Capstone Design Hub: Building the Capstone Design Community

Capstone design courses are common across engineering programs nationwide. Yet, many departments and programs rely on one faculty member or a small handful of faculty members to teach their capstone design course. As a result these faculty members find themselves isolated, with limited mechanisms for sharing ideas and networking with peers who have similar responsibilities and concerns. This paper reports on the ongoing efforts to support the broader capstone design community through the development of the Capstone Design Hub (CDHub) as a web resource for capstone design programs. The features and structure of the CDHub are being developed through capstone faculty input, including results from a survey of the capstone community. To build awareness of the CDHub as well as to solicit additional feedback from the community, this paper describes development of the hub to meet community needs, initial population of the hub with resources focused on communication, and plans for continued expansion of the hub. © 2012 American Society for Engineering Education

A pilot study of demographic and dopaminergic genetic contributions to weight change in kidney transplant recipients

Kidney transplant recipients often experience a significant amount of weight gain in the first year post-transplantation. While demographic factors such as age, race, and sex have been associated with weight gain in this population, these factors do not explain all of the variability seen. A number of studies have suggested that genetics also plays a critical role in weight changes. Recently, alterations in the activity of the neurotransmitter dopamine have been associated with weight change, and gene expression studies in kidney transplant recipients have supported this association. The purpose of this pilot study is to first examine the feasibility and methodology, and then to examine the associations of age, race, sex, and genotype for 13 SNPs and 3 VNTRs in 9 dopaminergic pathway genes (ANKK1, DRD2, DRD3, DRD4, SLC6A3/DAT1, MAOA, MAOB, COMT, CPE) for associations with percent weight change at 12 months post-transplantation. Seventy kidney transplant recipients had demographic and clinical data collected as a part of a larger observational study. DNA was extracted from repository buffy coat samples taken at the time of transplant, and genotyped using Taqman and PCR based methods. Three SNPs were independently associated with percent weight change: ANKK1 rs1800497 (r = -0.28, p = 0.05), SLC6A3/DAT1 rs6347 (p = 0.046), and CPE rs1946816 (p = 0.028). Stepwise regression modelling confirmed the combined associations of age (p = 0.0021), DRD4 VNTR 4/5 genotype (p = 0.0074), and SLC6A3/DAT1 rs6347 CC genotype (p = 0.0009) and TT genotype (p = 0.0004) with percent weight change in a smaller sample (n = 35) of these kidney transplant recipients that had complete genotyping. These associations indicate that there may be a genetic, and an age component to weight changes post transplantation

Parallel computing in information retrieval - An updated review

The progress of parallel computing in Information Retrieval (IR) is reviewed. In particular we stress the importance of the motivation in using parallel computing for Text Retrieval. We analyse parallel IR systems using a classification due to Rasmussen [1] and describe some parallel IR systems. We give a description of the retrieval models used in parallel Information Processing.. We describe areas of research which we believe are needed

A Hierarchical Approach to Multimodal Classification

Abstract. Data models that are induced in classifier construction often consists of multiple parts, each of which explains part of the data. Classi-fication methods for such models are called the multimodal classification methods. The model parts may overlap or have insufficient coverage. How to deal best with the problems of overlapping and insufficient cov-erage? In this paper we propose hierarchical or layered approach to this problem. Rather than seeking a single model, we consider a series of models under gradually relaxing conditions, which form a hierarchical structure. To demonstrate the effectiveness of this approach we imple-mented it in two classifiers that construct multi-part models: one based on the so-called lattice machine and the other one based on rough set rule induction. This leads to hierarchical versions of the classifiers. The classification performance of these two hierarchical classifiers is compared with C4.5, Support Vector Machine (SVM), rule based classifiers (with the optimisation of rule shortening) implemented in Rough Set Explo-ration System (RSES), and a method combining k-nn with rough set rule induction (RIONA in RSES). The results of the experiments show that this hierarchical approach leads to improved multimodal classifiers