• Journal of Information Technology Services
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• v.22 no.4
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• pp.171-184
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• 2023

FinTech, which has brought innovation to the financial industry thanks to the advancements in ICT since 2010, has contributed to the growth of the financial ecosystem and increased consumer benefits. Furthermore, there has been a growing demand for social responsibility and sustainability in financial institutions, which have a significant impact on governments, businesses, and people's lives. Despite this, many FinTech companies and traditional financial institutions are still in the early stages of establishing ESG (Environmental, Social, and Governance) management philosophy or lack long-term plans. In this study, we aim to examine the impact of ESG management on the FinTech industry, focusing on representative domestic cases, and derive policy and institutional measures to spread it in the financial industry. Specifically, we will adopt MSCI rating indicators, which are internationally accepted by various industries such as manufacturing, healthcare, and transportation, to evaluate the 35 ESG management subcategories of FinTech companies. As a result, a total of 22 compliance items were disclosed in the ESG report, and it was possible to confirm the detailed management. Through this, we intend to propose effective management strategies for the organizational structure, operations, programs, and performance evaluation of FinTech companies, which are positioning themselves as sustainable growth drivers in the domestic industry.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.35-46
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• 2023

In this study, we propose a Dual Approach methodology to enhance the accuracy of document classifiers by utilizing both contextual and keyword information. Firstly, contextual information is extracted using Google's BERT, a pre-trained language model known for its outstanding performance in various natural language understanding tasks. Specifically, we employ KoBERT, a pre-trained model on the Korean corpus, to extract contextual information in the form of the CLS token. Secondly, keyword information is generated for each document by encoding the set of keywords into a single vector using an Autoencoder. We applied the proposed approach to 40,130 documents related to healthcare and medicine from the National R&D Projects database of the National Science and Technology Information Service (NTIS). The experimental results demonstrate that the proposed methodology outperforms existing methods that rely solely on document or word information in terms of accuracy for document classification.

• Fisheries and Aquatic Sciences
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• v.26 no.1
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• pp.35-47
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• 2023

Myelophycus caespitosus, a brown alga belonging to genus Myelophycus, has been traditionally used as a food and medicinal resource in Northeastern Asia. However, few studies have been conducted on its pharmacological activity. In this study, we evaluated whether methanol extract of M. caespitosus (MEMC) could protect against oxidative damage caused by hydrogen peroxide (H₂O₂) in C2C12 murine myoblasts. Our results revealed that MEMC could suppress H₂O₂-induced growth inhibition and DNA damage while blocking the production of reactive oxygen species. In H₂O₂-treated cells, cell cycle progression was halted at the G2/M phase, accompanied by changes in expression of key cell cycle regulators. However, these effects were attenuated by MEMC. In addition, we found that MEMC protected cells from induction of apoptosis associated with mitochondrial impairment caused by H₂O₂ treatment. Furthermore, MEMC enhanced the phosphorylation of nuclear factor-erythroid-2 related factor 2 (Nrf2) and expression and activity of heme oxygenase-1 (HO-1) in H₂O₂-treaetd C2C12 myoblasts. However, such anti-apoptotic and cytoprotective effects of MEMC were greatly abolished by HO-1 inhibitor, suggesting that MEMC could increase Nrf2-mediated activity of HO-1 to protect C2C12 myoblasts from oxidative stress.

• Journal of The Korean Society of Integrative Medicine
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• v.11 no.2
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• pp.1-13
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• 2023

Purpose : Along with the rapid development of digital technology, the application of digital healthcare in the medical field is also increasing. According to many experts, increasing the amount of exercise and physical activity is a helpful way to prevent and manage physical problems in modern society. However, a lack of exercise, which is of the lifestyle of modern people, leads to the development of various diseases. This study aimed to examine the effects of digital exercise intervention using artificial intelligence (AI) on the physical abilities of adults whether digital exercise intervention can be a reliable and effective therapeutic option for musculoskeletal disorders in real-world clinical settings. Methods : In this study, exercise was conducted using a digital application to investigate the effects of an AI-based digital exercise intervention on the physical abilities of adults. A total of 13 adults were evaluated, and their physical abilities before and after the exercise intervention were compared. Hand-grip strength, functional leg muscle strength, dynamic balance, and quadriceps muscle strength were assessed. Exercise was performed using a digital application and in a non-face-to-face manner. AI identified the exercise status of each participant and adjusted the exercise difficulty level accordingly. The exercised daily for 4 weeks. Results : A total of 12 participants were analyzed for the final results. Significant improvements were observed in hand-grip strength, functional leg muscle strength (evaluated using the stand-up test), dynamic balance, and straight-gait ability (p<.05), indicating an increase in the overall muscular strength and physical function of the participants. Conclusions : Digital exercise intervention using AI is effective in improving physical abilities related to musculoskeletal function. It can be useful in clinical practice as an effective treatment option for patients with musculoskeletal disorders or muscle weakness.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.184-191
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• 2022

