• Progress in Medical Physics
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• v.23 no.1
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• pp.15-25
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• 2012

The aim of this study is to evaluate plan quality and dose accuracy for Volumetric Modulated Arc Therapy (VMAT) on the TG-119 and is to investigate the effects on variation of the selectable optimization parameters of VMAT. VMAT treatment planning was implemented on a Varian iX linear accelerator with ARIA record and verify system (Varian Mecical System Palo Alto, CA) and Oncentra MasterPlan treatment planning system (Nucletron BV, Veenendaal, Netherlands). Plan quality and dosimetric accuracy were evaluated by effect of varying a number of arc, gantry spacing and delivery time for the test geometries provided in TG-119. Plan quality for the target and OAR was evaluated by the mean value and the standard deviation of the Dose Volume Histograms (DVHs). The ionization chamber and $Delta^{4PT}$ bi-planar diode array were used for the dose evaluation. For treatment planning evaluation, all structure sets closed to the goals in the case of single arc, except for the C-shape (hard), and all structure sets achieved the goals in the case of dual arc, except for C-shape (hard). For the variation of a number of arc, the simple structure such as a prostate did not have the difference between single arc and dual arc, whereas the complex structure such as a head and neck showed a superior result in the case of dual arc. The dose distribution with gantry spacing of $4^{\circ}$ was shown better plan quality than the gantry spacing of $6^{\circ}$, but was similar results compared with gantry spacing of $2^{\circ}$. For the verification of dose accuracy with single arc and dual arc, the mean value of a relative error between measured and calculated value were within 3% and 4% for point dose and confidence limit values, respectively. For the verification on dose accuracy with the gantry intervals of $2^{\circ}$, $4^{\circ}$ and $6^{\circ}$, the mean values of relative error were within 3% and 5% for point dose and confidence limit values, respectively. In the verification of dose distribution with $Delta^{4PT}$ bi-planar diode array, gamma passing rate was $98.72{\pm}1.52%$ and $98.3{\pm}1.5%$ for single arc and dual arc, respectively. The confidence limit values were within 4%. The smaller the gantry spacing, the more accuracy results were shown. In this study, we performed the VMAT QA based on TG-119 procedure, and demonstrated that all structure sets were satisfied with acceptance criteria. And also, the results for the selective optimization variables informed the importance of selection for the suitable variables according to the clinical cases.

• Radiation Oncology Journal
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• v.19 no.1
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• pp.45-52
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• 2001

Purpose : Granulocyle-colony stimulating factor (G-CSF) has been widely used to treat neutropenia caused by chemotherapy or radiotherapy. The efficacy of recombinant human hematopoietic growth factors in improving oral mucositis after chemotherapy or radiotherapy has been recently demonstrated in some clinical studies. This study was designed to determine whether G-CSF can modify the radiation injury of the intestinal mucosa in mice. Materials and Methods : One hundred and five BALB/c mice weighing 20 grams were divided into nine subgroups including G-CSF alone group $(I:10\;{\mu}g/kg\;or\;II:100\;{\mu}g/kg)$, radiation alone group (7.5 or 12 Gy on the whole body), combination group with G-CSF and radiation (G-CSF I or II plus 7.5 Gy, G-CSF I or II plus 12 Gy), and control group. Radiation was administered with a 6 MV linear accelerator (Mevatron Siemens) with a dose rate of 3 Gy/min on day 0. G-CSF was injected subcutaneously for 3 days, once a day, from day -2 to day 0. Each group was sacrificed on the day 1, day 3, and day 7. The mucosal changes of jejunum were evaluated microscopically by crypt count per circumference, villi length, and histologic damage grading. Results : In both G-CSF I and II groups, crypt counts, villi length, and histologic damage scores were not significantly different from those of the control one (p>0.05). The 7.5 Gy and 12 Gy radiation alone groups showed significantly lower crypt counts and higher histologic damage scores compared with those of control one (p<0.05). The groups exposed to 7.5 Gy radiation plus G-CSF I or II showed significantly higher crypt counts and lower histologic damage scores on the day 3, and lower histologic damage scores on the day 7 compared with those of the 7.5 Gy radiation alone one (p<0.05). The 12 Gy radiation plus G-CSF I or II group did not show significant difference in crypt counts and histologic damage scores compared with those of the 12 Gy radiation alone one (p>0,05). Most of the mice in 12 Gy radiation with or without G-CSF group showed intestinal death within 5 days. Conclusion : These results suggest that G-CSF may protect the jejunal mucosa from the acute radiation damage following within the tolerable ranges of whole body irradiation in mice.

