• Mycobiology
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• v.45 no.4
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• pp.297-311
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• 2017

Saprolegniasis is one of the most devastating oomycete diseases in freshwater fish which is caused by species in the genus Saprolegnia including Saprolegnia parasitica. In this study, we isolated the strain of S. parasitica from diseased rainbow trout in Korea. Morphological and molecular based identification confirmed that isolated oomycete belongs to the member of S. parasitica, supported by its typical features including cotton-like mycelium, zoospores and phylogenetic analysis with internal transcribed spacer region. Pathogenicity of isolated S. parasitica was developed in embryo, juvenile, and adult zebrafish as a disease model. Host-pathogen interaction in adult zebrafish was investigated at transcriptional level. Upon infection with S. parasitica, pathogen/antigen recognition and signaling (TLR2, TLR4b, TLR5b, NOD1, and major histocompatibility complex class I), pro/anti-inflammatory cytokines (interleukin $[IL]-1{\beta}$, tumor necrosis factor ${\alpha}$, IL-6, IL-8, interferon ${\gamma}$, IL-12, and IL-10), matrix metalloproteinase (MMP9 and MMP13), cell surface molecules ($CD8^+$ and $CD4^+$) and antioxidant enzymes (superoxide dismutase, catalase) related genes were differentially modulated at 3- and 12-hr post infection. As an anti-Saprolegnia agent, plant based lawsone was applied to investigate on the susceptibility of S. parasitica showing the minimum inhibitory concentration and percentage inhibition of radial growth as $200{\mu}g/mL$ and 31.8%, respectively. Moreover, natural lawsone changed the membrane permeability of S. parasitica mycelium and caused irreversible damage and disintegration to the cellular membranes of S. parasitica. Transcriptional responses of the genes of S. parasitica mycelium exposed to lawsone were altered, indicating that lawsone could be a potential anti-S. parasitica agent for controlling S. parasitica infection.

• Journal of Chest Surgery
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• v.44 no.1
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• pp.44-50
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• 2011

Background: Cardiovascular complications are major causes of morbidity and mortality following non-cardiac thoracic operations. Recent studies have demonstrated that elevation of N-Terminal Pro-B-type natriuretic peptide (NT-proBNP) levels can predict cardiac complications following non-cardiac major surgery as well as cardiac surgery. However, there is little information on the correlation between lung resection surgery and NT-proBNP levels. We evaluated the role of NT-proBNP as a potential marker for the risk stratification of cardiac complications following lung resection surgery. Material and Methods: Prospectively collected data of 98 patients, who underwent elective lung resection from August 2007 to February 2008, were analyzed. Postoperative adverse cardiac events were categorized as myocardial injury, ECG evidence of ischemia or arrhythmia, heart failure, or cardiac death. Results: Postoperative cardiac complications were documented in 9 patients (9/98, 9.2%): Atrial fibrillation in 3, ECG-evidenced ischemia in 2 and heart failure in 4. Preoperative median NT-proBNP levels was significantly higher in patients who developed postoperative cardiac complications than in the rest (200.2 ng/L versus 45.0 ng/L, p=0.009). NT-proBNP levels predicted adverse cardiac events with an area under the receiver operating characteristic curve of 0.76 [95% confidence interval (CI) 0.545~0.988, p=0.01]. A preoperative NT-proBNP value of 160 ng/L was found to be the best cut-off value for detecting postoperative cardiac complication with a positive predictive value of 0.857 and a negative predictive value of 0.978. Other factors related to cardiac complications by univariate analysis were a higher American Society of Anesthesiologists grade, a higher NYHA functional class and a history of hypertension. In multivariate analysis, however, high preoperative NT-proBNP level (>160 ng/L) only remained significant. Conclusion: An elevated preoperative NT-proBNP level is identified as an independent predictor of cardiac complications following lung resection surgery.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.181-187
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• 2016

A thermal power plant has a heat exchanger tube to collect and convert the heat generated from the high temperature and pressure steam to energy, but the tubes are arranged in a complex manner. In the event that a leakage occurs in any of these tubes, the high-pressure steam leaks out and may cause the neighboring tubes to rupture. This leakage can finally stop power generation, and hence there is a dire need to establish a suitable technology capable of detecting tube leaks at an early stage even before it occurs. As shown in this paper, by applying acoustic emission (AE) technology in existing boiler tube leak detection equipment (BTLD), we developed a system that detects these leakages early enough and generates an alarm at an early stage to necessitate action; the developed system works better that the existing system used to detect fine leakages. We verified the usability of the system in a 560MW-class thermal power plant boiler by conducting leak tests by simulating leakages from a variety of hole sizes (ⵁ2, ⵁ5, ⵁ10 mm). Results show that while the existing fine leakage detection system does not detect fine leakages of ⵁ2 mm and ⵁ5 mm, the newly developed system could detect leakages early enough and generate an alarm at an early stage, and it is possible to increase the signal to more than 18 dB.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.194-207
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• 2003

