125,099 research outputs found

    Microscale ocean dynamics

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    The detailed dynamics of the micro-scale ocean surface phenomena are studied along with the relationships among the surface signatures with the underlying dynamical processes. The approach to advance the understanding in this area is as follows: (1) Conduct rigorous theoretical studies of the ocean surface wave dynamics and statistical properties; (2) Conduct process-oriented laboratory experiments to verify the theoretical results, and to provide guidance for further studies; and (3) Prepare testable hypotheses for field verifications and comparisons during the ONR/NASA sponsored Surface Wave Dynamics Experiment (SWADE). An analytic model was established for wave breaking probability to study the influence of wave breaking on the spectrum shape in both deep and finite-depth waters. The spectrum was processed along with the structures of the water surface under the influence of wind and existing waves

    Delay-Optimal User Scheduling and Inter-Cell Interference Management in Cellular Network via Distributive Stochastic Learning

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    In this paper, we propose a distributive queueaware intra-cell user scheduling and inter-cell interference (ICI) management control design for a delay-optimal celluar downlink system with M base stations (BSs), and K users in each cell. Each BS has K downlink queues for K users respectively with heterogeneous arrivals and delay requirements. The ICI management control is adaptive to joint queue state information (QSI) over a slow time scale, while the user scheduling control is adaptive to both the joint QSI and the joint channel state information (CSI) over a faster time scale. We show that the problem can be modeled as an infinite horizon average cost Partially Observed Markov Decision Problem (POMDP), which is NP-hard in general. By exploiting the special structure of the problem, we shall derive an equivalent Bellman equation to solve the POMDP problem. To address the distributive requirement and the issue of dimensionality and computation complexity, we derive a distributive online stochastic learning algorithm, which only requires local QSI and local CSI at each of the M BSs. We show that the proposed learning algorithm converges almost surely (with probability 1) and has significant gain compared with various baselines. The proposed solution only has linear complexity order O(MK)

    Inferring bulk self-assembly properties from simulations of small systems with multiple constituent species and small systems in the grand canonical ensemble

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    In this paper we generalize a methodology [T. E. Ouldridge, A. A. Louis, and J. P. K. Doye, J. Phys.: Condens. Matter {\bf 22}, 104102 (2010)] for dealing with the inference of bulk properties from small simulations of self-assembling systems of characteristic finite size. In particular, schemes for extrapolating the results of simulations of a single self-assembling object to the bulk limit are established in three cases: for assembly involving multiple particle species, for systems with one species localized in space and for simulations in the grand canonical ensemble. Furthermore, methodologies are introduced for evaluating the accuracy of these extrapolations. Example systems demonstrate that differences in cluster concentrations between simulations of a single self-assembling structure and bulk studies of the same model under identical conditions can be large, and that convergence on bulk results as system size is increased can be slow and non-trivial.Comment: Accepted by J. Chem. Phy
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