Temperature-Dependent Defect Dynamics in the Network Glass SiO2

We investigate the long time dynamics of a strong glass former, SiO2, below the glass transition temperature by averaging single-particle trajectories over time windows which comprise roughly 100 particle oscillations. The structure on this coarse-grained time scale is very well defined in terms of coordination numbers, allowing us to identify ill-coordinated atoms, which are called defects in the following. The most numerous defects are O-O neighbors, whose lifetimes are comparable to the equilibration time at low temperature. On the other hand, SiO and OSi defects are very rare and short lived. The lifetime of defects is found to be strongly temperature dependent, consistent with activated processes. Single-particle jumps give rise to local structural rearrangements. We show that in SiO2 these structural rearrangements are coupled to the creation or annihilation of defects, giving rise to very strong correlations of jumping atoms and defects.

Molecular Dynamics Simulations of the d(TAT) Triple Helix

Molecular dynamics (MD) simulations have been used to study the dynamical and time-averaged characteristics of the DNA triple helix d(T)10âd(A)10âd(T)10. The structures sampled during the trajectory resemble closely the B-type model for the DNA triplex proposed on the basis of NMR data, although there are some subtle differences. Alternative P- and A-type conformations for the triplex, suggested from X-ray experiments, are not predicted to contribute significantly to the structure of the DNA triplex in solution. Comparison with the best available experimental data supports the correctnes of the MD-generated structures. The analysis of the collected data gives a detailed picture of the characteristics of triple-helix DNA. A new and interesting pattern of hydration, specific for triplex DNA, is an important observation. The results suggest that molecular dynamics can be useful for the study of novel nucleic acid structures.

Active processes, morphology, and dynamics of icy debris fans: Landform evolution along rapidly degrading escarpments in alpine regions undergoing recent deglaciation

In the past few decades the impacts of climate warming have been significant in alpine glaciated regions. Many valley glaciers formerly linked as distributary glaciers to high-level icecaps have decoupled at their icefalls, exposing major escarpments and generating a suite of dynamic landforrns dominated by mass wasting. Ice-dominated landforms, here termed icy debris fans, develop rapidly by ice avalanching, rockfall, and icy debris flow. Field-based reconnaissance studies at two alpine settings, the Wrangell Mountains of Alaska and the Southern Alps of New Zealand, provide a preliminary morphogenetic model of spatial and temporal evolution of icy debris fans in a range of alpine settings. The influence of these processes on landform evolution is largely unrecognized in the literature dealing with post-glacial landform adjustment known as the paraglacial. A better understanding of these dynamic processes will be increasingly important because of the extreme geohazards characterizing these areas. Our field studies show that after glacier decoupling, icy debris fans begin to form along the base of bedrock escarpments at the mouths of catchments and prograde over valley glaciers. The presence of a distinct catchment, apex, and fan morphology distinguishes these landforms from other landforms common in periglacial hillslope settings receiving abundant clastic debris and ice. Ice avalanching is the most abundant process involved in icy debris fan formation. Fans developed below weakly incised catchments are dominated by ice avalanching and are composed primarily of ice with minor lithic detritus. Typically, avalanches fall into the fan catchments where sediments transform into grainflows that flow onto the fans. Once on the fans, avalanche deposits ablate rapidly, flattening and concentrating lithic fragments at the surface. Icy debris fans may become thick enough to become glaciers with splay crevasse systems. Fans developed below larger, more complex catchments are composed of higher proportions of lithic detritus resulting from temporary storage of ice and lithic detritus deposits within the catchment. Episodic outbursts of meltwater from the icecap may mix with the stored sediments and mobilize icy debris flows (mixture of ice and lithic clasts) onto the fans. Our observations indicate that the entire evolutionary cycle of icy debris fans probably occurs during an early paraglacial interval (i.e., decades to 100 years). Observations comparing avalanche frequency, volume, and fan morphologic evolution at the Alaska site between 2006 and 2010 illustrate complex response between icy debris fans even within the same cirque - where one fan may be growing while others are downwasting because of differences in ice supply controlled by their respective catchments and icecap contributions. As ice supply from the icecap diminishes through time, icy debris fans rapidly downwaste and eventually evolve into talus cones that receive occasional but ephemeral ice avalanches.

Cultural Dynamics and Ceramic Resource Use at Late Bronze Age/Early Iron Age Troy, Northwestern Turkey

Changes in resource use over time can provide insight into technological choice and the extent of long-term stability in cultural practices. In this paper we re-evaluate the evidence for a marked demographic shift at the inception of the Early Iron Age at Troy by applying a robust macroscale analysis of changing ceramic resource use over the Late Bronze and Iron Age. We use a combination of new and legacy analytical datasets (NAA and XRF), from excavated ceramics, to evaluate the potential compositional range of local resources (based on comparisons with sediments from within a 10 km site radius). Results show a clear distinction between sediment-defined local and non-local ceramic compositional groups. Two discrete local ceramic resources have been previously identified and we confirm a third local resource for a major class of EIA handmade wares and cooking pots. This third source appears to derive from a residual resource on the Troy peninsula (rather than adjacent alluvial valleys). The presence of a group of large and heavy pithoi among the non-local groups raises questions about their regional or maritime origin. (C) 2012 Elsevier Ltd. All rights reserved.

Dynamics of Spatial Game Theory Networks with Novel Modifications

Introduction: Advances in biotechnology have shed light on many biological processes. In biological networks, nodes are used to represent the function of individual entities within a system and have historically been studied in isolation. Network structure adds edges that enable communication between nodes. An emerging fieldis to combine node function and network structure to yield network function. One of the most complex networks known in biology is the neural network within the brain. Modeling neural function will require an understanding of networks, dynamics, andneurophysiology. It is with this work that modeling techniques will be developed to work at this complex intersection. Methods: Spatial game theory was developed by Nowak in the context of modeling evolutionary dynamics, or the way in which species evolve over time. Spatial game theory offers a two dimensional view of analyzingthe state of neighbors and updating based on the surroundings. Our work builds upon this foundation by studying evolutionary game theory networks with respect to neural networks. This novel concept is that neurons may adopt a particular strategy that will allow propagation of information. The strategy may therefore act as the mechanism for gating. Furthermore, the strategy of a neuron, as in a real brain, isimpacted by the strategy of its neighbors. The techniques of spatial game theory already established by Nowak are repeated to explain two basic cases and validate the implementation of code. Two novel modifications are introduced in Chapters 3 and 4 that build on this network and may reflect neural networks. Results: The introduction of two novel modifications, mutation and rewiring, in large parametricstudies resulted in dynamics that had an intermediate amount of nodes firing at any given time. Further, even small mutation rates result in different dynamics more representative of the ideal state hypothesized. Conclusions: In both modificationsto Nowak's model, the results demonstrate the network does not become locked into a particular global state of passing all information or blocking all information. It is hypothesized that normal brain function occurs within this intermediate range and that a number of diseases are the result of moving outside of this range.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.