• Journal of the Korean GEO-environmental Society
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• v.14 no.5
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• pp.57-63
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• 2013

In this study, the characteristics of degradation and mineralization of diazinon using a statistical approach based on Box-Behnken design (BBD, one of response surface method) was investigated in an E-beam process, and also the main factors with diazinon concentration ($X_1$), irradiatin intensity ($X_2$) and pH ($X_3$) which consisted of 3 levels in each factor was set up to determine the effects of factors and optimization. At first, effects of pH and diazinon concentration were investigated to determine the proper range of application on response surface method(RSM). In statistical approach, the regression analysis and analysis of variance (ANOVA) were applied to evaluate the quantitative comparison of each factors in order to obtain the effects were irradiation intensity>diazinon concentration>pH. The regression model predicted the optimization point using the response optimizer to consider the effects of operation conditions were $Y_1=81.73-5.58X_1+23.69X_2-14.23X{_2}^2+4.22X{_3}^2(R^2=99.7%)$, $Y_2=35.23-3.01X_1+10.79X_2-7.58X_2{^2}(R^2=97.9%)$ and 95.7% of diazinon degradation, 41.8% of TOC reduction at 12.75mg/L and 4.26kGy, respectively. The pH condition was not significantly affects on E-beam process than other advanced oxidation processes (AOPs).

• Advances in aircraft and spacecraft science
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• v.6 no.6
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• pp.529-550
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• 2019

The effect of multiple process parameters on a set of continuous response variables is, especially in experimental designs, difficult and intricate to determine. Due to the complexity in aeroacoustic and vibroacoustic studies, the often-performed simple one-factor-at-a-time method turns out to be the least effective approach. In contrast, the statistical Design of Experiments is a technique used with the objective to maximize the obtained information while keeping the experimental effort at a minimum. The presented work aims at giving insights on Design of Experiments applied to aeroacoustic and vibroacoustic problems while comparing different experimental designs and approximation models. For this purpose, an experimental rig of a ducted low-pressure fan is developed that allows gathering data of both, aerodynamic and aeroacoustic nature while analysing three independent process parameters. The experimental designs used to sample the design space are a Central Composite design and a Box-Behnken design, both used to model a response surface regression, and Latin Hypercube sampling to model an Artificial Neural network. The results indicate that Latin Hypercube sampling extracts information that is more diverse and, in combination with an Artificial Neural network, outperforms the quadratic response surface regressions. It is shown that the Latin Hypercube sampling, initially developed for computer-aided experiments, can also be used as an experimental design. To further increase the benefit of the presented approach, spectral information of every experimental test point is extracted and Artificial Neural networks are chosen for modelling the spectral information since they show to be the most universal approximators.

• Korean Journal of Organic Agriculture
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• v.20 no.1
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• pp.91-99
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• 2012

This study was conducted to develop a functional feed additive for pig with spent $Lycium$ $chinense$ Mill fruit. We investigated the optimum conditions for the extraction of polyphenol from spent $Lycium$ $chinense$ Mill using methanol. Methanol concentration as a solvent for extraction, extraction time and the volume of solvent per a gram of solid (ground spent Lyceum chinense Mill) were selected as parameters. Three levels of parameters were configured according to Box Behnken experiment design, a fractional factorial design, and total 15 trials were employed. Total polyphenol concentration from each trial was used as response from experiment system and effects of parameters on total polyphenol extraction efficiency were determined using response surface model. As a result, all terms in analysis of variance, regression ($p$ = 0.001), linear ($p$ = 0.002), square ($p$ = 0.017) and interaction ($p$ = 0.047) was significant and adjusted determination coefficient ($R^2$) was 94.7%. Total polyphenol extraction efficiency was elevated along increased methanol content and decreased solvent to solid ratio. However extraction time did not affect the efficiency. This study provides a primary information for the optimum extraction conditions to maximize total polyphenol recovery from spent Lycium chinens Mill fruit and this result could be applied to re-use of argo-industrial by-products and to develop of functional feed additives in organic farming.

