• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.161-173
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• 2020

As ICT convergence technology has spread and applied to various industrial fields and society in general, interest in rural entrepreneurship using smart farm as a means for solving many pending problems in agriculture is increasing. In this context, this study is to look at the influential factors in terms of perceived usefulness associated with the rural entrepreneurial intention using smart farm and suggest a proposal for spreading smart farms. The subjects were 296 general adults over 20 years old who were selected by simple random sampling method. The research method was exploratory factor analysis and multiple regression analysis using IBM SPSS 22.0. The perceived usefulness of smart farm, which are availability, reliability and economic efficiency were selected as independent variables to analyze the influential factors on rural entrepreneurial intention using smart farm and the moderating effect of personal innovation was observed. As a result, reliability and economic efficiency have a positive(+) influence on rural entrepreneurial intention using smart farm. And personal innovation moderates the relationship between the availability, reliability of smart farm and rural entrepreneurial intention using smart farm. The results of this study have significance in that we devised and empirically revealed factors affecting rural entrepreneurship intentions from the perspective of perceived usefulness of smart farms, away from studies of general entrepreneurship intention factors such as internal personal characteristics and external environmental factors. The implications of the study are expected to be utilized at the seeking direction of policy for potential entrepreneur using smart farm, the training and consulting in actual field of smart farm.

• Journal of Environmental Impact Assessment
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• v.28 no.5
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• pp.440-456
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• 2019

The controversy of the risk perception related to particulate matters becomes significant. Therefore, in order to understand the nature of the particulate matters, we gathered articles and comments in on-line community related to bicycling which is affected by exposure of the particulate matters. As a result, firstly, the government - led particulate matter policy was strengthened and segmented every period, butthe risk perception related to particulate matters in the bicycle community has become active and serious. Second, as a result of analyzing the perception change of outdoor activities related to particulate matters, bicycle users in community showed a tendency of outdoor activity depending on the degree of particulate matters ratherthan the weather. In addition, the level of the risk perception related to particulate matters has been moved from fears of serious threat in daily life and health, combined with the disregard of domestic particulate matter levels or mask performance. Ultimately, these risk perception related to particulate matters have led some of the bicycling that were mainly enjoyed outdoors to the indoor space. However, in comparison with outdoor bicycling enjoyed by various factors such as scenery, people, and weather, the monotonous indoor bicycling was converted into another type of indoor exercise such as fitness and yoga. In summary, it was derived from mistrust of excessive information or policy provided by the government or local governments. It is considered that environmental policy should be implemented after discussion of risk communication that can reduce the gap between public anxiety and concern so as to cope with the risk perception related to particulate matters. Therefore,this study should be provided as an academic basis for the effective communication direction when decision makers establish the policy related to particulate matters.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.