• Journal of Chest Surgery
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• v.37 no.5
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• pp.391-400
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• 2004

Xenotransplantation in discordant species results in immediate and irreversible hyperacute rejection due to natural antibodies, IgM. With this, antibody depletion is one option to reduce hyperacute rejection, we investigated the effect of PCPP (postcentrifugal plasmapheresis) on the depletion of natural antibodies and the effect of antibody titer on xenograft survival. Material and Method: Outbred swines (n=4) weighing 10∼20 kg were used as donors and mongrel dogs (n=4) weighing 25∼30 kg were used as recipients. Recipient canines underwent plasmapheresis (COBE TPE Laboratories, Lakewood. CO, USA). Pre-transplantation PCPP was peformed on day －2 and day 0. There were three groups (Group 0: no PCPP, Group 1: 1 pla sma-volume (PV) at day －2 and 2 PV at day 0, Group 2: 2 PV at day －2 and 2 PV at day 0). A swine heart was heterotopically transplanted into a recipient's abdominal infrarenal aorta and inferior vena cava. Mean percent depletion of total IgM and IgG in plasma of the recipients was calculated. Serum albumin, electrolyte, complement activity and coagulation factors were measured. Histopathologic examination of heart specimens was performed. Result: Mean percent depletion of IgM and IgG were 95.7$\pm$1.2%, 80.5$\pm$2.4% in the group 2 at the end of PCPP. The percent depletion of serum albumin concentration was decreased from 2.8 to 1.4 g/㎗ in the group 1 and 3.0 to 1.5 g/㎗ in the group 2. Complement hemolytic activity was decreased in group 1 and 2, but returned to normal level within 24 hours. Complement hemolytic activity was reduced to 10% of pre-PCPP level in group 2. Serum fibrinogen decreased to 20% or less and was recovered within 24 hours in group 2. Antithrombin III decreased but less than fibrinogen. PT and aPTT were sometimes but not always prolonged during plasmapheresis. After plasmapheresis, PT and aPTT were prolonged beyond the measurable level. D-dimer was not found during PCPP, but appeared and maintained from 10 minutes after trasplantation. Graft Survival time was 5 min in group 0, and it was 90$\pm$0 min in the group 2. Histopathologic changes were more typically characterized by edema, hemorrhages, thrombosis in all groups at the end of experiment. Conclusion: PCPP effectively removed immuoglobulins and reduced the titer of natural antibodies, as a result, significantly prololonged swine heart xenograft survival.

• Journal of Venture Innovation
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• v.5 no.1
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• pp.107-127
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• 2022

