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The condition of complex products in the transport industry, such as train couplings or aircraft turbines, is not exactly determinable before their disassembly and diagnosis in a maintenance plant. Thus, planning and control of their regeneration is impeded since work plans and spare part demand result at short notice. This paper presents a novel method, which combines a planning approach, the in-house pooling of components, and a controlling approach, the sequencing of components, by means of event-driven simulation. Thereby, mean cycle time, mean tardiness and on-time delivery can be optimized under the consideration of the volatile conditions. © 2017 The Authors. Published by Elsevier B.V. German Ministry of Economics and Energy

International audience; We examine the onset of inelastic collapse for a cluster of 10 ≤ N ≤ 50 grains colliding with a fixed wall. When the restitution coefficient ẽ is less than a threshold, the cluster does not bounce off the wall, but comes to rest after an infinite number of collisions. Near the collapse threshold, a nearly periodic sequence of collisions is established that can be identified by tracking the most energetic collisions. The length of this sequence grows exponentially with N. The dispersal of a colliding cluster can be compared to the decay of a radioactive nucleus: the cluster has a fixed probability per unit time (measured in collisions) of dispersing. The "half-life" of the cluster grows exponentially as the restitution coefficient is decreased. Thus the onset of inelastic collapse is characterized by an exponentially growing time scale.

The dynamics of the plant microtubule cytoskeleton is a paradigmatic example of the complex spatiotemporal processes characterising life at the cellular scale. This system is composed of large numbers of spatially extended particles, each endowed with its own intrinsic stochastic dynamics, and is capable of non-equilibrium self-organisation through collisional interactions of these particles. To elucidate the behaviour of such a complex system requires not only conceptual advances, but also the development of appropriate computational tools to simulate it. As the number of parameters involved is large and the behaviour is stochastic, it is essential that these simulations be fast enough to allow for an exploration of the phase space and the gathering of sufficient statistics to accurately pin down the average behaviour as well as the magnitude of fluctuations around it. Here we describe a simulation approach that meets this requirement by adopting an event-driven methodology that encompasses both the spontaneous stochastic changes in microtubule state as well as the deterministic collisions. In contrast with finite time step simulations this technique is intrinsically exact, as well as several orders of magnitude faster, which enables ordinary PC hardware to simulate systems of $\sim 10^3$ microtubules on a time scale $\sim 10^{3}$ faster than real time. In addition we present new tools for the analysis of microtubule trajectories on curved surfaces. We illustrate the use of these methods by addressing a number of outstanding issues regarding the importance of various parameters on the transition from an isotropic to an aligned and oriented state.

Simulating spiking neuron networks with a sequential event-driven approach consumes less computation time than clock-driven methods. On the other hand, a parallel computing support provides a larger amount of material ressources for optimizing simulation performance. This PhD dissertation proposes an event-driven, multithreaded and distributed framework for simulating large size spiking neuron networks. The name of the simulator is the acronym DAMNED, for Distributed And Multithreaded Neural Event-Driven simulation framework. DAMNED distributes the neurons and connections of the network on the material ressources synchronized through a decentralized global virtual time. DAMNED also couples local multithreaded processing to the distributed hardware. DAMNED allows to speed up the simulation and to manage wider neural networks than sequential processing. DAMNED is suited to run many models of spiking neurons and networks, and most material supports are workable. Using DAMNED is presented first on simple networks for different sizes, connectivities and activities. Next, DAMNED is applied to modelling the control of saccadic eye movements. Completely based on spiking neurons, the model studies interactions between the neural circuits of the saccadic system located in the brainstem. The model helps validating the hypothesis that the saccade amplitude could be encoded by a vector summation of the activities in the superior colliculus motor map rather than a vector average, from comparison with data obtained in the simulation. Even if further developments and improvements may be forecasted, the originality of the work is to couple event-driven and distributed programming. Moreover, among the parallel simulators for spiking neuron networks, DAMNED is the first one taking advantage of an event-driven strategy internal multithreading of the logic processes and a distributed architecture of physical processes. Hence DAMNED is an advance in the area of simulating spiking neuron networks, mainly for wide size networks. Experiments on simulating the control of saccadic eye movements by a spiking neural network model and their contributions for the neuroscience community confirm the perspectives for further uses of the present work; Ce travail de thèse propose un simulateur événementiel, multithreadé et distribué pour la simulation de réseaux de neurone impulsionnels de grande taille, nommé DAMNED, qui signifie Distributed And Multithreaded Neural Event-Driven simulation framework. Répartissant le réseau de neurones sur les ressources matérielles synchronisées par une méthode décentralisée de gestion du temps virtuel, DAMNED introduit également un fonctionnement multithread. DAMNED permet d'accélérer les calculs et de simuler des réseau de plus grande taille qu'en séquentiel. DAMNED pennet d'exploiter de nombreux modèles de réseaux et de neurones et la plupart des supports matériels sont exploitables. Nous présentons l'utilisation de DAMNED sur un modèle simple de réseau pour différentes taille, connectivités et dynamiques. Ensuite, nous proposons une application directe de DAMNED dans une modélisation du système saccadique restreint au tronc cérébral. On montre, à l'aide de ce modèle, que l'hypothèse selon laquelle une somme vectorielle (vectc summation) des activités de la carte motrice du colliculus supérieur coderait pour l'amplitude de la saccade correspond davantage au données obtenues pour le modèle, exécuté sur DAMNED, qu'à l'hypothèse d'un moyennage de vecteurs (vector average). L'originalité de ce travail, parmi les premiers simulateurs distribués de réseaux de neurones impulsionnels, réside dans le couplage d'une stratégie événementielle, d'un multithreading interne aux processus logiques et une architecture physique distribuée. Le simulateur DAMNE D constitue donc une avancée dans le domaine des réseaux de neurones impulsionnels de grande taille

