• Development and Reproduction
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• v.5 no.2
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• pp.175-180
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• 2001

The ectopic expression of gonadotropin releasing hormone(GnRH and luteinizing hormone(LH) in several tissues is a quite intriguing phenomenon. Recently, the presence of GnRH and its receptor has been clearly demonstrated in rodents and human mammary gland. In this context, one can postulate that the presence of local circuit composed of GnRH and LH in the gland. The present study was undertaken to elucidate whether there is a correlation between the LH expression in rat mammary gland and physiological status during the process of mammary differentiation. LH contents in mammary gland from cycling to weaning rats were measured by radioimmunoassay(RIA). In cycling rats, changes of the LH level in both serum and mammary gland showed similar pattern as the highest level in proestrus and the lowest level in diestrus II stage. While the serum LH levels were fluctuated from pregnant through involution stage, a sharp decline of mammary LH contents was observed in the lactating rats. This decrement was recovered in involuting rats to the level of proestrus stage. Reverse transcription-polymerase chain reaction (RT-PCR) and Southern blot analyses demonstrated that the transcriptional activities of the mammary LH and GnRH were increased from diestrus I stage to estrus stage, and the increased levels were maintained in pregnant, lactation and involution stages. To test the hypothesis that the alteration in mammary LH expression might be steroid-dependant, ovariectomy(OVX) and steroid supplement model was employed. As expected, supplement of estradiol(E$_2$) after OVX remarkably decreased serum LH level compared to that in serum from vehicle-only treated rats. Likewise, administration of E$_2$ significantly reduced the mammary LH content. The present study demonstrated that (i) the LH expression in mammary gland could be altered by some physiological parameters such as estrous cycle, pregnancy, lactation and involution, and (ii) ovarian steroid especially estrogen seems to be one of major endocrine factors which are responsible for regulation of mammary LH expression.

• Journal of the Mineralogical Society of Korea
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• v.25 no.4
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• pp.197-210
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• 2012

Hematite has been known to be the most stable form of various iron (oxyhydr)oxides in the surface environments. In this study, its properties as an adsorbent were examined and also adsorption of arsenic onto hematite was characterized as well. The specific surface area of hematite synthesized in our laboratory appeared to be $31.8g\;m^2/g$ and its point of zero salt effect, (PZSE) determined by potentiometric titration was observed 8.5. These features of hematite may contribute to high capacity of arsenic adsorption. From several adsorption experiments undertaken at the identical solution concentrations over pH 2~12, the adsorption of As(III) (arsenite) was greater than that of As(V) (arsenate). As of pH-dependent adsorption patterns, in addition, arsenite adsorption gradually increased until pH 9.2 and then sharply decreased with pH, whereas adsorption of arsenate was greatest at pH 2.0 and steadily decreased with the increasing pH from 2 to 12. The characteristics of these pH-dependent adsorption patterns might be caused by combined effects of the variation in the chemical speciation of arsenic and the surface charge of hematite. The experimental results on adsorption kinetics show that adsorption of both arsenic species onto hematite approached equilibrium within 20 h. Additionally, the pseudo-second-order model was evaluated to be the best fit for the adsorption kinetics of arsenic onto hematite, regardless of arsenic species, and the rate constant of As(V) adsorption was investigated to be larger than that of As(III).

• Journal of Chest Surgery
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• v.32 no.1
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• pp.1-4
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• 1999

Background: The transplantation of organs between phylogenetically disparate or harmonious species has invariably failed due to the occurrence of hyperacute rejection or accerelated acute rejection. But, concordant cardiac xenograft offer us an opportunity to study xenotransplantation in the absence of hyperacute rejection. Current therapeutics for the prolongation of survival of rodent concordant xenotransplantation are not ideal with many regimens having a high mortality rate. Cyclosporine A & Mycophenolate Mofetil are new immunosuppresive agent which has been shown to be effective at prolonging survival of allograft, as purine synthesis inhibitor. Material and Method: We used white mongrel rats as recipient and mice as donor, divided 4 groups(n=6), control group(Group 1) has no medication or pretreatment, Group 2 has splenectomy as pretreatment 7∼10 days before transplantation, Group 3 has Cyclosporine A treatment group, Group 4 has combined treatment of Cyclosporine A & Mycophenolate Mofetil(RS 61443). We compared survival time. Reuslt: We can't find significant difference of survival time between each groups. Conclusion: We concluded that rejection of cardiac xenograft was different from rejection of allograft, and new immunossuppresive Agent(Mycophenolate Mofetil, Cyclosporine A) was not effective for prolongation of survival time after cardiac xenograft.

• Journal of the Mineralogical Society of Korea
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• v.21 no.4
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• pp.425-434
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• 2008

