• Journal of Animal Science and Technology
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• v.48 no.2
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• pp.211-218
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• 2006

This experiment was conducted to study the effects of the natural deep sea water, which contained approximately 2.3% salt, and various minerals of K, Mg, Ca, Na, Fe, Mn, Zn, Cu etc, on the immune response and antioxidant activity in rats. 24 Sprague Dawley rats were randomly allotted to a control group and 3 treatment groups. Control rats were supplied with filtered tap water, and each treatment group rats were supplied with 0.5% deep sea water, 1% deep sea water and Jijangsoo, respectively, which is upper clear water separated from sediment by the clay. Feed and water were provided ad libitum throughout the experiment that lasted for 4 weeks. The results showed that 1% deep sea water group showed the highest values in weight gain, feed intake, and feed efficiency than those of other groups. The levels of water intake of 1%- and 0.5%-deep sea water, and Jijangsoo group were 49.1%, 22.8%, and 40.5% higher than that of control group, respectively. The Jijangsoo group rats showed that perirenal and epididymal adipose tissue weights were decreased by 32% and 25%(p<0.05), respectively, when compared to control group rats. There were no remarkable differences of serum glucose concentration among all experimental groups. However, insulin concentration of experimental groups were remarkably increased in order of Jijangsoo (4.54), 1% deep sea water (3.70), 0.5% deep sea water (3.25)(p<0.05). B cell and T cell stimulation were increased about 44.7% and 207%, respectively, by 0.5% deep sea water in comparison with control (p<0.05). TBARS values of 0.5 % deep sea water group were significantly lower than that of control(p<0.05). Catalase and SOD activities of 0.5 % deep sea water group were 200% and 47% higher than that of control, respectively. From the results, it can be concluded that the supply of natural deep sea water can slightly improve the physiological activity which modulates immune response and antioxidant activity in rats.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.