• Title/Summary/Keyword: 기하 활성 모델

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Changes in Sink capacity and Source Activity of Rice Cultivars in Response to Shift of Heading date (벼 품종들의 출수기에 따른 동화산물 생산능력 및 수용기관 크기 변화)

  • Lee, Sok-Young;Kwon, Yong-Woong
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.40 no.2
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    • pp.260-267
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    • 1995
  • In temperate zone planting rice at different date subjects the Crop to different climatic condition. The present study aimed at comparison of the change in source-sink relationship of the Japonica(J) and that of IndicaxJaponica(I$\times$J) type rice cultivars caused by shift of heading date. Two J- and two I$\times$J-type cultivars were made to head on August 16, August 26, and September 5. Sink capacity was changed by shift of heading date in different mode between the types of cultivars. In both types major determinant of sink capacity was number of effective tillers, and the number of spikelets per panicle was the minor. In J-type earlier planting/heading was beneficial to increased panicle numbers and this was due mainly to a larger diurnal difference in temperature. I$\times$J-type cultivars favored a higher daily mean temperature to increase the sink capacity. The ability of source at heading, in terms of leaf area per panicle, chlorophyll content per spiklet, photosynthetic ability of leaves per unit area at 25$\^{\circ}C$, carbohydrate and N contents of leaves, was not so different among different heading dates in both types. However, the source activity was governed principally by temperature during grain filling. The J-type cultivars headed on Sept. 5 and I$\times$J-type cultivars headed later than August 16 could not have had sufficient source activity in grain filling due to lower temperature.

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Comparison of Property Changes of Black Jujube and Zizyphus jujube Extracts during Lactic Acid Fermentation (흑대추와 일반 건조대추의 추출 및 유산발효과정 중 특성 변화)

  • Auh, Mi Sun;Kim, Yi Seul;Ahn, Seung Joon;Ahn, Jun Bae;Kim, Kwang Yup
    • Journal of the Korean Society of Food Science and Nutrition
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    • v.41 no.10
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    • pp.1346-1355
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    • 2012
  • This study was carried out to investigate the characteristics of black jujube and Zizyphus jujube extracts during lactic acid fermentation. Both extracts were fermented using Lactobacillus fermentum YL-3. As a result, viable cell number rapidly increased until 24 hours, after which it gradually decreased. Before lactic acid fermentation, the $IC_{50}$ of black jujube, which was 0.014 mg/mL, was lower than that of Zizyphus jujube. Further, black jujube showed stronger antioxidant activity (374.21 mg AA eq/g) than Zizyphus jujube. Contents of total polyphenolics in both extracts were 15.46 mg/g and 13.61 mg/g, respectively, whereas contents of total flavonoids were 374.21 ${\mu}g/g$ and 64.25 ${\mu}g/g$. After lactic acid fermentation, there was no significant increase in DPPH or ABTS free radical scavenging activity. Total polyphenolic content of Zizyphus jujube decreased to 12.39 mg/g upon fermentation, whereas flavonoid content significantly increased to 291.58 ${\mu}g/g$. Further, polyphenolic and flavonoid contents of black jujube increased from 15.46 mg/g to 17.46 mg/g and from 374.21 ${\mu}g/g$ to 1,135.29 ${\mu}g/g$, respectively. These results demonstrate that 9-Times Steamed and Dried increased functional components. Especially, lactic acid fermented black jujube showed remarkably high antioxidant activity. These results confirm the potential use of lactic acid fermented black jujube as a valuable resource for the development of functional foods.

A Study on Construction and Application of Nuclear Grade ESF ACS Simulator (원자력등급 ESF 공기정화계통 시뮬레이터 제작 및 활용에 관한 연구)

  • Lee, Sook-Kyung;Kim, Kwang-Sin;Sohn, Soon-Hwan;Song, Kyu-Min;Lee, Kei-Woo;Park, Jeong-Seo;Hong, Soon-Joon;Kang, Sun-Haeng
    • Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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    • v.8 no.4
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    • pp.319-327
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    • 2010
  • A nuclear plant ESF ACS simulator was designed, built, and verified to perform experiment related to ESF ACS of nuclear power plants. The dimension of 3D CAD model was based on drawings of the main control room(MCR) of Yonggwang units 5 and 6. The CFD analysis was performed based on the measurement of the actual flow rate of ESF ACS. The air flowing in ACS was assumed to have $30^{\circ}C$ and uniform flow. The flow rate across the HEPA filter was estimated to be 1.83 m/s based on the MCR ACS flow rate of 12,986 CFM and HEPA filter area of 9 filters having effective area of $610{\times}610mm^2$ each. When MCR ACS was modeled, air flow blocking filter frames were considered for better simulation of the real ACS. In CFD analysis, the air flow rate in the lower part of the active carbon adsorber was simulated separately at higher than 7 m/s to reflect the measured value of 8 m/s. Through the CFD analyses of the ACSes of fuel building emergency ventilation system, emergency core cooling system equipment room ventilation cleanup system, it was confirmed that all three EFS ACSes can be simulated by controlling the flow rate of the simulator. After the CFD analysis, the simulator was built in nuclear grade and its reliability was verified through air flow distribution tests before it was used in main tests. The verification result showed that distribution of the internal flow was uniform except near the filter frames when medium filter was installed. The simulator was used in the tests to confirm the revised contents in Reg. Guide 1.52 (Rev. 3).

