5,288 research outputs found

    Design, fabrication, and characterization of deep-etched waveguide gratings

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    One-dimensional (1-D) deep-etched gratings on a specially grown AlGaAs wafer were designed and fabricated. The gratings were fabricated using state-of-the-art electron beam lithography and high-aspect-ratio reactive ion etching (RIE) in order to achieve the required narrow deep air slots with good accuracy and reproducibility. Since remarkable etch depths (up to 1.5 /spl mu/m), which completely cut through the waveguide core layer, have been attained, gratings composed of only five periods (and, thus, shorter than 6 /spl mu/m) have a bandgap larger than 100 nm. A defect was introduced by increasing the width of the central semiconductor tooth to create microcavities that exhibit a narrow transmission peak (less than 7 nm) around the wavelength of 1530 nm. The transmission spectra between 1460 and 1580 nm have been systematically measured, and the losses have been estimated for a set of gratings, both with and without a defect, for different periods and air slot dimensions. Numerical results obtained via a bidirectional beam propagation code allowed the evaluation of transmissivity, reflectivity, and diffraction losses. By comparing experimental results with the authors' numerical findings, a clear picture of the role of the grating's geometric parameters in determining its spectral features and diffractive losses is illustrated

    OWA-based fuzzy m-ary adjacency relations in Social Network Analysis.

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    In this paper we propose an approach to Social Network Analysis (SNA) based on fuzzy m-ary adjacency relations. In particular, we show that the dimension of the analysis can naturally be increased and interesting results can be derived. Therefore, fuzzy m-ary adjacency relations can be computed starting from fuzzy binary relations and introducing OWA-based aggregations. The behavioral assumptions derived from the measure and the exam of individual propensity to connect with other suggest that OWA operators can be considered particularly suitable in characterizing such relationships.reciprocal relation; fuzzy preference relation; priority vector; normalization

    Probing turbulent superstructures in Rayleigh-B\'{e}nard convection by Lagrangian trajectory clusters

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    We analyze large-scale patterns in three-dimensional turbulent convection in a horizontally extended square convection cell by Lagrangian particle trajectories calculated in direct numerical simulations. A simulation run at a Prandtl number Pr =0.7=0.7, a Rayleigh number Ra =105=10^5, and an aspect ratio Γ=16\Gamma=16 is therefore considered. These large-scale structures, which are denoted as turbulent superstructures of convection, are detected by the spectrum of the graph Laplacian matrix. Our investigation, which follows Hadjighasem {\it et al.}, Phys. Rev. E {\bf 93}, 063107 (2016), builds a weighted and undirected graph from the trajectory points of Lagrangian particles. Weights at the edges of the graph are determined by a mean dynamical distance between different particle trajectories. It is demonstrated that the resulting trajectory clusters, which are obtained by a subsequent kk-means clustering, coincide with the superstructures in the Eulerian frame of reference. Furthermore, the characteristic times τL\tau^L and lengths λUL\lambda_U^L of the superstructures in the Lagrangian frame of reference agree very well with their Eulerian counterparts, τ\tau and λU\lambda_U, respectively. This trajectory-based clustering is found to work for times tττLt\lesssim \tau\approx\tau^L. Longer time periods tτLt\gtrsim \tau^L require a change of the analysis method to a density-based trajectory clustering by means of time-averaged Lagrangian pseudo-trajectories, which is applied in this context for the first time. A small coherent subset of the pseudo-trajectories is obtained in this way consisting of those Lagrangian particles that are trapped for long times in the core of the superstructure circulation rolls and are thus not subject to ongoing turbulent dispersion.Comment: 12 pages, 7 downsized figures, to appear in Phys. Rev. Fluid

    Power-law scaling in dimension-to-biomass relationship of fish schools

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    Motivated by the finding that there is some biological universality in the relationship between school geometry and school biomass of various pelagic fishes in various conditions, I here establish a scaling law for school dimensions: the school diameter increases as a power-law function of school biomass. The power-law exponent is extracted through the data collapse, and is close to 3/5. This value of the exponent implies that the mean packing density decreases as the school biomass increases, and the packing structure displays a mass-fractal dimension of 5/3. By exploiting an analogy between school geometry and polymer chain statistics, I examine the behavioral algorithm governing the swollen conformation of large-sized schools of pelagics, and I explain the value of the exponent.Comment: 25 pages, 6 figures, to appear in J. Theor. Bio
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