• Macromolecular Research
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• v.17 no.11
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• pp.829-841
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• 2009

This study introduces a specified group-contribution method for predicting the phase equilibria in polymer solutions. The method is based on a modified double lattice model developed previously. The proposed model includes a combinatorial energy contribution that is responsible for the revised Flory-Huggins entropy of mixing, the van der Waals energy contribution from dispersion, a polar force and specific energy contribution. Using the group-interaction parameters obtained from data reduction, the solvent activities for a large variety of mixtures of polymers and solvents over a wide range of temperatures can be predicted with good accuracy. This method is simple but provides improved predictions compared to those of the other group contribution methods.

• Macromolecular Research
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• v.16 no.4
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• pp.320-328
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• 2008

We established a thermodynamic framework of group contribution method based on modified double lattice (MDL) model. The proposed model included the long-range interaction contribution caused by the Coulomb electrostatic forces, the middle-range interaction contribution from the indirect effects of the charge interactions and the short-range interaction from modified double lattice model. The group contribution method explained the combinatorial energy contribution responsible for the revised Flory-Huggins entropy of mixing, the van der Waals energy contribution from dispersion, the polar force, and the specific energy contribution from hydrogen bonding. We showed the solvent activities of various polymer solution systems in comparison with theoretical predictions based on experimental data. The proposed model gave a very good agreement with the experimental data.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.589-603
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• 2012

Mahalanobis-Taguchi system(MTS) is a statistical tool for classifying the normal group and abnormal group in multivariate data structures. In addition to the classification itself, the MTS uses a method for selecting variables useful for the classification. This method can be used efficiently especially when the abnormal group data are scattered without a specific directionality. When the feedback adjustment procedure through the measurements of the process output for controlling process input variables is not practically possible, the reset procedure can be an alternative one. This article proposes a reset procedure using the MTS. Moreover, a method for identifying input variables to reset is also proposed by the use of the contribution. The identification of the root-cause parameters using the existing dimension-reduced contribution tends to be difficult due to the variety of correlation relationships of multivariate data structures. However, it became possible to provide an improved decision when used together with the location-centered contribution and the individual-parameter contribution.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.11 no.1
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• pp.125-132
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• 1999

Studies are being done to replace conventional refrigerants with alternatives that have low or no ozone depletion and greenhouse warming Potentials, yet possess appropriate pro perties for a refrigeration cycle. To achieve this goal, a consistent set of thermodynamic properties of the working fluid is required. A common problem with the possible alternative refrigerants is that sufficient experimental data do not exist, thus making it difficult to develp complete equations of state that can predict properties in all regions including the vapor-liquid equilibrium. One solution is the use of the generalized equation of state correlations that can predict thermodynamic properties with a minimum number of characteristic parameters. Characteristic parameters required for the generalized equation of state are, in general, critical temperature, critical pressure, critical volume and normal boiling temperature. In this study, estimation of these characteristic parameters of refrigerants by group contribution method is developed.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.61.4-62
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• 2017

In this study we have made a statistical investigation on the kinematic classification of coronal mass ejections (CMEs) using about 4,000 SOHO/LASCO CMEs from 1996 to 2015. For this we use their SOHO/LASCO C3 data and exclude all poor events. Using the constant acceleration model, we classify these CMEs into three groups: Acceleration group, Constant Velocity group, and Deceleration group. For classification we adopt four different methods: Acceleration method, Velocity Variation method, Height Contribution method, and Visual Inspection method. Our major results are as follows. First, the fractions of three groups depend on the method used. Second, the results of the Height Contribution method are most consistent with those of the Visual Inspection method, which is thought to be most promising. Third, the fractions of different kinematic groups for the Height contribution method are: Acceleration (35%), Constant speed (47%), and Deceleration (18%). Fourth, the fraction strongly depend on CME speed; the fraction of Acceleration decreases from 0.6 to 0.05 with CME speed; the fraction of Constant increases from 0.3 to 0.7; the fraction of Deceleration increases from 0.1 to 0.3. Finally we present dozens of CMEs with non-constant accelerations. It is found that about 40 % of these CMEs show quasi-periodic oscillations.