Study on the Short-Term Hemodynamic Effects of Experimental Cardiomyoplasty in Heart Failure Model (심부전 모델에서 실험적 심근성형술의 단기 혈역학적 효과에 관한 연구)

  • Jeong, Yoon-Seop;Youm, Wook;Lee, Chang-Ha;Kim, Wook-Seong;Lee, Young-Tak;Kim, Won-Gon
    • Journal of Chest Surgery
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    • v.32 no.3
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    • pp.224-236
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    • 1999
  • Background: To evaluate the short-term effect of dynamic cardiomyoplasty on circulatory function and detect the related factors that can affect it, experimental cardiomyoplasties were performed under the state of normal cardiac function and heart failure. Material and Method: A total of 10 mongrel dogs weighing 20 to 30kg were divided arbitrarily into two groups. Five dogs of group A underwent cardiomyoplasty with latissimus dorsi(LD) muscle mobilization followed by a 2-week vascular delay and 6-week muscle training. Then, hemodynamic studies were conducted. In group B, doxorubicin was given to 5 dogs in an IV dose of 1 mg/kg once a week for 8 weeks to induce chronic heart failure, and simultaneous muscle training was given for preconditioning during this period. Then, cardiomyoplasties were performed and hemodynamic studies were conducted immediately after these cardiomyoplasties in group B. Result: In group A, under the state of normal cardiac function, only mean right atrial pressure significantly increased with the pacer-on(p<0.05) and the left ventricular hemodynamic parameters did not change significantly. However, with pacer-on in group B, cardiac output(CO), rate of left ventricular pressure development(dp/dt), stroke volume(SV), and left ventricular stroke work(SW) increased by 16.7${\pm}$7.2%, 9.3${\pm}$3.2%, 16.8${\pm}$8.6%, and 23.1${\pm}$9.7%, respectively, whereas left ventricular end-diastole pressure(LVEDP) and mean pulmonary capillary wedge pressure(mPCWP) decreased by 32.1${\pm}$4.6% and 17.7${\pm}$9.1%, respectively(p<0.05). In group A, imipramine was infused at the rate of 7.5mg/kg/hour for 34${\pm}$2.6 minutes to induce acute heart failure, which resulted in the reduction of cardiac output by 17.5${\pm}$2.7%, systolic left ventricular pressure by 15.8${\pm}$2.5% and the elevation of left ventricular end-diastole pressure by 54.3${\pm}$15.2%(p<0.05). With pacer-on under this state of acute heart failu e, CO, dp/dt, SV, and SW increased by 4.5${\pm}$1.8% and 3.1${\pm}$1.1%, 5.7${\pm}$3.6%, and 6.9${\pm}$4.4%, respectively, whereas LVEDP decreased by 11.7${\pm}$4.7%(p<0.05). Comparing CO, dp/dt, SV, SW and LVEDP that changed significantly with pacer-on, both under the state of acute and chronic heart failure, augmentation widths of these left ventricular hemodynamic parameters were significantly larger under the state of chronic heart failure(group B) than acute heart failure(group A)(p<0.05). On gross inspection, variable degrees of adhesion and inflammation were present in all 5 dogs of group A, including 2 dogs that showed no muscle contraction. No adhesion and inflammation were, however, present in all 5 dogs of group B, which showed vivid muscle contractions. Considering these differences in gross findings along with the following premise that the acute heart failure state was not statistically different from the chronic one in terms of left ventricular parameters(p>0.05), the larger augmentation effect seen in group B is presumed to be mainly attributed to the viability and contractility of the LD muscle. Conclusion: These results indicate that the positive circulatory augmentation effect of cardiomyoplasty is apparent only under the state of heart failure and the preservation of muscle contractility is important to maximize this effect.

