• Journal of Oral Medicine and Pain
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• v.31 no.3
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• pp.223-229
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• 2006

The objectives of this study was to investigate the amount of tooth ablation and the change of intrapulpal temperature by Er:YAG laser as it relates to pulse energy and pulse repetition rate at the identical power and, thereby, to reveal which of the two parameters strongly relates with ablation efficiency and intrapulpal temperature. Extracted healthy human molar teeth were sectioned into two pieces and each specimen was irradiated within the combination of pulse energy and pulse repetition time at the same power of 3W; $300mJy{\times}10Hz$ group, $200mJy{\times}15Hz$ group, and $150mJy{\times}20Hz$ group. Each specimen comprised ten tooth specimens. A laser beam with conjunction of a water flow rate of 1.6 ml/min was applied over enamel surfaces of the specimens during 3 seconds and the ablation amount was determined by difference in weight before and after irradiation. To investigate the temperature change in the pulp according to the above groups, another five extracted healthy human molar teeth were prepared. Each tooth was embedded into resin block and the temperature-measuring probes were kept on the irradiated and the opposite walls in the dental pulp during lasing. When the power was kept constant at 3W, ablation amount increased with pulse energy rather than pulse repetition rate (p=0.000). Although intrapulpal temperature increased with pulse repetition rate, there were no significant differences among the groups and between the irradiated and the opposite pulpal walls, except at a condition of $150y{\times}20Hz$ (p=0.033). Conclusively, it is suggested that ablation efficacy is influenced by pulse energy rather than pulse repetition rate.

• Journal of the Korean Society of Radiology
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• v.13 no.6
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• pp.879-887
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• 2019

The purpose of this study is to establish a physiological injection protocol according to body weight, in order to minimize amount of contrast medium and optimize contrast enhancement in pediatric patients performing thoracic CT examinations. The 80 pediatric patients under the age of 10 were studied. Intravenous contrast material containing 300 mgI/ml was used. The group A injected with a capacity of 1.5 times its weight, and groups B, C and D added 5 to 15 ml of normal saline with a 10% decrease in each. The physiologic model which can be calculated by weight about amount of injection of contrast medium and normal saline, flow rate and delay time were applied. To assess image quality, measured average HU value and SNR of superior vena cava, pulmonary artery, ascending and descending aorta, right and left atrium, right and left ventricle. CT numbers of subclavian vein and superior vena cava were compared to identify the effects of reducing artifacts due to normal saline. Comparing SNR according to the contrast medium injection protocol, significant differences were found in superior vena cava and pulmonary artery, descending aorta, right and left ventricle, and CT numbers showed significant differences in all organs. In particular, B group with a 10% decrease in contrast medium and an additional injection of saline showed a low degree of contrast enhancement in groups with a decrease of more than 20%. In addition, the group injected with normal saline greatly reduced contrast enhancement of subclavian vein and superior vena cava, and the beam hardening artifact by contrast medium was significantly attenuated. In conclusion, the application of physiological protocol for injection of contrast medium in pediatric thoracic CT examinations was able to reduce artifacts by contrast medium, prevent unnecessary use of contrast medium and improve the effect of contrast enhancement.

• Clinical and Experimental Pediatrics
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• v.46 no.4
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• pp.376-381
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• 2003

