Thermally conductive functionalized multilayer graphene sheets (fMGs) are efficiently aligned in large-scale by a vacuum filtration method at room temperature, as evidenced by SEM images and polarized Raman spectroscopy. A remarkably strong anisotropy in properties of aligned fMGs is observed. High electrical (∼386 S cm(-1)) and thermal conductivity (∼112 W m(-1) K(-1) at 25 °C) and ultralow coefficient of thermal expansion (∼-0.71 ppm K(-1)) in the in-plane direction of A-fMGs are obtained without any reduction process. Aligned fMGs are vertically assembled between contacted silicon/silicon surfaces with pure indium as a metallic medium. Thus-constructed three-dimensional vertically aligned fMG thermal interfacial material (VA-fMG TIM) architecture has significantly higher equivalent thermal conductivity (75.5 W m(-1) K(-1)) and lower contact thermal resistance (5.1 mm2 K W(-1)), compared with their counterpart from A-fMGs that are recumbent between silicon surfaces. This finding provides a throughout approach for a graphene-based TIM assembly as well as knowledge of vertically aligned graphene architectures, which may not only facilitate graphene's application in current demanding thermal management but also promote its widespread applications in electrodes of energy storage devices, conductive polymeric composites, etc.

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The knowledge of channel statistics can be very helpful in making sound opportunistic spectrum access decisions. It is therefore desirable to be able to efficiently and accurately estimate channel statistics. In this paper we study the problem of optimally placing sensing times over a time window so as to get the best estimate on the parameters of an on-off renewal channel. We are particularly interested in a sparse sensing regime with a small number of samples relative to the time window size. Using Fisher information as a measure, we analytically derive the best and worst sensing sequences under a sparsity condition. We also present a way to derive the best/worst sequences without this condition using a dynamic programming approach. In both cases the worst turns out to be the uniform sensing sequence, where sensing times are evenly spaced within the window. With these results we argue that without a priori knowledge, a robust sensing strategy should be a randomized strategy. We then compare different random schemes using a family of distributions generated by the circular β ensemble, and propose an adaptive sensing scheme to effectively track time-varying channel parameters. We further discuss the applicability of compressive sensing for this problem.

At home and broad, more wind power is being installed in electricity markets, the influence brought by wind power become more important on power system stability, especially the fluctuation, the uncertainty in wind power production and multi-time scale of the wind. In order to forecast ramp events before the power system encountering failure, so that the operator can adopt some limited controlling strategy. This paper introduces the present status of the wind power ramp prediction at home and abroad. And it gives out four kinds of definitions of ramp events, which are used by many scholars, then provides various forecasting error algorithm. In the aspect of prediction models, it comes up with physical models and statistical models, and enumerates various examples of different models. Finally, it prospects the tendency of the model improvement about the wind power ramp events forecasting, which would be significant for ramp research.