4,543 research outputs found
Stand Up for Inclusive Public Services: An illustrated guide and case studies
An illustrated guide and case studies on building gender responsive and inclusive public services in the thematic area of water, transport, health, education and Gender based violence against women and girls services
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Avoiding structural collapses in refurbishment - a decision support system (HSE research report)
Communication framework to support more effective onsite construction monitoring
The UK construction industry has recently witnessed an increasing demand for cost-reduction strategies due to the strict government regulations on BIM implementation. This adoption will certainly lead to a continuous work improvement, better project delivery and communication. Although the UK government has set a target of 15–20% saving on the costs of capital projects by the full implementation of BIM level 2 in 2016, this figure is unlikely to be met since the majority of construction companies are still spending approximately £20 billion per year on rebuilding and repairing the construction defects caused by miscommunication. This research addresses the problem of communication using traditional methods (i.e. communication through paper-based documents and drawings) and its impact during the construction phase in relation to clash detection. Next, we will present a communication framework using advanced visualisation technique such as augmen ted reality (AR) combined with a BIM model with an easy access to the IFC f ile on site for a compliance checking between the BIM model and the actual co nstruction site. Subsequently, site inspection can be performed more efficiently, and with more reliability. Furthermore, early warning on future occu rring clashes can be given. To reach our objectives, the research has been designed using real case scenario, following two phases of implementation. The first phase include the communication study and consists of determining users requiring a ssistance with regard to site monitoring and inspection, whereas the second, built on the results of the first phase to specify and implement the mobile AR syste
On the Detectability of Oxygen X-ray Fluorescence and its Use as a Solar Photospheric Abundance Diagnostic
Monte Carlo calculations of the O Kalpha line fluoresced by coronal X-rays
and emitted just above the temperature minimum region of the solar atmosphere
have been employed to investigate the use of this feature as an abundance
diagnostic. While quite weak, we estimate line equivalent widths in the range
0.02-0.2 AA, depending on the X-ray plasma temperature. The line remains
essentially uncontaminated by blends for coronal temperatures T =< 3e6 K and
should be quite observable, with a flux >~ 2 ph/s/arcmin^2. Model calculations
for solar chemical mixtures with an O abundance adjusted up and down by a
factor of 2 indicate 35-60% changes in O Kalpha line equivalent width,
providing a potentially useful O abundance diagnostic. Sensitivity of
equivalent width to differences between recently recommended chemical
compositions with ``high'' and ``low'' complements of the CNO trio important
for interpreting helioseismological observations is less accute, amounting to
20-26% at coronal temperatures T ~< 2e6 K. While still feasible for
discriminating between these two mixtures, uncertainties in measured line
equivalent widths and in the models used for interpretation would need to be
significantly less than 20%. Provided a sensitive X-ray spectrometer with
resolving power >= 1000 and suitably well-behaved instrumental profile can be
built, X-ray fluorescence presents a viable means for resolving the solar
``oxygen crisis''.Comment: To appear in the Astrophysical Journa
Preprocessing Solar Images while Preserving their Latent Structure
Telescopes such as the Atmospheric Imaging Assembly aboard the Solar Dynamics
Observatory, a NASA satellite, collect massive streams of high resolution
images of the Sun through multiple wavelength filters. Reconstructing
pixel-by-pixel thermal properties based on these images can be framed as an
ill-posed inverse problem with Poisson noise, but this reconstruction is
computationally expensive and there is disagreement among researchers about
what regularization or prior assumptions are most appropriate. This article
presents an image segmentation framework for preprocessing such images in order
to reduce the data volume while preserving as much thermal information as
possible for later downstream analyses. The resulting segmented images reflect
thermal properties but do not depend on solving the ill-posed inverse problem.
This allows users to avoid the Poisson inverse problem altogether or to tackle
it on each of 10 segments rather than on each of 10 pixels,
reducing computing time by a factor of 10. We employ a parametric
class of dissimilarities that can be expressed as cosine dissimilarity
functions or Hellinger distances between nonlinearly transformed vectors of
multi-passband observations in each pixel. We develop a decision theoretic
framework for choosing the dissimilarity that minimizes the expected loss that
arises when estimating identifiable thermal properties based on segmented
images rather than on a pixel-by-pixel basis. We also examine the efficacy of
different dissimilarities for recovering clusters in the underlying thermal
properties. The expected losses are computed under scientifically motivated
prior distributions. Two simulation studies guide our choices of dissimilarity
function. We illustrate our method by segmenting images of a coronal hole
observed on 26 February 2015
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