• International Journal of Industrial Entomology and Biomaterials
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• v.9 no.2
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• pp.207-215
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• 2004

The present study was carried out to evaluate the ISSR and RAPD markers for their efficiency as genetic marker systems to establish the relationships between 18 mulberry genotypes. A total of 36 from 56 (64%) RAPD primers and 12 from 48 (25%) ISSR primers produced reproducible amplification patterns. A high proportion of polymorphic bands ranging from 44 to 91% was observed respectively with RAPD and ISSR markers. The average Resolving Power (Rp) of ISSR primers was higher than RAPD primers. The ISSR primers, UBC 825, 868 and 873, and RAPD primers, UBC 712, 720 and 729, possessed the highest Rp values and could in each instance distinguish all the 18 genotypes. Similarity matrix values were estimated based on Jaccards coefficient, considering 109 polymorphic ISSR and 212 polymorphic RAPD bands and two dendrograms were constructed. The dendrograms obtained with ISSR and RAPD markers distinguished the eight exotic genotypes from the ten indigenous (Indian) genotypes. A significant correlation value (r=0.959; p=0.001) for the cophenetic matrix between the RAPD and ISSR matrices was observed. The results indicated that the ISSR and RAPD markers could assist in the differentiation of genotypes and permit the determination of genetic distances that might be exploited by mulberry breeders in improvement programs.

• 한국가시화정보학회:학술대회논문집
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• 2002.11a
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• pp.19-22
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• 2002

A process of 3-D particle image velocimetry, called here, as '3-D stereo PIV' was developed for the measurement of a section field of 3-D complex flows. The present method includes modeling of camera by a calibrator based on the homogeneous coordinate system, transfromation of oblique-angled image to transformed image, identification of 2-D velocity vectors by 2-D cross-correlation equation, stereo matching of 2-D velocity vectors of two cameras, accurate calculation of 3-D velocity vectors by homogeneous coordinate system and finally 3-D animation as the post processing. In principle, as two frame images only are necessary for the single instantaneous analysis of a section field of 3-D flow, more effective vectors are obtainable contrary to the previous multi-frame vector algorithm. An experimental system was also used for the application of the proposed method. Three analog CCD cameras and an Argon-Ion Laser(300mW) for illumination were adopted to capture the wake flow behind a bluff obstacle.

• Bulletin of the Korean Chemical Society
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• v.22 no.1
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• pp.68-76
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• 2001

We have employed chemical modification to identify amino acids essential for the catalytic activity of alliinase (EC 4.4.1.4) from garlic (Allium sativum). Alliinase degrades S-alkyl-L cysteine sulfoxides, causing the characteristic odor of garlic. The activity of alliinase was rapidly and completely inactivated by N-bromosuccinimide(NBS) and slightly decreased by succinic anhydride and N-acetylimidazole. These results indicate that tryptophanyl, lysyl, and tyrosyl residues play an important role in enzyme catalysis. The reaction of alliinase with NBA yielded a characteristic decrease in both the absorbance at 280 nm and the intrinsic fluorescence at 332 nm with increasing reagent concentration of NBS, consistent with the oxidation of tryptophan residues. Kinetic analysis, fluorometric titration of tryptophans and correlation to residual alliinase activity showed that modification of only one residue present on alliinase led to complete inhibition of alliinase activity. To identify this essential tryptophan residue, we employed chemical modification by NBS in the presence and absence of the protecting substrate analogue, S-ethyl-L-cysteine (SEC) and N-terminal sequence analysis of peptide fragment isolated by reverse phase-HPLC. A fragment containing residues 179-188 was isolated. We conclude that Trp182 is essential for alliinase activity.

• Journal of Powder Materials
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• v.26 no.5
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• pp.369-374
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• 2019

The properties of powder metallurgy products are related to their densities. In the present work, we demonstrate a method to apply artificial neural networks (ANNs) trained on experimental data to predict the bulk density of barium titanates. The density is modeled as a function of pressure, press rate, heating rate, sintering temperature, and soaking time using the ANN method. The model predictions with the training and testing data result in a high coefficient of correlation (R2 = 0.95 and Pearson's r = 0.97) and low average error. Moreover, a graphical user interface for the model is developed on the basis of the transformed weights of the optimally trained model. It facilitates the prediction of an infinite combination of process parameters with reasonable accuracy. Sensitivity analysis performed on the ANN model aids the identification of the impact of process parameters on the density of barium titanates.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1100-1108
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• 2021

Supercritical water has great potential as a coolant for nuclear reactor. Its use will lead to higher thermal efficiency of Rankine cycle. However, in certain conditions heat transfer may get deteriorated which may lead to undesirable high clad surface temperature. It is necessary to estimate the operating conditions in which heat transfer deterioration (HTD) will take place, so as to establish thermal margins for safe reactor operation. In the present work, the heat flux corresponding to onset of HTD for vertically upward flow of supercritical water in a pipe is obtained over a wide range of system parameters, namely pressure, mass flux, and pipe diameter. This is done by performing large number of simulations using an in-house CFD code, which is especially developed and validated for this purpose. The identification of HTD is based on observance of one or more peak/s in the computed wall temperature profile. The existing correlations for predicting the onset of HTD are compared against the results obtained by present simulations as well as available sets of experimental data. It is found that the prediction accuracy of the correlation proposed by Dongliang et al. is best among the existing correlations.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.369-378
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• 2018

