• Nutrition Research and Practice
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• v.12 no.4
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• pp.315-323
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• 2018

BACKGROUND/OBJECTIVES: Available data suggest that seasonal changes may influence the nutritional status and overall health of elderly individuals. Therefore, this study was conducted to investigate the effects of seasonal changes and related factors on energy and nutrient intake of older adults. SUBJECTS/METHODS: Individuals aged 65 years or over were prospectively enrolled in this single-center study (male: 11, female: 20). Data were collected between May 2013 and February 2014 during winter, spring, summer and autumn. Food consumption and biochemical parameters were taken during each season to assess the seasonal nutrition status of the elderly. Upon analysis of biochemical parameters (retinol, vitamin D and vitamin C), an high-performance liquid chromatography device was utilized whereas an Immulite 2000 device was utilized during analysis of serum folic acid and parathyroid hormone. RESULTS: Fruit, fat, egg and bread consumption varied seasonally in males and females (P < 0.05). During winter, daily energy intake was found to be greater than in other seasons in males (557 kcal) and females (330 kcal) (P < 0.05). Additionally, carbohydrates, vegetable protein, n-3 fatty acid and sodium intake increased in winter, while the n-6/n-3 ratio increased in summer among males (P < 0.05). Dietary fiber and sodium intake in winter, vitamin C, iron and zinc intake in spring, and cholesterol, retinol, vitamin D and niacin intake in autumn were found to be higher in females when compared to other seasons (P < 0.05). Serum parathyroid hormone level was higher in winter, and vitamin D level was higher in autumn in both genders (P < 0.05). In males, blood folic acid level was higher in winter, while vitamin C level was higher in females, and there was no seasonal variation in retinol concentration (P < 0.05). CONCLUSION: Food consumption and biochemical parameters showed significant seasonal variations in older adults. It is not clear if nutrition plans in older adults will benefit from consideration of seasonal changes in eating habits.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.673-689
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• 2022

To understand seasonal changes it is necessary to understand the relationship between celestial bodies in a three-dimensional space, and to this end, modeling activities in which students directly construct, use, evaluate, and modify three-dimensional models are important. In this study, the process of elementary school students using globes and light bulbs to model Earth's motion in a three-dimensional space as a cause of seasonal changes was analyzed. Seventeen sixth graders participated in the modeling process. After exploring phenomena and concepts related to seasonal change, students constructed models using globes and bulbs and used them to explain seasonal changes. Video data recording students' modeling process, students' activity sheets, and transcripts of post-interview were used as research data, and data triangulation was conducted. The modeling level analysis framework was also developed based on previous studies. In particular, the framework was developed in detail in this study in consideration of the concept of Earth's motion as well as understanding model and implementing modeling. In the final analysis framework, the 3D modeling level was classified from level 1 to level 3, and student performance that may appear at each level was specified. As a result of the study, there were two main levels of modeling using globes for elementary school students to explain seasonal changes. The rotation and tilt of the axis of rotation and revolution of the earth were considered but the level at which empirical evidence was not used (level 2), the level at which empirical evidence was used to explain seasonal chages (level 3). However, even when students use empirical evidence, it did not lead to the construction of a scientific model. In this study, the cause was explored in relation to the characteristics of the tool used for modeling.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.21-29
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• 2019

This study analyzed the variability of drought risk based on trend analysis of dry-seasonal dam inflow located in upper river basins. To this, we used areal averaged precipitation and dam inflow of three upper river dams such as Soyang dam, Chungju dam, and Andong dam. We employed Mann-Kendall trend analysis and change point detection method to identify the significant trends and changing point in time series. Our results showed that significant decreasing trends (95% confidence interval) in dry-seasonal runoff rates (= dam inflow/precipitation) in three-dam basins. We investigated potential causes of decreasing runoff rates trends using changes in potential evapotranspiration (PET) and precipitation indices. However, there were no clear relation among changes in runoff rates, PET, and precipitation indices. Runoff rate reduction in the three dams may increase the risk of dam operational management and long-term water resource planning. Therefore, it will be necessary to perform a multilateral analysis to better understand decreasing runoff rates.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.3 no.2
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• pp.39-51
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• 2007

