• Journal of Veterinary Clinics
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• v.33 no.2
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• pp.107-112
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• 2016

The objective of this study was to determine potential contributing factors associated with biosecurity level of farrow-to-finish pig farms and to develop a classification tree model to explore how these factors related to each other based on prediction model. To this end, the author analyzed data (n = 193) extracted from a cross-sectional study of 344 farrow-to-finish farms which was conducted between March and September 2014 aimed to explore swine disease status at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. For the classification of the data sets regarding biosecurity level as a dependent variable and predictor variables, Chi-squared Automatic Interaction Detection (CHAID) algorithm was applied for modeling classification tree. The statistics of misclassification risk was used to evaluate the fitness of the model in terms of prediction results. Categorical multivariate input data (40 variables) was used to construct a classification tree, and the target variable was biosecurity level dichotomized into low versus high. In general, the level of biosecurity was lower in the majority of farms studied, mainly due to the limited implementation of on-farm basic biosecurity measures aimed at controlling the potential introduction and transmission of swine diseases. The CHAID model illustrated the relative importance of significant predictors in explaining the level of biosecurity; maintenance of medical records of treatment and vaccination, use of dedicated clothing to enter the farm, installing fence surrounding the farm perimeter, and periodic monitoring of the herd using written biosecurity plan in place. The misclassification risk estimate of the prediction model was 0.145 with the standard error of 0.025, indicating that 85.5% of the cases could be classified correctly by using the decision rule based on the current tree. Although CHAID approach could provide detailed information and insight about interactions among factors associated with biosecurity level, further evaluation of potential bias intervened in the course of data collection should be included in future studies. In addition, there is still need to validate findings through the external dataset with larger sample size to improve the external validity of the current model.

• Korean Journal of Social Welfare Studies
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• v.49 no.2
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• pp.5-38
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• 2018

The purpose of this study is to examine work-life balance by analyzing time schedules of married couple. The 2014 Korea Time Use Survey dataset was used for the analysis. Finally, 6,262 time diaries of 3,131 households were included in the analysis. The study used sequence analysis in particular, by applying the Lesnard(2014)'s dynamic hamming matching (DHM) method, which is useful for the time diary analysis where timing is a key factor. This study explored daily schedules of each man and woman according to 9 types of couple-combined work-schedules, which had been already derived by cluster analysis. The daily schedules were identified according to the activities divided as labor, housework, sleep, self-management, active leisure, passive leisure, and others. Here, time allocation was analyzed through various graphs showing average time amount and modal states by time period. Based on the analysis, it summarized that "long working hours as a main factor of work-life imbalance", "gender inequality of time use", "non-standard hours work impairing quality of life and "poverty of leisure time"as characteristics of work-life imbalance. Finally this study discussed the social policy implications to support work-life balance.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.1-7
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• 2019

This study analyzed the effects of labor aging on labor productivity using panel statistics of 16 local governments from 1995 to 2017. The aging of the labor force, defined as the proportion of workers aged 60 or older in total employment, in the results of the panel regression analysis considering regional fixed effects and various adjustment variables, has a very consistent and significant negative effect on labor productivity. For every 1% increase in aging, labor productivity decreases by about 0.14 ~ 0.20%. In addition, the per capita capital stock and human capital considered as adjustment variables contributed to the increase of labor productivity, and the unemployment rate, which is a proxy variable of the economic fluctuation, has a significant negative effect on labor productivity as expected. The coefficient of the industrial structure, which represents the share of the service industry in the whole industry, was positive, but is not significant. The results of this study suggest that the design and construction of economic and educational policies that can maintain and expand human capital are necessary to curb the reduction in labor productivity expected by the aging workforce.

• Korean Journal of Environmental Biology
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• v.36 no.3
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• pp.352-358
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• 2018

This study aimed to develop a species identification method for the egg and fry of the three Korean bitterling fishes (Pisces: Acheilognathinae), including Acheilognathus signifer, Acheilognathus yamatsutae and Rhodeus uyekii based on the PCR-based Restriction Fragment Length Polymorphism (RFLP) markers. We conducted a field survey on the Deokchicheon River from the North Han River basin, where the three Acheilognathinae species co-occur, and also analyzed the existing sequence dataset available from the GenBank. We found coexistence of the three species at the study site. The egg and fry were obtained from the host mussels (Unio douglasiae sinuolatus) by hand from May to June 2015 and in May 2017. To develop PCR-based RFLP markers for species identification of the three Acheilognathinae fish species, restriction enzymes pinpointing species-specific single nucleotide variation (SNV) sites in mitochondrial DNA COI (cytochrome oxidase I) and cyt b (cytochrome b) genes were determined. Genomic DNA was extracted from the egg and fry and RFLP experiments were carried out using restriction enzymes Apal I, Stu I and EcoR V for A. signifer, A. yamatsutae and R. uyekii, respectively. Consequently, unambiguous discrimination of the three species was possible, as could be seen in DNA band patterns from gel electrophoresis. Our developed PCR-based RFLP markers will be useful for the determination of the three species for the young and would assist in studying the spawning patterns and reproductive ecology of Acheilognathinae fishes. Furthermore, we believe the obtained information will be of importance for future maintenance, management and conservation of these natural and endangered species.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.43-65
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• 2021

