• Food Science and Preservation
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• v.31 no.1
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• pp.99-111
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• 2024

The antioxidant potentials of ethanolic extracts derived from Aster yomena (A. yomena) were evaluated by assessing their total phenolic and flavonoid contents and radical scavenging activities. Our findings revealed that the 60% ethanolic extract of A. yomena exhibited the most robust antioxidant properties among all extracts tested. Specifically, the IC₅₀ values for the 2,2'-azino-bis (3-ethyl benzothiazoline-6-sulfonic acid) and 1,1-diphenyl-2-picrylhydrazyl radical scavenging activities of the 60% ethanolic extract from A. yomena were determined to be 1,640.30 ㎍/mL and 2,655.10 ㎍/mL, respectively. Moreover, the inhibitory effect on malondialdehyde increased with the 60% ethanolic extract from A. yomena. To assess the neuroprotective effects, we examined the impact of the 60% ethanolic extract from A. yomena against H₂O₂-induced cytotoxicity in HT-22 (mouse hippocampal neuronal cell line) and SK-N-MC (human neuroblastoma cell line) cells. The results demonstrated a significant improvement in cell viability and reduced intracellular oxidative stress. Furthermore, the major bioactive compounds present in the 60% ethanolic extract from A. yomena were identified as chlorogenic acid and rutin through high-performance liquid chromatography (HPLC) analysis.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Journal of Physiology & Pathology in Korean Medicine
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• v.38 no.2
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• pp.49-58
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• 2024

The rapid advancement of generative large language models has revolutionized various real-life domains, emphasizing the importance of exploring their applications in healthcare. This study aims to examine how generative large language models are implemented in the medical domain, with the specific objective of searching for the possibility and potential of integration between generative large language models and East Asian medicine. Through a comprehensive current state analysis, we identified limitations in the deployment of generative large language models within East Asian medicine and proposed directions for future research. Our findings highlight the essential need for accumulating and generating structured data to improve the capabilities of generative large language models in East Asian medicine. Additionally, we tackle the issue of hallucination and the necessity for a robust model evaluation framework. Despite these challenges, the application of generative large language models in East Asian medicine has demonstrated promising results. Techniques such as model augmentation, multimodal structures, and knowledge distillation have the potential to significantly enhance accuracy, efficiency, and accessibility. In conclusion, we expect generative large language models to play a pivotal role in facilitating precise diagnostics, personalized treatment in clinical fields, and fostering innovation in education and research within East Asian medicine.

• Advances in nano research
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• v.16 no.6
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• pp.623-638
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• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Journal of Arbitration Studies
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• v.34 no.1
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• pp.75-101
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• 2024

"ESG management" guides companies to prioritize corporate social responsibility and sustainable development as key management objectives, going beyond mere financial performance pursuits. This approach involves creating a sustainable and robust supply chain by urging companies, acting as 'supply chain managers', to implement ESG management practices alongside their 'supply chain partners'. The domestic business community has been quick to respond to this trend, recognizing that failure to adhere to ESG standards set by organizations such as the EU and SEC could lead to severe repercussions, including exclusion from international trade and reputational damage. POSCO and NAVER, two leading Korean companies, are at the forefront of practicing ESG management effectively. They have both produced and publicly disclosed ESG management reports, showcasing their success in enhancing supply chain ESG management. However, as supply chain managers enforce ESG-related obligations on their suppliers, the likelihood of disputes between the parties may increase. In scenarios where supply chain ESG management leads to conflicts between supply chain managers and suppliers, commercial arbitration emerges as a viable solution for dispute resolution. This method offers several advantages, including the arbitrators' expertise, time and cost efficiency, the binding nature of decisions akin to a court's final judgment, international recognition under the New York Convention, confidentiality, and ample opportunity for parties to be heard. Our analysis focuses on the emerging disputes between supply chain managers and suppliers within the context of supply chain ESG management, particularly examining the cases of POSCO and NAVER. By categorizing the expected types of disputes and assessing the appropriateness of commercial arbitration for their resolution, we highlight the effectiveness of this approach. Furthermore, we propose leveraging the Korean Commercial Arbitration Board's role to enhance the use of arbitration in resolving supply chain ESG disputes, underscoring its potential as a strategic tool for maintaining sustainable and harmonious supply chain relationships.

