South Korea Research Articles

Meta learning regression framework for energy consumption prediction in retrofitted buildings: A case study of South Korea

Building energy consumption (BEC) prediction is crucial for optimizing energy efficiency in building retrofit projects, particularly during the early design phase. However, existing machine learning (ML) models often face challenges with overfitting and complexity, limiting their accuracy and generalizability. This study addresses these issues by proposing a novel meta-learning regression framework for energy prediction in green retrofitted buildings. The framework employs optimized base regressors using various ML models and integrates them through stacking regression to enhance predictive accuracy and generalization. The model’s performance is evaluated against advanced methods like Deep Neural Networks and Ensemble Learning Regression using data from over 811 green retrofitting projects in South Korea. The proposed method demonstrates superior efficiency, achieving the lowest mean absolute error (19.899 for Primary Energy Consumption and 11.301 for Energy Required Amount), lowest root mean squared error (29.494 for Primary Energy Consumption and 19.977 for Energy Required Amount), and highest R-squared score (0.786 for Primary Energy Consumption and 0.542 for Energy Required Amount). This research contributes a novel approach to ensemble modeling for BEC prediction, showcasing its superior accuracy, generalizability, and practical applicability in the context of green building retrofits.

Can stimulus checks to households save the local economy? The impact of South Korea’s COVID-19 stimulus on small business sales

During economic downturns, stimulus checks are often used to help households but may fail to reach the most affected sectors, such as local small businesses during COVID-19. Although the aggregate impact of these measures is well-studied, their distributional effects on local economies are less explored. Using a difference-in-differences analysis, this study examines weekly local sales data during 2020 from Gyeonggi and Incheon, South Korea, to assess the impact of Gyeonggi’s stimulus, distributed to every household for spending exclusively at local stores, contrasting with Incheon’s lack of such stimulus. We find that Gyeonggi’s stimulus initially boosted local sales by 4.5%, though this impact gradually diminished. This translates into a local-sales multiplier of 1.09, indicating that more than distributed stimulus was spent due to spillover and propagation effects in local in-person spending. Our findings emphasize the importance of tailored fiscal policy designs and assessing the distributional impacts of fiscal measures.

Beauty Capital of the World: A Synthetic Analysis of the Historical Evolution of Cosmetic Surgery in South Korea

Beauty Capital of the World: A Synthetic Analysis of the Historical Evolution of Cosmetic Surgery in South Korea

Frequency domain cross-quantile coherency and connectedness network of exchange rates: Evidence from ASEAN+3 countries

This study examines the frequency domain connectedness and synchronization between the exchange rates of Association of Southeast Asian Nations (ASEAN) member countries and those of China, Japan, and South Korea across quantile levels. We propose a quantile cross-spectrum of exchange rates to establish the coherency of connectedness and synchronization measurements. Our empirical results are as follows: First, the return connectedness between the exchange rates is heterogeneous, being stronger in the long run than in the short run and more pronounced under normal market conditions than under extreme market conditions. Second, the dynamic return connectedness among the exchange rates follows a similar trend in the monthly and yearly cycles. Third, exchange rate returns and volatility exhibit long-term synchronization. However, short-term heterogeneity persists across market conditions and investment horizons. Overall, these findings offer valuable insights for monetary authorities in their efforts to maintain exchange rate stability and for investors in making informed portfolio decisions.

Comprehensive vulnerability assessment for environmental facility depending on spatial characteristics in South Korea