Simulating spiking neuron networks with a sequential event-driven approach consumes less computation time than clock-driven methods. On the other hand, a parallel computing support provides a larger amount of material ressources for optimizing simulation performance. This PhD dissertation proposes an event-driven, multithreaded and distributed framework for simulating large size spiking neuron networks. The name of the simulator is the acronym DAMNED, for Distributed And Multithreaded Neural Event-Driven simulation framework. DAMNED distributes the neurons and connections of the network on the material ressources synchronized through a decentralized global virtual time. DAMNED also couples local multithreaded processing to the distributed hardware. DAMNED allows to speed up the simulation and to manage wider neural networks than sequential processing. DAMNED is suited to run many models of spiking neurons and networks, and most material supports are workable. Using DAMNED is presented first on simple networks for different sizes, connectivities and activities. Next, DAMNED is applied to modelling the control of saccadic eye movements. Completely based on spiking neurons, the model studies interactions between the neural circuits of the saccadic system located in the brainstem. The model helps validating the hypothesis that the saccade amplitude could be encoded by a vector summation of the activities in the superior colliculus motor map rather than a vector average, from comparison with data obtained in the simulation. Even if further developments and improvements may be forecasted, the originality of the work is to couple event-driven and distributed programming. Moreover, among the parallel simulators for spiking neuron networks, DAMNED is the first one taking advantage of an event-driven strategy internal multithreading of the logic processes and a distributed architecture of physical processes. Hence DAMNED is an advance in the area of simulating spiking neuron networks, mainly for wide size networks. Experiments on simulating the control of saccadic eye movements by a spiking neural network model and their contributions for the neuroscience community confirm the perspectives for further uses of the present work, Ce travail de thèse propose un simulateur événementiel, multithreadé et distribué pour la simulation de réseaux de neurone impulsionnels de grande taille, nommé DAMNED, qui signifie Distributed And Multithreaded Neural Event-Driven simulation framework. Répartissant le réseau de neurones sur les ressources matérielles synchronisées par une méthode décentralisée de gestion du temps virtuel, DAMNED introduit également un fonctionnement multithread. DAMNED permet d'accélérer les calculs et de simuler des réseau de plus grande taille qu'en séquentiel. DAMNED pennet d'exploiter de nombreux modèles de réseaux et de neurones et la plupart des supports matériels sont exploitables. Nous présentons l'utilisation de DAMNED sur un modèle simple de réseau pour différentes taille, connectivités et dynamiques. Ensuite, nous proposons une application directe de DAMNED dans une modélisation du système saccadique restreint au tronc cérébral. On montre, à l'aide de ce modèle, que l'hypothèse selon laquelle une somme vectorielle (vectc summation) des activités de la carte motrice du colliculus supérieur coderait pour l'amplitude de la saccade correspond davantage au données obtenues pour le modèle, exécuté sur DAMNED, qu'à l'hypothèse d'un moyennage de vecteurs (vector average). L'originalité de ce travail, parmi les premiers simulateurs distribués de réseaux de neurones impulsionnels, réside dans le couplage d'une stratégie événementielle, d'un multithreading interne aux processus logiques et une architecture physique distribuée. Le simulateur DAMNE D constitue donc une avancée dans le domaine des réseaux de neurones impulsionnels de grande taille

We study the dynamics of monodisperse hard ellipsoids via a new event-driven molecular dynamics algorithm as a function of volume fraction $\phi$ and aspect ratio $X_0$. We evaluate the translational $D_{trans}$ and the rotational $D_{rot}$ diffusion coefficient and the associated isodiffusivity lines in the $\phi-X_0$ plane. We observe a decoupling of the translational and rotational dynamics which generates an almost perpendicular crossing of the $D_{trans}$ and $D_{rot}$ isodiffusivity lines. While the self intermediate scattering function exhibits stretched relaxation, i.e. glassy dynamics, only for large $\phi$ and $X_0 \approx 1$, the second order orientational correlator $C_2(t)$ shows stretching only for large and small $X_0$ values. We discuss these findings in the context of a possible pre-nematic order driven glass transition., Comment: accepted by Phys. Rev. Lett

In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementations of SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand, simulations of large scale neural networks can take advantage of distributing the neurons on a set of processors (either workstation cluster or parallel computer). This article presents DAMNED, a large scale SNN simulation framework able to gather the benefits of EDS and parallel computing. Two levels of parallelism are combined: Distributed mapping of the neural topology, at the network level, and local multithreaded allocation of resources for simultaneous processing of events, at the neuron level. Based on the causality of events, a distributed solution is proposed for solving the complex problem of scheduling without synchronization barrier., Comment: 6 pages