• Tuberculosis and Respiratory Diseases
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• v.84 no.2
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• pp.96-104
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• 2021

Background: Many chronic obstructive pulmonary disease (COPD) patients receiving monotherapy continue to experience symptoms, exacerbations and poor quality of life. This study aimed to assess the efficacy and safety of direct switch from once-daily tiotropium (TIO) 18 ㎍ to indacaterol/glycopyrronium (IND/GLY) 110/50 ㎍ once daily in COPD patients in Korea. Methods: This was a randomized, open-label, parallel group, 12-week trial in mild-to-moderate COPD patients who received TIO 18 ㎍ once daily for ≥12 weeks prior to study initiation. Patients aged ≥40 years, with predicted post-bronchodilator forced expiratory volume in 1 second (FEV₁) ≥50%, post-bronchodilator FEV₁/forced vital capacity <0.7 and smoking history of ≥10 pack-years were included. Eligible patients were randomized in a 1:1 ratio to either IND/GLY or TIO. The primary objective was to demonstrate superiority of IND/GLY over TIO in pre-dose trough FEV₁ at week 12. Secondary endpoints included transition dyspnea index (TDI) focal score, COPD assessment test (CAT) total score, and rescue medication use following the 12-week treatment, and safety assessment. Results: Of the 442 patients screened, 379 were randomized and 347 completed the study. IND/GLY demonstrated superiority in pre-dose trough FEV₁ versus TIO at week 12 (least squares mean treatment difference [Δ], 50 mL; p=0.013). Also, numerical improvements were observed with IND/GLY in the TDI focal score (Δ, 0.31), CAT total score (Δ, -0.81), and rescue medication use (Δ, -0.09 puffs/day). Both treatments were well tolerated by patients. Conclusion: A direct switch from TIO to IND/GLY provided improvements in lung function and other patient-reported outcomes with an acceptable safety profile in patients with mild-to-moderate airflow limitation.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.spc
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• pp.108-114
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• 2008

Near infrared spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. The applicability of near infrared reflectance spectroscopic method was tested to determine the color value (L, a, b) of green tea. A total of 162 green tea calibration samples and 82 validation samples were used for NIRS equation development and validation, respectively. In the developed NIRS equation for analysis of the color value (L, a, b), the most accurate equation for L value was obtained at 2, 8, 6, 1 (2nd derivative, 8 nm gap, 6 points smoothing, and 1pointsecond smoothing), and for a, and b value were obtained at 1, 4, 4, 1 (1st derivative, 4 nm gap, 4points smoothing, and 1 point second smoothing) math treatment condition with SNVD (Standard Normal Variate and Detrend) scatter correction method and entire spectrum ($400{\sim}2,500\;nm$) by using MPLS (Modified Partial Least Squares) regression. Validation results of these NIRS equations showed very low bias (L: 0.005%, a: 0.003%, b: -0.013%) and standard error of prediction (SEP, L: 0.361%, a: 0.141%, b: 0.306%) as well as high coefficient of determination ($R^2$, L: 0.905, a: 0.986, b: 0.931). Therefore, these NIRS equations can be applicable and reliable for determination of color value (L, a, b) of green tea, and NIRS method could be used as a mass screening technique for breeding programs and quality control in the green tea industry.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.353-356
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• 2005

In this paper, the proposed $gamma({\gamma})$ line system is developed for reducing the error between non-linear gamma curve produced by a formula and result produced by hardware implementation. The proposed algorithm and system is based on the specific gamma value 2.2, namely the formula is represented by {0,1}$^{2.2}$ and the bit width of input and out data is 10bit. In order to reduce the error, the system is using least squares polynomial of the numerical method which is calculating the best fitting polynomial through a set of points. The proposed gamma line is consisting of nine kinds of quadratic equations, each with their own overlap sections to get more precise. Based on the algorithm verified by $MATLAB^{TM}$ 7.0, the proposed system is implemented by using Verilog-HDL. The proposed system has 2 clock latency; 1 result per clock. The error range (LSB) is -4 and +3. Its standard deviation is 1.287956238. The total gate count of system is 2,083 gates and the maximum timing is 15.56[ns].