Among environmental facilities, wastewater treatment facilities have a crucial role in sustaining human life, and any occurrence of an earthquake or flood within these facilities can result in various social, economic, and environmental issues, either directly or indirectly. Therefore, a quantitative vulnerability assessment of wastewater treatment facilities is necessary to minimize and prevent damage from earthquakes and flood disasters. For this reason, this study introduces a novel indicator to assess the susceptibility of disasters, considering aspects of exposure, sensitivity, and adaptive capacity. The newly proposed indicator encompasses numerous evaluation criteria, topography, natural surroundings, hydraulic systems, structural composition, and non-structural features. Also, Weights derived using the combined weight calculation (CWC) method, which combined the analytic hierarchy process (AHP) and entropy weight method were applied to the indicator. It was tested across 23 cities to validate its efficacy, revealing a substantial correlation between the vulnerability index and the specific attributes of the city's wastewater treatment facilities. Therefore, this study analyzed wastewater treatment facilities by comparing the attributes of the urban areas under investigation, such as topological characteristics, urbanization levels, population density, infrastructure quality, and disaster preparedness resources available. The suggested methodology can facilitate the development of strategies aimed at averting damage caused by earthquakes or floods and reducing the adverse impact on wastewater treatment facilities while considering the unique characteristics of the urban setting in question.

The factors related to depressive symptoms in urban older adults in South Korea: a study based on the Seoul Aging Survey

PurposeThis study aims to comprehensively examine the determinants of depression among urban older adults in Seoul, utilizing the social-ecological model to address multifaceted influences.MethodsA comprehensive analysis was conducted using data from the 2022 Seoul Aging Survey, which surveyed 2,914 individuals aged 65 and above. A multiple logistic regression analysis was performed to identify factors contributing to depressive symptoms, including socio-demographic characteristics, health behaviors and status, research accessibility, social support, and environmental influences.ResultsThis study revealed that poor subjective health (OR = 1.47) and the presence of multiple chronic diseases (OR = 1.59) significantly increased the risk of depressive symptoms among urban older adults. From a social support standpoint, living alone was associated with a higher risk of depression (OR = 1.66), low food security (OR = 2.56), and low digital competency (OR = 2.70) were all significant predictors of depressive symptoms. Additionally, a lack of engagement with cultural facilities (OR = 2.15) was identified as a critical environmental factor contributing to depression.ConclusionsThe findings underscore the need for comprehensive policy and practical interventions aimed at preventing chronic disease, enhancing social support networks, improving digital literacy, ensuring food security, and expanding access to healthcare and cultural facilities. Such measures are crucial in mitigating depression among urban older adults, thereby enhancing their overall well-being and quality of life.

Knowledge of and attitude toward diabetes care as predictors of school nurses' partnership with parents: A cross-sectional study

BackgroundCollaboration between parents and school nurses is important for effective healthcare in schools. This study focuses on the competency of school nurses, which encompasses their knowledge and self-efficacy in diabetes care, and investigates how these factors, along with workload, influence healthcare partnerships in schools. However, it is unknown whether school nurses' knowledge and self-efficacy about diabetes care, as well as their workload, affect school healthcare partnerships concerning children with type 1 diabetes. AimThis study aimed to investigate the impact of school nurses' self-efficacy, knowledge, attitude, and role overload on healthcare partnerships with parents of children with type 1 diabetes in schools. DesignA cross-sectional, descriptive design. Setting and participantsBetween December 2023 and January 2024 in South Korea, 142 elementary- and middle-school nurses participated in this study. MethodsSchool healthcare partnership, self-efficacy in diabetes education, knowledge of and attitude toward school healthcare for type 1 diabetes, and the role-overload scale were utilized in the analysis. Data were analyzed using multiple regression. ResultsKnowledge of school healthcare (β = 0.34, p < .001) and attitude toward it (β = 0.29 p = .001) for type 1 diabetes, as well as the grade level of the current employing school (β = −0.15, p = .039) were predictors of school healthcare partnerships. These three variables explained 30.3 % of the total variance in school healthcare partnerships (F = 21.44, p < .001). ConclusionsKnowledge of school healthcare and attitudes toward it for type 1 diabetes were identified as factors in school nurses' school healthcare partnerships. Therefore, interventions to strengthen school nurses' competencies should be developed to improve school healthcare partnerships.