This study aims to provide basic information necessary to find an efficient management plan for patients using auto insurance. The analysis was conducted on the five-year auto insurance medical expenses review data registered in the health care bigdata Hub from 2016 to 2020. As a result of the analysis, the number one composition ratio of auto insurance inpatient treatment expenses was treatment and surgery fees for Certified tertiary hospitals, hospitalization fees for general hospitals, hospitals and clinics, and treatment and surgery fees for oriental medical institutions and dental hospitals. outpatient treatment expenses was doctor's fee for medical institution, treatment and surgery fees for oriental medical institutions and dental hospitals. The ratio of medication, anesthesia, and special equipment significantly affected the cost of inpatient. And the ratio of physical therapy significantly affected the cost of outpatient.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.270-279
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• 2022

The purpose of this study was to find the influential factors of nursing students' behavioral intention of Recommended Immunizations for Healthcare Personnel(RIHP). The survey was performed on 260 nursing students. Data were collected using a structured questionnaires and analyzed using t-test, ANOVA, Pearson correlation coefficient, and hierarchical regression with SPSS 23.0 program. Results of this study revealed that the influential factors on the behavioral intention of RIHP were the cues to action, self-efficacy, perceived benefits and senior grade. And the explanation power of the regression model appeared as being 36.4%(F=13.35, p<.001). Based on the study findings, further development and application of specific programs to improve nursing students' intention of RIHP in consideration of grade, to emphasize benefits of immunization, are needed to prevent infection in clinical practice.

• Convergence Security Journal
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• v.22 no.1
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• pp.129-134
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• 2022

A smart platform is defined as an evolved platform that realizes physical and virtual space into a hyper-connected environment by combining the existing platform and advanced IT technology. The hyper-connection that is the connection between information and information, infrastructure and infrastructure, infrastructure and information, or space and service, enables the realization and provision of high-quality services that significantly change the quality of life and environment of users. In addition, it is providing everyone with the effect of significantly improving the social safety net and personal health management level by implementing smart government and smart healthcare. A lot of information produced and consumed in these processes can act as a factor threatening the basic rights of the public and individuals by the informations themselves or through big data analysis. In particular, as the smart platform as a core function that forms the ecosystem of a smart city is naturally and continuously expanded, it faces a huge security burden in data processing and network operation. In this paper, platform components as core functions of smart city and appropriate security threats and countermeasures are studied.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.107-115
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• 2022

The purpose of this study was to investigate the level of infection control knowledge and standard precaution practice and to identify effecting factors on standard precaution practice among nurses in small and midium-sized hospitals. A cross-sectional study was conducted with a sample of 258 nurses from 7 hospitals between July and August 2021. The mean score of infection control knowledge and standard precaution practice was 7.25 and 3.61, respectively. There were significant differences in standard precaution practice according to clinical experience (r=.123, p=.047) and position (F=5.356, p=.005). Infection control knowledge and standard precaution practice were closely correlated (r=.421, p<.001). Position (β=-.187, p=.025) and infection control knowledge (β=.408, p<.001) had an effect on standard precaution practice. It could be possible to enhance the standard precaution practice through convergence education program related to infection control among staff nurses.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.761-770
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• 2023

This study was conducted to identify factors affecting depression in the elderly using three-year Community Health Survey data from 2020, when COVID-19 was declared an epidemic. Differences in depression according to general characteristics, health behavior, subjective health level, and medical use among 220,921 elderly were analyzed using complex samples t-test and ANOVA, and multiple regression analysis was performed to identify factors affecting depression it was carried out. As a result of the study, the level of depression among elderly women was found to be high, with an average of 1.21±0.01 for elderly men and 1.74±0.02 for elderly women, and there was a difference in generation type, with depression being higher in the first generation for elderly men and the third generation for elderly women. Variables that had a great influence on depression were the experience of depression and perceived stress.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3907-3912
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• 2023

Radiation portal monitors (RPMs) installed at airports and harbors to prevent illicit trafficking of radioactive materials generally use large plastic scintillators. However, their energy resolution is poor and radionuclide identification is nearly unfeasible. In this study, to improve isotope identification, a RPM system based on a multi-array plastic scintillator and convolutional neural network (CNN) was evaluated by measuring the spectra of radioactive sources. A multi-array plastic scintillator comprising an assembly of 14 hexagonal scintillators was fabricated within an area of 50 × 100 cm². The energy spectra of ¹³⁷Cs, ⁶⁰Co, ²²⁶Ra, and ⁴K (KCl) were measured at speeds of 10-30 km/h, respectively, and an energy-weighted algorithm was applied. For the CNN, 700 and 300 spectral images were used as training and testing images, respectively. Compared to the conventional plastic scintillator, the multi-arrayed detector showed a high collection probability of the optical photons generated inside. A Compton maximum peak was observed for four moving radiation sources, and the CNN-based classification results showed that at least 70% was discriminated. Under the speed condition, the spectral fluctuations were higher than those under dwelling condition. However, the machine learning results demonstrated that a considerably high level of nuclide discrimination was possible under source movement conditions.