• Korean Chemical Engineering Research
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• v.55 no.6
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• pp.807-815
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• 2017

This study investigated the removal efficiency of residual pentane from paclitaxel according to the drying temperature in the case of rotary evaporation, and performed a kinetic and thermodynamic analysis of the drying process. At all the temperatures (25, 30, 35, 40, and $45^{\circ}C$), a large amount of the residual solvent was initially removed during the drying, and the drying efficiency increased when increasing the drying temperature. Five drying models (Newton, Page, modified Page, Henderson and Pabis, Geometric) were then used for the kinetic analysis, where the Henderson and Pabis model showed the highest coefficient of determination ($r^2$) and lowest root mean square deviation (RMSD), indicating that these models were the most suitable. Furthermore, in the thermodynamic analysis of the rotary evaporation, the activation energy ($E_a$) was 4.9815 kJ/mol and the standard Gibbs free energy change (${\Delta}G^0$) was negative, whereas the standard enthalpy change (${\Delta}H^0$) and standard entropy change (${\Delta}S^0$) were both positive, indicating that the drying process was spontaneous, endothermic, and irreversible.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.2
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• pp.12-27
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• 2023

In this study, among the construction-type parks in Gangdong-gu, targeting parks with high environmental and ecological value located on the urban green axis, a plan was prepared for the ecological renewal of urban parks, and a design that applied to them was proposed. The renewal target site was selected by analyzing the general condition of Gangdong-gu and urban parks, the land use and green area ratio, park green area, and the green axis of Gangdong-gu. Gangdong-gu has 54 parks, including 2 neighborhood parks and 52 children's parks. In the first stage of the current status review, 17 parks were extracted through locational value analysis, such as location and adjacency to the natural axis and green axis. In the second stage, eight parks were selected among the first-stage extraction parks based on the ratio of green spaces and open spaces within each park service area. In the third stage, two of the second stage extraction parks were selected based on whether the legal standard of the park area was met, and in the fourth stage, one of the third stage extraction parks was selected through an aging survey of the park. As for the urban ecological status of the renewal target site, the status of land use in the aspect of entropy reduction, the status of soil cover in the aspect of water circulation, and the status of planting structure in the aspect of biodiversity were investigated. As for the status of the three renewal sites, the green area was insufficient at 18.3-45.3%, and the facility area was 54.7%-81.7%, which was judged to have low urban temperature reduction effects. The impervious pavement area accounted for 34.5% to 48.9% of the park area, accounting for most of the facility area, and it was judged that the water circulation function was insufficient. The planting structure consisted of a single layer and a double layer structure, and although the tree layer was good, the lower vegetation was poor, and there was no planting site of edible plants or large hardwood trees, so the biodiversity was low. After the ecological renewal design of Seonrin Children's Park, Dangmal Children's Park, and Saemmul Children's Park, which were selected as the renewal targets in this study, the ecological area ratio of each park increased by 1.4 to 3 times than before the renewal. If the urban parks located on the urban green axis are examined from the perspective of the urban ecosystem and renewed ecologically, it is judged that the expected effect will be high in reducing entropy, improving water circulation, and laying the foundation for biodiversity in terms of the urban ecosystem.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.6953-6961
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• 2015

