Fabrication and transport critical currents of multifilamentary MgB2/Fe wires and tapes

Multifilamentary MgB2/Fe wires and tapes with high transport critical current densities have been fabricated using a straightforward powder-in-tube (PIT) process. After annealing, we measured transport jc values up to 1.1 * 105 A/cm2 at 4.2 K and in a field of 2 T in a MgB2/Fe square wire with 7 filaments fabricated by two-axial rolling, and up to 5 * 104 A/cm2 at 4.2 K in 1 T in a MgB2/Fe tape with 7 filaments. For higher currents these multifilamentary wires and tapes quenched due to insufficient thermal stability of filaments. Both the processing routes and deformation methods were found to be important factors for fabricating multifilamentary MgB2 wires and tapes with high transport jc values.Comment: 13 pages, 7 figure

Transport Properties and Exponential n-values of Fe/MgB2 Tapes With Various MgB2 Particle Sizes

Fe/MgB2 tapes have been prepared starting with pre-reacted binary MgB2 powders. As shown by resistive and inductive measurements, the reduction of particle size to a few microns by ball milling has little influence on Bc2, while the superconducting properties of the individual MgB2 grains are essentially unchanged. Reducing the particle size causes an enhancement of Birr from 14 to 16 T, while Jc has considerably increased at high fields, its slope Jc(B) being reduced. At 4.2K, values of 5.3*10^4 and 1.2*10^3 A/cm^2 were measured at 3.5 and 10 T, respectively, suggesting a dominant role of the conditions at the grain interfaces. A systematic variation of these conditions at the interfaces is undertaken in order to determine the limit of transport properties for Fe/MgB2 tapes. The addition of 5% Mg to MgB2 powder was found to affect neither Jc nor Bc2. For the tapes with the highest Jc values, very high exponential n factors were measured: n = 148, 89 and 17 at 3.5, 5 and 10T, respectively and measurements of critical current versus applied strain have been performed. The mechanism leading to high transport critical current densities of filamentary Fe/MgB2 tapes based on MgB2 particles is discussed.Comment: Presented at ICMC 2003, 25-28 May 200

The U(1)A anomaly in noncommutative SU(N) theories

We work out the one-loop $U(1)_A$ anomaly for noncommutative SU(N) gauge theories up to second order in the noncommutative parameter $\theta^{\mu\nu}$. We set $\theta^{0i}=0$ and conclude that there is no breaking of the classical $U(1)_A$ symmetry of the theory coming from the contributions that are either linear or quadratic in $\theta^{\mu\nu}$. Of course, the ordinary anomalous contributions will be still with us. We also show that the one-loop conservation of the nonsinglet currents holds at least up to second order in $\theta^{\mu\nu}$. We adapt our results to noncommutative gauge theories with SO(N) and U(1) gauge groups.Comment: 50 pages, 5 figures in eps files. Some comments and references adde

Vegetation Mapping of a Coastal Dune Complex Using Multispectral Imagery Acquired from an Unmanned Aerial System

Vegetation mapping, identifying the distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environment changes on vegetation, and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper represents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an Unmanned Aerial System (UAS) with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. UAS, also known as Unmanned Aerial Vehicles (UAV’s) or drones, have enabled high-resolution and high-accuracy ground-based data to be gathered quickly and easily on-site. The Sequoia multispectral camera used in this study has green, red, red-edge and near infrared wavebands, and a normal RGB camera, to capture both visible and NIR images of the land surface. The workflow of 3D vegetation mapping of the study site included establishing ground control points, planning the flight mission and camera parameters, acquiring the imagery, processing the image data and performing features classification. The data processing outcomes include an orthomosiac model, a 3D surface model and multispectral images of the study site, in the Irish Transverse Mercator coordinate system, with a planimetric resolution of 0.024m and a georeferenced Root-Mean-Square (RMS) error of 0.111m. There were 235 sample area (1m×1m) used for the accuracy assessment of the classification of the vegetation mapping. Feature classification was conducted using three different classification strategies to examine the efficiency of multispectral sensor data for vegetation mapping. Vegetation type classification accuracies ranged from 60% to 70%. This research illustrates the efficiency of data collection at Buckroney dune complex and the high-accuracy and high-resolution of the vegetation mapping of the site using a multispectral sensor mounted on UAS

Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose {\sf Dipole}, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation

Controlled formation and disappearance of creases

Soft, elastic materials are capable of large and reversible deformation, readily leading to various modes of instability that are often undesirable, but sometimes useful. For example, when a soft elastic material is compressed, its initially flat surface will suddenly form creases. While creases are commonly observed, and have been exploited to control chemical patterning, enzymatic activity, and adhesion of surfaces, the conditions for the formation and disappearance of creases have so far been poorly controlled. Here we show that a soft elastic bilayer can snap between the flat and creased states repeatedly, with hysteresis. The strains at which the creases form and disappear are highly reproducible, and are tunable over a large range, through variations in the level of pre-compression applied to the substrate and the relative thickness of the film. The introduction of bistable flat and creased states and hysteretic switching is an important step to enable applications of this type of instability.Engineering and Applied Science

The role of substrate pre-stretch in post-wrinkling bifurcations

When a stiff film on a soft substrate is compressed, the surface of the film forms wrinkles, with tunable wavelengths and amplitudes that enable a variety of applications. As the compressive strain increases, the film undergoes post-wrinkling bifurcations, leading to period doubling and eventually to formation of localized folds or ridges. Here we study the post-wrinkling bifurcations in films on pre-stretched substrates. Through a combination of experiments and simulations, we demonstrate that pre-stretched substrates not only show substantial shifts in the critical strain for the onset of post-wrinkling bifurcations, but also exhibit qualitatively different post-wrinkled states. In particular, we report on the stabilization of wrinkles in films on pre-tensioned substrates and the emergence of ‘chaotic’ morphologies in films on pre-compressed substrates.Engineering and Applied Science

On a minimal model for estimating climate sensitivity

In a recent issue of this journal, Loehle (2014) presents a "minimal model" for estimating climate sensitivity, identical to that previously published by Loehle and Scafetta (2011). The novelty in the more recent paper lies in the straightforward calculation of an estimate of transient climate response based on the model and an estimate of equilibrium climate sensitivity derived therefrom, via a flawed methodology. We demonstrate that the Loehle and Scafetta model systematically underestimates the transient climate response, due to a number of unsupportable assumptions regarding the climate system. Once the flaws in Loehle and Scafetta's model are addressed, the estimates of transient climate response and equilibrium climate sensitivity derived from the model are entirely consistent with those obtained from general circulation models, and indeed exclude the possibility of low climate sensitivity, directly contradicting the principal conclusion drawn by Loehle. Further, we present an even more parsimonious model for estimating climate sensitivity. Our model is based on observed changes in radiative forcings, and is therefore constrained by physics, unlike the Loehle model, which is little more than a curve-fitting exercise