Developing a Supply Chain Strategy for a Midsize Resturant Chain

In this paper, we develop a supply chain strategy for a growing midsize restaurant chain. Based on a case research of The HoneyBaked Ham Company of Ohio, we propose that an integrated approach should be applied to handle the challenges presented in the midsize restaurant distribution system. Specifically, we focus on action plans for mitigating inefficiencies found in the previous supply chain of HBH. As the success of supply chain management has increasingly become part of the competitive advantage of many firms, our work provides managerial insights to practitioners and researchers in the area of chain restaurant management where supply chain is often overlooked as a standard "back-office" function

Learning-based deformable image registration for infant MR images in the first year of life

Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (1) large anatomical changes due to fast brain development and (2) dynamic appearance changes due to white matter myelination

Adaptive clustering with feature ranking for DDoS attacks detection

Distributed Denial of Service (DDoS) attacks pose an increasing threat to the current internet. The detection of such attacks plays an important role in maintaining the security of networks. In this paper, we propose a novel adaptive clustering method combined with feature ranking for DDoS attacks detection. First, based on the analysis of network traffic, preliminary variables are selected. Second, the Modified Global K-means algorithm (MGKM) is used as the basic incremental clustering algorithm to identify the cluster structure of the target data. Third, the linear correlation coefficient is used for feature ranking. Lastly, the feature ranking result is used to inform and recalculate the clusters. This adaptive process can make worthwhile adjustments to the working feature vector according to different patterns of DDoS attacks, and can improve the quality of the clusters and the effectiveness of the clustering algorithm. The experimental results demonstrate that our method is effective and adaptive in detecting the separate phases of DDoS attacks. © 2010 IEEE

Herd Behavior In Global Online Shopping Carnival

We have witnessed the magnificent power of herd behavior exhibited by the frantic crowd during the Alibaba’s global online shopping carnival (OSC) which has made 9 Ginness world’s records within 24 hours. This study explores the cognitive herding process and the critical factors facilitating herd behavior in OSC. Meanwhile, applying the theory of carnival, this study identifies three OSC behaviors which spread through the OSC herd. Using 473 samples from OSC participants, the hypotheses are supported by the empirical results. Information incentive (e.g. promotion motivation and review information) and social influence (e.g. peer mimicry and endorsement influence) are two crucial preconditions for herd behavior; participation, interaction and playfulness capture the essence of OSC behavior. The results provide insights to: (1). the cognitive process of herd behavior; (2). the critical factors facilitating herd behavior in OSC; (3) the important OSC behavior imitated during the herding process

A Novel Approach to Recovering Depth from Defocus

This paper proposes a novel approach to recovering depth from defocus, which is a deterministic approach in spatial domain. Two defocused gray images from the same scene are obtained by changing two parameters (image distance and focal length of camera) other than only parameter (image distance). The idea of this approach is to convert the gray images into the gradient images by Canny operator other than Sobel operator, then calculate the ratio of the area of region with large gradient value to that of the whole image region in each block for each defocused image by moment-preserving method, and recover depth from scene according to the ratio of the ratio of one gradient image to that of the other gradient image. The experimental results show that the proposed approach is more accurate and efficient than the traditional approach