1,721,023 research outputs found

    Swapping a Failing Edge of a Shortest Paths Tree by Minimizing the Average Stretch Factor

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    We consider a 2-edge connected, undirected graph G = (V,E), with n nodes and m non-negatively real weighted edges, and a single source shortest paths tree (SPT) T of G rooted at an arbitrary node r. If an edge in T is temporarily removed, it makes sense to reconnect the nodes disconnected from the root by adding a single non-tree edge, called a swap edge, instead of rebuilding a new optimal SPT from scratch. In the past, several optimality criteria have been considered to select a best possible swap edge. In this paper we focus on the most prominent one, that is the minimization of the average distance between the root and the disconnected nodes. To this respect, we present an O(m log2 n) time and linear space algorithm to find a best swap edge for every edge of T, thus improving for m = o(n2/log2 n) the previously known O(n2) time and space complexity algorithm

    Swapping a failing edge of a shortest paths tree by minimizing the average stretch factor

    No full text
    AbstractWe consider a two-edge connected, undirected graph G=(V,E), with n nodes and m non-negatively real weighted edges, and a single source shortest paths tree (SPT) T of G rooted at an arbitrary node r. If an edge in T is temporarily removed, it makes sense to reconnect the nodes disconnected from the root by adding a single non-tree edge, called a swap edge, instead of rebuilding a new optimal SPT from scratch. In the past, several optimality criteria have been considered to select a best possible swap edge. In this paper we focus on the most prominent one, that is the minimization of the average distance between the root and the disconnected nodes. To this respect, we present an O(mlog2n) time and O(m) space algorithm to find a best swap edge for every edge of T, thus improving for m=o(n2/log2n) the previously known O(n2) time and space complexity algorithm

    Pharmacokinetics and residue depletion of erythromycin in gilthead sea bream Sparus aurata L. after oral administration

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    Erythromycin (ERY) is an antibiotic effective against Streptococcus iniae, a microorganism responsible for significant losses in aquaculture. No data are available on the pharmacokinetics and residue depletion of ERY in sea bream. The aim of this study was thus to evaluate the pharmacokinetics of ERY in this species after a single oral administration at 75 mg kg-1 b.w. and to assess its residue depletion from tissues after prolonged treatment for 10 days. ERY was rapidly absorbed in sea bream (Cmax = 10.04 μg g-1 and Tmax=1 h), with a half-life of 9.35 h and an AUC0-24 of 56.81 (h μg mL-1). The data obtained and the evaluation of pharmacokinetic/pharmacodynamic parameters allowed us to hypothesize that dosage used in this study should be effective against S. iniae. A rapid reduction in erythromycin concentrations was observed in tissues, with the drug being detectable only during the first day post-treatment. In Europe, the use of ERY in aquaculture is allowed by off-label prescription with a withdrawal time of 500 °C day-1. The absence of ERY residues in tissues already at 24 h post-treatment suggests that ERY in sea bream should not pose human food safety issues

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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