The radiated energy budget of Chromospheric Plasma in a major solar flare deduced from multi-wavelength observations

This paper presents measurements of the energy radiated by the lower solar atmosphere, at optical, UV, and EUV wavelengths, during an X-class solar flare (SOL2011-02-15T01:56) in response to an injection of energy assumed to be in the form of nonthermal electrons. Hard X-ray observations from RHESSI were used to track the evolution of the parameters of the nonthermal electron distribution to reveal the total power contained in flare accelerated electrons. By integrating over the duration of the impulsive phase, the total energy contained in the nonthermal electrons was found to be >2 × 1031 erg. The response of the lower solar atmosphere was measured in the free–bound EUV continua of H i (Lyman), He i, and He ii, plus the emission lines of He ii at 304 Å and H i (Lyα) at 1216 Å by SDO/EVE, the UV continua at 1600 Å and 1700 Å by SDO/AIA, and the white light continuum at 4504 Å, 5550 Å, and 6684 Å, along with the Ca ii H line at 3968 Å using Hinode/SOT. The summed energy detected by these instruments amounted to ~3 × 1030 erg; about 15% of the total nonthermal energy. The Lyα line was found to dominate the measured radiative losses. Parameters of both the driving electron distribution and the resulting chromospheric response are presented in detail to encourage the numerical modeling of flare heating for this event, to determine the depth of the solar atmosphere at which these line and continuum processes originate, and the mechanism(s) responsible for their generation.

A Significance-Driven Programming Framework for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for energy-con\-strained environments, provided that applications have the mechanisms to produce outputs of the highest possible quality within the given energy budget.
We introduce a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows users to express the relative importance of computations for the quality of the end result, as well as minimum quality requirements. The significance-aware runtime system uses an application-specific analytical energy model to identify the degree of concurrency and approximation that maximizes quality while meeting user-specified energy constraints. Evaluation on a dual-socket 8-core server shows that the proposed
framework predicts the optimal configuration with high accuracy, enabling energy-constrained executions that result in significantly higher quality compared to loop perforation, a compiler approximation technique.

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints. Evaluation on a dual-socket 16-core Intel platform using 9 benchmark kernels shows that the proposed framework picks the optimal configuration with high accuracy. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.

Foraging ecology, fluctuating food availability and energetics of wintering brent geese

Changing energy requirements and dramatic shifts in food availability are major factors driving behaviour and distribution of herbivores. We investigate this in wintering East Canadian High Arctic light-bellied brent geese Branta bernicla hrota in Northern Ireland. They followed a sequential pattern of habitat use, feeding on intertidal Zostera spp. in autumn and early winter before moving to predominantly saltmarsh and farmland in late winter and early spring. Night-time feeding occurred throughout and made a considerable contribution to the birds' daily energy budget, at times accounting for > 50% of energy intake. Nocturnal feeding, however, is limited to the intertidal, possibly because of predation risk on terrestrial habitat, and increases with moonlight. The amount of Zostera spp., declined dramatically after the arrival of birds, predominantly, but not entirely, due to consumption by the birds. Birds gained fat reserves in the first 2 months but then this was dramatically lost as their major food source collapsed and their daily energy intake declined. Single birds consistently fared worse than paired birds and pairs with juveniles fared better than those without suggesting a benefit of having a family to compete for food. Many birds leave the Lough at this time of reduced Zostera spp. for other sea inlets in Ireland but some remain. Body condition of the latter gradually improved in early spring and reflected a heavy reliance on terrestrial habitats, particularly farmland, to meet the birds' daily energy requirements. However, even in the period immediately before migration to the breeding ground, the birds did not regain the amount of abdominal fatness observed in November. The dramatic changes in available food and requirements of the birds drive the major changes seen in foraging behaviour as the birds evade starvation in the wintering period.

The VLT-FLAMES Tarantula Survey XI. A census of the hot luminous stars and their feedback in 30 Doradus