Background: Tobacco and alcohol contain or may generate carcinogenic compounds related to cancers. CYP1A1 enzymes act upon these carcinogens before elimination from the body. The aim of this study was to investigate whether CYP1A1 T3801C polymorphism modulates the relationship between tobacco and alcohol-associated head and neck cancer (HNC) susceptibility among the northeast Indian population. Materials and Methods: One hundred and seventy histologically confirmed HNC cases and 230 controls were included within the study. The CYP1A1 T3801C polymorphism was determined using PCR-RFLP, and the results were confirmed by DNA sequencing. Logistic regression (LR) and multifactor dimensionality reduction (MDR) approaches were applied for statistical analysis. Results: The CYP1A1 CC genotype was significantly associated with HNC risk (P=0.045). A significantly increased risk of HNC (OR=6.09; P<0.0001) was observed in individuals with combined habits of smoking, alcohol drinking and tobacco-betel quid chewing. Further, gene-environment interactions revealed enhanced risks of HNC among smokers, alcohol drinkers and tobacco-betel quid chewers carrying CYP1A1 TC or CC genotypes. The highest risk of HNC was observed among smokers (OR=7.55; P=0.009) and chewers (OR=10.8; P<0.0001) carrying the CYP1A1 CC genotype. In MDR analysis, the best model for HNC risk was the three-factor model combination of smoking, tobacco-betel quid chewing and the CYP1A1 variant genotype (CVC=99/100; TBA=0.605; P<0.0001); whereas interaction entropy graphs showed synergistic interaction between tobacco habits and CYP1A1. Conclusions: Our results confirm that the CYP1A1 T3801C polymorphism modifies the risk of HNC and further demonstrated importance of gene-environment interaction.

• Journal of Wetlands Research
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• v.18 no.4
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• pp.404-412
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• 2016

This paper not only aims to establish a plan to acquire the water stage data in a constant and proper manner by using limited manpower and costs, but also establishes the fundamental technology for acquiring the water level observation data or the stage data. For this, this paper focuses on how to acquire the stage data, in a uniform manner, that can represent each basin by developing the technology for establishing the optimal observational network. For that, this paper identifies the current status of the stage gauge stations installed in the ChungJu dam including wetland basin mainly along the national rivers. Then, thus obtained factors are used to develop the representative unit hydrograph. After that, the data are converted into the probability density function. Then, the stations are calculated information transfer amount. As a last step, we establish the optimized stage gauge network by the location of the stage station and space impact that takes into account for the combinations of the number of the stations. In other words, we consider the combination of the stage gauge station with information transfer amount and spatial correlation analysis for estimation.

• Journal of radiological science and technology
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• v.35 no.3
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• pp.227-235
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• 2012

Digital Mammography makes it possible to reproduce the entire breast image. And it is used to detect microcalcification and mass which are the most important point of view of nonpalpable early breast cancer, so it has been used as the primary screening test of breast disease. It is reported that microcalcification of breast lesion is important in diagnosis of early breast cancer. In this study, six types of texture features algorithms are used to detect microcalcification on breast US images and the study has analyzed recognition rate of lesion between normal US images and other US images which microcalification is seen. As a result of the experiment, Computer aided diagnosis recognition rate that distinguishes mammography and breast US disease was considerably high 70~98%. The average contrast and entropy parameters were low in ROC analysis, but sensitivity and specificity of four types parameters were over 90%. Therefore it is possible to detect microcalcification on US images. If not only six types of texture features algorithms but also the research of additional parameter algorithm is being continually proceeded and basis of practical use on CAD is being prepared, it can be a important meaning as pre-reading. Also, it is considered very useful things for early diagnosis of breast cancer.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.5
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• pp.47-62
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• 2020

Rice paddy accounts for approximately 52.5% of all farmlands in South Korea, and it is closely related to the water environment. Climate change is expected to affect not only agricultural productivity also the water and the nutrient circulation. Therefore this study was aimed to evaluate changes of nitrogen load from rice paddy considering climate change scenario uncertainty. APEX-Paddy model which reflect rice paddy environment by modifying APEX (Agricultural Policy and Environmental eXtender) model was used. Using the AIMS (APCC Integrated Modeling Solution) offered by the APEC Climate Center, bias correction was conducted for 9 GCMs using non-parametric quantile mapping. Bias corrected climate change scenarios were applied to the APEX-Paddy model. The changes and uncertainty in runoff and nitrogen load were evaluated using multi-model ensemble. Paddy runoff showed a change of 23.1% for RCP4.5 scenario and 45.5% for RCP8.5 scenario compared the 2085s (2071 to 2100) against the base period (1976 to 2005). The nitrogen load was found to be increased as 43.9% for RCP4.5 scenario and 76.0% for RCP8.5 scenario. The uncertainty analysis showed that the annual standard deviation of nitrogen loads increased in the future, and the maximum entropy indicated an increasing tendency. And Duncan's analysis showed significant differences among GCMs as the future progressed. The result of this study seems to be used as a basis for mid- and long-term policies for water resources and water system environment considering climate change.

