66 research outputs found
Spectral hole burning quantum efficiency and electron traps in Sm21-ion-doped aluminosilicate glasses
Persistent spectral hole burning (PSHB) in the 7F0-5D0 transition and the electron excitation in the 7F0-4f5d transition of Sm2+ doped in Al2O3-SiO2 glasses were studied from the measurements of hole burning efficiency and the refilling of the burnt hole. The PSHB at low temperature is attributed to the optically activated rearrangement of OH bonds surrounding Sm2+ ions. On the other hand, the PSHB at high temperature is attributed to one-step electron tunneling in the excitation state. The barrier heights for hole filling corresponding to the two mechanisms were determined to be ?0.27 and ?0.90 eV, respectively. Thermal depth of the trap that captures electrons by two-step ionization via the 4f5d state was determined to be ?0.35 eV below the conduction band. A model was proposed for describing the PSHB and electron excitation of Sm2+ doped in Al2O3-SiO2 glasses. In addition, the dependence of hole burning efficiency on the Al2O3 concentration, temperature, and burning wavelength was also studied.application/pdfjournal articl
Computed tomography perfusion examination can detect the impairment of cerebral circulation and may help predict the outcome of patients with a neurysmal subarachnoid hemorrhage
秋田大学博士(医学)thesi
成長ひずみ法による円筒コイルばねの素線断面形状最適化解析
Optimum cross sections of cylindrically coiled spring are analyzed by the growth-strain method. this method was previously proposed as a shape optimization method which deforms shapes by generating bulk strain. In the present analyses, the helix angle is neglected. The stress distributions are analyzed by the finite element method based on the stress function. The growth deformation is analyzed by the finite element method on the assumption of a roll of coil as a rotationally symmetric ring under free restriction. The bulk strain is generated based on the strain energy density in a manner in which volume change in the cross section is absorbed in the circuit direction. In consequence, a convergent curve of mass improvement ratio and corresponding cross sections are obtained. The cross sections are egg-shaped tapering to the outside of the coil.journal articl
Why did Executives of Local Companies Enter Graduate School of Local University?
application/pdfIn 2008, 7 executives of local companies entered the Graduate School of Medicine of Mie University. They were young executives or backbone executives who succeeded to the business and were thinking of doing a new trial in their suc-ceeded business. At the first stage of our challenge, each ex-ecutive brought their own problems in research and develop-ment to University and discussed their own possible technical solution with a professor who supervises them. This individual approach was effective but not enough for their expected hopes as the reward of time and efforts which they spend at Univer-sity. We then tried the other approach to satisfy their purpose of entering a University. Since their wish was to find out the own way in their succeeded business, we started the cross dis-cussion meeting in which all executive students and other graduate students participated. We indicated the activity, which executives bring both of the problems regarding man-agement and R&D of their company and discuss these matters with other executive student and professors in a University, is effective for the foothold which works out a management strategy for the executive. In this reports, we discuss a possible activity which will be effective for regional innovative from a local University.departmental bulletin pape
Power curves for testing interaction under the additive-additive interaction model.
<p><b>A</b>. Assuming no main effect at both loci (<i>OR<sub>G</sub></i> = <i>OR<sub>H</sub></i> = 1.0); <b>B</b>. Assuming main effect at one locus (<i>OR<sub>G</sub></i> = 2.0). G-MI: <i>GenoMI</i>; G-CMI: <i>GenoCMI</i>; H-CMI: <i>GameteCMI</i>; Wu-adj: Adjusted Wu statistics; JE: Joint Effects statistics; Logit_1 df: logistic regression model with 1 df test; Logit_4 df: logistic regression model with 4 df test. Disease prevalence was chosen at 0.02.</p
False positive rates (type 1 error rates) for testing interaction in common disease with main effect at one locus (Schema 2).
a<p>logistic regression model with 1 df test for the correct genetic model.</p>b<p>logistic regression model with 4 df test by coding genotypes as factors.</p><p>The disease prevalence is assumed 0.02. The significance level is set as 0.01.</p
To Control False Positives in Gene-Gene Interaction Analysis: Two Novel Conditional Entropy-Based Approaches
<div><p>Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects.</p></div
Null distribution of the <i>GenoCMI</i> and <i>GameteCMI</i> metrics.
<p><b>A</b>. The empirically null distribution of <i>GenoCMI</i>, compared to its theoretical distribution <i>χ</i><sup>2</sup><sub>(8)</sub>. <b>B</b>. The empirically null distribution of <i>GameteCMI</i>, compared to its theoretical distribution <i>χ</i><sup>2</sup><sub>(2)</sub>.</p
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