An inverse method to obtain porosity, fibre diameter and density of fibrous sound absorbing materials

Characterization of sound absorbing materials is essential to predict its acoustic behaviour. The most commonly used models to do so consider the flow resistivity, porosity, and average fibre diameter as parameters to determine the acoustic impedance and sound absorbing coefficient. Besides direct experimental techniques, numerical approaches appear to be an alternative to estimate the material’s parameters. In this work an inverse numerical method to obtain some parameters of a fibrous material is presented. Using measurements of the normal incidence sound absorption coefficient and then using the model proposed by Voronina, subsequent application of basic minimization techniques allows one to obtain the porosity, average fibre diameter and density of a sound absorbing material. The numerical results agree fairly well with the experimental data.

Procedure for calculating the chloride diffusion coefficient and surface concentration from a profile having a maximum beyond the concrete surface

Chlorides induce local corrosion in the steel reinforcements when reaching the bar surface. The measurement of the rate of ingress of these ions, is made by mathematically fitting the so called “error function equation” into the chloride concentration profile, obtaining so the diffusion coefficient and the chloride concentration at the concrete surface. However, the chloride profiles do not always follow Fick’s law by having the maximum concentration at the concrete surface, but often the profile shows a maximum concentration more in the interior, which indicates a different composition and performance of the most external concrete layer with respect to the internal zones. The paper presents a procedure prepared during the time of the RILEM TC 178-TMC: “Testing and modeling chloride penetration in concrete”, which suggests neglecting the external layer where the chloride concentration increases and using the maximum as an “apparent” surface concentration, called C max and to fit the error function equation into the decreasing concentration profile towards the interior. The prediction of evolution should be made also from the maximum.

An R script to plot the conditioned Gini (or skewness) coefficient in the bivariate case

In the cs.index.zip file we provide an R script which let us plot the conditioned Gini (or skewness) coefficient used in the working paper entitled "On conditional skewness with applications in environmental data" submitted to Environmental and Ecological Statistics. On the other hand, the ReadMe.pdf explains how to use the cs.index.R script.

Making it easy to do the right thing in healthcare: Advancing improvement science education through accredited pan European higher education modules

Background: Numerous international policy drivers espouse the need to improve healthcare. The application of Improvement Science has the potential to restore the balance of healthcare and transform it to a more person-centred and quality improvement focussed system. However there is currently no accredited Improvement Science education offered routinely to healthcare students. This means that there are a huge number of healthcare professionals who do not have the conceptual or experiential skills to apply Improvement Science in everyday practise. Methods: This article describes how seven European Higher Education Institutions (HEIs) worked together to develop four evidence informed accredited inter-professional Improvement Science modules for under and postgraduate healthcare students. It outlines the way in which a Policy Delphi, a narrative literature review, a review of the competency and capability requirements for healthcare professionals to practise Improvement Science, and a mapping of current Improvement Science education informed the content of the modules. Results: A contemporary consensus definition of Healthcare Improvement Science was developed. The four Improvement Science modules that have been designed are outlined. A framework to evaluate the impact modules have in practise has been developed and piloted. Conclusion: The authors argue that there is a clear need to advance healthcare Improvement Science education through incorporating evidence based accredited modules into healthcare professional education. They suggest that if Improvement Science education, that incorporates work based learning, becomes a staple part of the curricula in inter-professional education then it has real promise to improve the delivery, quality and design of healthcare.