• Food Science and Preservation
• /
• v.18 no.6
• /
• pp.980-985
• /
• 2011

This study was conducted to determine the acetic-acid fermentation properties of apple juice (initial alcohol content, apple juice concentration, acetic-acid content, and inoculum size) in flask scale. At the acetic-acid fermentation of apple juice with 3, 5, 7, and 9% initial alcohol content, the maximum acidity after 10-day fermentation was 5.88% when the initial alcohol content was 5%. The acetic-acid fermentation did not proceed normally when the initial alcohol content was 9%. When the initial Brix was $1^{\circ}$, the acidity gradually increased, and the acidity after 12-day acetic-acid fermentation was 4.48%. Above 4% acidity was attained faster when the apple juice concentration was 5 and 10 $^{\circ}Brix$ than when it was 1 and 14 $^{\circ}Brix$. When the initial acidity was 1% or above (0.3, 0.5, 1.0, and 2.0%), the acetic-acid fermentation proceeded normally. The acetic-acid fermentation also proceeded normally when the inoculum sizes were 10 and 15%, and the acidity after eight-day acetic-acid fermentation was 5.60 and 6.05%, respectively. Therefore, the following were considered the optimal acetic-acid fermentation conditions for apple cider vinegar: 5% initial alcohol content, 5 $^{\circ}Brix$ or above apple juice concentration, 1.0% or above initial acidity, and 10% or above inoculum size. Apple cider vinegar with above 5% acidity can be produced within 48 h under the following acetic-acid fermentation conditions: 7% initial alcohol content, about 1% initial acidity, and 10% inoculum volume at $30^{\circ}C$, 30 rpm, and 1.0 vvm, using 14 $^{\circ}Brix$ apple juice in a mini-jar fermentor as a pre-step for industrial-scale adaptation.

• Proceedings of the KSAR Conference
• /
• 2001.03a
• /
• pp.31-31
• /
• 2001

This study was to establish in uitro culture system of mouse preantral follicles and to obtain higher in vitro development rates and production of live young. Preantral follicles were obtained from 12-day-old FI mouse (C57BL $\times$ CBA) by enzymatical methods. Oocyte-granulosa cell complexes (OGCs) of preantral follicles were loaded on Transwell-COL insert and cultured in $\alpha$MEM supplemented with 5% FBS, 100 mIU/$m\ell$ FSH and 100 mIU/$m\ell$ hMG for IVG. IVM was performed in $\alpha$MEM supplemented 1.5 IU/$m\ell$ hCG for 18 hrs and IVF was carried out in Ml6 medium. Embryos were cultured in modified Ml6 medium supplemented 10% FBS for 4 days. The effect of the OGCs size on the nuclear/cytoplasmic maturation was significantly higher in 120-150 ${\mu}{\textrm}{m}$ (MII: 33.0%, $\geq$2-cell: 36.7%, $\geq$morula: 20.9%) than in 70-110 ${\mu}{\textrm}{m}$ (MII: 12.2%, $\geq$2-cell: 10.2%, $\geq$morula: 4.8%) (p<0.001). In period of the IVG days, the rate of $\geq$2-cell was significantly higher in 10 days(38.2%) than in 12 days (20.0%) (p<0.01). In period of IVF time, 9 hrs ($\geq$2-cell: 31.5%, $\geq$ morula: 14.3%) indicated significantly higher cytoplasmic maturation rate than 4 hrs ($\geq$2-cell: 17.5%, This study was to establish in vitro culture system of mouse preantral follicles and to obtain higher in vitro development rates and production of live young. Preantral follicles were obtained from 12-day-old FI mouse (C57BL $\times$ CBA) by enzymatical methods. Oocyte-granulosa cell complexes (OGCs) of preantral follicles were loaded on Transwell-COL insert and cultured in $\alpha$MEM supplemented with 5% FBS, 100 mIU/$m\ell$ FSH and 100 mIU/$m\ell$ hMG for IVG. IVM was performed in $\alpha$MEM supplemented 1.5 IU/$m\ell$ hCG for 18 hrs and IVF was carried out in Ml6 medium. Embryos were cultured in modified Ml6 medium supplemented 10% FBS for 4 days. The effect of the OGCs size on the nuclear/cytoplasmic maturation was significantly higher in 120-150 ${\mu}{\textrm}{m}$ (MII: 33.0%, $\geq$2-cell: 36.7%, $\geq$morula: 20.9%) than in 70-110 ${\mu}{\textrm}{m}$ (MII: 12.2%, $\geq$2-cell: 10.2%, $\geq$morula: 4.8%) (p<0.001). In period of the IVG days, the rate of $\geq$2-cell was significantly higher in 10 days(38.2%) than in 12 days (20.0%) (p<0.01). In period of IVF time, 9 hrs ($\geq$2-cell: 31.5%, $\geq$ morula: 14.3%) indicated significantly higher cytoplasmic maturation rate than 4 hrs ($\geq$2-cell: 17.5%, $\geq$morula: 4.8%) and 7 hrs ($\geq$2-cell: 20.4%, $\geq$morula: 6.1%) (p<0.01). However, there was no difference in cytoplasmic maturation between co-cultured preantral follicle ( $\geq$morula: 17.4%) and preantral follicle cultured in Ml6 ( $\geq$morula: 17.4%). 22 morula and blastocysts produced in above optimal condition were transferred to uterus of 2 pseudopregnant recipients, 1 recipient was pregnant and then born 1 live young. This result demonstrates that in vitro culture system of preantral follicles can be used efficiently as another method to supply mouse oocyte.morula: 4.8%) and 7 hrs (2-cell: 20.4%, $\geq$morula: 6.1%) (p<0.01). However, there was no difference in cytoplasmic maturation between co-cultured preantral follicle ( $\geq$morula: 17.4%) and preantral follicle cultured in Ml6 ( $\geq$morula: 17.4%). 22 morula and blastocysts produced in above optimal condition were transferred to uterus of 2 pseudopregnant recipients, 1 recipient was pregnant and then born 1 live young. This result demonstrates that in vitro culture system of preantral follicles can be used efficiently as another method to supply mouse oocyte.

