The Associated Metric for a Particle in a Quantum Energy Level

We show that the probabilistic distribution over the space in the spectator world, can be associated via noncommutative geometry (with some modifications) to a metric in which the particle lives. According to this geometrical view, the metric in the particle world is ``contracted'' or ``stretched'' in an inverse proportion to the probability distribution.Comment: 14 pages, latex, epsf, 3 figures. Some clarifications were adde

Formation And Characterization of Amorphous Erbium-Based Alloys Prepared By Near-Isothermal Cold-Rolling of Elemental Composites

The article originally appeared in Journal of Applied Physics 58, 3885 (1985) and may also be found at The article originally appeared in Journal of Applied Physics 58, 3865 (1985) and may also be found at ttp://jap.aip.org/resource/1/japiau/v58/i10/p3865_s1Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83409/1/Atzmon_Johnson_JAP1985.pd

Deformation-induced nanocrystallization - a comparison of two amorphous, Al-based, alloys

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83394/1/Jiang_JMR_2005.pd

Isolation of Microstructure in Proton-Irradiated Steels. Topical Report

OAK B188 Isolation of Microstructure in Proton-Irradiated Steels. Topical Report. Component degradation by irradiation is a primary concern in both current reactor systems as well as advanced designs and concepts where the demand for higher efficiency and performance will be considerably greater. In advanced reactor systems, core components will be expected to operate under increasingly hostile (temperature, pressure, radiation flux, dose, etc.) conditions, The current strategy for assessing radiation effects for the development of new materials is impractical in that the costs and time required to conduct reactor irradiations are becoming increasingly prohibitive, and the facilities for conducting these irradiations are becoming increasingly scarce. The next generation reactor designs will require more extreme conditions (temperature, flux, fluence), yet the capability for conducting irradiations for materials development and assessment in the next 20 years is significantly weaker than over the past 20 years. Short of building new test reactors, what is needed now are advanced tools and capabilities for studying radiation damage in materials that can keep pace with design development requirements. The most successful of these irradiation tools has been high energy (several MeV) proton irradiation. Proton irradiation to several tens of dpa can be conducted in short amounts of time (weeks), with relatively inexpensive accelerators, and result in negligible residual radioactivity. All of these factors combine to provide a radiation damage assessment tool that reduces the time and cost to develop and assess reactor materials by factors of 10-100. What remains to be accomplished, is the application of this tool to specific materials problems and the extension of the technique to a wider range of problems in preparation for advanced reactor materials development and assessment. In this project, we plan to approach the mechanism of irradiation assisted stress corrosion cracking (IASCC) by isolating the irradiated microstructure. This report focuses on the microstructure of proton irradiated stainless steel and model alloys for reactor pressure vessel (RPV) steels

Formation And Growth of Amorphous Phases By Solid-State Reaction In Elemental Composites Prepared By Cold-Working

The article originally appeared in Journal of Applied Physics 99, 083504 (2006) and may also be found at http://apl.aip.org/resource/1/applab/v45/i10/p1052_s1Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83390/1/AtzmonVerhoevenJohnson_APL1984.pd

Dirac Operators and the Calculation of the Connes Metric on arbitrary (Infinite) Graphs

As an outgrowth of our investigation of non-regular spaces within the context of quantum gravity and non-commutative geometry, we develop a graph Hilbert space framework on arbitrary (infinite) graphs and use it to study spectral properties of graph-Laplacians and graph-Dirac-operators. We define a spectral triplet sharing most of the properties of what Connes calls a spectral triple. With the help of this scheme we derive an explicit expression for the Connes-distance function on general directed or undirected graphs. We derive a series of apriori estimates and calculate it for a variety of examples of graphs. As a possibly interesting aside, we show that the natural setting of approaching such problems may be the framework of (non-)linear programming or optimization. We compare our results (arrived at within our particular framework) with the results of other authors and show that the seeming differences depend on the use of different graph-geometries and/or Dirac operators.Comment: 27 pages, Latex, comlementary to an earlier paper, general treatment of directed and undirected graphs, in section 4 a series of general results and estimates concerning the Connes Distance on graphs together with examples and numerical estimate

Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10−3) and candidate genes from knockout mice (P = 5.2 × 10−3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000–185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts

Gene-Gene Interactions Lead to Higher Risk for Development of Type 2 Diabetes in an Ashkenazi Jewish Population

Evidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to evaluate interactions among SNPs within genes frequently identified as associated with T2D.Logistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P<0.0001, P<0.0002, respectively]. Interaction between these SNPs were also strong using parametric or nonparametric methods: the unadjusted odds of being affected with T2D was 3 times greater in subjects with the HNF4A and WFS1 risk alleles than those without either (95% CI = [1.7-5.3]; P<or=0.0001). Although the univariate association between the TCF7L2 SNP and T2D was relatively modest [P = 0.02], when paired with the HNF4A SNP, the OR for subjects with risk alleles in both SNPs was 2.4 [95% CI = 1.7-3.4; P<or=0.0001]. The KCNJ11 variant reached significance only when paired with either the HNF4A or WFSI SNPs: unadjusted ORs were 2.0 [95% CI = 1.4-2.8; P<or=0.0001] and 2.3 [95% CI = 1.2-4.4; P<or=0.0001], respectively. MDR and GMDR results were consistent with the parametric findings.These results provide evidence of strong independent associations between T2D and SNPs in HNF4A and WFS1 and their interaction in our Ashkenazi sample. We also observed an interaction in the nonparametric analysis between the HNF4A and KCNJ11 SNPs (P<or=0.001), demonstrating that an independently non-significant variant may interact with another variant resulting in an increased disease risk