Applications of simulation within the healthcare context

This is a pre-print of an article published in Journal of the Operation Research Society. The definitive publisher-authenticated version Katsaliaki, K., Mustafee, N.,(2010). Applications of simulation within the healthcare context. Journal of the Operation Research Society. 62, 1431-1451 is available online at: http://www.palgrave-journals.com/jors/journal/v62/n8/full/jors201020a.htmlA large number of studies have applied simulation to a multitude of issues related to healthcare. These studies have been published over a number of unrelated publishing outlets, and this may hamper the widespread reference and use of such resources. In this paper we analyse existing research in healthcare simulation in order to categorise and synthesise it in a meaningful manner. Hence, the aim of this paper is to conduct a review of the literature pertaining to simulation research within healthcare in order to ascertain its current development. A review of approximately 250 high quality journal papers published between 1970 and 2007 on healthcare-related simulation research was conducted. The results present: a classification of the healthcare publications according to the simulation techniques they employ; the impact of published literature in healthcare simulation; a report on demonstration and implementation of the studies’ results; the sources of funding; and the software used. Healthcare planners and researchers will benefit from this study by having ready access to an indicative article collection of simulation techniques applied in healthcare problems that are clustered under meaningful headings. This study facilitates the understanding of the potential of different simulation techniques for solving diverse healthcare problems

A Petri net model for railway bridge maintenance

This article describes the application of the Petri net modelling approach to managing the maintenance process of railway bridges. The Petri net model accounts for the degradation, inspection and repair processes of individual bridge elements in investigating the effectiveness of alternative maintenance strategies. The times governing the degradation and repair processes considered are stochastic and defined by the appropriate Weibull distribution. The model offers a capability for modelling the bridge asset which overcomes the limitations in the currently used modelling techniques reported in the literature. The bridge model also provides a means of predicting the future asset condition as a result of adopting different maintenance strategies. The solution of the Petri net model is performed using a Monte Carlo simulation routine. The application of the model to a typical metal railway bridge is also presented in the article

Gamification and Simulation

Gamification and simulation methods are two of the most important components of serious games. In order to create an effective training tool, it is imperative to understand these methods and their relationship to each other. If designed correctly, gamification techniques can build upon simulations to provide an effective training medium, which enhances learning, engagement and motivation in users. This chapter discusses their uses, strengths and weaknesses whilst identifying how to most effectively utilise them in developing serious games

A novel asynchronous access method with binary interfaces

© 2008 Silva et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Short term traffic congestion forecasting using hybrid metaheuristics and rule based methods a comparative study

In this paper, a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, and literature techniques is presented. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Different types of datasets have been chosen. The used time horizons are 5, 15 and 30 min. Results show that the hybrid technique improves those results obtained by the techniques of the state of the art. In this way, the performed experimentation shows the competitiveness of the proposal in this area of application. Document type: Part of book or chapter of boo

A unified data representation theory for network visualization, ordering and coarse-graining

Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form.Comment: 13 pages, 5 figure

Efficient Sparse Coding in Early Sensory Processing: Lessons from Signal Recovery

Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as well as for using the large and sparse representations in an efficient way. We argue that higher level overcompleteness becomes computationally tractable by imposing sparsity on synaptic activity and we also show that such structural sparsity can be facilitated by statistics based decomposition of the stimuli into typical and atypical parts prior to sparse coding. Typical parts represent large-scale correlations, thus they can be significantly compressed. Atypical parts, on the other hand, represent local features and are the subjects of actual sparse coding. When applied on natural images, our decomposition based sparse coding model can efficiently form overcomplete codes and both center-surround and oriented filters are obtained similar to those observed in the retina and the primary visual cortex, respectively. Therefore we hypothesize that the proposed computational architecture can be seen as a coherent functional model of the first stages of sensory coding in early vision