• Korean Journal of Exercise Nutrition
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• v.13 no.1
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• pp.15-21
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• 2009

Ergogenic aids are substances, devices, and practices that enhance an individual's energy use and production, and recovery from fatigue. L-carnitine increases enhance performance and aerobic capacity by stimulating lipid oxidation in muscle cells during long term exercise. L-carnitine is a well known and widely used ergogenic aid. In the present study, the effect of L-carnitine at concentrations of 100 nM, 1 μM, 10 μM, and 100 μM on the amplitude of field potential in rat cardiac muscle slices was measured using multi-channel extracellular recording (MED 64) system. In the present result, L-carnitine was shown to enhance field potential as a does-dependent manner. The increasing effect of the L-carnitine on field potential was not affected by application of the glibenclamide, an ATP-dependant K⁺ channel antagonist. The increasing effect of L-carnitine on field potential was suppressed by application of the diazoxide, an ATP-dependent K⁺ channel agonist. Present data show that L-carnitine potentiates field potentials by inhibition on ATP-dependant K⁺ channel in cardiac muscles. The enhancing effect of the L-carnitine on the field potential in cardiac muscles can be suggested as one of the underlying mechanism of ergogenic aid of the L-carnitine.

• Korean Journal of Community Nutrition
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• v.28 no.6
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• pp.480-494
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• 2023

Objectives: This study sought to assess the effectiveness of community-based nutrition counseling on improving nutritional status, managing complex chronic diseases, and enhancing the quality of life for elderly individuals with chronic conditions, particularly in older adults with high levels of food insecurity and multiple chronic illnesses. Methods: Thirty elderly subjects with diabetes and hypertension who were registered at local Senior Welfare Center received individualized nutrition counseling, based on their Nutrition Quotient for the Elderly (NQ-E) index. Over a 16-week period, they received tailored counseling and underwent various health and nutritional assessments. The final analysis included 28 participants after two dropped out. Data analysis was conducted using the SPSS v28.0. Results: The subjects were over 70, with multiple chronic diseases including diabetes and hypertension and predominantly female. After 16 weeks, significant improvements were observed in the subjects' grip strength, and HbA1c levels, as well as in their NQ-E scores, indicating improved dietary balance and diversity. There were no significant improvements in the 'Moderation' subdomain of the NQ-E index, suggesting that this aspect requires further attention in nutritional counseling. The subjects' nutritional risk scores (NSI) were also significantly decreased, indicating less nutritional risk. Lastly, as measured by the SF-36K, the subjects' quality of life showed significant improvement in several domains including physical role performance and social function. Conclusions: This study demonstrates that tailored nutrition counseling, based on the NQ-E index, can improve elderly health, manage chronic diseases, and enhance quality of life. This approach potentially broadens the scope of community nutritionists' roles within an aging society. However, additional research is necessary to evaluate these interventions' long-term effects and sustainability.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.126-133
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• 2023

In this study, electromyography was obtained in the six muscle areas that move the joints of the two legs, and by analyzing it, an exercise robot system capable of gait rehabilitation was proposed in consideration of the individual's muscle state. Through this, the system was constructed to prevent the effect of exercise from decreasing because the patient's will was not reflected when walking exercise was simply provided automatically. As a result of the evaluation of the developed system, it was confirmed that the pedestrian rehabilitation robot system manufactured through this study had performance suitable for the design requirements, and it was also confirmed that the usability evaluation was comprehensively satisfactory. The results of this study are thought to be of great help to patients who are having difficulty in gait rehabilitation, and are believed to be helpful in the development of electromyography signal-based gait robot systems.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.207-228
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• 2023

After COVID-19, organizations have widely adopted platforms such as zoom or developed their proprietary online real-time systems for remote work, with recent forays into incorporating the metaverse for meetings and publicity. While ongoing studies investigate the impact of avatar customization, expansive virtual environments, and past virtual experiences on participant satisfaction within virtual reality or metaverse settings, the utilization of the metaverse as a dedicated workspace is still an evolving area. There exists a notable gap in research concerning the factors influencing the performance of the metaverse as a workspace, particularly in non-immersive work-type metaverses. Unlike studies focusing on immersive virtual reality or metaverses emphasizing immersion and presence, the majority of contemporary work-oriented metaverses tend to be non-immersive. As such, understanding the factors that contribute to the success of these existing non-immersive metaverses becomes crucial. Hence, this paper aims to empirically analyze the factors impacting personal outcomes in the non-immersive metaverse workspace and derive implications from the results. To achieve this, the study adopts the Embodied Social Presence (ESP) model as a theoretical foundation, modifying and proposing a research model tailored to the non-immersive metaverse workspace. The findings validate that the impact of presence on task engagement and task involvement exhibits a moderating effect based on the metaverse platform used. Following interviews with participants engaged in non-immersive metaverse workplaces (specifically Gather Town and Ifland), a survey was conducted to gather comprehensive insights.