Purpose : This study aimed to demonstrate the possible pathogenesis of granulopoiesis in patients of Kawasaki disease(KD) using quantitative analysis of G-CSF, GM-CSF and their CSFr. Methods : The plasma levels of G-CSF, GM-CSF, G-CSFr and GM-CSFr were studied in 14 patients in the acute phase of KD; 13 children with normal peripheral white blood cell counts were used as the normal control group. The plasma concentration of G-CSF, GM-CSF were analyzed by ELISA. The G-CSFr and GM-CSFr on the peripheral granulocytes were analyzed by a quantitative flow cytometric assay and QuantiBRITE, and the quantitative changes of receptors which did not combine with G-CSF and GM-CSF were measured. Results : The total number of leukocytes in KD was similar to normal control group, but the leukocytes increased according to the number of neutrophils. The plasma concentration of G-CSF were decreased similar to normal control group(P=0.133), but that of GM-CSF decreased more than the normal control group(P=0.227). The quantity of G-CSFr, GM-CSFr were revealed to be no less than the normal control(P=0.721, P=0.912). After incubation with excessive G-CSF, the expressed G-CSFr on the neutrophils were decreased in both groups(P=0.554). The quantities of expressions of GM-CSFr on the neutrophil after incubation with the excessive GM-CSF were always increased in both groups(P=0.255). The amount of GM-CSFr of neutrophils are in proportion to total white blood cells (r=0.788, P=0.035), but it wasn't in the case of KD(P=0.644). Conclusion : The leukocytosis in KD that mediated by increasing neutrophil was not correlated with the plasma concentrations of G-CSF and GM-CSF, and the amount of expression of G-CSFr and GM-CSFr on granulocyte. It is possible that the reduction of concentration of GM-CSF results by increasing the active GM-CSFr.

• Journal of Chest Surgery
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• v.33 no.7
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• pp.531-540
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• 2000

Baclgrpimd; Recent studies have suggested that the cardioprotective effect of ischemic preconditioning(IP) is closely related to glycogen depletion and attenuation of intracellular acidosis. In the present study, the authors tested this hypothesis by perfusion isolated rabbit hearts with glucose(G) is closely related to glycogen depletion and attenuation of intracellular acidosis. In the present study, the authors tested this hypothesis by perfusion isolated rabbit hearts with glucose(G)-free perfusate. Material and Method; Hearts isolated from New Zealand white rabbits(1.5~2.0 kg body weight) were perfused with Tyrode solution by Langendorff technique. After stabilization of baseline hemodynamics, the hearts were subjected to 45 min global ischemia followed by 120 min reperfusion with IP(IP group, n=13) or without IP(ischemic control group, n=10). IP was induced by single episode of 5 min global ischemia and 10 min reperfusion. In the G-free preconditioned group(n=12), G depletion was induced by perfusionwith G-free Tyrode solution for 5 min and then perfused with G-containing Tyrode solution for 10 min; and 45 min ischemia and 120 min reperfusion. Left ventricular functionincluding developed pressure(LVDP), dP/dt, heart rate, left ventricular end-distolic pressure(LVEDP) and coronary flow (CF) were measured. Myocardial cytosolic and membrane PKC activities were measured by 32P-${\gamma}$-ATP incorporation into PKC-specific peptide and PKC isozymes were analyzed by Western blot with monoclonal antibodies. Infarct size was determined by staining with TTC(tetrazolium salt) and planimetry. Data were analyzed by one-way analysis of variance (ANOVA) and Turkey's post-hoc test. Result ; In comparison with the ischemic control group, IP significantly enhanced functional recovery of the left ventricle; in contrast, functional significantly enhanced functional recovery of the left ventricle; in contrast, functional recovery were not significantly different between the G-free preconditioned and the ischemic control groups. However, the infarct size was significantly reduced by IP or G-free preconditioning(39$\pm$2.7% in the ischemic control, 19$\pm$1.2% in the IP, and 15$\pm$3.9% in the G-free preconditioned, p<0.05). Membrane PKC activities were increased significantly after IP (119%), IP and 45 min ischemia(145%), G-free [recpmdotopmomg (150%), and G-free preconditioning and 45 min ischemia(127%); expression of membrane PKC isozymes, $\alpha$ and $\varepsilon$, tended to be increased after IP or G-free preconditioning. Conclusion; These results suggest that in isolated Langendorff-perfused rabbit heart model, G-free preconditioning (induced by single episode of 5 min G depletion and 10 min repletion) colud not improve post-ischemic contractile dysfunction(after 45-minute global ischemia); however, it has an infarct size-limiting effect.