Arsenic contamination in soil and groundwater has recently been one of the most serious environmental concerns. This arsenic contamination can be originated from natural or anthropogenic sources. It has been well known that arsenic behavior in geo-environmental is controlled by various oxides or hydroxides, such as those of iron, manganese, and aluminum, and clay minerals. Among those, particularly, iron (oxy)hydroxides are the most effective scavengers for arsenic. For this reason, this study characterized arsenic adsorption of magnetite which is a kind of iron oxide in nature. The physicochemcial features of the magnetite were investigated to evaluate adsorption of arsenite [As(III)] and arsenate [As(V)] onto magnetite. In addition to experiments on adsorption equilibria, kinetic experiments were also conducted. The point of zero charge (PZC) and specific surface area of the laboratory-synthesized magnetite used as an arsenic adsorbent were measured 6.56 and $16.6\;g/m^2$, which values seem to be relatively smaller than those of the other iron (oxy)hydroxides. From the results of equilibria experiments, arsenite was much more adsorbed onto magnetite than arsenate, indicating the affinity of arsenite on magnetite is larger than arsenate. Arsenite and arsenate showed adsorption maxima at pHs 7 and 2, respectively. In particular, adsorption of arsenate decreased with increase in pH as a result of electrical repulsion caused by anionic arsenate and negatively-charged surface of magnetite. These results indicate that the surface charge of magnetite and the chemical speciation of arsenic should be considered as the most crucial factors in controlling arsenic. The results of kinetic experiments show that arsenate was adsorbed more quickly than arsenite and adsorption of arsenic was investigated to be mostly completed within the duration of 4 hours, regardless of chemical speciation of arsenic. When the results of kinetic experiments were fitted to a variety of kinetic models proposed so far, power function and elovich model were evaluated to be the most suitable ones which can simulate adsorption kinetics of two kinds of arsenic species onto magnetite.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1447-1460
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• 2021

Urbanization increases the amount of impervious surface and artificial heat emission, resulting in urban heat island (UHI) effect. Local climate zones (LCZ) are a classification scheme for urban areas considering urban land cover characteristics and the geometry and structure of buildings, which can be used for analyzing urban heat island effect in detail. This study aimed to examine the UHI effect by urban structure in Suwon and Daegu using the LCZ scheme. First, the LCZ maps were generated using Landsat 8 images and convolutional neural network (CNN) deep learning over the two cities. Then, Surface UHI (SUHI), which indicates the land surface temperature (LST) difference between urban and rural areas, was analyzed by LCZ class. The results showed that the overall accuracies of the CNN models for LCZ classification were relatively high 87.9% and 81.7% for Suwon and Daegu, respectively. In general, Daegu had higher LST for all LCZ classes than Suwon. For both cities, LST tended to increase with increasing building density with relatively low building height. For both cities, the intensity of SUHI was very high in summer regardless of LCZ classes and was also relatively high except for a few classes in spring and fall. In winter the SUHI intensity was low, resulting in negative values for many LCZ classes. This implies that UHI is very strong in summer, and some urban areas often are colder than rural areas in winter. The research findings demonstrated the applicability of the LCZ data for SUHI analysis and can provide a basis for establishing timely strategies to respond urban on-going climate change over urban areas.

• Economic and Environmental Geology
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• v.53 no.6
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• pp.667-675
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• 2020

An Alum-sludge based adsorbent (ASBA) was synthesized by the hydrothermal treatment of alum sludge obtained from settling basin in water treatment plant. ASBA was applied to remove fluoride and arsenic in artificially-contaminated aqueous solutions and mine drainage. The mineralogical crystal structure, composition, and specific surface area of ASBA were identified. The result revealed that ASBA has irregular pores and a specific surface area of 87.25 ㎡ g-1 on its surface, which is advantageous for quick and facile adsorption. The main mineral components of the adsorbent were found to be quartz(SiO2), montmorillonite((Al,Mg)2Si4O10(OH)2·4H2O) and albite(NaAlSi3O8). The effects of pH, reaction time, initial concentration, and temperature on removal of fluoride and arsenic were examined. The results of the experiments showed that, the adsorbed amount of fluoride and arsenic gradually decreased with increasing pH. Based on the results of kinetic and isotherm experiments, the maximum adsorption capacity of fluoride and arsenic were 7.6 and 5.6 mg g-1, respectively. Developed models of fluoride and arsenic were suitable for the Langmuir and Freundlich models. Moreover, As for fluoride and arsenic, the increase rate of adsorption concentration decreased after 8 and 12 hr, respectively, after the start of the reaction. Also, the thermodynamic data showed that the amount of fluoride and arsenic adsorbed onto ASBA increased with increasing temperature from 25℃ to 35℃, indicating that the adsorption was endothermic and non-spontaneous reaction. As a result of regeneration experiments, ASBA can be regenerated by 1N of NaOH. In the actual mine drainage experiment, it was found that it has relatively high removal rates of 77% and 69%. The experimental results show ASBA is effective as an adsorbent for removal fluoride and arsenic from mine drainage, which has a small flow rate and acid/neutral pH environment.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.2
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• pp.101-109
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• 2022

The mobility and transport of radioactive cesium are crucial factors to consider for the safety assessment of high-level radioactive waste disposal sites in granite. The retardation of radionuclides in the fractured crystalline rock is mainly controlled by the hydrochemical condition of groundwater and surface reactions with minerals present in the fractures. This paper reports the experimental results of cesium sorption to the Wonju Granite, a typical Mesozoic granite in Korea, performed in an anaerobic chamber that mimics the anoxic environment of a deep disposal site. We measured the rates and amounts of cesium (¹³³Cs) removed by crushed granite samples in different electrolyte (NaCl, KCl, and CaCl₂) solutions and a synthetic groundwater solution, with variations in the initial cesium concentration (10^-5, 5×10^-6, 10^-6, 5×10^-7 M). The cesium sorption kinetic and isotherm data were successfully simulated by the pseudo-second-order kinetic model (r²= 0.99) and the Freundlich isotherm model (r²= 0.99), respectively. The sorption distribution coefficient of granite increased almost linearly with increasing biotite content in granite samples, indicating that biotite is an effective cesium scavenger. The cesium removal was minimal in KCl solution compared to that in NaCl or CaCl₂ solution, regardless of the ionic strength and initial cesium concentration that we examined, showing that K⁺ is the most competitive ion against cesium in sorption to granite. Because it is the main source mineral of K⁺ in fracture fluids, biotite may also hinder the sorption of cesium, which warrants further research.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.