• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.417-426
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• 2023

Rapid climate change has resulted in glacial retreat and increased meltwater inputs in the Antarctic Peninsula, including King George Island where Marian Cove is located. Consequently, these phenomena are expected to induce changes in the water column light properties, which in turn will affect phytoplankton communities. To comprehend the effects of glacial retreat on the marine ecosystem in Marian Cove, we investigated on phytoplankton biomass (chlorophyll-a, chl-a) and various environment parameters in this area in December 2021 and January 2022. The average temperature at the euphotic depth in January 2022 (1.41 ± 0.13 ℃) was higher than that in December 2021 (0.87 ± 0.17 ℃). Contrastingly, the average salinity was lower in January 2022 (33.9 ± 0.10 psu) than in December 2021 (34.1 ± 0.12 psu). Major nutrients, including dissolved inorganic nitrogen, phosphate, and silicate, were sufficiently high, and thus, did not act as limiting factors for phytoplankton biomass. In December 2021 and January 2022, the mean chl-a concentrations were 1.03 ± 0.64 and 0.66 ± 0.15㎍ L^-1, respectively. The mean concentration of suspended particulate matter (SPM) was 24.9 ± 3.54 mgL^-1 during the study period, with elevated values observed in the vicinity of the inner glacier. However, relative lower chl-a concentrations were observed near the inner glacier, possibly due to high SPM load from the glacier, resulting in reduced light attenuation by SPM shading. Furthermore, the proportion of nanophytoplankton exceeded 70% in the inner cove, contributing to elevated mean fractions of nanophytoplankton in the glacier retreat marine ecosystem. Overall, our study indicated that freshwater and SPM inputs from glacial meltwater may possibly act as main factors controlling the dynamics of phytoplankton communities in glacier retreat areas. The findings may also serve as fundamental data for better understanding the carbon cycle in Marian Cove.

• Korean Journal of Environmental Agriculture
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• v.42 no.1
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• pp.52-62
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• 2023

This study focused on determining the specific causes and prevention methods of mass fish deaths occurred in five reservoirs (Gagugi, Neupgokgi, Danggokgi, Sagokji, and Hangokji) in Andong-si. For this purpose, a survey of agricultural land and livestock in the upper part of the reservoirs and analysis of water quality in the reservoir irrespective of whether it rains or not were conducted. We attempted to examine the changes in dissolved oxygen (DO) in the surface and bottom layers of reservoirs and changes in DO depending on the amount of livestock compost and time. Based on the above investigations, treatment plans were established to efficiently control the inflow of contaminated water into reservoirs. The rainfall and farmland areas in the upper part of the reservoir were investigated using Google and aviation data provided by the Ministry of Land, Infrastructure, and Transport. The current status of livestock farms distributed around the reservoirs was also examined because compost from these farms can flow into the reservoir when it rains. Various water quality parameters, such as phosphate phosphorus (PO₄-P) and ammonium nitrogen (NH₃-N), were analyzed and compared for each reservoir during the rainy season. Changes in the DO concentration and electrical conductivity (EC) were also observed at the inlet of the reservoir during raining using an automated instrument. In addition, DO was measured until the concentration reached 0 ppm in 10 min by adding livestock compost at various concentrations (0.05%, 0.1%, 0.3%, and 0.5% by wt.), where the concentration of the livestock compost represents the relative weight of rainwater. The DO concentration in the surface layer of reservoirs was 3.7 to 5.3 ppm, which is sufficient for fish survival. However, the fish could not survive at the bottom layer with DO concentration of 0.0-2.1 ppm. When the livestock compost was 0.3%, DO required 10-19 h to reach 0 ppm. Considering these results, it was confirmed that the DO in the bottom layer of the reservoir could easily change to an anaerobic state within 24 h when the livestock compost in the rainwater exceeds 0.3%. The results show that the direct cause of fish mortality is the inflow of excessive livestock compost into reservoirs during the first rainfall in spring. All the surveyed reservoirs had relatively good topographical features for the inflow of compost generated from livestock farms. This keeps the bottom layer of the reservoir free of oxygen. Therefore, to prevent fish death due to insufficient DO in the reservoir, measures should be undertaken to limit the amount of livestock compost flowing into the reservoir within 0.3%, which has been experimentally determined. As a basic countermeasure, minerals such as limestone, dolomite, and magnesia containing calcium and magnesium should be added to the compost of livestock farms around the reservoir. These minerals have excellent pollutant removal capabilities when sprayed onto the compost. In addition, measures should be taken to prevent fish death according to the characteristics of each reservoir.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.75-89
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• 2023

