• Korean Journal of Plant Resources
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• v.37 no.2
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• pp.101-109
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• 2024

In this study, we investigated the antioxidant effects and active ingredients of Gynura Procumbens extracts obtained through various extraction methods for the development of natural-based cosmetics and pharmaceutical materials. The contents of compounds, total flavonoids, chlorogenic acid, and caffeic acid were compared at different concentrations, revealing the highest content of active ingredients in the 100% ethanol extract. Antioxidant activity assessed by DPPH and ABTS radical scavenging assays showed a concentration-dependent increase in antioxidant activity with the ethanol concentration. Additionally, we validated the DNA damage inhibition and anti-inflammatory effects of Gynura Procumbens extracts through UVB irradiation on Hs68 cell models. The 100% ethanol extract demonstrated significant inhibition of the expression of p-p53, γ-H2AX, iNOS, and COX-2 induced by UVB, indicating its potential in alleviating photodamage effects. Consequently, the efficient extraction of Gynura Procumbens for skin functional material development was confirmed, suggesting the suitability of ethanol or alcohol-based solvents.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.53-63
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• 2024

Biodiversity holds significant importance within the framework of environmental impact assessment, being utilized in site selection for development, understanding the surrounding environment, and assessing the impact on species due to disturbances. The field of environmental impact assessment has seen substantial research exploring new technologies and models to evaluate and predict biodiversity more accurately. While current assessments rely on data from fieldwork and literature surveys to gauge species richness indices, limitations in spatial and temporal coverage underscore the need for high-resolution biodiversity assessments through species richness mapping. In this study, leveraging data from the 4th National Ecosystem Survey and environmental variables, we developed a species distribution model using Random Forest. This model yielded mapping results of 24 mammalian species' distribution, utilizing the species richness index to generate a 100-meter resolution map of species richness. The research findings exhibited a notably high predictive accuracy, with the species distribution model demonstrating an average AUC value of 0.82. In addition, the comparison with National Ecosystem Survey data reveals that the species richness distribution in the high-resolution species richness mapping results conforms to a normal distribution. Hence, it stands as highly reliable foundational data for environmental impact assessment. Such research and analytical outcomes could serve as pivotal new reference materials for future urban development projects, offering insights for biodiversity assessment and habitat preservation endeavors.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.85-93
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• 2024

In this paper, we propose a method for establishing a state-space equation model for the motion analysis of floating structures subjected to wave loads, by applying system-identification techniques. Traditionally, the motion of floating structures has been analyzed in the time domain by integrating the Cummins equation over time, which utilizes a convolution integral term to account for the effects of the retardation function. State-space equation models have been studied as a way to efficiently solve floating-motion equations in the time domain. The proposed approach outlines a procedure to derive the target transfer function for the load-displacement input/output relationship in the frequency domain and subsequently determine the state-space equation that closely approximates it. To obtain the state-space equation, the method employs the N4SID system-identification method and an optimization approach that treats the coefficients of the numerator and denominator polynomials as design variables. To illustrate the effectiveness of the proposed method, we applied it to the analysis of a single-degree-of-freedom model and the motion of a six-degree-of-freedom barge. Our findings demonstrate that the presented state-space equation model aligns well with the existing analysis results in both the frequency and time domains. Notably, the method ensures computational accuracy in the time-domain analysis while significantly reducing the calculation time.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.119-131
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• 2024

Pulse-like ground motion can cause greater damage to structures than nonpulse-like ground motion. Currently, much research is being conducted to determine the presence or absence of velocity pulses and to quantify them from seismic-acceleration records. Existing ground motion is divided into far-field (FF) and near-fault ground motion, based on the distance of the measurement point from the fault. Near-fault ground motion is further classified into near-fault pulse-like (NFP) and near-fault nonpulse-like (NFNP) ground motion by quantifying the presence or absence of velocity pulses. For each ground motion group, 40 FF, 40 NFP, and 40 NFNP ground motions are selected; thus, 120 ground motions are used in the seismic analysis to assess the seismic fragility of sample bridges. Probabilistic seismic demand models (PSDMs) are created by evaluating the seismic responses of two types of sample bridges with lead-rubber and elastomeric rubber bearings using three groups of ground motions. Seismic fragility analysis is performed using the PSDM, and from these results, the effect of the presence or absence of seismic velocity pulses on the seismic fragility is evaluated. From the comparison results of the seismic fragility curve, the seismic fragility of NFP ground motion appears to be approximately three to five times greater than that of NFNP ground motion, according to the presence or absence of a velocity pulse of seismic waves. This means that the damage to the bridge is greater in the case of NFP ground motion than that in the case of NFNP ground motion.

• Journal of Korea Water Resources Association
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• v.57 no.4
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• pp.289-300
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• 2024

The purpose of this study is to evaluate the applicability of the GRM (Grid based rainfall-Runoff Model) to the continuous simulation by simulating the dam inflow. The GRM was previously developed for the simulation of rainfall-runoff events but has recently been improved to enable continuous simulation. The target watersheds are Chungju dam, Andong dam, Yongdam dam, and Sumjingang dam basins, and runoff models were constructed with the spatial resolution of 500 m × 500 m. The simulation period is 21 years (2001 to 2021). The simulation results were evaluated over the 17 year period (2005 to 2021), and were divided into three data periods: total duration, wet season (June to September), and dry season (October to May), and compared with the observed daily inflow of each dam. Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), correlation coefficient (CC), and total volume error (VE) were used to evaluate the fitness of the simulation results. As a result of evaluating the simulated dam inflow, the observed data could be well reproduced in the total duration and wet season, and the dry season also showed good simulation results considering the uncertainty of low-flow data. As a result of the study, it was found that the continuous simulation technique of the GRM model was properly implemented and the model was sufficiently applicable to the simulation of dam inflow in this study.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.127-134
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• 2024

