Impact of Supercapacitors on a Fuel-Cell-Based Triple Hybrid for Small Unmanned Aircraft

Hybrid fuel-cell-based propulsion systems have the potential to transform the use of small electric powered unmanned aircraft. Offering the possibility of greatly increased flight endurance and range over existing battery systems, hybrid systems also overcome some of the limitations inherent with fuel cell only systems such as low specific power and comparatively slow dynamic response. However, although there have been many fuel-cell/battery hybrid systems developed for unmanned aerial vehicle (UAV) propulsion, alternative power storage devices such as supercapacitors have not been adequately explored. Supercapacitors are fast acting with a high specific power and cycle lifetime, making them ideal candidates for use in a fuel cell hybrid system. This research develops and evaluates the use of hybrid fuel cell propulsion systems incorporating supercapacitors in the overall hybrid architecture. First, the performance of supercapacitors is evaluated and compared with the performance of fuel cells and batteries to enable an assessment of the strengths and weaknesses of the different energy sources. Next, the integration of supercapacitors with fuel cells is performed in a robust and efficient manner that ensures the hybrid system architecture maximises the benefits inherent in each of the power sources. A comparison is made between fuel-cell/battery, fuel-cell/supercapacitor, and fuel-cell/battery/supercapacitor hybrids for a UAV propulsion application through hardware-in-the-loop simulation. Finally, flight testing of a fuel-cell-based triple hybrid in a small UAV is performed to validate the operation and performance of the power system

Compound C inhibits nonsense-mediated RNA decay independently of AMPK

The nonsense mediated RNA decay (NMD) pathway safeguards the integrity of the transcriptome by targeting mRNAs with premature translation termination codons (PTCs) for degradation. It also regulates gene expression by degrading a large number of non-mutant RNAs (including mRNAs and noncoding RNAs) that bear NMD-inducing features. Consequently, NMD has been shown to influence development, cellular response to stress, and clinical outcome of many genetic diseases. Small molecules that can modulate NMD activity provide critical tools for understanding the mechanism and physiological functions of NMD, and they also offer potential means for treating certain genetic diseases and cancer. Therefore, there is an intense interest in identifying small-molecule NMD inhibitors or enhancers. It was previously reported that both inhibition of NMD and treatment with the AMPK-selective inhibitor Compound C (CC) induce autophagy in human cells, raising the possibility that CC may be capable of inhibiting NMD. Here we show that CC indeed has a NMD-inhibitory activity. Inhibition of NMD by CC is, however, independent of AMPK activity. As a competitive ATP analog, CC does not affect the kinase activity of SMG1, an essential NMD factor and the only known kinase in the NMD pathway. However, CC treatment down-regulates the protein levels of several NMD factors. The induction of autophagy by CC treatment is independent of ATF4, a NMD target that has been shown to promote autophagy in response to NMD inhibition. Our results reveal a new activity of CC as a NMD inhibitor, which has implications for its use in basic research and drug development

Perceptual Quality Assessment Based on Visual Attention Analysis

Most existing quality metrics do not take the human attention analysis into account. Attention to particular objects or regions is an important attribute of human vision and perception system in measuring perceived image and video qualities. This paper presents an approach for extracting visual attention regions based on a combination of a bottom-up saliency model and semantic image analysis. The use of PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural SIMilarity) in extracted attention regions is analyzed for image/video quality assessment, and a novel quality metric is proposed which can exploit the attributes of visual attention information adequately. The experimental results with respect to the subjective measurement demonstrate that the proposed metric outperforms the current methods