• Korean Journal of Social Welfare
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• v.65 no.3
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• pp.239-263
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• 2013

This research was carried out in order to develop and validate the Korean Version of Working Relationship Scale for Mentally Disabled Persons, which measures the working relationship between a mentally disabled person and his or her case manager. The first step taken to develop this scale was to construct sample items for the Working Relationship Scale using literature research and three focus group interviews of mentally disabled persons who use local mental health services. Secondly, mentally disabled people were surveyed with these sample items and two professors from the department of social work who specialize in mental health social work and two licensed mental health social workers working in the community mental health field reviewed these sample items to select and compile a final version of the scale. Lastly, the scale's reliability and validity was verified through an empirical study of 569 mentally disabled persons who surveyed the final selection of items. An explanatory factor analysis showed that the sample items can be grouped into three factors. Factor 1 is 'Professional Contribution Factor,' which is related to the professional practice of the case manager; Factor 2 is 'Negative Working Relationship Factor'; and Factor 3 is 'Emotional Bond Factor,' which measures the intimacy between the case manager and the mentally disabled person. A confirmatory analysis of the three-factor format that was discovered in the explanatory factor analysis was carried out with the rest of the randomly divided data, which showed that the model demonstrated a goodness-of-fit. The convergence validity between similar concepts appeared to be appropriate as well. Based on these results, the Korean Version of Working Relationship Scale for Mentally Disabled Persons consisting of a final 33 items is developed and proposed and its implications in social work are discussed.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.189-212
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• 2005

This study examines the interactions between KTB spot and futures markets using the daily prices from March 4, 2002 to January 31, 2005. We use Granger causality test, impulse Response Analysis and Variance Decomposition through vector autoregressive analysis (VAR). However, considering the long-term relationships between the level variables of KTB spot and futures, we introduced Vector Error Correction Model. The main results are as follows. According to the results of Granger-causality test and impulse response analysis, we find that the yields of KTB forward have a great influence on the change of KTB spot but not vice versa. In terms of volatility analysis, there is no inter-dependence between KTB forward and spot markets. In the variance decomposition analysis we find that the short-term KTB forward has much more impact on the KTB spot market than the long-term KTB forward does. We think these results are meaningful for bond investors who are in charge of capital asset pricing valuation, risk management and international portfolio management.

• Journal of the Korea Concrete Institute
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• v.22 no.5
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• pp.727-736
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• 2010

This paper describes results of an preliminary study to produce strain hardening cement-based composites (SHCCs)with consideration of sustainability for infrastructure applications. The aims of this study are to evaluate the influence of recycled materials on the mechanical characteristics of SHCCs, such as compressive, four-point bending, and direct tensile behaviors, and to give basic data for constitutive model for analyzing and designing infra structures with SHCCs. In this study, silica sand, cement, and PVA fibers, were partially replaced with recycled sand, fly-ash, and FET fibers in the mixture of SHCCs, respectively. Test results indicated that fly-ash could improve both bending and direct tensile performance of SHCCs due to increasing chemical bond strength at the interface between PVA fibers and cement matrices. However, SHCCs replaced with PET fibers showed much lower performance in bending and direct tensile tests due to originally low mechanical properties of own fibers, although compressive behavior is similar to PVA2.0 specimen. Also, it was noted that the recycled sand would increase elastic modulus of SHCCs due to larger grain size compared to silica sand. Based on pre-set target value to maintain the performance of SHCCs, it was concluded that the replacement ratio below 20% of fly-ash or below 50% of recycled sands would be desirable for creating sustainable SHCCs.

