Evapotranspiration (ET) is expected to increase by a considerable amount because of the impact of future temperature increase. Nowadays, the daily to seasonal ET maps can be used to provide information for a sustainable and adaptive watershed eco-environment. This study attempts to estimate the spatial ET of South Korea (99,900 km 2 ), located within the latitudes of 33 • 06 N to 43 • 01 N and the longitudes of 124 • 04 E to 131 • 05 E, on a daily basis. The satellite-based image-processing model Surface Energy Balance Algorithms for Land (SEBAL) was adopted and modified to generate the spatial ET data. The SEBAL was calibrated using two years (2012)(2013) of measured ETs by an eddy covariance (EC) flux tower at three locations (two in a mixed forest area and one in a rice paddy area). The primary inputs for the model were land surface temperature/emissivity (LST/E), the Normalized Distribution Vegetation Index (NDVI), albedo (Ab) from a Terra Moderate-resolution Imaging Spectroradiometer (MODIS) satellite, a digital elevation model, and wind speed and solar radiation (Rs) from 76 ground-based weather stations. When LST data were unavailable because of clouds and/or snow, the bias-corrected ground temperature measured at the weather stations was used. The NDVI and Ab were used as the monthly average value to maintain relatively stable values rather than using the original time interval data. The determination coefficient (R 2 ) between SEBAL and the flux tower ET was 0.45-0.54 for the two mixed forest towers and 0.79 for the rice paddy tower reflecting the known characteristics of closed and open space ET estimation. The spatial distribution of SEBAL showed that the spatial ET reflected the geographical characteristics, revealing that the ET of lowland areas was higher than that of highland areas.

Nucleophosmin (NPM)/B23, a multifunctional nucleolar phosphoprotein, plays an important role in ribosome biogenesis, cell cycle regulation, apoptosis and cancer pathogenesis. The role of NPM in cells is determined by several factors, including total expression level, oligomerization or phosphorylation status, and subcellular localization. In the nucleolus, NPM participates in rRNA maturation to enhance ribosomal biogenesis. Consistent with this finding, NPM expression is increased in rapidly proliferating cells and many types of human cancers. In response to ribosomal stress, NPM is redistributed to the nucleoplasm, where it inactivates mouse double minute 2 homologue to stabilize p53 and inhibit cell cycle progression. These observations indicate that nucleolus-nucleoplasmic mobilization of NPM is one of the key molecular mechanisms that determine the role of NPM within the cell. However, the regulatory molecule(s) that control(s) NPM stability and subcellular localization, crucial to the pluripotency of intercellular NPM, remain(s) unidentified. In this study, we showed that nucleolar protein GLTSCR2/Pict-1 induced nucleoplasmic translocation and enhanced the degradation of NPM via the proteasomal polyubiquitination pathway. In addition, we showed that GLTSCR2 expression decreased the transforming activity of cells mediated by NPM and that the expression of NPM is reciprocally related to that of GLTSCR2 in cervical cancer tissue. In this study, we demonstrated that GLTSCR2 is an upstream negative regulator of NPM.

Abstract:This study attempts to estimate spatial soil moisture in South Korea (99,000 km 2 ) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation index (NDVI) data. The MODIS NDVI was used to reflect vegetation variations. Observed precipitation was measured using the automatic weather stations (AWSs) of the Korea Meteorological Administration (KMA), and soil moisture data were recorded at 58 stations operated by various institutions. Prior to MLR analysis, satellite LST data were corrected by applying the conditional merging (CM) technique and observed LST data from 71 KMA stations. The coefficient of determination (R 2 ) of the original LST and observed LST was 0.71, and the R 2 of corrected LST and observed LST was 0.95 for 3 selected LST stations. The R 2 values of all corrected LSTs were greater than 0.83 for total 71 LST stations. The regression coefficients of the MLR model were estimated seasonally considering the five-day antecedent precipitation. The p-values of all the regression coefficients were less than 0.05, and the R 2 values were between 0.28 and 0.67. The reason for R 2 values less than 0.5 is that the soil classification at each observation site was not completely accurate. Additionally, the observations at most of the soil moisture monitoring stations used in this study started in December 2014, and the soil moisture measurements did not stabilize. Notably, R 2 and root mean square error (RMSE) in winter were poor, as reflected by the many missing values, and uncertainty existed in observations due to freezing and mechanical errors in the soil. Thus, the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model. Specifically, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of droughts with the variability of soil moisture.

No abstract

A model was developed for the location of rapid charging stations for electric vehicles (EVs) in urban areas, taking into account the batteries' state of charge and users' charging and traveling behaviors. EVs are one means of preparing for the energy crisis and of reducing greenhouse gas emissions. To help relieve range anxiety, an adequate number of EV charging stations must be constructed. Rapid charging stations are needed in urban areas because there is inadequate space for slow-charging equipment. The objective function of the model is to minimize EVs' travel fail distance and the total travel time of the entire network when the link flow is determined by a user equilibrium assignment. The remaining fuel range (RFR) at the origin node is assumed to follow a probabilistic distribution to reflect users' charging behavior or technical development. The results indicate that the model described in this paper can identify locations for charging stations by using a probabilistic distribution function for the RFR. The location model, which was developed on the basis of user equilibrium assignment, is likely to consider the congested traffic conditions of urban areas, to avoid locating charging stations where they could cause additional traffic congestion. The proposed model can assist decision makers in developing policies that encourage the use of EVs, and it will be useful in developing an appropriate budget for implementing the plan.