• Economic and Environmental Geology
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• v.54 no.2
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• pp.299-308
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• 2021

Fe(II) released from mining activities is precipitated as various Fe(III)-oxyhydroxides when exposed to an oxidizing environment including mine drainage. Ferrihydrite, one of the representative precipitated Fe(III) minerals, is easy to adsorb heavy metals and other pollutants due to the large specific surface area caused by very low crystallinity. Ferrihydrite is transformed to thermodynamically more stable goethite in the natural environment. Hence, information on the transformation of ferrihydrite to goethite and the related mobility of heavy metals in the acid mine drainage is important to predict the behaviors of those elements during ferrihydrite to goethite transition. The behaviors of heavy metals during the transformation of ferrihydrite to goethite were investigated for core samples collected from an AMD treatment system in the Heungjin-Taemaek coal mine by using X-ray diffraction (XRD), chemical analysis, and statistical analysis. XRD results showed that ferrihydrite gradually transformed to goethite from the top to the bottom of the core samples. Chemical analysis showed that the relative concentration of As was significantly high in the core samples compared with that in the drainage, indicating that As was likely to be adsorbed strongly on or coprecipitated with iron oxyhydroxide. Correlation analysis also indicated that As can be easily removed from mine drainage during iron mineral precipitation due to its high affinity to Fe. The concentration ratio of As, Cd, Co, Ni, and Zn to Fe generally decreased with depth in the core samples, suggesting that mineral transformation can increase those concentrations in the drainage. In contrast, the concentration ratio of Cr to Fe increased with depth, which can be explained by the chemical bond of iron oxide and chromate, and surface charge of ferrihydrite and goethite.

• Journal of Christian Education in Korea
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• v.71
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• pp.121-154
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• 2022

This study is to summarize the relationship between youth in terms of developmental psychology, university education, faith, and spirituality in order to form and improve relationships, which are major developmental tasks of youth, and to suggest Christian youth education by the elements of education. Relationships are formed when you are connected to another person and community, feel interested in each other, feel a sense of bond and belonging, and maintain a stable and satisfactory relationship. This is not skill or technology, but is related to life attitude and value, and continuous learning and training are required. Various developmental tasks in youth have something in common with relationships. Relationships positively affect the lives of young people, such as satisfaction with college life in the early stages of youth, adaptation to college life, personality, and career decision. Relationships are also very important in faith because human existence and faith are defined and formed through relationships. The relationship between the community and others plays an important role in spiritual development for the meaning of life and inner growth. In the aspects of learners and educational environment, it was suggested to understand learners with desire for relationships, the generation they live in, and the educational environment in which the relationship between young people occurs. In terms of teachers, teachers have to try to change their roles such as facilitators, guides, managers, and mentors. For the educational purpose and content, it was suggested that relationships should be the ultimate purpose and the educational content for this was presented in three different types of relationships and each main contents to be dealt with. In terms of educational method, it was proposed to select a learner-centered group learning method that induces communication and active participation of learners to cause interaction by considering other elements of education according to the content of the relationship in the cognitive, emotional, and behavioral dimensions. In the aspects of educational results and evaluation, it was proposed to confirm that what was considered during the educational planning stage was effectively carried out in actual education, to evaluate various evaluation methods, various aspects, and to summarize the evaluation results for the specific application.

• Journal of the Korean Wood Science and Technology
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• v.16 no.2
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• pp.1-41
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• 1988

For the purpose of utilizing the sawdust having poor combining properties as board raw material and resulting in dimensional instability of board, polypropylene chip (abbreviated below as PP chip) or oriented PP thread was combined with sawdust particle from white meranti(Shorea sp.). The PP chip was prepared from PP thread in length of 0.25, 0.5, 1.0 and 1.5 cm for conventional blending application. Thereafter, the PP chip cut as above was combined with the sawdust particle by 3, 6, 9, 12 and 15% on the weight basis of board. Oriented PP threads were aligned with spacing of 0.5, 1.0 and 1.5cm along transverse direction of board. The physical and mechanical properties on one, two and three layer boards manufactured with the above combining conditions were investigated. The conclusions obtained at this study were summarized as follows: 1. In thickness swelling, all one layer boards combined with PP chips showed lower values than control sawdustboard, and gradually clear decreasing tendendy with the increase of PP chip composition. Two layer board showed higher swelling value than one layer board, but the majority of boards lower values than control sawdustboard. All three layer boards showed lower swelling values than control sawdustboard. 2. In the PP chip and oriented thread combining board, the swelling values of boards combining 0.5cm spacing oriented thread with 1.0 or 1.5cm long PP chip in 12 and 15% by board weight were much lower than the lowest of one or three layer. 3. In specific gravity of 0.51, modulus of rupture of one layer board combined with 3% PP chip showed higher value than control sawdustboard. However, moduli of rupture of the boards with every PP chip composition did not exceed 80kgf/cm2, the low limit value of type 100 board, Korean Industrial Standard KS F 3104 Particleboards. Moduli of rupture of 6%, 1.5cm-long and 3% PP chip combined boards in specific gravity of 0.63 as well as PP chip combined board in specific gravity of 0.72 exceeded 80kgf/$cm^2$ on KS F 3104. Two layer boards combined with every PI' chip composition showed lower values than control sawdustboard and one layer board. Three layer boards combined with.1.5cm long PP chip in 3, 6 and 9% combination level showed higher values than control sawdustboard, and exceeded 80kgf/$cm^2$ on KS F 3104. 4. In modulus of rupture of PP thread oriented sawdustboard, 0.5cm spacing oriented board showed the highest value, and 1.0 and 1.5cm spacing oriented boards lower values than the 0.5cm. However, all PP thread oriented sawdustboards showed higher values than control saw-dustboard. 5. Moduli of rupture in the majority of PP chip and oriented thread combining boards were higher than 80kgf/$cm^2$ on KS F 3104. Moduli of rupture in the boards combining longer PP chip with narrower 0.5cm spacing oriented thread showed high values. In accordance with the spacing increase of oriented thread, moduli of rupture in the PP chip and oriented thread combining boards showed increasing tendency compared with oriented sawdustboard. 6. Moduli of elasticity in one, two and three layer boards were lower than those of control sawdustboard, however, moduli of elasticity of oriented sawdustboards with 0.5, 1.0 and 1.5cm spacing increased 20, 18 and 10% compared with control sawdustboard, respectively. 7. Moduli of elasticity in the majority of PP chip and oriented thread combining boards in 0.5, 1.0 and 1.5cm oriented spacing showed much higher values than control sawdustboard. On the whole, moduli of elasticity in the oriented boards combined with 9% or less combination level and 0.5cm or more length of PP chip showed higher values than oriented sawdustboard. The increasing effect on modulus of elasticity was shown by the PP chip composition in oriented board with narrow spacing. 8. Internal bond strengths of all one layer PP chip combined boards showed lower values than control sawdust board, however, the PP chip combined boards in specific gravity of 0.63 and 0.72 exceeded 1.5kgf/$cm^2$, the low limit value of type 100 board and 3kgf/$cm^2$, type 200 board on KS F 3104, respectively. And also most of all two, three layer-and oriented boards exceeded 3kgf/$cm^2$ on KS F. 9. In general, screw holding strength of one layer board combined with PP chip showed lower value than control sawdustboard, however, that of two or three layer board combined with PP chip did no decreased tendency, and even screw holding strength with the increase of PP chip composition. In the PP chip and oriented PP thread combining boards, most of the boards showed higher values than control sawdustboard in 9% or less PP chip composition.

• 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.