Purpose: The aim of this study was to construct a saponin content-predicting model using shortwave infrared imaging spectroscopy. Methods: The experiment used a shortwave imaging spectrometer and ENVI spectral acquisition software sampling a spectrum of 910 nm-2500 nm. The corresponding preprocessing and mathematical modeling analysis was performed by Unscrambler 9.7 software to establish a ginsenoside nondestructive spectral testing prediction model. Results: The optimal preprocessing method was determined to be a standard normal variable transformation combined with the second-order differential method. The coefficient of determination, $R^2$, of the mathematical model established by the partial least squares method was found to be 0.9999, while the root mean squared error of prediction, RMSEP, was found to be 0.0043, and root mean squared error of calibration, RMSEC, was 0.0041. The residuals of the majority of the samples used for the prediction were between ${\pm}1$. Conclusion: The experiment showed that the predicted model featured a high correlation with real values and a good prediction result, such that this technique can be appropriately applied for the nondestructive testing of ginseng quality.

• Journal of dental hygiene science
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• v.19 no.2
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• pp.86-95
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• 2019

The modern era of microbial genome analysis began in earnest in the 2000s with the generalization of metagenomics and gene sequencing techniques. Studying complex microbial community such as oral cavity and colon by a pure culture is considerably ineffective in terms of cost and time. Therefore, various techniques for genomic analysis have been developed to overcome the limitation of the culture method and to explore microbial communities existing in the natural environment at the gene level. Among these, DNA fingerprinting analysis and microarray chip have been used extensively; however, the most recent method of analysis is metagenomics. The study summarily examined the overview of metagenomics analysis techniques, as well as domestic and foreign studies on disease genomics and cluster analysis related to oral metagenome. The composition of oral bacteria also varies across different individuals, and it would become possible to analyze what change occurs in the human body depending on the activity of bacteria living in the oral cavity and what causality it has with diseases. Identification, isolation, metabolism, and presence of functional genes of microorganisms are being identified for correlation analysis based on oral microbial genome sequencing. For precise diagnosis and treatment of diseases based on microbiome, greater effort is needed for finding not only the causative microorganisms, but also indicators at gene level. Up to now, oral microbial studies have mostly involved metagenomics, but if metatranscriptomic, metaproteomic, and metabolomic approaches can be taken together for assessment of microbial genes and proteins that are expressed under specific conditions, then doing so can be more helpful for gaining comprehensive understanding.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.6
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• pp.744-751
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• 2018

In this paper, we propose a fast detection and identification algorithm of chemical gases with a passive FTIR spectrometer. We use a pre-detection algorithm that can reduce the spatial region effectively for gas detection and the candidates of the target. It is possible to remove background spectra effectively from measured spectra with the least-squares method. The CC(Correlation Coefficients) and the SNR(Signal-to-Noise Ratio) methods are used for the detection of target gases. The proposed pre-detection algorithm allows the total process of chemical gas detection to be performed with lower complexity compared with the conventional algorithms. This paper can help developing real-time chemical detection instruments and various applications of FTIR spectrometers.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.1033-1045
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• 2021

Corporate social responsibility for employees is associated with employees' requirements related to the work and the organization's ability to meet such needs. The study aims to examine the impact of corporate social responsibility for workers on employee management effectiveness amongst businesses in Vietnam. Using the quantitative method, through SEM linear structural model analysis, the research surveys 619 samples of employees at businesses in Vietnam. The study results show that corporate social responsibility for workers has both a direct and indirect impact on the effectiveness of employee management through intermediary factors such as organizational identity, organizational commitment, and knowledge sharing of employees. In addition, the study also demonstrates that commitment has a positive correlation with both organizational identification and knowledge sharing of employees in the businesses. On that basis, the study proposes several recommendations to improve employee administration efficiency. This study's findings have shown the importance and impact of corporate social responsibility in many respects on employee administration efficiency. These are meaningful contributions in theoretical and practical aspects that help businesses get a more in-depth insight into employee administration and the need to care and promote corporate social responsibility for employees, which is an important basis for further research.

• Journal of Physiology & Pathology in Korean Medicine
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• v.34 no.4
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• pp.209-214
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• 2020

The association of cold-heat (CH) pattern and anthropometry/body composition has been suggested in that they are related to thermoregulation. We aimed to study the association of CH pattern and anthropometry/body composition. A total of 1479 individuals aged 50-80 years were included in the study, and their CH pattern were evaluated by a self-administered questionnaire. After adjustment for age and sex, the CH score were significantly correlated with weight, BMI (body mass index), body surface area, waist-hip ratio, fat free mass, body fat mass, body cell mass, intracellular water, extracellular water, and basal metabolic rate; however, the correlation coefficients were mostly low (0.15-0.24). The selected variables for predicting CH score were various according to the methods used for variable selection; however, the adjusted R-squared of the final models were all around 0.12. Thus the most parsimonious model could be the one that includes sex and BMI. In conclusion, various anthropometry and body composition indices were associated with CH pattern. Future studies are necessary to improve the performance of the prediction model.