As the Mekong River basin is a nationally shared river, it is difficult to collect precipitation data, and the quantitative and qualitative quality of the data sets differs from country to country, which may increase the uncertainty of hydrological analysis results. Recently, with the development of remote sensing technology, it has become easier to obtain grid-based precipitation products(GPPs), and various hydrological analysis studies have been conducted in unmeasured or large watersheds using GPPs. In this study, rainfall-runoff simulation in the Mekong River basin was conducted using the SWAT model, which is a quasi-distribution model with three satellite GPPs (TRMM, GSMaP, PERSIANN-CDR) and two GPPs (APHRODITE, GPCC). Four water level stations, Luang Prabang, Pakse, Stung Treng, and Kratie, which are major outlets of the main Mekong River, were selected, and the parameters of the SWAT model were calibrated using APHRODITE as an observation value for the period from 2001 to 2011 and runoff simulations were verified for the period form 2012 to 2013. In addition, using the ConvAE, a convolutional neural network model, spatio-temporal correction of original satellite precipitation products was performed, and rainfall-runoff performances were compared before and after correction of satellite precipitation products. The original satellite precipitation products and GPCC showed a quantitatively under- or over-estimated or spatially very different pattern compared to APHPRODITE, whereas, in the case of satellite precipitation prodcuts corrected using ConvAE, spatial correlation was dramatically improved. In the case of runoff simulation, the runoff simulation results using the satellite precipitation products corrected by ConvAE for all the outlets have significantly improved accuracy than the runoff results using original satellite precipitation products. Therefore, the bias correction technique using the ConvAE technique presented in this study can be applied in various hydrological analysis for large watersheds where rain guage network is not dense.

• Journal of Korean Society of Forest Science
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• v.111 no.1
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• pp.136-149
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• 2022

In this study, the performance of a nonlinear mixed-effects (NLME) model used to estimate the stem taper of Pinus densiflora in Gangwon Province was compared with that of a nonlinear fixed-effects (NLFE) model using several performance measures. For the diameters of whole tree stems, the NLME model improved on the performance of the NLFE model by 26.4%, 42.9%, 43.1%, and 0.9% in terms of BIAS, MAB, RMSE, and FI, respectively. For the cross-section areas of whole tree stems, the NLME model improved on the performance of the NLFE model by 67.7%, 44.7%, 45.8%, and 1.0% in terms of BIAS, MAB, RMSE, and FI, respectively. Based on the analysis of 12 relative height classes of tree stems, stem taper estimation performance was also reasonably improved by the NLME model, which showed better MAB, RMSE, and FI at every relative height class compared with those of the NLFE model. In some classes, the NLFE model had better BIAS than the NLME model (stem diameter: 0.05, 0.2, 0.3, and 0.8; stem cross-section area: 0.05, 0.3, 0.5, 0.6, and 1.0). However, the NLME model enhanced the performance of stem diameter and cross-section area estimations at the lowest stem part (0.2 m from the ground). Improvements for stem diameter in terms of BIAS, MAB, RMSE, and FI were 84.2%, 69.8%, 68.7%, and 3.1%, respectively. For stem cross-section areas, the improvements in BIAS, MAB, RMSE, and FI were 98.5%, 70.1%, 68.7%, and 3.1%, respectively. The cross-section area at 0.2 m from the ground occupied 22.7% of total cross-section area. Improvements in estimation of cross-section area at the lowest stem part indicate that stem volume estimation performance could also be enhanced. Although NLME models are more difficult to fit than NLFE models, the use of NLME models as a standard method for the estimating the parameters of stem taper equations should be considered.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.1-11
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• 2016

