• Journal of Technologic Dentistry
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• v.35 no.2
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• pp.157-171
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• 2013

Purpose: This study aims to present a database for the development of a healthcare management program based on the survey and analysis of self reported psychosomatic symptoms among the current dental technology students. Methods: Subjects of our study are 480 dental technology major students enrolled in a third year college located Gyeonggi, Chungcheong, and Gangwon province. Using a random sampling, we conducted a self-report survey from August 30, 2011 to October 28, 2011 and 418 reports were collected as feedback and we put an analysis on them. Results: 1. The average physical self symptom was 20.49, which is higher than the average mental self symptom(18.54). Of the subcategories of psychosomatic self symptom, we observed multiple subjective symptoms as the highest one(37.77), and aggression as the lowest(13.77). 2. As to gender, both physical and mental self symptom were statistically significant with the scale score of(p<.001). The scale score of subcategories is as follows; multiple subjective symptoms(I, p<.001), eye and skin(B, p<.001), digestive(C, p<.001), impulsiveness(H, p<.001), lie scale(L, p<.001), mental instability(J, p<.001), depression(K, p<.001), aggression(F, p<.001), irregularity of life(G, p<.001), mouth and anal(D, p<.05), nervousness(E, p<.05). 3. As for obesity, statistical significance was shown with the scale scores of physical self symptom(p<.001), multiple subjective symptoms(I, p<.001), digestive(C, p<.001), aggression(F, p<.001), depression(K, p<.01), irregularity of life(G, p<.01), respiratory(A, p<.05), eye and skin(B, p<.05), impulsiveness(H, p<.05), mental instability(J, p<.05). The scale scores in the environmental quality and life satisfaction were shown as follows; depression(K, p<.001), lie scale(L, p<.01), and irregularity of life(G, p<.05). 4. We employed multiple regression analysis to take account of general factors affecting psychosomatic self symptoms, and drew that the explanatory power of the model was proved with the scales of physical self symptom(4.1%) and mental self-symptom(3.6%). Obesity was a factor that affects physical self symptom with the scale score of(p<.01), and environmental quality and life satisfaction(p<.01) and obesity(p<.05) affect mental self symptom. Conclusion: In this analysis we observed obesity of dental technology students can influence their psychosomatic self symptoms. In this sense, it would be reasonable to develop a healthcare management and education programs that help the students maintain a healthy weight and promote their health.

• Journal of Life Science
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• v.24 no.9
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• pp.981-987
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• 2014

Foeniculum vulgare has long been prescribed in traditional medicine for the treatment of inflammation diseases. In this study, we aimed to investigate the inhibitory effects of the fruits of F. vulgare on lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophage cells under non-cytotoxic ($100{\mu}g/ml$) conditions. The 80% methanol extract was subsequently partitioned successively with hexane, methylene chloride, ethyl acetate, and n-butanol, and the fractions so obtained were also examined for their anti-inflammatory effects. Among them, the hexane, methylene chloride, and ethyl acetate fractions inhibited nitric oxide (NO) and prostaglandin E2 (PGE2) production in LPS stimulated macrophages. The methylene chloride and ethyl acetate fractions also suppressed the productions of interleukin $(IL)-1{\beta}$ and IL-6 by down-regulating their mRNA levels in LPS stimulated RAW 264.7 cells. Furthermore, the ethyl acetate fraction strongly suppressed tumor necrosis factor (TNF)-${\alpha}$ at the protein and mRNA levels in LPS stimulated RAW 264.7 cells. These observations suggest that the anti-inflammatory actions of F. vulgare are due to inhibitions of the productions of NO, PGE2, and pro-inflammatory cytokines.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.169-177
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• 2020

In South Korea, the results of R&D in science and technology are submitted to the National Science and Technology Information Service (NTIS) in reports that have Korea national science and technology standard classification codes (K-NSCC). However, considering there are more than 2000 sub-categories, it is non-trivial to choose correct classification codes without a clear understanding of the K-NSCC. In addition, there are few cases of automatic document classification research based on the K-NSCC, and there are no training data in the public domain. To the best of our knowledge, this study is the first attempt to build a highly performing K-NSCC classification system based on NTIS report meta-information from the last five years (2013-2017). To this end, about 210 mid-level categories were selected, and we conducted preprocessing considering the characteristics of research report metadata. More specifically, we propose a convolutional neural network (CNN) technique using only task names and keywords, which are the most influential fields. The proposed model is compared with several machine learning methods (e.g., the linear support vector classifier, CNN, gated recurrent unit, etc.) that show good performance in text classification, and that have a performance advantage of 1% to 7% based on a top-three F1 score.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Journal of Trauma and Injury
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• v.37 no.3
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• pp.201-208
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• 2024