• Fisheries and Aquatic Sciences
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• v.17 no.1
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• pp.47-57
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• 2014

Thai Jasmine rice (Oryza sativa, long grain Indica var.) is popular in southeastern Asia and China due to its non-glutinous, fluffy texture and fragrant smell. However it has a high starch digestibility, which leads to an increased glycemic index (GI). Therefore it may require modified cooking methods for diabetes patients. The objectives of this study were to optimize the ratio of Thai Jasmine rice, sea tangle, and olive oil (CLTR) based on consumers' acceptance. The GI of plain cooked Thai Jasmine rice (CLR) was measured as a control. Sensory evaluation and response surface methodology were used to determine the optimal ratio. Texture analysis and nutritional evaluation were also performed on the optimal recipe of cooked Jasmine rice with sea tangle. A multiple regression equation was developed in quadratic canonical polynomial models. We used 26 trained Chinese panelists in their forties to rate color, flavor, adhesiveness, and glossiness, which we determined were highly correlated with overall acceptability. The optimal CLTR formula was 34.8% rice, 2.8% sea tangle, 61.9% water, and 0.5% olive oil. Compared to CLR, CLTR had a lower hardness, but a higher springiness and cohesiveness. However, CLR and CLTR had the same adhesiveness and chewiness. The addition of sea tangle and olive oil delayed retro-gradation of starch in CLTR and increased total dietary fiber, and protein and ash contents. The degree of gelatinization, and in vitro protein and starch digestibility of CLTR were lower than those of CLR. Based on Wolver' method, the GI of CLTR (52.9, incremental area under the glycemic-response curve, ignoring the area below fasting, as used for calculating the GI [Inc]) was lower compared with that of CLR (70.94, Inc), which indicates that CLTR is effective in decreasing and stabilizing blood glucose level, owing to its lower degree of gelatinization and starch digestibility. Our results show that CLTR can contribute to the development of a healthier meal for families and the fast food industry.

• Horticultural Science & Technology
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• v.30 no.3
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• pp.270-277
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• 2012

Greenhouse industry has been growing in many countries due to both the advantage of stable year-round crop production and increased demand for fresh vegetables. In greenhouse cultivation, $CO_2$ concentration plays an essential role in the photosynthesis process of crops. Continuous and accurate monitoring of $CO_2$ level in the greenhouse would improve profitability and reduce environmental impact, through optimum control of greenhouse $CO_2$ enrichment and efficient crop production, as compared with the conventional management practices without monitoring and control of $CO_2$ level. In this study, a mathematical model was developed to estimate the $CO_2$ emission from soil as affected by environmental factors in greenhouses. Among various model types evaluated, a linear regression model provided the best coefficient of determination. Selected predictor variables were solar radiation and relative humidity and exponential transformation of both. As a response variable in the model, the difference between $CO_2$ concentrations at the soil surface and 5-cm depth showed are latively strong relationship with the predictor variables. Segmented regression analysis showed that better models were obtained when the entire daily dataset was divided into segments of shorter time ranges, and best models were obtained for segmented data where more variability in solar radiation and humidity were present (i.e., after sun-rise, before sun-set) than other segments. To consider time delay in the response of $CO_2$ concentration, concept of time lag was implemented in the regression analysis. As a result, there was an improvement in the performance of the models as the coefficients of determination were 0.93 and 0.87 with segmented time frames for sun-rise and sun-set periods, respectively. Validation tests of the models to predict $CO_2$ emission from soil showed that the developed empirical model would be applicable to real-time monitoring and diagnosis of significant factors for $CO_2$ enrichment in a soil-based greenhouse.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.4
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• pp.567-571
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• 2005

In this study, the effects of varying the amount of water, soaking time at $25^{\circ}C$ and drying time after soaking at $45^{\circ}C$ on the induction of resveratrol were evaluated to optimize the soaking treatment by response surface methodology (RSM). After response surface regression (RSREG), the second-order polynomial equation was fitted to the experimental data. The analysis of variance showed that the model appeared to be adequate $(R^2=0.9547)$ with no significant lack of fit (p>0.1). From statistical analysis, amount of water and soaking time were found to be significant factors. On the other hand, drying time was not significant. Ridge analysis showed that the optimized parameters were $67.15\%$ for amount of water, 19.58 hr for soaking time, 65.56 hr for drying time. It was confirmed that resveratrol content was increased from $0.1\;{\mu}g/g$ to $4.55\;{\mu}g/g$ under the optimized conditions. In addition, the experimental values at the optimized condition agreed with values predicted by ridge analysis. The analytical method validation parameters such as accuracy, precision, and specificity were calculated to ensure the method's validity.

• Journal of Korean Society of Environmental Engineers
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• v.35 no.12
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• pp.918-928
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• 2013

The inequitable flow distribution to flocculation basins is an important problem faced in many water treatment plants. This is caused by the structure of a distribution channel, the height differences of outlet weirs etc. But, a modified approach for the structures has no effectiveness to achieve flow equality. The aim of this study is to reduce the inequality by adopting optimized inlet valve opening (%) of the flocculation basins using DOE (Design of Experiments) and RSA (Response Surface Analysis). The inlet valve openings (%) and inflow distributions (%) of 6 paralleled basins were set as factors (X) and characteristics(Y) respectively. 2 level factorial experiments and RSA were conducted for optimization and regression analysis (Y = f(X) + Const.). Adopting the optimized inlet valve opening (%) at field, standard deviation of flow distribution (%) and effluent turbidity was declined from 3.80% to 0.42% and from 0.29 NTU to 0.03 NTU respectively.

