The purpose of this study was to analysis know the important oral environmental factors which affect halitosis components of the adult in order to provide basic data for halitosis prevention and establish a device to eliminate halitosis efficiently. The 97 adults who visited at the Dental Clinic in Metropolis (M=68, F=30) participated in this study that performed from March in 2009 to in 2010. The obtained results through items as caries status, periodontal status, salivary flow, the viscosity, pH, Snyder test, plaque deposit, tongue plaque and halitosis check were as followings. The average shame of halitosis components appeared at hydrogen sulfide 36.71 ppb methyl mercaptan 31.46ppb dimethyl sulfide 54.33 ppb and Ammonia 22.60 ppm. The normality and the detection comparative result dimethyl sulfide above reverse appeared highly at 46.9%, ammonia appeared highly at 52%. According to the Hydrogen sulfide level was a high relationship among age, CPI, tongue coat status, DMFT index which were statistically significant (p<0.05). According to the quantity of hydrogen sulfide level there was relationship where tongue coat status Saliva flow rate considers statistically(p<0.05). The quantity of methyl mercaptan level there was relationship where Dimethyl sulfide level, tongue coat status, Saliva flow rate considers statistically(p<0.05). The quantity of Dimethyl sulfide level there was relationship where Hydrogen sulfide level, ammonia level, tongue coat status, Saliva pH and Saliva flow rate considers statistically(p<0.05). Ammonia level there was relationship where Methyl mercaptan level, CPI, and Saliva flow rate considers statistically(p<0.05).
As the frequency and intensity of extreme weather events due to climate change are increasing in recent years, it is very important to evaluate and analyze climate conditions to manage and respond to the negative effects of climate change in advance. In this study, the trends and characteristics of regional climate change were analyzed by calculating the climate indices for the Chungcheong Province. Annual and monthly UNEP-MP, UNEP-PM and MDM indices were calculated using daily data from 1973-2020 collected from 10 synoptic meteorological stations operated by the Korea Meteorological Administration. The normality of climate data was analyzed through the KS test, and the climate change trend was analyzed by applying the Spearman and Pearson methods. The Chungcheongnam-do region had a relatively humid climate than the Chungcheongbuk-do region, and the annual climate indices showed a dry climate trend in Cheongju and Chungju, while the climate of Seosan and Buyeo was becoming humid. Based on the monthly trend change analysis, a humid climate trend was observed in summer and autumn, while a dry climate trend was observed in spring and winter. Comparison of climate indices during the past (2001-2010) and the recent (2011-2020) years showed a higher decrease in the average climate indices during the last 10 years and a gradually drying climate change trend was recorded.
Purpose. The purpose of this study was to evaluate the effect of surface treatments on the shear bond strength of two types of zirconia (3-TZP and 5Y-PSZ) with resin cement. Materials and methods. Two different types of zirconia specimens with a fully sintered size of 14.0×14.0×2.0 mm3 were prepared, polished with 400, 600, and 800 grit silicon carbide paper, and buried in epoxy resin. They were classified into four groups each control, sandblasting, primer, and sandblasting & primer. Cylindrical resin adhered to the surface-treated zirconia with resin cement. It was stored in distilled water (37℃) for 24 hours, and a shear bond strength test was performed. The normality of the experimental group was confirmed with the Kolmogorov-Smirnov & Shapiro-Wilk test. The interaction and statistical difference were analyzed using a two-way ANOVA. A post-hoc analysis was performed using Dunnett T3. Results. As a result of two-way ANOVA, there was no significant difference in shear bonding strength between zirconia types (P > .05), but there was a significant correlation in the sandblasting, primer, and alumina sandblasting & primer group (P < .05). Dunnett T3 post-test showed that, regardless of the type of zirconia, shear bonding strength was sandblasting & primer > Primer > sandblasting > control group (P < .05). Conclusion. There was no difference in shear bond strength between the types of zirconia. The highest shear bond strength was shown when the mechanical and chemical treatments of the zirconia surface was performed simultaneously.
Purpose: The purpose of this study was to compare and analyze the wear of a prosthesis for 6 months after restoration with implant-supported fixed dental prosthesis made of either zirconia or gold. Materials and Methods: This study was conducted on patients requiring implant-supported fixed dental prostheses on first or second molar from January, 2015 to January, 2016. A total of 47 prostheses and antagonists were examined. Occlusal surface was recorded by impression of each prosthesis and antagonist 1 week and 6 months after prosthesis delivery. The digital files were created by impression scan. Occlusal shapes of 1 week and 6 months were compared and wear of prostheses and antagonists was analyzed. The Mann-Whitney test was used to analyzed the result data underwent normality test using SPSS (Version 23.0, IBM Corporation) Results: Mann-Whitney test revealed that there was no statistically significant difference in the median amount of mean vertical wear for 6 months in zirconia (
Purpose Estradiol (E2) is a steroid hormone mainly produced in women and is a useful indicator for diagnosis of gynecological diseases, menstrual cycle, menopause, and precocious puberty. E2 measurement is performed by diluting the
Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70