Search

In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment. This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence. The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples. [ABSTRACT FROM AUTHOR]

In many parts of the world Echinococcus granulosus is a widespread infection in sheep and dogs with a consequential spill over into the human population. In the past, mathematical models have been derived to define the transmission dynamics of this parasite, principally in the sheep-dog life cycle. These models have characterized the cycles of infection as lacking in density dependent constraints in both the definitive or intermediate hosts. This suggested that there was little, if any, induced host immunity by the parasite in either host in natural infections. However, recent evidence from both Tunisia and Kazakhstan, where young dogs are the most heavily parasitised, suggests the possibility of significant definitive host immunity. This may have an effect on the control effort needed to destabilize the parasite. A preliminary computer simulation model (based on an Excel spreadsheet) to attempt to predict the results of a control programme has been written. This demonstrates that there could be significantly different results if there is indeed protective immunity in the dog than in the absence of immunity. In the former the parasite needs a greater control effort to push the parasite towards extinction than in the latter. The computer simulation is based on a mathematical model of the parasite''s life cycle and is flexible so that different values of parameters can be used in different situations where the transmission of the parasite may be at different levels. Because of the flexibility of the computer simulation it is anticipated that this programme can be applied in most situations, although initial parameters for a particular location or strain of the parasite will have to be first predetermined with base line field surveys and possibly experimental infections. The programme also has an additional flexibility to enable simulations if some parameters cannot be accurately estimated through Monte-Carlo techniques. In the latter situation, worst and best case scenarios can be estimated and likely frequency distributions of the unknown parameters can be included in the model. [Copyright &y& Elsevier]

In this paper a method to determine the appropriateness of energy resources in rural applications is discussed. Feasible energy resources are comparatively evaluated using eight attributes representing the criteria of appropriateness. 'Appropriateness' is defined as a linear combination of attribute weights multiplied by attribute attainment levels which have been mapped into utility for decision-makers. The uncertainty in data is handled using Monte-Carlo techniques. Sample results indicate a set of dominant energy resources for a particular task. This method can be applied in real-life decision making concerning energy resources for rural applications. [ABSTRACT FROM AUTHOR]