• Journal of Microbiology and Biotechnology
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• v.18 no.1
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• pp.110-117
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• 2008

Three kinds of prenylated flavonols, icariside I, icariside II, and icaritin, were isolated from an icariin hydrolysate and their effects on melanogenesis evaluated based on mushroom tyrosinase inhibition and quantifying the melanin contents in melanocytes. Although none of the compounds had an effect on tyrosinase activity, icariside II and icaritin both effectively inhibited the melanin contents with an $IC_{50}$ of 10.53 and $11.13{\mu}M$, respectively. Whereas icariside II was obtained from a reaction with ${\beta}$-glucosidase and cellulase, the icariin was not completely converted into icariside II. Thus, for the high-purity production of icariside II, the reaction was optimized using the response surface methodology, where an enzyme concentration of 5.0mg/ml, pH 7, $37.5^{\circ}C$, and 8 h reaction time were selected as the central conditions for the central composite design (CCD) for the enzymatic hydrolysis of icariin into icariside II using cellulase. Empirical models were developed to describe the relationships between the operating factors and the response (icariside II yield). A statistical analysis indicated that all four factors had a significant effect (p<0.01) on the icariside II production. The coefficient of determination $(R^2)$ was good for the model (0.9853), and the optimum production conditions for icariside II was an enzyme concentration of 7.5mg/ml, pH 5, $50^{\circ}C$, and 12 h reaction time. A good agreement between the predicted and experimental data under the designed optimal conditions confirmed the usefulness of the model. A laboratory pilot scale was also successful.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2002.10a
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• pp.154-154
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• 2002

Benzoyl peroxide is a High Production Volume Chemical, which is produced about 1,375 tons/year in Korea as of 2001 survey. The substance is mainly used as initiators in polymerization, catalysts in the plastics industry, bleaching agents for flour and medication for acne vulgaris. The substance is one of seven chemicals of which human health and environmental risks are being assessed by National Institute of Environmental Research (NIER) under the frame of OECD SIDS Program. In this study, Quantitative Structure-Activity Relationships (QSAR) is used for getting adequate information on the physical-chemical property and the environmental fate of this chemical. For the assessment of benzoyl peroxide, models such as MPBPWIN for vapor pressure, KOWWIN for octanol/water partition coefficient, HENRYWIN for Henry's Law constant, AOPWIN for photolysis and BCFWN for bioconcentration factor (BCF) were used. These (Q)SAR model programmes were worked by using the SHILES (Simplified Molecular Input Line Entry System) notations. The physical-chemical properties and the environmental fate of benzoyl peroxide were estimated as followed : vapor pressure =0.00929 Pa, Log Kow = 3.43, Henry's Law constant = 0.00000354 atm-㎥/mole at 25 $^{\circ}C$, the half-life of photodegradation = 3 days, bioconcentration factor (BCF) = 92

• Clean Technology
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• v.18 no.4
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• pp.347-354
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• 2012

In this study, simulation works have been performed for the $CO_2$ removal process contained in the DME production process using NMP (N-methyl-2-pyrrolidone) as a solvent. PRO/II with PROVISION release 9.1 at Invensys was used as a chemical process simulator and NRTL activity coefficient model with Henry's law option and Soave-Redlich-Kwong equation of state were used for thermodynamic models. For the determination of the binary interaction parameters in NRTL model, regression works have been performed to match the experimental thermodynamic data. Optimal feed tray location which minimizes the reboiler heat duty was determined.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.594-603
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• 2021

This study explored the moderating effects of task difficulty for flow, learning motivation, and interesting in distant PBL classes of pre-service teachers. For this, the research model was constructed by analyzing previous studies. The research model verification was conducted by 105 students of taking courses in the curriculum. The distant PBL used a real-time video conference system. Cooperative activities were carried out in real time. After the end of the distant PBL activity, the level of learning motivation, interesting, flow, and task difficulty perception were measured. The collected data were analyzed using a test of the structural model invariance across the groups using a structural equation model. This analysis verifies the difference in path coefficients between measurement models. The control effect of task difficulty was verified through the difference in path coefficient. As a result, it was verified that interesting mediates the influence of learning motivation on flow. And the moderating effect of the perceived task difficulty appeared on the path from learning motivation to interesting.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.6
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• pp.884-892
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• 2014

The purpose of this study was to investigate the drying characteristics and drying models of Ainsliaea acerifolia Sch. Bip. using far-infrared thin layer drying. Far-infrared thin layer drying test on Ainsliaea acerifolia Sch. Bip. was conducted at two air velocities of 0.6 and 0.8 m/sec, as well as three drying temperatures of 40, 45, and $50^{\circ}C$ respectively. The drying models were estimated using coefficient of determination and root mean square error. Drying characteristics were analyzed based on factors such as drying rate, leaf color changes, antioxidant activity, and contents of polyphenolics and flavonoids. The results revealed that increases in drying temperature and air velocity caused a reduction in drying time. The Thompson model was considered suitable for thin layer drying using far-infrared radiation for Ainsliaea accerifolia Sch. Bip. Greenness and yellowness values decreased and lightness values increased after far-infrared thin layer drying, and the color difference (${\Delta}E$) values at $40^{\circ}C$ were higher than those at $45^{\circ}C$ and $50^{\circ}C$. The antioxidant properties of Ainsliaea acerifolia Sch. Bip. decreased under all far-infrared thin layer drying conditions, and the highest polyphenolic content (37.9 mg/g), flavonoid content (22.7 mg/g), DPPH radical scavenging activity (32.5), and ABTS radical scavenging activity (31.1) were observed at a drying temperature of $40^{\circ}C$ with an air velocity of 0.8 m/sec.

