• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.97-105
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• 2022

This study deals with the credibility of citizens when investing in uncertain project companies, as well as the Social Performance Compensation Project (SIB) and the IT sharing economy. This allows the convergence of the three sectors to address investment inequalities in economic effectiveness and social security. Activates the model of the overall Social Impact Bond (SIB) process that successfully activates the exchange of information. The empirical presentation of the operations and techniques for social IT service finance examines how the innovation ecosystem can be created with social performance and reward projects. The analysis shows that small sharing institutions or citizens can participate directly to create the ability to connect with private investors, identify the possibility of recognizing non-shared barriers to participation, and show the great impact of citizen trust in IT sharing projects in uncertain areas. As a result, for the sake of social sharing and IT cooperation promoted by the City of Seoul, before the project has the ability to design directly, it will be responsible for reliability and safety in the planning of the project. Therefore, non-shared citizens can also participate in the platform that has been effectively constructed and created.

• The Korean Journal of Pesticide Science
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• v.11 no.1
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• pp.8-17
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• 2007

Three-dimensional quantitative structure-activity relationships (3D-QSARs) on the fungicidal activity against damping-off (Pythium ultimum) with N-phenylbenzenesulfonamide and N-phenyl-2-thienylsulfonamide analogues (1-34) were studied quantitatively using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indeces analysis) methodologies. On the whole, the statistical qualities of CoMSIA models with field fit alignment (FF1-FF5) were slightly higher than that of atom based fit alignment (AF1-AF5) but, the deviations of statistical quality between two alignments in case of CoMFA models were slightly lower. The statistical results of CoMFA and CoMSIA model showed that the optimized CoMSIA model (FF1: $r_{cv.}^2\;(q^2)=0.674$ & $r_{ncv.}^2=0.964$) for damping-off is better predictability and fitness for fungicidal activities than CoMFA model (AF5: $r_{cv.}^2\;(q^2)=0.616$ & $r_{ncv.}^2=0.930$). The fungicidal activities according to the information of the CoMSIA (FF1) model were dependence upon the electrostatic and hydrophobic field of the N-phenylbenzene sulfonamide analogues. Therefore, from the results of graphical analyses on the contour maps with CoMSIA (FF3) model, it is expected that the characters of R4-substituent on the N-phenyl ring as hydrophobic and hydrogen bond acceptor will be contributed to the fungicidal activity against damping-off.

• Bulletin of the Korean Chemical Society
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• v.32 no.1
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• pp.201-207
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• 2011

Peroxisome proliferator-activated receptors (PPARs) are members of nuclear receptors and their activation induces regulation of fatty acid storage and glucose metabolism. Therefore, the $PPAR\gamma$ is a major target for the treatment of type 2 diabetes mellitus. In order to generate pharmacophore model, 1080 known agonists database was constructed and a training set was selected. The Hypo7, selected from 10 hypotheses, contains four features: three hydrogen-bond acceptors (HBA) and one general hydrophobic (HY). This pharmacophore model was validated by using 862 test set compounds with a correlation coefficient of 0.903 between actual and estimated activity. Secondly, CatScramble method was used to verify the model. Hence, the validated Hypo7 was utilized for searching new lead compounds over 238,819 and 54,620 chemical structures in NCI and Maybridge database, respectively. Then the leads were selected by screening based on the pharmacophore model, predictive activity, and Lipinski's rules. Candidates were obtained and subsequently the binding affinities to $PPAR\gamma$ were investigated by the molecular docking simulations. Finally the best two compounds were presented and would be useful to treat type 2 diabetes.

• The Korean Journal of Pesticide Science
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• v.10 no.1
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• pp.7-14
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• 2006

Three-dimensional quantitative structure-activity relationships (3D-QSARs) on the herbicidal activities against in-vitro pre-emergence rice plant (Oryza sativa L.) and barnyard grass (Echinochloa crus-galli) by new 5-benzofuryl-2-[1-(alkoxyimino)alkyl]-3-hydroxycyclohex-2-en-1-one derivatives were studied quantitatively using comparative molecular similarity indices analysis (CoMSIA) methodology. The optimized CoMSIA model(A5: $r^2_{cv.}=0.569$ & $r^2_{ncv.}=0.941$) for rice plant exhibited a good correlation with steric (31.6%) and hydrophobic (39.7%) factors of the substrate molecules, and the model (B4: $r^2_{cv.}=0.569$ & $r^2_{ncv.}=0.941$) for barnyardgrass exhibited a good correlation with electrostatic (46.7%) and H-bond acceptor field (30.8%), respectively. The predicted $R_1=SF_5,\;R_2=R_3=R_4=H(P1)$ substituent (Rice plant: $pI_{50}=4.84$ & Barnyardgrass: $pI_{50}=7.21$, ${\Delta}pI_{50}=2.37$) by the model (B4) not only exhibited to the highest herbicidal activity against barnyardgrass, but also exhibited to the highest selecticity between two plants.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.5
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• pp.397-403
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• 2016