The article highlights the problems of the digitalization of the educational process, which affect the pedagogical cluster and are of a psychological nature. The authors investigate the transformational changes in education in general and the individual beliefs of each subject of the educational process, caused by both the change in the format of learning (distance, mixed), and the use of new technologies (digital, communication). The purpose of the article is to identify the strategic trend of the educational process, which is a synergistic combination of pedagogical methodology and psychological practice and avoiding dialectical opposition of these components of the educational space. At the same time, it should be noted that the introduction of digital technologies in the educational process allows for short-term difficulties, which is a usual phenomenon for innovations in the educational sphere. Consequently, there is a need to differentiate the fundamental problems and temporary shortcomings that are inherent in the new format of learning (pedagogical features). Based on the awareness of this classification, it is necessary to develop psychological techniques that will prevent a negative reaction to the new models of learning and contribute to a painless moral and spiritual adaptation to the realities of the present (psychological characteristics). The methods used in the study are divided into two main groups: general-scientific, which investigates the pedagogical component (synergetic, analysis, structural and typological methods), and general-scientific, which are characterized by psychological direction (dialectics, observation, and comparative analysis). With the help of methods disclosed psychological and pedagogical features of the process of digitalization of education in a mixed learning environment. The result of the study is to develop and carry out methodological constants that will contribute to the synergy for the new pedagogical components (digital technology) and the psychological disposition to their proper use (awareness of the effectiveness of new technologies). So, the digitalization of education has demonstrated its relevance and effectiveness in the pedagogical dimension in the organization of blended and distance learning under the constraints of the COVID-19 pandemic. The task of the psychological cluster is to substantiate the positive aspects of the digitalization of the educational process.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.35-49
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• 2024

Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.48-59
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• 2024

As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access to PCI devices, thus giving a high I/O performance by minimizing the need for hypervisor or operating system interventions. With SR-IOV, network I/O acceleration can be realized in virtualization systems that have relatively long I/O paths compared to bare-metal systems and frequent context switches between the user area and kernel area. To take performance advantages of SR-IOV, network resource management policies that can derive optimal network performance when SR-IOV is applied to an instance such as a virtual machine(VM) or container are being actively studied.This paper evaluates and analyzes the network performance of SR-IOV implementing I/O acceleration is compared with Virtio in terms of 1) network delay, 2) network throughput, 3) network fairness, 4) performance interference, and 5) multi-network. The contributions of this paper are as follows. First, the network I/O process of Virtio and SR-IOV was clearly explained in the virtualization system, and second, the evaluation results of the network performance of Virtio and SR-IOV were analyzed based on various performance metrics. Third, the system overhead and the possibility of optimization for the SR-IOV network in a virtualization system with high VM density were experimentally confirmed. The experimental results and analysis of the paper are expected to be referenced in the network resource management policy for virtualization systems that operate network-intensive services such as smart factories, connected cars, deep learning inference models, and crowdsourcing.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.135-144
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• 2024

In this study, we propose an automatic classification model for quantitative multidimensional analysis based on facet theory to understand public opinions and demands on major issues through big data analysis. Civil complaints, as a form of public feedback, are generated by various individuals on multiple topics repeatedly and continuously in real-time, which can be challenging for officials to read and analyze efficiently. Specifically, our research introduces a new classification framework that utilizes facet theory and political analysis models to analyze the characteristics of citizen complaints and apply them to the policy-making process. Furthermore, to reduce administrative tasks related to complaint analysis and processing and to facilitate citizen policy participation, we employ deep learning to automatically extract and classify attributes based on the facet analysis framework. The results of this study are expected to provide important insights into understanding and analyzing the characteristics of big data related to citizen complaints, which can pave the way for future research in various fields beyond the public sector, such as education, industry, and healthcare, for quantifying unstructured data and utilizing multidimensional analysis. In practical terms, improving the processing system for large-scale electronic complaints and automation through deep learning can enhance the efficiency and responsiveness of complaint handling, and this approach can also be applied to text data processing in other fields.

• Journal of the Korean GEO-environmental Society
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• v.25 no.3
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• pp.5-11
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• 2024

Promising geological structures for carbon dioxide capture and subsurface storage include aquifers, depleted reservoirs, and gas fields. Among these, aquifers are gaining attention due to their potential for storing significant amounts of carbon dioxide compared to other geological structures. Therefore, there is a growing interest in enhancing carbon dioxide storage efficiency by understanding the characteristics of aquifers and developing technologies tailored to their properties. In this study, the storage efficiency of carbon dioxide injection following surfactant pre-injection into porous micro-models was evaluated. The results indicate that as the concentration of the surfactant solution injected prior to carbon dioxide injection increases, storage efficiency improves. Conversely, lower concentrations require more surfactant injection to enhance storage efficiency. Furthermore, under identical surfactant concentration conditions, the storage efficiency from surfactant pre-injection prior to supercritical carbon dioxide injection is approximately 30% lower compared to surfactant-co-solvent substitution as observed in previous studies. However, under the maximum concentration conditions investigated in this study, similar storage efficiencies to those of previous studies were achieved. These findings are expected to guide concentration determinations for surfactant application aimed at enhancing carbon dioxide storage efficiency in aquifers in future studies.