With the advancement of information and communications technology, a means to recruit IT professional has fundamentally changed. Nowadays recruiters search for candidate information from the Web as well as traditional information sources such as résumés or interviews. Particularly, open-source software development (OSSD) platforms have become an opportunity for developers to demonstrate their IT capabilities, making it a way for recruiters to find the right candidates, whom they need. Therefore, this study aims to investigate the impact developers' profiles in an OSSD platform on their finding a job. This study examined four antecedents of developer information that can accelerate their job search: job-seeking status, personal-information posting, learning activities and knowledge contribution activities. For the empirical analysis, we developed a Web crawler and gathered a dataset on 4,005 developers from GitHub, which is a well-known OSSD platform. Proportional hazards regression was used for data analysis because shorter job-seeking period implies more successful result of job change. Our results indicate that developers, who explicitly posted their job-seeking status, had shorter job-seeking periods than those who did not. The other antecedents (i.e., personal-information posting, learning, and knowledge contribution activities) also contributed in reducing the job-seeking period. These findings imply values of OSSD platforms for recruiters to find proper candidates and for developers to successfully find a job.

• Atmosphere
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• v.31 no.2
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• pp.241-249
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• 2021

A smart city utilizes data collected from various sensors through the internet of things (IoT) and improves city operations across the urban area. Recently substantial research is underway to examine all aspects of data that requires for the smart city operation. Atmospheric data are an essential component for successful smart city implementation, including Urban Air Mobility (UAM), infrastructure planning, safety and convenience, and traffic management. Unfortunately, the current level of conventional atmospheric data does not meet the needs of the new city concept. New and innovative approaches to developing high spatiotemporal resolution of observational and modeling data, resolving the complex urban structure, are expected to support the future needs. The geographic information system (GIS) integrates the atmospheric data with the urban structure and offers information system enhancement. In this study we proposed the necessity and applicability of the high resolution urban meteorological dataset based on heavy fog cases in the smart city region (e.g., Sejong and Pusan) in Korea.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.1
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• pp.49-61
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• 2021

The repeated shipboard measurements that have been conducted by the National Institute of Fisheries Science (NIFS) for more than a half century, provide the valuable long-term hydrographic data with high spatial-temporal resolution. However, this unprecedent dataset has been rarely used for oceanic climate sciences because of its reliability issue. In this study, temporal variability of salinity error in the NIFS data was quantified by means of extremely small variability of salinity in the deep layer of the south-western East Sea, in order to contribute to studies on long-term variability of the East Sea. The NIFS salinity errors estimated on the isothermal surfaces of 1℃ have a remarkable temporal variation, such as ~0.160 g/kg in the year of 1961~1980, ~0.060 g/kg in 1981~1994,~0.020 g/kg in 1995~2002, and ~0.010 g/kg in 2003~2014 on average, which basically represent bias error. In the recent years, even though the quality of salinity has been improved, there still remain relatively large bias errors in salinity data presumably due to failure of salinity sensor managements, especially in 2011, 2013, and 2014. On the contrary, the salinity in the year of 2012 was very accurate and stable, whose error was estimated as about 0.001 g/kg comparable to the salinity sensor accuracy. Thus, as long as developing proper data quality control procedures and sensor management systems, I expect that the NIFS shipboard hydrographic data could have good enough quality to support various studies on ocean response to climate variabilities. Additionally, a few points to improve the current NIFS shipboard measurements were suggested in the discussion section.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.112-125
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• 2021

Marine spatial planning(MSP) designates the marine as nine kinds of use zones for the systematic and rational management of marine spaces. One of them is the fishery protection zone, which is necessary for the sustainable production of fishery products, including the protection and fosterage of fishing activities. This study intends to quantitatively identify the fishing activity space, one of the elements necessary for the designation of fisheries protection zones, by mapping of fishery activities using V-Pass data and deriving the fishery activity concentrated zone. To this end, pre-processing of V-Pass data was performed, such as constructing a dataset that combines static and dynamic information, calculating the speed of fishing vessels, extracting fishing activity points, and removing data in non-fishing activity zone. Finally, using the selected V-Pass point data, a fishery activity map was made by kernel density estimation, and the concentrated space of fishery activity was analyzed. In addition, it was confirmed that there is a difference in the spatial distribution of fishing activities according to the type of fishing vessel and the season. The pre-processing technique of large volume V-Pass data and the mapping method of fishing activities performed through this study are expected to contribute to the study of spatial characteristics evaluation of fishing activities in the future.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.113-126
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• 2021

The future energy consumption pattern will show a very different pattern from the present due to the increase of distributed power sources such as renewable energy and the birth of the concept of prosumers, etc. Accordingly, it can be predicted that the direction of establishment of an appropriate production and supply plan considering the stability and consumption efficiency of the entire power grid will also be different from now. This paper proposes a simulation model that can test a new operational strategy when faced with a number of possible future environments. Through the proposed model, it is possible to simulate and analyze power consumed and supplied in a future Smart Grid environment, in which a large amount of new concepts including energy storage service (ESS) and distributed energy resources (DER) will be added. In particular, it is possible to model complex systems structurally by using DEVS formalism among the ABM (Agent-Based Model) methodologies that can model decision-making for each agent existing in the grid, and several factors can be easily added to the grid. The simulation model was verified using given dataset in the current situation, and scenario analysis was performed by simply adding an ESS, one of the main elements of the smart grid, to the model.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.