• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.4
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• pp.213-221
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• 2013

This paper introduces a mesh continuation scheme for a one-dimensional inverse medium problem to reconstruct the spatial distribution of elastic wave velocities in heterogeneous semi-infinite solid domains. To formulate the inverse problem, perfectly-matched-layers(PMLs) are introduced as wave-absorbing boundaries that surround the finite computational domain truncated from the originally semi-infinite extent. To tackle the inverse problem in the PML-truncated domain, a partial-differential-equations(PDE)-constrained optimization approach is utilized, where a least-squares misfit between calculated and measured surface responses is minimized under the constraint of PML-endowed wave equations. The optimization problem iteratively solves for the unknown wave velocities with their updates calculated by Fletcher-Reeves conjugate gradient algorithms. The optimization is performed using a mesh continuation scheme through which the wave velocity profile is reconstructed in successively denser mesh conditions. Numerical results showed the robust performance of the mesh continuation scheme in reconstructing target wave velocity profile in a layered heterogeneous solid domain.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.42-55
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• 2017

Since the phases and patterns of the climate adaptability of vegetation can greatly differ from region to region, an intensive pixel scale approach is required. In this study, Partial Least Squares (PLS) regression on satellite image-based vegetation index is conducted for to assess the effect of climate factors on vegetation productivity and to predict future productivity of forests vegetation in South Korea. The results indicate that the mean temperature of wettest quarter (Bio8), mean temperature of driest quarter (Bio9), and precipitation of driest month (Bio14) showed higher influence on vegetation productivity. The predicted 2050 EVI in future climate change scenario have declined on average, especially in high elevation zone. The results of this study can be used in productivity monitoring of climate-sensitive vegetation and estimation of changes in forest carbon storage under climate change.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.178-181
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• 2015

The purpose of this study was aimed to analyze factors affecting on continuous intention to use of mobile based on the caused motivation of cyber language. Predictor factors were selected economic motivation, represent motivation, entertainment motivation, relationship strength motivation and psychological free motivation suggested on the previous study. Participants of this study were 76 mobile users in Gyeongnam and Jeonbuk province in accordance with convenience sampling. IBM SPSS Statistics 19 were employed for descriptive statistics, Smart PLS(partial least squares) was employed for confirmatory factor analysis and path analysis of casual relationship among variables and effect. Analytical results show that paths from economic motivation to the percieved value and relationship strength motivation are significant. And analytical results show that path from economic motivation to the percieved risk are significant. This study suggests practical and theoretical implications based on the results.

• Biomolecules & Therapeutics
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• v.21 no.6
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• pp.470-475
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• 2013

Ultraviolet (UV) radiation is a major environmental factor that leads to acute and chronic reactions in the human skin. UV exposure induces wrinkle formation, DNA damage, and generation of reactive oxygen species (ROS). Most mechanistic studies of skin physiology and pharmacology related with UV-irradiated skin have focused on proteins and their related gene expression or single-targeted small molecules. The present study identified and analyzed the alteration of skin metabolites following UVB irradiation and topical retinyl palmitate (RP, 5%) treatment in hairless mice using direct analysis in real time (DART) time-of-flight mass spectrometry (TOF-MS) with multivariate analysis. Under the negative ion mode, the DART ion source successfully ionized various fatty acids including palmitoleic and linolenic acid. From DART-TOF-MS fingerprints measured in positive mode, the prominent dehydrated ion peak (m/z: 369, M+H-$H_2O$) of cholesterol was characterized in all three groups. In positive mode, the discrimination among three groups was much clearer than that in negative mode by using multivariate analysis of orthogonal partial-least squares-discriminant analysis (OPLS-DA). DART-TOF-MS can ionize various small organic molecules in living tissues and is an efficient alternative analytical tool for acquiring full chemical fingerprints from living tissues without requiring sample preparation. DART-MS measurement of skin tissue with multivariate analysis proved to be a powerful method to discriminate between experimental groups and to find biomarkers for various experiment models in skin dermatological research.

