Filtering of Acoustic Emission Data Through Principal Frequency Component Extraction

Rapid editing of acoustic emission (AE) data is required in order to make real-time acoustic emission flaw growth systems a viable testing method for materials and setups that contain noisy signals. It was hypothesized that extracting major frequency components from the acoustic emission signal would therefore provide a representative acoustic signature of the major waveforms occurring due to defect growth This research has verified that the aforementioned filtering technique does, in fact, extract a representative signal from the composite and metal specimens utilized herein These findings were verified both through visual analysis of the data as well as the low error occurrence in backpropagation neural network predictions and good classification in self-organizing map type neural networks applied to the testing data

Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels

Acoustic emission data were collected during the hydroburst testing of eleven 15 inch diameter filament wound composite overwrapped pressure vessels. A neural network burst pressure prediction was generated from the resulting AE amplitude data. The bottles shared commonality of graphite fiber, epoxy resin, and cure time. Individual bottles varied by cure mode (rotisserie versus static oven curing), types of inflicted damage, temperature of the pressurant, and pressurization scheme. Three categorical variables were selected to represent undamaged bottles, impact damaged bottles, and bottles with lacerated hoop fibers. This categorization along with the removal of the AE data from the disbonding noise between the aluminum liner and the composite overwrap allowed the prediction of burst pressures in all three sets of bottles using a single backpropagation neural network. Here the worst case error was 3.38 percent

Field theory of directed percolation with long-range spreading

It is well established that the phase transition between survival and extinction in spreading models with short-range interactions is generically associated with the directed percolation (DP) universality class. In many realistic spreading processes, however, interactions are long ranged and well described by L\'{e}vy-flights, i.e., by a probability distribution that decays in $d$ dimensions with distance $r$ as $r^{-d-\sigma}$. We employ the powerful methods of renormalized field theory to study DP with such long range, L\'{e}vy-flight spreading in some depth. Our results unambiguously corroborate earlier findings that there are four renormalization group fixed points corresponding to, respectively, short-range Gaussian, L\'{e}vy Gaussian, short-range DP and L\'{e}vy DP, and that there are four lines in the $(\sigma, d)$ plane which separate the stability regions of these fixed points. When the stability line between short-range DP and L\'{e}vy DP is crossed, all critical exponents change continuously. We calculate the exponents describing L\'{e}vy DP to second order in $\epsilon$-expansion, and we compare our analytical results to the results of existing numerical simulations. Furthermore, we calculate the leading logarithmic corrections for several dynamical observables.Comment: 12 pages, 3 figure

Effects of surfaces on resistor percolation

We study the effects of surfaces on resistor percolation at the instance of a semi-infinite geometry. Particularly we are interested in the average resistance between two connected ports located on the surface. Based on general grounds as symmetries and relevance we introduce a field theoretic Hamiltonian for semi-infinite random resistor networks. We show that the surface contributes to the average resistance only in terms of corrections to scaling. These corrections are governed by surface resistance exponents. We carry out renormalization group improved perturbation calculations for the special and the ordinary transition. We calculate the surface resistance exponents \phi_{\mathcal S \mathnormal} and \phi_{\mathcal S \mathnormal}^\infty for the special and the ordinary transition, respectively, to one-loop order.Comment: 19 pages, 3 figure

Transport on Directed Percolation Clusters

We study random lattice networks consisting of resistor like and diode like bonds. For investigating the transport properties of these random resistor diode networks we introduce a field theoretic Hamiltonian amenable to renormalization group analysis. We focus on the average two-port resistance at the transition from the nonpercolating to the directed percolating phase and calculate the corresponding resistance exponent $\phi$ to two-loop order. Moreover, we determine the backbone dimension $D_B$ of directed percolation clusters to two-loop order. We obtain a scaling relation for $D_B$ that is in agreement with well known scaling arguments.Comment: 4 page

On the relevance of percolation theory to the vulcanization transition

The relationship between vulcanization and percolation is explored from the perspective of renormalized local field theory. We show rigorously that the vulcanization and percolation correlation functions are governed by the same Gell--Mann-Low renormalization group equation. Hence, all scaling aspects of the vulcanization transition are reigned by the critical exponents of the percolation universality class.Comment: 9 pages, 2 figure

Noisy random resistor networks: renormalized field theory for the multifractal moments of the current distribution

We study the multifractal moments of the current distribution in randomly diluted resistor networks near the percolation treshold. When an external current is applied between to terminals $x$ and $x^\prime$ of the network, the $l$th multifractal moment scales as $M_I^{(l)} (x, x^\prime) \sim | x - x^\prime |^{\psi_l /\nu}$, where $\nu$ is the correlation length exponent of the isotropic percolation universality class. By applying our concept of master operators [Europhys. Lett. {\bf 51}, 539 (2000)] we calculate the family of multifractal exponents $\{\psi_l \}$ for $l \geq 0$ to two-loop order. We find that our result is in good agreement with numerical data for three dimensions.Comment: 30 pages, 6 figure

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

ADAMDEC1 maintains a growth factor signaling loop in cancer stem cells

Glioblastomas (GBM) are lethal brain tumors where poor outcome is attributed to cellular heterogeneity, therapeutic resistance, and a highly infiltrative nature. These characteristics are preferentially linked to GBM cancer stem cells (GSCs), but how GSCs maintain their stemness is incompletely understood and the subject of intense investigation. Here, we identify a novel signaling loop that induces and maintains GSCs consisting of an atypical metalloproteinase, a disintegrin and metalloproteinase domain-like protein decysin 1 (ADAMDEC1), secreted by GSCs. ADAMDEC1 rapidly solubilizes fibroblast growth factor-2 (FGF2) to stimulate FGF receptor 1 (FGFR1) expressed on GSCs. FGFR1 signaling induces upregulation of Zinc-finger E-box-binding homeobox 1 (ZEB1) via ERK1/2 that regulates ADAMDEC1 expression through miR-203, creating a positive feedback loop. Genetic or pharmacological targeting of components of this axis attenuates self-renewal and tumor growth. These findings reveal a new signaling axis for GSC maintenance and highlight ADAMDEC1 and FGFR1 as potential therapeutic targets in GB

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure