The objective of this study was to develop a simultaneous method for 8 amino acids including alanine, arginine, glutathione, lysine, ornithine, methionine, threonine and tryptophan in veterinary products using LC-MS/MS. To optimize MS analytical condition of 8 amino acids, each parameter was established by multiple reaction monitoring in positive mode. The chromatogram separation was achieved on a C18 column with mobile phase of 0.1% formic acid in D.W. and 0.1% formic acid in acetonitrile for green technology at a flow rate of 0.4mL/min for 5 min with gradient elution. The developed method was validated for mass accuracy, precision, linearity in veterinary products. Calibration curves were linear over the calibration ranges (0.5 – 10 mg/L) for all the analytes r2>0.99. Average recoveries were 92.96 – 105.61% and relative standard deviations (RSD) were 0.27 – 3.5%. The limit of detection (LOD) and the limit of quantification (LOQ) were 0.04 – 0.83 mg/L and 0.12 – 2.52 mg/L, respectively. All values were corresponded with the criteria ranges requested by CD 2002/657/EC. The application of this method will be helpful in quality control analysis of amino acids in veterinary products.

This study investigated EFL college-level learners’ expectation on and their experience in an online English-medium instruction (EMI) course focusing on how participants interacted with their classmates and the instructor in their online class (zoom session) based on assumptions and rationales of Interaction Hypothesis and classroom interaction research. Analyses of questionnaire, observation, and interview data revealed that participants’ experience of interaction and their perception of interaction opportunities in the zoom session were significantly related to how they would evaluate the course-taking experience. It was also found that cognitive strategy such as participants’ preparation for each class rather than L2 confidence was more relevant to their level of satisfaction with the course. Results of analyses suggested that an online class could be more effective than a face-to-face class in terms of engaging EFL adult learners in an academic course offered in participants’ L2, English. Based on study results, suggestions on how to increase interaction opportunities in online EMI course are made.

농촌진흥청 국립원예특작과학원에서는 2018년 오리엔탈-트럼펫(OT) 종간잡종나리 ‘Pink Bella’를 개발하였다. 2008년 연노란색 OT 종간잡종나리 ‘Valparadiso’와 붉은색의 오리엔탈나리 ‘Scalini’를 각각 모본과 부본으로 화주 절단 수분법과 주 두교배법으로 각 3화를 인공교배하였고, 교배 후 미숙한 3개의 꼬투리를 수확하여 배가 형성된 배주를 기내에서 배양하여 잡종을 획득한 후 재배하였다. 육묘한 배양묘로부터 2011년 분홍색의 OT 종간잡종 나리 ‘OTO-11-43’ 계통을 개체 선발하였다. 2012년부터 2017년까지 선발된 계통은 자구와 인편번식, 조직배양을 이용하여 번식 및 양구한 후 1, 2차 생육특성 검정을 실시하였다. 2018년 3차 생육특성검정 및 소비자 기호도 평가를 수행한 결과 화색 및 화형에 대한 기호도가 높은 분홍색(RHS, RP62C)의 조기개화성 절화용 OT 종간잡종 나리 ‘Pink Bella’를 육성하였다. 3배체의 OT 종간잡종 나리로 초장은 131.7cm로 초장신장성이 우수하였다. ‘Pink Bella’의 화폭은 18.6cm이며 대조품종 ‘Table Dance’의 18.4cm와 유사한 크기였으며, 내화피의 폭, 길이 역시 대조품종과 통계적인 차이가 없었다. ‘Pink Bella’의 개화기는 6월 15일로 대조품종 ‘Table Dance’의 6월 28일에 비교하여 개화기가 13일 단축된 것으로 나타났으며 통계적으로 유의하였다.

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

Chrysanthemum boreal, C. indicum, and C. indicum var. albescens are well-known wild Chrysanthemum species used for traditional medicine in Korea. In this study, volatile compounds from three wild Chrysanthemums were identified according to four different flowering stages and analyzed using HS-SPME-GC-MS to determine the temporal variation of the volatiles. As a result, 132, 151, and 142 peaks were identified from C. boreale, C. indicum, and C. indicum var. albescens, respectively. Furthermore, 70 out of 132 peaks were identified in C. boreale with a matching ratio of >90% from library search. In addition, 85/151 and 76/142 peaks were identified from C. indicum and C. indicum var. albescens. Forty-nine volatile compounds were found commonly in all three wild Chrysanthemums through all four different flowering stages. However, six, seven, and five unique compounds were detected only in C. boreale, C. indicum, and C. indicum var. albescens, respectively. One hundred volatile compounds were selected for multivariate analysis considering volatile compounds overlapped with each other. The one-way ANOVA (p < 0.05) detected significant differences from 77 out of 100 volatile compounds. In addition, PLS-DA showed the different profiles of volatile compounds according to four different flowering stages in each wild Chrysanthemum. PC1 of each Chrysanthemum accounted for 45.8 56.9, and 11.9% in C. boreale, C. indicum, and C. indicum var. albescens, respectively. PC1 of C. boreale and C. indicum clearly separated the BF stage and the other three stages. Conversely, PC1 of C . indicum var. albescens showed a difference in the composition of volatile compounds between the BF/BO and HO/FO stages. In addition, the different profiles of volatile compounds could be visualized using a heatmap from three wild Chrysanthemums according to four different flowering stages. This study will help improve particular volatile compounds in three wild Chrysanthemums both in quality and quantity.