• Tuberculosis and Respiratory Diseases
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• v.45 no.4
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• pp.813-822
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• 1998

Background: Extubation is recommended to be performed at minimum pressure support (PSmin) during the pressure support ventilation (PSV). In field, physicians sometimes perform additional 1 hr T-piece trial to the patient at PSmin to reduce re-intubation risk. Although it provides confirmation of patient's breathing reserve, weaning could be delayed due to increased airway resistance by endotracheal tube. Methods: To investigate the effect of additional 1 hr T-piece trial on weaning outcome, a prospective study was done in consecutive 44 patients who had received mechanical ventilation more than 3 days. Respiratory mechanics, hemodymic, and gas exchange measurements were done and the level of PSmin was calculated using the equation (PSmin=peak inspiratory flow rate $\times$ total ventilatory system resistance) at the 15cm $H_2O$ of pressure support. At PSmin, the patients were randomized into intervention (additional 1 hr T-piece trial) and control (extubation at PSmin). The measurements were repeated at PSmm, during weaning process (in cases of intervention), and after extubation. The weaning success was defined as spontaneous breathing more than 48hr after extubation. In intervention group, failure to continue weaning process was also considered as weaning failure. Results: Thirty-six patients with 42 times weaning trial were satisfied to the protocol. Mean PSmin level was 7.6 (${\pm}1.9$)cm $H_2O$. There were no differences in total ventilation times (TVT), APACHE III score, nutritional indices, and respiratory mechanics at PSmin between 2 groups. The weaning success rate and re-intubation rate were not different between intervention group (55% and 18% in each) and control group (70% and 20% in each) at first weaning trial. Work of breathing, pressure time product, and tidal volume were aggravated during 1 hr T-piece trial compared to those of PSmin in intervention group ($10.4{\pm}1.25$ and $1.66{\pm}1.08$ J/L in work of breathing) ($191{\pm}232$ and $287{\pm}217$cm $H_2O$ s/m in pressure time product) ($0.33{\pm}0.09$ and $0.29{\pm}0.09$ L in tidal volume) (P<0.05 in each). As in whole, TVT, and tidal volume at PSmin were significantly different between the patients with weaning success ($246{\pm}195$ hr, $0.43{\pm}0.11$ L) and the those with weaning failure ($407{\pm}248$ hr, $0.35{\pm}0.10$L) (P<0.05 in each). Conclusion : There were no advantage to weaning outcome by addition of 1 hr T-piece trial compared to prompt extubation to the patient at PS min.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• IMMUNE NETWORK
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• v.8 no.1
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• pp.21-28
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• 2008

Background: A co-inhibitory molecule, B7-H4, is believed to negatively regulate T cell immunity by suppressing T cell proliferation and inhibiting cytokine production. However, the mechanism behind B7-H4-mediated tolerance remains unclear. Methods: Balb/c $(H-2^d)$ mice were fed with dendritic cell line, DC2.4 $(H-2^d)$ every day for 10 days. Meantime, mice were hydrodynamically injected with recombinant plasmid expressing B7-H4 fusion protein (B7-H4.hFc) or hFc via tail vein. One day after last feeding, mice were immunized with allogeneic B6 spleen cells. 14 days following immunization, mice were challenged with B6 spleen cells to ear back and the ear swelling was determined the next day. Subsequently, a mixed lymphocyte reaction (MLR) was also performed and cytokines profiles from the reaction were examined by sandwich ELISA. Frequency of immunosuppressive cell population was assayed with flow cytometry and mRNA for FoxP3 was determined by RT-PCR. Results: Tolerant mice given plasmid expressing B7-H4.hFc showed a significant reduction in ear swelling compared to control mice. In addition, T cells from mice given B7-H4.hFc plasmid revealed a significant hyporesponsiveness of T cells against allogeneic spleen cells and showed a significant decrease in Th1 and Th2 cytokines such as IFN-${\gamma}$, IL-5, and TNF-${\alpha}$. Interestingly, flow cytometric analysis showed that the frequency of CD4+CD25+FoxP3+ Tregs in spleen was increased in tolerant mice given recombinant B7-H4.hFc plasmid compared to control group. Conclusion: Our results demonstrate that B7-H4 synergistically potentiates oral tolerance induced by allogeneic cells by increasing the frequency of FoxP3+ CD4+CD25+ Treg and reducing Th1 and Th2 cytokine production.

