• Journal of Preventive Medicine and Public Health
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• v.30 no.4 s.59
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• pp.719-728
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• 1997

To guarantee the inter-reviewer reliability is very important in evaluating the quality of large number of clinical research papers by multiple reviewers. We cannot find reports on statistical methods for evaluating reliability for multiple raters in clinical research field. The purpose of this paper is to introduce the statistical methods focused on kappa statistic and five kinds of loglinear models for, which can be applied when evaluating the reliability of multiple raters. We have applied these methods to the result of a project, in which seven reviewers have evaluated the quality of 33 papers with regard to four aspects of paper contents including study hypothesis, study design, study population, study method, data analysis and interpretation. Among the five loglinear models including Symmetry model, Conditional symmetry model, Quasi-symmetry model, Independence model, and Quasi-independence model, Quasi-symmetry model shows the best model of fitting. And the level of reliability among seven reviewers revealed to be acceptable as meaningful.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.143-143
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• 2021

Climate change indicators, mainly frequent drought which has happened since the drought of 1994, 1995, and 2012 causing the devastating effect to the agricultural sector, and could be more disruptive given the context of climate change indicators by increasing the temperature and more variable and extreme precipitation. Changes in frequency, duration, and severity of droughts will have enormous impacts on agriculture production and water management. Since both the possibility of drought manifestation and substantial yield losses, we are propositioning an integrated method for evaluating past and future agriculture drought hazards that depend on models' simulations in the Hung-up watershed. to discuss the question of how climate change might influence the impact of extreme agriculture drought by assessing the potential changes in temporal trends of agriculture drought. we will calculate the temporal trends of future drought through drought indices Standardized Precipitation Evapotranspiration Index, Standardized Precipitation Index, and Palmer drought severity index by using observed data of (1991-2020) from Wonju meteorological station and projected climate change scenarios (2021-2100) of the Representative Concentration Pathways models (RCPs). expected results confirmed the frequency of extreme agricultural drought in the future projected to increase under all studied RCPs. at present 100 years drought is anticipated to happen since the result showing under RCP2.6 will occur every 24 years, RCP4.5 every 17 years, and RCPs8.5 every 7 years, and it would be double in the largest warming scenarios. On another side, the result shows unsupportable water management, could cause devastating consequences in both food production and water supply in extreme events. Because significant increases in the drought magnitude and severity like to be initiate at different time scales for each drought indicator. Based on the expected result that the evaluating the impacts of extreme agricultural droughts and recession could be used for the development of proactive drought risk management, policies for future water balance, prioritize sustainable strengthening and mitigation strategies.

• BMB Reports
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• v.56 no.1
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• pp.32-42
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• 2023

Heart disease is one of the major life-threatening diseases with high mortality and incidence worldwide. Several model systems, such as primary cells and animals, have been used to understand heart diseases and establish appropriate treatments. However, they have limitations in accuracy and reproducibility in recapitulating disease pathophysiology and evaluating drug responses. In recent years, three-dimensional (3D) cardiac tissue models produced using tissue engineering technology and human cells have outperformed conventional models. In particular, the integration of cell reprogramming techniques with bioengineering platforms (e.g., microfluidics, scaffolds, bioprinting, and biophysical stimuli) has facilitated the development of heart-on-a-chip, cardiac spheroid/organoid, and engineered heart tissue (EHT) to recapitulate the structural and functional features of the native human heart. These cardiac models have improved heart disease modeling and toxicological evaluation. In this review, we summarize the cell types for the fabrication of cardiac tissue models, introduce diverse 3D human cardiac tissue models, and discuss the strategies to enhance their complexity and maturity. Finally, recent studies in the modeling of various heart diseases are reviewed.

• Journal of The Korean Association For Science Education
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• v.16 no.1
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• pp.13-34
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• 1996

The purpose of this study is to develop appropriate science teaching models which can be applied effectively to relevant situations. Five science teaching models; cognitive conflict teaching models, generative teaching model, learning cycle teaching model, hypothesis verification teaching model and discovery teaching model, were identified from the existing models. The teaching models were modified and in primary and secondary students using a nonequivalent pretest-posttest control group design. Major findings of this study were as follows: 1. For teaching science concepts, three teaching models were found more effective; cognitive conflict teaching model, generative teaching model and discovery teaching model. 2. For teaching inquiry skills, two teaching models were found more effective; learning cycle teaching model and hypothesis verification teaching model. 3. For teaching scientific attitudes, two teaching models were found more effective; learning cycle teaching models and discovery teaching model. Each teaching model requires specific learning environment. It is strongly suggested that teachers should select a suitable teaching model carefully after evaluating the learning environment including teacher and student variables, learning objectives and curricular materials.

