• Journal of Animal Science and Technology
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• v.44 no.3
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• pp.327-340
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• 2002

This study was conducted to investigate the effects of domestic clay minerals on feed efficiency, meat quantity, meat quality and economic traits in 24 head of Hanwoo steers(166.1kg in body weight) for 540 days from six to 24 months in age. Feeding trial was conducted with 4 treatment(six heads/treatment) which were T1(Control), T2(Control+Kaolinite), T3(Control+Bentonite), T4(Control+Illite). The results obtained are summarized as follows; The range of average daily gains were 0.682 to 0.713, 0.669 to 0.714, 0.690 to 0.840 and 0.699 to 0.756kg in growing, fattening, finishing and over-all period, respectively, and the gains were high in T1 for growing and fattening period but in clay mineral groups for finishing and over-all period, especially it was high in Illite and Bentonite groups. Concentrates and TDN intakes per unit of kg gains were lower in clay mineral groups than in control and was lower especially in Bentonite groups. In carcass characteristics, dressed carcass and fresh meat and retailed cut percent were not apparently difference by treatments, and yield index was 69.3, 68.9, 68.8 and 68.6 in T3, T2, T4 and T1, respectively. Marbling scores were 5.1, 4.6, 4.4 and 3.3 in T3, T2, T4 and T1, respectively, and the range of shear force by treatment was from 3.51 to 6.02kg/cm2. and were improved with significant difference(P<0.05) in clay mineral groups than in control. Also in palatability traits, panel test scores of juiciness, tenderness and flavor were improved in clay mineral feeding groups, especially the flavor was improved with highly significant difference(P<0.01) in clay mineral groups than in control. In total fatty acid contents, the rate of SFA(saturated fatty acid) in longissimus muscle of beef was higher in the order of T2, T3, T1 and T4 while the rate of MUFA(monounsaturated fatty acid) was high in the order of T4, T3, T1 and T2. The content of oleic acid which is major influential factor at the flavor of beef was higher in Illite groups than in any other groups. In composition of amino acids in longissimus muscles of beef, the rate of essential amino acids was high in the order of T1, T2, T3 and T4. and the rate of amino acids in clay mineral groups was smaller than in control.In chemical component in Gom-Tang(soup of bone) made by Hanwoo steer’s leg-bone, the ranges of crude protein, ether extract, and crude ash was 0.81 to 1.24, 0.17 to 0.35 and 0.07 to 0.09%, respectively. In mineral composition, the ranges of Ca, P, Na and Mg was 14.01 to 15.77, 11.45 to 16.40, 37.92 to 49.99 and 0.26 to 0.46ppm, respectively. Chemical composition were not apparently different but mineral composition was increased in clay mineral groups than in control. Income by treatments was 967,096 to 1,524,055 Won per head for 540 days and income of clay mineral groups in comparison with control’s increased by 23.7 to 57.6 percent, and especially it was higher in bentonite and(or) Illite groups than others. According to the above results it may be concluded that clay mineral to growing-fattening Hanwoo steers can be improved the meat quantity, meat quality and income. Especially the effect of bentonite and illite is large and can be recommended for usage to improve animal performance as feed additives of growing-fattening Hanwoo steers.

• 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.

• Asia Marketing Journal
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• v.12 no.4
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• pp.55-78
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• 2011

Recently strategic alliance between business firms has become prevalent to overcome increasing competitive threats and to supplement resource limitation of individual firms. As one of allianced sales promotion activities, a new type of discount program, so called "Alliance Card Discount", is introduced with the partnership of credit cards and loyalty cards. The program mainly pursues short-term sales growth by larger discount scheme while spends less through cost share among alliance partners. Thus this program can be regarded as cost efficient discount promotion. But because there is no solid evidence that it can really deliver profitable sales growth, an empirical study for its effects on sales and profit should be conducted. This study has two basic research questions concerning the effects of allianced discount program ; 1)the possibility of sales increase 2) the profitability of the discount driven sales. In F&B industry, sales increase mainly comes from increased guest count. Especially in family restaurants, to increase the number of guests we need to enlarge the size of visitor group (number of visitors for one group) because customers visit by group in a special occasion. And because they pay the bill by group(table), the increase of sales per table is a key measure for sales improvement. The past researches for price & discount sensitivity and reference discount rate explain that price sensitive consumers have narrow reference discount zone and make rational purchase decision. Differently from all time discount scheme of regular sales promotions, the alliance card discount program only provides the right to get discount like discount coupon. And because it is usually once a month opportunity given by the past month usage level, customers tend to perceive alliance card discount as a rare chance to get. So that we can expect customers try to maximize the discount effect when they use the limited discount opportunity. Considering group visiting practice and low visit frequency of family restaurants, the way to maximize discount effect should be the increase the size of visit group. And their sensitivity to discount and rational consumption behavior defer the additional spending for ordering high price menu, even though they get considerable amount of savings from the discount. From the analysis of sales data paid by alliance discount cards for four months, we found the below. 1) The relation between discount rate and number of guest per table is positive : 25% discount results one additional guest 2) The relation between discount rate and the spending per guest is negative. 3) However, total profit amount per table is increased when discount rate is increased. 4) Reward point accumulation & redemption did not show any significant relationship with the increase of number of guests. These results suggest that the allianced discount program substantially contributes to sales increase and profit improvement by increasing the number of guests per table. Though the spending per guest is decreased by discount rate increase, the total amount of profit per table is improved. It seems the incremental profit by increased guest count offsets the profit decrease. Additional intriguing finding is the point reward system does not have any significant impact on the increase of number of guest, even if the point accumulation & redemption of loyalty program are usually regarded as another saving offers by customers. In sum, because it is proved that allianced discount program with credit cards and loyalty cards is effective to both sales drive and profit increase, the alliance card program could be recommended as strategically buyable program.