Germanium-lead perovskite light-emitting diodes.

Reducing environmental impact is a key challenge for perovskite optoelectronics, as most high-performance devices are based on potentially toxic lead-halide perovskites. For photovoltaic solar cells, tin-lead (Sn-Pb) perovskite materials provide a promising solution for reducing toxicity. However, Sn-Pb perovskites typically exhibit low luminescence efficiencies, and are not ideal for light-emitting applications. Here we demonstrate highly luminescent germanium-lead (Ge-Pb) perovskite films with photoluminescence quantum efficiencies (PLQEs) of up to ~71%, showing a considerable relative improvement of ~34% over similarly prepared Ge-free, Pb-based perovskite films. In our initial demonstration of Ge-Pb perovskite LEDs, we achieve external quantum efficiencies (EQEs) of up to ~13.1% at high brightness (~1900 cd m-2), a step forward for reduced-toxicity perovskite LEDs. Our findings offer a new solution for developing eco-friendly light-emitting technologies based on perovskite semiconductors

Event-Based Optical Flow Estimation with Spatio-Temporal Backpropagation Trained Spiking Neural Network

The advantages of an event camera, such as low power consumption, large dynamic range, and low data redundancy, enable it to shine in extreme environments where traditional image sensors are not competent, especially in high-speed moving target capture and extreme lighting conditions. Optical flow reflects the target’s movement information, and the target’s detailed movement can be obtained using the event camera’s optical flow information. However, the existing neural network methods for optical flow prediction of event cameras has the problems of extensive computation and high energy consumption in hardware implementation. The spike neural network has spatiotemporal coding characteristics, so it can be compatible with the spatiotemporal data of an event camera. Moreover, the sparse coding characteristic of the spike neural network makes it run with ultra-low power consumption on neuromorphic hardware. However, because of the algorithmic and training complexity, the spike neural network has not been applied in the prediction of the optical flow for the event camera. For this case, this paper proposes an end-to-end spike neural network to predict the optical flow of the discrete spatiotemporal data stream for the event camera. The network is trained with the spatio-temporal backpropagation method in a self-supervised way, which fully combines the spatiotemporal characteristics of the event camera while improving the network performance. Compared with the existing methods on the public dataset, the experimental results show that the method proposed in this paper is equivalent to the best existing methods in terms of optical flow prediction accuracy, and it can save 99% more power consumption than the existing algorithm, which is greatly beneficial to the hardware implementation of the event camera optical flow prediction., laying the groundwork for future low-power hardware implementation of optical flow prediction for event cameras

Effects of Population Density on Revegetation of <i>Artemisia sphaerocephala</i> and Soil Traits in a Desert Ecosystem

Soil desertification is a serious problem in arid northwestern China that threatens ecological sustainability. Artemisia sphaerocephala, a dominant shrub species, play an important role in the conservation of water and the restoration of soil in the desert ecosystem. However, the poor establishment of A. sphaerocephala often limits plant revegetation, and the optimal population density for sustainable growth is largely unknown. Here, we determined key soil properties and plant growth characteristics associated with different population densities of A. sphaerocephala (including from 1.1, 2.1, 3.1, 3.9 to 5.3 plants per m2) in the resource-limited Alashan desert of northwestern China. The results showed that plant population density was the primary factor determining the revegetation of A. sphaerocephala, followed by soil water availability. Soil N, P and K content, and soil fractal dimensions also contributed to the vegetation and productivity. Soil nutrients were mostly accumulated in the topsoil layers, coincidental with the root distribution pattern in which 57% to 82% of total roots were distributed in the top 20 cm soil layer. The concentrations of soil nutrients in higher population densities (3.9 to 5.3 plants per m2) were greater than those in lower population densities (1.1 to 2.1 plants per m2), suggesting that A. sphaerocephala may have the ability to promote nutrient cycling in the desert ecosystem. We conclude that the optimal population density for the best growth of revegetated A. sphaerocephala was 3 plants per m2

Progress and Perspectives of Desalination in China

In recent decades, the ever-growing demands for clean water in households and industries have urged researchers to take every possible step to deal with the global water crisis. Seawater desalination has turned out to be the most promising and efficient way to provide clean water. Owing to the advancement of synthetic chemistries and technologies, great success has been achieved in the desalination and utilization of seawater worldwide. China, with the world’s largest population, has pushed the development of desalination and multipurpose utilization of seawater further in respect of materials, technologies and services, etc. This review reports recent progress of desalination technologies accomplished in China, from the viewpoints of facilities and equipment, collaborations, technologies, applications, research abilities, services, and standard systems. Inspired by the Fourteenth Five-year Plan, it also proposes future perspectives of desalination in China

Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment

To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model&rsquo;s understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model

Genome-Wide Analysis and Expression of the <i>GRAS</i> Transcription Factor Family in <i>Avena sativa</i>

The GRAS transcription factor is an important transcription factor in plants. In recent years, more GRAS genes have been identified in many plant species. However, the GRAS gene family has not yet been studied in Avena sativa. We identified 100 members of the GRAS gene family in A. sativa (Avena sativa), named them AsGRAS1~AsGRAS100 according to the positions of 21 chromosomes, and classified them into 9 subfamilies. In this study, the motif and gene structures were also relatively conserved in the same subfamilies. At the same time, we found a great deal related to the stress of cis-acting promoter regulatory elements (MBS, ABRE, and TC-rich repeat elements). qRT-PCR suggested that the AsGRAS gene family (GRAS gene family in A. sativa) can regulate the response to salt, saline–alkali, and cold and freezing abiotic stresses. The current study provides original and detailed information about the AsGRAS gene family, which contributes to the functional characterization of GRAS proteins in other plants

Measurements (in mm) of the type series of three sympatric <i>Xenophrys</i> species in the middle Luoxiao Mountains, southeast China.

<p>The range, mean value, and standard deviation are given for each parameter.</p