• Asia pacific journal of information systems
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• v.28 no.2
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• pp.114-132
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• 2018

Quantified-self technologies (QSTs) provide functions for users to collect, track, and monitor personal data for self-reflection and acquisition of self-knowledge. Although QSTs require prolonged use to reap the attendant benefits, many users stop using their devices or tracking within weeks or months. To address this issue, this study seeks to determine ways to sustain the use of QSTs. Combining motivational affordance theory with technology continuance theory, this study develops a theoretical model that accounts for an individual's continued intention to use a QST. Within the proposed model, unique QST affordances were identified as antecedents of individual motivation in relation to technology continuance, and their different roles in stimulating hedonic, utilitarian, and eudaimonic motivations were examined. The model was tested using data collected from 180 QST users. Results demonstrate that although utilitarian and eudaimonic motivations are complementary forces in determining continuance intention, hedonic motivation loses its predictive power in favor of eudaimonic motivation. Tracking, visualizing, and sharing affordances play different roles in elevating user motivations. The sharing affordance does not influence utilitarian and eudaimonic motivations, but it positively influences hedonic motivation. This research contributes to the literature on technology continuance by shifting scholarly attention from hedonic-utilitarian duality to eudaimonic motivation, characterized by meaning, self-growth, and pursuit of excellence.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4102-4111
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• 2023

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

• Korean Journal of Environmental Biology
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• v.42 no.2
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• pp.207-218
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• 2024

Food webs have received global attention as next-generation biomonitoring tools; however, it remains challenging because revealing trophic links between species is costly and laborious. Although a link-extrapolation method utilizing published trophic link data can address this difficulty, it has limitations when applied to construct food webs in domestic streams due to the lack of information on endemic species in global literature. Therefore, this study aimed to develop a link extrapolation-based food web model adapted to Korean stream ecosystems. We considered taxonomic similarity of predation and dominance of generalists in aquatic ecosystems, designing taxonomically higher-level matching methods: family matching for all fish (Family), endemic fish (Family-E), endemic fish playing the role of consumers (Family-EC), and resources (Family-ER). By adding the commonly used genus matching method (Genus) to these four matching methods, a total of five matching methods were used to construct 103 domestic food webs. Predictive power of both individual links and food web indices were evaluated by comparing constructed food webs with corresponding empirical food webs. Results showed that, in both evaluations, proposed methods tended to perform better than Genus in a data-poor environment. In particular, Family-E and Family-EC were the most effective matching methods. Our model addressed domestic data scarcity problems when using a link-extrapolation method. It offers opportunities to understand stream ecosystem food webs and may provide novel insights into biomonitoring.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.11-23
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• 2024

Triage is a practice of accurately prioritizing patients in emergency department (ED) based on their medical condition to provide them with proper treatment service. The variation in triage assessment among medical staff can cause mis-triage which affect the patients negatively. Developing ED triage system based on machine learning (ML) techniques can lead to accurate and efficient triage outcomes. This study aspires to develop a triage system using machine learning techniques to predict ED triage levels using patients' information. We conducted a retrospective study using Security Forces Hospital ED data, from 2021 through 2023 during Hajj period in Saudia Arabi. Using demographics, vital signs, and chief complaints as predictors, two machine learning models were investigated, naming gradient boosted decision tree (XGB) and deep neural network (DNN). The models were trained to predict ED triage levels and their predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and confusion matrix. A total of 11,584 ED visits were collected and used in this study. XGB and DNN models exhibit high abilities in the predicting performance with AUC-ROC scores 0.85 and 0.82, respectively. Compared to the traditional approach, our proposed system demonstrated better performance and can be implemented in real-world clinical settings. Utilizing ML applications can power the triage decision-making, clinical care, and resource utilization.

• The Korean journal of internal medicine
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• v.39 no.1
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• pp.25-33
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• 2024

A high level of particulate matter (PM) in air is correlated with the onset and development of chronic respiratory diseases. We conducted a systematic literature review, searching the MEDLINE, EMBASE, and Cochrane databases for studies of biomarkers of the effect of PM exposure on chronic respiratory diseases and the progression thereof. Thirty-eight articles on biomarkers of the progression of chronic respiratory diseases after exposure to PM were identified, four of which were eligible for review. Serum, sputum, urine, and exhaled breath condensate biomarkers of the effect of PM exposure on chronic obstructive pulmonary disease (COPD) and asthma had a variety of underlying mechanisms. We summarized the functions of biomarkers linked to COPD and asthma and their biological plausibility. We identified few biomarkers of PM exposure-related progression of chronic respiratory diseases. The included studies were restricted to those on biomarkers of the relationship of PM exposure with the progression of chronic respiratory diseases. The predictive power of biomarkers of the effect of PM exposure on chronic respiratory diseases varies according to the functions of the biomarkers.

