• The Korea Journal of Herbology
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• v.38 no.5
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• pp.9-19
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• 2023

Objectives : Osteoporosis is a systemic skeletal disorder characterized by reduced bone mineral density and increased risk of fractures. Bisphosphonates and selective estrogen receptors, which are bone resorption inhibitors that are currently widely used as osteoporosis treatments, show serious side effects when administered for a long time. Research on bone resorption inhibitors that complement the problems of existing treatments is needed. The purpose of this study was to investigate the effect of inhibiting osteoclast differentiation and activity on the tuberous root of Ampelopsis japonica (Thunb.) Makino (AM). Methods : After extracting AM using distilled water and ethanol, the inhibitory effects of the two solvents on osteoclast differentiation were compared using the RANKL-induced in vitro experimental model and the TRAP assay kit. The impact of AM on bone resorption was investigated through the pit formation assay, and its effect on F-actin formation was assessed through fluorescent staining. Additionally, protein and mRNA expression levels of osteoclast differentiation markers (NFATc1, c-Fos, TRAP and ATP6v0d2) and resorption markers (MMP-9, CTK, and CA2) were analyzed via western blot and RT-PCR. Results : AM treatment significantly decreased the number of TRAP-positive cells and pit formation area. Furthermore, AM suppressed both the protein and mRNA expression of NFATc1 and c-Fos, key transcription factors involved in osteoclast differentiation and it downregulated the expression of osteoclast-associated genes such as TRAP, CTK, MMP-9, CA2, and ATP6v0d2. Conclusions : These results suggest that AM can inhibit bone resorption and osteoclast differentiation, indicating its potential for use in the treatment and prevention of osteoporosis.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.177-188
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• 2023

The purpose of this study is to develop and validate a scale for measuring digital literacy by identifying the factors consisting of digital literacy and extracting items for each factor. Preliminary items for the Delphi study were developed through the analysis of previous literature and the deliberation of the research team. As a result of two rounds of the expert Delphi study, 65 items were selected for the main survey. The validation of the items was carried out in the process of exploratory and confirmatory factor analyses, reliability test, and criterion validity test using the data collected in the main survey. As a result, a 4-factor structure composed of 31 questions(factor 1: digital technology & data literacy- 9 questions, factor 2: digital content & media literacy- 8 questions, factor 3: digital communication & community literacy- 9 questions, factor 4: digital wellness literacy - 5 questions) was confirmed. Also, the goodness of fit indices of the model were found to be good and the result of reliability test revealed the scale had a very appropriate level of Cronbach's alpha(α=.956). In addition, a statistically significantly positive correlations(p<.001) were found between digital literacy and internet self-efficacy and between digital literacy and self-directed learning ability, which were predicted in the existing evidence, therefore the criterion validity of the developed scale was secured. Finally, practical and academic implications of the study are provided and future study and limitations of the study are discussed.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1253-1253
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• 2022

Although the construction industry is changing from a 2D-based to a 3D BIM-based management process, 2D drawings are still used as standards for permits and construction. For this reason, 2D deliverables extracted from 3D BIM are one of the essential achievements of BIM projects. However, due to technical and institutional problems that exist in practice, the process of extracting 2D deliverables from BIM requires additional work beyond generating 3D BIM models. In addition, the consistency of data between 3D BIM models and 2D deliverables is low, which is a major factor hindering work productivity in practice. To solve this problem, it is necessary to build BIM data that meets information requirements (IRs) for extracting 2D deliverables to minimize the amount of work of users and maximize the utilization of BIM data. However, despite this, the additional work that occurs in the BIM process for drawing creation is still a burden on BIM users. To solve this problem, the purpose of this study is to increase the productivity of the BIM process by automating the process of extracting 2D deliverables from BIM and securing data consistency between the BIM model and 2D deliverables. For this, an expert interview was conducted, and the requirements for automation of the process of extracting 2D deliverables from BIM were analyzed. Based on the requirements, the types of drawings and drawing expression elements that require automation of drawing generation in the design development stage were derived. Finally, the method for developing automation technology targeting elements that require automation was classified and analyzed, and the process for automatically extracting BIM-based 2D deliverables through templates and rule-based automation modules were derived. At this time, the automation module was developed as an add-on to Revit software, a representative BIM authoring tool, and 120 rule-based automation rulesets, and the combinations of these rulesets were used to automatically generate 2D deliverables from BIM. Through this, it was possible to automatically create about 80% of drawing expression elements, and it was possible to simplify the user's work process compared to the existing work. Through the automation process proposed in this study, it is expected that the productivity of extracting 2D deliverables from BIM will increase, thereby increasing the practical value of BIM utilization.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.87-96
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• 2022

The Ministry of Land, Infrastructure and Transport has also been promoting the commercialization of land transport technology to commercialize the technologies owned by small and medium-sized venture companies, and to support the transfer and commercialization of public technologies. At this point, in order to improve the investment effect of subsequent new projects and to select excellent research institutes, it is necessary to establish a valid evaluation index system suitable for the purpose of the project. The evaluation index system for subsequent new projects should be linked to the project objectives and goals of the preceding project, and should be selected in consideration of existing evaluation indicators to prevent interruption of research results. Therefore, this thesis sets the evaluation index system into multiple scenarios through hierarchical cluster analysis using the evaluation result data for each evaluation committee for small and medium venture companies participating in the land transportation technology commercialization support project, and then analyzes the structural equation model. As a result of scenario analysis, considering the measurement effect of each path representing the causal relationship between evaluation indicators and the effect of each evaluation index on evaluation items, the scenario with the highest impact on the evaluation result was selected as an improvement plan.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.685-690
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• 2023

