68,546 research outputs found

    Shock and vibration response of multistage structure

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    Study of the shock and vibration response of a multistage structure employed analytically, lumped-mass, continuous-beam, multimode, and matrix-iteration methods. The study was made on the load paths, transmissibility, and attenuation properties along a longitudinal axis of a long, slender structure with increasing degree of complexity

    Effects of the complex mass distribution of dark matter halos on weak lensing cluster surveys

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    Gravitational lensing effects arise from the light ray deflection by all of the mass distribution along the line of sight. It is then expected that weak lensing cluster surveys can provide us true mass-selected cluster samples. With numerical simulations, we analyze the correspondence between peaks in the lensing convergence κ\kappa-map and dark matter halos. Particularly we emphasize the difference between the peak κ\kappa value expected from a dark matter halo modeled as an isolated and spherical one, which exhibits a one-to-one correspondence with the halo mass at a given redshift, and that of the associated κ\kappa-peak from simulations. For halos with the same expected κ\kappa, their corresponding peak signals in the κ\kappa-map present a wide dispersion. At an angular smoothing scale of θG=1arcmin\theta_G=1\hbox{arcmin}, our study shows that for relatively large clusters, the complex mass distribution of individual clusters is the main reason for the dispersion. The projection effect of uncorrelated structures does not play significant roles. The triaxiality of dark matter halos accounts for a large part of the dispersion, especially for the tail at high κ\kappa side. Thus lensing-selected clusters are not really mass-selected. (abridged)Comment: ApJ accepte

    On controllability of neuronal networks with constraints on the average of control gains

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    Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently
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