Risk tolerance towards research and development investment: the role of firm size and technology intensity

ABSTRACT This research examines how risk tolerance towards research and development (R&D) investment varies by firm size and technology intensity using a case study of four groups of manufacturing firms (i.e. large high-tech firms, small high-tech firms, large low-tech firms, and small low-tech firms) in South Korea. Our research contributes to the literature by offering the first empirical application quantifying different risk tolerances towards R&D investment of firms of varying sizes and technology intensities using total factor productivity and optimal portfolio frontiers. We find that the risk tolerance towards R&D investment is proportional to firm size, and high-tech firms tend to be more risk tolerant than low-tech firms given the same firm size. Furthermore, we find that large low-tech firms have a greater tolerance for risk than small high-tech firms when the present guaranteed return is lower, but risk tolerance is reversed as the current guaranteed return increases. These findings could be used to inform R&D project funding decisions in particular, our finding of higher risk tolerance of small high-tech firms with higher present guaranteed return suggests that government subsidies for R&D investment may function as the present guaranteed return for small high-tech businesses.

Psychosocial Factors and Quality of Life in Older Adults Discharged After Isolation Due to COVID-19 Infection.

To identify the impact of psychosocial factors on quality of life (QoL) of older adults hospitalized and isolated after contracting coronavirus disease 2019 (COVID-19). Data were collected between April 30 and June 18, 2022, from 117 discharged older adults who were in isolation for COVID-19 treatment in a tertiary hospital in South Korea. Of participants, 33.3% were at high risk for posttraumatic stress symptoms (PTSS), and 21.4% of participants were identified as having severe depressive symptoms. Participants' QoL negatively correlated with PTSS, depressive symptoms, and fear of social stigma. Depressive symptoms were the primary psychosocial factor identified as significantly affecting QoL (β = -0.682, p < 0.001), and the explanatory power of the regression model was 41.2%. To enhance QoL of older adults who have experienced hospitalization and isolation due to COVID-19, identifying pertinent psychosocial factors, especially depressive symptoms, is necessary. [Journal of Gerontological Nursing, 50(8), 29-36.].

Correlation Between OTs’ Motivational Strategies Used With Stroke Clients &amp; Job Efficacy

Abstract Date Presented 03/23/24 This study explored the relationship between OTs’ motivational strategies with stroke clients and job efficacy. Surveying 212 therapists in South Korea, it found a positive correlation between the strategies used and job efficacy. Primary Author and Speaker: Yujin Lee Contributing Authors: Seong-A Lee

Risk of Depression in Amyotrophic Lateral Sclerosis: A Nationwide Cohort Study in South Korea

Depression is frequently reported in amyotrophic lateral sclerosis (ALS) due to the disastrous prognosis of progressive motor impairment, but the risk of depression in ALS is still unclear. Therefore, we investigated the risk of depression in ALS and analyzed the effect of ALS-related physical disability on the risk of developing depression using the Korean National Health Insurance Service (KNHIS) database. A total of 2,241 ALS patients, as defined by the International Classification Diseases (ICD, G12.21) and Rare Intractable Disease codes (V123), and 1:10 sex- and age-matched controls were selected from the KNHIS. After applying exclusion criteria (non-participation in national health screening, history of depression, or having missing data), 595 ALS patients and 9,896 non-ALS individuals were finally selected. Primary outcome is newly diagnosed depression during follow-up duration defined by ICD code (F32 or F33). A Cox regression model was used to examine the hazard ratios (HRs) after adjustment for potential confounders. During the follow-up period, 283 cases of depression in the ALS group and 1,547 in the controls were recorded. The adjusted HR for depression in ALS was 9.1 (95% confidence interval [CI] 7.87−10.60). The risk of depression was slightly higher in the disabled ALS group (aHR 10.1, 95% CI 7.98−12.67) than in the non-disabled ALS group (aHR 8.78, 95% CI 7.42−10.39). The relative risk of depression was higher in younger patients than in older patients, and in obese patients than in non-obese patients. Our study showed that ALS patients have an increased risk of depression compared to non-ALS individuals.

