2010 Research Fair poster sessionshttp://hdl.handle.net/11124/862024-03-29T06:19:22Z2024-03-29T06:19:22ZQuasiparticle spectrum of 2-D Dirac vortices in optical latticesHaddad, Laith H.Carr, Lincoln D.http://hdl.handle.net/11124/706302022-05-23T08:24:38ZQuasiparticle spectrum of 2-D Dirac vortices in optical lattices
Haddad, Laith H.; Carr, Lincoln D.
Bose-Einstein condensates (BEC's) in a honeycomb optical lattice are described by a nonlinear Dirac equaton (NLDE) in the long wavelength, mean field limit [1]. The bipartite structure of the lattice appears as pseudospin in the multi-component BEC with states above and below the Dirac point playing the roles of particles and antiparticles. Although much work has been done on NLDE's, the bulk of the literature deals with models with Poincare invariant nonlinearites. In contrast our equations break Poincare symmetry providing an opportunity to study phenomenological models in cosmology and particle physics where this symmetry is not manifest. We present the associated linear stability equations and apply them to the case of weak contact interactions to obtain the quasiparticle energies, states, and stabilities of vortex solutions of the mean field equations. We discuss future applications of our results to problems at the interface between condensed matter and particle physics. [1] L. H. Haddad and L. D. Carr, "The Nonlinear Dirac Equation in Bose-Einstein Condensates: Foundation and Symmetries," Physica D: Nonlinear Phenomena, v. 238, p. 1413 (2009) . http://arxiv.org/pdf/0803.3039v1.
Microbial diversity associated with single cell protein produced from a brewery wastewater pilot plantLee, Jackson Z.Spear, John R.http://hdl.handle.net/11124/706362022-05-23T08:24:45ZMicrobial diversity associated with single cell protein produced from a brewery wastewater pilot plant
Lee, Jackson Z.; Spear, John R.
Background: Brewery wastewater typically contains large untapped amounts of useful dissolved carbon measured as Biological Oxygen Demand (BOD) that can be utilized for protein production for fish feed in the form of Single Cell Protein (SCP). Protein is produced from the growth of bacteria as it consumes carbon and is harvested and dried into a fishmeal replacement. Here we present results of a 1-year biodiversity monitoring study of a brewery wastewater treatment pilot plant tuned to produce a dried bacterial Single Cell Protein (SCP) fishfeed replacement product. The plant consistently produced 55-60% (w/w) crude protein SCP at about 15 kg/day. The key to this consistency was the addition of micronutrients to the wastewater during aerobic growth, but the exact microbial response to this addition was not well understood. Materials and Methods: An initial survey of the brewery wastewater operations was conducted over the year 2008 using Sanger sequencing of the 16S SSU rRNA gene with the 8F/1492R primer. Samples were taken from throughout the brewery treatment works and pilot plant to establish a time course. Next, A 454 FLX pyrosequencing run was also completed using normalized DNA from the same samples as above with the bacterial 27F/338R primers and sample barcoding. Pyrotags were processed and clustered into Operational Taxonomic Units (OTUs) by the MOTHUR bioinformatics package. Results: Ribosomal Database Project classifications of Sanger data showed that while the order level diversity was relatively simple, the consortia varied considerably both in time and in location. Pyrotag data (55,000 sequences) was characterized by a high degree of singleton OTUs. No single sequence comprised more than 2% of all sequences and no two samples (in either time or space) contained more than 10% OTU similarity. Phylum-level pyrotag diversity of the pilot production tank revealed dominance by Bacteroidetes followed by Firmicutes and beta/gamma-Proteobacteria. Fast UniFrac results show that SCP product and pilot plant environments sometimes clustered together, and that some temporal clustering also occurs. More significantly, Fast UniFrac results show that each segment of the treatment works was highly selective. In order to understand where variations in Fast UniFrac data exist, a taxonomic rank abundance plot was made which details distributions of sequences within various phylum. Results show that major contributors to community structure lie in Firmicutes and Beta-proteobacteria, but the majority of dominant organisms come from Bacteroidetes, particularly from genus Prevotella, a group of carbohydrate metabolizing anaerobes commonly associated with tooth decay. Conclusion: These results indicated that the bulk of diversity in the pilot plant were low count species and that high turnover led to considerable shifts in diversity within several major phyla, particularly from phylum Bacteroidetes, though overall protein concentration of the system remained consistent for the production of SCP. These results indicate that minute changes in reactor conditions commonly seen in day-to-day operations at any treatment plant can cause wide fluctuations in reactor diversity without impacting process stability.
Predictive bio-computational wear modeling for joint replacementsArmstrong, Jeffrey R.Petrella, Anthony J.http://hdl.handle.net/11124/180042022-05-23T08:24:30ZPredictive bio-computational wear modeling for joint replacements
Armstrong, Jeffrey R.; Petrella, Anthony J.
