• Journal of Korea Trade
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• v.24 no.8
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• pp.63-80
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• 2020

Purpose - Recent empirical studies have shown that FDI is expected to be strongly associated with democratic governance, political stability, and sound macroeconomic conditions of the host country. We attempt to take it a step further to see if governments implement a major change in institutional characteristics, will the institutional reform toward better governance have a substantive effect in enhancing FDI inflows. This paper thus aims to analyze the importance of good governance as an important factor in the attractiveness of FDI inflows in ASEAN+3 (Korea, China, Japan) countries. Design/methodology - To determine the effects of good governance on FDI inflows across ASEAN+3 countries recorded between 1996-2018, the Worldwide Governance Indicators (WGI) are used to investigate the impact of good governance on FDI inflows. The model has been estimated by using fixed effects to show the robustness of the results. Findings - Our main findings can be summarized as follows: Political Stability, Rule of Law, and Voice and Accountability have a statistically significant impact on the inflow of FDI in the ASEAN+3 Countries, especially for Korean economy. Moreover, GDP growth continue to exert their positive influence. However, Regulatory Quality, Government Effectiveness and Control of Corruption, though equally important, are insignificant to attract FDI inflows. The key finding is that good governance has a significant impact on inward FDI in the ASEAN+3 countries. Originality/value - Existing studies focus on the impact of political factors on FDI across countries. This paper instead attempts to investigate which type of good governance is the most important in promoting FDI inflows across ASEAN+3 countries, which is essential for multinationals to consider when choosing a foreign site as a possible FDI destination.

• Journal of Korea Trade
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• v.24 no.7
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• pp.29-37
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• 2020

Purpose - This paper empirically investigates the relationship between country governance quality and worker remittances from foreign countries. Because remittances can be a source of funds for economic development and smoothing economic crises in developing countries, the related topic has been a concern for policy-makers and academic researchers. This paper divides the motives of remittances into altruistic and investment motives through existing papers, and then considers the governance quality the remittance receiving country as one of the determinants of remittances. Design/methodology - Our empirical model considers whether governance quality can affect the volume of remittances, and uses altruistic and investment factors studied in the literature. To do this, a two-step approach is taken. First, the panel data are examined via pooled OLS, random effects, and Tobit estimation. Second, the paper reduces six governance indicators into one variable, Governance, using the principal component technique (PCA) for a robustness check. Findings - The main findings can be summarized as follows. The negative governance variable in the estimation results shows a lower governance quality that induces workers to send savings to their home countries. This means that a country with poor governance quality seems to have more remittance inflows from abroad. It also reveals that poor governance quality is more relevant to an altruistic motive rather than an investment motive, in general. The positive per capita GDP variable shows the investment motive for developed countries. Originality/value - Existing papers have focused on various factors related to the motives of remittances. However, governance quality effects on remittance inflows have not been fully studied so far. This paper considers governance quality in an estimation equation explicitly as one of the determinants of remittances. This area of study is needed, in theory and empirically, in order to fully understand the relationship between governance and remittances.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Journal of Acupuncture Research
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• v.38 no.4
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• pp.312-319
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• 2021

Background: Lumbar herniated intervertebral disc (LHIVD) is a frequently presented condition/disease in Korean medical institutions. In this study, the economics of thread embedding acupuncture (TEA) was evaluated in a randomized controlled trial comparing TEA with sham TEA (STEA). Methods: This economic evaluation was analyzed from a limited social perspective, and the per-protocol set was from a basic analysis perspective. The cost-effectiveness analysis was based on the change in visual analog scale score, and the cost-utility analysis was based on the quality-adjusted life years. The final results were expressed as the average cost-effectiveness ratio and incremental cost-effectiveness ratio, and furthermore sensitivity analysis was performed to confirm the robustness of the results observed. Results: The cost-effectiveness analysis showed that TEA was 9,908 won lower than STEA, while the decrease in 100 mm visual analog scale score was 8.5 mm greater in the TEA group compared with the STEA group (p > 0.05). The cost-utility analysis showed that TEA was 9,908 won lower than STEA, while the quality-adjusted life years of TEA was 0.0026 years higher than STEA (p > 0.05). These results were robust in the sensitivity analysis, but were not statistically significant. Conclusion: In treating LHIVD, TEA appeared to have cost-effectiveness and cost-utility compared with STEA. However, there were no significant differences between the groups in terms of cost, effectiveness, and utility indicators. Therefore, results must be interpreted prudently; this study was the 1^st to conduct an economic evaluation of TEA for LHIVD.

• Analyses & Alternatives
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• v.6 no.2
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• pp.155-183
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• 2022

The purpose of this study is to econometrically analyze the negative impact of the May 18 Democratic Uprising on the Gwangju/Jeonnam regionional economy using the Synthetic Control Method (SCM). The SCM SCM is a methodology similar to the difference-in-difference(DID) method of microeconometrics. It is applied to macroeconomic variables such as country, region, etc. to estimate the causal relationship between specific events and the dependent variable. In this study, as of 1980, local tax revenue data of metropolitan local governments were used as a proxy variable for the economy of the region, and the impact of the May 18 Democratic Uprising on the economy of Gwangju/Jeonnam region was analyzed through various socio-economic indicators. In this study, data were used to analyze from 1971 to 2000, and as a result of empirical analysis, local tax revenues in Gwangju/Jeonnam area were less collected than normal routes up to 17%. In addition, the significance of this analysis was confirmed through in-time placebo effect analysis and in-space placebo effect analysis, which are methods of analyzing the robustness of the control group synthesis method.