• Bulletin of the Korean Chemical Society
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• v.31 no.1
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• pp.52-58
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• 2010

Human histamine H1 receptor (HHR1) is a G protein-coupled receptor and a primary target for antiallergic therapy. Here, the ligand-based three-dimensional pharmacophore models were built from a set of known HHR1 inverse agonists using HypoGen module of CATALYST software. All ten generated pharmacophore models consist of five essential features: hydrogen bond acceptor, ring aromatic, positive ionizable and two hydrophobic functions. Best model had a correlation coefficient of 0.854 for training set compounds and it was validated with an external test set with a high correlation value of 0.925. Using this model Maybridge database containing 60,000 compounds was screened for potential leads. A rigorous screening for drug-like compounds unveiled RH01692 and SPB00834, two novel molecules for HHR1 with good CATALYST fit and estimated activity values. The new lead molecules were docked into the active site of constructed HHR1 homology model based on recently crystallized squid rhodopsin as template. Both the hit compounds were found to have critical interactions with Glu177, Phe432 and other important amino acids. The interpretations of this study may effectively be deployed in designing of novel HHR1 inverse agonists.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.136-152
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• 2017

The thermodynamic models, PC-SAFT (Perturbed-Chain Statistical Associated Fluid Theory) state equation and the Two-model approach liquid activity coefficient model NRTL (Non Random Two Liquid) + Henry + Peng-Robinson, for modeling the Rectisol process using methanol aqueous solution as the $CO_2$ removal solvent were compared. In addition, to determine the new binary interaction parameters of the PC-SAFT state equations and the Henry's constant of the two-model approach, absorption equilibrium experiments between carbon dioxide and methanol at 273.25K and 262.35K were carried out and regression analysis was performed. The accuracy of the newly determined parameters was verified through the regression results of the experimental data. These model equations and validated parameters were used to model the carbon dioxide removal process. In the case of using the two-model approach, the methanol solvent flow rate required to remove 99.00% of $CO_2$ was estimated to be approximately 43.72% higher, the cooling water consumption in the distillation tower was 39.22% higher, and the steam consumption was 43.09% higher than that using PC-SAFT EOS. In conclusion, the Rectisol process operating under high pressure was designed to be larger than that using the PC-SAFT state equation when modeled using the liquid activity coefficient model equation with Henry's relation. For this reason, if the quantity of low-solubility gas components dissolved in a liquid at a constant temperature is proportional to the partial pressure of the gas phase, the carbon dioxide with high solubility in methanol does not predict the absorption characteristics between methanol and carbon dioxide.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• Journal of the Korean Wood Science and Technology
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• v.15 no.4
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• pp.32-44
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• 1987

This study was carried out to analyze and furecast the domestic demand for plywood in Korea by regression models with time-series data for 16 years(1970-85). The results obtained were summarized as follows. 1. To analyze domestic demand for plywood, GNP, PWI and CWI were used as independant variables. The domestic demand equation was computed as follows: $^{in}DDP$=0.65186+1.29412 $^{in}GNP$-0.28385 $^{in}PWI$-1.05011 $^{in}CWI$ Where DDP : Domestic demand for plywood(1000 S/F) GNP: Gross national product (Billion won) PWI : Real wholesale price index of plywood CWI: Real wholesale price index of construction materials. 2. Among independant variables reflecting on the production activity of plywood industry, GNP was the most decisive in forecasting the domestic demand for plywood. 3. The significance can be recognized highly because the decision coefficient of the forecasting model which is obtained by using time series data is 0.9. 4. According to the estimated regression coefficients for GNP, PWI and CWI, GNP shows positive relation while PWI and CWI show negative relation. 5. An annual average increase rate of demand for plywood was 9.4 percent during expect period. Therefore, it was decreased slightly than that of 10.2 percent during sample period.

• Reproductive and Developmental Biology
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• v.32 no.3
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• pp.141-146
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• 2008

To search a new porcine pheromonal odorant, the models of four type (2D-QSAR, HQSAR, CoMFA & CoMSlA) were derived from quantitative structure-activity relationship (QSAR) between tetrahydrofuran-2-yl family compounds and their observed binding affinity constants (Obs.p$[Od]_{50}$). The optimized CoMFA model (predictability; $r^{2}_{cv.}(q^2)$=0.886 & correlation coefficient: $r^{2}_{ncv.}$=0.984) from ligand based approaches was confirmed as the best model among them. The $N^{1}$-allyl-$N^{2}$-(tetrahydrofuran-2-yl)methyl)oxalamide (P1), 2-(4-trimethylammoniummethylcyclohexyloxy)tetrahydrofurane (P5) and 2-(3-trimethylammoniummethylcyclohexyloxy)tetrahydrofurane (P6) molecules predicted as porcine pheromonal odorant by the CoMFA model were showed relatively high binding affinity constant values (Pred.p$[Od]_{50}=8{\sim}10$) and very lower toxicity values against some sorts of toxicity.