The analysis of networks and in particular the identification of communities, or clusters, is a topic of active research with application arising in many domains. Several models were proposed for its solution. In [Cafieri et al., Phys. Rev. E 81(2):026105, 2010], a criterion is proposed for a graph bipartition to be optimal: one seeks to maximize the minimum for both classes of the bipartition of the ratio of inner edges to cut edges (edge ratio), and it is used in a hierarchical divisive algorithm for community identification in networks. In this paper, we develop a VNS-based heuristic for hierarchical divisive edge ratio network clustering. A k-neighborhood is defined as move of k entities, i.e., k entities change their membership from one to another cluster. A local search is based on 1-changes and k-changes are used for shaking the incumbent solution. Computational results on datasets from the literature validate the proposed approach.Résumé : L'analyse de réseaux et en particulier l'identification de communautés, ou classes, est un sujet de recherche très actif dont les applications sont nombreuses dans de multiples domaines. Plusieurs modèles ontété proposés pour sa résolution. Dans [Cafieri et al., Phys. Rev. E 81(2):026105, 2010], on propose un critère pour que la bipartition d'un graphe soit optimale : on chercheà maximiser le minimum pour les deux classes de la bipartition du rapport du nombre d'arêtes internes au nombre d'arêtes coupées (edgeratio). Ce critère est utilisé dans un algorithme hiérarchique divisif pour l'identification de communautés dans les réseaux. Dans le présent article, nous développons une heuristique pour la classification hiérarchique descendante basée sur la métaheuristique de rechercheà voisinage variable. Un k-voisinage est défini comme le mouvement de k entités, c'est-à-dire que k entités changent leur appartenance d'une classeà l'autre. Une recherche locale est basée sur les 1-échanges et les k-échanges sont utilisés pour perturber la meilleure solution connue. Les résultats de calcul sur des données de la littérature valident l'approche proposée.

The inventory routing problem (IRP) combines inventory management and delivery route‐planning decisions. This work presents a simheuristic approach that integrates Monte Carlo simulation within a variable neighborhood search (VNS) framework to solve the multiperiod IRP with stochastic customer demands. In this realistic variant of the problem, our goal is to establish the optimal refill policies for each customer–period combination, that is, those individual refill policies that minimize the total expected cost over the periods. This cost is the aggregation of both expected inventory and routing costs. Our simheuristic algorithm allows to consider the inventory changes between periods generated by the realization of the random demands in each period, which have an impact on the quantities to be delivered in the next period and, therefore, on the associated routing plans. A range of computational experiments are carried out in order to illustrate the potential of our simulation–optimization approach.

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Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to

SummaryUsing Schnider's pharmacokinetic model, propofol pharmacodynamics were modelled during total intravenous anaesthesia. The method involved adjusting a pharmacokinetic/pharmacodynamic model according to data obtained from 42 patients having operative procedures with remifentanil analgesia. Parameters C e50 and c were estimated for induction and maintenance by analysing patients' bispectral index. The pharmacodynamic models were different for induction and maintenance. The mean (95% CI) C e50 for induction and maintenance was C e50 = 3.35 (2.79-3.91) mg.l À1 and 2.23 (1.95-2.51) mg.l À1