During the rapid economic growth in Korea since the 1970s, many underground facilities were constructed such as under passes and railways. Seismic design has been mandated in 1988, but the structures built before 1988 were not reflected on the seismic design. Accordingly, these underground structures require effective seismic reinforcing methods to ensure safety when the earthquake happens. By these reasons, in this study, using the proposed pre-flexed members, RC box structure was analyzed for seismic reinforcement of the corner. This method is based on a principle that enlarging the resistance against the external force by installing the pre-flexed member to the box structure corner. To evaluate validity, a newly developed member with CornerSafe was compared with traditional type reinforcement using experiments and finite element analysis. In finite element mode, nonlinearity of steel was modeled based on J2 plasticity model and concrete was based on CEB FIP MODEL CODE 1990. Also, composite ratios of box and pre-flexed member were computed for design application. The reinforcement and box structure were analyzed under the bond condition completely attached by the tie, and the results of experiment and finite element analysis were same in the force-displacement curve.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.670-676
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• 2009

Bankruptcy of firms in Korea can cause distress of financial institutions because these institutions have disterssed bond. Accordingly, social and economical spill-over effects by these results are very big. Even after the difficult times of IMF crisis had ended, bankruptcy of information-based small-medium companies and venture firms listed on the KOSDAQ has been continued. In this context, this study developed and adopted failure prediction models for which discriminant analysis was used. Samples of this study was 81 firms respectively for both failed and non-failed firms listed on the KOSDAQ between the year of 2000 and 2007. The results of this study are as follows. First, the accuracy of classification of the model by years was $74.5%{\sim}76.5%$, and the accuracy of classification of the mean model was $69.6%{\sim}80.4%$. Among the models, the mean model of -one year, -two years, and -three years was highest in accuracy of classification (80.4%). Second, accuracy of prediction of final model adopted on validation samples showed 85% before one year of bankruptcy. The results of this study may be significant in that the results may be used as early warning system for bankruptcy prediction of KOSDAQ firms.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.93-103
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• 2021

Despite the efforts of financial authorities in conducting the direct management and supervision of collection agents and bond-collecting guideline, the illegal and unfair collection of debts still exist. To effectively prevent such illegal and unfair debt collection activities, we need a method for strengthening the monitoring of illegal collection activities even with little manpower using technologies such as unstructured data machine learning. In this study, we propose a classification model for illegal debt collection that combine machine learning such as Support Vector Machine (SVM) with a rule-based technique that obtains the collection transcript of loan companies and converts them into text data to identify illegal activities. Moreover, the study also compares how accurate identification was made in accordance with the machine learning algorithm. The study shows that a case of using the combination of the rule-based illegal rules and machine learning for classification has higher accuracy than the classification model of the previous study that applied only machine learning. This study is the first attempt to classify illegalities by combining rule-based illegal detection rules with machine learning. If further research will be conducted to improve the model's completeness, it will greatly contribute in preventing consumer damage from illegal debt collection activities.

• Applied Biological Chemistry
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• v.48 no.1
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• pp.82-88
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• 2005

3D QSAR studies for the fungicidal activities against resistive phytophthora blight (RPC; 95CC7303) and sensitive phytophthora blight (Phytopthora capsici) (SPC; 95CC7105) by a series of new 2-alkoxyphenyl-3-phenylthioisoindoline-1-one derivatives (X: A=propynyl & B=2-chloropropenyl) were studied using comparative molecular field analyses (CoMFA) methodology. The CoMFA models were generated from the two different alignment, atom based fit (AF) alignment and field fit (FF) alignment. The atom based alignment exhibited a higher statistical results than that of field fit alignment. The best models, A3 and A7 using combination fields of H-bond field, standard field, LUMO and HOMO molecular orbital field as additional descriptors were selected to improve the statistic of the present CoMFA models. The statistical results of the two models showed the best predictability of the fungicidal activities based on the cross-validated value $q^2\;(r^2_{cv.}=RPC:\;0.625\;&\;SPC:\;0.834)$, non cross-validated value $(r^2_{ncv.}=RPC:\;0.894\;&\;SPC:\;0.915)$ and PRESS value (RPC: 0.105 & SPC: 0.103), respectively. Based on the findings, the predictive ability and fitness of the model for SPC was better than that of the model for RPC. The fugicidal activities exhibited a strong correlation with steric $(66.8{\sim}82.8%)$, electrostatic $(10.3{\sim}4.6%)$ and molecular orbital field (SPC: HOMO, 12.6% and RPC: LUMO, 22.9%) factors of the molecules. The novel selective character for fungicidal activity between two fungi depend on the positive charge of ortho, meta-positions on the N-phenyl ring and size of hydrophilicity of a substituents on the S-phenyl ring.