Purpose: The number of elderly patients with trauma is increasing; therefore, precise models are necessary to estimate the mortality risk of elderly patients with trauma for informed clinical decision-making. This study aimed to develop machine learning based predictive models that predict 30-day mortality in severely injured elderly patients with trauma and to compare the predictive performance of various machine learning models. Methods: This study targeted patients aged ≥65 years with an Injury Severity Score of ≥15 who visited the regional trauma center at Chungbuk National University Hospital between 2016 and 2022. Four machine learning models-logistic regression, decision tree, random forest, and eXtreme Gradient Boosting (XGBoost)-were developed to predict 30-day mortality. The models' performance was compared using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, specificity, F1 score, as well as Shapley Additive Explanations (SHAP) values and learning curves. Results: The performance evaluation of the machine learning models for predicting mortality in severely injured elderly patients with trauma showed AUC values for logistic regression, decision tree, random forest, and XGBoost of 0.938, 0.863, 0.919, and 0.934, respectively. Among the four models, XGBoost demonstrated superior accuracy, precision, recall, specificity, and F1 score of 0.91, 0.72, 0.86, 0.92, and 0.78, respectively. Analysis of important features of XGBoost using SHAP revealed associations such as a high Glasgow Coma Scale negatively impacting mortality probability, while higher counts of transfused red blood cells were positively correlated with mortality probability. The learning curves indicated increased generalization and robustness as training examples increased. Conclusions: We showed that machine learning models, especially XGBoost, can be used to predict 30-day mortality in severely injured elderly patients with trauma. Prognostic tools utilizing these models are helpful for physicians to evaluate the risk of mortality in elderly patients with severe trauma.

• Korean Journal of Plant Resources
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• v.28 no.1
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• pp.101-110
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• 2015

Parameters of mating system and pollen flow of a Pinus densiflora population in Buan, South Korea, were estimated using seven nuclear microsatellite markers. The expected heterozygosity ($H_e$) was 0.614 in mother trees and 0.624 in seeds. Fixation index (F) was 0.018 and 0.087 in each generation. There was no significant genetic difference between the generations (P > 0.05). From MLTR, the outcrossing rate ($t_m$), the biparental inbreeding ($t_m-t_s$), and the correlation of paternity ($r_p$) were 0.967, 0.057, and 0.012, respectively. tm was larger but $t_m-t_s$ and $r_p$ were smaller than those of allozyme markers in Pinus densiflora. These values were similar to those of microsatellite markers in other pine species. The optimal pollen dispersal model from TwoGener was the normal dispersal model with the effective density of 220 trees/ha and its level of genetic differentiation in pollen pool structure (${\Phi}_{ft}$) was 0.021. The average radial distance of pollen flow (${\delta}$) was calculated as 11.42 m, but no correlation between the pairwise-${\Phi}_{ft}$ and the geographical distance among mother trees was at Mantel test (r = -0.141, P > 0.05). Although the effective pollen dispersal in the population seems to be restricted, the amount of genetic variation might be maintained in each generation without a loss of genetic diversity. It might be because the genetic diversity in pollen pool was high but the genetic difference between pollen donors was small under the complete random mating condition in the Pinus densiflora population in Buan.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Tuberculosis and Respiratory Diseases
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• v.45 no.1
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• pp.140-152
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• 1998