Feasibility of Big Data Analytics in Disaster Psychiatry: Impact of Seoul Itaewon Tragedy on Sentiment Distribution on Twitter

Numerous studies have highlighted the significant impact of disasters on mental health, often leading to psychiatric disorders among affected individuals. Timely identification of disaster-related mental health problems is crucial to prevent long-term negative consequences and improve individual and community resilience. To address the limitations of prior research that has focused solely on isolated incidents, we analyzed the impact of a recurring Halloween event in Itaewon, South Korea, which culminated tragically in a crowd crush incident in 2022. We conducted sentiment analysis on big data from Korean Twitter to gauge the impact of this disaster on public sentiment. We collected tweets 2 weeks before and after the annual festival from 2020 to 2022, allowing for the consideration of variability across years and days before the disaster. Using a pre-trained RoBERTa neural network model fine-tuned with public sentiment datasets, we categorized tweets into seven pre-defined emotional categories: Anger, sadness, happiness, disgust, fear, surprise, and neutrality. These sentiments were then analyzed as daily time-series data. The overall tweet volume across all sentiment categories increased, particularly showing an increase in the number of tweets indicating “Sadness” in 2022 compared with that in previous years. Post-disaster, a substantial increase was noted in the proportion of tweets expressing “Sadness” and “Fear.” This trend was confirmed by Seasonal Autoregressive Integrated Moving Average with Exogenous Regressor models. Notably, there was an increase in the number of tweets expressing all sentiments, including “Happy.” However, significant changes in proportions were observed only in tweets categorized as expressing “Sadness” [0.046 (95% CI: 0.024–0.068, P<0.0001)] and “Fear” [0.033 (95% CI: 0.014–0.051, P<0.0001)]. Our study demonstrates the feasibility of using sentiment data from social media, combined with sentiment classification, to assess distinct public mental health features following disasters. This approach provides valuable insights into the emotional impact of each event.

From Funds to Footprints: Unravelling the Asymmetric Association between Nuclear Energy Technology and Environmental Quality

Innovation and investment in research and development (R&D) for renewable energy are critical in improving environmental quality and creating a more sustainable energy landscape. This research delves into the asymmetric correlation between nuclear energy technology budget and environmental quality in the top 10 economies with the largest nuclear energy R&D budgets (Germany, South Korea, Japan, the UK, France, Russia, China, USA, India, and Canada). Earlier investigations have used panel data methodologies that submit usual outcomes related to the link between these variables, although some countries did not show a similar relationship independently. Moreover, previous studies analyzed the overall impact, while our investigation focuses on examining the asymmetric impact on an individual country level. On the other hand, we apply the Quantile-on-Quantile tool to probe the time-series dependence in every country, bestowing worldwide yet country-specific evidence for the variables' interconnection. The findings suggest that budgets related to nuclear energy technology positively impact environmental quality by reducing ecological footprint in most economies, especially at the specific distribution of data points. In addition, this research highlights that the degree of asymmetry between the variables deviates across economies, heightening the importance of carefully implementing policies related to nuclear energy technology and environmental quality.

Source Attribution of Health Burdens From Ambient PM2.5, O3, and NO2 Exposure for Assessment of South Korean National Emission Control Scenarios by 2050.

We quantify anthropogenic sources of health burdens associated with ambient air pollution exposure in South Korea and forecast future health burdens using domestic emission control scenarios by 2050 provided by the United Nations Environment Programme (UNEP). Our health burden estimation framework uses GEOS-Chem simulations, satellite-derived NO2, and ground-based observations of PM2.5, O3, and NO2. We estimate 19,000, 3,300, and 8,500 premature deaths owing to long-term exposure to PM2.5, O3, and NO2, respectively, and 23,000 NO2-associated childhood asthma incidences in 2016. Next, we calculate anthropogenic emission contributions to these four health burdens from each species and grid cell using adjoint sensitivity analysis. Domestic sources account for 56%, 38%, 87%, and 88% of marginal emission contributions to the PM2.5-, O3-, and NO2-associated premature deaths and the NO2-associated childhood asthma incidences, respectively. We project health burdens to 2050 using UNEP domestic emission scenarios (Baseline and Mitigation) and population forecasts from Statistics Korea. Because of population aging alone, there are 41,000, 10,000, and 20,000 more premature deaths associated with PM2.5, O3, and NO2 exposure, respectively, and 9,000 fewer childhood asthma incidences associated with NO2. The Mitigation scenario doubles the NO2-associated health benefits over the Baseline scenario, preventing 24,000 premature deaths and 13,000 childhood asthma incidences by 2050. It also slightly reduces PM2.5- and O3-associated premature deaths by 9.9% and 7.0%, unlike the Baseline scenario where these pollutants increase. Furthermore, we examine foreign emission impacts from nine SSP/RCP-based scenarios, highlighting the need for international cooperation to reduce PM2.5 and O3 pollution.

