UMBC Center for Interdisciplinary Research and Consulting (CIRC)

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http://hdl.handle.net/11603/11404

The Center for Interdisciplinary Research and Consulting (CIRC) is a consulting service for mathematics and statistics provided by the Department of Mathematics and Statistics at UMBC. Established in 2003, CIRC is dedicated to supporting interdisciplinary research for both the UMBC campus community and the general public. We provide a full range of consulting services from free initial consulting to long term support for research programs.

CIRC offers mathematical and statistical expertise in broad areas of applications, including biological sciences, engineering, and the social sciences. On the mathematics side, particular strengths include techniques of parallel computing and assistance with software packages such as MATLAB and COMSOL Multiphysics. On the statistics side, areas of particular strength include Toxicology, Industrial Hygiene, Bioequivalence, Biomechanical Engineering, Environmental Science, Finance, Information Theory, and packages such as SAS, SPSS, and S-Plus.

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Recent Submissions

Now showing 1 - 4 of 4
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    Statistical Meta-Analysis: Air Pollution & Children’s Health
    (University of Rajshahi, 2011) Stanwyck, Elizabeth; Wei, Rong
    There have been numerous studies seeking to establish an association between air pollution and children’s adverse health outcomes, and the ultimate findings are often varied. A few studies found a statistically significant association between an increase in a specific pollutant and an adverse health effect among children, while others find a non-significant association between the same pair of variables. These conflicting results undermine confidence in the final conclusions, and this leads naturally to a novel application of the so-called statistical meta-analysis whose primary objective is to integrate or synthesize the findings from independent and comparable studies. In this paper we first review a recent statistical meta-analysis paper by Weinmayr et al. (2010) dealing with studies on the effects of NO₂ and PM₁₀ on some aspects of children’s health. In the second part of this paper, we conduct our own meta-analysis focusing on the association between children’s (binary) health outcomes (such as cough and respiratory symptoms) and four pollutants: PM₁₀, NO₂, SO₂, and O₃. While we find a statistically significant association with every pollutant, it turns out that for PM₁₀, NO₂, and SO₂, there is significant heterogeneity among the estimated effect sizes (odds ratios). Finally, we explore the techniques of meta-regression by incorporating distinct study features to meaningfully explain the heterogeneity.
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    The Effects of Population Density on the Incidence of Developmental Deformities in Chemosensory Organs of Tobacco Hornworm Larvae (Lepidoptera: Sphingidae)
    (Oxford University Press, 2020-07-17) Hanson, Frank; Stanwyck, Elizabeth; Bohorquez, Alexander
    Cultures of Manduca sexta Johanssen in our laboratory were found to have larvae with missing or deformed mouthparts or antennae. Hypothesizing that these developmental deformities were caused by crowded rearing conditions, we reared larvae in four different population densities and recorded the incidence (% of larvae affected) and types of chemoreceptor deformities. Results showed that the incidence of these deformities was directly proportional to larval population density. Deformities of the maxilla and palp were the most frequent, followed by those of the antenna, epipharynx and maxillary styloconica. Life history traits of larval mass, food consumption, and rate of development were inversely related to larval density for both normal and deformed larvae. We discuss possible causes and mechanisms of these deformities and of changes to life history traits.
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    An Implementation of Binomial Method of Option Pricing using Parallel Computing
    Popuri, Sai K.; Raim, Andrew M.; Neerchal, Nagaraj K.; Gobbert, Matthias K.
    The Binomial method of option pricing is based on iterating over discounted option payoffs in a recursive fashion to calculate the present value of an option. Implementing the Binomial method to exploit the resources of a parallel computing cluster is non-trivial as the method is not easily parallelizable. We propose a procedure to transform the method into an “embarrassingly parallel” problem by mapping Binomial probabilities to Bernoulli paths. We have used the parallel computing capabilities in R with the Rmpi package to implement the methodology on the cluster tara in the UMBC High Performance Computing Facility, which has 82 compute nodes with two quad-core Intel Nehalem processors and 24 GB of memory on a quad-data rate InfiniBand interconnect. With high-performance clusters and multi-core desktops becoming increasingly accessible, we believe that our method will have practical appeal to financial trading firms.