• Progress in Medical Physics
• /
• v.25 no.3
• /
• pp.128-138
• /
• 2014

In gated radiation therapy (gRT), due to residual motion, beam delivery is intended to irradiate not only the true extent of disease, but also neighboring normal tissues. It is desired that the delivery covers the true extent (i.e. clinical target volume or CTV) as a minimum, although target moves under dose delivery. The objectives of our study are to validate if the intended dose is surely delivered to the true target in gRT and to quantitatively understand the trend of dose delivery on it and neighboring normal tissues when gating window (GW), motion amplitude (MA), and CTV size changes. To fulfill the objectives, experimental and computational studies have been designed and performed. A custom-made phantom with rectangle- and pyramid-shaped targets (CTVs) on a moving platform was scanned for four-dimensional imaging. Various GWs were selected and image integration was performed to generate targets (internal target volume or ITV) for planning that included the CTVs and internal margins (IM). The planning was done conventionally for the rectangle target and IMRT optimization was done for the pyramid target. Dose evaluation was then performed on a diode array aligned perpendicularly to the gated beams through measurements and computational modeling of dose delivery under motion. This study has quantitatively demonstrated and analytically interpreted the impact of residual motion including penumbral broadening for both targets, perturbed but secured dose coverage on the CTV, and significant doses delivered in the neighboring normal tissues. Dose volume histogram analyses also demonstrated and interpreted the trend of dose coverage: for ITV, it increased as GW or MA decreased or CTV size increased; for IM, it increased as GW or MA decreased; for the neighboring normal tissue, opposite trend to that of IM was observed. This study has provided a clear understanding on the impact of the residual motion and proved that if breathing is reproducible gRT is secure despite discontinuous delivery and target motion. The procedures and computational model can be used for commissioning, routine quality assurance, and patient-specific validation of gRT. More work needs to be done for patient-specific dose reconstruction on CT images.

