26 research outputs found
Radio emission from Supernova Remnants
The explosion of a supernova releases almost instantaneously about 10^51 ergs
of mechanic energy, changing irreversibly the physical and chemical properties
of large regions in the galaxies. The stellar ejecta, the nebula resulting from
the powerful shock waves, and sometimes a compact stellar remnant, constitute a
supernova remnant (SNR). They can radiate their energy across the whole
electromagnetic spectrum, but the great majority are radio sources. Almost 70
years after the first detection of radio emission coming from a SNR, great
progress has been achieved in the comprehension of their physical
characteristics and evolution. We review the present knowledge of different
aspects of radio remnants, focusing on sources of the Milky Way and the
Magellanic Clouds, where the SNRs can be spatially resolved. We present a brief
overview of theoretical background, analyze morphology and polarization
properties, and review and critical discuss different methods applied to
determine the radio spectrum and distances. The consequences of the interaction
between the SNR shocks and the surrounding medium are examined, including the
question of whether SNRs can trigger the formation of new stars. Cases of
multispectral comparison are presented. A section is devoted to reviewing
recent results of radio SNRs in the Magellanic Clouds, with particular emphasis
on the radio properties of SN 1987A, an ideal laboratory to investigate
dynamical evolution of an SNR in near real time. The review concludes with a
summary of issues on radio SNRs that deserve further study, and analyzing the
prospects for future research with the latest generation radio telescopes.Comment: Revised version. 48 pages, 15 figure
The Timing of the Cognitive Cycle
We propose that human cognition consists of cascading cycles of recurring brain
events. Each cognitive cycle senses the current situation, interprets it with
reference to ongoing goals, and then selects an internal or external action in
response. While most aspects of the cognitive cycle are unconscious, each cycle
also yields a momentary “ignition” of conscious broadcasting.
Neuroscientists have independently proposed ideas similar to the cognitive
cycle, the fundamental hypothesis of the LIDA model of cognition. High-level
cognition, such as deliberation, planning, etc., is typically enabled by
multiple cognitive cycles. In this paper we describe a timing model LIDA's
cognitive cycle. Based on empirical and simulation data we propose that an
initial phase of perception (stimulus recognition) occurs 80–100 ms from
stimulus onset under optimal conditions. It is followed by a conscious episode
(broadcast) 200–280 ms after stimulus onset, and an action selection phase
60–110 ms from the start of the conscious phase. One cognitive cycle would
therefore take 260–390 ms. The LIDA timing model is consistent with brain
evidence indicating a fundamental role for a theta-gamma wave, spreading forward
from sensory cortices to rostral corticothalamic regions. This posteriofrontal
theta-gamma wave may be experienced as a conscious perceptual event starting at
200–280 ms post stimulus. The action selection component of the cycle is
proposed to involve frontal, striatal and cerebellar regions. Thus the cycle is
inherently recurrent, as the anatomy of the thalamocortical system suggests. The
LIDA model fits a large body of cognitive and neuroscientific evidence. Finally,
we describe two LIDA-based software agents: the LIDA Reaction Time agent that
simulates human performance in a simple reaction time task, and the LIDA Allport
agent which models phenomenal simultaneity within timeframes comparable to human
subjects. While there are many models of reaction time performance, these
results fall naturally out of a biologically and computationally plausible
cognitive architecture
Predicting patch occupancy in fragmented landscapes at the rangewide scale for an endangered species: An example of an American warbler
Aim Our objective was to identify the distribution of the endangered golden-cheeked warbler (Setophaga chrysoparia) in fragmented oak-juniper woodlands by applying a geoadditive semiparametric occupancy model to better assist decision-makers in identifying suitable habitat across the species breeding range on which conservation or mitigation activities can be focused and thus prioritize management and conservation planning. Location Texas, USA. Methods We used repeated double-observer detection/non-detection surveys of randomly selected (n=287) patches of potential habitat to evaluate warbler patch-scale presence across the species breeding range. We used a geoadditive semiparametric occupancy model with remotely sensed habitat metrics (patch size and landscape composition) to predict patch-scale occupancy of golden-cheeked warblers in the fragmented oak-juniper woodlands of central Texas, USA. Results Our spatially explicit model indicated that golden-cheeked warbler patch occupancy declined from south to north within the breeding range concomitant with reductions in the availability of large habitat patches. We found that 59% of woodland patches, primarily in the northern and central portions of the warbler's range, were predicted to have occupancy probabilities ≤0.10 with only 3% of patches predicted to have occupancy probabilities >0.90. Our model exhibited high prediction accuracy (area under curve=0.91) when validated using independently collected warbler occurrence data. Main conclusions We have identified a distinct spatial occurrence gradient for golden-cheeked warblers as well as a relationship between two measurable landscape characteristics. Because habitat-occupancy relationships were key drivers of our model, our results can be used to identify potential areas where conservation actions supporting habitat mitigation can occur and identify areas where conservation of future potential habitat is possible. Additionally, our results can be used to focus resources on maintenance and creation of patches that are more likely to harbour viable local warbler populations. © 2011 Blackwell Publishing Ltd