Birth-death processes with mass annihilation and state-dependent immigration

A birth-death process is subject to mass annihilation at rate β with subsequent mass immigration occurring into state j at rateα j . This structure enables the process to jump from one sector of state space to another one (via state 0) with transition rate independent of population size. First, we highlight the difficulties encountered when using standard techniques to construct both time-dependent and equilibrium probabilities. Then we show how to overcome such analytic difficulties by means of a tool developed in Chen and Renshaw (1990, 1993b); this approach is applicable to many processes whose underlying generator on E\{0} has known probability structure. Here we demonstrate the technique through application to the linear birth-death generator on which is superimposed an annihilation/immigration process.

An ontological characterization of time-series and state-sequences for data mining

Time-series and sequences are important patterns in data mining. Based on an ontology of time-elements, this paper presents a formal characterization of time-series and state-sequences, where a state denotes a collection of data whose validation is dependent on time. While a time-series is formalized as a vector of time-elements temporally ordered one after another, a state-sequence is denoted as a list of states correspondingly ordered by a time-series. In general, a time-series and a state-sequence can be incomplete in various ways. This leads to the distinction between complete and incomplete time-series, and between complete and incomplete state-sequences, which allows the expression of both absolute and relative temporal knowledge in data mining.

Forward and inverse transport of particulate matter and gaseous pollutants affecting the region bordering the English Channel

The ATTMA "Aerosol Transport in the Trans-Manche Atmosphere" project investigates the transportation and dispersion of air pollutants across the English Channel, in collaboration with local authorities and other Universities in Southern England and Northern France. The research is concerned with both forward and inverse (receptor based) tracking. Two alternative dispersion simulation methods are used: (a) Lagrangian Particle Dispersion (LPD) models, (b) Eulerian Finite Volume type models. This paper is concerned with part (a), the simulations based on LPD models. Two widely applied LPD models are used and compared. Since in many observed episodes the source of pollution is traced outside the region of interest, long range, trans-continental transport is also investigated.

Application of simulation technologies in the analysis of granular material behaviour during transport and storage

Problems in the preservation of the quality of granular material products are complex and arise from a series of sources during transport and storage. In either designing a new plant or, more likely, analysing problems that give rise to product quality degradation in existing operations, practical measurement and simulation tools and technologies are required to support the process engineer. These technologies are required to help in both identifying the source of such problems and then designing them out. As part of a major research programme on quality in particulate manufacturing computational models have been developed for segregation in silos, degradation in pneumatic conveyors, and the development of caking during storage, which use where possible, micro-mechanical relationships to characterize the behaviour of granular materials. The objective of the work presented here is to demonstrate the use of these computational models of unit processes involved in the analysis of large-scale processes involving the handling of granular materials. This paper presents a set of simulations of a complete large-scale granular materials handling operation, involving the discharge of the materials from a silo, its transport through a dilute-phase pneumatic conveyor, and the material storage in a big bag under varying environmental temperature and humidity conditions. Conclusions are drawn on the capability of the computational models to represent key granular processes, including particle size segregation, degradation, and moisture migration caking.