• Journal of the Korean Chemical Society
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• v.20 no.1
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• pp.19-34
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• 1976

The crystal structure of sodium sulfisoxazole hexahydrate, $C_{11}H_{12}N_3O_3SNa{\cdot}6H_2O$,has been determined by X-ray diffraction method. The compound crystallizes in the monoclinic space group $$P2_1}c$$ with a = 15.68(3), b = 7.70(2), c = 17.94(4)${\AA}$, ${\beta}$ = $118(2)^{\circ}$ and Z = 4. A total of 1717 observed reflections were collected by the Weissenberg method with $CuK{\alpha}$ radiation. Structure was solved by heavy atom method and refined by block-diagonal least-squares methods to the R value of 0.14. The conformational angle formed by the S-C(l) bond with that of N(2)-C(7), when the projection in taken along the S-N(2), is $73^{\circ}.$ The benzene ring is planar and makes an angle of $60^{\circ}$ with the plane of the isoxazole ring, which is also planar. The sodium atom has a distorted octahedral coordination of N(l) and five oxygen atoms from hydrate molecules. Sodium sulfisoxazole hexahydrate shows fourteen different hydrogen bondings in the crystal. These are six $O-H{\cdots}O-H bonds, three $O-H{\cdots}O$ bonds, two $O-N{\cdots}N,$ one $N-H{\cdots}O,O-H{\cdots}N,N-H{\cdots}O-H$ bond, with the distances in the range of 2.71 to $3.04{\AA}.$.

• Reproductive and Developmental Biology
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• v.37 no.3
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• pp.123-127
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• 2013

The objective of this study was to compare the effects of the levels of inbreeding on body weight traits between two breed populations, Hanwoo and Korea Brindle cattle. Birth weight (BW), weaning weight (WW), body weight at 6 months of age (W6) and yearling weight (YW). Records of 1,745 calves (1,513 from Hanwoo, and 232 from Korea Brindle calves) were collected from Livestock Research Institutes in Kangwon, Gyeongbuk and Chungbuk provinces. The least squares means (LSM) and their standard errors for BW, WW, W6 and YW were $25.4{\pm}0.1$ kg, $81.0{\pm}1.8$ kg, $146.1{\pm}3.7$ kg and $291.5{\pm}2.4$ kg, respectively in Hanwoo calves and $22.6{\pm}0.3$ kg, $79.9{\pm}2.3$ kg, $137.6{\pm}4.6$ kg and $249.3{\pm}6.6$ kg, respectively in Korea Brindle calves. Pedigree data showed that 14.8% (316 out of 2131) of Hanwoo was inbred and the average inbreeding coefficient was 0.0209 (2.09%). Inbreeding coefficients of ten calves out of 316 total inbred Hanwoo calves were 12.5% or higher, whereas those of the other 306 calves were less than 12.5%. In both breeds, calves were divided into three groups of inbreeding classes - highly inbred group($F{\geq}0.125$), lowly to medially inbred group(0

• Journal of the Korean Geographical Society
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• v.51 no.4
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• pp.509-523
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• 2016

The soil prediction can provide quantitative soil information for sustainable mountainous ecosystem management. Visible near infrared spectroscopy, one of soil prediction methods, has been applied to predict several soil properties with effective costs, rapid and nondesctructive analysis, and satisfactory accuracy. Spectral preprocessing is a essential procedure to correct noisy spectra for visible near infrared spectroscopy. However, there are no attempts to evaluate various spectral preprocessing methods. We tested 5 different pretreatments, namely continuum removal, Savitzky-Golay filter, discrete wavelet transform, 1st derivative, and 2nd derivative to predict soil carbon(C) and nitrogen(N). Partial least squares regression was used for the prediction method. The total of 153 soil samples was split into 122 samples for calibration and 31 samples for validation. In the all range, absorption was increased with increasing C contents. Specifically, the visible region (650nm and 700nm) showed high values of the correlation coefficient with soil C and N contents. For spectral preprocessing methods, continuum removal had the highest prediction accuracy(Root Mean Square Error) for C(9.53mg/g) and N(0.79mg/g). Therefore, continuum removal was selected as the best preprocessing method. Additionally, there were no distinct differences between Savitzky-Golay filter and discrete wavelet transform for visual assessment and the methods showed similar validation results. According to the results, we also recommended Savitzky-Golay filter that is a simple pre-treatment with continuum removal.

• Journal of Distribution Science
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• v.14 no.2
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• pp.135-144
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• 2016

