A framework for long term capacity decisions in advanced manufacturing systems

Adelaide - Australi

TN-Grid and gene@home project: volunteer computing for bioinformatics

The ability to reconstruct and find genes that belong or are connected to a gene regulatory network is of essential importance in biology, in order to understand how the biological processes of an organism work. The main limitation in performing gene network expansion is related to the huge amount of computations needed to discover new candidate genes. Given these premises we decided to adopt the BOINC platform that allows us to use the very powerful computational resources of the volunteers. We set up a BOINC server in which we developed a specific work generator that implements our gene network expansion algorithm. Furthermore, we developed an ad hoc version of the PC algorithm (PC++) able to run in the BOINC environment, on the client computers. We present and discuss some statistics and preliminary scientific results of the gene@home BOINC project, the first one hosted by the TN-Grid infrastructur

An integrated approach to measuring the whole journey traveller experience

Transport authorities and providers use various standardized indicators in order to evaluate the system performance. The main objective of this study is to identify the most significant variables that describe travellers' satisfaction. An original survey and stakeholder consultation were conducted across Europe. The relations between overall satisfaction and travel experience variables, subjective well-being indices, travel-related attitudes as well as individual- and trip-specific attributes were investigated for individual trip stages as well as the whole journey experience. The segmentation of the population into distinguished travellers' groups revealed distinctively different sets of main determinants of their satisfaction with various trip stages. The results of this survey will facilitate the development of a traveller satisfaction measurement too

An integrated approach to measuring the whole journey traveller experience

Transport authorities and providers use various standardized indicators in order to evaluate the system performance. The main objective of this study is to identify the most significant variables that describe travellers’ satisfaction. An original survey and stakeholder consultation were conducted across Europe. The relations between overall satisfaction and travel experience variables, subjective well-being indices, travel-related attitudes as well as individual- and trip-specific attributes were investigated for individual trip stages as well as the whole journey experience. The segmentation of the population into distinguished travellers’ groups revealed distinctively different sets of main determinants of their satisfaction with various trip stages. The results of this survey will facilitate the development of a traveller satisfaction measurement tool

Discovering candidates for gene network expansion by variable subsetting and ranking aggregation

We present a method that produces a list of genes that are candidates for Network Expansion by Subsetting and Ranking Aggregation (NESRA) and its application to gene regulatory networks. Our group has recently developed gene@home [3], a BOINC project [1] that permits to search for candidate genes for the expansion of a gene regulatory network using gene expression data. The project adopts intensive variable-subsetting strategies enabled by the computational power provided by the volunteers who join the project by means of the BOINC client, and exploits the PC algorithm for discovering putative causal relationships within each subset of variables. The PC algorithm, whose name derives from the initials of its authors [7] and PC* [2] are algorithms that discover causal relationships among variables. In particular, PC is based on the systematic testing for conditional independence of variables given subsets of other variables, comprehensively presented and evaluated by Kalish and colleagues [4] who proposed it also for gene network reconstruction [5]. NESRA is an algorithm which runs as a postprocessor of the gene@home project that has: 1) a procedure that systematically subsets the variables, runs the PC and ranks the genes; the subsetting is iterated several times and a ranked list of candidates is produced by counting the number of times a relationship is found; 2) several ranking steps are executed with different values of the dimension of the subsets and with different number of iterations producing several ranked lists; 3) the ranked lists are aggregated by using a state-of-the-art ranking aggregator. Here we show that a single ranking step is enough to outperform PC and PC*, but with some dependency on the parameters. Moreover, we show that the output ranking aggregation method is better that the average performance of the single ranking steps. Evaluations are done by means of the gene@home project on Arabidopsis thaliana including a comparison against ARACNE [6] (Table 1). Method k=5 k=10 k=20 k=55 NESRA 0.90 0.80 0.60 0.42 ARACNE 0.2 0.3 0.35 0.45 Table 1: A. thaliana, Expansion of the Flower Organ Specification Gene Regulatory Network. NESRA and ARACNE (default parameters) precision for different values k of the length of the gene lis

NES2RA: network expansion by stratified variable subsetting and ranking aggregation

Gene network expansion is a task of the foremost importance in computational biology. Gene network expansion aims at finding new genes to expand a given known gene network. To this end, we developed gene@home, a BOINC-based project that finds candidate genes that expand known local gene networks using NESRA. In this paper, we present NES2RA, a novel approach that extends and improves NESRA by modeling, using a probability vector, the confidence of the presence of the genes belonging to the local gene network. NES2RA adopts intensive variable-subsetting strategies, enabled by the computational power provided by gene@home volunteers. In particular, we use the skeleton procedure of the PC-algorithm to discover candidate causal relationships within each subset of variables. Finally, we use state-of-the-art aggregators to combine the results into a single ranked candidate genes list. The resulting ranking guides the discovery of unknown relations between genes and a priori known local gene networks. Our experimental results show that NES2RA outperforms the PC-algorithm and its order-independent PC-stable version, ARACNE, and our previous approach, NESRA. In this paper we extensively discuss the computational aspects of the NES2RA approach and we also present and validate expansions performed on the model plant Arabidopsis thaliana and the model bacteria Escherichia coli