• Korean Journal of Microbiology
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• v.40 no.2
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• pp.104-110
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• 2004

To determine evolution and genotype of new chromosomal AmpC $\beta$-lactamases among clinical isolates of Enterobacter species, we performed antibiotic susceptibility testing, pI determination, sequencing, and phy-logenetic analysis using developed expression/secretion vector. Six isolates have shown to produce AmpC $\beta$-lactamases. Six genes of AmpC $\beta$-lactamases that are responsible for the resistance to cephamycins (cefoxitin and cefotetan), amoxicillin, cephalothin, and amoxicillin-clavulanic acid were cloned and characterized in pMSG12119. Insert fragment containing the ampC genes was sequenced and found to have an open reading frame coding for 381-amino-acid $\beta$-lactamase. The nucleotide sequence of four ampC genes ($bla_EcloK992004.l$, $bla_EcloK995120.1$, $bla_EcloK99230$, and $bla_EareK9911729$) shared considerable homology with that of chromosomal ampC gene ($bla_EcloMHN1$) of E. cloacae MHN1 (more than 99.6% identity). The sequences of two ampC genes ($bla_EcloK9973$ and $bla_EcloK9914325$) showed close similarity to the chromosomal ampC gene ($bla_EcloQ908R$) of E. clo-acae 908R (99.7% identity). The results from phylogenetic analysis suggested that six ampC genes could be originated from $bla_EcloMHN1$ / or $bla_EcloQ908R$ / MIC patterns and exact pI values of six transformants indicated that the developed expression/secretion vector (pMSG1219) was suitable for the characterization of foreign genes in E. coli strain.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.416-422
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• 2016

Lots of plastics are widely used in a variety of industrial field. And the amount of plastic waste is massively produced. In the study of waste recycling, it is emerged as an important issue to prevent the waste of potentially useful resource materials as well as to reduce ecological damage. So, the recycling of plastic waste has been currently paid attention to from the view point of reuse. Existing automatic sorting system consist of near infrared ray (NIR) sensors to classify the types of plastics. But the classification of black plastics still remains a challenge. Black plastics which contains carbon black are not almost classified by NIR because of the characteristic of the light absorption of black plastics. This study is focused on handling how to identify black plastics instead of NIR. Raman spectroscopy is used to get qualitative as well as quantitative analysis of black plastics. In order to improve the performance of identification, Support Vector Machine(SVM) classifier and Principal Component Analysis(PCA) are exploited to more preferably classify some kinds of the black plastics, and to analyze the characteristic of each data.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.209-216
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• 2024

Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a variety of unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transaction prices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a News Sentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index, the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutral sentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used for real estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM prediction model, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117 for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724, and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254, and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413, and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.461-466
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• 2024

Korean local governments operates the participatory budgeting system autonomously. This study is to classify these entities into clusters. Among the diverse machine learning methodologies(Neural Network, Rule Induction(CN2), KNN, Decision Tree, Random Forest, Gradient Boosting, SVM, Naïve Bayes), the Support Vector Machine technique emerged as the most efficacious in the analysis of 2022 Korean municipalities data. The first cluster C1 is characterized by minimal committee activity but a substantial allocation of participatory budgeting; another cluster C3 comprises cities that exhibit a passive stance. The majority of cities falls into the final cluster C2 which is noted for its proactive engagement in. Overall, most Korean local government operates the participatory busgeting system in good shape. Only a small number of cities is less active in this system. We anticipate that analyzing time-series data from the past decade in follow-up studies will further enhance the reliability of classifying local government types regarding participatory budgeting.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.201-208
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• 2000

A numerical analysis algorithm for thermally loaded structures has been proposed and compared with the general free vibration approach to determine the characteristics of thermal load effects in vibration structures. The field of numerical inspection includes free vibration analysis, transient heat transfer analysis and thermal stress analysis. The key point of the analysis of thermally loaded structure is the method of parallel time integration between transient heat transfer and free vibration simultaneously. The results of the study demonstrate the computation of the specific total external force vector and stiffness matrix. The proposed analysis method can be applied to both heated and cooled structure vibration analysis.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.11
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• pp.1644-1651
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• 2014

Transgenic animals have become important tools for the production of therapeutic proteins in the domestic animal. Production efficiencies of transgenic animals by conventional methods as microinjection and retrovirus vector methods are low, and the foreign gene expression levels are also low because of their random integration in the host genome. In this study, we investigated the homologous recombination on the porcine ${\beta}$-casein gene locus using a knock-in vector for the ${\beta}$-casein gene locus. We developed the knock-in vector on the porcine ${\beta}$-casein gene locus and isolated knock-in fibroblast for nuclear transfer. The knock-in vector consisted of the neomycin resistance gene (neo) as a positive selectable marker gene, diphtheria toxin-A gene as negative selection marker, and 5' arm and 3' arm from the porcine ${\beta}$-casein gene. The secretion of enhanced green fluorescent protein (EGFP) was more easily detected in the cell culture media than it was by western blot analysis of cell extract of the HC11 mouse mammary epithelial cells transfected with EGFP knock-in vector. These results indicated that a knock-in system using ${\beta}$-casein gene induced high expression of transgene by the gene regulatory sequence of endogenous ${\beta}$-casein gene. These fibroblasts may be used to produce transgenic pigs for the production of therapeutic proteins via the mammary glands.

