• Journal of Yeungnam Medical Science
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• v.40 no.1
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• pp.23-29
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• 2023

The pathological hallmark of rheumatoid arthritis (RA) is a synovial pannus that comprises proliferating and invasive fibroblast-like synoviocytes, infiltrating inflammatory cells, and an associated neoangiogenic response. Animal models have been established to study these pathological features of human RA. Spontaneous and induced animal models of RA primarily reflect inflammatory aspects of the disease. Among various induced animal models, collagen-induced arthritis (CIA) and collagen antibody-induced arthritis (CAIA) models are widely used to study the pathogenesis of RA. Improved transplantation techniques for severe combined immunodeficiency (SCID) mouse models of RA can be used to evaluate the effectiveness of potential therapeutics in human tissues and cells. This review provides basic information on various animal models of RA, including CIA and CAIA. In addition, we describe a SCID mouse coimplantation model that can measure the long-distance migration of human RA synoviocytes and cartilage destruction induced by these cells.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.161-176
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• 2022

There has been active research in image classification using deep learning convolutional neural network (CNN) models. ImageNet large-scale visual recognition challenge (ILSVRC) (2010-2017) was one of the most important competitions that boosted the development of efficient deep learning algorithms. This paper introduces and compares six monumental models that achieved high prediction accuracy in ILSVRC. First, we provide a review of the models to illustrate their unique structure and characteristics of the models. We then compare those models under a unified framework. For this reason, additional devices that are not crucial to the structure are excluded. Four popular data sets with different characteristics are then considered to measure the prediction accuracy. By investigating the characteristics of the data sets and the models being compared, we provide some insight into the architectural features of the models.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.28 no.1
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• pp.1-21
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• 2024

This review comprehensively explores the evolution, theoretical underpinnings, variations, and applications of diffusion models. Originating as a generative framework, diffusion models have rapidly ascended to the forefront of machine learning research, owing to their exceptional capability, stability, and versatility. We dissect the core principles driving diffusion processes, elucidating their mathematical foundations and the mechanisms by which they iteratively refine noise into structured data. We highlight pivotal advancements and the integration of auxiliary techniques that have significantly enhanced their efficiency and stability. Variants such as bridges that broaden the applicability of diffusion models to wider domains are introduced. We put special emphasis on the ability of diffusion models as a crucial foundation model, with modalities ranging from image, 3D assets, and video. The role of diffusion models as a general foundation model leads to its versatility in many of the downstream tasks such as solving inverse problems and image editing. Through this review, we aim to provide a thorough and accessible compendium for both newcomers and seasoned researchers in the field.

• BMB Reports
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• v.57 no.8
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• pp.352-362
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• 2024

Hematopoietic stem cell transplantation (HSCT) remains an indispensable therapeutic strategy for various hematological diseases. This review discusses the pivotal role of bone marrow (BM) niches in influencing the efficacy of HSCT and evaluates the current animal models, emphasizing their limitations and the need for alternative models. Traditional animal models, mainly murine xenograft, have provided significant insights, but due to species-specific differences, are often constrained from accurately mimicking human physiological responses. These limitations highlight the importance of developing alternative models that can more realistically replicate human hematopoiesis. Emerging models that include BM organoids and BM-on-a-chip microfluidic systems promise enhanced understanding of HSCT dynamics. These models aim to provide more accurate simulations of the human BM microenvironment, potentially leading to improved preclinical assessments and therapeutic outcomes. This review highlights the complexities of the BM niche, discusses the limitations of current models, and suggests directions for future research using advanced model systems.

• Korean Journal of Computational Design and Engineering
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• v.21 no.1
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• pp.78-89
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• 2016

When a plant owner requests plant 3D CAD models in the format that a shipbuilding company does not use, the shipyard manually re-models plant 3D CAD models according to the owner's requirement. Therefore, it is important to develop a technology to compare the re-modeled plant 3D CAD models with original ones and to quantitatively evaluate similarity between two models. In the previous study, we developed a graphic user interface (GUI)-based comparison system where a user evaluates similarity between original and re-modeled plant 3D CAD models for piping design at the level of unit. However, an offshore plant consists of thousands of units and thus a system which compares several plant 3D CAD models at unit-level without human intervention is necessary. For this, we developed a new batch model comparison system which automatically evaluates similarity of several unit-level plant 3D CAD models using an extensible markup language (XML) file storing file location and name data about a set of plant 3D CAD models. This paper suggests system configuration of a batch-mode-based comparison system and discusses its core functions. For the verification of the developed system, comparison experiments for offshore plant 3D piping CAD models using the system were performed. From the experiments, we confirmed that similarities for several plant 3D CAD models at unit-level were evaluated without human intervention.

