• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.851-863
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• 2023

The mandatory use of BIM(Building Information Model) in larger Korean public construction projects necessitates participants to have a comprehensive understanding of the relevant procedures and technologies, especially during the bidding stage. However, most small and medium-sized construction and engineering companies possess limited BIM proficiency and understanding. This hampers their ability to recognize bidding requirements and make informed decisions. To address this challenge, our study introduces a method to gauge the complexity of BIM requirements in bidding documents. This is achieved by integrating a morphological analyzer, which encompasses BIM bidding terminology, with a deep learning model. We investigated the effects of the parameters in our proposed deep learning model and examined its predictive validity. The results revealed an F1-score of 0.83 for the test data, indicating that the model's predictions align closely with the actual BIM performance challenges.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.37-43
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• 2023

In today's business landscape, collaboration and interoperability are crucial for organizational success and profitability. However, integrating operations across multiple organizations is challenging due to differing roles and policies in Identity and Access Management (IAM). User-centric identity (UCI) adopts a personalized approach to digital identity management, centering on the end-user for authentication and access control. It provides a decentralized system that ensures secure and customized access for each user. UCI aims to address complex security challenges by aligning access privileges with individual user requirements. This research delves into UCI's ability to streamline resource access amidst conflicting IAM roles and protocols across various organizations. The study presents a UCI-based multi-domain access control (MDAC) framework, which encompasses an ontology, a unified method for articulating access roles and policies across domains, and software services melding with UCI infrastructure. The goal is to enhance organizational resource management and decision-making by offering clear guidelines on access roles and policy management across diverse domains, ultimately boosting companies' return on investment.

• Genomics & Informatics
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• v.21 no.4
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• pp.50.1-50.9
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• 2023

Vaccine development is one of the key efforts to control the spread of coronavirus disease 2019 (COVID-19). However, it has become apparent that the immunity acquired through vaccination is not permanent, known as the waning effect. Therefore, monitoring the proportion of the population with immunity is essential to improve the forecasting of future waves of the pandemic. Despite this, the impact of the waning effect on forecasting accuracies has not been extensively studied. We proposed a method for the estimation of the effective immunity (EI) rate which represents the waning effect by integrating the second and booster doses of COVID-19 vaccines. The EI rate, with different periods to the onset of the waning effect, was incorporated into three statistical models and two machine learning models. Stringency Index, omicron variant BA.5 rate (BA.5 rate), booster shot rate (BSR), and the EI rate were used as covariates and the best covariate combination was selected using prediction error. Among the prediction results, Generalized Additive Model showed the best improvement (decreasing 86% test error) with the EI rate. Furthermore, we confirmed that South Korea's decision to recommend booster shots after 90 days is reasonable since the waning effect onsets 90 days after the last dose of vaccine which improves the prediction of confirmed cases and deaths. Substituting BSR with EI rate in statistical models not only results in better predictions but also makes it possible to forecast a potential wave and help the local community react proactively to a rapid increase in confirmed cases.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.53-71
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• 2024

Generative Artificial Intelligence (AI) has become the focus of societal attention due to its wide range of applications and profound impact. This paper constructs a comprehensive theoretical model based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), integrating variables such as Personal Innovativeness and Perceived Risk to study the key factors influencing enterprises' adoption of Generative AI. We employed Structural Equation Modeling (SEM) to verify the hypothesized paths and used the Bootstrapping method to test the mediating effect of Behavioral Intention. Additionally, we explored the moderating effect of Perceived Risk through Hierarchical Regression Analysis. The results indicate that Performance Expectancy, Effort Expectancy, Social Influence, Price Value, and Personal Innovativeness have significant positive impacts on Behavioral Intention. Behavioral Intention plays a significant mediating role between these factors and Use Behavior, while Perceived Risk negatively moderates the relationship between Behavioral Intention and Use Behavior. This study provides theoretical and empirical support for how enterprises can effectively adopt Generative AI, offering important practical implications.

• Journal of Cardiovascular Imaging
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• v.31 no.4
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• pp.191-199
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• 2023

BACKGROUND: Assessment of left ventricular (LV) function plays a pivotal role in the management of patients with valvular heart disease, including those caused by rheumatic heart disease. Noninvasive LV pressure-strain loop analysis is emerging as a new echocardiographic method to evaluate global LV systolic function, integrating longitudinal strain by speckle-tracking analysis and noninvasively measured blood pressure to estimate myocardial work. The aim of this study was to characterize global LV myocardial work efficiency in patients with severe rheumatic mitral stenosis (MS) with preserved ejection fraction (EF). METHODS: We retrospectively included adult patients with severe rheumatic MS with preserved EF (> 50%) and sinus rhythm. Healthy individuals without structural heart disease were included as a control group. Global LV myocardial work efficiency was estimated with a proprietary algorithm from speckle-tracking strain analyses, as well as noninvasive blood pressure measurements. RESULTS: A total of 45 individuals with isolated severe rheumatic MS with sinus rhythm and 45 healthy individuals were included. In healthy individuals without structural heart disease, the mean global LV myocardial work efficiency was 96% (standard deviation [SD], 2), Compared with healthy individuals, median global LV myocardial work efficiency was significantly worse in MS patients (89%; SD, 4; p < 0.001) although the LVEF was similar. CONCLUSIONS: Individuals with isolated severe rheumatic MS and preserved EF, had global LV myocardial work efficiencies lower than normal controls.

