Strain distributions in lattice-mismatched semiconductor core-shell nanowires

The authors study the elastic deformation field in lattice-mismatched core-shell nanowires with single and multiple shells. The authors consider infinite wires with a hexagonal cross section under the assumption of translational symmetry. The strain distributions are found by minimizing the elastic energy per unit cell using the finite element method. The authors find that the trace of the strain is discontinuous with a simple, almost piecewise variation between core and shell, whereas the individual components of the strain can exhibit complex variations.Comment: 4 pages, 3 figure

Analysis of Renewable Energy Research Hotspots and Trends Based on Bibliometric and Patent Survey

In recent years, renewable energy has taken on an increasingly important role as a result of the depletion of traditional fossil fuels and the pressure of climate change. Due to the advantages of clean energy production and wide availability, research on renewable energy has increased worldwide. We collected data from the Web of Science and the Derwent Innovations Index to analyze research trends in the field of renewable energy. It was found that the number of research achievements in this field has developed rapidly worldwide since 2005. The United States ranks first in the quantity and quality of literature and fourth in the number of authorized patents. China ranks second and first regarding the quantity of literature and authorized patents, respectively. Biomass energy, wind energy, and solar energy are trending research topics in various stages of development. China has maintained close cooperation with the United States, the United Kingdom, Australia, and other countries

Breaking Free from Fusion Rule: A Fully Semantic-driven Infrared and Visible Image Fusion

Infrared and visible image fusion plays a vital role in the field of computer vision. Previous approaches make efforts to design various fusion rules in the loss functions. However, these experimental designed fusion rules make the methods more and more complex. Besides, most of them only focus on boosting the visual effects, thus showing unsatisfactory performance for the follow-up high-level vision tasks. To address these challenges, in this letter, we develop a semantic-level fusion network to sufficiently utilize the semantic guidance, emancipating the experimental designed fusion rules. In addition, to achieve a better semantic understanding of the feature fusion process, a fusion block based on the transformer is presented in a multi-scale manner. Moreover, we devise a regularization loss function, together with a training strategy, to fully use semantic guidance from the high-level vision tasks. Compared with state-of-the-art methods, our method does not depend on the hand-crafted fusion loss function. Still, it achieves superior performance on visual quality along with the follow-up high-level vision tasks

SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation

Accurate medical image segmentation especially for echocardiographic images with unmissable noise requires elaborate network design. Compared with manual design, Neural Architecture Search (NAS) realizes better segmentation results due to larger search space and automatic optimization, but most of the existing methods are weak in layer-wise feature aggregation and adopt a ``strong encoder, weak decoder" structure, insufficient to handle global relationships and local details. To resolve these issues, we propose a novel semi-supervised hybrid NAS network for accurate medical image segmentation termed SSHNN. In SSHNN, we creatively use convolution operation in layer-wise feature fusion instead of normalized scalars to avoid losing details, making NAS a stronger encoder. Moreover, Transformers are introduced for the compensation of global context and U-shaped decoder is designed to efficiently connect global context with local features. Specifically, we implement a semi-supervised algorithm Mean-Teacher to overcome the limited volume problem of labeled medical image dataset. Extensive experiments on CAMUS echocardiography dataset demonstrate that SSHNN outperforms state-of-the-art approaches and realizes accurate segmentation. Code will be made publicly available.Comment: Submitted to ICASSP202

Health monitoring device design and application for large synchronously excited multi-shaker vibration test facility

There are different kinds of equipments distributed in different locations for a large complicated multi-shaker vibration test facility, so it is challenging to monitor the real state of test facility thoroughly during its operation. Long-term operation of this test facility will lead to the degradation of reliability and malfunction, and sometimes the emergency stop of the whole test system that threatens the safety of the spacecraft seriously. This paper presents in detail the design and application of a set of health monitoring device for a large multi-shaker vibration test facility which is capable of monitoring the operation state in real time and predicting the potential malfunction of the whole test facility to ensure the reliability of this large test system and safety of the spacecraft during its environmental vibration test

Conditional DETR for Fast Training Convergence

The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7x faster for the backbones R50 and R101 and 10x faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.Comment: Accepted by ICCV 2021. The first two authors share first authorship, and the order was determined by rolling dic

External financing, channel power structure and product green R&D decisions in supply chains

Purpose – This study aims to focus on the optimal green R&D of a capital-constrained supply chain under different channel power structures as well as the impact of capital constraint, financing cost, channel power structure and cost-reducing efficiency on green R&D and supply chain profitability. Design/methodology/approach – A two-echelon supply chain is considered. The upstream firm engages in green R&D but has capital constraints that can be overcome by external financing. Green R&D is beneficial to reduce production costs and increase consumer demand. Based on whether or not the upstream firm is capital constrained and dominates the supply chain, four models are developed. Findings – Capital constraints significantly lower green R&D and supply chain profitability. Transferring leadership from the upstream to the downstream firms leads to higher green R&D levels and downstream firm profitability, whereas the upstream firm's profitability is increased (decreased) if green R&D investment efficiency is high (low) enough. Greater financing costs reduce green R&D and downstream firm profitability; however, the upstream firm's profitability under the model in which it functions as the follower increases if the initial capital is sufficient. More importantly, empirical analysis based on practice data is used to verify the theoretical results reported above. Practical implications – This study reveals how upstream firms in supply chains decide green R&D decisions in situations with capital constraints, providing managers and governments with an understanding of the impact of capital constraint, channel power structure, financing cost and cost-reducing efficiency on supply chain green R&D and profitability. Originality/value – The major contributions are the exploration of supply chain green R&D by taking into consideration channel power structures and cost-reducing efficiency and the validation of theoretical results using practice data