Education fever in South Korea: Rite of passage versus children’s rights

Education fever in South Korea: Rite of passage versus children’s rights

A machine learning model to predict liver-related outcomes after the functional cure of chronic hepatitis B.

The risk of hepatocellular carcinoma (HCC) and hepatic decompensation persists after hepatitis B surface antigen (HBsAg) seroclearance. This study aimed to develop and validate a machine learning model to predict the risk of liver-related outcomes (LROs) following HBsAg seroclearance. A total of 4,787 consecutive patients who achieved HBsAg seroclearance between 2000 and 2022 were enrolled from 6 centers in South Korea and a territory-wide database in Hong Kong, comprising the training (n=944), internal validation (n=1,102), and external validation (n=2,741) cohorts. Three machine learning-based models were developed and compared in each cohort. The primary outcome was the development of any LRO, including HCC, decompensation, and liver-related death. During a median follow-up of 55.2 (interquartile range=30.1-92.3) months, 123 LROs were confirmed (1.1%/person-year) in the Korean cohort. A model with the best predictive performance in the training cohort was selected as the final model (designated as PLAN-B-CURE), which was constructed using a gradient boosting algorithm and 7 variables (age, sex, diabetes, alcohol consumption, cirrhosis, albumin, and platelet count). Compared to previous HCC prediction models, PLAN-B-CURE showed significantly superior accuracy in the training cohort (c-index: 0.82 vs. 0.63-0.70, all P<0.001; area under the receiver operating characteristic curve: 0.86 vs. 0.62-0.72, all P<0.01; area under the precision-recall curve: 0.53 vs. 0.13-0.29, all P<0.01). PLAN-B-CURE showed a reliable calibration function (Hosmer-Lemeshow test P>0.05) and these results were reproduced in the internal and external validation cohorts. This novel machine learning model consisting of 7 variables provides reliable risk prediction of LRO after HBsAg seroclearance that can be used for personalized surveillance.

New technologies for beam spectrometry, quality assurance, real-time monitoring and microdosimetry in BNCT

Boron neutron capture therapy (BNCT) is a next-generation radiotherapy that combines neutron beams with boron compounds that selectively accumulate in cancer cells. Because this therapy requires neutrons for irradiation, compact accelerator-based neutron source devices that can be installed in hospitals are being developed around the world. Clinical trials of several devices have been conducted in Japan, China, and South Korea. The most advanced device was approved by the Japanese regulatory authorities in 2020. As a result, BNCT with the device is being implemented as an insured therapy for recurrent head and neck cancer at two hospitals in Japan. Thus, the development of accelerator-based neutron generators is proceeding in the field of BNCT. However, much remains to be done to establish this therapy as a common cancer treatment and to make it available worldwide. Dosimetry methods in BNCT are one of the issues. In the treatment, the dose given to a patient by neutron irradiation has to be estimated accurately. However, it is difficult to measure neutrons accurately and in real time. Currently, the neutron dose delivered to patients during treatment is not measured. Instead, the current of the charged particles that irradiate the neutron target material is measured to indirectly evaluate the neutron fluence and dose. In addition, accurate dose estimation based on reactions with neutrons and various elements in a human body uses the Monte Carlo method, which is computationally time-consuming. To address these dosimetry issues, several neutron fluence and dose measurement methods are being developed. This review briefly describes the current dosimetry methods and then presents several methods under development that allow real-time neutron measurements applicable to BNCT.

