Mapping cumulative noise from shipping to inform marine spatial planning

Including ocean noise in marine spatial planning requires predictions of noise levels on large spatiotemporal scales. Based on a simple sound transmission model and ship track data (Automatic Identification System, AIS), cumulative underwater acoustic energy from shipping was mapped throughout 2008 in the west Canadian Exclusive Economic Zone, showing high noise levels in critical habitats for endangered resident killer whales, exceeding limits of “good conservation status” under the EU Marine Strategy Framework Directive. Error analysis proved that rough calculations of noise occurrence and propagation can form a basis for management processes, because spending resources on unnecessary detail is wasteful and delays remedial action

Extreme magnesium isotope fractionation at outcrop scale records the mechanism and rate at which reaction fronts advance

Isotopic fractionation of cationic species during diffusive transport provides novel means of constraining the style and timing of metamorphic transformations. Here we document a major (~1‰) decrease in the Mg isotopic composition of the reaction front of an exhumed contact between rocks of subducted crust and serpentinite, in the Syros mélange zone. This isotopic perturbation extends over a notable length-scale (~1 m), implicating diffusion of Mg through an intergranular fluid network over a period of ~100 kyr. These novel observations confirm models of diffusion-controlled growth of reaction zones formed between rocks of contrasting compositions, such as found at the slab-mantle interface in subduction zones. The results also demonstrate that diffusive processes can result in exotic stable isotope compositions of major elements with implications for mantle xenoliths and complex intrusions

Current Issues in Environmental Law

Materials from the Current Issues in Environmental Law seminar held by UK/CLE in April 1995

Modelling and Analysing Genetic Networks: From Boolean Networks to Petri Nets

Abstract. In order to understand complex genetic regulatory networks researchers require automated formal modelling techniques that provide appropriate analysis tools. In this paper we propose a new qualitative model for genetic regulatory networks based on Petri nets and detail a process for automatically constructing these models using logic mini-mization. We take as our starting point the Boolean network approach in which regulatory entities are viewed abstractly as binary switches. The idea is to extract terms representing a Boolean network using logic minimization and to then directly translate these terms into appropri-ate Petri net control structures. The resulting compact Petri net model addresses a number of shortcomings associated with Boolean networks and is particularly suited to analysis using the wide range of Petri net tools. We demonstrate our approach by presenting a detailed case study in which the genetic regulatory network underlying the nutritional stress response in Escherichia coli is modelled and analysed.

Machine and Process System Diagnostics Using One-Step Prediction Maps

This paper describes a method for machine or process system diagnostics that uses one-step prediction maps. The method uses nonlinear time series analysis techniques to form a one-step prediction map that estimates the next time series data point when given a sequence of previously measured time series data point. The difference between the predicted and measured time series values is a measure of the map error. The average value of this error should remain within some bound as long as both the dynamic system and its operating condition remain unchanged. However, changes in the dynamic system or operating condition will cause an increase in average map error. Thus, for a constant operating condition, monitoring the average map error over time should indicate when a change has occurred in the dynamic system. Furthermore, the map error itself forms a time series that can be analyzed to detect changes in system dynamics. The paper provides technical background in the nonlinear analysis techniques used in the diagnostic method, describes the creation of one-step prediction maps and their application to machine or process system diagnostics, and then presents results obtained from applying the diagnostic method to simulated and measured data

Order-Based Dependent Dirichlet Processes

In this article we propose a new framework for Bayesian nonparametric modeling with continuous covariates. In particular, we allow the nonparametric distribution to depend on covariates through ordering the random variables building the weights in the stick-breaking representation. We focus mostly on the class of random distributions that induces a Dirichlet process at each covariate value. We derive the correlation between distributions at different covariate values and use a point process to implement a practically useful type of ordering. Two main constructions with analytically known correlation structures are proposed. Practical and efficient computational methods are introduced. We apply our framework, through mixtures of these processes, to regression modeling, the modeling of stochastic volatility in time series data, and spatial geostatistical modeling