• Economic and Environmental Geology
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• v.55 no.6
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• pp.727-736
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• 2022

Ice wedges are subsurface ice mass structures that formed mainly by freezing precipitation with airborne dust and surrounding soil particles flowed through the active layer into the cracks growing by repeating thermal contractions in the deeper permafrost layer over time. These ice masses characteristically contain high concentrations of solutes and solids. Because of their unique properties and distribution, the possibility of harnessing ice wedges as an alternative archive for reconstructing paleoclimate and paleoenvironment has been recently suggested despite limited studies. It is imperative to preserve the physicochemical properties of the ice wedge (e.g., solute concentration, mineral particles) without any potential alteration to use it as a proxy for reconstructing the paleo-information. Thawing the ice wedge samples is prerequisite for the assessment of their physicochemical properties, during which the paleo-information could be unintentionally altered by any methodological artifact. This study examined the effect of thawing conditions and procedures on the physicochemical properties of solutes and solid particles in ice wedge samples collected from Cyuie, East Siberia. Four different thawing conditions with varying temperatures (4 and 23℃) and oxygen exposures (oxic and anoxic) for the ice wedge sample treatment were examined. Ice wedge samples thawed at 4℃ under anoxic conditions, wherein biological activity and oxidation were kept to a minimum, were set as the standard thawing conditions to which the effects of temperature and oxygen were compared. The results indicate that temperature and oxygen exposure have negligible effects on the physicochemical characteristics of the solid particles. However, the chemical features of the solution (e.g., pH, electric conductivity, alkalinity, and concentration of major cations and trace elements) at 4℃ under oxic conditions were considerably altered, compared to those measured under the standard thawing conditions. This study shows that the thawing condition of ice wedge samples can affect their chemical features and thereby the geochemical information therein for the reconstruction of the paleoclimate and/or paleoenvironment.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.655-667
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• 2023

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.

• Journal of Communication Design
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• v.68
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• pp.278-288
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• 2019

The purpose of this study is to analyze the effects of risk and convenience on purchase intention in the IOT market, and I want to analyze the moderating effect of emotional consumption value. In this study, two products were selected from three product groups. There are three major methods of research. First, theoretical considerations. Second, survey analysis. Reliability analysis and factor analysis were performed using descriptive statistics using SPSS. Third, we measured changes of EEG according to in - depth interview and indirect experience. As a result of the hypothesis of this study, it was confirmed that convenience of use of IoT product influences purchase intention. Risk was predicted to have a negative effect on purchase intentions, but not significant in this study. This implies that IoT products tend to be neglected in terms of monetary loss such as cost of purchase, cost of use, and disposal cost when purchasing. In-depth interviews and EEG analysis revealed that there is a desire to purchase and try out the IoT product due to the nature of the product, the novelty of new technology, and the vague idea that it will benefit my life. The aesthetic, symbolic, and pleasure factors, which are sub - elements of emotional consumption value, were found to have a great influence. This is consistent with previous research showing that emotional consumption value has a positive effect on purchase intention. In-depth interviews and EEG analyzes also yielded the same results. This study has revealed that emotional consumption value affects the intention to purchase IoT products. It seems that companies producing IoT products need to concentrate on marketing with more emotional consumption value.