• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1571-1576
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• 2022

This study aims to analyze the interest of modern people in non-face-to-face medical counseling in the medical industrys. Big data was collected on two social platforms, 지식인, a platform that allows experts to receive medical counseling, and YouTube. In addition to the top five keywords of telephone counseling, "internal medicine", "general medicine", "department of neurology", "department of mental health", and "pediatrics", a data set was built from each platform with a total of eight search terms: "specialist", "medical counseling", and "health information". Afterwards, pre-processing processes such as morpheme classification, disease extraction, and normalization were performed based on the crawled data. Data was visualized with word clouds, broken line graphs, quarterly graphs, and bar graphs by disease frequency based on word frequency. An emotional classification model was constructed only for YouTube data, and the performance of GRU and BERT-based models was compared.

• Journal of Microbiology and Biotechnology
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• v.34 no.9
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• pp.1836-1847
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• 2024

Polyethylene terephthalate (PET), one of the most widely used plastics in the world, causes serious environmental problems. Recently, scientists have been focused on the enzymatic degradation of PET, an environmentally friendly method that offers an attractive approach to the degradation and recycling of PET. In this work, PET hydrolase from Streptomyces sp. W2061 was biochemically characterized, and the biodegradation of PET was performed using the PET model substrate bis (2-hydroxyethyl terephthalate) (BHET). PET hydrolase has an isoelectric point of 5.84, and a molecular mass of about 50.31 kDa. The optimum pH and temperature were 7.0 and 40℃, respectively. LC-MS analysis of the enzymatic products showed that the PET hydrolase successfully degraded a single ester bond of BHET, leading to the formation of MHET. Furthermore, in silico characterization of the PET hydrolase protein sequence and its predicted three-dimensional structure was designed and compared with the well-characterized IsPETase from Ideonella sakaiensis. The structural analysis showed that the (Gly-x1-Ser-x2-Gly) serine hydrolase motif and the catalytic triad (Ser, Asp, and His) were conserved in all sequences. In addition, we integrated molecular dynamics (MD) simulations to analyze the variation in the structural stability of the PET hydrolase in the absence and presence of BHET. These simulations showed the formation of a stable complex between the PET hydrolase and BHET. To the best of our knowledge, this is the first study on Streptomyces sp. W2061 to investigate the BHET degradation activity of PET hydrolase, which has potential application in the biodegradation of plastics in the environment.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.8 no.1
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• pp.14-22
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• 1972

For the development of acoustic fishing method, the noises of fishes have been recorded and analy/'ed by many scientists. Some specimens of fishes were selected as such Cyprinus carpio, Ctenopharyngodon idellus Carassius carassius, and pagrosol1ms major in this experiment. The noises such as feeding noise, driving away noise, jumping noise and fi llip noise were recorded by the tape recorder, Sony Model 262, through the underwa te r microph I one, Oki ST 6582, and analyzed in frequencies bv octave band analyzer, Rion SA-55, and sound pressure level of source by sound level meter, Rion NA-opNN The supplied feed was placed within 5em apart from the hydrophone. The result of analyzed noises were as follow. Cyprinus carjJio; Feeding noise 250- 500 cps, 92- 99 dB Driving away noise 125-2, 000 eps, 101-112 dB Jumping noise 125-2, 000 eps, 99-116.5 dB Ctenopharyngodon idcllus; Driving away noise 125-1, 000 cps, 96-109 dB Carassius carassius; Feeding noise 250- 500 cps, 91. 5- 99.5 dB Driving away noise 125-1, 000 eps, 99-108 dB Carassius auratus Feeding noise 250 eps, 94-101 dB Driving away noise 125-1, 000 cps, 98-110 dB Pagrosomus major Feeding noise 230-500 cps, 90-101 dB Fillip noise 500 cps, 98-108 dB (1) Feeding noise was produced as like as snap noise of twig and gulping down saliva noise in human and dominant frequency range of the noise is 250-500 cps and noise level 90-101 dB. (2) It was found that feeding noise were not a monotonic but a complex tones though fish took the same food. (3) Driving away noise was produced not so keen and the wave form of the noise is rising very sharp and big amplitude in the oscillograph. Dominant frequency range of this noise was about 150-1, 000 cps and noise level 96-112 dB except thut of carp. (4) The frequency of snapper's fillip noise, when it produced by caudal fin in swimming at the surface of water, was 500 cps and noise level 93-108 dB snd that of jumping noise of carp about 150-2, 000 cps and noise level 99-116.5 dB.

• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.3 s.116
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• pp.120-138
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• 2006

A city evolves over time. It grows, transforms, and sometimes degrades. Chuncheon is at a turning point from a city souggling with regulations regarding clean water supply and a military encampment to a masterpiece city with a sustainable vision. The city is getting ready to restructure itself to become a world-famous culture and tourism complex expanding its physical boundary across the Camp Page site and absorbing Jungdo as a major tourist attraction. The landscape in the future blueprint of Chuncheon will play a great role in restructuring urban form. The regenerated in will have a new networked open space system as well as re-evaluated landscape resources. The hybrid theoretical practice called 'landscape urbanism' burgeoning in the fields between 'landscape architecture' and 'urbanism' can guide us in considering the terms of the relationship between a city and landscape when we design a future city Landscape urbanism is considered to be an effective framework by which we can diagnose the current status of a landscape in our contemporary urban design practice in Korea. This paper tries to provide a different perspective from the viewpoint of landscape urbanism to decipher the hidden implications of the social agreement on the role of landscape in urban structure by re-reading eight design proposals presented for the ChunCheon G5 international design competition based on the main principles of landscape urbanism. The G5 design competition is a great opportunity to test out new ideas on a city, demonstrating the relative values among various urban-design professional realms. First, this paper provides an overview of the main ideas of landscape urbanism based on the literature review and case studies. Second, framework categories are suggested in order to extract the explicit and implicit ideas on the landscape. Third, eight proposals are reviewed according to the suggested categories to situate the current landscape design of Korea within the mainstream of contemporary practice of landscape urbanism. Based on the review of eight proposals, the following diagnostic conclusions are made; first, the ideas of landscape urbanism have not been actively introduced in large-scaled urban landscape projects in Korea like Chuncheon G5. Second, it remains to be a big task for landscape professions to be able to participate in design consortiums on an equal footing. Third, In order to introduce and reify the ideas of landscape urbanism in Korea, it is inevitable and critical to test the ideas in both academic fields and professional practices to find the appropriately adjusted model of landscape urbanism.

• Journal of Global Scholars of Marketing Science
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• v.17 no.4
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• pp.1-22
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• 2007

Customer satisfaction is important in an increasingly competitive and global marketplace. This implies that customer service is a critical factor for many organizations. In service encounter context, customer satisfaction is affected by employees' attitudes and behaviors. Accordingly, service firms have been focusing on selecting high quality of service employees, which resulted the ability to identify and select quality service- or customer- oriented employees to become critical for an organization's success. It was suggested that customer service orientation links to performance and subsequent organizational revenue. Moreover, it was found that service encounter failures were among the major reasons for customers' service switch. Therefore, the selection of customer service oriented employees is a key factor in establishing customer service - a potential source of sustained competitive advantage. However, the measurement of employee service orientation is more confusing than that of definitive answers. The difficulty of measuring service orientation is attributed to the use of broad versus narrow measures of personality. Advocates for the broad perspective prefer using basic personality constructs, such as the Big Five personality traits. On the contrary, the latter prefer a construct-oriented approach of personality research that provides a better measure of job performance because it requires the specification of the relationship of the personality traits with multiple dimensions of job performance. The customer service orientation was defined as "a set of basic individual predispositions and an inclination to provide service, to be courteous and to be helpful in dealing with customers and associates." Similarly, it is a fact that the Big five personality traits are predictors of customer orientation, and employee's self- and supervisor performance. They propose that basic personality traits may be too far removed from focal service behaviors to be able to predict specific service behaviors (customer orientation) and service worker performance. Also, customer orientation is defined as "an employee's tendency or predisposition to meet customer needs in an on-the-job context." This means that people who have job-relevant personality traits such as concern, empathy, and conscientiousness will be more adept at customer service than people who do not possess these traits. However, little attention has been given to the exploration of the service orientation of customer-contact employees who play a key role in creating satisfactory service encounters in the hospitality industry except for Kim, McCahon, & Miller (2003)'s study, especially in family restaurants context. Thus, the purposes of this study are to examine and validate the customer service orientation of customer-contact employees using the instrument developed by Donavan (1999) in Korean family restaurants, because the scale was developed to measure the personality traits related job behaviors. And this study explores the relationships between customer service orientation, job satisfaction, organizational commitment, and self service performance using structural equation modeling (SEM). And this study explores the relationships between customer service orientation, job satisfaction, organizational commitment, and self service performance using structural equation modeling (SEM). For these purposes the author developed several hypotheses as follows: H1: Employee's service orientation is associated with service performance. H2: Employee's service orientation is positively associated with job satisfaction. H3: Employee's service orientation is positively associated with organizational commitment. H4: Service performance is positively associated with job satisfaction. H5: Service performance is positively associated with organizational commitment. H6: Job satisfaction is negatively associated with organizational commitment. The data were collected from 278 employees in 5 deluxe hotels located in Pusan, Korea. The researcher contacted the manager of the restaurants, and managers consented to administer surveys to their employees. The survey was executed during one month period in the October of 2007. The data were analyzed with structural equation modeling with LISREL 8.7 W. The result of the overall model analysis appeared as follows: $X^2$=122.638 (p = 0.00), df=59, GFI=.936, AGFI=.901, NFI=.948, CFI=.971, RMSEA=.0625. Since the result of the overall model analysis demonstrated a good fit, we could further analyze our data. The findings can be summarized as follows: First, the greater the employee service orientation, the greater the service performance. Second, the greater the employee service orientation, the greater the job satisfaction. Third, the greater the employee service orientation, the greater the organizational commitment. Fourth, the greater the service performance, the greater the job satisfaction. Fifth, the greater the service performance, the greater the organizational commitment. Finally, the greater the job satisfaction, the greater the organizational commitment. Seventh, the greater the customer satisfaction, the greater the customer loyalty.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.396-407
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• 2020