With increasing interest, there have been studies on LiDAR(Light Detection And Ranging)-based DEM(Digital Elevation Model) to acquire three dimensional topographic information. For producing LiDAR DEM with better accuracy, Filtering process is crucial, where only surface reflected LiDAR points are left to construct DEM while non-surface reflected LiDAR points need to be removed from the raw LiDAR data. In particular, the changes of input values for filtering algorithm-constructing parameters are supposed to produce different products. Therefore, this study is aimed to contribute to better understanding the effects of the changes of the levels of GroundFilter Algrothm's Mean parameter(GFmn) embedded in FUSION software on the accuracy of the LiDAR DEM products, using LiDAR data collected for Hwacheon, Yangju, Gyeongsan and Jangheung watershed experimental area. The effect of GFmn level changes on the products' accuracy is estimated by measuring and comparing the residuals between the elevations at the same locations of a field and different GFmn level-produced LiDAR DEM sample points. In order to test whether there are any differences among the five GFmn levels; 1, 3, 5, 7 and 9, One-way ANOVA is conducted. In result of One-way ANOVA test, it is found that the change in GFmn level significantly affects the accuracy (F-value: 4.915, p<0.01). After finding significance of the GFmn level effect, Tukey HSD test is also conducted as a Post hoc test for grouping levels by the significant differences. In result, GFmn levels are divided into two subsets ('7, 5, 9, 3' vs. '1'). From the observation of the residuals of each individual level, it is possible to say that LiDAR DEM is generated most accurately when GFmn is given as 7. Through this study, the most desirable parameter value can be suggested to produce filtered LiDAR DEM data which can provide the most accurate elevation information.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.34.3-34.3
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• 2020

Faint z ~ 5 quasars with M1450 ~ -23 mag are known to be the potentially important contributors to the ultraviolet ionizing background in the post-reionization era. However, their number density has not been well determined, making it difficult to assess their role in the early ionization of the intergalactic medium (IGM). In this work, we present the updated results of our z ~ 5 quasar survey using the Infrared Medium-deep Survey (IMS), a near-infrared imaging survey covering an area of 85 square degrees. From our spectroscopic observations with the Gemini Multi-Object Spectrograph (GMOS) on the Gemini-South 8 m Telescope, we discovered eight new quasars at z ~ 5 with -26.1 ≤ M1450 ≤ -23.3. Combining our IMS faint quasars with the brighter Sloan Digital Sky Survey (SDSS) quasars, we derive, for the first time, the z ~ 5 quasar luminosity function (QLF) without any fixed parameters down to the magnitude limit of M1450 = -23 mag. We find that the faint-end slope of the QLF is very flat (-1.2) with a characteristic luminosity of -25.7 mag. The number density of z ~ 5 quasars from the QLF gives lower ionizing emissivity and ionizing photon density than those in previous works. These results imply that quasars are responsible for only 10-20% of the photons required to completely ionize the IGM at z ~ 5, disfavoring the idea that quasars alone could have ionized the IGM at z ~ 5.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• Korean Journal of Mineralogy and Petrology
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• v.36 no.4
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• pp.289-302
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• 2023

