Facility selection model for BOPS service for an omni-channel retail chain

Retail sector around the world is going through a transition due to digitalization. Customers now demand convenience of online channel with instant fulfilment of offline channel. As a result, the concept of omni-channel retailing has come into existence. In omni-channel retailing, both the channels of a firm are fully or partially integrated. There are many strategies that may be developed by retailers for efficient functioning of the omni-channel retail chain. The choice of such a strategy depends upon the comparative benefits that can be derived from them. One such strategy gaining immense attention of retailers is the Buy Online Pickup in Store (BOPS) strategy. In order to understand this BOPS, we conducted a detail case study of an Indian multi-channel retail chain who is considering the option of launching BOPS option in few of its retail stores. The focus is to evaluate and select a fixed number of operational retail stores to also act as pick-up points for online deliveries. K-means clustering is utilized for formation of retail zones as per the number of retail stores to be selected. The retail stores in each zone are evaluated on the basis of a number of criteria such as demand, inventory carrying capacity, cost, and population characteristics using Analytical Hierarchy Process (AHP) and Complex Proportional Assessment-Grey (COPRAS-G). Based on these evaluations the best suitable retail store is selected for expansion from each zone. The result implications drawn in the study can provide an understanding of the future strategies that can be developed by the decision makers of the firm for launching the BOPS option. The study can also be beneficial to other Indian retail firms who can derive meaningful insights from the findings

An effective non-parametric method for globally clustering genes from expression profiles

Clustering is widely used in bioinformatics to find gene correlation patterns. Although many algorithms have been proposed, these are usually confronted with difficulties in meeting the requirements of both automation and high quality. In this paper, we propose a novel algorithm for clustering genes from their expression profiles. The unique features of the proposed algorithm are twofold: it takes into consideration global, rather than local, gene correlation information in clustering processes; and it incorporates clustering quality measurement into the clustering processes to implement non-parametric, automatic and global optimal gene clustering. The evaluation on simulated and real gene data sets demonstrates the effectiveness of the algorithm. <br /

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

What do we know and when do we know it?

Two essential aspects of virtual screening are considered: experimental design and performance metrics. In the design of any retrospective virtual screen, choices have to be made as to the purpose of the exercise. Is the goal to compare methods? Is the interest in a particular type of target or all targets? Are we simulating a ‘real-world’ setting, or teasing out distinguishing features of a method? What are the confidence limits for the results? What should be reported in a publication? In particular, what criteria should be used to decide between different performance metrics? Comparing the field of molecular modeling to other endeavors, such as medical statistics, criminology, or computer hardware evaluation indicates some clear directions. Taken together these suggest the modeling field has a long way to go to provide effective assessment of its approaches, either to itself or to a broader audience, but that there are no technical reasons why progress cannot be made

An early warning method for agricultural products price spike based on artificial neural networks prediction

In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation of employment related to the rural environment. These characteristics differentiate the agricultural sector from other economic sectors. On the other hand, the volatility of prices payed by producers, the high cost of raw materials, and the instability of both domestic and international markets are factors which have eroded the competitiveness and profitability of the agricultural sector. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in agricultural products, considering macro and micro economic variables and factors related to the seasons of the year

Conditional mouse models demonstrate oncogene-dependent differences in tumor maintenance and recurrence

Diversity in the pathophysiology of breast cancer frustrates therapeutic progress. We need to understand how mechanisms activated by specific combinations of oncogenes, tumor suppressors, and hormonal signaling pathways govern response to therapy and prognosis. A recent series of investigations conducted by Chodosh and colleagues offers new insights into the similarities and differences between specific oncogenic pathways. Expression of three oncogenes relevant to pathways activated in human breast cancers (c-myc, activated neu and Wnt1) were targeted to murine mammary epithelial cells using the same transgenic tetracycline-responsive conditional gene expression system. While the individual transgenic lines demonstrate similarly high rates of tumor penetrance, rates of oncogene-independent tumor maintenance and recurrence following initial regression are significantly different, and are modifiable by mutations in specific cooperating oncogenes or loss of tumor suppressor gene expression. The experiments make three notable contributions. First, they illustrate that rates of tumor regression and recurrence following initial regression are dependent upon the pathways activated by the initiating oncogene. The experiments also demonstrate that altered expression or mutation of specific cooperating oncogenes or tumor suppressor genes results in different rates of tumor regression and recurrence. Finally, they exemplify the power of conditional mouse models for elucidating how specific molecular mechanisms give rise to the complexity of human cancer

Knee Arthroplasty: Disabilities in Comparison to the General Population and to Hip Arthroplasty Using a French National Longitudinal Survey

International audienceBACKGROUND: Knee arthroplasty is increasing exponentially due to the aging of the population and to the broadening of indications. We aimed to compare physical disability and its evolution over two years in people with knee arthroplasty to that in the general population. A secondary objective was to compare the level of disabilities of people with knee to people with hip arthroplasty. METHODOLOGY/PRINCIPAL FINDINGS: 16,945 people representative of the French population were selected in 1999 from the French census and interviewed about their level of disability. This sample included 815 people with lower limb arthroplasty. In 2001, 608 of them were re-interviewed, among whom 134 had knee arthroplasty. Among the other participants re-interviewed, we identified 68 who had undergone knee arthroplasty and 145 hip arthroplasty within the last two years (recent arthroplasty). People with knee arthroplasty reported significantly greater difficulties than the general population with bending forward (odds ratio [OR] = 4.7; 95% confidence interval [CI]: 1.7, 12.6), walking more than 500 meters (OR = 6.0; 95% CI: 1.5, 24.7) and carrying 5 kg kilograms for 10 meters (OR = 4.6; 95% CI: 1.3, 16.4). However, the two years evolution in disability was similar to that in the general population for most activities. The level of mobility was similar between people with recent knee arthroplasty and those with recent hip arthroplasty. Nevertheless, people with recent knee arthroplasty reported a lower level of disability than the other group for washing and bending forward (OR = 0.3; 95% CI: 0.1, 0.6 and OR = 0.4; 95% CI: 0.1, 0.9, respectively). CONCLUSIONS/SIGNIFICANCE: People with knee arthroplasty reported a higher risk of disability than the general population for common activities of daily living but a similar evolution. There was no relevant difference between recent knee and hip arthroplasties for mobility