This paper presents an optimal design methodology for the multidimensional nested attribute index (MD-NAI) that uses a multidimensional index structure for indexing the nested attributes in object databases. The MD-NAI efficiently supports complex queries involving both nested attributes and class hierarchies, which are not supported by the nested attribute index using one-dimensional index structure such as $B^+$-tree. However, the performance of the MD-NAI is very degraded in some cases of user's query types. In this paper, for the performance enhancement of the MD-NAI, we first determine the optimal shape of index page region by using the query information about the nested predicates, and then construct an optimal MD NAI by applying a region splitting strategy that makes the shape of the page regions of the MD-NAI as close as possible to the predetermined optimal one. For performance evaluation, we perform extensive experiments with the MD-NAI using various types of nested predicates and object distribution. The results indicate that our proposed method builds optimal MD-NAI regardless of the query types and object distributions. When the interval ratio of a three-dimensional query region is 1:16:236, the performance of the proposed method is enhanced by as much as 5.5 times over that of the conventional method employing the cyclic splitting strategy.

• Journal of the Korean Magnetics Society
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• v.17 no.3
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• pp.141-146
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• 2007

A new class of LC-resonator for micro magnetic sensor device was invented and fabricated by means of MEMS technique. The micro LC-resonator consists of a solenoidal micro-inductor with a bundle of soft magnetic microwire cores and a capacitor connected in parallel to the micro-inductor. The core magnetic material is a tiny glass coated $Co_{83.2}B_{3.3}Si_{5.9}Mn_{7.6}$ microwire fabricated by a glasscoated melt spinning technique. The core materials were annealed at various temperatures $150^{\circ}C,\;200^{\circ}C\;,250^{\circ}C\;,$ and $300^{\circ}C$ for 1 hour in a vacuum to improve soft magnetic properties. The solenoidal micro-inductors fabricated by MEMS technique were $500{\sim}1,000{\mu}m$ in length with $10{\sim}20$ turns. The changes of inductance as a function of external magnetic field in micro-inductors with properly annealed microwire cores were varied as much as 370%. Since the permeability of ultra soft magnetic microwire is changing rapidly as a function of external magnetic field. The inductance ratio as well as magnetoimpedance ratio (MIR) in a LC-resonator was varied drastically as a function of external magnetic field. The MIR curves can be tuned very precisely to obtain maximum sensitivity. A prototype magnetic sensor device consisting of the developed microinductors with a multivibrator circuit was test successfully.

• Journal of Korean Society of Disaster and Security
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• v.14 no.3
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• pp.17-27
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• 2021

In this study, classification models were built using machine learning techniques that can classify the soil creep risk into three classes from A to C (A: risk, B: moderate, C: good). A total of six machine learning techniques were used: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting and then their classification accuracy was analyzed using the nationwide soil creep field survey data in 2019 and 2020. As a result of classification accuracy analysis, all six methods showed excellent accuracy of 0.9 or more. The methods where numerical data were applied for data training showed better performance than the methods based on character data of field survey evaluation table. Moreover, the methods learned with the data group (R1~R4) reflecting the expert opinion had higher accuracy than the field survey evaluation score data group (C1~C4). The machine learning can be used as a tool for prediction of soil creep if high-quality data are continuously secured and updated in the future.

• Korean Journal of Microbiology
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• v.54 no.4
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• pp.428-435
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• 2018