• Toxicological Research
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• v.20 no.2
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• pp.89-100
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• 2004

The comet assay (also called the single-cell gel electrophoresis assay) has been widely used for detecting DNA damage and repair in individual cells. Since the conventional methods of evaluating comet assay data using frequency statistics are unsatisfactory we developed a new quantitative measure of DNA damage/repair that is based on all information residing in the dose/time-response curves of a comet experiment. Blood samples were taken from 25 breast cancer patients before undergoing radiotherapy. The comet assay was performed under alkaline conditions using isolated lymphocytes. Tail DNA, tail length, tail moment and tail inertia of the comet were measured for each patient at four doses of $\gamma$-rays (0, 2, 4 and 8 Gy) and at four time points after irradiation (0, 10, 20 and 30 min) using 100 cells each. The resulting three-dimensional dose-time response surface was modeled by multiple regression, and the second derivative, termed 2D, on dose and time was determined. A software module was programmed in SAS/AF to compute 2D values. We applied the new method successfully to data obtained from cancer patients to be assessed for their radiation sensitivity. We computed the 2D values for the four damage measures, i.e., tail moment, tail length, tail DNA and tail inertia, and examined the pairwise correlation coefficients of 2D both on the log scale and the unlogged scale. 2D values based on tail moment and tail DNA showed a high correlation and, therefore, these two damage measures can be used interchangeably as far as DNA repair is concerned. 2D values based on tail inertia have a correlation profile different from the other 2D values which may reflect different facets of DNA damage/repair. Using the dose-time response surface, other statistical models, e.g., the proportional hazards model, become applicable for data analysis. The 2D approach can be applied to all DNA repair measures, Le., tail moment, tail length, tail DNA and tail inertia, and appears to be superior to conventional evaluation methods as it integrates all data of the dose/time-response curves of a comet assay.

• Korean journal of food and cookery science
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• v.27 no.1
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• pp.75-84
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• 2011

The aim of this study was to develop a convenient brown sauce product with long shelf life that has similar taste and quality characteristics with sauce used in restaurants. Response surface analysis was carried out to optimize brown sauce. Extracted brown sauce powder was subjected to hot air drying, infrared drying, freeze drying, and spray drying to determine the appropriate drying method for brown sauce manufacturing. The optimum extraction conditions were set by superimposing and reading each reaction surface that satisfied all of the sensory characteristics such as color, smell, taste, concentration, and overall preference level in order to set the optimum conditions for brown sauce production. The optimum extraction conditions for brown sauce were determined to be heating time 30 min, gelatin addition quantity 9.00%, and tomato paste addition quantity 11.25%. Reliability test showed a similar value to the predicted scope when compared to the experimental value obtained under the same conditions as the predicted value according to RSM (response surface methodology), enabling verification of the derived regression formula. Product powder of ideal brown sauce by heating, infrared radiation, freezing, and spray drying and investigate result for functional tests of color, flavor, taste, viscosity, overall acceptability and show highly acceptability on powder by infrared rays and freeze-drying methods. Especially, infrared radiation method resulted in favorable color and flavor values while freeze-drying method produced good taste and viscosity values and high overall acceptability. Therefore, infrared radiation drying method and freeze-drying method to product powder.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.501-506
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• 2001

Computer modeling is essential to evaluate possible design of suspension for a railway vehicles. By creating a simulation, the engineers are able to assess the feasibility of a given design and change the design factors to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have turned to surrogate modeling. A surrogate model is essentially a regression performed on a data sampling of the simulation. In the most general sense, metamodels(surrogate model) take the form $y(x)=f(x)+{\varepsilon}$, where y(x) is the true simulation output, f(x) is the metamodel output, and $\varepsilon$ is the error between the two. In this paper, a second order polynomial equation is partially used as a metamodel to represent the forty-six dynamic performances for high speed train. The number of factors as design variables of the metamodel is twenty-nine, which are composed the dynamic characteristics of suspension. This metamodel is used to search the optimum values of suspension characteristics which minimize the dynamic responses for high speed train. This optimization is a multi-objective problem which have many design variables. This paper shows that the response surface model which is made through the design of analysis of computer experiments method is very efficient to solve this complex optimization problem.