Selenium induced selenocysteine methyltransferase gene expression and antioxidant enzyme activities in Astragalus chrysochlorus

Astragalus sp. are used in folk medicine because of their biological activities and are known for the ability to accumulate high levels of selenium (Se). The purpose of this study was to explore gene expression of selenocysteine methyltransferase (SMT), responsible for forming MeSeCys, and activities of ascorbate peroxidase (APX), peroxidase (POX), catalase (CAT) and glutathione reductase (GR) enzymes in callus tissues of Astragalus chrysochlorus growing in different Se-containing media. Quantitative real-time polymerase chain reaction assay was done for quantification of SMT gene transcript and it was normalized to actin gene. It was found that transcript level of callus tissues grown at 5.2 μM and 26.4 μM Se-enriched media was lower than that of the control callus. In contrast, a high level of Se (132.3 μM) in the medium caused an approximately 4.26 times higher level of SMT transcript in callus than the control. APX, POX, CAT and GR enzymes were all effected by different Se concentrations. While POX and APX activities were higher then control, CAT and GR activities decreased. These results show that an increase of SMT gene expression led to a rise in APX and POX, but a suppression of CAT and GR enzymes activities in Astragalus chrysochlorus. This suggests that Se could be involved in the antioxidant metabolism in Astragalus chrysochlorus

Generation of long-living entanglement between two separate atoms

A scheme for non-conditional generation of long-living maximally entangled states between two spatially well separated atoms is proposed. In the scheme, $\Lambda$-type atoms pass a resonator-like equipment of dispersing and absorbing macroscopic bodies giving rise to body-assisted electromagnetic field resonances of well-defined heights and widths. Strong atom-field coupling is combined with weak atom-field coupling to realize entanglement transfer from the dipole-allowed transitions to the dipole-forbidden transitions, thereby the entanglement being preserved when the atoms depart from the bodies and from each other. The theory is applied to the case of the atoms passing by a microsphere.Comment: 13 pages, 5 figure

STORE SEGMENTATION OF RETAIL CHAINS VIA SOCIO-ECONOMIC APPROACH

Günümüzde perakende zincir mağazaların sayısı giderek artmaktadır. Sayıları binlerle ifade edilen bu zincirler, sayıları milyonlarla ifade edilebilecek müşterilere sosyoekonomik açıdan farklı özelliklerdeki şehirlerde hatta aynı şehirlerin farklı özelliklerdeki semtlerinde hizmet vermektedirler. Bu bakımdan, zincir firmalar tarafından özellikle pazarlama süreçleri için kararlar alınırken genel olarak mağazaların tamamına değil de bölgelere veya belirli mağaza gruplarına yönelik stratejiler geliştirmek gerekmektedir. Bu çalışmada, zincir mağazalara sahip perakende firmalarının kümeleme analizini kullanarak sosyoekonomik faktörlere göre mağazalarını nasıl segmentlere ayırabileceği araştırılmaktadır. Bu amaçla, bir perakendecinin İstanbul’daki 175 mağazasına ait farklı veriler çeşitli kaynaklardan bir araya getirilmiş ve Ward’ın kümeleme tekniği kullanılarak mağazalar altı segmente ayrılmıştır. Mağaza segmentasyonu sonucunda elde edilen segmentler incelendiğinde segmentlerin gerçekten farklı özelliklerde olduğu konum olarak birbirine yakın olan mağazaların bile farklı segmentlerde yer alabilecekleri tespit edilmiştir.The number of the retail chain stores is increasing very fast nowadays. These chains which may have thousands of stores and millions of customers are serving at the cities with different socio-economic characteristics and even at the same city of different areas with different characteristics. So in most of the decision processes, especially in marketing decisions, the chains should focus to the areas or store groups rather than focusing to the entire. In this study, our goal is to develop a methodology for retailers on how to segment their stores with cluster analysis based on multiple data sources. A retailer’s 175 stores in Istanbul have been segmented into six segments with Ward’s clustering algorithm using the data of socio-economic factors which are gathered from different sources. After clustering the stores and analyzing the characteristics of segments one would notice that the stores even closer to each other are segmented into different groups

A recommendation engine by using association rules

This study represents a recommendation engine which was developed to personalize an e-commerce website. Here, the personalization approach is collaborative filtering and the technique is association rule mining. The software was developed by the programming language C# and association rules were generated by Apriori algorithm. The recommendation engine had been tested by existing data before it was deployed to an e-commerce website. Testing phase was evaluated by accuracy and coverage while the deployment phase was evaluated by basket ratio, which is the ratio of the number of products added to the shopping cart to the number of keywords searched by users. The application has taken three weeks. Results show that the recommendation engine increases the basket ratio. (C) 2012 Published by Elsevier Ltd. Selection and/or peer review under responsibility of Prof. Dr. Huseyin Arasl

Retail Store Segmentation for Target Marketing

In this paper, we use Data Mining techniques such as clustering and association rules, for the purpose of target marketing strategy. Our goal is to develop a methodology for retailers on how to segment their stores based on multiple data sources and how to create marketing strategies for each segment rather than mass marketing. We have analyzed a supermarket chain company, which has 73 stores located in the Istanbul area in Turkey. First, stores are segmented in 5 clusters using a hierarchical clustering method and then association rules are applied for each cluster

Retail analytics: store segmentation using Rule-Based Purchasing behavior analysis

Retailers are facing challenges in making sense of the significant amount of data available for a better understanding of their customers. While retail analytics plays an increasingly important role in successful retailing management, comprehensive store segmentation based on Data Mining-based Retail Analytics is still an under-researched area. This study seeks to address this gap by developing a novel approach to segment the stores of retail chains based on 'purchasing behavior of customers' and applying it in a case study. The applicability and benefits of using Data Mining techniques to examine purchasing behavior and identify store segments are demonstrated in a case study of a global retail chain in Istanbul, Turkey. Over 600 K transaction data of a global grocery retailer are analyzed and 175 stores in Istanbul are successfully segmented into five segments. The results suggest that the proposed new retail analytics approach enables the retail chain to identify clusters of stores in different regions using all transaction data and advances our understanding of store segmentation at the store level. The proposed approach will provide the retail chain the opportunity to manage store clusters by making data-driven decisions in marketing, customer relationship management, supply chain management, inventory management and demand forecasting