• Restorative Dentistry and Endodontics
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• v.27 no.2
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• pp.158-174
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• 2002

The aim of this study was to investigate the effect of light irradiation modes on polymerization shrinkage, degree of cure and microleakage of a composite resin. VIP$^{TM}$ (Bisco Dental Products, Schaumburg, IL, USA) and Optilux 501$^{TM}$ (Demetron/Kerr, Danbury, CT, USA) were used for curing Filtek$^{TM}$ Z-250 (3M Dental Products, St. Paul., MN, USA) composite resin using following irradiation modes: VIP$^{TM}$ (Bisco) 200mW/$\textrm{cm}^2$ (V2), 400mW/$\textrm{cm}^2$ (V4), 600mW/$\textrm{cm}^2$ (V6), Pulse-delay (200 mW/$\textrm{cm}^2$ 3 seconds, 5 minutes wait, 600mW/$\textrm{cm}^2$ 30seconds, VPD) and Optilux 501$^{TM}$ (Demetron/Kerr) C-mode (OC), R-mode (OR). Linear polymerization shrinkage of the composite specimens were measured using Linometer (R&B, Daejeon, Korea) for 90 seconds for V2, V4, V6, OC, OR groups and for up to 363 seconds for VPD group (n=10, each). Degree of conversion was measured using FTIR spectrometer (IFS 120 HR, Bruker Karlsruhe, Germany) at the bottom surface of 2 mm thick composite specimens V2, Y4, V6, OC groups were measured separately at five irradiation times (5, 10, 20, 40, 60 seconds) and OR, VPD groups were measured in the above mentioned irradiation modes (n=5 each). Microhardness was measured using Digital microhardness tester (FM7, Future-Tech Co., Tokyo, Japan) at the top and bottom surfaces of 2mm thick composite specimens after exposure to the same irradiation modes as the test of degree of conversion(n=3, each). For the microleakage test, class V cavities were prepared on the distal surface of the ninety extracted human third molars. The cavities were restored with one of the following irradiation modes : V2/60 seconds, V4/40 seconds, V6/30 seconds, VPD , OC and OR. Microleakage was assessed by dye penetration along enamel and dentin margins of cavities. Mean polymerization shrinkage, mean degree of conversion and mean microhardness values for all groups at each time were analyzed using one-way ANOVA and Duncan's multiple range test, and using chi-square test far microleakage values. The results were as follows : . Polymerization shrinkage was increased with higher light intensity in groups using VIP$^{TM}$ (Bisco) : the highest with 600mW/$\textrm{cm}^2$, followed by Pulse-delay, 400mW/$\textrm{cm}^2$ and 200mW/$\textrm{cm}^2$ groups, The degree of polymerization shrinkage was higher with Continuous mode than with Ramp mode in groups using Optilux 501$^{TM}$ (Demetron/Kerr). . Degree of conversion and microhardness values were higher with higher light intensity. The final degree of conversion was in the range of 44.7 to 54.98% and the final microhardness value in the range of 34.10 to 56.30. . Microleakage was greater in dentin margin than in enamel margin. Higher light intensity showed more microleakage in dentin margin in groups using VIP$^{TM}$ (Bisco). The microleakage was the lowest with Continuous mode in enamel margin and with Ramp mode in dentin margin when Optilux 501$^{TM}$ (Demetron/Kerr) was used.

• Journal of Animal Science and Technology
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• v.45 no.5
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• pp.689-694
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• 2003

Weight records of Hanwoo cows from birth to 36 months of age collected in Daekwanryeong branch, National Livestock Research Institute(NLRI) were fitted to Gompertz, von Bertalanffy and Logistic functions. For the growth curve parameters fitted on individual records using Gompertz model, the mean estimates of mature weight(A), growth ratio(b) and growth rate(k) were 383.42 ${\pm}$ 97.29kg, 2.374 ${\pm}$ 0.340 and 0.0037 ${\pm}$ 0.0012, respectively, and mean estimates of body weight, age and daily gain rate at inflection were 141.05 ${\pm}$ 35.79kg, 255.63 ${\pm}$ 109.09 day and 0.500 ${\pm}$ 0.123kg, respectively. For von BertalanfTy model, the mean estimates of A, b and k were 410.47 ${\pm}$ 117.98kg, 0.575${\pm}$0.057 and 0.003 ${\pm}$ 0.001, and mean estimates of body weight, age and daily gain at inflection were 121.62 ${\pm}$ 34.94kg, 211.02 ${\pm}$ 105.53 and 0.504 ${\pm}$ O.l24kg. For Logistic model, the mean estimates of A, b and k were 347.64 ${\pm}$ 97.29kg, 6.73 ${\pm}$ 0.34 and 0.006 ${\pm}$ 0.0018, and mean estimates of body weight, age and daily gain at inflection were 173.82 ${\pm}$ 37.25kg, 324.47 ${\pm}$ 126.85 and 0.508 ${\pm}$ 0.131kg. Coefficients of variation for the A, b and k parameter estimates were 25.3%, 14.3% and 32.4%, respectively, for Gompertz model, 28.70/0, 9.9% and 33.3% for von Bertalanffy model, and 27.9°/0, 5.0% and 30.0% for Logistic model.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.167-194
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• 2019

