Browsing by Subject "Computational modeling"
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Item Computational analysis of meditation(2011-08) Saggar, Manish; Miikkulainen, Risto; Saron, Clifford D.; Schnyer, David M.; Ballard, Dana H.; Ravikumar, PradeepMeditation training has been shown to improve attention and emotion regulation. However, the mechanisms responsible for these effects are largely unknown. In order to make further progress, a rigorous interdisciplinary approach that combines both empirical and theoretical experiments is required. This dissertation uses such an approach to analyze electroencephalogram (EEG) data collected during two three-month long intensive meditation retreats in four steps. First, novel tools were developed for preprocessing the EEG data. These tools helped remove ocular artifacts, muscular artifacts, and interference from power lines in a semi-automatic fashion. Second, in order to identify the cortical correlates of meditation, longitudinal changes in the cortical activity were measured using spectral analysis. Three main longitudinal changes were observed in the retreat participants: (1) reduced individual alpha frequency after training, similar reduction has been consistently found in experienced meditators; (2) reduced alpha-band power in the midline frontal region, which correlated with improved vigilance performance; and (3) reduced beta-band power in the parietal-occipital regions, which correlated with daily time spent in meditation and enhanced self-reported psychological well-being. Third, a formal computational model was developed to provide a concrete and testable theory about the underlying mechanisms. Four theoretical experiments were run, which showed, (1) reduced intrathalamic gain after training, suggesting enhanced alertness; (2) increased cortico-thalamic delay, which strongly correlated with the reduction in individual alpha frequency (found during spectral analysis); (3) reduction in intrathalamic gain provided increased stability to the brain; and (4) anterior-posterior division in the modeled reticular nucleus of the thalamus (TRN) layer and increased connectivity in the posterior region of TRN after training. Fourth, correlation analysis was performed to ground the changes in cortical activity and model parameters into changes in behavior and self-reported psychological functions. Through these four steps, a concrete theory of the mechanisms underlying focused-attention meditation was constructed. This theory provides both mechanistic and teleological reasoning behind the changes observed during meditation training. The theory further leads to several predictions, including the possibility that customized meditation techniques can be used to treat patients suffering from neurodevelopmental disorders and epilepsy. Lastly, the dissertation attempts to link the theory to the long-held views that meditation improves awareness, attention, stability, and psychological well-being.Item Computational modeling of fluctuations and phase behavior in polymeric systems(2015-08) Pandav, Gunja Rajesh; Ganesan, Venkat; Ellison, Christopher; Sanchez, Isaac; Truskett, Thomas; Henkelman, Graeme; Johnston, KeithThis research focuses on the computational modeling of fluctuations, interactions, phase behavior and structural characteristics of multicomponent polymeric systems. The role of fluctuations is studied in the context of block copolymer melts and polymer blends stabilized using copolymers exhibiting different sequence architectures. The relationship between interparticle interactions and structural characteristics of the aggregates formed in particle-polymer solutions is examined for charged nanoparticle-polymer and charged dendrimer-polyelectrolyte system. A hybrid Monte Carlo and self consistent field theory approach employed in single chain in mean field simulations (SCMF) is utilized in order to achieve the equilibrium morphologies/aggregates in such polymeric systems. We examine the effect of composition fluctuations on the phase behavior of polydisperse block copolymer melts quantified in terms of fluctuation-induced shift in the order-disorder transition temperature from the corresponding mean-field predictions. Fluctuation effects can also play an important role in stabilizing bicontinuous microemulsions phases. To study this effect, we examine polymer blend systems compatibilized by a copolymer having different sequence architectures such as monodisperse and polydisperse block copolymer, and gradient copolymer. We systematically assess the efficiency of such system in forming bicontinuous microemulsions phases. We also study the effect of sequence architecture on the phase behavior of gradient copolymer solutions. We extend above framework to account for electrostatic effects arising from charged polymers and dendrimers. Using such a framework, we characterize the clusters formed due to electrostatic binding between oppositely charged dendrimers and polyelectrolytes. Our results indicate that, the binding is maximum when the charge on dendrimers is balanced by the charge on the polyelectrolytes. We extend the above study to probe the phase behavior of charged nanoparticles suspended in polymer solutions. We examine the influence of polymer concentration, particle volume fraction, and particle charge on the structure and size of clusters. We also examine the influence of multibody effects on the resulting structure of nanoparticle clusters. The charged nanoparticle-polymer solution is seen to exhibit significant multibody effects and the effective two-body interparticle potentials are seen to be a function of nanoparticle density.Item Depth resolved diffuse reflectance spectroscopy(2015-05) Hennessy, Richard J.; Markey, Mia Kathleen; Tunnell, James W.This dissertation focuses on the development of computational models and algorithms related to diffuse reflectance spectroscopy. Specifically, this work aims to advance diffuse reflectance spectroscopy to a technique that is capable of measuring depth dependent properties in tissue. First, we introduce the Monte Carlo lookup table (MCLUT) method for extracting optical properties from diffuse reflectance spectra. Next, we extend this method to a two-layer tissue geometry so that it can extract depth dependent properties in tissue. We then develop a computational model that relates photon sampling depth to optical properties and probe geometry. This model can be used to aid in design of application specific diffuse reflectance probes. In order to provide justification for using a two-layer model for extracting tissue properties, we show that the use of a one-layer model can lead to significant errors in the extracted optical properties. Lastly, we use our two-layer MCLUT model and a probe that was designed based on our sampling depth model to extract tissue properties from the skin of 80 subjects at 5 anatomical locations. The results agree with previously published values for skin properties and show that can diffuse reflectance spectroscopy can be used to measured depth dependent properties in tissue.Item A multicomponent membrane model for the vanadium redox flow battery(2012-08) Michael, Philip Henry; Meyers, Jeremy P.; Chen, Dongmei, Ph. D.With its long cycle life and scalable design, the vanadium redox flow battery (VRB) is a promising technology for grid energy storage. However, high materials costs have impeded its commercialization. An essential but costly component of the VRB is the ion-exchange membrane. The ideal VRB membrane provides a highly conductive path for protons, prevents crossover of reactive species, and is tolerant of the acidic and oxidizing chemical environment of the cell. In order to study membrane performance and optimize cell design, mathematical models of the separator membrane have been developed. Where previous VRB membrane models considered minimal details of membrane transport, generally focusing on conductivity or self-discharge at zero current, the model presented here considers coupled interactions between each of the major species by way of rigorous material balances and concentrated solution theory. The model describes uptake and transport of sulfuric acid, water, and vanadium ions in Nafion membranes, focusing on operation at high current density. Governing equations for membrane transport are solved in finite difference form using the Newton-Raphson method. Model capabilities were explored, leading to predictions of Ohmic losses, vanadium crossover, and electro-osmotic drag. Experimental methods were presented for validating the model and for further improving estimates of uptake parameters and transport coefficients.