Bayesian Inference for Reliability of Systems and Networks Using the Survival Signature.

The concept of survival signature has recently been introduced as an alternative to the signature for reliability quantification of systems. While these two concepts are closely related for systems consisting of a single type of component, the survival signature is also suitable for systems with multiple types of component, which is not the case for the signature. This also enables the use of the survival signature for reliability of networks. In this article, we present the use of the survival signature for reliability quantification of systems and networks from a Bayesian perspective. We assume that data are available on tested components that are exchangeable with those in the actual system or network of interest. These data consist of failure times and possibly right-censoring times. We present both a nonparametric and parametric approach.

Adopting primary plastic trickling filters as a solution for enhanced nitrification.

The wastewater industry is under pressure to optimize performance of sewage treatment works (STW), while simultaneously reducing energy consumption. Using a process configuration selection matrix, this paper explores the practicability of placing a hypothetical cross flow structured plastic media (CFSP) trickling filter (TF) immediately ahead of an existing conventional trickling filter process (CTFP), without intermediate clarification. The viability of this configuration is subsequently demonstrated using an empirical multispecies TF model. This predicts the enhanced nitrification performance of the CTFP by simulating prior removals of biochemical oxygen demand (BOD). The model predictions propose that prior 50-80% BOD removals can allow for further reductions in effluent ammoniacal nitrogen (NH4-N) concentrations of 40-70%, respectively. This illustrates that adopting low energy TF technologies can eliminate the requirement for more energy intensive alternatives, such as submerged aerated filters (SAF). Moreover, this configuration maximizes the potential of existing assets, while simultaneously improving nitrification robustness when compared with tertiary nitrification processes.

Bayesian spatiotemporal model of fMRI data using transfer functions.

This research describes a new Bayesian spatiotemporal model to analyse BOLD fMRI studies. In the temporal dimension, we describe the shape of the hemodynamic response function (HRF) with a transfer function model. The spatial continuity and local homogeneity of the evoked responses are modelled by a Gaussian Markov random field prior on the parameter indicating activations. The proposal constitutes an extension of the spatiotemporal model presented in a previous approach [Quirós, A., Montes Diez, R. and Gamerman, D., 2010. Bayesian spatiotemporal model of fMRI data, Neuroimage, 49: 442-456], offering more flexibility in the estimation of the HRF and computational advantages in the resulting MCMC algorithm. Simulations from the model are performed in order to ascertain the performance of the sampling scheme and the ability of the posterior to estimate model parameters, as well as to check the model sensitivity to signal to noise ratio. Results are shown on synthetic data and on a real data set from a block-design fMRI experiment.