It was analyzed that the volume of deep excavation works adjacent to existing underground structures is increasing according to the population growth and density of cities. Currently, many underground structures and tracks are damaged by external factors, and the cause is analyzed based on the measurement results in the tunnel, and measurements are being made for post-processing, not for prevention. The purpose of this study is to analyze the effect on the deformation of the structure due to the excavation work adjacent to the urban railway track in use. In addition, the safety of structures is evaluated through machine learning techniques for displacement of structures before damage and destruction of underground structures and tracks due to external factors. As a result of the analysis, it was analyzed that the model suitable for predicting the structure management standard value time in the analyzed dataset was a polynomial regression machine. Since it may be limited to the data applied in this study, future research is needed to increase the diversity of structural conditions and the amount of data.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.2
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• pp.21-33
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• 2023

While the main purpose of irrigation reservoirs is to supply agricultural water, the needs of environmental flow and flood control has been expanded. The agricultural reservoirs have been operated in the form of carry-over system until now. Therefore, the supply of agricultural water is difficult when the storage rate is not sufficiently secured after large volume of irrigation. In addition, there are regulation of the upper storage rate for some large reservoirs during the flood season, but lower storage rate is not regulated. Accordingly, this study aims to evaluate the capacity of agricultural water and environmental flow supply by setting the management lower storage rate of reservoir. The changes in the supply of agricultural and environmental flow was simulated according to the three different regulating lower storage rate scenarios. As a result, it was judged effective in terms of water supply managing the lower storage rate up to 30% when the initial storage rate of farming period is above annual average for the Naju reservoir considering existing water management practice. If the lower storage rate would have been controlled above 30%, the supply of agricultural water might be increased and non-effective discharge amount would be decreased compared to other scenarios during dry period of 2016-2018.

• Journal of Korea Trade
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• v.26 no.4
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• pp.1-22
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• 2022

Purpose - This paper empirically investigates the effect of a rise in the global value chain (GVC) on the industry-level efficiency of resource allocation (based on plant-level inefficiency measures) in Korea, with a focus on various channels through which a rise in the GVC can increase competition among firms and thus induce resources to be allocated more efficiently across firms. Design/methodology - We empirically investigate the relationship between the industry-specific importance of GVC and the industry-level allocative inefficiency that is measured as the dispersion of the plant-level marginal revenue of capital (MRK) as in Hsieh and Klenow's (2009) influential model. We compute MRK dispersion for industries sorted by various characteristics that are closely related to firm/industry sensitivity to the GVC. In other words, we compute the average industry-level MRK dispersion for industries sorted by industry-specific importance of GVC and compute the difference between the two groups of industries (higher vs. lower than the median GVC); we also calculate the difference between industries sorted by industry-specific export (import) intensity. This is our difference-in-difference estimate of the MRK dispersion associated with the GVC for the export (import)-intensive industry versus the non-export (non-import)-intensive industry. This difference-in-difference estimate of the MRK dispersion conditional vs. unconditional on firm-level productivity is then calculated further (triple-difference estimate). Findings - A rise in GVC is associated with a decrease in the MRK dispersion in the export-intensive industry compared to the non-export-intensive industry. The same is true for industries that rely heavily on imports versus those that do not (i.e., import intensive vs. non-intensive). Furthermore, the reduction in the MRK dispersion in the export-intensive industry associated with an increase in the GVC is disproportionately greater for high-productivity firms. In contrast, the negative relationship between GVC and MRK dispersion in the import-intensive industry is disproportionately smaller for high-productivity firms. Originality/value - Existing studies focus on the relationship between GVC and aggregate output, exports, and imports at the country level. We investigate detailed firm/industry-level mechanisms that determine the relationship between GVC, trade, and productivity. Using the plant-level data in South Korea, we investigate how GVC is related to the cross-firm MRK dispersion, an important measure of allocative inefficiency, based on Hsieh and Klenow's (2009) influential economic theory. This is the first study to provide plant-level evidence of how GVC affects MRK dispersion. Furthermore, we examine how the relationship between GVC and MRK-dispersion varies across export intensity, import intensity, and firm-level productivity, providing insight into how GVC can affect firms' exposure to competition in the global market differently depending on market conditions and thus generate trade-related productivity gains.

• Journal of the Korean Geotechnical Society
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• v.24 no.4
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• pp.5-13
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• 2008

Laboratory model test with soft silty ground (ML) and polluted silty ground with wastewater and factory waste oil ($ML_p$) was conducted and the applicability of changes of bearing capacity from the increase of pollutants was compared and analyzed with existing findings. As silty ground polluted with wastewater and factory waste oil had increased contents of pollutants, plasticization of ground was fostered compared to soft silt ground due to the influence of pollutants, and characteristics of ground strength decreased. Critical surcharge value of soft silty ground $q_{cr}=4.14c_u$, ultimate bearing capacity value $q_{ult}=9.53c_u$, critical surcharge value of silty ground polluted with wastewater and factory waste oil $q_{cr}=1.78c_u$ and ultimate bearing capacity value $q_{ult}=4.39c_u$. Critical surcharge and ultimate bearing capacity of silty ground polluted with wastewater and factory waste oil were less than those of soft silty ground. It meant that shearing resistance due to the increase of pollutants decreased and rather a smaller value was obtained.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.37-51
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• 2023

The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.