• Journal of the Korean Society of Safety
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• v.36 no.1
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• pp.1-8
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• 2021

To simulate a process model in the field of chemical engineering, it is very important to identify the physical properties of novel materials as well as existing materials. However, it is difficult to measure the physical properties throughout a set of experiments due to the potential risk and cost. To address this, this study aims to develop a property prediction model based on the group contribution method for aromatic chemical compounds including benzene rings. The benzene rings of aromatic materials have a significant impact on their physical properties. To establish the prediction model, 42 important functional groups that determine the physical properties are considered, and the total numbers of functional groups on 147 aromatic chemical compounds are counted to prepare a dataset. Support vector regression is employed to prepare a prediction model to handle sparse and high-dimensional data. To verify the efficacy of this study, the results of this study are compared with those of previous studies. Despite the different datasets in the previous studies, the comparison indicated the enhanced performance in this study. Moreover, there are few reports on predicting the physical properties of aromatic compounds. This study can provide an effective method to estimate the physical properties of unknown chemical compounds and contribute toward reducing the experimental efforts for measuring physical properties.

• Journal of Korean Society on Water Environment
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• v.35 no.6
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• pp.497-509
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• 2019

After the Total Maximum Daily Loads(TMDLs) was applied, it became beyond the limit of concentration management. However, it does not adequately reflect the characteristics of various watersheds, and causes problems with local governments because of the standard flow set. Thus, in this study, the Han River system is organized into four groups in estimating the Pollution Contribution by applying the Flow Duration Curve(FDC) created by the daily flow of data from the HSPF. And the method of this study is expected to be valuable as basic data for the TMDLs. As a result, Group I contains the main watersheds with no large hydraulic structures and tributary watersheds. There is no specificity in the FDC and the Pollution Contribution is estimated as rainfall runoff. Group II contains watersheds near the city where the FDC is maintained above a certain level during the Low Flow Conditions and the Pollution Contribution is estimated as the discharge flow of large scale point pollution facilities. Group III contains the main watersheds in which the large hydraulic structures are installed and FDC is curved in the Low Flow Conditions. So the Pollution Contribution is estimated as the water quality of the large hydraulic structures. Group IV contains the upstream in mainstream watersheds in which the large hydraulic structures are installed and the FDC is disabled before the Low Flow Conditions. As the flow is concentrated in the High Flow Conditions, the non-point pollution sources are estimated as the Pollution Contribution.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.53-74
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• 2012