Background: Prone position improves oxygenation in patients with ARDS probably by reducing shunt Reduction of shunt in prone position is thought to be effected by lowering of the critical opening pressure (COP) of the dorsal lung because the pleural pressure becomes less positive in prone position compared to supine position. It can then be assumed that prone position would bring about greater improvement in oxygenation when PEEP applied in supine position is just beneath COP than when PEEP is above COP. Hemodynamically, prone position is expected to attenuate the lifting of cardiac fossa induced by PEEP. Based on these backgrounds, we investigated whether the effect of prone position on oxygenation differs in magnitude according to the level of PEEP applied in supine position, and whether impaired cardiac output in supine position by PEEP can be restored in prone position. Methods: In seven mongrel dogs, $PaO_2/F_1O_2$(P/F) was measured in supine position and at prone position 30 min. Cardiac output (CO), stroke volume (SV), pulse rate (PR), and pulmonary artery occlusion pressure (PAOP) were measured in supine position, at prone position 5 min, and at prone position 30 min. After ARDS was established with warmed saline lavage(P/F ratio $134{\pm}72$ mm Hg), inflection point was measured by constant flow method($6.6{\pm}1.4cm$ $H_2O$), and the above variables were measured in supine and prone positions under the application of Low PEEP($5.0{\pm}1.2cm$ $H_2O$), and Optimal PEEP($9.0{\pm}1.2cm$ $H_2O$)(2 cm $H_2O$ below and above the inflection point, respectively) consecutively. Results : P/F ratio in supine position was $195{\pm}112$ mm Hg at Low PEEP and $466{\pm}63$ mm Hg at Optimal PEEP(p=0.003). Net increase of P/F ratio at prone position 30 min, however, was far greater at Low PEEP($205{\pm}90$ mm Hg) than at Optimal PEEP($33{\pm}33$ mm Hg)(p=0.009). Compared to CO in supine position at Optimal PEEP($2.4{\pm}0.5$ L/min), CO in prone improved to $3.4{\pm}0.6$ L/min at prone position 5 min (p=0.0180) and $3.6{\pm}0.7$ L/min at prone position 30 min (p=0.0180). Improvement in CO was attributable to the increase in SV: $14{\pm}2$ ml in supine position, $20{\pm}2$ ml at prone position 5 min (p=0.0180), and $21{\pm}2$ ml at prone position 30 min (p=0.0180), but not to change in PR or PAOP. When the dogs were turned to supine position again, MAP ($92{\pm}23$ mm Hg, p=0.009), CO ($2.4{\pm}0.5$ L/min, p=0.0277) and SV ($14{\pm}1$ ml, p=0.0277) were all decreased compared to prone position 30 min. Conclusion: Prone position in a dog with saline-lavaged acute lung injury appeared to augment the effect of relatively low PEEP on oxygenation, and also attenuate the adverse hemodynamic effect of relatively high PEEP. These findings suggest that a PEEP lower than Optimal PEEP can be adopted in prone position to achieve the goal of alveolar recruitment in ARDS avoiding the hemodynamic complications of a higher PEEP at the same time.

• Journal of Korea Water Resources Association
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• v.34 no.6
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• pp.659-672
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• 2001

Generally, design flood for a hydraulic structure is estimated using statistical analysis of runoff data. However, due to the lack of runoff data, it is difficult that the statistical method is applied for estimation of design flood. In this case, the synthetic unit hydrograph method is used generally and the models such as NYMO method, Snyder method, SCS method, and HYMO method have been widely used in Korea. In this study, these methods and KICT method, which is developed in year 2000, are compared and analyzed in 10 study areas. Firstly, peak flow and peak time of representative unit hydrograph and synthetic unit hydrograph in study area are compared, and secondly, the shape of unit hydrograph is compared using a root mean square error(RMSE). In Nakayasu method developed in Japan, synthetic unit hydrograph is very different from peak flow, peak time, and the shape of representative unit hydrograph, and KICT method(2000) is superior to others. Also, KICT method(2000) is superior to others in the aspects of using hydrologic and topographical data. Therefore, Nakayasu method is not a proper in hydrological practice. Moreover, it is considered that KICT model is a better method for the estimation of design flood. However, if other model, i.e. SCS method, Nakayasu method, and HYMO method, is used, parameters or regression equations must be adjusted by analysis of real data in Korea.

• Journal of Microbiology and Biotechnology
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• v.21 no.6
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• pp.637-645
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• 2011

Production of recombinant proteins by excretory expression has many advantages over intracellular expression in Escherichia coli. Hyperexpression of a secretory exoglucanase, Exg, of Cellulomonas fimi was previously shown to saturate the SecYEG pathway and result in dramatic cell death of E. coli. In this study, we demonstrated that overexpression of the PspA in the JM101(pM1VegGcexL-pspA) strain enhanced excretion of Exg to 1.65 U/ml using shake-flask cultivation, which was 80% higher than the highest yield previously obtained from the optimized JM101(pM1VegGcexL) strain. A much higher excreted Exg activity of 4.5 U/ml was further achieved with high cell density cultivation using rich media. Furthermore, we showed that the PspA overexpression strain enjoyed an elevated critical value (CV), which was defined as the largest quotient between the intracellular unprocessed precursor and its secreted mature counterpart that was still tolerable by the host cells prior to the onset of cell death, improving from the previously determined CV of 20/80 to the currently achieved CV of 45/55 for Exg. The results suggested that the PspA overexpression strain might tolerate a higher level of precursor Exg making use of the SecYEG pathway for secretion. The reduced lethal effect might be attributable to the overexpressed PspA, which was postulated to be able to reduce membrane depolarization and damage. Our findings introduce a novel strategy of the combined application of metabolic engineering and construct optimization to the attainment of the best possible E. coli producers for secretory/excretory production of recombinant proteins, using Exg as the model protein.