Global challenges such as the corona pandemic, climate change and the war-on-tech ensure that the demand who the technologies of the future develops and monitors prominently for will be on the agenda. Development of, and applications in, agrifood, biotech, high-tech, medtech, quantum, AI and photonics are the basis of the future earning capacity of the Netherlands and contribute to solving societal challenges, close to home and worldwide. To be like the Netherlands and Europe a strategic position in the to obtain knowledge and innovation chain, and with it our autonomy in relation to from China and the United States insurance, clear choices are needed. Brainport Eindhoven: Building on Philips' knowledge base, there is create an innovative ecosystem where more than 7,000 companies in the High-tech Systems & Materials (HTSM) collaborate on new technologies, future earning potential and international value chains. Nearly 20,000 private R&D employees work in 5 regional high-end campuses and for companies such as ASML, NXP, DAF, Prodrive Technologies, Lightyear and many others. Brainport Eindhoven has a internationally leading position in the field of system engineering, semicon, micro and nanoelectronics, AI, integrated photonics and additive manufacturing. What is being developed in Brainport leads to the growth of the manufacturing industry far beyond the region thanks to chain cooperation between large companies and SMEs. South-Holland: The South Holland ecosystem includes companies as KPN, Shell, DSM and Janssen Pharmaceutical, large and innovative SMEs and leading educational and knowledge institutions that have more than Invest €3.3 billion in R&D. Bearing Cores are formed by the top campuses of Leiden and Delft, good for more than 40,000 innovative jobs, the port-industrial complex (logistics & energy), the manufacturing industry cluster on maritime and aerospace and the horticultural cluster in the Westland. South Holland trains thematically key technologies such as biotech, quantum technology and AI. Twente: The green, technological top region of Twente has a long tradition of collaboration in triple helix bandage. Technological innovations from Twente offer worldwide solutions for the large social issues. Work is in progress to key technologies such as AI, photonics, robotics and nanotechnology. New technology is applied in sectors such as medtech, the manufacturing industry, agriculture and circular value chains, such as textiles and construction. Being for Twente start-ups and SMEs of great importance to the jobs of tomorrow. Connect these companies technology from Twente with knowledge regions and OEMs, at home and abroad. Wageningen in FoodValley: Wageningen Campus is a global agri-food magnet for startups and corporates by the national accelerator StartLife and student incubator StartHub. FoodvalleyNL also connects with an ambitious 2030 programme, the versatile ecosystem regional, national and international - including through the WEF European food innovation hub. The campus offers guests and the 3,000 private R&D put in an interesting programming science, innovation and social dialogue around the challenges in agro production, food processing, biobased/circular, climate and biodiversity. The Netherlands succeeded in industrializing in logistics countries, but it is striving for sustainable growth by creating an innovative ecosystem through a regional industry-academic research model. In particular, the Brainport Cluster, centered on the high-tech industry, pursues regional innovation and is opening a new horizon for existing industry-academic models. Brainport is a state-of-the-art forward base that leads the innovation ecosystem of Dutch manufacturing. The history of ports in the Netherlands is transforming from a logistics-oriented port symbolized by Rotterdam into a "port of digital knowledge" centered on Brainport. On the basis of this, it can be seen that the industry-academic cluster model linking the central government's vision to create an innovative ecosystem and the specialized industry in the region serves as the biggest stepping stone. The Netherlands' innovation policy is expected to be more faithful to its role as Europe's "digital gateway" through regional development centered on the innovation cluster ecosystem and investment in job creation and new industries.

• The korean journal of orthodontics
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• v.36 no.6
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• pp.412-421
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• 2006

Objective: The result of finite element analysis depends on material properties, structural expression, density of element, and boundar or loading conditions. To represent proper elastic behavior, a finite element model was made using Hounsfield unit (HU) values in CT images. Methods: A 13 year 6 month old male was used as the subject. A 3 dimensional visualizing program, Mimics, was used to build a 3D object from the DICOM file which was acquired from the CT images. Model 1 was established by giving 24 material properties according to HU. Model 2 was constructed by the conventional method which provides 2 material properties. Protraction force of 500g was applied at a 45 degree downward angle from Frankfort horizontal (FH) plane. Results: Model 1 showed a more flexible response on the first premolar region which had more forward and downward movement of the maxillary anterior segment. Maxilla was bent on the sagittal plane and frontal plane. Model 2 revealed less movement in all directions. It moved downward on the anterior part and upward on the posterior part, which is clockwise rotation of the maxilla. Conclusion: These results signify that different outcomes of finite element analysis can occur according to the given material properties and it is recommended to use HU values for more accurate results.

• Journal of the Korean Geophysical Society
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• v.2 no.1
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• pp.9-26
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• 1999

The Ulleung Basin (Tsushima Basin) in the southwestern East Sea (Japan Sea) is floored by a crust whose affinity is not known whether oceanic or thinned continental. This ambiguity resulted in unconstrained mechanisms of basin evolution. The present work attempts to define the nature of the crust of the Ulleung Basin and its tectonic evolution using seismic wide-angle reflection and refraction data recorded on ocean bottom seismometers (OBSs). Although the thickness of (10 km) of the crust is greater than typical oceanic crust, tau-p analysis of OBS data and forward modeling by 2-D ray tracing suggest that it is oceanic in character: (1) the crust consists of laterally consistent upper and lower layers that are typical of oceanic layers 2 and 3 in seismic velocity and gradient distribution and (2) layer 2C, the transition between layer 2 and layer 3 in oceanic crust, is manifested by a continuous velocity increase from 5.7 to 6.3 km/s over the thickness interval of about 1 km between the upper and lower layers. Therefore it is not likely that the Ulleung Basin was formed by the crustal extension of the southwestern Japan Arc where crustal structure is typically continental. Instead, the thickness of the crust and its velocity structure suggest that the Ulleung Basin was formed by seafloor spreading in a region of hotter than normal mantle surrounding a distant mantle plume, not directly above the core of the plume. It seems that the mantle plume was located in northeast China. This suggestion is consistent with geochemical data that indicate the influence of a mantle plume on the production of volcanic rocks in and around the Ulleung Basin. Thus we propose that the opening models of the southwestern East Sea should incorporate seafloor spreading and the influence of a mantle plume rather than the extension of the crust of the Japan Arc.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Progress in Medical Physics
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• v.22 no.1
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• pp.42-51
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• 2011