• Mass Spectrometry Letters
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• v.4 no.4
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• pp.59-66
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• 2013

A metabolomics study was conducted to identify urinary biomarkers for breast cancer, using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), analyzed by principal components analysis (PCA) as well as a partial least squares-discriminant analysis (PLS-DA) for a metabolic pattern analysis. To find potential biomarkers, urine samples were collected from before- and after-mastectomy of breast cancer patients and healthy controls. Androgens, corticoids, estrogens, nucleosides, and polyols were quantitatively measured and urinary metabolic profiles were constructed through PCA and PLS-DA. The possible biomarkers were discriminated from quantified targeted metabolites with a metabolic pattern analysis and subsequent screening. We identified two biomarkers for breast cancer in urine, ${\beta}$-cortol and 5-methyl-2-deoxycytidine, which were categorized at significant levels in a student t-test (p-value < 0.05). The concentrations of these metabolites in breast cancer patients significantly increased relative to those of controls and patients after mastectomy. Biomarkers identified in this study were highly related to metabolites causing oxidative DNA damage in the endogenous metabolism. These biomarkers are not only useful for diagnostics and patient stratification but can be mapped on a biochemical chart to identify the corresponding enzyme for target identification via metabolomics.

• Journal of Agricultural Extension & Community Development
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• v.15 no.4
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• pp.659-681
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• 2008

The purpose of this study is to empirically analyze factors that influence farm managers' intentions to use an agricultural farm management information systems. It focused on "LFcenter System," a leading information system operated by the Rural Development Administration for farm management. Participants of this study are classified into two groups: a group of leading farm managers and a group fo regular farm managers. A total of 192 survey samples on users' intentions are collected; 85 samples from leading farm managers and 107 from regular farm managers. The theoretical background of this study is developed based on Theory of Reasoned Action(TRA), Technology Acceptance Model(TAM), Diffusion of Innovation(DOI), Social Cognitive Theory(SCT), and Theory of Planned Behavior(TPB). Partial Least Squares(PLS) method is used to test a proposed Structural Equation Model(SEM), including nine hypotheses. The differences between two groups are investigated using Smith-Satterthwait test. The findings from this study are: First of all, in terms of average comparison of most variables used in this study, a group of leading farm managers shows higher value that the other group in most cases. Second, hypothesis tests how that "subjective norms", "goal to study", "perceived usefulness", "perceived enjoyment", and "intention to use" significantly influence the intention to use an agricultural management information system in the group of leading farm managers. However, "subjective norms", "goal to study", "perceived ease of use", "perceived usefulness", "perceived enjoyment", and "intention to use" turned out to significantly influence the intention to use an agricultural management information system in the group of regular farms managers. Based on the results of Smith-Satterthwait test, compared with a group of leading farms managers, the impact of "goal to study" on "intention to use" is significantly stronger. On the other hand, in the group of leading farms managers, "perceived usefulness" and "perceived enjoyment" turned out to be main drivers of "intention to use."

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.5
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• pp.643-648
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• 2005

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal grains and forages. If samples could be analyzed without drying and grinding, then sample preparation time and costs may be reduced. This study was conducted to develop robust NIRS equations to predict fermentation quality of corn (Zea mays) silage and to select acceptable sample preparation methods for prediction of fermentation products in corn silage by NIRS. Prior to analysis, samples (n = 112) were either oven-dried and ground (OD), frozen in liquid nitrogen and ground (LN) and intact fresh (IF). Samples were scanned from 400 to 2,500 nm with an NIRS 6,500 monochromator. The samples were divided into calibration and validation sets. The spectral data were regressed on a range of dry matter (DM), pH and short chain organic acids using modified multivariate partial least squares (MPLS) analysis that used first and second order derivatives. All chemical analyses were conducted with fresh samples. From these treatments, calibration equations were developed successfully for concentrations of all constituents except butyric acid. Prediction accuracy, represented by standard error of prediction (SEP) and $R^2_{v}$ (variance accounted for in validation set), was slightly better with the LN treatment ($R^2$ 0.75-0.90) than for OD ($R^2$ 0.43-0.81) or IF ($R^2$ 0.62-0.79) treatments. Fermentation characteristics could be successfully predicted by NIRS analysis either with dry or fresh silage. Although statistical results for the OD and IF treatments were the lower than those of LN treatment, intact fresh (IF) treatment may be acceptable when processing is costly or when possible component alterations are expected.

