• Clinical and Experimental Reproductive Medicine
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• v.48 no.4
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• pp.322-336
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• 2021

Objective: Endometriosis is a chronic debilitating inflammatory condition characterized by the presence of endometrial tissues outside the uterine cavity. Pelvic soreness and infertility are the usual association. Due to the poor effectiveness of the hormone therapy and the high incidence of recurrence following surgical excision, there is no single effective option for management of endometriosis. Mesenchymal stem cells (MSCs) are multipotent stromal cells studied for their broad immunoregulatory and anti-inflammatory properties; however, their efficiency in endometriosis cases is still a controversial issue. Our study aim was to evaluate whether adipose tissue-derived MSCs (AD-MSCs) could help with endometriosis through their studied anti-inflammatory role. Methods: Female Wistar rats weighting 180 to 250 g were randomly divided into two groups: group 1, endometriosis group; established by transplanting autologous uterine tissue into rats' peritoneal cavities and group 2, stem cell treated group; treated with AD-MSCs on the 5th day after induction of endometriosis. The proliferative activity of the endometriosis lesions was evaluated through Ki67 staining. Quantitative estimation of interferon γ, tumor necrosis factor-α, interleukin (IL)-6, IL-1β, IL-10, and transforming growth factor β expression, as well as immunohistochemical detection of CD68 positive macrophages, were used to assess the inflammatory status. Results: The size and proliferative activity of endometriosis lesions were significantly reduced in the stem cell treated group. Stem cells efficiently mitigated endometriosis associated chronic inflammatory reactions estimated through reduction of CD68 positive macrophages and the expression of the proinflammatory cytokines. Conclusion: Stem cell therapy can be considered a novel remedy in endometriosis possibly through its anti-inflammatory and antiproliferative properties.

• Atmosphere
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• v.29 no.3
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• pp.269-282
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• 2019

While it is important to obtain the accurate information on snowfall data due to the increase in damage caused by the heavy snowfall in the winter season, it is not easy to observe the snowfall quantitatively. Recently, snow measurements using a weighing precipitation gauge have been carried out, but there is a problem that high snowfall intensity results in low accuracy. Also, the observed snowfall data are sensitive depending on wind speed, temperature, and humidity. In this study, a new process of quality control for snow water equivalent (SWE) data of the weighing precipitation gauge were proposed to cover the low accuracy of snow data and maximize the data utilization. Snowfall data (SWE) observed by Pluvio, Parsivel, snow-depth meter using laser or ultrasonic, and rainfall gauge in Cloud Physics Observation Site (CPOS) were compared and analyzed. Applying the QC algorithm including the use of number of hydrometeor particles as reference, the increased SWE per the unit time was determined and the data noise was removed and marked by flag. The SWE data converted by the number concentration of hydrometeor particles are tested as a method to restore the QC-removed data, and show good agreement with those of the weighing precipitation gauge, though requiring more case studies. The three events data for heavy snowfall disaster in Pyeongchang area was analyzed. The SWE data with improved quality was showed a good correlation with the eye-measured data ($R^2$ > 0.73).

• Journal of the Korean Geotechnical Society
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• v.35 no.3
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• pp.37-46
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• 2019

Recently, the use of CPT-based liquefaction assessment method has increased by providing more accurate results than other field tests. In CPT-based liquefaction evaluation, various soil properties are predicted and they are used for liquefaction potential assessment. In particular, fines content is one of the important input parameters in CPT-based liquefaction assessment, so it is very important to use correct prediction model and to make quantitative evaluation of estimating variability of fines content. In this study, the error evaluation of existing models for prediction of fines content through CPT was performed, and the most suitable model was selected for Pohang area, where the liquefaction phenomenon was observed in the 2017. In addition, the inherent variability of soil was analyzed, and the estimating variability of fines content was evaluated quantitatively considering the inherent variability of soil, measurement error of CPT and transformation uncertainty of selected model.

• Journal of the Korea Institute of Building Construction
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• v.21 no.5
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• pp.495-506
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• 2021

A Conceptual cost estimate, which is computed in the preliminary step of a project, is important for decision-making by a contractor in terms of the project budget, economic feasibility and validity analysis, and alternative comparisons. Therefore, a high error rate of a prediction model for a conceptual cost estimate can lead to various problems including excessive project expenditures and a delayed break-even point. this study proposed optimal impact factors by configuring quantitative impact factors computable in a preliminary step in various cases(combinations of impact factors). subsequently, the accuracy of different cases was comparatively analyzed by using the cases as input values of a prediction model using regression analysis. when the optimal combination of impact factors proposed in this study and other combination of impact factors were applied to the prediction model, the regression analysis-based prediction model exhibited 0.2-4.7% improvements in accuracy, respectively. the optimal combination of impact factors proposed in this study improved the accuracy of the prediction model of a conceptual cost estimate by removing unnecessary impact factor.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.1
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• pp.60-70
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• 2021