As fossil fuels are depleted worldwide, alternative resources is required to replace fossil fuels, and biofuels are in the spotlight as alternative resources. Biofuels are produced from biomass, which is a renewable resource to produce biofuels or bio-chemicals. Especially, in order to substitute fossil fuels, the research focusing the biofuel (biodiesel) production based on CO₂ and biomass achieves more attention recently. To produce biomass-based biodiesel, the development of a supply chain network is required considering the amounts of feedstocks (ex, CO₂ and water) required producing biodiesel, potential locations and capacities of bio-refineries, and transportations of biodiesel produced at biorefineries to demand cities. Although many studies of the biomass-based biodiesel supply chain network are performed, there are few types of research handled the uncertainty in CO₂ supply which influences the optimal strategies of microalgae-based biodiesel production. Because CO₂, which is used in the production of microalgae-based biodiesel as one of important resources, is captured from the off-gases emitted in power plants, the uncertainty in CO₂ supply from power plants has big impacts on the optimal configuration of the biodiesel supply chain network. Therefore, in this study, to handle those issues, we develop the two-stage stochastic model to determine the optimal strategies of the biodiesel supply chain network considering the uncertainty in CO₂ supply. The goal of the proposed model is to minimize the expected total cost of the biodiesel supply chain network considering the uncertain CO₂ supply as well as satisfy diesel demands at each city. This model conducted a case study satisfying 10% diesel demand in the Republic of Korea. The overall cost of the stochastic model (US$ 12.9/gallon·y) is slightly higher (23%) than that of the deterministic model (US$ 10.5/gallon·y). Fluctuations in CO₂ supply (stochastic model) had a significant impact on the optimal strategies of the biodiesel supply network.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.266-274
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• 2021

Ship officers are subject to excessive job stress, which has negative physical and psychological impacts and may adversely affect the smooth supply and demand of human resources. In this study, a mobile web application was developed as a tool for systematic job stress measurement and management of officers and verified through quality evaluation. Requirement analysis was performed by ship officers and staff in charge of human resources of shipping companies, and the results were reflected in the application configuration step. The application was designed according to the waterfall model, which is a traditional software development method, and functions were implemented using JSP and Spring Framework. Performance evaluation on the user interface, confirmed that proper input and output results were implemented, and the respondent results and the database were configured in the administrator interface. The results of evaluation questionnaires for quality evaluation of the interface based on ISO/IEC 9126-2 metric were significant 4.60 for the user interface and 4.65 for the administrator interface in a 5-point scale. In the future, it is necessary to conduct follow-up research on the development of data analysis system through utilization of the collected big-data sets.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.3
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• pp.281-290
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• 2014

This study was conducted to evaluate the distribution characteristics of $PM_{10}$ and heavy metals concentrations in the ambient air of Gyeonggi-do area by region and season from February, 2013 to March, 2014. The regression model for the prediction of formation characteristics and contamination degree of $PM_{10}$ and heavy metals by correlation analysis and regression analysis for using the multivariate statistical analysis was also established. The main wind direction during the investigation period was South East (SE) and West South West (WSW) winds, and the concentration of $SO_2$ at Ansan with industrial region showed 1.6 times higher than Suwon, Euiwang with residential region. The concentrations (median) of Pb, Cu and Ni at Ansan showed 3.2~4.5, 1.9~2.2 and 1.7~2.6 times respectively higher than those at Suwon. By the seasonal concentration variation, the concentrations of $PM_{10}$, Pb, Fe and As in winter and spring (December to May) showed 1.7, 1.9, 1.9 and 2.7 times respectively higher than those in summer and fall (June to November). As, Fe and $PM_{10}$ had a big difference by the seasonal factors, and Cu and Ni were evaluated to be influenced by the regional factors. From the results of correlation analysis among the target items, the correlation coefficient of PM and Mn had 0.82 (p/0.01) and that of Fe and Mn had 0.82 (p/0.01), which showed high correlation. And the correlation coefficients for $SO_2$ and Pb, CO and $PM_{10}$ were 0.66 (p/0.01) and 0.62 (p/0.01) respectively. The multiple linear regression models for $PM_{10}$, Pb, Cu, Cr, As, Ni, Fe and Mn were established by independent variables of CO, $SO_2$ and meteorological factors (wind speed, relative humidity). In the regression models, independent variable $SO_2$ was in cause-and-effect relationship with all dependent variables, and $PM_{10}$, Fe and Mn were influenced by CO and wind speed, and Pb, Cu, Ni and As had a main factor of $SO_2$.