• Information Systems Review
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• v.16 no.3
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• pp.89-112
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• 2014

This tutorial presents an approach to perform the PLS structural equation modeling using the R. For this purpose, the practical guide defines the criteria for the PLS structural equation modeling by reviewing previous studies, and shows how to analyze the research model with an example using the "plspm" which is the R package for the performing PLS path analysis against the criteria. This practical guide will be useful for the study of the PLS model analysis for new researchers and will provide the knowledge base for in-depth analysis through the new PLS structural equation modeling technique using R which is the integrated statistical software operating environment for the researchers familiar with the PLS structural equation modeling.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.12
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• pp.1948-1956
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• 2020

Objective: The purpose of this study was to reveal the metabolic shift in the fungus cocultured with the methanogen (Methanobrevibacter thaueri). Methods: Gas chromatography-mass spectrometry was used to investigate the metabolites in anaerobic fungal (Pecoramyces sp. F1) cells and the supernatant. Results: A total of 104 and 102 metabolites were detected in the fungal cells and the supernatant, respectively. The partial least squares-discriminant analysis showed that the metabolite profiles in both the fungal cell and the supernatant were distinctly shifted when co-cultured with methanogen. Statistically, 16 and 30 metabolites were significantly (p<0.05) affected in the fungal cell and the supernatant, respectively by the co-cultured methanogen. Metabolic pathway analysis showed that co-culturing with methanogen reduced the production of lactate from pyruvate in the cytosol and increased metabolism in the hydrogenosomes of the anaerobic fungus. Citrate was accumulated in the cytosol of the fungus co-cultured with the methanogen. Conclusion: The co-culture of the anaerobic fungus and the methanogen is a good model for studying the microbial interaction between H₂-producing and H₂-utilizing microorganisms. However, metabolism in hydrogenosome needs to be further studied to gain better insight in the hydrogen transfer among microorganisms.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.1
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• pp.79-90
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• 2020

Objective: In the present study, an liquid chromatography/mass spectrometry (LC/MS) metabolomics approach was performed to investigate potential biomarkers of milk production in high- and low-milk-yield dairy cows and to establish correlations among rumen fluid metabolites. Methods: Sixteen lactating dairy cows with similar parity and days in milk were divided into high-yield (HY) and low-yield (LY) groups based on milk yield. On day 21, rumen fluid metabolites were quantified applying LC/MS. Results: The principal component analysis and orthogonal correction partial least squares discriminant analysis showed significantly separated clusters of the ruminal metabolite profiles of HY and LY groups. Compared with HY group, a total of 24 ruminal metabolites were significantly greater in LY group, such as 3-hydroxyanthranilic acid, carboxylic acids, carboxylic acid derivatives (L-isoleucine, L-valine, L-tyrosine, etc.), diazines (uracil, thymine, cytosine), and palmitic acid, while the concentrations of 30 metabolites were dramatically decreased in LY group compared to HY group, included gentisic acid, caprylic acid, and myristic acid. The metabolite enrichment analysis indicated that protein digestion and absorption, ABC transporters and unsaturated fatty acid biosynthesis were significantly different between the two groups. Correlation analysis between the ruminal microbiome and metabolites revealed that certain typical metabolites were exceedingly associated with definite ruminal bacteria; Firmicutes, Actinobacteria, and Synergistetes phyla were highly correlated with most metabolites. Conclusion: These findings revealed that the ruminal metabolite profiles were significantly different between HY and LY groups, and these results may provide novel insights to evaluate biomarkers for a better feed digestion and may reveal the potential mechanism underlying the difference in milk yield in dairy cows.