Nuclear medicine images (SPECT, PET) were widely used tool for assessment of myocardial viability and perfusion. However it had difficult to define accurate myocardial infarct region. The purpose of this study was to investigate methodological approach for automatic measurement of rat myocardial infarct size using polar map with adaptive threshold. Rat myocardial infarction model was induced by ligation of the left circumflex artery. PET images were obtained after intravenous injection of 37 MBq $^{18}F$-FDG. After 60 min uptake, each animal was scanned for 20 min with ECG gating. PET data were reconstructed using ordered subset expectation maximization (OSEM) 2D. To automatically make the myocardial contour and generate polar map, we used QGS software (Cedars-Sinai Medical Center). The reference infarct size was defined by infarction area percentage of the total left myocardium using TTC staining. We used three threshold methods (predefined threshold, Otsu and Multi Gaussian mixture model; MGMM). Predefined threshold method was commonly used in other studies. We applied threshold value form 10% to 90% in step of 10%. Otsu algorithm calculated threshold with the maximum between class variance. MGMM method estimated the distribution of image intensity using multiple Gaussian mixture models (MGMM2, ${\cdots}$ MGMM5) and calculated adaptive threshold. The infarct size in polar map was calculated as the percentage of lower threshold area in polar map from the total polar map area. The measured infarct size using different threshold methods was evaluated by comparison with reference infarct size. The mean difference between with polar map defect size by predefined thresholds (20%, 30%, and 40%) and reference infarct size were $7.04{\pm}3.44%$, $3.87{\pm}2.09%$ and $2.15{\pm}2.07%$, respectively. Otsu verse reference infarct size was $3.56{\pm}4.16%$. MGMM methods verse reference infarct size was $2.29{\pm}1.94%$. The predefined threshold (30%) showed the smallest mean difference with reference infarct size. However, MGMM was more accurate than predefined threshold in under 10% reference infarct size case (MGMM: 0.006%, predefined threshold: 0.59%). In this study, we was to evaluate myocardial infarct size in polar map using multiple Gaussian mixture model. MGMM method was provide adaptive threshold in each subject and will be a useful for automatic measurement of infarct size.

• The korean journal of orthodontics
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• v.32 no.6 s.95
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• pp.401-411
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• 2002

Precise bracket positioning is essential in modem orthodontics. However, there can be alterations in the vertical position of a bracket due to several reasons. The purpose of this study was to evaluate the effect of variations in the vertical bracket position on the crown inclination in Korean patients with normal occlusion. From a larger group of what was considered to be normal occlusions obtained from the Department of Orthodontics, College of Dentistry, Seoul National University, each of the final 10 subjects (6 males and 4 females, with an average age of 22.3 yews) was selected. The dental models of each of the subjects were scanned three-dimensionally by a laser scanner, and measurements drawn from these were made on the scanned dental casts of the subjects were input into the computer program. From this the occlusal plane and the bracket plane were determined. The tooth plane was then constructed to measure the crown inclination on the bracket plane of each tooth. From a practical standpoint, information was obtained on the extent to which the torque of a tooth would be changed as the bracket position was to be moved vertically (in ${\pm}0.5mm,\;{\pm}1.0mm,\;{\pm}1.5mm$) from its ideal position. A one way analysis of the variance (ANOVA) was used to compare each group of the different vertical distances from the bracket plane on a specific tooth. Duncan's multiple comparison test was then performed. There were statistically significant differences in the crown inclination among the groups of different vertical distances for the upper central incisor, upper lateral incisor, upper canine, upper first and second molars, lower first and second premolars, and lower first and second molars (p<0.05). On the upper anterior teeth, upper molars, lower premolars and lower molars, the resultant torque values due to the vertical displacement of the bracket were different depending on the direction of the displacement, occlusal or gingival. This study implies that the torque of these teeth should be handled carefully during the orthodontic treatment. In circumstances in which the bracket must be positioned more gingivally or occlusally due to various reasons, it would be useful to provide the chart of torque alteration of each tooth referred to in this study with its specified bracket prescription.