• Radiation Oncology Journal
• /
• v.20 no.4
• /
• pp.295-302
• /
• 2002

Purpose : The Purpose of this study was to establish general guidelines for the treatment of patients with early glottic carcinoma (T1-2N0M0), by assessing the role of primary radiotherapy and by analyzing the tumor-related and treatment-related factors that have an influence on the treatment results. Materials and Methods : This retrospective study was composed of 80 patients who suffered from early glottic carcinoma and were treated by primary radiotherapy at Pusan National University Hospital, between August 1987 and December 1996. The distribution of patients according to T-stage was 66 for stage T1 and 14 for stage T2. All of the patients were treated with conventional radical radiotherapy using a 6MV photon beams, a total tumor dose of $60\~75.6\;Gy$ (median 68.4 Gy), administered in 5 weekly fractions of $1.8\~2.0\;Gy$. The overall radiation treatment time was from 40 to 87 days, median 51 days. All patients were followed up for at least 3 years. Univariate and multivariate analysis was done to identify the prognostic factors affecting the treatment results. Results : The five-years survival rate was $89.2\%$ for all patients, $90.2\%$ for T1 and $82.5\%$ for T2. The local control rate was $81.3\%$ for all patients, $83.3\%$ for T1 and $71.4\%$ for T2. However, when salvage operations were taken into account, the ultimate local control rate was $91.3\%,\;T1\;94.5\%,\;T2\;79.4\%$, reprosenting an increase of $8\~12\%$ in the local control rate. The voice preservation rate was $89.2\%,\;T1\;94.7\%,\;T2\;81.3\%$. Fifteen patients suffered a relapse after radiotherapy, among whom 12 patients underwent salvage surgery. We included T-stage, tumor location, total radiation dose, fraction size, field size and overall radiation treatment time as potential prognostic factors. T-stage and overall treatment time were found to be statistically significant in the univariate analysis, but in the multivariate analysis, only the over-all treatment time was found to be significant. Conclusion : The high cure and voice preservation rates obtained when using a procedure, comprising a combination of radical radiotherapy and salvage surgery, may make this the treatment of choice for patients with early glottic carcinoma. However, the prognostic factors affecting the treatment results must be kept in mind, and more accurate treatment planning and further optimization of the radiation dose are necessary.

• Environmental and Resource Economics Review
• /
• v.31 no.2
• /
• pp.165-204
• /
• 2022

This study organized data from 2000 to 2014 for 20 grid-connected countries in Europe and analyzed the determinants of carbon emissions through the panel GLS method considering the problem of heteroscedasticity and autocorrelation. At the same time, the effect of introducing ETS was considered by dividing the sample period as of 2005 when the European emission trading system was introduced. Carbon emissions from individual countries were used as dependent variables, and proportion of generation by each source, power self-sufficiency ratio of neighboring countries, power production from resource-holding countries, concentration of power sources, total energy consumption per capita in the industrial sector, tax of electricity, net electricity export per capita, and size of national territory per capita. According to the estimation results, the proportion of nuclear power and renewable energy generation, concentration of power sources, and size of the national territory area per capita had a negative (-) effect on carbon emissions both before and after 2005. On the other hand, the proportion of coal power generation, the power supply and demand rate of neighboring countries, the power production of resource-holding countries, and the total energy consumption per capita in the industrial sector were found to have a positive (+) effect on carbon emissions. In addition, the proportion of gas generation had a negative (-) effect on carbon emissions, and tax of electricity were found to have a positive (+) effect. However, all of these were only significant before 2005. It was found that net electricity export per capita had a negative (-) effect on carbon emissions only after 2005. The results of this study suggest macroscopic strategies to reduce carbon emissions to green growth, suggesting mid- to long-term power mix optimization measures considering the electricity trade market and their role.

• Journal of Korean Society of Transportation
• /
• v.18 no.1
• /
• pp.75-85
• /
• 2000