Purpose - The purpose of this paper is to empirically investigate the moderating effects of social capital on antecedents factors path of export performance in the materials and components SMEs(Small and Medium-sized Enterprises) of Busan and Kyungnam region. In case of materials and components SMEs, they are always trying to achieve business performance including export sales and market share, but it is difficult for them to increase performance due to the limitation of inner & tangible resources. Therefore intangible asset such as technology capability and its antecedents factors which are technology innovation and learning orientation are getting more important to SMEs. In addition, it is supposed that social capital such as local network including distribution channel in overseas market plays an essential role to enhance export performance. Accordingly, the main goal of this study is to find out the relationship between export performance and antecedents factors and the validity of social capital as a moderating valuable. Research design, data, and methodology - Technology innovation, learning orientation and technology capability have been used as antecedents factors for export performance and social capital such as network diversity and intensity has been used for moderating effects analysis. In order to select these valuables mentioned above, this study examined the existing researches on a basis of Resources Based View, Organizational Learning Theory and Social Capital theory. To achieve the objective of this paper, 7 hypotheses including the moderating effects have been proposed with 6 potential variables measured by 24 questions. The survey was carried out from December 2014 to March 2015 and 137 samples out of total 175 were selected for the analysis. PLS(Partial Least Squares) has been used for the methodology of empirical analysis for both antecedents factors path and moderating effects. Results - Research findings are as follows. First, technology innovation has a significant impact on learning orientation, learning orientation has a positive effect on the technology capability and technology capability also has a significant impact on export performance. Therefore 3 valuables are proved as antecedents factors of export performance. Second, the social capital(both network diversity and intensity) plays a moderating role with learning orientation to technology capability. However, there is no moderating effects between both of social capital and technology capability regarding export performance. Conclusions - According to path analysis results, it is suggested that the materials and components SMEs should raise technology innovation and learning orientation in order to improve technology capability and export performance. Meantime, the moderating effect analysis shows that SMEs should consider local network diversity and intensity along with learning orientation to add up technology capability. But local network diversity and intensity does not work systematically with technology capability for export performance and it means that SMEs should find the appropriate local partners for the purpose of establishing concrete distribution channels based on marketing perspective, not for improving technology capability.

• Journal of The Korean Society of Grassland and Forage Science
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• v.26 no.1
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• pp.45-52
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• 2006

This study was carried out to explore the accuracy of Near Infrared Reflectance Spectroscopy (NIRS) fer the prediction of digestibility and energy value of corn silages. The spectral data were regressed against a range of digestibility and energy parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with first and second order derivatization, with scatter correction procedure(SNV-Detrend) to reduce the effect of extraneous noise. Calibration models for NIRS measurements gave multivariate correlation coefficients of determination$(R^2)$ and standard errors of cross validation of 0.92(SECV 1.73), 0.91(SECV 1.13) and 0.93(SECV 1.74) for in vitro dry matter digestibility(IVDMD), in vitro true digestibility(IVTD), and cellulase dry matter digestibility(CDMD), respectively. The standard error of prediction(SEP) and the multiple correlation coefficient of validation$(R^2v)$ on the validation set(n=39) was used in comparing the prediction accuracy. The SEP value was 0.30(TDN), 0.01(NEL), and 0.01(ME). The relative ability of NIRS to predict digestibility and energy value was very good for CDMD, total digestible nutrients(TDN), net energy fer lactation(NEL) and metabolizable energy(ME). This paper shows the potential of NIRS to predict the digestibility and energy value of con silage as a routine method in feeding programmes and for giving advice to farmers.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1266-1266
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• 2001

Pasture composition, an important attribute determining sward condition and value, is normally assessed by hand separation, drying and measuring weight contribution of each species in the mixture. This is a tedious, time and labour consuming procedure. NIRS has demonstrated the potential for predicting botanical composition of swards, but most of the work has been carried out on dry samples. The aim of this work was to evaluate the feasibility of developing NIR models for predicting the white clover and ryegrass content in fresh or dry mixtures artificially prepared from pure samples of both species. Mixtures from pure stands of white clover(Trifolium repens) and perennial ryegrass (Lolium perenne) were prepared with different proportions (0 to 100%) of each species (fresh weight). A total of 55 samples were made (11 mixtures,5 cuts). Spectra (400 to 2500 nm) were taken from fresh chopped (rectangular cuvettes, transport sample module) samples, in a NIR Systems 6500 scanning monochromator controlled by the software NIRS 3 (Infrasoft International), which was also utilized for calibration development. Different math treatments (derivative order, subtraction gap and smooth segment) and a scatter correction treatment of the spectra (SNV and Detrend) were tested. Equations were developed by modified partial least squares. Prediction accuracy evaluated by cross-validation, showed that percentage of clover or ryegrass, as contribution in dry weight, can be successfully percentage of clover or ryegrass, as contribution in dry weight, can be successfully predicted either on fresh or dried samples, with equations developed by different math treatments. Best equations for fresh samples were developed including a first, second, or third derivative, whereas for dry samples best equations included a second or third derivative. Standard errors of ross validation were about 6% for fresh and 3.6% for dry samples, Coefficient of determination of cross validation (1-VR) were over 0.95 times the value of SECV for fresh samples and over 8 times the value of SECV for dry samples. Scatter correction (SNV and Detrend) in general improved prediction accuracy. It is concluded more precise on dried and ground samples, it can be used with an acceptable error level and less time and labour, on fresh samples.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1244-1244
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.