• Journal of Dental Rehabilitation and Applied Science
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• v.35 no.2
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• pp.55-63
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• 2019

The aim of this article was to review various methods used to evaluate the accuracy of digital dental models. When evaluating the accuracy of digital models, the errors can be reduced by educating examiners and using artificial landmarks. The accuracy evaluation methods of digital dental models are divided into linear measurement, 2-dimensional cross-sectional analysis, and 3-dimensional best fit measurement. As the technology of scanners develops, many studies have been conducted to compare the accuracy of digital impression and conventional impression. According to improvement of scan technologies and development of 3-dimensional model analysis software, the ability to evaluate the accuracy of digital models is becoming more efficient. In this article, we describe the methods for evaluating the accuracy of a digital model and investigate effective accuracy analysis methods for each situation.

• Structural Engineering and Mechanics
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• v.36 no.2
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• pp.225-241
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• 2010

In this study, a hybrid search algorithm combining genetic programming with orthogonal least squares (GP/OLS) is utilized to generate prediction models for compressive strength of high performance concrete (HPC) mixes. The GP/OLS models are developed based on a comprehensive database containing 1133 experimental test results obtained from previously published papers. A multiple least squares regression (LSR) analysis is performed to benchmark the GP/OLS models. A subsequent parametric study is carried out to verify the validity of the models. The results indicate that the proposed models are effectively capable of evaluating the compressive strength of HPC mixes. The derived formulas are very simple, straightforward and provide an analysis tool accessible to practicing engineers.

• Computers and Concrete
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• v.33 no.4
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• pp.409-423
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• 2024

Using an extensive database, a sensitivity analysis across fifteen machine learning (ML) classifiers was conducted to evaluate the impact of various data manipulation techniques, evaluation metrics, and explainability tools. The results of this sensitivity analysis reveal that the examined models can achieve an accuracy ranging from 72-93% in predicting the fire-induced spalling of concrete and denote the light gradient boosting machine, extreme gradient boosting, and random forest algorithms as the best-performing models. Among such models, the six key factors influencing spalling were maximum exposure temperature, heating rate, compressive strength of concrete, moisture content, silica fume content, and the quantity of polypropylene fiber. Our analysis also documents some conflicting results observed with the deep learning model. As such, this study highlights the necessity of selecting suitable models and carefully evaluating the presence of possible outcome biases.

• Journal of ILASS-Korea
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• v.29 no.1
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• pp.1-6
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• 2024

This paper is a numerical analysis study for evaluating the energy efficiency of electric vehicles. Currently, the methods for testing and evaluating the energy consumption efficiency of electric vehicles have limitations such as resources and time. Therefore, there is a need for research on developing models to predict the energy consumption efficiency of electric vehicles. In this study, a numerical analysis research is conducted to predict the energy efficiency of electric vehicles using a vehicle dynamics numerical analysis model. To validate the accuracy of the simulation model, it is compared the results of dynamometer tests with the simulation results and used the Unified Diagnostic Services (UDS) protocol to acquire internal data from the electric vehicle. It is ensured the reliability of the simulation model by comparing data such as motor speed, battery voltage, current, state of charge (SOC), regenerative braking power generation, and total driving distance of the test vehicle with dynamometer test data and simulation model results.

• Korean Management Science Review
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• v.19 no.2
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• pp.91-124
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• 2002

The use of network planning models and tools is essential for effective KII (Korea Information Infrastructure) planning and analysis in that it will significantly reduce the risk and uncertainty embeded in the development and the provision of future broadband services. The purpose of this study is to develop a theoretical framework and a computer tool for modeling the various aspects of the KII topology and architecture and evaluating the techno-economic feasibility of the KII implementation strategy.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.95-100
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• 2005

In this paper a class of multifactor designs for estimating the slope of second order response surface regression models with correlated errors is considered. General conditions for second order slope rotatability over all directions and also with respect to the maximum directional variance in case of k=2 have been derived assuming errors have a general correlated error structure. And we consider the measures for evaluating slope rotatability with correlated errors similar to in case of uncorrelated error structures.