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  • 1
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    Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning (2019)
    Hindawi
    Details
    Publication Date: 2019-11-14
    Description: The largest project managers and adjudicators of a country, both by number of projects and by cost, are public procurement agencies. Therefore, knowing and characterising public procurement announcements (tenders) is fundamental for managing public resources well. This article presents the case of public procurement in Spain, analysing a dataset from 2012 to 2018: 58,337 tenders with a cost of 31,426 million euros. Many studies of public procurement have been conducted globally or theoretically, but there is a dearth of data analysis, especially regarding Spain. A quantitative, graphical, and statistical description of the dataset is presented. Mainly, the analysis is of the relation between the award price and the bidding price. An award price estimator is proposed that uses the random forest regression method. A good estimator would be very useful and valuable for companies and public procurement agencies. It would be a key tool in their project management decision making. Finally, a similar analysis, employing a dataset from European countries, is presented to compare and generalise the results and conclusions. Hence, this is a novel study which fills a gap in the literature.
    Print ISSN: 1076-2787
    Electronic ISSN: 1099-0526
    Topics: Computer Science , Mathematics
    Published by Hindawi
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  • 2
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    Strategies to Identify Recognition Signals and Targets of SUMOylation (2012)
    Hindawi
    Details
    Publication Date: 2012-01-01
    Description: SUMOylation contributes to the regulation of many essential cellular factors. Diverse techniques have been used to explore the functional consequences of protein SUMOylation. Most approaches consider the identification of sequences on substrates, adaptors, or receptors regulating the SUMO conjugation, recognition, or deconjugation. The large majority of the studied SUMOylated proteins contain the sequence [IVL]KxE. SUMOylated proteins are recognized by at least 3 types of hydrophobic SUMO-interacting motifs (SIMs) that contribute to coordinate SUMO-dependent functions. Typically, SIMs are constituted by a hydrophobic core flanked by one or two clusters of negatively charged amino acid residues. Multiple SIMs can integrate SUMO binding domains (SBDs), optimizing binding, and control over SUMO-dependent processes. Here, we present a survey of the methodologies used to study SUMO-regulated functions and provide guidelines for the identification ofcisandtranssequences controlling SUMOylation. Furthermore, an integrative analysis of known and putative SUMO substrates illustrates an updated landscape of several SUMO-regulated events. The strategies and analysis presented here should contribute to the understanding of SUMO-controlled functions and provide rational approach to identify biomarkers or choose possible targets for intervention in processes where SUMOylation plays a critical role.
    Print ISSN: 2090-2247
    Electronic ISSN: 2090-2255
    Topics: Biology , Chemistry and Pharmacology
    Published by Hindawi
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  • 3
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    A Methodology for Classifying Search Operators as Intensification or Diversification Heuristics (2020)
    Hindawi
    Details
    Publication Date: 2020-02-13
    Description: Selection hyper-heuristics are generic search tools that dynamically choose, from a given pool, the most promising operator (low-level heuristic) to apply at each iteration of the search process. The performance of these methods depends on the quality of the heuristic pool. Two types of heuristics can be part of the pool: diversification heuristics, which help to escape from local optima, and intensification heuristics, which effectively exploit promising regions in the vicinity of good solutions. An effective search strategy needs a balance between these two strategies. However, it is not straightforward to categorize an operator as intensification or diversification heuristic on complex domains. Therefore, we propose an automated methodology to do this classification. This brings methodological rigor to the configuration of an iterated local search hyper-heuristic featuring diversification and intensification stages. The methodology considers the empirical ranking of the heuristics based on an estimation of their capacity to either diversify or intensify the search. We incorporate the proposed approach into a state-of-the-art hyper-heuristic solving two domains: course timetabling and vehicle routing. Our results indicate improved performance, including new best-known solutions for the course timetabling problem.
    Print ISSN: 1076-2787
    Electronic ISSN: 1099-0526
    Topics: Computer Science , Mathematics
    Published by Hindawi
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