The study area was Gangnim-myeon, Hoengseong-gun, Gangwon-do, composed of the Chiaksan gneiss complex, and it was revealed that the concentrations of uranium (U) and thorium (Th) within the groundwater of the study area exceeded their water quality standards. Hence, artificial weathering experiments were conducted to elucidate mineralogically the mechanisms of their leaching using drilling cores obtained from the corresponding groundwater aquifers. First of all, the mineralogical compositions of core samples were observed, and the results indicated that the content of clinochlore, a member of the chlorite group of minerals that can form through low- and intermediate-temperature metamorphisms, was relatively higher. In addition, the Th concentration was measured ten times higher than that of U. The results of artificial weathering experiments suggested that the Th concentrations gradually increased through the dissolution of radioactive-element-bearing minerals up to the first day, and then they tended to decrease. It could be attributed to the fact that Th was leached with the dissolution of thorite, which might be a secondary mineral, and then dissolved Th was re-precipitated as the various forms of salt, such as sulfate. Even though the U content was lower than that of Th in the core samples, the U concentration was one hundred times higher than that of Th after the weathering experiments. It is likely caused by the gradual dissolution and desorption of U included in intensively weathered thorite or adsorbed as a form of UO₂²⁺ on the mineral surface. In addition, the leaching tendency of U and Th was positively correlated with the bicarbonate concentration. However, the concentrations between U and Th in groundwater exhibited a relatively lower correlation, which might result from the fact that they occurred from different sources, as aforementioned. Among various kinetic models, the parabolic diffusion and pseudo-second-order kinetic models were confirmed to best fit the dissolution kinetics of both elements. The period that would be taken for the U concentration to exceed its drinking-water standard was inferred using the regressed parameters of the best-fitted models, and the duration of 29.4 years was predicted in the neutral-pH aquifers with relatively higher concentrations of HCO₃, indicating that U could be relatively quickly leached out into groundwater.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.1
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• pp.5-30
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• 2024

The purpose of this study is to extract topics from experimental data using the topic modeling methods(LDA, Top2Vec, and BERTopic) and compare the characteristics and differences between these models. The experimental data consist of 55,442 papers published in 85 academic journals in the field of library and information science, which are indexed in the Web of Science(WoS). The experimental process was as follows: The first topic modeling results were obtained using the default parameters for each model, and the second topic modeling results were obtained by setting the same optimal number of topics for each model. In the first stage of topic modeling, LDA, Top2Vec, and BERTopic models generated significantly different numbers of topics(100, 350, and 550, respectively). Top2Vec and BERTopic models seemed to divide the topics approximately three to five times more finely than the LDA model. There were substantial differences among the models in terms of the average and standard deviation of documents per topic. The LDA model assigned many documents to a relatively small number of topics, while the BERTopic model showed the opposite trend. In the second stage of topic modeling, generating the same 25 topics for all models, the Top2Vec model tended to assign more documents on average per topic and showed small deviations between topics, resulting in even distribution of the 25 topics. When comparing the creation of similar topics between models, LDA and Top2Vec models generated 18 similar topics(72%) out of 25. This high percentage suggests that the Top2Vec model is more similar to the LDA model. For a more comprehensive comparison analysis, expert evaluation is necessary to determine whether the documents assigned to each topic in the topic modeling results are thematically accurate.

• Journal of Life Science
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• v.34 no.7
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• pp.509-519
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• 2024

A previous study showed that krill oil improved recognition and memory through anti-oxidative effects in an amyloid β model, but the authors noted that further investigations are necessary of alterations to neurotransmitters' states and of serum lipid profile improvements related to serum lipid peroxidation. Accordingly, in this study, ICR mice were pre-treated intraperitoneally with scopolamine prior to induced neurotransmission impairment, and the effects of krill oil provision on their capabilities of cognition were tested by performing a passive avoidance test (PAT), water maze test (WMT), and novel object recognition test. Then, parameters including the acetylcholine (ACh) concentration, acetylcholinesterase activity (AChE), lipid peroxidation, serum lipid levels, and nerve cell proliferation were investigated. The results showed that krill oil improved the mice's abilities in recognition and memory as the times taken to complete the PAT and WMT were reduced compared to the mice in a comparison scopolamine-treated group. Krill oil produced an increased concentration of Ach, and this was accompanied by a decrease in AChE. As shown in a scopolamine-treated SH-SY5Y cell line, krill oil reduced the activity of AChE. Moreover, the suppression of lipid peroxidation-reflected in the finding that malondialdehyde was decreased with krill oil provision-is speculated to affect the recorded serum triglyceride and cholesterol decreases and LDL cholesterol increase. The intake of krill oil was also found to produce an improvement in brain-derived neurotrophic factor expression by stimulating the activation of cyclic AMP response element binding protein in the brain tissue. Overall, the current results imply that the provision of krill oil raises the cognition and memory by elevating neurotransmitters and by improving the serum lipid profile and nerve cell proliferation, which occur as lipid peroxidation is suppressed in the brain tissue.