• Korean Journal of Environmental Agriculture
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• v.39 no.3
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• pp.171-177
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• 2020

BACKGROUND: Recently, biomass conversion from agricultural wastes to carbon-rich materials such as biochar has been recognized as a promising option to maintain or increase soil productivity, reduce nutrient losses, and mitigate greenhouse gas emissions from the agro-ecosystem. This experiment was conducted to select an optimum conditions for enhancing the NH₄-N adsorption capacity of rice hull activated biochar. METHODS AND RESULTS: For deciding the proper molarity of KOH for enhancing its porosity, biochars treated with different molarity of KOH (0, 1, 2, 4, 6, 8) were carbonized at 600℃ in the reactor. The maximum adsorption capacity was 1.464 mg g^-1, and an optimum molarity was selected to be 6 M KOH. For the effect of adsorption capacity to different carbonized temperatures, 6 M KOH-treated biochar was carbonized at 600℃ and 800℃ under the pyrolysis system. The result has shown that the maximum adsorption capacity was 1.76 mg g^-1 in the rice hull activated biochar treated with 6 M KOH at 600℃ of pyrolysis temperature, while its non-treated biochar was 1.17 mg g^-1. The adsorption rate in the rice hull activated biochar treated with 6 M KOH at 600℃ was increased at 62.18% compared to that of the control. Adsorption of NH₄-N in the rice hull activated biochar was well suited for the Langmuir model because it was observed that dimensionless constant (R_L) was 0.97 and 0.66 at 600℃ and 800℃ of pyrolysis temperatures, respectively. The maximum adsorption amount (q_m) and the bond strength constants (b) were 0.092 mg g^-1 and 0.001 mg L^-1, respectively, for the rice hull activated biochar treated with 6 M KOH at 600℃ of pyrolysis. CONCLUSION: Optimum condition of rice hull activated biochar was 6M KOH at 600℃ of pyrolysis temperature.

• Journal of the Korean Regional Science Association
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• v.33 no.1
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• pp.69-85
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• 2017

The positive changes in urban space and urban activities resulting from the urban services provided by Smart City is not accomplished by simply creating the physical environment built on ICT but through the cooperation and participation of citizens and private sector, which is in fact key to success. In this study, this research analyzed the role and structure of smart governance, which is a system of horizontal cooperation between public and private sector, and analyze its role in developing Smart City. The study also proposes various ways to facilitate such development in each of critical categories. For this purpose, this research studied 228 smart services across 11 categories registered in the Ministry of Land and Transport in Korea to analyze the spatial distribution of smart services by spatial hierarchy and their characteristics Also, the research conducted a case study of MetroGIS, which is an information collaboration governance system for the greater area of Minneapolis and St. Paul in the state of Minnesota, United States, to explore how governance is formed; its organizational structure; the role of sub-level organization hierarchy and their interrelationship. The results of the analysis suggest that the following conditions are required to create a smart city: first, public sector resource assistance for building community; second, enhanced communication system within the community and with outside the community; securing financial stability and establishing a model of sustainable development to induce the community to evolve into a governance form.

• Journal of the Korean Chemical Society
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• v.29 no.6
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• pp.565-574
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• 1985

The rates of reaction between 4-substituted-2,6-dinitrochlorobenzenes with para-substituted anilines in methanol-acetonitrile mixtures were measured by conductometry. It was observed that the rate constant increases in the order of X = 4-$NO_2 {\gg}4-CN {\gg}4- CF_3$, where X is a substituent in the substrate. The rate constant also increases in the order of Y = p-O$CH_3{\gg}p- CH_3{\gg}H {\gg}p-Cl{\gg}m- NO_2$, where Y is a substituent in the aniline ring. Kinetic studies in the methanol-acetonitrile solvent system with various nucleophiles showed that the N-C bond forming step is making a great contribution to the overall second order rate constant. The electrophilic catalysis by methanol probably consists of the hydrogen bonding between alcoholic hydrogen and leaving chloride in the transition state. The nucleophilic catalysis by methanol may be ascribed to the formation of hydrogen bonds between alcoholic oxygen and hydrogens of amines in the transition state. All these experimental facts are supporting the operation of $S_N$Ar machanism with the second step being the rate determining. This mechanism can be successfully fitted to the PES model.