• Journal of Embryo Transfer
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• v.31 no.3
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• pp.161-168
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• 2016

Nucleotide metabolism in endothelium is variable between different species. Recent studies demonstrated that this variability could contribute coagulation dysfunction, even though organs of the alpha 1,3-galactosyltransferase gene knockout pig were transplanted into the primate. CD73 (ecto-5'-nucelotidase) is an enzyme at cell surface catalyzing the hydrolysis of adenosine triphosphate to adenosine, which plays role on a substance for anti-inflammatory and anti-coagulant. Thus, overexpression of CD73 in endothelial cells of the pig is considered as an approach to reduce coagulopathy. In this study, we constructed a human CD73 expression vector under control of porcine Icam2 promoter (pIcam2-hCD73), which is expressed specifically at endothelial cells, and of CMV promoter as a control (CMV-CD73). First, we transfected the CMV-CD73 vector into HEK293 cells, and then confirmed CD73 expression at cell surface by flow cytometry analysis. Next, we transfected the pIcma2-CD73 and CMV-CD73 vectors into primary porcine fibroblasts and endothelial cells. Consequence was that the pIcma2-CD73 vector was expressed only at the porcine endothelial cells, meaning that the pIcam2 promoter lead to endothelial cell-specific expression of CD73 in vitro. Finally, we nucleofected the pIcam2-hCD73 vector into passage 3 fibroblasts, and enforced hygromycin selection of 400mg/ml. We were able to obtain forty three colonies harboring pIcam2-CD73 to provide donor cells for transgenic cloned porcine production.

• The Korean Journal of Pesticide Science
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• v.20 no.4
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• pp.286-292
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• 2016

Malaria is mainly transmitted by Anopheles sinensis which is dominant species in malaria high-risk area, northern part of Gyeonggi province in Korea. Pyrethroid insecticide is used for malaria vector, An. sinensis in Korea and the previous investigation consistently reported insecticide resistance from the vector. This study investigated insecticide susceptible and resistant alleles from An. sinensis and the status of malaria vector control in malaria high-risk area. For the study, An. sinensis collected from Paju, Gimpo and Ganghwa were sequenced for kdr detection. In Paju, there was no homozygous susceptibility and all of tested samples had homozygous or heterozygous resistance. There were 6.7% for susceptible homozygosity and 93.3% for resistant homozygosity or heterozygosity in Gimpo. Furthermore, the percentages of homozygous susceptibility and homozygous or heterozygous resistance in Ganghwa were 5.7% and 94.3% respectively. The results showed that the frequency of the insecticide resistance from An. sinensis in malaria high-risk area were increased much more than the previous investigation. Hence, this study suggests that malaria vector control programs should have to be prepared for the management of pyrethroid insecticide resistance.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.63-72
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• 2018

Purpose - This work analyzes, in detail, the specification of vector error correction model (VECM) and thus examines the relationships and impact among seven economic variables for USA - balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), a measure of the money supply that includes total currency as well as large time deposits, institutional money market funds, short-term repurchase agreements and other larger liquid assets (M3), real rate of interest (IR_REAL) and household credits (LOAN). In particular, we search for the main explanatory variables that have an effect on stock and real estate market, respectively and investigate the causal and dynamic associations between them. Research design, data, and methodology - We perform the time series vector error correction model to infer the dynamic relationships among seven variables above. This work employs the conventional augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root techniques to test for stationarity among seven variables under consideration, and Johansen cointegration test to specify the order or the number of cointegration relationship. Granger causality test is exploited to inspect for causal relationship and, at the same time, impulse response function and variance decomposition analysis are checked for both short-run and long-run association among the seven variables by EViews 9.0. The underlying model was analyzed by using 108 realizations from Q1 1990 to Q4 2016 for USA. Results - The results show that all the seven variables for USA have one unit root and they are cointegrated with at most five and three cointegrating equation for USA. The vector error correction model expresses a long-run relationship among variables. Both IR_REAL and M3 may influence real estate market, and GDP does stock market in USA. On the other hand, GDP, IR_REAL, M3, STOCK and LOAN may be considered as causal factors to affect real estate market. Conclusions - The findings indicate that both stock market and real estate market can be modelled as vector error correction specification for USA. In addition, we can detect causal relationships among variables and compare dynamic differences between countries in terms of stock market and real estate market.