74 research outputs found
Co-sputtered MoRe thin films for carbon nanotube growth-compatible superconducting coplanar resonators
Molybdenum rhenium alloy thin films can exhibit superconductivity up to
critical temperatures of . At the same time, the films are
highly stable in the high-temperature methane / hydrogen atmosphere typically
required to grow single wall carbon nanotubes. We characterize molybdenum
rhenium alloy films deposited via simultaneous sputtering from two sources,
with respect to their composition as function of sputter parameters and their
electronic dc as well as GHz properties at low temperature. Specific emphasis
is placed on the effect of the carbon nanotube growth conditions on the film.
Superconducting coplanar waveguide resonators are defined lithographically; we
demonstrate that the resonators remain functional when undergoing nanotube
growth conditions, and characterize their properties as function of
temperature. This paves the way for ultra-clean nanotube devices grown in situ
onto superconducting coplanar waveguide circuit elements.Comment: 8 pages, 6 figure
Optomechanical coupling and damping of a carbon nanotube quantum dot
Carbon nanotubes are excellent nano-electromechanical systems, combining high
resonance frequency, low mass, and large zero-point motion. At cryogenic
temperatures they display high mechanical quality factors. Equally they are
outstanding single electron devices with well-known quantum levels and have
been proposed for the implementation of charge or spin qubits. The integration
of these devices into microwave optomechanical circuits is however hindered by
a mismatch of scales, between typical microwave wavelengths, nanotube segment
lengths, and nanotube deflections. As experimentally demonstrated recently in
[Blien et al., Nat. Comm. 11, 1363 (2020)], coupling enhancement via the
quantum capacitance allows to circumvent this restriction. Here we extend the
discussion of this experiment. We present the subsystems of the device and
their interactions in detail. An alternative approach to the optomechanical
coupling is presented, allowing to estimate the mechanical zero point motion
scale. Further, the mechanical damping is discussed, hinting at hitherto
unknown interaction mechanisms.Comment: 17 pages, 13 figures, 3 table
Development in Regional Labour Market in Germany: a Comparative Analysis of the Forecasting Performance of Competing Statistical Models
The aim of this paper is to forecast regional employment patterns in West German regions. After a brief exposition of key labour market issues, Artificial Neural Network (ANN) techniques are proposed as a new tool to generate reliable short term employment forecasts at a regional level. A variety of ANN models are developed and compared. Comparison with methods commonly applied to panel data, such as GMM (Generalised Method of Moments), confirms the ability of ANNs to capture complex data structures in a multi-regional context
A Rank-Order Test on the Statistical Performance of Neural Network Models for Regional Labor Market Forecasts
Using a panel of 439 German regions we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for regional employment growth. Because of relevant differences in data availability between the former East and West Germany, the NN models are computed separately for the two parts of the country.
The comparisons of the models and their ex post forecasts are carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance. Furthermore, the Friedman statistic is indifferent to the scale on which the data are measured.
The evaluation of the ex post forecasts suggests that NN models are generally able to correctly identify the fastest-growing and the slowest-growing regions, and hence predict rather well the correct ranking of regions in terms of their employment growth. The comparison among NN models \u2013 on the basis of several criteria \u2013 suggests that the choice of the variables used in the model may influence the model\u2019s performance and the reliability of its forecasts
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