Protein Adequacy, Plant Protein Proportion, and Main Plant Protein Sources Consumed Across Vegan, Vegetarian, Pescovegetarian, and Semivegetarian Diets: A Systematic Review

BackgroundThere are several types of plant-based diets, with unknown differences across diets on total/plant protein intake and variety of plant protein sources consumed. ObjectivesThis systematic review aimed to compare total protein intake, proportion of plant proteins, and main plant protein sources consumed across 4 primarily plant-based diets: vegan, vegetarian, pescovegetarian, and semivegetarian. MethodsWe included observational studies reporting on protein intake and/or protein sources in generally healthy adults that were published between 2002 and 2023. We determined the following: 1) % energy from total and plant protein; 2) the proportion of plant protein relative to total protein intake; and 3) main plant protein sources (median percentage contribution of each source to total plant protein intake; interquartile range) consumed across the 4 diets. The plant protein sources were broadly classified into the following United States Department of Agriculture food groups: grains; nuts and seeds; soy products; and beans, peas, and lentils. ResultsWe included 13 studies reporting on protein intake/sources that were conducted in the United States, Europe, and South Korea. Of these, 7 reported on vegan, 11 on vegetarian, 7 on pescovegetarian, and 7 on semivegetarian diets with total protein intake ranging from 10% to 17.4%. Vegan diets had the highest plant protein proportion (range: 77%–98%) and semivegetarian diets the lowest (range: 37%–83%). Plant protein source contribution was the highest from grains (range: 60%–78%). Nuts and seeds were the most consumed in vegetarian diets (7.9%; 2.9%–10.3%) and least in semivegetarian diets (3.7%; 2%–14.8%). Soy products and beans, peas, and lentils were most consumed in vegan diets (17.3%; 16.3%–19.9, and 19.6%; 14.6%–21.3, respectively) and least in semivegetarian (3.7%; 1.3%–13.9%, and 8.5%; 5.2%–10.2%) diets. ConclusionsVegan diets has the highest plant protein proportion and a variety of plant protein sources, while semivegetarian diets has the lowest plant protein proportion and mainly relied on grains as a plant protein source.

Posttraumatic growth and psychopathological symptoms as predictors in the integration of North Korean adolescent refugees: Latent profile analysis.

This study is to explore identifiable psychological differences and predictors for the subgroup of refugees who at a very early age moved from a totalitarian country to a westernized democracy, ultimately to improve policies and reduce the maladaptation of refugees to a new society. More specifically, it is to fill in significant gaps in understanding and predicting refugee tendencies toward depression, social withdrawal, aggression, and posttraumatic growth. Latent class analysis was conducted to identify subgroups based on symptom profiles within a sample of 201 North Korean adolescent refugees residing in South Korea. Four subgroups were identified: (a) High growth, (b) Low trauma with high adaptive aggression, (c) Low trauma with high social withdrawal/depression, and (d) High trauma with high comorbidity. Predictors were physical health, self-esteem, peer support, teacher support, and school adjustment. Current refugee policies in South Korea would benefit from refining services to allow customization to the specific needs and characteristics of each adolescent rather than continue the current model of homogeneity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Analysis of Prescription Trends for Narcotic Appetite Suppressants: Utilizing the Narcotics Information Management System.

The aim of this study was to systematically analyze the prescription trends of medical narcotic appetite suppressants in South Korea. Data was extracted from the Narcotics Information Management System dataset from 2020, which encompasses nationwide information concerning the use of medical narcotics. The selected variables for this study included the types of prescribed medical narcotic appetite suppressants, gender, age, region, and the category of medical institution. Regional prescription trends were compared by utilizing the defined daily doses for statistical purposes (S-DDD). The prescription of medical narcotic appetite suppressants was predominantly for females (94%), with the highest prescription rates identified in the 30-40 age group. The majority of these prescriptions were dispensed by clinics. Within the category of narcotic appetite suppressants, phentermine and phendimetrazine were found to have higher prescription rates. Notably, the region of Daegu recorded the highest S-DDD value (12.66) in phentermine consumption. Our findings underscore the need for governmental policy and guidance to address the risks linked to the long-term use of medical narcotic appetite suppressants. This is crucial to ensure their safe and efficacious prescription and administration.