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

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 $\sim$10 segments rather than on each of $\sim$10$^7$ pixels, reducing computing time by a factor of $\sim$10$^6$. 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