• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.204-204
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• 2021

Terrestrial water storage (TWS) includes all components of water (e.g., surface water, groundwater, snow and ice) over the land. So accurately predicting and estimating TWS is important in water resource management. Although many land surface models are used to predict the TWS, model output has errors and biases in comparison to the observation data due to the model deficiencies in the model structure, atmospheric forcing datasets, and parameters. In this study, Gravity Recovery And Climate Experiment (GRACE) satelite TWS data is assimilated in the Community Land Model version 5 with a biogeochemistry module (CLM5.0-BGC) over East Asia from 2003 to 2010 by employing the Ensemble Adjustment Kalman Filter (EAKF). Results showed that TWS over East Asia continued to decrease during the study period, and the ability to simulate the surface water storage, which is the component of the CLM derived TWS, was greatly improved. We further investigated the impact of assimilated TWS on the vegetated and carbon related variables, including the leaf area index and primary products of ecosystem. We also evaluated the simulated total ecosystem carbon and calculated its correlation with TWS. This study shows that how the better simulated TWS plays a role in capturing not only water but also carbon fluxes and states.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.4
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• pp.412-418
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• 2023

As advances in artificial intelligence (AI) increasingly approach areas once relegated to the realm of science fiction, there is growing public interest in using these technologies for practical everyday tasks in both the home and the workplace. This paper explores the applications of and implications for of using ChatGPT, a conversational AI model based on GPT-3.5 and GPT-4.0, in the field of occupational health and safety. After gaining over one million users within five days of its launch, ChatGPT has shown promise in addressing issues ranging from emergency response to chemical exposure to recommending personal protective equipment. However, despite its potential usefulness, the integration of AI into scientific work and professional settings raises several concerns. These concerns include the ethical dimensions of recognizing AI as a co-author in academic publications, the limitations and biases inherent in the data used to train these models, legal responsibilities in professional contexts, and potential shifts in employment following technological advances. This paper aims to provide a comprehensive overview of these issues and to contribute to the ongoing dialogue on the responsible use of AI in occupational health and safety.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.9-22
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• 2023

In this paper, we propose a balanced approach to AI regulation, focused on harnessing the potential benefits of artificial intelligence while upholding fairness and ethical responsibility. With the increasing integration of AI systems into daily life, it is essential to develop regulations that prevent harmful biases and the unfair disadvantage of certain demographics. Our approach involves analyzing regulatory frameworks and case studies in AI applications to ensure responsible development and application. We aim to contribute to ongoing discussions around AI regulation, helping to establish policies that balance innovation with fairness, thereby driving economic progress and societal advancement in the age of artificial intelligence.

• Journal of Korean Biological Nursing Science
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• v.25 no.4
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• pp.243-253
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• 2023

Purpose: This study conducted a concept analysis to clarify the meaning of anxiety in hospitalized patients with infectious diseases who have been placed in isolation. Methods: This study used Walker and Avant's process of concept analysis. Results: Anxiety in hospitalized patients with infectious diseases who have been placed in isolation can be defined by the following attributes: 1) fear, 2) guilt, 3) isolation, 4) discrimination, 5) frustration, 6) shame, and 7) uncertainty. The antecedents of anxiety were as follows: 1) a lack of information about infectious diseases, 2) restrictions of movement, 3) blockage of the social support system, 4) helplessness, and 5) negative biases. The consequences of anxiety were 1) internalized stigma, 2) loss of confidence, 3) lack of social activities and avoidance, 4) insomnia, 5) poor quality of life. Conclusion: The definition and attributes of anxiety identified in this study can be applied to enhance the understanding of anxiety in hospitalized patients with infectious diseases who have been placed in isolation. Systematic suppose should also be provided to reduce anxiety in these patients.

• Korean Journal of Artificial Intelligence
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• v.12 no.2
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• pp.9-16
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• 2024

In this study, we delve into the effects of personalization algorithms on the creation of "filter bubbles," which can isolate individuals intellectually by reinforcing their pre-existing biases, particularly through personalized Google searches. By setting up accounts with distinct ideological learnings-progressive and conservative-and employing deep neural networks to simulate user interactions, we quantitatively confirmed the existence of filter bubbles. Our investigation extends to the deployment of an LSTM model designed to assess political orientation in text, enabling us to bias accounts deliberately and monitor their increasing ideological inclinations. We observed politically biased search results appearing over time in searches through biased accounts. Additionally, the political bias of the accounts continued to increase. These results provide numerical evidence for the existence of filter bubbles and demonstrate that these bubbles exert a greater influence on search results over time. Moreover, we explored potential solutions to mitigate the influence of filter bubbles, proposing methods to promote a more diverse and inclusive information ecosystem. Our findings underscore the significance of filter bubbles in shaping users' access to information and highlight the urgency of addressing this issue to prevent further political polarization and media habit entrenchment. Through this research, we contribute to a broader understanding of the challenges posed by personalized digital environments and offer insights into strategies that can help alleviate the risks of intellectual isolation caused by filter bubbles.