In existing models in optimization, the crisp data improve has been used in the objective or constraints to derive the optimal solution, Besides, the subjective environments are eliminated because the complex and uncertain circumstances were regarded as Probable ambiguity, In other words those optimal solutions in the existing models could be the complete satisfactory solutions to the objective functions in the Process of application for industrial engineering methods to minimize risks of decision-making. As a result of those, decision-makers in location Problems couldn't face appropriately with the variation of demand as well as other variables and couldn't Provide the chance of wide selection because of the insufficient information. So under the circumstance. it has been to develop the model for the location and size decision problems of logistics facility in the use of the fuzzy theory in the intention of making the most reasonable decision in the Point of subjective view under ambiguous circumstances, in the foundation of the existing decision-making problems which must satisfy the constraints to optimize the objective function in strictly given conditions in this study. Introducing the Process used in this study after the establishment of a general mixed integer Programming(MIP) model based upon the result of existing studies to decide the location and size simultaneously, a fuzzy mixed integer Programming(FMIP) model has been developed in the use of fuzzy theory. And the general linear Programming software, LINDO 6.01 has been used to simulate, to evaluate the developed model with the examples and to judge of the appropriateness and adaptability of the model(FMIP) in the real world.

• Journal of fish pathology
• /
• v.6 no.2
• /
• pp.111-122
• /
• 1993

The mechanism by which $V_H$ region gene segments is selected in B lymphocyte is not known. Moreover, evidence for both random and nonrandom expression of $V_H$ genes in matured B cells has been presented previously. In this report, the technique of in situ hybridization allowed us to analyze expressed $V_H$ gene families in normal B lymphocyte at the single cell level. The analysis of normal B cells in this study eliminated any posssible bias resulting from transformation protocols used previously and minimized limitation associated with sampling size. Therefore, an accurate measure of the functional and expressed $V_H$ gene repertoire in B lymphocyte could be made. One of the most important controls for the optimization of in situ hybridization is to establish probe concentration and washing stringency due to the degree of nucleotide sequence similarlity between different families which in some cases can be as high as 70%. When the radioactive $C{\mu}$ and $V_{H}J558$ RNA probes are tested on LPS-stimulated adult spleen cells, $2{\sim}4{\times}106cpm$/slide shows low background and reasonable frequency of specific positive cells. For the washing condition. 40~50% formamide at $54^{\circ}C$ is found to be optimum for the $C{\mu}$. $V_{H}S107$ and $V_{H}J558$ probes. The analyzed results clearly demonstrate that the level of each different $V_H$ gene family expression is dependent upon the complexity or size of that family. These findings are also extended to the level of $V_H$ gene family expression in separated bone marrow B cells depend upon the various stage of differentiation and conclude no preferential utilization of specific $V_H$ gene family. Thus, the utilization of VH gene segments in B lymphocyte of adult BALB/c mice is random and is not regulated or changed during the differentiation of B cells.

• Korean Journal of Ecology and Environment
• /
• v.47 no.4
• /
• pp.312-318
• /
• 2014

This study was conducted to establish models of filtrating rate and production of feces of a native freshwater bivalve, Anodonta woodiana, on removal organic matters in eutrophic waters. Among the applied shell size (4.3~15.5 cm), the filtrating rate and production of feces of Anodonta woodiana was $0.08{\sim}0.86L\;g^{-1}\;h^{-1}$ (average $0.24L\;g^{-1}\;h^{-1}$), $0.00{\sim}11.10mg\;g^{-1}\;h^{-1}$ (average $0.94mg\;g^{-1}\;h^{-1}$), respectively. In two different water current (high $48Lh^{-1}$, low $24Lh^{-1}$), the filtrating rate of Chl-a was $0.02{\sim}0.10L\;g^{-1}\;d^{-1}$ (average $0.05L\;g^{-1}\;d^{-1}$), $0.02{\sim}0.11L\;g^{-1}\;d^{-1}$ (average $0.07L\;g^{-1}\;d^{-1}$) and the removal rate was 65.4%, 82.1%, respectively. Response surface methodology, with a central composite design comprising 3 levels and 2 variables, was used to identify the optimal removal condition of shell length, water current and filtrating rate or feces production by bivalves. The optimum removal conditions were found that had optimized $6.21L\;mussel^{-1}\;d^{-1}$ at shell length 14.3~15.6 and water current $22{\sim}30Lh^{-1}$. The optimal conditions of production of feces ($4.2mg\;g^{-1}\;d^{-1}$) by freshwater mussels were shell length 14.3~16.3 cm and water current $36{\sim}44Lh^{-1}$.

• Information Systems Review
• /
• v.16 no.3
• /
• pp.161-177
• /
• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.135-149
• /
• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.