A challenge in fostering virtual communities is the continuous supply of knowledge, namely members' willingness to contribute knowledge to their communities. Previous research argues that giving away knowledge eventually causes the possessors of that knowledge to lose their unique value to others, benefiting all except the contributor. Furthermore, communication within virtual communities involves a large number of participants with different social backgrounds and perspectives. The establishment of mutual understanding to comprehend conversations and foster knowledge contribution in virtual communities is inevitably more difficult than face-to-face communication in a small group. In spite of these arguments, evidence suggests that individuals in virtual communities do engage in social behaviors such as knowledge contribution. It is important to understand why individuals provide their valuable knowledge to other community members without a guarantee of returns. In virtual communities, knowledge is inherently rooted in individual members' experiences and expertise. This personal nature of knowledge requires social interactions between virtual community members for knowledge transfer. This study employs the social capital theory in order to account for interpersonal relationship factors and identity theory for individual and group factors that may affect knowledge contribution. First, social capital is the relationship capital which is embedded within the relationships among the participants in a network and available for use when it is needed. Social capital is a productive resource, facilitating individuals' actions for attainment. Nahapiet and Ghoshal (1997) identify three dimensions of social capital and explain theoretically how these dimensions affect the exchange of knowledge. Thus, social capital would be relevant to knowledge contribution in virtual communities. Second, existing research has addressed the importance of identity in facilitating knowledge contribution in a virtual context. Identity in virtual communities has been described as playing a vital role in the establishment of personal reputations and in the recognition of others. For instance, reputation systems that rate participants in terms of the quality of their contributions provide a readily available inventory of experts to knowledge seekers. Despite the growing interest in identities, however, there is little empirical research about how identities in the communities influence knowledge contribution. Therefore, the goal of this study is to better understand knowledge contribution by examining the roles of social capital and identity in virtual communities. Based on a theoretical framework of social capital and identity theory, we develop and test a theoretical model and evaluate our hypotheses. Specifically, we propose three variables such as cohesiveness, reciprocity, and commitment, referring to the social capital theory, as antecedents of knowledge contribution in virtual communities. We further posit that members with a strong identity (self-presentation and group identification) contribute more knowledge to virtual communities. We conducted a field study in order to validate our research model. We collected data from 192 members of virtual communities and used the PLS method to analyse the data. The tests of the measurement model confirm that our data set has appropriate discriminant and convergent validity. The results of testing the structural model show that cohesion, reciprocity, and self-presentation significantly influence knowledge contribution, while commitment and group identification do not significantly influence knowledge contribution. Our findings on cohesion and reciprocity are consistent with the previous literature. Contrary to our expectations, commitment did not significantly affect knowledge contribution in virtual communities. This result may be due to the fact that knowledge contribution was voluntary in the virtual communities in our sample. Another plausible explanation for this result may be the self-selection bias for the survey respondents, who are more likely to contribute their knowledge to virtual communities. The relationship between self-presentation and knowledge contribution was found to be significant in virtual communities, supporting the results of prior literature. Group identification did not significantly affect knowledge contribution in this study, inconsistent with the wealth of research that identifies group identification as an important factor for knowledge sharing. This conflicting result calls for future research that examines the role of group identification in knowledge contribution in virtual communities. This study makes a contribution to theory development in the area of knowledge management in general and virtual communities in particular. For practice, the results of this study identify the circumstances under which individual factors would be effective for motivating knowledge contribution to virtual communities.

• Clean Technology
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• v.11 no.3
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• pp.117-122
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• 2005

N-vinyl caprolactam (NVCL), a kind of N-vinyl amide monomer, must be dissolved in continuous phase ($scCO_2$) for dispersion polymerization in supercritical carbon dioxide. Phase behavior of $CO_2$+NVCL is very important and necessary for determining initial polymerization condition and for monomer extraction from final polymer. There is the limitation of experimental method for obtaining pure properties of the monomer because of the possibility of polymerization. And N-methyl caprolactam (NMCL) is the useful solvent for the gas treating process. In the viewpoint of molecular thermodynamics, NVCL and NMCL have same functional group i.e. ${\varepsilon}$-caprolactam. In the case of NVCL, hydrogen of amide group is substituted with vinyl group and for NMCL, hydrogen of amide group is substituted with methyl group. We suggested modified group contribution method for this ${\varepsilon}$-caprolactam derivatives. This new group contribution parameter was applied to correlate $CO_2$ + N-vinyl caprolactam or N-methyl caprolactam system.

• Clean Technology
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• v.11 no.3
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• pp.147-152
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• 2005

For estblishing the best technique for the micronization of Ibuprofen using supercritical fluids, the solubility should be known. The solubility of Ibuprofen in supercritical carbon dioxide was measured by observing the cloud point. The cloud point was observed using high pressure equipment equipped a variable volume view cell between temperature of 35, 40 and $45^{\circ}C$. The solubility data was correlated by the Peng-Robinson equation of state Solute physical properties, such as critical temperature (Tc), critical pressure (Pc) and acentric factor (${\omega}$) were estimated by the some group contribution method. As pressure was increased, the solubility increased at constant temperature. The retrograde phenomenon by a solute vapor pressure and a density of solvent was observed at the pressure of around 150bar. It was found that $CO_2$ can be used as a supercritical solvent in micronization of ibuprofen by RESS.