Passivation and hydrophobic effects can significantly enhance the stability and optoelectronic properties of MAPbI3 films.

A rapid and simple process to prepare CH3NH3PbI3 perovskite solar cells in ambient air by adding 2-pyridylthiourea in the precursor solution was reported. The newly developed PSC exhibited an enhanced PCE of 18.2% along with enhanced stability under heat and humidity.

Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet transformed domain via a relatively new spectral feature processing technique -singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine (SVM) classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracies over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artefacts introduced during the data acquisition process as well. By adding an extra spatial post-processing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral-spatial classification methods.Index Terms-Hyperspectral imaging (HSI), the curvelet transform, singular spectrum analysis (SSA), classification, support vector machine (SVM), spatial post-processing.Manuscript received on October xx, 2015. This work is partially funded by the following projects:

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Although research on detection of saliency and visual attention has been active over recent years, most of the existing work focuses on still image rather than video based saliency. In this paper, a deep learning based hybrid spatiotemporal saliency feature extraction framework is proposed for saliency detection from video footages. The deep learning model is used for the extraction of high-level features from raw video data, and they are then integrated with other high-level features. The deep learning network has been found extremely effective for extracting hidden features than that of conventional handcrafted methodology. The effectiveness for using hybrid high-level features for saliency detection in video is demonstrated in this work. Rather than using only one static image, the proposed deep learning model take several consecutive frames as input and both the spatial and temporal characteristics are considered when computing saliency maps. The efficacy of the proposed hybrid feature framework is evaluated by five databases with human gaze complex scenes. Experimental results show that the proposed model outperforms five other state-of-the-art video saliency detection approaches. In addition, the proposed framework is found useful for other video content based applications such as video highlights. As a result, a large movie clip dataset together with labeled video highlights is generated.

Increasing evidence showed that abnormal proliferation and migration of vascular smooth muscle cells (VSMCs) are common event in the pathophysiology of many vascular diseases, including atherosclerosis and restenosis after angioplasty. Among the underlying mechanisms, oxidative stress is one of the principal contributors to the proliferation and migration of VSMCs. Oxidative stress occurs as a result of persistent production of reactive oxygen species (ROS). Recently, the protective effects of peroxisome proliferator-activated receptor γ (PPARγ) against oxidative stress/ROS in other cell types provide new insights to inhibit the suggests that PPARγ may regulate VSMCs function. However, it remains unclear whether activation of PPARγ can attenuate oxidative stress and further inhibit VSMC proliferation and migration. In this study, we therefore investigated the effect of PPARγ on inhibiting VSMC oxidative stress and the capability of proliferation and migration, and the potential role of mitochondrial uncoupling protein 2 (UCP2) in oxidative stress. It was found that platelet derived growth factor-BB (PDGF-BB) induced VSMC proliferation and migration as well as ROS production; PPARγ inhibited PDGF-BB-induced VSMC proliferation, migration and oxidative stress; PPARγ activation upregulated UCP2 expression in VSMCs; PPARγ inhibited PDGF-BB-induced ROS in VSMCs by upregulating UCP2 expression; PPARγ ameliorated injury-induced oxidative stress and intimal hyperplasia (IH) in UCP2-dependent manner. In conclusion, our study provides evidence that activation of PPARγ can attenuate ROS and VSMC proliferation and migration by upregulating UCP2 expression, and thus inhibit IH following carotid injury. These findings suggest PPARγ may represent a prospective target for the prevention and treatment of IH-associated vascular diseases.

In the last few decades, organic solar cells (OSCs) have drawn broad interest owing to their advantages such as being low cost, flexible, semitransparent, non-toxic, and ideal for roll-to-roll large-scale processing. Significant advances have been made in the field of OSCs containing high-performance active layer materials, electrodes, and interlayers, as well as novel device structures. Particularly, the innovation of active layer materials, including novel acceptors and donors, has contributed significantly to the power conversion efficiency (PCE) improvement in OSCs. In this review, high-performance acceptors, containing fullerene derivatives, small molecular, and polymeric non-fullerene acceptors (NFAs), are discussed in detail. Meanwhile, highly efficient donor materials designed for fullerene- and NFA-based OSCs are also presented. Additionally, motivated by the incessant developments of donor and acceptor materials, recent advances in the field of ternary and tandem OSCs are reviewed as well.

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