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Transfer of Isolated Mitochondria to Bovine Oocytes by Microinjection (미세주입을 이용한 난자로의 분리된 미토콘드리아 전달)

  • Baek, Sang-Ki;Byun, June-Ho;Kim, Bo Gyu;Lee, A ram;Cho, Young-Soo;Kim, Ik-Sung;Seo, Gang-Mi;Chung, Se-Kyo;Lee, Joon-Hee;Woo, Dong Kyun
    • Journal of Life Science
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    • v.27 no.12
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    • pp.1445-1451
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    • 2017
  • Mitochondria play a central role in energy generation by using electron transport coupled with oxidative phosphorylation. They also participate in other important cellular functions including metabolism, apoptosis, signaling, and reactive oxygen species production. Therefore, mitochondrial dysfunction is known to contribute to a variety of human diseases. Furthermore, there are various inherited diseases of energy metabolism due to mitochondrial DNA (mtDNA) mutations. Unfortunately, therapeutic options for these inherited mtDNA diseases are extremely limited. In this regard, mitochondrial replacement techniques are taking on increased importance in developing a clinical approach to inherited mtDNA diseases. In this study, green fluorescence protein (GFP)-tagged mitochondria were isolated by differential centrifugation from a mammalian cell line. Using microinjection technique, the isolated GFP-tagged mitochondria were then transferred to bovine oocytes that were triggered for early development. During the early developmental period from bovine oocytes to blastocysts, the transferred mitochondria were observed using fluorescent microscopy. The microinjected mitochondria were dispersed rapidly into the cytoplasm of oocytes and were passed down to subsequent cells of 2-cell, 4-cell, 8-cell, morula, and blastocyst stages. Together, these results demonstrate a successful in vitro transfer of isolated mitochondria to oocytes and provide a model for mitochondrial replacement implicated in inherited mtDNA diseases and animal cloning.

Using Effective Temperatures to Determine Safety Cultivation Season in Direct Seeding Rice on Dry Paddy (작물생육 유효기온 출현시기를 이용한 건답직파 벼의 지역별 안전작기 설정)

  • 최돈향;윤경민;윤성호;박무언
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.42 no.6
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    • pp.666-672
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    • 1997
  • Twenty years' daily mean air temperature data was used to calculate the critical early seeding date(CESD), the optimum heading date(OHD), the critical late heading date for stable ripening(CHDR) and the critical late ripening date(CLRD) for rice seeded on dry paddy in different agroclimatic zones in Korea. The CESD was defined as the first day with mean air temperature of 13$^{\circ}C$, and the OHD as the first day of the 40 consecutive days with mean air temperature of 22$^{\circ}C$ or above after heading. The CHDR was defined as the date after which the cumulative daily mean air temperature would be at least 76$0^{\circ}C$. Lastly, the CLRD was defined as the last day when daily mean air temperature remains above 15$^{\circ}C$. This information was used for the estimation of periods from the earliest date of seeding to optimum heading date, the latest possible date of heading and the latest possible date of ripening in respective regions. For instance, in Suwon, those respective periods mentioned were found to be 104days, 124days, and 165days.

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Analysis of Surgical Risk Factors in Pulmonary (폐국균종의 수술위험인자 분석)

  • 김용희;이은상;박승일;김동관;김현조;정종필;손광현
    • Journal of Chest Surgery
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    • v.32 no.3
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    • pp.281-286
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    • 1999
  • Background: The purpose of this study is to analyze the types of complications, the incidences of complications, and preoperative and postoperative risk factors affecting the incidence of the complication. Material and Method: Between August 1990 and August 1997 in Asan Medical Center, 42 patients(24 men and 18 women) underwent surgical resection for pulmonary aspergilloma. The mean age was 46.6${\pm}$11.5 years(range 29 to 69 years). Hemoptysis(90%) was the most common presentation. Pulmonary tuberculosis was the most common predisposing cause(81%). The associated diseases were bronchiectasis(n=11), active puolmonary tuberculosis(n=9), diabetes mellitus(n=8), lung carcinoid(n=1), and acute myeloblastic leukemia(n=1). Lobectomy was done in 32 cases(76%), segmentectomy or wedge resection in 4, pneumonectomy in 2, and lobectomy combined with segmentectomy in 4. Result: Operative mortality was 2%. The most common postoperative complication was persistent air leakage(n=6). The variables such as age, sex, pulmonary function test, amount and duration of hemoptysis, associated diseases(diabetes mellitus, active pulmonary tuberculosis), mode of preoperative management(steroid, antifungal agent, bronchial arterial embolization), and modes of operative procedures were statistically insignificant. The radiologic extent of infiltration to normal lung parenchyme was statistically significant(p=0.04). Conclusion: We conclude that the extent of the infiltration to normal lung parenchyme in preoperative radiologic studies should be carefully evaluated to reduce the postoperative complications in surgery for pulmonary aspergilloma.

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Feasibility of Deep Learning Algorithms for Binary Classification Problems (이진 분류문제에서의 딥러닝 알고리즘의 활용 가능성 평가)

  • Kim, Kitae;Lee, Bomi;Kim, Jong Woo
    • Journal of Intelligence and Information Systems
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    • v.23 no.1
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    • pp.95-108
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    • 2017
  • Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.