• Journal of the Korean Regional Science Association
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• v.35 no.2
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• pp.73-86
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• 2019

The purpose of this paper is to empirically analyze the effects of related and unrelated varieties of industry and occupation on regional economic growth. Recent studies dealing with the mechanism of economic growth argue that occupation as well as industry act as the driving force of regional economic growth by inducing knowledge externalities. Therefore, this study comprehensively analyzed the effects of occupational diversity along with industrial diversity. For the empirical analysis, we set the regional labor market areas as the spatial units of analysis. Dependent variables include regional per capita GRDP and employment growth between 2010 and 2015, and related and unrelated variety of industry and occupations measured based on the entropy approach are used as key explanatory variables. Our empirical results show that the related variety of industry has a positive effect on per capita GRDP in the region, and the related variety of occupation has a positive effect on regional employment growth. On the other hand, the unrelated variety of industries shows a negative correlation with regional employment growth. Based on the empirical results, this paper provides regional policy implications for strengthening economic vitality by dividing the diversity of industry and occupation into related and unrelated varieties and analyzing how they affect regional economic growth.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.23-30
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• 2021

In this study, we present a comparative analysis of major autoencoder(AE)-based anomaly detection methods for quality determination in the manufacturing process and a new anomaly discrimination criterion. Due to the characteristics of manufacturing site, anomalous instances are few and their types greatly vary. These properties degrade the performance of an AI-based anomaly detection model using the dataset for both normal and anomalous cases, and incur a lot of time and costs in obtaining additional data for performance improvement. To solve this problem, the studies on AE-based models such as AE and VAE are underway, which perform anomaly detection using only normal data. In this work, based on Convolutional AE, VAE, and Dilated VAE models, statistics on residual images, MSE, and information entropy were selected as outlier discriminant criteria to compare and analyze the performance of each model. In particular, the range value applied to the Convolutional AE model showed the best performance with AUC PRC 0.9570, F1 Score 0.8812 and AUC ROC 0.9548, accuracy 87.60%. This shows a performance improvement of an accuracy about 20%P(Percentage Point) compared to MSE, which was frequently used as a standard for determining outliers, and confirmed that model performance can be improved according to the criteria for determining outliers.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Bulletin of the Korean Chemical Society
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• v.24 no.6
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• pp.864-880
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• 2003

A unified picture for magnetism, superconductivity, quantum optics and other properties of molecule-based materials has been presented on the basis of effective model Hamiltonians, where necessary parameter values have been determined by the first principle calculations of cluster models and/or band models. These properties of the matetials are qualitatively discussed on the basis of the spin and pseudo-spin Hamiltonian models, where several quantum operators are expressed by spin variables under the two level approximation. As an example, ab initio broken-symmetry DFT calculations are performed for cyclic magnetic ring constructed of 34 hydrogen atoms in order to obtain effective exchange integrals in the spin Hamiltonian model. The natural orbital analysis of the DFT solution was performed to obtain symmetry-adapted molecular orbitals and their occupation numbers. Several chemical indices such as information entropy and unpaired electron density were calculated on the basis of the occupation numbers to elucidate the spin and pair correlations, and bonding characteristic (kinetic correlation) of this mesoscopic magnetic ring. Both classical and quantum effects for spin alignments and singlet spin-pair formations are discussed on the basis of the true spin Hamiltonian model in detail. Quantum effects are also discussed in the case of superconductivity, atom optics and quantum optics based on the pseudo spin Hamiltonian models. The coherent and squeezed states of spins, atoms and quantum field are discussed to obtain a unified picture for correlation, coherence and decoherence in future materials. Implications of theoretical results are examined in relation to recent experiments on molecule-based materials and molecular design of future molecular soft materials in the intersection area between molecular and biomolecular materials.