• Convergence Security Journal
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• v.23 no.4
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• pp.93-100
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• 2023

This paper presents a novel approach for assigning group keys within a dynamic swarm unmanned system environment. In this environment, multiple groups of unmanned systems have the flexibility to merge into a single group or a single unmanned system group can be subdivided into multiple groups. The proposed protocol encompasses two key steps: group key generation and sharing. The responsibility of generating the group key rests solely with the leader node of the group. The group's leader node employs a secret sharing scheme to fragment the group key into multiple fragments, which are subsequently transmitted. Nodes that receive these fragments reconstruct a fresh group key by combining their self-generated secret fragment with the fragment obtained from the leader node. Subsequently, they validate the integrity of the derived group key by employing the hash function. The efficacy of the proposed technique is ascertained through an exhaustive assessment of its security and communication efficiency. This analysis affirms its potential for robust application in forthcoming swarm unmanned system operations scenarios characterized by frequent network group modifications.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.466-475
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• 2024

Matched-Field Processing (MFP) is a model-based approach that requires accurate knowledge of the ocean environment and array geometry (e.g., array tilt) to localize underwater acoustic sources. Consequently, it is inherently sensitive to model mismatches. In contrast, the waveguide invariant-based approach (also known as array invariant) offers a simple and robust means for source-range estimation in shallow waters. This approach solely exploits the beam angles and travel times of multiple arrivals separated in the beam-time domain, requiring no modeling of the acoustic fields, unlike MFP. This paper extends the waveguide invariant-based approach to shallow water environments featuring a shallow pit, where the waveguide invariant is not defined due to the complex bathymetry. An in-depth performance analysis is conducted using experimental data and numerical simulations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.801-809
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• 2024

In the digital age, the importance of personal information has magnified, underscoring the need for enhanced personal information protection, especially within public institutions. Despite ongoing efforts since 2007, significant breaches in public sector information underline persistent vulnerabilities. This study advocates for a transition from a diagnostic to an assessment framework to fortify privacy management in public institutions, as mandated by recent legislative revisions. The amended Personal Information Protection Act introduces an assessment approach, aiming to comprehensively assess and mitigate risks by expanding the scope of evaluation and implementing robust regulatory measures. This study examines the limitations of the current diagnostic practices through literature review and case analysis and proposes a systematic approach to adopting the new assesment system. By enhancing the assessment framework, the study expects to improve the effectiveness of personal information management in public institutions, thereby restoring public trust and ensuring a stable progression into a more secure digital era. The transition to an assessment system is designed not only to address the gaps in the current framework but also to provide a methodical assessment that supports ongoing improvement and compliance with enhanced legal standards.

• Journal of the Korean Society of Clothing and Textiles
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• v.48 no.4
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• pp.793-807
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• 2024

In response to the growing focus on products utilizing character IP in retail, this study investigated the impact of consumers' recollection of character IP fashion products on brand relationship formation. Furthermore, it explores the moderating effect of consumer preferences for collaboration series on this relationship. Participants were consumers who recalled character IP fashion products, and the analysis was conducted using PLS-SEM. The findings show that consumer recollection associated with character IP in fashion products positively affects brand attachment, leading to a positive impact on brand attitude and brand loyalty. The study also highlights the effectiveness of repeated exposure to similar products as an effective marketing strategy. Furthermore, a positive synergy between brand attachment and preferences for collaboration series enhances brand loyalty by influencing consumer attitudes. The study suggests that collaboration series marketing strategies affect consumers' perceptions of novelty and alter their expectations. This research has significant academic and practical implications, demonstrating the effectiveness of marketing through collaboration series and revealing how character IP in fashion products, commonly used by fashion brands, shapes brand-consumer relationships. The findings provide a robust theoretical foundation for marketing strategies leveraging character IP.

• Advances in nano research
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• v.17 no.2
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• pp.181-195
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• 2024

This study introduces a novel functionally graded material model, termed the "Coated Functionally Graded Graphene-Reinforced Composite (FG GRC)" model, for investigating the free vibration response of plates, highlighting its potential to advance the understanding and application of material property variations in structural engineering. Two types of coated FG GRC plates are examined: Hardcore and Softcore, and five distribution patterns are proposed, namely FG-A, FG-B, FG-C, FG-D, and FG-E. A modified displacement field is proposed based on the higher-order shear deformation theory, effectively reducing the number of variables from five to four while accurately accounting for shear deformation effects. To solve the equations of motion, an analytical solution based on the Galerkin approach was developed for FG GRC plates resting on a viscoelastic Winkler/Pasternak foundation, applicable to various boundary conditions. A comprehensive parametric analysis elucidates the impact of multiple factors on the fundamental frequencies. These factors encompass the types and distribution patterns of the coated FG GRC plates, gradient material distribution, porosities, nonlocal length scale parameter, gradient material scale parameter, nanoplate geometry, and variations in the elastic foundation. Our theoretical research aims to overcome the inherent challenges in modeling structures, providing a robust alternative to experimental analyses of the mechanical behavior of complex structures.