• Journal of Environmental Health Sciences
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• v.50 no.2
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• pp.125-137
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• 2024

Background: The Korea Risk Information Surveillance System (K-RISS) was developed to enable the early detection of food and drug safety-related issues. Its goal is to deliver real-time risk indicators generated from ongoing food and drug risk monitoring. However, the existing K-RISS system suffers under several limitations. Objectives: This study aims to augment K-RISS with more detailed indicators and establish a severity standard that takes into account structural changes in the daily time series of K-RISS values. Methods: First, a Delphi survey was conducted to derive the required weights. Second, a control chart, commonly used in statistical process controls, was utilized to detect outliers and establish caution, attention, and serious levels for K-RISS values. Furthermore, Bai and Perron's method was employed to determine structural changes in K-RISS time series. Results: The study incorporated 'closeness to life' and 'sustainability' indicators into K-RISS. It obtained the necessary weights through a survey of experts for integrating variables, combining indicators by data source, and aggregating sub K-RISS values. We defined caution, attention, and serious levels for both average and maximum values of daily K-RISS. Furthermore, when structural changes were detected, leading to significant variations in daily K-RISS values according to different periods, the study systematically verified these changes and derived respective severity levels for each period. Conclusions: This study enhances the existing K-RISS system and introduces more advanced indicators. K-RISS is now more comprehensively equipped to serve as a risk warning index. The study has paved the way for an objective determination of whether the food safety risk index surpasses predefined thresholds through the application of severity levels.

• Journal of Distribution Science
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• v.22 no.4
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• pp.1-10
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• 2024

Purpose: This study is aimed to introduce the application of phantom approach with structural equation modelling method for online learning. By integrating these innovative methodologies, the research seeks to advance the understanding of how the phantom approach can effectively complement and augment structural equation modeling techniques. Research design, data and methodology: A theoretical framework of Technology Acceptance Model (TAM) was modified and updated. A questionnaire was developed and used to extract information from 189 instructors who used online learning as their primary medium. The Covariance Based Structural Equation Modelling (CBSEM) was applied to test the direct effects and the phantom approach is used to handle the 2 mediators in the model. Results:social influence, perceived usefulness, and perceived ease of use exerted discernible impacts on instructors' intentionsto engage in online learning. These findings illuminate the intricate dynamics influencing instructor behavior within the realm of online education, underscoring the significance of various factors in shaping their intentions. Conclusions: In additions, the perceived usefulness and perceived ease of use had mediated the effect of social influence and instructor intention using phantom approach. Therefore, one can have concluded that this modified model was also confirmed, thereby reinforcing distribution strategies to online learning and overall education presence.

• Journal of Science Education
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• v.48 no.2
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• pp.111-120
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• 2024

The purpose of this study was to develop and implement a wetland education program using ecotourism and investigate its effect on middle school students' environmental literacy. The wetland education program was developed by analyzing the 2015 revised curriculum and integrating content from science, social studies, ethics, technology, and home economics. The subjects of this study were 49 second-grade middle school students in Jeju City. The analysis revealed that the wetland education program using ecotourism improved environmental literacy in both the cognitive and psychomotor domains. In particular, the students' awareness of the 'causes of climate change' and 'responses to climate change' appeared to have increased. Although environmental literacy in the affective domain did not improve, there were positive effects in specific areas, such as attitudes toward pollution and proactive behavior. Based on the results of this study, follow-up research should further explore using ecotourism as an effective method to enhance wetland education.

• Journal of IKEEE
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• v.28 no.3
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• pp.451-457
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• 2024

This paper proposes a method for predicting agricultural product prices by utilizing various variables such as price, climate factors, demand, and import volume as data, and applying the Long Short-Term Memory (LSTM) model. The analysis of prediction performance using the LSTM model, which learns the long-term dependencies of time series data, showed that integrating diverse data improved performance compared to traditional methods. Furthermore, even when predicting without price data as a dependent variable, meaningful results were achieved using only independent variables, indicating the potential for further model development. Moreover, it was found that using a multi-variable model could further enhance prediction performance, suggesting that this complex approach is effective in improving the accuracy of cabbage price predictions.

• Knowledge Management Research
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• v.25 no.3
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• pp.99-119
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• 2024

This research aims to develop high-quality generative AI services by overcoming the limitations of existing Retrieval-Augmented Generation (RAG) models and implementing an enhanced graph-based RAG system to improve knowledge-based question answering (QA) systems. While traditional RAG models demonstrate high accuracy and fluency by utilizing retrieved information, their accuracy can be compromised due to the use of pre-loaded knowledge without rework. Additionally, the inability to incorporate real-time data after the RAG configuration leads to a lack of contextual understanding and potential biased information. To address these limitations, this study implements an enhanced RAG system utilizing graph technology. This system is designed to efficiently search and utilize information. In particular, LangGraph is employed to evaluate the reliability of retrieved information and to generate more accurate and improved answers by integrating various information. Furthermore, the specific operation method, key implementation steps, and case studies are presented with implementation code and verification results to enhance understanding of Advanced RAG technology. This research provides practical guidelines for actively implementing enterprise services utilizing Advanced RAG, making it significant.