A decision support system based on Electre III for safety analysis in a suburban road network

The aim of this paper is to develop a method for supporting decision makers in transport planning. When funds are insufficient to cover the interventions required to ensure safe driving conditions, it is necessary to optimize resources for the most critical sections. In this analysis, the multicriteria ranking method based on the ELECTRE III algorithms is applied to a real case, involving different sections of a motorway. This analysis is based on a comparison of different road sections in regard to safety conditions. The rank of more critical sections identifies intervention priorities

Consensus and meta-analysis regulatory networks for combining multiple microarray gene expression datasets

Microarray data is a key source of experimental data for modelling gene regulatory interactions from expression levels. With the rapid increase of publicly available microarray data comes the opportunity to produce regulatory network models based on multiple datasets. Such models are potentially more robust with greater confidence, and place less reliance on a single dataset. However, combining datasets directly can be difficult as experiments are often conducted on different microarray platforms, and in different laboratories leading to inherent biases in the data that are not always removed through pre-processing such as normalisation. In this paper we compare two frameworks for combining microarray datasets to model regulatory networks: pre- and post-learning aggregation. In pre-learning approaches, such as using simple scale-normalisation prior to the concatenation of datasets, a model is learnt from a combined dataset, whilst in post-learning aggregation individual models are learnt from each dataset and the models are combined. We present two novel approaches for post-learning aggregation, each based on aggregating high-level features of Bayesian network models that have been generated from different microarray expression datasets. Meta-analysis Bayesian networks are based on combining statistical confidences attached to network edges whilst Consensus Bayesian networks identify consistent network features across all datasets. We apply both approaches to multiple datasets from synthetic and real (Escherichia coli and yeast) networks and demonstrate that both methods can improve on networks learnt from a single dataset or an aggregated dataset formed using a standard scale-normalisation

Monitoring the defoliation of hardwood forests in Pennsylvania using LANDSAT

An automated system for conducting annual gypsy moth defoliation surveys using LANDSAT MSS data and digital processing techniques is described. A two-step preprocessing procedure was developed that uses multitemporal data sets representing forest canopy conditions before and after defoliation to create a digital image in which all nonforest cover types are eliminated or masked out of a LANDSAT image that exhibits insect defoliation. A temporal window for defoliation assessment was identified and a statewide data base was established. A data management system to interface image analysis software with the statewide data base was developed and a cost benefit analysis of this operational system was conducted

A chi-squared time-frequency discriminator for gravitational wave detection

Searches for known waveforms in gravitational wave detector data are often done using matched filtering. When used on real instrumental data, matched filtering often does not perform as well as might be expected, because non-stationary and non-Gaussian detector noise produces large spurious filter outputs (events). This paper describes a chi-squared time-frequency test which is one way to discriminate such spurious events from the events that would be produced by genuine signals. The method works well only for broad-band signals. The case where the filter template does not exactly match the signal waveform is also considered, and upper bounds are found for the expected value of chi-squared.Comment: 18 pages, five figures, RevTex

Application of learning algorithms to traffic management in integrated services networks.

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