Energy transfer and enhanced 1.54 μm emission in Erbium-Ytterbium disilicate thin films.

α-(Yb1-xErx)2Si2O7 thin films on Si substrates were synthesized by magnetron co-sputtering. The optical emission from Er3+ ions has been extensively investigated, evidencing the very efficient role of Yb-Er coupling. The energy-transfer coefficient was evaluated for an extended range of Er content (between 0.2 and 16.5 at.%) reaching a maximum value of 2 × 10⁻¹⁶ cm⁻³s⁻¹. The highest photoluminescence emission at 1535 nm is obtained as a result of the best compromise between the number of Yb donors (16.4 at.%) and Er acceptors (1.6 at.%), for which a high population of the first excited state is reached. These results are very promising for the realization of 1.54 μm optical amplifiers on a Si platform.

The Effect of Inlet Guide Vanes on Inlet Flow Distortion Transfer and Transonic Fan Stability

An investigation was carried out into the effects of variable inlet guide vanes (VIGVs) on the performance and stability margin of a transonic fan in the presence of inlet flow distortion. The study was carried out using computational fluid dynamics (CFD) and validated with experimental data. The capability of CFD to predict the changes in performance with or without VIGVs in the presence of an inlet flow distortion is assessed. Results show that the VIGVs improve the performance and stability margin and do so by reducing the amount of swirl at inlet to the rotor component of the fan.

Beyond dataset bias: Multi-task unaligned shared knowledge transfer

Many visual datasets are traditionally used to analyze the performance of different learning techniques. The evaluation is usually done within each dataset, therefore it is questionable if such results are a reliable indicator of true generalization ability. We propose here an algorithm to exploit the existing data resources when learning on a new multiclass problem. Our main idea is to identify an image representation that decomposes orthogonally into two subspaces: a part specific to each dataset, and a part generic to, and therefore shared between, all the considered source sets. This allows us to use the generic representation as un-biased reference knowledge for a novel classification task. By casting the method in the multi-view setting, we also make it possible to use different features for different databases. We call the algorithm MUST, Multitask Unaligned Shared knowledge Transfer. Through extensive experiments on five public datasets, we show that MUST consistently improves the cross-datasets generalization performance. © 2013 Springer-Verlag.

Human pavlovian-instrumental transfer.

The vigor with which a participant performs actions that produce valuable outcomes is subject to a complex set of motivational influences. Many of these are believed to involve the amygdala and the nucleus accumbens, which act as an interface between limbic and motor systems. One prominent class of influences is called pavlovian-instrumental transfer (PIT), in which the motivational characteristics of a predictor influence the vigor of an action with respect to which it is formally completely independent. We provide a demonstration of behavioral PIT in humans, with an audiovisual predictor of the noncontingent delivery of money inducing participants to perform more avidly an action involving squeezing a handgrip to earn money. Furthermore, using functional magnetic resonance imaging, we show that this enhanced motivation was associated with a trial-by-trial correlation with the blood oxygenation level-dependent (BOLD) signal in the nucleus accumbens and a subject-by-subject correlation with the BOLD signal in the amygdala. Our data dovetails well with the animal literature and sheds light on the neural control of vigor.

Turbine blade tip heat transfer in low speed and high speed flows

In this paper, high and low speed tip flows are investigated for a high-pressure turbine blade. Previous experimental data are used to validate a CFD code, which is then used to study the tip heat transfer in high and low speed cascades. The results show that at engine representative Mach numbers the tip flow is predominantly transonic. Thus, compared to the low speed tip flow, the heat transfer is affected by reductions in both the heat transfer coefficient and the recovery temperature. The high Mach numbers in the tip region (M>1.5) lead to large local variations in recovery temperature. Significant changes in the heat transfer coefficient are also observed. These are due to changes in the structure of the tip flow at high speed. At high speeds, the pressure side corner separation bubble reattachment occurs through supersonic acceleration which halves the length of the bubble when the tip gap exit Mach number is increased from 0.1 to 1.0. In addition, shock/boundary-layer interactions within the tip gap lead to large changes in the tip boundary-layer thickness. These effects give rise to significant differences in the heat-transfer coefficient within the tip region compared to the low-speed tip flow. Compared to the low speed tip flow, the high speed tip flow is much less dominated by turbulent dissipation and is thus less sensitive to the choice of turbulence model. These results clearly demonstrate that blade tip heat transfer is a strong function of Mach number, an important implication when considering the use of low speed experimental testing and associated CFD validation in engine blade tip design. Copyright © 2009 by ASME.