• Journal of Sensor Science and Technology
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• v.33 no.1
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• pp.1-6
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• 2024

This study aims to suggest the partition ratio (Q-factor) of healthy Koreans and the comparison results of breath alcohol concentration (BAC) measurements using two methods (photoacoustic and electrochemical methods) for developing breath alcohol ignition interlock devices (BAIIDs). Given the relationship between BACs and BrACs and the Q-factor, the alcohol metabolism of healthy Koreans (96 males and 91 females) is revealed for understanding the digestion of alcohol and surveying the fundamental data of alcohol-related problems, CO₂ concentrations vs. alcohol concentrations, and the performance of alcohol sensors in the marketplace. The average Q-factor of healthy Korean males and females are 1,913 (95% confidence interval from 1,889-1,937) and 1,991 (95% confidence interval from 1,945-2,036). Photoacoustic measurements could be applied to predict the BACs of drinkers, which is confirmed by the Bland-Altman plots presented in this study. The biases based on the partition ratios (Q=1,913 and Q=1,991) in the Bland-Altman plots were -0.0004% (95% CI from -0.0011 to +0.0003% for males) and -0.0017% (95% CI from -0.020 to +0.017% for females).

• The Journal of Economics, Marketing and Management
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• v.12 no.2
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• pp.1-10
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• 2024

Purpose: This study conducted an empirical study to estimate the loss aversion rate of individual investors in the Seoul condominium market. Research design, data and methodology: A survey was conducted with Seoul residents ranging from 30's to 60's with various backgrounds. Descriptive statistical analysis and a paired sample t-test were conducted using SPSS 27.0 statistical package. Results: The results of the t-test showed that Seoul residents are indeed more sensitive to loss than gains, as pointed out in various researches related to behavioral economics. Also, the loss aversion rate associated with KRW 50 million risk was found to be 2.14. Finally, the same question was asked with KRW 100 million risk, doubled associated risk of previous question, using the same scenario, and it's been verified that the loss aversion rate increases as the associated risk or stake increases. The loss aversion rate with double risk is 2.26 which is about 5% higher than the one with KRW 50 million risk. Conclusions: This study can help many groups of people in society who need to establish rewards and punishment policies within any organization. In particular, incorporating human cognitive biases, such as loss aversion can help the South Korean government shape more effective reward and punishment policies when building rewards and punishments using taxes.

• New & Renewable Energy
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• v.20 no.1
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• pp.102-109
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• 2024

The Typical Meteorological Year (TMY) dataset compiles 12 months of data that best represent long-term climate patterns, focusing on global horizontal irradiance and other weather-related variables. However, the irradiance measured on the plane of the array (POA) shows certain distinct distribution characteristics compared with the irradiance in the TMY dataset, and this may introduce some biases. Our research recalculated POA irradiance using both the Isotropic and DIRINT models, generating an updated dataset that was tailored to POA characteristics. Our analysis showed a 28% change in the selection of typical meteorological months, an 8% increase in average irradiance, and a 40% reduction in the range of irradiance values, thus indicating a significant shift in irradiance distribution patterns. This research aims to inform stakeholders about accurate use of TMY datasets in potential decision-making. These findings underscore the necessity of creating a typical dataset by using the time series of POA irradiance, which represents the orientation in which PV panels will be deployed.

• Korean Circulation Journal
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• v.52 no.12
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• pp.853-864
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• 2022

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations. However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world's population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.35 no.1
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• pp.57-65
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• 2024

Autism spectrum disorder (ASD) can be associated with eating problems. However, currently, there is a lack of established guidelines for assessing and addressing eating behaviors in individuals with ASD. This gap in research exists due to the challenges associated with using traditional assessment methods, which may lead to discrepancies in responses and unintentional potential biases from caregivers. In this review, we provided a comprehensive overview of various eating behaviors commonly observed in individuals with ASD. These behaviors include 1) food neophobia, 2) selective eating, 3) binge eating, 4) food avoidance, 5) chewing and swallowing problems, 6) pica, 7) rumination, 8) rituals, and 9) problematic behaviors. Furthermore, we provide a perspective of utilizing digital tools: 1) augmentative and alternative communication; 2) ecological momentary assessment; and 3) video analysis, behavioral analysis, and facial expression analysis. This review explores existing assessment methods and suggests novel assessment aiding together.