This research starts from the four basic concepts of incentive incompatibility, limited information, myopia and decision variable which are confronted when making decisions in keyword bidding. In order to make these concept concrete, four framework approaches are designed as follows; Strategic approach for the incentive incompatibility, Statistical approach for the limited information, Alternative optimization for myopia, and New model approach for decision variable. The purpose of this research is to propose the statistical optimization model in constructing the portfolio of Sponsored Search Advertising (SSA) in the Sponsor's perspective through empirical tests which can be used in portfolio decision making. Previous research up to date formulates the CTR estimation model using CPC, Rank, Impression, CVR, etc., individually or collectively as the independent variables. However, many of the variables are not controllable in keyword bidding. Only CPC and Rank can be used as decision variables in the bidding system. Classical SSA model is designed on the basic assumption that the CPC is the decision variable and CTR is the response variable. However, this classical model has so many huddles in the estimation of CTR. The main problem is the uncertainty between CPC and Rank. In keyword bid, CPC is continuously fluctuating even at the same Rank. This uncertainty usually raises questions about the credibility of CTR, along with the practical management problems. Sponsors make decisions in keyword bids under the limited information, and the strategic portfolio approach based on statistical models is necessary. In order to solve the problem in Classical SSA model, the New SSA model frame is designed on the basic assumption that Rank is the decision variable. Rank is proposed as the best decision variable in predicting the CTR in many papers. Further, most of the search engine platforms provide the options and algorithms to make it possible to bid with Rank. Sponsors can participate in the keyword bidding with Rank. Therefore, this paper tries to test the validity of this new SSA model and the applicability to construct the optimal portfolio in keyword bidding. Research process is as follows; In order to perform the optimization analysis in constructing the keyword portfolio under the New SSA model, this study proposes the criteria for categorizing the keywords, selects the representing keywords for each category, shows the non-linearity relationship, screens the scenarios for CTR and CPC estimation, selects the best fit model through Goodness-of-Fit (GOF) test, formulates the optimization models, confirms the Spillover effects, and suggests the modified optimization model reflecting Spillover and some strategic recommendations. Tests of Optimization models using these CTR/CPC estimation models are empirically performed with the objective functions of (1) maximizing CTR (CTR optimization model) and of (2) maximizing expected profit reflecting CVR (namely, CVR optimization model). Both of the CTR and CVR optimization test result show that the suggested SSA model confirms the significant improvements and this model is valid in constructing the keyword portfolio using the CTR/CPC estimation models suggested in this study. However, one critical problem is found in the CVR optimization model. Important keywords are excluded from the keyword portfolio due to the myopia of the immediate low profit at present. In order to solve this problem, Markov Chain analysis is carried out and the concept of Core Transit Keyword (CTK) and Expected Opportunity Profit (EOP) are introduced. The Revised CVR Optimization model is proposed and is tested and shows validity in constructing the portfolio. Strategic guidelines and insights are as follows; Brand keywords are usually dominant in almost every aspects of CTR, CVR, the expected profit, etc. Now, it is found that the Generic keywords are the CTK and have the spillover potentials which might increase consumers awareness and lead them to Brand keyword. That's why the Generic keyword should be focused in the keyword bidding. The contribution of the thesis is to propose the novel SSA model based on Rank as decision variable, to propose to manage the keyword portfolio by categories according to the characteristics of keywords, to propose the statistical modelling and managing based on the Rank in constructing the keyword portfolio, and to perform empirical tests and propose a new strategic guidelines to focus on the CTK and to propose the modified CVR optimization objective function reflecting the spillover effect in stead of the previous expected profit models.