophysiology
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• v.13 no.1
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• pp.22-26
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• 2006

Circadian rhythms have been observed to be disturbed in mood disorders, especially seasonal affective disorder (SAD). Clock related gene variants also have been suggested to be associated with seasonality (seasonal variations in mood and behavior). This study tested the potential association between a length polymorphism of Period3 gene and seasonal variations in mood and behavior. 297 Korean college students were genotyped for the Period3 polymorphism and were for evaluated the seasonal variation by Seasonal Pattern Assessment Questionnaire (SPAQ). The genotype frequencies were 0.76 for 4R/4R, 0.22 for 4R/5R and 0.013 for 5R/5R. The global seasonality score was not different among Period3 gene variants (4R/4R, 4R/5R and 5R/5R) except for 'sleep length' subscore. The 5R/5R genotype showed the higher 'sleep length' subscore than others (p=0.024). The comparison between seasonals (syndromal plus subsyndromal SAD determined by SPAQ) and non-seasonals did not show any significant difference in frequencies of genotypes. These findings suggest that there is a possibility that the investigated Period3 polymorphism may play a partial role in the susceptibility of seasonal variations in a Korean population.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.11 no.2
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• pp.55-64
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• 1978

The present investigations were carried out for the purpose of making clear the fundamental features of the seasonal variations in carrageenan content and its chemical composition. The samples, Chondrus ocellatus, were collected every month from the same locality on the coast of Haewundae from March, 1975 to February, 1976, and analyzed monthly to determine their carrageenan content, sulphate and 3,6-anhydro-D-galactose over a year period. En addition, some chemical characteristics were also tested on the fractions separated by the different concentrations of potassium chloride. In seasonal variation, the maximum carrageenan content occurred from summer through autumn, and the minimum in winter, especially in February. It is noted in the seasonal variations of the sulphate content of total carrageenan and three fractions that the maximums appreared in October and minimums in february. With seasonal variations of percent 3,6-anhydro-D-galactose of total carrageenan and three fractions, maximum values occur in June and minimum values in December. Seasonal variations in sulphate and 3,6-anhydro-D-galactose contents of the three fractions were on the whole similar in mode of variation, and there could be no substancial differences. Seeing seasonal variations in the sulphate and 3,6-anhydro-D-galactose contents of three fractions, carrageenan extracted from the algae gathered in spring shelved higher portion of fraction I than that gathered in autumn. In these respects, it seems that a more weakly gelling k-carrageenan exists in the spring than in the autumn.

• Ocean and Polar Research
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• v.29 no.2
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• pp.113-121
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• 2007

The sound wave in the sea propagates under the effect of water depth, sound speed structure, sea surface roughness, bottom roughness, and acoustic properties of bottom sediment. In shallow water, the bottom sediments are distributed very variously with place and the sound speed structure varying with time and space. In order to investigate the seasonal propagation characteristics of low-frequency sound wave in the Yellow Sea, propagation experiments were conducted along a track in the middle part of the Yellow Sea in spring, summer, and autumn. In this paper we consider seasonal variations of the sound speed profile and propagation loss based on the measurement results. Also we quantitatively investigate variation of bottom loss by dividing the propagation loss into three components: spreading loss, absorption loss, and bottom loss. As a result, the propagation losses measured in summer were larger than the losses in spring and autumn, and the propagation losses measured in autumn were smaller than the losses in spring. The spreading loss and the absorption loss did not show seasonal variations, but the bottom loss showed seasonal variations. So it was thought that the seasonal variation of the propagation loss was due to the seasonal change of the bottom loss and the seasonal variation of the bottom loss was due to the change of the sound speed profile by season.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.695-703
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• 2005

This paper extends the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. The finite sample distribution of the associated rank test for dally data is also presented.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.31-39
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• 2001

For estimating parameters of possibly nonlinear and/or non-stationary seasonal autoregressive(AR) processes, we introduce a new instrumental variable method which use the direction vector of the regressors in the same period as an instrument. On the basis of the new estimator, we propose new seasonal random walk tests whose limiting null distributions are standard normal regardless of the period of seasonality and types of mean adjustments. Monte-Carlo simulation shows that he powers of he proposed tests are better than those of the tests based on ordinary least squares estimator(OLSE).

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.869-876
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• 2012

In this paper, we applied seasonal time series models such as ARIMA, FARIMA, AR-GARCH and Holt-Winters in consideration of seasonality to forecast short-term electricity demand data. The results for performance evaluation on the time series models show that seasonal FARIMA and seasonal Holt-Winters models perform adequately under the criterion of Mean Absolute Percentage Error(MAPE).

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.13-15
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• 2005

In this paper, we propose an extension of the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. We presented the finite sample distribution of the associated rank test statistics for daily data.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.447-456
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• 2007

On the basis of marginal likelihood of the residual vector which is free of nuisance mean parameters, we propose new Lagrange Multiplier seasonal unit root tests in seasonal autoregressive process. The limiting null distribution of the tests is the standardized ${\chi}^2-distribution$. A Monte-Carlo simulation shows the new tests are more powerful than the tests based on the ordinary least squares (OLS) estimator, especially for large number of seasons and short time spans.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.501-522
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• 2007

We propose an estimation procedure that can be used for detecting structural changes in the seasonal cointegrated vector autoregressive model. The asymptotic properties of the estimates and the test statistics for the parameter change are provided. A simulation example is presented to illustrate this method and its concept.

• Atmosphere
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• v.22 no.1
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• pp.117-128
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• 2012

Does snow depth initialization have a quantitative impact on sub-seasonal to seasonal prediction skill? To answer this question, a snow depth initialization technique for seasonal forecast system has been implemented and the impact of the initialization on the seasonal forecast of surface air temperature during the wintertime is examined. Since the snow depth observation can not be directly used in the model simulation due to the large systematic bias and much smaller model variability, an anomaly rescaling method to the snow depth initialization is applied. Snow depth in the model is initialized by adding a rescaled snow depth observation anomaly to the model snow depth climatology. A suite of seasonal forecast is performed for each year in recent 12 years (1999-2010) with and without the snow depth initialization to evaluate the performance of the developed technique. The results show that the seasonal forecast of surface air temperature over East Asian region sensitively depends on the initial snow depth anomaly over the region. However, the sensitivity shows large differences for different timing of the initialization and forecast lead time. Especially, the snow depth anomaly initialized in the late winter (Mar. 1) is the most effective in modulating the surface air temperature anomaly after one month. The real predictability gained by the snow depth initialization is also examined from the comparison with observation. The gain of the real predictability is generally small except for the forecasting experiment in the early winter (Nov. 1), which shows some skillful forecasts. Implications of these results and future directions for further development are discussed.