• Journal of Oral Medicine and Pain
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• v.30 no.4
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• pp.457-464
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• 2005

The purpose of our study was to investigate whether the intrapulpal temperature during cavity preparation of enamel or dentin with Er:YAG laser still remained in range of safety for dental pulp protection when combined with appropriate water flow rate. The effect of different pulse repetition rates at the same pulse energy during ablation was evaluated as well. Caries-free, restoration-free extracted human molar teeth were prepared for the specimen and divided two experimental groups of enamel and dentin. Each group comprised 5 specimens and each of tooth specimens were embedded into a resin block each and measuring probe was placed on the irradiated pulpal walls. For experiments of dentin ablation, enamel layers were prepared to produce dentin specimen with a same dentin thickness of 2 mm. A pulse energy of Er:YAG laser was set to 300 mJ and three different pulse repetition rates of 20 Hz, 15 Hz and 10 Hz were employed. Laser beam was delivered with 3 seconds and less per application over enamel and dentin surfaces constant sized by $3\;mm{\times}2\;mm$ and water spray added during irradiation was a rate of 1.6 ml/min. Temperature change induced by Er:YAG laser irradiation was monitored and recorded While enamel was ablated, there was no significant difference of temperature related to pulse repetition rates(p=0.358) and temperature change at any pulse repetition rate was negligible. Significant statistical difference in temperature changes during cavity preparation in dentin existed among three different pulse groups(p=0.001). While temperature rise was noticeable when the dentinal wall was perforated, actual change of temperature due to Er:YAG laser irradiation was not enough to compromise safety of dental pulp when irradiation was conjugated with appropriate water spray. Conclusively, it can be said that cavity preparation on enamel or dentin with an Er:YAG laser is performed safely without pulp damage if appropriate volume of water is sprayed properly over the irradiated site.

• The Korean Journal of Financial Management
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• v.27 no.2
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• pp.35-62
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• 2010

This paper examines the existence of the fund performance persistence and the smart money effect in Korean stock market and tests the flow-induced price pressure (FIPP) hypothesis, that is, fund flows affect individual stock returns and mutual fund performance. This paper also tests whether the FIPP effect can cause the performance persistence using the monthly returns and stock holdings data of 2,702 Korean mutual funds from January 2002 to June 2008. The empirical results indicate that the performance persistence exists significantly for a long time but the smart money effect does not. The hedge portfolio constructed by buying funds with the highest past 12 months performance and selling funds with the lowest past 12 months performance earns 0.11%~1.05% monthly abnormal returns, on average, in 3 years from portfolio formation month, but the hedge portfolio constructed by buying funds with the highest past net fund inflows and selling funds with the lowest past net fund inflows cannot earn positive monthly abnormal returns and the size of negative abnormal returns of the portfolio increase as time goes on. We find the evidence that the FIPP hypothesis is significantly supported. We first estimate the FIPP measure for each individual stock using the trading volume resulting from past fund flows and then construct the hedge portfolio by buying stocks with the highest FIPP measure and selling stocks with the lowest FIPP measure. That portfolio earns significantly positive abnormal return, 1.01% at only portfolio formation month and cannot earn significant abnormal returns after formation month. But, the FIPP effect cannot cause the performance persistence because, within the same FIPP measure group, funds with higher past performance still earn higher monthly abnormal returns than those with lower past performance by 0.08%~0.77%, on average, in 2 years. These results imply that the main cause of the performance persistence in Korean stock market is the difference of fund managers' ability rather than the FIPP effect.