• The Korean Journal of Pesticide Science
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• v.9 no.1
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• pp.26-34
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• 2005

3D-QSAR studies for the fungicidal activities against resistance phytophthora blight (RPC; 95CC7303) and sensitive phytophthora blight (Phytopthora capsici) (SPC; 95CC7105) by a series of new 2-alkoxyphenyl-3-phenylthioisoindoline-1-one derivatives (A & B) were studieded using comparative molecular similarity indices analyses (CoMSIA) methodology. From the based on the results, the two CoMSIA models, R5 and S1: as the best models were derivated. The statistical results of the models showed the best predictability and fitness for the fungicidal activities based on the cross- validated value ($q^2=0.714{\sim}0.823$) and non cross-validated, value ($r^2_{ncv.}=0.918{\sim}0.954$), respectively. The model R5 for fungicidal activity of RPC generated from the field fit alignment and combination of electrostatic field, H-bond acceptor field and LUMO molecular orbital field. The model S1 (or S5) for fungicidal activity of SPC generated from the atom based fit alignment and combination of steric field and HOMO molecular orbital field. The models also shows that inclusion of H-bond acceptor field (A) improved the statistical significance of the models. From the based graphical analyses of CoMSIA contribution maps, it was revealed that the novel selective character for fungicidal activities between the two fungi by modify of X-sub-stituent on the N-phenyl group and R-substituent on the S-phenyl group will be able to achivement.

• Asia Marketing Journal
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• v.12 no.2
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• pp.53-81
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• 2010

Brand experience has received much attention from considerable marketing research. When consumers consume and use brands, they are exposed to various specific brand-related stimuli. These brand-related stimuli include brand identity and brand communications(e.g., colors, shapes, designs, slogans, mascots, brand characters) components. Brakus, Schmitt, and Zarantonello(2009) conceptualized brand experience as subjective and internal consumer responses evoked by brand-related stimuli. They demonstrated that brand experience can be broken down into four dimensions(sensory, affective, intellectual, and behavioral). Because experiences result from stimulations and lead to pleasurable outcomes, we expect consumers to want to repeat theses experiences. That is, brand experiences, stored in consumer memory, should affect brand loyalty. Consumers with positive experiences should be more likely to buy a brand again and less likely to buy an alternative brand(Fournier 1998; Oliver 1997). Brand attachment, one of dimensions of the consumer-brand relationship, is defined as an emotional bond to the specific brand(Thomson, MacInnis, and Park 2005). Brand attachment is target-specific bond between the consumer and the specific brand. Thus, strong attachment is attended by a rich set of schema that link the brand to the consumer. Previous researches propose that brand attachments should affect consumers' commitment to the brand. Brand experience differs from affective construct such as brand attachment. Brand attachment is based on interaction between a consumer and the brand. In contrast, brand experience occurs whenever there is a direct and indirect interaction with the brand. Furthermore, brand experience is not an emotional relationship concept. Brakus et al.(2009) suggest that brand experience may result in brand attachment. This study aims to distinguish brand experience dimensions and investigate the effects of brand experience on brand attachment and brand commitment. We test research problems with data from 265 customers having brand experiences in various product categories by using multiple regression and structural equation model. The empirical results can be summarized as follows. First, the paths from affective, behavior, and intellectual experience to the brand attachment were found to be positively significant whereas the effect of sensory experience to brand attachment was not supported. In the consumer literature, sensory experiences for consumers are often equated with aesthetic pleasure. Over time, these pleasure experiences can affect consumer satisfaction. However, sensory pleasures are not linked to attachment such as consumers' strong emotional bond(i.e., hot affect). These empirical results confirms the results of previous studies. Second, brand attachment including passion and connection influences brand commitment positively but affection does not influence brand commitment. In marketing context, consumers with brand attachment have intention to have a willingness to stay with the relationship. The results also imply that consumers' emotional attachment is characterized by a set of brand experience dimensions and consumers who are emotionally attached to the brand are committed. The findings of this research contribute to develop differences between brand experience and brand attachment and to provide practical implications on the brand experience management. Recently, many brand managers have focused on short-term view. According to this study, we suggest that effective brand experience management requires taking a long-term view of marketing decisions.