How green in our library?

An environment group has had a presence within Leeds Met Libraries since December 2004. Initially the group chose to look at simple things which could have a positive impact on how we use resources in the workplace. We introduced recycling bins for plastic cups and cans in the staff tearooms, provided dispensers for plastic carrier bags near the self-service machines and arranged training hours for library staff to learn more about environmental issues and to look at their carbon footprint. In May 2006, the group decided to continue their work via an e-mail discussion forum where any issues of environmental concern were raised, with dedicated members of the group continuing to action if appropriate. We found that as time went on, there was an increased interest in environmental matters, so much so that in April 2009 the group reformed, with a new membership and a new title of Green Initiatives Group. The remit of the group is: - To raise awareness of and promote green issues amongst library staff. - To exchange best practice in the university and in the Higher Education library sector. - To build relationships with other ethical organisations working in the Leeds area. One of our aims is to be aware of initiatives taking place in the wider university and to be able to communicate relevant information to library staff about what the university is involved in environmentally. Below is a brief outline of what we have achieved as a group and current projects we are working on

Thick film resistors on dielectrics

This thesis investigates the viability of using thick film materials on a range of thick film dielectrics. They are usually formulated so as to be compatible with 96% alumina substrates. It is sometimes desirable, however, to deposit them on thick film dielectrics. This may produce both technical and commercial advantages. The electrical changes produced and the chemical interactions observed are discussed. It is concluded that viable systems are possible provided the materials are carefully selected. In particular, there is evidence that the presence of barium in a resistor material affects the conduction mechanisms. This means that, in cases where the dielectric contains barium, the effects on the properties of the resistors may be very marked. It is observed, however, that ceramic filled dielectrics with a high content of glasses should form a satisfactory class of dielectric for use as an underlayer for thick film resistors. This conclusion appears to be in agreement with recently published commercial developments. The thesis also discusses materials selection and the preparation of samples and gives detailed results of electrical tests. The figures includes plots of the results of SEM/EDX analyses made on polished cross-sections of samples

Likelihood based inference for diffusion driven models

This paper provides methods for carrying out likelihood based inference for diffusion driven models, for example discretely observed multivariate diffusions, continuous time stochastic volatility models and counting process models. The diffusions can potentially be non-stationary. Although our methods are sampling based, making use of Markov chain Monte Carlo methods to sample the posterior distribution of the relevant unknowns, our general strategies and details are different from previous work along these lines. The methods we develop are simple to implement and simulation efficient. Importantly, unlike previous methods, the performance of our technique is not worsened, in fact it improves, as the degree of latent augmentation is increased to reduce the bias of the Euler approximation. In addition, our method is not subject to a degeneracy that afflicts previous techniques when the degree of latent augmentation is increased. We also discuss issues of model choice, model checking and filtering. The techniques and ideas are applied to both simulated and real data.Bayes estimation, Brownian bridge, Non-linear diffusion, Euler approximation, Markov chain Monte Carlo, Metropolis-Hastings algorithm, Missing data, Simulation, Stochastic differential equation.

Smooth particle filters for likelihood evaluation and maximisation

In this paper,a method is introduced for approximating the likelihood for the unknown parameters of a state space model.The approximation converges to the true likelihood as the simulation size goes to infinity. In addition,the approximating likelihood is continuous as a function of the unknown parameters under rather general conditions.The approach advocated is fast, robust and avoids many of the pitfalls associated with current techniques based upon importance sampling.We assess the performance of the method by considering a linear state space model, comparing the results with the Kalman filter, which delivers the true likelihood. We also apply the method to a non-Gaussian state space model, the Stochastic Volatility model, finding that the approach is efficient and effective. Applications to continuous time finance models are also considered. A result is established which allows the likelihood to be estimated quickly and efficiently using the output from the general auxiliary particle filter