• Journal of Music and Human Behavior
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• v.21 no.1
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• pp.1-27
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• 2024

This study investigates the link between music integration in the lives of adults in their twenties and their school music experiences. Ten individuals in their twenties were interviewed to explore their experiences based on the self-determination theory's fundamental psychological needs (autonomy, competence, and relatedness). Participants were categorized into an active music engagement group (5 individuals) and an inactive group (5 individuals) for individual interviews. Transcripts were analyzed following the five steps of grounded theory data analysis technique. Results indicated a strong connection between music activities during school years and current motivation for music integration, associated with the fulfillment of psychological needs outlined in the self-determination theory. Particularly, this study identified the instructional methods, school music activities, and performance evaluations as closely related to autonomy, competence, and relatedness. It offers a comprehensive analysis of how experiences in these areas during school music activities correlate with values and motivations for music integration in adulthood. Additionally, the study suggests ways to promote the voluntary incorporation of music into life through positive experiences of autonomy, competence, and relatedness in music activities.

• Journal of Cadastre & Land InformatiX
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• v.54 no.1
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• pp.19-32
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• 2024

This study examined the feasibility of image-based surveys by detecting objects in facilities and agricultural land using the YOLO algorithm based on drone images and comparing them with the land category by law. As a result of detecting objects through the YOLO algorithm, buildings showed a performance of detecting objects corresponding to 96.3% of the buildings provided in the existing digital map. In addition, the YOLO algorithm developed in this study detected 136 additional buildings that were not located in the digital map. Plastic greenhouses detected a total of 297 objects, but the detection rate was low for some plastic greenhouses for fruit trees. Also, agricultural land had the lowest detection rate. This result is because agricultural land has a larger area and irregular shape than buildings, so the accuracy is lower than buildings due to the inconsistency of training data. Therefore, segmentation detection, rather than box-shaped detection, is likely to be more effective for agricultural fields. Comparing the detected objects with the land category by law, it was analyzed that some buildings exist in agricultural and forest areas where it is difficult to locate buildings. It seems that it is necessary to link with administrative information to understand that these buildings are used illegally. Therefore, at the current level, it is possible to objectively determine the existence of buildings in fields where it is difficult to locate buildings.

• Knowledge Management Research
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• v.25 no.2
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• pp.95-114
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• 2024

Because it is difficult to respond to a constantly changing environment with individual ability and creativity alone, many organizations are forming teams and seeking ways to make the teams more active. Team learning behavior allows team members to and create better performance based on such accumulated knowledge and experience within a team. In particular, the process of team learning not only explicit and formalized knowledge but also implicit and informal experiences is important from the perspective of knowledge management. However, there were limitations in utilizing research results on team learning behavior because the concepts were fragmented and the measurements were different for each researcher. In this study, an integrated model was presented by examining concepts related to team learning behaviors. Moreover, the measurement model of team learning behaviors was validated for the Korean context. The measurement model consisted of five factors: sharing and elaboration, constructive conflict, team reflection, team activity, and storage and utilization. This tool was confirmed through exploratory factor analysis and confirmatory factor analysis. The results of this study are expected to have implications for team researchers and practitioners who diagnose and improve the level of team learning behavior within an organization.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Korean Journal of Radiology
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• v.23 no.8
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• pp.811-820
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• 2022

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

• Journal of Physiology & Pathology in Korean Medicine
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• v.38 no.1
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• pp.1-9
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• 2024

Cognitive reserve (CR) is a concept that can explain the discrepancies between the pathologic burden of the disease and clinical manifestations. It refers to the individual susceptibility to age-related brain changes and pathologies related to Alzheimer's disease, thus recognized as a factor affecting the trajectories of the disease. The purpose of this study was to explore the current states of clinical studies on neural substrates of CR in Alzheimer's disease using functional magnetic resonance imaging. We searched for clinical studies on CR using fMRI in the Pubmed, Cochrane library, RISS, KISS and ScienceON on August 14, 2023. Once the online search was finished, studies were selected manually by the inclusion criteria. Finally, we analyzed the characteristics of selected articles and reviewed the neural substrates of CR. Total thirty-four studies were included in this study. As surrogate markers of CR, not only education and occupational complexity, but also composite score and questionnaire-based method, which cover various areas of life, were mainly used. The most utilized methods in resting-state fMRI were independent component analysis, seed-based analysis, and graph theory analysis. Through the analysis, we demonstrated that neuroimaging techniques could capture the neural substrates associated with cognitive reserve. Moreover, functional connectivity of brain regions centered on prefrontal and parietal cortex and network areas such as default mode network showed a significant correlation with CR, which indicated a significant association with cognitive performance. CR may induce differential effects according to the disease status. We hope that this perspective on cognitive reserve would be helpful when conducting clinical researches on the mechanisms of traditional Korean medicine for Alzheimer's disease in the future.