• Journal of Radiation Protection and Research
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• v.33 no.3
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• pp.105-112
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• 2008

To measure the soil-to-plant transfer factors ($TF_a,\;m^2\;kg^{-1}$-fresh) of radionuclides deposited during the growing season of potato, a radioactive solution containing $^{54}Mn,\;^{60}Co,\;^{85}Sr$ and $^{137}Cs$ was applied to the soil surfaces in soil boxes 2 d before seeding and three different times during the plant growth. For the pre-seeding application (PSA), radionuclides were mixed with the topsoil (loamy sand and 5.2 in pH). The plant parts investigated were leaves, stems, tuber skin and tuber flesh. The $TF_a$ values of $^{54}Mn,\;^{60}Co,\;^{85}Sr$ and $^{137}Cs$ from the PSA were in the ranges of $1.9{\times}10^{-4}{\sim}1.5{\times}10^{-2}$, $1.8{\times}10^{-4}{\sim}7.5{\times}10^{-4}$, $4.0{\times}10^{-4}{\sim}1.6{\times}10^{-2}$, $1.5{\times}10^{-4}{\sim}3.9{\times}10^{-4}$ respectively, for different plant parts. The TFa values from the growing-time applications were on the whole a few times lower than those from the PSA. For $^{54}Mn,\;^{85}Sr$ and $^{137}Cs$, the $TF_a$ values from the early- or middle-growth-stage application were higher than those from the late-growth-stage application, whereas the opposite was true for $^{60}Co$. Leaves and tuber flesh had the highest and lowest $TF_a$ values, respectively, in most cases. The total uptake from soil by the four plant parts was in the range of $0.05{\sim}3.16%$. In the third year following the PSA, the $TF_a$ values of $^{54}Mn,\;^{60}Co$ and $^{137}Cs$ were $11{\sim}25%$, $21{\sim}25%$ and $38{\sim}67%$ of those in the first year, respectively, depending on the plant parts. The present results can be used for estimating the radiological impact of an acute radioactive deposition during the growing season of potato and for testing the validity of relevant food-chain models.

• Journal of Dental Rehabilitation and Applied Science
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• v.36 no.2
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• pp.70-79
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• 2020

Purpose: The purpose of this study was to evaluate the effect of attachments and palatal coverage on stress distribution in maxillary implant overdenture using finite element analysis. Materials and Methods: Four maxillary overdenture 3-D models with four implants placed in the anterior region were fabricated with computer-aided design. 1) Ball-F: Non-splinted ball attachment and full palatal coverage, 2) Ball-P: Non-splinted ball attachment and U-shaped partial palatal coverage, 3) Bar-F: Splinted milled bar attachment and full palatal coverage, 4) Bar-P: Splinted milled bar attachment and U-shaped partial palatal coverage. Stress distribution analysis was performed with ANSYS workbench 14. 100 N vertical load was applied at the right first molar unilaterally and maximum stress was calculated at the implant, peri-implant bone and mucosa. Results: The use of the ball attachment showed lower maximum stress on implant and peri-implant bone than the use of the milled bar attachment. But it showed contrary tendency in the mucosa. Regardless of attachment, full palatal coverage showed lower maximum stress on implant, peri-implant bone and mucosa. Conclusion: Within the limitation of this study, ball attachment improved stress distribution on implant and peri-implant bone rather than milled bar attachment in maxillary implant overdenture. Also, full palatal coverage is more favorable in stress distribution.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.