• Korean Journal of Environmental Agriculture
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• v.37 no.3
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• pp.189-196
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• 2018

BACKGROUND: Water quality management of river requires quantification of pollutant loads and implementation of measures through monitoring study, but it requires labour and costs. Therefore, many researchers are performing nonpoint source pollution analysis using computer models. However, calibration of model parameters needs observed data. Nitrogen concentration in rainfall is one of the factors to be considered when estimating the pollutant loads through application of the nonpoint source pollution model, but the default value provided by the model is used when there are no observed data. Therefore, this study aims to provide the representative nitrogen concentration of the rainfall for the administrative district ensuring rational modeling and reliable results. METHODS AND RESULTS: In this study, rainfall monitoring data from June 2015 to December 2017 were used to determine the nitrogen concentration in rainfall for each administrative district. Range of the $NO_3{^-}$ and $NH_4{^+}$ concentrations were 0.41~6.05 mg/L, 0.39~2.27 mg/L, respectively, and T-N concentration was 0.80~7.71 mg/L. Furthermore, the national average of T-N concentration in this study was $2.84{\pm}1.42mg/L$, which was similar to the national average of T-N 3.03 mg/L presented by the Ministry of Environment in 2015. Therefore, the nitrogen concentrations suggested in this study can be considered to be resonable values. CONCLUSION: The nitrogen concentrations estimated in this study showed regional differences. Therefore, when estimating the pollutant loads through application of the nonpoint source pollution model, resonable parameter estimation of nitrogen concentration in rainfall is possible by reflecting the regional characteristics.

• Knowledge Management Research
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• v.22 no.2
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• pp.119-144
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• 2021

Recently, as competition in the market evolves from the competition among companies to the competition among their supply chains, companies are struggling to enhance their supply chain management (hereinafter SCM). In particular, as blockchain technology with various technical advantages is combined with SCM, a lot of domestic manufacturing and distribution companies are considering the adoption of blockchain-oriented SCM (BOSCM) services today. Thus, it is an important academic topic to examine the factors affecting the use of blockchain-oriented SCM. However, most prior studies on blockchain and SCMs have designed their research models based on Technology Acceptance Model (TAM) or the Unified Theory of Acceptance and Use of Technology (UTAUT), which are suitable for explaining individual's acceptance of information technology rather than companies'. Under this background, this study presents a novel model of blockchain-oriented SCM acceptance model based on the Technology-Organization-Environment (TOE) framework to consider companies as the unit of analysis. In addition, Value-based Adoption Model (VAM) is applied to the research model in order to consider the benefits and the sacrifices caused by a new information system comprehensively. To validate the proposed research model, a survey of 126 companies were collected. Among them, by applying PLS-SEM (Partial Least Squares Structural Equation Modeling) with data of 122 companies, the research model was verified. As a result, 'business innovation', 'tracking and tracing', 'security enhancement' and 'cost' from technology viewpoint are found to significantly affect 'perceived value', which in turn affects 'intention to use blockchain-oriented SCM'. Also, 'organization readiness' is found to affect 'intention to use' with statistical significance. However, it is found that 'complexity' and 'regulation environment' have little impact on 'perceived value' and 'intention to use', respectively. It is expected that the findings of this study contribute to preparing practical and policy alternatives for facilitating blockchain-oriented SCM adoption in Korean firms.

• Journal of Korean Dental Hygiene Science
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• v.6 no.1
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• pp.37-50
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• 2023

Background: This study investigated the structural relationships among justice, test anxiety, and school reliability s non-face-to-face tests of dental hygiene students. Methods: A survey was conducted with 267 dental hygiene students. The survey items included general characteristics, opinions on evaluation, the fairness of non-face-to-face tests (distributive, procedural, and interactional justice), school satisfaction, and school reliability. For statistical analysis, independent-sample t-tests, one-way ANOVA, and structural modeling analyses were performed. Results: Among factors that directly affected distributive justice and reliability towards non-face-to-face tests, the higher the interactional justice (β=0.401, p<0.001) and distributive justice (β=0.232, p=0.002) levels, the higher the school satisfaction. The higher the school satisfaction (β=0.606, p<0.001) and procedural justice (β=0.299, p<0.001) levels, the higher the perceived reliability of the school. Factors that indirectly affected school reliability included interactional justice (β=0.243, p=0.010) and distributive justice (β=0.141, p=0.010). Interactional justice (β=0.592, p=0.010) and distributive justice (β=0.208, p=0.010) were the factors affecting school satisfaction. Moreover, factors that influenced school reliability were distributive justice (β=0.56, p=0.010), interactional justice (β=0.332, p=0.010), procedural justice (β=0.229, p=0.010), and distributive justice (β=0.116, p=0.010). Conclusions: Students will trust and be satisfied with schools when schools and professors sufficiently provide information on face-to-face tests and ensure proper procedures to achieve reasonable grades as rewards for exerted time and effort. Furthermore, this study provides a reference base for developing a variety of content for fair, non-face-to-face tests, thereby allowing students to trust their schools.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.