Correlation between density of states distributions and the fermi level position in interfacial regions of amorphous silicon thin film transistors

This paper investigates the variation of the integrated density of states with conduction activation energy in hydrogenated amorphous silicon thin film transistors. Results are given for two different gate insulator layers, PECVD silicon oxide and thermally grown silicon dioxide. The different gate insulators produce transistors with very different initial transfer characteristics, but the variation of integrated density of states with conduction activation energy is shown to be similar.

Velocity distributions for low pressure turbines

A parametric set of velocity distributions has been investigated using a flat plate experiment. Three different diffusion factors and peak velocity locations were tested. These were designed to mimic the suction surfaces of Low Pressure (LP) turbine blades. Unsteady wakes, inherent in real turbomachinery flows, were generated using a moving bar mechanism. A turbulence grid generated a freestream turbulence level that is believed to be typical of LP turbines. Measurements were taken across a Reynolds number range of 50,000-220,000 at three reduced frequencies (0.314, 0.628, 0.942). Boundary layer traverses were performed at the nominal trailing edge using a Laser Doppler Anemometry system and hot-films were used to examine the boundary layer behaviour along the surface. For every velocity distribution tested, the boundary layer separated in the diffusing flow downstream of the peak velocity. The loss production is dominated by the mixing in the reattachment process, mixing in the turbulent boundary layer downstream of reattachment and the effects of the unsteady interaction between the wakes and the boundary layer. A sensitive balance governs the optimal location of peak velocity on the surface. Moving the velocity peak forwards on the blade was found to be increasingly beneficial when bubblegenerated losses are high, i.e. at low Reynolds number, at low reduced frequency and at high levels of diffusion. Copyright © 2008 by ASME.

Foraging habitats and utilization distributions of Black-necked Cranes wintering at the Napahai Wetland, China

One of the most endangered populations of Black-necked Cranes (Grus nigricollis), the central population, is declining due to habitat loss and degradation, but little is known about their space use patterns and habitat preferences. We examined the space use and habitat preferences of Black-necked Cranes during the winter of 2007-2008 at the Napahai wetland in northwest Yunnan, China, where approximately 300 Black-necked Cranes (>90% of the total central population) spent the winter. Euclidean distance analysis was employed to determine the habitat preferences of Black-necked Cranes, and a local nearest-neighbor, convex-hull construction method was used to examine space use. Our results indicate that Black-necked Cranes preferred shallow marsh and wet meadow habitats and avoided farmland and dry grassland. Core-use areas (50% isopleths) and total-use areas (100% isopleths) accounted for only 1.2% and 28.2% of the study area, respectively. We recommend that habitat protection efforts focus on shallow marsh and wet meadow habitats to maintain preferred foraging sites. Core-use areas, such as the primary foraging areas of Black-necked Cranes, should be designated as part of the core zone of the nature reserve. Monthly shifts in the core-use areas of the cranes also indicate that the reserve should be large enough to permit changes in space use. In addition to preserving habitat, government officials should also take measures to decrease human activity in areas used by foraging Black-necked Cranes.

Restricting exchangeable nonparametric distributions

Distributions over exchangeable matrices with infinitely many columns, such as the Indian buffet process, are useful in constructing nonparametric latent variable models. However, the distribution implied by such models over the number of features exhibited by each data point may be poorly- suited for many modeling tasks. In this paper, we propose a class of exchangeable nonparametric priors obtained by restricting the domain of existing models. Such models allow us to specify the distribution over the number of features per data point, and can achieve better performance on data sets where the number of features is not well-modeled by the original distribution.