Calculation of the effect of random superfluid density on the temperature dependence of the penetration depth

Microscopic variations in composition or structure can lead to nanoscale inhomogeneity in superconducting properties such as the magnetic penetration depth, but measurements of these properties are usually made on longer length scales. We solve a generalized London equation with a non-uniform penetration depth, lambda(r), obtaining an approximate solution for the disorder-averaged Meissner effect. We find that the effective penetration depth is different from the average penetration depth and is sensitive to the details of the disorder. These results indicate the need for caution when interpreting measurements of the penetration depth and its temperature dependence in systems which may be inhomogeneous

Bayesian spatial extreme value analysis of maximum temperatures in County Dublin, Ireland

In this study, we begin a comprehensive characterisation of temperature extremes in Ireland for the period 1981-2010. We produce return levels of anomalies of daily maximum temperature extremes for an area over Ireland, for the 30-year period 1981-2010. We employ extreme value theory (EVT) to model the data using the generalised Pareto distribution (GPD) as part of a three-level Bayesian hierarchical model. We use predictive processes in order to solve the computationally difficult problem of modelling data over a very dense spatial field. To our knowledge, this is the first study to combine predictive processes and EVT in this manner. The model is fit using Markov chain Monte Carlo (MCMC) algorithms. Posterior parameter estimates and return level surfaces are produced, in addition to specific site analysis at synoptic stations, including Casement Aerodrome and Dublin Airport. Observational data from the period 2011-2018 is included in this site analysis to determine if there is evidence of a change in the observed extremes. An increase in the frequency of extreme anomalies, but not the severity, is observed for this period. We found that the frequency of observed extreme anomalies from 2011-2018 at the Casement Aerodrome and Phoenix Park synoptic stations exceed the upper bounds of the credible intervals from the model by 20% and 7% respectively

Water sources and mixing in riparian wetlands revealed by tracers and geospatial analysis

Acknowledgments We thank the European Research Council (ERC) (project GA 335910 VEWA) and Natural Environment Research Council (NERC) (project NE/K000268/1) for funding and the Airborne Research and Survey Facility for conducting the aerial survey. The data used are available from the authors. In addition, we would like to thank the additional support from Audrey Innes for the sample analysis and Maria Blumstock and Mike Kennedy for assisting with field work.Peer reviewedPublisher PD

Outlining multi-purpose forest inventories to assess the ecosystem approach in forestry

A summary and discussion of selected published results on the current and potential role of forest inventories (with particular reference to the national ones) are presented in the light of the challenges posed by society and policy decisions in the environmental sector. The analysis concentrates mainly on the ecological and socio-economic aspects of the question and on forest inventories’ potential contribution to achieving sustainable forest management.L'articolo è diponibile sul sito dell'editore wwww.tandf.co.uk/journals

Generalized Whittle-MatEˊ\acute{\text{E}}rn random field as a model of correlated fluctuations

This paper considers a generalization of Gaussian random field with covariance function of Whittle-Mat$\acute{\text{e}}$rn family. Such a random field can be obtained as the solution to the fractional stochastic differential equation with two fractional orders. Asymptotic properties of the covariance functions belonging to this generalized Whittle-Mat$\acute{\text{e}}$rn family are studied, which are used to deduce the sample path properties of the random field. The Whittle-Mat$\acute{\text{e}}$rn field has been widely used in modeling geostatistical data such as sea beam data, wind speed, field temperature and soil data. In this article we show that generalized Whittle-Mat$\acute{\text{e}}$rn field provides a more flexible model for wind speed data.Comment: 22 pages, 10 figures, accepted by Journal of Physics

Incorporating Model Uncertainty into Spatial Predictions

We consider a modeling approach for spatially distributed data. We are concerned with aspects of statistical inference for Gaussian random fields when the ultimate objective is to predict the value of the random field at unobserved locations. However the exact statistical model is seldom known before hand and is usually estimated from the very same data relative to which the predictions are made. Our objective is to assess the effect of the fact that the model is estimated, rather than known, on the prediction and the associated prediction uncertainty. We describe a method for achieving this objective. We, in essence, consider the best linear unbiased prediction procedure based on the model within a Bayesian framework. These ideas are implemented for the spring temperature over the region in the north- ern United States based on the stations in the United States historical climatological network reported in Karl, Williams, Quinlan & Boden (1990)

Assessing the impact of a health intervention via user-generated Internet content

Assessing the effect of a health-oriented intervention by traditional epidemiological methods is commonly based only on population segments that use healthcare services. Here we introduce a complementary framework for evaluating the impact of a targeted intervention, such as a vaccination campaign against an infectious disease, through a statistical analysis of user-generated content submitted on web platforms. Using supervised learning, we derive a nonlinear regression model for estimating the prevalence of a health event in a population from Internet data. This model is applied to identify control location groups that correlate historically with the areas, where a specific intervention campaign has taken place. We then determine the impact of the intervention by inferring a projection of the disease rates that could have emerged in the absence of a campaign. Our case study focuses on the influenza vaccination program that was launched in England during the 2013/14 season, and our observations consist of millions of geo-located search queries to the Bing search engine and posts on Twitter. The impact estimates derived from the application of the proposed statistical framework support conventional assessments of the campaign

Hierarchical Bayesian level set inversion

The level set approach has proven widely successful in the study of inverse problems for inter- faces, since its systematic development in the 1990s. Re- cently it has been employed in the context of Bayesian inversion, allowing for the quantification of uncertainty within the reconstruction of interfaces. However the Bayesian approach is very sensitive to the length and amplitude scales in the prior probabilistic model. This paper demonstrates how the scale-sensitivity can be cir- cumvented by means of a hierarchical approach, using a single scalar parameter. Together with careful con- sideration of the development of algorithms which en- code probability measure equivalences as the hierar- chical parameter is varied, this leads to well-defined Gibbs based MCMC methods found by alternating Metropolis-Hastings updates of the level set function and the hierarchical parameter. These methods demon- strably outperform non-hierarchical Bayesian level set methods