• Asia-Pacific Journal of Business
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• v.15 no.2
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• pp.71-88
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• 2024

Purpose - This study provides an overview of existing research and practices related to market risk premiums(MRP), and empirically estimates the MRP in Korea, particularly using the related option prices. We also seek to improve the current MRP practices and explore alternative solutions. Design/methodology/approach - We present the option price-based MRP estimation method, as proposed by Martin (2017), and implement it within the context of the Korean stock market. We then juxtapose these results with those derived from other methods, and compare the characteristics with those of the United States. Findings - We found that the lower limit of the MRP in the Korean stock market shows a much lower value compared to the US. There seems to be the possibility of a market crash, exchange rate volatility, or a lack of option trading data. We investigated the predictive power of the estimated values and discovered that the weighted average of the results of various methodologies using the Principal Component Analysis (PCA) is superior to the individual method's results. Research implications or Originality - It is required to explore various methods of estimating MRP that are suitable for the Korean stock market. In order to improve the estimation methodology based on option prices, it is necessary to develop the methods using the higher-order(third order or above) moments, or consider additional risk factors such as the possibility of a crash.

• Journal of Environmental Science International
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• v.33 no.8
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• pp.583-589
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• 2024

Seawater movement analyses and dye diffusion experiments were conducted to understand the characteristics of eddy diffusion in the southwest coastal zone of Korea. The findings indicate that pollutants entering the study area were most influenced by tidal currents and showed temporal and spatial variations according to the turbulent characteristics of the tidal current. Pollutants entering the study area are likely to travel a distance of approximately 2 km (within 1 h) following the direction of the tidal currents and show a spreading distance (diameter of the diffusion area) of within 10% of the travel distance (within 200 m). The dispersion of the diffusion area is expected to increase in proportion to the elapsed time raised to a power of 1.19 to 1.23. The results are expected to provide a basis for using the eddy diffusion coefficient as a temporally variable value (previously assumed to be a constant based on empirical data), thereby contributing to improving the predictive accuracy of ocean diffusion models.

• Journal of muscle and joint health
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• v.31 no.2
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• pp.65-74
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• 2024

Purpose: This study was conducted to verify fall predictive power and reasonable fall risk assessment tool by a comparative analysis of the sensitivity, specificity, positive forecast and negative forecast of each tool by applying Morse Fall Scale (MFS), Johns Hopkins Fall Risk Assessment Tool (JHFRAT), and Fall Assessment Scale-Korean version (FAS-K) through electronic medical records to adult patients hospitalized in a general hospital in Korea. Methods: We performed a retrospective evaluation study from January to December 2018, 123 fall groups experiencing falls during hospitalization and 123 non-falls groups were selected. Data presented a reasonable assessment tool that predicts and distinguishes fall high-risk patients through area comparison based on the ROC curve for each tool. Results: In the ROC curve analysis by fall risk assessment group, the AUC of MFS is shown to be .706 (good), JHFRAT is shown to be .649 (sufficient) and FAS-K is shown to be .804 (very good). FAS-K at a cut-off score of 4, sensitivity, specificity, and positive and negative prediction values were 83.7%, 60.2%, 67.8%, and 78.7%, respectively. Conclusion: Based on the above findings, it is believed that the FAS-K was presented as a suitable and reasonable tool for predicting falls for adult patients in general hospitals.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.749-756
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• 2018

In this study, we developed a prediction model for greenhouse control using machine learning technique. The prediction model was developed using measured data (2016) on greenhouse in the Protected Horticulture Research Institute. In order to improve the predictive performance of model and to ensure the reliability of data, the dimension of the data was reduced by correlation analysis. The dataset were divided into spring, summer, autumn, and winter considering the seasonal characteristics. An artificial neural network, recurrent neural network, and multiple regression model were constructed as a machine leaning based prediction model and evaluated by comparative analysis with real dataset. As a result, ANN showed good performance in selected dataset, while MRM showed good performance in full dataset.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8753-8759
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• 2015

Establishment of analysis procedures and validated performance measurements for generator output is required to maintain stable management of generator output in turbine power generation cycle. We developed turbine expansion model and measurement validation model for the performance calculation of generator using turbine output based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). We also developed verification model for uncertain measurement data related to the turbine and generator output. Although the model in previous researches was developed using artificial neural network and kernel regression, the verification model in this paper was based on algorithms through Support Vector Machine (SVM) model to overcome the problems of unmeasured data. The selection procedures of related variables and data window for verification learning was also developed. The model reveals suitability in the estimation procss as the learning error was in the range of about 1%. The learning model can provide validated estimations for corrective performance analysis of turbine cycle output using the predictions of measurement data loss.