Volatile organic compounds (VOCs) in plants are various organic compounds with small molecular weight and high vapor pressure. The metabolomics approach was recently introduced to analyze VOCs involved in biological processes, such as abiotic and biotic stresses, spatial and temporal distribution, and genotypic differences. In addition, this approach is widely used in combination with identification of VOCs analysis and statistical analysis using multivariate analysis, such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), hierarchical cluster analysis (HCA), etc. First, in this review, the current condition of the metabolomics approach to analyze VOCs synthesized in plants using head space-solid phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) is discussed. In addition, metabolomics approach, such as extraction and analysis of VOCs using HS-SPME-GC-MS, conversion, and processing of mass spectral (MS) data, a database for VOCs identification, useful statistical methods, and statistical tools and applications, are explained. Finally, multi-omics in combination with other omics techniques, such as genomics, transcriptomics, etc. are suggested as prospects of a metabolomics approach for VOC analysis in floricultural plants using HS-SPMEGC- MS. Therefore, the metabolomics approach of HS-SPMEGC- MS will facilitate our understanding of VOCs synthesized in plants. Furthermore, the multi-omics approach will help understand gene functions involved in the biosynthesis of VOCs and help develop new development cultivars with nicer floral scents by contributing to the development of the floricultural industry.

Recently, the demand for more precise and demand-oriented customized spatial information is increasing due to the 4th industrial revolution. In particular, the use of 3D spatial information and digital twins which based on spatial information, and research for solving social problems in cities by using such information are continuously conducted. Globally, non-face-to-face services are increasing due to COVID-19, and the national policy direction is also rapidly progressing digital transformation, digitization and virtualization of the Korean version of the New Deal, which means that 3D spatial information has become an important factor to support it. In this study, physical objects for cities defined by world organizations such as ISO, OGC, and ITU were selected and the target of the 3D object model was limited to buildings. Based on CityGML2.0, the data collected using a drone suitable for building a 3D model of a small area is selected to be updated through road name address and building ledger, which are administrative information related to this, and LoD2.5 data is constructed and urban space. It was intended to suggest an object update method for a 3D building among data.

Using the frequency-based decomposition, I decompose the consumption growth to explain well-known patterns of stock returns in the Korean market. To be more specific, the consumption growth is decomposed by its half-life of shocks. The component over four years of half-life is called the business-cycle consumption component, and the components with half-lives under four years are short-run components. I compute the long-run and short-run components of stock excess returns as well and use component- by-component sensitivities to price stock portfolios. As a result, the business-cycle consumption risk with half-life of over four years is useful in explaining the cross-section of size-book-to-market portfolios and size-momentum portfolios in the Korean stock market. The short-run components have their own pricing abilities with mixed direction, so that the restricted one short-term factor model is rejected. The explanatory power with short- and long-run components is comparable to that of the Fama-French three-factor model. The components with one- to four-year half-lives are also helpful in explaining the returns. The results about the long-run components emphasize the importance of long-run component in consumption growth to explain the asset returns.

This study explores multiple variables of an OTT service for discovering hidden relationship between rating and the other variables of each successful and failed content, respectively. In order to extract key variables that are strongly correlated to the rating across the contents, this work analyzes 170 Netflix original dramas and 419 movies. These contents are classified as success and failure by using the rating site IMDb, respectively. The correlation between the contents, which are classified via rating, and variables such as violence, lewdness and running time are analyzed to determine whether a certain variable appears or not in each successful and failure content. This study employs a regression analysis to discover correlations across the variables as a main analysis method. Since the correlation between independent variables should be low, check multicollinearity and select the variable. Cook's distance is used to detect and remove outliers. To improve the accuracy of the model, a variable selection based on AIC(Akaike Information Criterion) is performed. Finally, the basic assumptions of regression analysis are identified by residual diagnosis and Dubin Watson test. According to the whole analysis process, it is concluded that the more director awards exist and the less immatatable tend to be successful in movies. On the contrary, lower fear tend to be failure in movies. In case of dramas, there are close correlations between failure dramas and lower violence, higher fear, higher drugs.