Fundamental Mechanical-behavior Studies of Annealed and Nano-particle-strengthened Nickel-based Alloys Using In-situ Neutron-diffraction Experiments

This dissertation addresses two issues concerning the fundamental mechanical behavior of the nickel-based superalloys: (1) the deformation mechanisms and (2) the nano-precipitate-strengthening effect. The precipitates are known to fortify the mechanical behavior of the metallic alloys. These precipitates can interact with the matrix upon the applied load. While the precipitation strengthening has been facilitated for many purposes, this research puts forward the mechanistic understanding. The dissertation considers the thesis that Deformation Mechanisms and Nanoprecipitate Strengthening and their effects on the microstructure are central to the mechanical behavior of nickel-based superalloys. The experimental methods employed in this research are in-situ neutrondiffraction measurements, in-situ thermal characterization, ex-situ small-angle neutron-scattering, and electron microscopy experiments. The microscopic structural information obtained from the diffraction profiles is compared with the electronmicroscopy images to be complementary to each other. The microscopic features are connected with the macroscopic states, such as the applied stresses and temperature evolution to bridge the understanding of the bulk property. This dissertation assumes that the macroscopic-material responses are the convolution of two contributions: the linear-elastic contribution and the plasticityinduced intra/inter-granular contribution. Within the context of this analysis, the mechanistic understanding of the deformation of the alloys is presented

The Apparent Constant-Phase-Element Behavior of an Ideally Polarized Blocking Electrode

Two numerical methods were used to calculate the influence of geometry-induced current and potential distributions on the impedance response of an ideally polarized disk electrode. A coherent notation is proposed for local and global impedance which accounts for global, local, local interfacial, and both global and local ohmic impedances. The local and ohmic impedances are shown to provide insight into the frequency dispersion associated with the geometry of disk electrodes. The high-frequency global impedance response has the appearance of a constant-phase element CPE but can be considered to be only an apparent CPE because the CPE exponent is a function of frequency

The Apparent Constant-Phase-Element Behavior of a Disk Electrode with Faradaic Reactions

Geometry-induced current and potential distributions modify the global impedance response of a disk electrode subject to faradaic reactions. The problem was treated for both linear and Tafel kinetic regimes. The apparent capacity of a disk electrode embedded in an insulating plane was shown to vary considerably with frequency. At frequencies above the characteristic frequency for the faradaic reaction, the global impedance response has a quasi-constant-phase element (CPE) character, but with a CPE coefficient alpha that is a function of both dimensionless frequency K and dimensionless current density J. For small values of J, alpha approached unity, whereas, for larger values of J, alpha reached values near 0.78. The calculated values of alpha are typical of those obtained in impedance measurements on disk electrodes. For determining the interfacial capacitance, the influence of current and potential distributions on the impedance response cannot be neglected, even if the apparent CPE exponent alpha has values close to unity. Several methods taken from the literature were tested to determine their suitability for extracting interfacial capacitance values from impedance data on disk electrodes. The best results were obtained using a formula which accounted for both ohmic and charge-transfer resistances

Local electrochemical impedance spectroscopy: A review and some recent developments

Local electrochemical impedance spectroscopy (LEIS), which provides a powerful tool for exploration of electrode heterogeneity, has its roots in the development of electrochemical techniques employing scanning of microelectrodes. The historical development of local impedance spectroscopy measurements is reviewed, and guidelines are presented for implementation of LEIS. The factors which control the limiting spatial resolution of the technique are identified. The mathematical foundation for the technique is reviewed, including definitions of interfacial and local Ohmic impedances on both local and global scales. Experimental results for the reduction of ferricyanide show the correspondence between local and global impedances. Simulations for a single Faradaic reaction on a disk electrode embedded in an insulator are used to show that the Ohmic contribution, traditionally considered to be a real value, can have complex character in certain frequency ranges

Relativistic Correction to J/\psi Production at Hadron Colliders

Relativistic corrections to the color-singlet J/\psi hadroproduction at the Tevatron and LHC are calculated up to O(v^2) in nonrelativistic QCD (NRQCD). The short distance coefficients are obtained by matching full QCD with NRQCD results for the subprocess g+g\to J/\psi+g. The long distance matrix elements are extracted from observed J/\psi hadronic and leptonic decay widths up to O}(v^2). Using the CTEQ6 parton distribution functions, we calculate the LO production cross sections and relativistic corrections for the process p+\bar{p}(p)\to J/\psi+X at the Tevatron and LHC. We find that the enhancement of O(v^2) relativistic corrections to the cross sections over a wide range of large transverse momentum p_t is negligible, only at a level of about 1 %. This tiny effect is due to the smallness of the correction to short distance coefficients and the suppression from long distance matrix elements. These results indicate that relativistic corrections can not help to resolve the large discrepancy between leading order prediction and experimental data for J/\psi production at the Tevatron.Comment: 9 pages, 5 figure

Sensor Selection and Integration to Improve Video Segmentation in Complex Environments

Background subtraction is often considered to be a required stage of any video surveillance system being used to detect objects in a single frame and/or track objects across multiple frames in a video sequence. Most current state-of-the-art techniques for object detection and tracking utilize some form of background subtraction that involves developing a model of the background at a pixel, region, or frame level and designating any elements that deviate from the background model as foreground. However, most existing approaches are capable of segmenting a number of distinct components but unable to distinguish between the desired object of interest and complex, dynamic background such as moving water and high reflections. In this paper, we propose a technique to integrate spatiotemporal signatures of an object of interest from different sensing modalities into a video segmentation method in order to improve object detection and tracking in dynamic, complex scenes. Our proposed algorithm utilizes the dynamic interaction information between the object of interest and background to differentiate between mistakenly segmented components and the desired component. Experimental results on two complex data sets demonstrate that our proposed technique significantly improves the accuracy and utility of state-of-the-art video segmentation technique. © 2014 Adam R. Reckley et al

Predicting genome-wide redundancy using machine learning

<p>Abstract</p> <p>Background</p> <p>Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as <it>Arabidopsis thaliana</it>, the test case used here.</p> <p>Results</p> <p>Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in <it>Arabidopsis </it>showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1), suggesting that redundancy is stable over long evolutionary periods.</p> <p>Conclusions</p> <p>Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for <it>Arabidopsis </it>provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.</p