Context. The VLT-FLAMES Tarantula Survey has an extensive view of the copious number of massive stars in the 30 Doradus (30 Dor) star forming region of the Large Magellanic Cloud. These stars play a crucial role in our understanding of the stellar feedback in more distant, unresolved star forming regions. Aims. The first comprehensive census of hot luminous stars in 30 Dor is compiled within a 10 arcmin (150 pc) radius of its central cluster, R136. We investigate the stellar content and spectroscopic completeness of the early type stars. Estimates were made for both the integrated ionising luminosity and stellar wind luminosity. These values were used to re-assess the star formation rate (SFR) of the region and determine the ionising photon escape fraction. Methods. Stars were selected photometrically and combined with the latest spectral classifications. Spectral types were estimated for stars lacking spectroscopy and corrections were made for binary systems, where possible. Stellar calibrations were applied to obtain their physical parameters and wind properties. Their integrated properties were then compared to global observations from ultraviolet (UV) to far-infrared (FIR) imaging as well as the population synthesis code, Starburst99. Results. Our census identified 1145 candidate hot luminous stars within 150 pc of R136 of which >700 were considered to be genuine early type stars and contribute to feedback. We assess the survey to be spectroscopically complete to 85% in the outer regions (>5 pc) but only 35% complete in the region of the R136 cluster, giving a total of 500 hot luminous stars in the census which had spectroscopy. Only 31 were found to be Wolf-Rayet (W-R) or Of/WN stars, but their contribution to the integrated ionising luminosity and wind luminosity was ~ 40% and ~ 50%, respectively. Similarly, stars with M > 100 M (mostly H-rich WN stars) also showed high contributions to the global feedback, ~ 25% in both cases. Such massive stars are not accounted for by the current Starburst99 code, which was found to underestimate the integrated ionising luminosity of R136 by a factor ~ 2 and the wind luminosity by a factor ~ 9. The census inferred a SFR for 30 Dor of 0.073 ± 0.04 M yr . This was generally higher than that obtained from some popular SFR calibrations but still showed good consistency with the far-UV luminosity tracer as well as the combined Hα and mid-infrared tracer, but only after correcting for Hα extinction. The global ionising output was also found to exceed that measured from the associated gas and dust, suggesting that ~6 % of the ionising photons escape the region. Conclusions. When studying the most luminous star forming regions, it is essential to include their most massive stars if one is to determine a reliable energy budget. Photon leakage becomes more likely after including their large contributions to the ionising output. If 30 Dor is typical of other massive star forming regions, estimates of the SFR will be underpredicted if this escape fraction is not accounted for.

Massive MIMO with Optimal Power and Training Duration Allocation

We consider the uplink of massive multicell multiple-input multiple-output systems, where the base stations (BSs), equipped with massive arrays, serve simultaneously several terminals in the same frequency band. We assume that the BS estimates the channel from uplink training, and then uses the maximum ratio combining technique to detect the signals transmitted from all terminals in its own cell. We propose an optimal resource allocation scheme which jointly selects the training duration, training signal power, and data signal power in order to maximize the sum spectral efficiency, for a given total energy budget spent in a coherence interval. Numerical results verify the benefits of the optimal resource allocation scheme. Furthermore, we show that more training signal power should be used at low signal-to-noise ratio (SNRs), and vice versa at high SNRs. Interestingly, for the entire SNR regime, the optimal training duration is equal to the number of terminals.

Selecting users in energy-efficient collaborative spectrum sensing

Cognitive radio network is defined as an intelligent wireless communication network that should be able to adaptively reconfigure its communication parameters to meet the demands of the transmission network or the user. In this context one possible way to utilize unused licensed spectrum without interfering with incumbent users is through spectrum sensing. Due to channel uncertainties, single cognitive (opportunistic) user cannot make a decision reliably and hence collaboration among multiple users is often required. Here collaboration among large number of users tends to increase power consumption and introduces large communication overheads. In this paper, the number of collaborating users is optimized in order to maximize the probability of detection for any given power budget in a cognitive radio network, while satisfying constraints on the false alarm probability. We show that for the maximum probability of detection, collaboration of only a subset of available opportunistic users is required. The robustness of our proposed spectrum sensing algorithm is also examined under flat Rayleigh fading and AWGN channel conditions.

Behaviour-time budget and functional habitat use of a free-ranging European badger (Meles meles)

Background: The European badger (Melesmeles) is involved in the maintenance of bovine tuberculosis infection and onward spread to cattle. However, little is known about how transmission occurs. One possible route could be through direct contact between infected badgers and cattle. It is also possible that indirect contact between cattle and infected badger excretory products such as faeces or urine may occur either on pasture or within and around farm buildings. A better understanding of behaviour patterns in wild badgers may help to develop biosecurity measures to minimise direct and indirect contact between badgers and cattle. However, monitoring the behaviour of free-ranging badgers can be logistically challenging and labour intensive due to their nocturnal and semi-fossorial nature.We trialled a GPS and tri-axial accelerometer-equipped collar on a free-ranging badger to assess its potential value to elucidate behaviour-time budgets and functional habitat use. Results: During the recording period between 16:00 and 08:00 on a single night, resting was the most commonly identified behaviour (67.4%) followed by walking (20.9%), snuffling (9.5%) and trotting (2.3%).When examining accelerometer data associated with each GPS fix and habitat type (occurring 2 min 30 s before and after), walking was themost common behaviour in woodland (40.3%) and arable habitats (53.8%), while snuffling was themost common behaviour in pasture (61.9%). Several nocturnal resting periods were also observed. The total distance travelled was 2.28 km. Conclusions: In the present report, we demonstrate proof of principle in the application of a combined GPS and accelerometer device to collect detailed quantitative data on wild badger behaviour. Behaviour-time budgets allow us to investigate how badgers allocate energy to different activities and how thismight change with disease status. Such information could be useful in the development of measures to reduce opportunities for onward transmission of bovine tuberculosis from badgers to cattle.