Unconsolidated and permeable alluvial deposit composed of sand and gravel is distributed along the fluvial plain at the Iryong study area. Previous studies on the area show that a single alluvial well can produce at least 1,650m3d-1 of bank infilterated shallow groundwater(BIGW) from the deposit. This study is aimed to evaluate and simulate the influence that seasonal variation of water levels and temperatures of the river have an effect on those of BIGW under the pumping condition and also to compare seasonal variation of COPs when indirectly pumped BIGW or directly pumped surface water are used for a water to water heat pump system as an heat source and sink using 3 D flow and heat transport model of Feflow. The result shows that the magnitude influenced to water level of BIGW by fluctuation of river water level in summer and winter is about 48% and 75% of Nakdong river water level separately. Seasonal change of river water temperature is about $23.7^{\circ}C$, on other hand that of BIGW is only $3.8^{\circ}C$. The seasonal temperatures of BIGW are ranged from minimum $14.5^{\circ}C$ in cold winter(January) and maximum $18.3^{\circ}C$ in hot summer(July). It stands for that BIGW is a good source of heat energy for heating and cooling system owing to maintaining quite similar temperature($16^{\circ}C$) of background shallow groundwater. Average COPh in winter time and COPc in summer time of BIGW and surface water are estimated about 3.95, 3.5, and about 6.16 and 4.81 respectively. It clearly indicates that coefficient of performance of heat pump system using BIGW are higher than 12.9% in winter time and 28.1% in summer time in comparision with those of surface water.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Atmosphere
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• v.31 no.5
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• pp.511-523
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• 2021

This study explores the optimal ensemble size to improve the prediction performance of the Korea Meteorological Administration's operational climate prediction system, global seasonal forecast system version 5 (GloSea5). The GloSea5 produces an ensemble of hindcast data using the stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. An experiment to increase the hindcast ensemble from 3 to 14 members for four initial dates was performed and the improvement and effect of the prediction performance considering Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), ensemble spread, and Ratio of Predictable Components (RPC) were evaluated. As the ensemble size increased, the RMSE and ACC prediction performance improved and more significantly in the high variability area. In spread and RPC analysis, the prediction accuracy of the system improved as the ensemble size increased. The closer the initial date, the better the predictive performance. Results show that increasing the ensemble to an appropriate number considering the combination of initial times is efficient.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1153-1159
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• 2000

This paper applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the lido utilization that QoS of the network is greatly influenced by and proposes the prediction algorithm of threshold violation in line utilization using the seasonal ARIMA model. We can predict the time of threshold violation in line utilization and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold and a detection probability, it thus appears that we have maximized the performance of this algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1257-1264
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• 2018

This paper proposes Autoregressive Integrated Moving Average (ARIMA)-based forecasting algorithms using meteorological indices to predict seasonal peak load. First of all, this paper observes a seasonal pattern of the peak load that appears intensively in winter and summer, and generates ARIMA models to predict the peak load of summer and winter. In addition, this paper also proposes hybrid ARIMA-based models (ARIMA-Hybrid) using a discomfort index and a sensible temperature to enhance the conventional ARIMA model. To verify the proposed algorithm, both ARIMA and ARIMA-Hybrid models are developed based on peak load data obtained from 2006 to 2015 and their forecasting results are compared by using the peak load in 2016. The simulation result indicates that the proposed ARIMA-Hybrid models shows the relatively improved performance than the conventional ARIMA model.

• Atmosphere
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• v.17 no.1
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• pp.27-44
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• 2007

The purpose of this study is to investigate predictability of the seasonal simulation by the METRI (Meteorological Research Institute) AGCM (Atmospheric General Circulation Model), which is a long-term prediction model for the METRI 3-month prediction system. We examine the performance skill of climate simulation and predictability by the analysis of variance of the METRI AGCM, focusing on the precipitation, 850 hPa temperature, and 500 hPa geopotential height. According to the result, the METRI AGCM shows systematic errors with seasonal march, and represents large errors over the equatorial region, compared to the observation. Also, the response of the METRI AGCM by the variation of the sea surface temperature is obvious for the wintertime and springtime. However, the METRI AGCM does not show the significant ENSO-related signal in autumn. In case of prediction over the east Asian region, errors between the prediction results and the observation are not quite large with the lead-time. However, in the predictability assessment using the analysis of variance method, longer lead-time makes the prediction better, and the predictability becomes better in the springtime.

• Asian Journal of Atmospheric Environment
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• v.8 no.4
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• pp.192-201
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• 2014

A comprehensive profile of volatile organic compounds (VOCs) in public spaces is needed for interpreting indoor air measurements. Seasonal differences in profiles are critical for epidemiological study and risk assessment. The purposes of this study were to establish profiles for individual VOCs in 50 indoor public places in Korea and to determine seasonal variations in their concentrations. Air samples were taken during working hours. Seventy-two of the 91 targeted VOCs were identified using multiple standards. Six VOCs detected in all summer and winter samples were toluene, acetone, m,p-xylenes, ethylbenzene, benzene, and styrene. In summer, methyl ethyl ketone and 1-butanol were also found in all samples. In both seasons, the dominant indoor VOCs were toluene, m,p-xylenes, ethylbenzene, acetone, and isopropyl alcohol. Other chemicals associated with gasoline emissions were dominant in summer. Limonene was dominant only in winter due to the consumption of tangerines. The nine VOCs with the highest concentrations comprised 64.8% and 49.6% of the TVOC in summer and winter, respectively. Comparing two types of adsorbent tube, a single adsorbent tube with Tenax-TA had similar detection performance as a double adsorbent tube with Tenax and Carbotrap.