In this study, we isolated and identified bacteria from freshwater and soil collected from Osang reservoir, to screen antimicrobial bacteria against various pathogenic bacteria. 38 strains were isolated and assigned to the class Proteobacteria (22 strains), Actinobacteria (7 strains), Bacteroidets (6 strains), and Firmicutes (3 strains) based on 16S rRNA gene sequence analysis. Among them, strain OS17 showed a good growth inhibition against 5 methicillin-resistant Staphylococcus aureus subsp. aureus strains and Bacillus cereus, Bacillus subtilis, Filobasidium neoformans. As a result of the 16S rRNA gene sequence analysis, strain OS17 show the high similarity with Burkholderia ambifaria $AMMD^T$, B. diffusa $AM747629^T$, B. tettitorii $LK023503^T$ 99.8%, 99.7%, 99.6%, respectively. We investigated cell growth and antimicrobial activity according to commercial culture medium, temperature, pH for culture optimization of strain OS17. Optimal conditions for growth and antimicrobial activity in strain OS17 were found to be: YPD medium, $35^{\circ}C$ and pH 6.5. When the strain was cultured in LB, NB, TSB, R2A media at $20^{\circ}C$ and $25^{\circ}C$, the antimicrobial activity did not show. Culture filtrate of strain OS17 showed antimicrobial activity against 5 MRSA strains, Bacillus cereus, Bacillus subtilis, and Filobasidium neoformans with inhibition zones from 2 to 8 mm. Optimal reaction time was 48 h in YPD medium, 100 rpm and 0.3 vvm in 2 L-scale fed-batch fermentation process for antimicrobial activity. Culture optimization of strain OS17 can be improved on antimicrobial activity. Therefore, the antimicrobial activity of Burkholderia sp. OS17 had potential as antibiotics for pathogens including MRSA.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.35-49
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• 2019

This study proposes a new method for categorizing the hydrological soil groups by considering the recent revision results of soil series. Also, the proposed method is evaluated by comparing the categorizing result with those based on existing three different methods. As an example, the proposed method is applied to Jeju Island to estimate the CN value, which is then compared with CN values estimated by applying the existing three different methods. Summaries of the results are as follow. (1) The revision result since 2007 shows that the soil texture has been changed in the 43 soil series, the drainage class in the 1 soil series, the permeability in the 15 soil series, and the impermeable layer in the 26 soil series. (2) The categorizing result of hydrological soil groups by applying the proposed method shows that the group B is the most dominant group covering up to 49.25%. On the other hand, one of the existing method of 1987 provides the group C as the most dominant group (46.43%). Method of 1995 defines the group B as the most dominant group (27.69%). The other method of 2007 distinguishes the group D (35.82%) to be the most dominant group. (3) Also, the CN value estimated by applying the proposed method to Jeju Island is found to be smaller than those based on existing three methods. This result indicates the possible overestimation of the CN value when applying the existing three methods.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.1
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• pp.20-25
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• 2021

The cement clinker, the main raw material of cement, is manufactured using limestone as the main material. Depending on the quality of limestone, the use of subsidiary materials changes, and has a great influence on the production of cement clinkers. In this study, the effect of CaO content of limestone, a cement clinker material, on Raw Mill grinding and sintering of cement clinker was investigated. The grinding time of the union materials changed in the content of limestone CaO was measured to identify the grinding properties. The raw material combination was cleaned within a range of 1,350-1,500℃. The sintering performance of cement clinker by Burnability index calculation was identified. The lower the grade of limestone, the lower the grinding quality of the raw material combination. The lower the CaO content of limestone, the greater the variation in F-CaO for sintering temperature. The lower the class of limestone, the higher B. I. value was calculated, indicating the lower cement clinker sintering. In addition, the mineral analysis results of cement clinker showed that if the F-CaO value was low due to the increase in sintering temperature, the Belite content decreased and the Alite content increased. In the case of Alite, the ratio of R-type decreased and that of M-type increased as the content of limestone CaO increased.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.133-140
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• 2022

We investigate the performance of deep learning-based Korean language models on a task of predicting the score range of Korean essays written by foreign students. We construct a data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job ('job'), conditions of a happy life ('happ'), relationship between money and happiness ('econ'), and definition of success ('succ'). These essays were labeled according to four letter grades (A, B, C, and D), and a total of eleven essay score range prediction experiments were conducted (i.e., five for predicting the score range of 'job' essays, five for predicting the score range of 'happiness' essays, and one for predicting the score range of mixed topic essays). Three deep learning-based Korean language models, KoBERT, KcBERT, and KR-BERT, were fine-tuned using various training data. Moreover, two traditional probabilistic machine learning classifiers, naive Bayes and logistic regression, were also evaluated. Experiment results show that deep learning-based Korean language models performed better than the two traditional classifiers, with KR-BERT performing the best with 55.83% overall average prediction accuracy. A close second was KcBERT (55.77%) followed by KoBERT (54.91%). The performances of naive Bayes and logistic regression classifiers were 52.52% and 50.28% respectively. Due to the scarcity of training data and the imbalance in class distribution, the overall prediction performance was not high for all classifiers. Moreover, the classifiers' vocabulary did not explicitly capture the error features that were helpful in correctly grading the Korean essay. By overcoming these two limitations, we expect the score range prediction performance to improve.