Recently, with the development of technologies for the fourth industrial revolution, convergence and complex technology are being applied to aircraft, electronic home appliances and mobile devices, and the number of parts used is increasing. Increasing the number of parts and the application of convergence technologies such as HW (hardware) and SW (software) are increasing the No Defect Found (NDF) phenomenon in which the defect is not reproduced or the cause of the defect cannot be identified in the subsequent investigation systems after the discovery of the defect in the product. The NDF phenomenon is a major problem when dealing with complex technical systems, and its consequences may be manifested in decreased safety and dependability and increased life cycle costs. Until now, NDF-related prior studies have been mainly focused on the NDF cost estimation, the cause and impact analysis of NDF in qualitative terms. And there have been no specific methodologies or examples of a working-level perspective to reduce NDF. The purpose of this study is to present a practical methodology for reducing NDF phenomena through data mining methods using quantitative data accumulated in the enterprise. In this study, we performed a cluster analysis using market defects and design-related variables of mobile devices. And then, by analyzing the characteristics of groups with high NDF ratios, we presented improvement directions in terms of design and after service policies. This is significant in solving NDF problems from a practical perspective in the company.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.171-181
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• 2022

The real estate price index plays key roles as quantitative data in real estate market analysis. International organizations including OECD publish the real estate price indexes by country, and the Korea Real Estate Board announces metropolitan-level and municipal-level indexes. However, when the index is set on the smaller spatial unit level than metropolitan and municipal-level, problems occur: missing values. As the spatial scope is narrowed down, there are cases where there are few or no transactions depending on the unit period, which lead index calculation difficult or even impossible. This study suggests a supervised learning-based machine learning model to compensate for missing values that may occur due to no transaction in a specific range and period. The models proposed in our research verify the accuracy of predicting the existing values and missing values.

• The korean journal of orthodontics
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• v.52 no.3
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• pp.182-200
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• 2022

Objective: This study aims to examine the effectiveness of miniscrew assisted rapid palatal expansion (MARPE) treatment in late adolescents and adult patients using cone-beam computed tomography (CBCT). Methods: Literature search was conducted in five electronic databases (PubMed, Embase, Scopus, Web of Science, and Cochrane Library) based on the PICOS keyword design focusing on MARPE. Out of the 18 CBCT screened outcomes, only nine parameters were sufficient for the quantitative meta-analysis. The parameters were classified into three main groups: 1) skeletal changes, 2) alveolar change, and 3) dental changes. Heterogeneity test, estimation of pooled means, publication bias, sensitivity analysis and risk of bias assessment were also performed. Results: Upon database searching, only 14 full-text articles were qualified from the 364 obtained results. Heterogeneity test indicated the use of the random-effects model. The pooled mean estimate were as follows: 1) Skeletal expansion: zygomatic width, 2.39 mm; nasal width, 2.68 mm; jugular width, 3.12 mm; and midpalatal suture at the posterior nasal spine and anterior nasal spine, 3.34 mm and 4.56 mm, respectively; 2) Alveolar molar width expansion, 4.80 mm; and 3) Dental expansion: inter-canine width, 3.96 mm; inter-premolar width, 4.99 mm and inter-molar width, 5.99 mm. The percentage of expansion demonstrated a skeletal expansion (PNS) of 55.76%, alveolar molar width expansion of 24.37% and dental expansion of 19.87%. Conclusions: In the coronal view, the skeletal and dental expansion created by MARPE was of the pyramidal pattern. MARPE could successfully expand the constricted maxilla in late adolescents and adult patients.

• Journal of Environmental Impact Assessment
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• v.31 no.6
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• pp.438-448
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• 2022

This study is to improve the quantitative estimation index of biotop area ratio, which is an environmental planning index and environmental ecological planning technique, as a planning means that can induce the improvement of ecological soundness in the spatial planning stage. It is intended to identify the relative importance of space types and calculation indicators currently in operation, and to find alternatives through opinion analysis on improvement of space types and weights. As the method of this study, AHP analysis was performed to evaluate the relative importance of spatial types for in-depth analysis of spatial types and calculation indicators. In order to secure the reliability and objectivity of the study, 50 experts participated. Through this study, it can be linked with the improvement of technologies and construction methods, maintenance efficiency, economic feasibility, and construction technology, which are developed through analysis on the limitations and improvements by type of biotop area ratio. And it is expected to contribute to the improvement of the urban environment and vitalization of the biotop area ratio through the application of the biotop area ratio.

• Journal of Wetlands Research
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• v.25 no.1
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• pp.1-13
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• 2023

Coastal shellfish in the shallow aquaculture waters form carbon contained shells as they grow. The existing researches showed that carbon flux can be improved, if the shells are re-treated by the carbon stored methods. In the present study, firstly, the mechanism and the quantitative flux of carbon dioxide in the shellfish individual have been analyzed. The re-treated methods of the useful by-product in the shellfish aquaculture, shells, have been reviewed. Finally, the potential effects to reduce the greenhouse gas has been suggested, if the shells can be properly re-treated.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.277-285
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• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.