Polyethylene wear has long been a topic of concern for the longevity of joint replacement systems as bearing failure is the leading cause for the need of revision surgery. Experimental simulations are costly and time consuming; therefore, a more efficient solution for predicting wear is computer simulation. Predictive computational modeling of the adhesive/abrasive wear mechanism has been in use for over a decade, but the accuracy of such models is still under debate [1-7]. Recent studies have shown that cross-path motion, as seen in joint replacements, results in elevated wear and shortens the life of the polyethylene bearing surface [8-10]. Modern computer simulations have attempted to address the effects of cross-path motion and range from simple to complex formulations [9, 11-13]. Current models are limited by their complexity, computational efficiency, joint-specificity, or motion-cycle path dependence. In this study, an adaptive finite element (FE) model was used to implement a modified form of Archard's Wear law [1] that accounts for the effects of cross-path motion and polymer chain realignment. The proposed model was validated to three separate experimental wear systems, each with three loading scenarios. As seen in Equation 1, the proposed Modified Archard's law sums the effects of unidirectional and cross-path motion and also accounts for polymer chain realignment, referred to as 'memory'. This Modified Archard's law is simple and generally applicable to any wear system: [Eqn 1.] where 'k0' and 'k*' are experimentally derived wear coefficients for uni-directional and cross-path sliding, respectively. The variable 'p' refers to contact pressure and the variable s is the magnitude of incremental sliding distance. The variable 'm' incorporates memory and sliding trajectory effects; its full definition can be found in [14]. Validation of the proposed wear model was completed through comparisons to published experimental data for three wear systems. The first system was a pin-on-disk wear experiment by Dressler et al. [15]. They concluded that wear was elevated by changes in direction but that the elevated wear diminished with sliding in a consistent direction up to 5 millimeters. Application of previous models to this experimental system resulted in incorrect wear predictions. Application of the proposed Modified Archard's law was able to predict the experimental wear volume results exactly. Further validation was confirmed when the Modified Archard's law was applied to FE models of a cervical disk replacement and a total knee replacement, as seen in Figure 1. The cervical disk model was made in accordance with the experimental setup by Bushelow et al. [16]. The total knee replacement model was made in accordance to the setup by McEwen et al. [10]. Experimental wear depth and volume results were compared to predictions from both the classical and Modified forms of Archard's Wear law for each of the two experiments three distinct loading scenarios. Wear coefficients were scaled to a standard loading scenario for each system. In each of the two predicted scenarios of both experiments, the Modified Archard's Wear law showed a better fit to the experimental data than the classical Archard's Wear formulation. Bibliography: [1] Archard 1953; [2] Maxian et al. 1995; [3] Kang et al 2009; [4] Knight et al 2007; [5] Pal et al 2008; [6] Ghiglieri et al 2008; [7] Goreham-Voss 2009; [8] Bragdon et al 1996; [9] Turrel et al 2003; [10] McEwen et al 2005; [11] Wang 2001; [12] Hamilton et al 2005; [13] Knight et al 2006; [14] Petrella et al 2009; [15] Dressler et al 2009; [16] Bushelow et al 2009.
Statistical shape model for probabilistic studies of the lumbar spine, AHuls, Kelli S.Petrella, Anthony J.http://hdl.handle.net/11124/706352022-05-23T08:24:31ZStatistical shape model for probabilistic studies of the lumbar spine, A
Huls, Kelli S.; Petrella, Anthony J.
Introduction: Computational modeling of the spine has become a promising option for evaluating the performance of new spinal implants and procedures before they are used in patients. Most models in the literature only represent a single subject and neglect normal variation that exists between specimens. However, using a probabilistic simulation (select input variables from a normal distribution and determine how they affect outputs) of virtual patients, whose geometries are representative of actual patients, may lead to viable options for pre-clinical evaluation of devices and procedures. One of the major challenges to overcome when applying probabilistic modeling techniques to biologic systems is to capture normal shape variation between subjects. Methods: Vertebral body geometries from 8 normal CT scans were used to develop a statistical shape model (SSM) of the lumbar spine. The SSM model is comprised of eigenvalues and eigenvectors calculated with a principle component analysis. Eigenvectors, also called modes, represent how shape varies in the geometry and eigenvalues represent how important each mode is to the overall shape of the geometry. Any specimen can be represented by P= P_mean+Sum_(j=1,m)[b_j*c_j] where bj are scalar coefficients and cj are the eigenvectors. Virtual specimens can be created by randomly sampling the normal curve for each b coefficient. A finite element model, shown in figure 1, was developed containing eight ligaments (non-linear springs), an intervertebral disc (hyperelastic annulus, fluid cavity for nucleus), and linear elastic cartilage (2mm thick). A compression load of 800 N and 7.5 Nm of axial rotation was applied to the model and it was solved using Abaqus. The model was validated using experimental range of motion data. For the purpose of probabilistic analysis, tens to hundreds of model runs are desired. To facilitate the generation of these models, a procedure was developed to incorporate geometry from the SSM, automatically place ligaments and generate cartilage. Results: The first mode shape was a scaling mode, the second was associated with shape and angulation of the facet joints and the third produced variations in transverse processes. Higher modes were not visually obvious. Models created from virtual geometries demonstrated noticeable shape variation but they mated quantitatively similar to models generated from CT scans (naturals). Qualitatively, differences between virtual specimens and natural specimens were not statistically different for either area or average pressure. For manual and automatic cartilage, contact area was observed in the same general location and contact pressure was similar, 2.43 MPa compared to 2.22 MPa. Discussion: Shape variation in the lumbar spine was characterized using a statistical shape model. Models created with virtual specimens demonstrated facet contact similar to models generated with natural specimens. Cartilage generation that was automated for a probabilistic study resulted in quality meshes that fit flush into the facet geometry. With the success of models developed using virtual specimens, the same methods can be applied to the entire lumbar spine. Next steps for creating a probabilistic model are to automate the alignment of vertebral bones and implementation of probabilistic computations using a Monte Carlo method.