• Journal of Korea Water Resources Association
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• v.30 no.3
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• pp.279-291
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• 1997

This study is to evaluate the accuracy and practicality of the existing four conceptual models, two linear models of Clark and Nash model and two nonlinear models of Laruenson and WBN model, and to select an appropriate model to simulate the rainfall-runoff process in a given catchment. The variability of parameters for linear models is generally larger than that of nonlinear models. The errors in peak discharge are similar among the four conceptual models buy the errors in time to peak are quite different. Nonlinear models produce better results for time distribution than linear models. A comparison of the conceptual models to predict overall hydrograph using Friedman two-way analysis of variance by rank test indicates that nonlinear models are slightly better than linear models.

• The korean journal of orthodontics
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• v.44 no.2
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• pp.69-76
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• 2014

Objective: This study aimed to evaluate the accuracy and precision of polyurethane (PUT) dental arch models fabricated using a three-dimensional (3D) subtractive rapid prototyping (RP) method with an intraoral scanning technique by comparing linear measurements obtained from PUT models and conventional plaster models. Methods: Ten plaster models were duplicated using a selected standard master model and conventional impression, and 10 PUT models were duplicated using the 3D subtractive RP technique with an oral scanner. Six linear measurements were evaluated in terms of x, y, and z-axes using a non-contact white light scanner. Accuracy was assessed using mean differences between two measurements, and precision was examined using four quantitative methods and the Bland-Altman graphical method. Repeatability was evaluated in terms of intra-examiner variability, and reproducibility was assessed in terms of interexaminer and inter-method variability. Results: The mean difference between plaster models and PUT models ranged from 0.07 mm to 0.33 mm. Relative measurement errors ranged from 2.2% to 7.6% and intraclass correlation coefficients ranged from 0.93 to 0.96, when comparing plaster models and PUT models. The Bland-Altman plot showed good agreement. Conclusions: The accuracy and precision of PUT dental models for evaluating the performance of oral scanner and subtractive RP technology was acceptable. Because of the recent improvements in block material and computerized numeric control milling machines, the subtractive RP method may be a good choice for dental arch models.

• Journal of Korean Elementary Science Education
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• v.30 no.3
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• pp.353-366
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• 2011

An alternative vision for science inquiry that appears to be important and challenging is model-based inquiry in which students generate, evaluate and revise their explanatory model. Pre-service teachers should be given opportunities to develop and use their mechanistic explanatory models in order to participate in the practice of science and to have a sound understanding of science. With this view, this study described a case of pre-service elementary teachers' scientific modeling in magnetism. The aims of this study were to explore difficulties preservice elementary teachers encountered while they engaged in a model-based inquiry, and to examine how their understandings of the nature of scientific models changed after the model-based inquiry. The data analysis revealed that the pre-service teachers had difficulties in drawing and writing their own thinking because they had little experience of expressing their own science ideas. When asked to predict what would happen, they could not understand what it meant to make a prediction "based on their model". They did not know how to use or consider their model in making a prediction. At the end of the model-based inquiry they reached a final consensus of a best model. However, they were very anxious about whether the model was the "correct" answer. With respect to the nature of scientific models, almost all of the pre-service teachers initially viewed models only as a communication tool among scientists or students and teachers to help understand others' ideas. After the model-based inquiry, however, many of them understood that they could create, test, and revise their "own" models "by themselves". They also realized the key aspects of scientific models that a model can be changed as evidence is accumulated and a model is a knowledge production tool as well as a communication tool. The results indicated that pre-service elementary teachers' understandings of the nature of scientific models and their previous school science experiences could affect their performance on a model-based inquiry, and their experience of scientific modeling could help them enhance their understandings of the nature of scientific models.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.123.1-123.1
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• 2012

We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms ($Kp{\geq}6$), we present contingency tables and estimate statistical parameters such as probability of detection yes (PODy), false alarm ratio (FAR), bias, and critical success index (CSI). From a comparison of these statistical parameters, we found that the SVM models (model 2 and model 4) are better than the NN models (model 1 and model 3). The values of PODy and CSI of the model 4 are the highest among these models (PODy: 0.57 and CSI: 0.48). From these results, we suggest that the NN models are better than the SVM models for predicting Kp and the SVM models are better than the NN models for forecasting geomagnetic storms.

• Education of Primary School Mathematics
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• v.22 no.1
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• pp.29-48
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• 2019

Previous studies on double scale models, visual models with two different scales (Chong, 2015), have focused on double number line and little research has been conducted on how to employ double scale models in the elementary mathematics textbooks series. Given this, we analyzed the characteristics of double scale models in the Japanese elementary mathematics textbooks in the following aspects: (a) the contents of units where double scale models were used; (b) the purposes of using such models; (c) the types of such models tailored to the contents and grade levels; and (d) the characteristics of problem contexts dealing with the models. The results of this study showed that double scale models were effectively used to connect the contents related to multiplication, specifically for the contexts of ratio. Such models were addressed for students in a systematic and gradual way as the grade levels went up. Based on these results, this paper describes implications on how to use double scale models in mathematics textbooks.