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No description available.

In this thesis, a systematic study of the structural characterization of the capsular polysaccharides of Streptococcus pneumoniae is conducted using Molecular Modelling methods. S.pneumoniae causes invasive pneumococcal disease (IPD), a leading cause of death in children under five. The serotypes in group 6 are amongst the most common of IPD causing serotypes. We performed structural characterization of serogroup 6 to understand the structural relationships between serotypes 6A, 6B, 6C and 6D in an attempt to understand the cross protection seen within the group. The 6B saccharide has been included in the early conjugate vaccine (PCV-7), and has shown to elicit protection against the 6B as well as offer some cross-protection against 6A. 6A has since been included in the latter conjugate vaccines in the hopes of eliciting stronger protection against 6A and 6C. Molecular Dynamics simulations were used to investigate the conformations of oligosaccharides with the aim of elucidating a conformational rationale for why small changes in the carbohydrate primary structure result in variable efficacy. We began by examining the Potential of Mean Force (PMF) plots of the disaccharide subunits which make up the Serogroup 6 oligosaccharides. The PMFs showed the free energy proles along the torsional angles space of the disaccharides. This conformational information was then used to build the four oligosaccharides on which simulations were conducted. These simulations showed that serotype pairs 6A/6C and 6B/6D have similar structures.

An understanding of network traffic behavior is essential in the evolution of today's wireless networks, and thus leads to a more efficient planning and management of the network's scarce bandwidth resources. Prior reservation of radio resources at the future locations of a user's mobile travel path can assist with optimizing the allocation of the network's limited resources. Such actions are intended to support the network with sustaining a desirable Quality-of-Service (QoS) level. To help ensure the availability of the network services to its users at anywhere and anytime, there is the need to predict when and where a user will demand any network usage. In this thesis, the mobility behavior of the wireless users are modeled as a Markov renewal process for predicting the likelihoods of the next-cell transition. The model also includes anticipating the duration between the transitions for an arbitrary user in a wireless network. The proposed prediction technique is further extended to compute the likelihoods of a user being in a particular state after $N$ transitions. This technique can also be applied for estimating the future spatial-temporal traffic load and activity at each location in a network's coverage area. The proposed prediction method is evaluated using some real traffic data to illustrate how it can lead to a significant improvement over some of the conventional methods. The work considers both the cases of mobile users with homogeneous applications (e.g. voice calls) and data connectivity with varying data loads being transferred between the different locations.

The hemp plant is an attractive source of raw material for multiple products. Processing hemp requires the separation of fibre and core components of the plant. Peel tests were conducted for hemp stems to evaluate the strength required to peel fibre from the core. The average peeling force for the Alyssa variety was 0.39 N and that for the USO-14 variety was 0.87 N. The Ising model was implemented to produce a stochast ic model. The simulated peel test behaved similarly to the experimental peel test. A discrete element model (DEM) of a planetary ball mill was developed to predict the energy requirement of grinding hemp for fibre. Hemp grinding tests were performed on variety USO-31 using a planetary ball mill for model calibration purposes. Power draw measurements increased linearly increasing at greater grinding speeds. The DEM approximated power draw with relative error below 10% for grinding speeds below 400 rpm.

The Engage to Excel (PCAST) report, the National Research Council's Framework for K-12 Science Education, and the Next Generation Science Standards all call for transforming the physics classroom into an environment that teaches students real scientific practices. This work describes the early stages of one such attempt to transform a high school physics classroom. Specifically, a series of model-building and computational modeling exercises were piloted in a ninth grade Physics First classroom. Student use of computation was assessed using a proctored programming assignment, where the students produced and discussed a computational model of a baseball in motion via a high-level programming environment (VPython). Student views on computation and its link to mechanics was assessed with a written essay and a series of think-aloud interviews. This pilot study shows computation's ability for connecting scientific practice to the high school science classroom.

Metabolic models were constructed from the metagenome of a methanogenic benzene-degrading community to understand the metabolite interactions among the key microbes in the culture. The metagenomic sequences were assembled, and it was found that assembling the short DNA fragments before they were combined with longer reads can contribute to the overall lengths of the resulting sequences. The metagenome was then taxonomically classified into the domain of archaea and bacteria, and domain-specific models were built. A mathematical framework to fill metabolic gaps at the community level was then developed and applied to the benzene-degrading community model to study how metabolic gaps can be filled by via interspecies metabolite transfer, and it suggested that among other metabolites, acetate, hydrogen, formate, coenzyme A and histidine produced by the bacteria population could potentially contribute to the growth of the methanogens. The computational framework demonstrated its ability to generate testable hypotheses about microbial interactions.