• The Korean Journal of Financial Management
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• v.2 no.1
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• pp.55-75
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• 1986

This article is to analyse the economic efficiency of capital market, which plays a role of resource allocation in terms of financial claims such as stock and bond. It provides various contributions to the welfare theoretical aspects of modern capital market theory. The key feature that distinguishes the theory described here from traditional welfare theory is the presence of uncertainty. Securities has time dimensions and the state and outcome of the future are really uncertain. This problem resulting from this uncertainty can be solved by complete market, but it has a weak power to explain real stock market. Capital Market is faced with the uncertainity because it is a kind of incomplete market. Individuals and firms in capital market made their consumption-investment decision by their own criteria, i. e. the maximization of expected utility form intertemporal consumption and the maximization of the market value of firm. We noted that allocative decisions that had to be made in the economy could be naturally subdivided into two groups. One set of decisions concerned the allocation of first-period resources among consumption $C_i$, investment in risky firms $I_j$, and riskless investment M. The other decisions concern the distribution among individuals of income available in the second period $Y_i(\theta)$. Corresponing to this grouping, the theoretical analysis of efficiency has also been dichotomized. The optimality of the distribution of output in the second period is distributive efficiency" and the optimality of the allocation of first-period resources is 'the efficiency of investment'. We have found in the distributive efficiency that the conditions for attainability is the same as the conditions for market optimality. The necessary and sufficient conditions for attainability or market optimality is that (1) all utility functions are such that -$\frac{{U_i}^'(Y_i)}{{U_i}^"(Y_i)}={\mu}_i+{\lambda}Y_i$-linear risk tolerance function where the coefficients ${\mu}_i$ and $\lambda$ are independent of $Y_i$, and (2) there are homogeneous expectations, i. e. ${\Large f}_i(\theta)={\Large f}(\theta)$ for every i. On the other hand, the efficiency of investment has disagreement about optimal investment level. The investment level for market rule will not generally lead to Pareto-optimal allocation of investment. This suboptimality is caused by (1)the difference of Diamond's decomposable production function and mean-variance valuation model and (2) the selection of exelusive investment or competitive investment. In conclusion, this article has made an analysis of conditions and processes of Pareto-optimal allocation of resources in capital marker and tried to connect with significant issues in modern finance.

• KDI Journal of Economic Policy
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• v.36 no.2
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• pp.103-136
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• 2014

The covered interest rate parity condition (CIRP) has been widely used in open macroeconomic analysis, risk management, exchange rate forecasts, and so forth. Due to the recent global financial crises, there have been remarkable changes in the financial markets of the emerging markets. These changes possibly influenced the dynamics of the covered interest rate parity condition. In this paper, we investigate whether the CIRP dynamics has changed, and what is the nature of the regime changes. To do this, we propose and estimate multiple-state Markov regime switching models using a Bayesian MCMC method. Our estimation results indicate that the default risk or the deviation from the CIRP has been decreased after the crisis. It seems to be associated with the more active interaction between the short-term bond market and the short-term foreign exchange market than before. The tightened relation of these two financial markets is caused by the arbitrage transaction of foreign investors.

• Composites Research
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• v.36 no.3
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• pp.199-204
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• 2023

A combination of a metallic mesh and an adhesive layer of metallic particle/epoxy composite was introduced as an intermediate layer to enhance the adhesion between cold-sprayed particles and fiber-reinforced composites (FRCs). Aluminum was considered for both the metallic particles in the adhesive and the metallic mesh. To predict the mechanical characteristics of the intermediate bond layer under a high strain rate, the properties of the adhesive layer needed to be calculated or measured. Therefore, in this study, the Al particle/epoxy adhesive was homogenized by using a rule of mixture. To verify the homogenization, the penetration depth, and the thickness decrease after the cold spray deposition from the undeformed surface, was monitored with FE analysis and compared with experimental observation. The comparison displayed that the penetration depth was comparable to the diameters of one cold spray particle, and thus the homogenization approach can be reasonable for the prediction of the stress level of particulate polymer composite interlayer under a high strain rate for cold spray processing.

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