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Microwave absorbing material is a kind of material that can absorb microwave and electromagnetic (EM) energy and has less reflection and scattering. Qualified microwave absorbers can effectively prevent EM pollution and protect human health. This review briefly introduces the principles of microwave absorption (MA), more importantly, the research progress in the nanostructure design and composition regulation of carbon spheres (CSs) are reviewed. The nanostructure design is dominated by hollow and porous nature, and the composition regulation is based on conductive/dielectric, magnetic, as well as conductive/dielectric-magnetic components. Due to the synergistic effect among varieties of components and the clever nanostructure design, the CSs and their composites can achieve preferable MA performance than traditional carbon materials. At the same time, the challenges and prospects of the research on CSs are put forward, which provide new ideas for further study of microwave absorption based on CSs. This review mainly reports the structure design and composition regulation of carbon based absorbers with tunable absorbent performance. Structure design is given priority to with hollow and hollow porous structures, and the composition regulation is adjusted mainly by adding conductive, dielectric and magnetic materials. [Display omitted] [ABSTRACT FROM AUTHOR]

Correction for 'Morphology control of eco-friendly chitosan-derived carbon aerogels for efficient microwave absorption at thin thickness and thermal stealth' by Xinting Chen et al., Green Chem., 2022, 24, 5280–5290, https://doi.org/10.1039/d2gc01604d. [ABSTRACT FROM AUTHOR]

• A multi-level structure absorber was established by different scale combinations. • The electromagnetic wave absorptivity of 99.999 % was achieved at 1.8 mm. • The broad bandwidth almost covered the entire Ku -band. • The difference temperature between the surface and the ambient background was less than 5 °C. • In the far-field condition, the radar cross-section reduction value was reduced by ∼ 21.5 dB m2. Given the complex practical environment and application fields, it is urgent to integrate various functions into one material. Based on the introduction of nano-sites, the creation of multilevel structures synergy, a light weight electromagnetic wave-infrared stealth compatible composite was prepared for the first time. In which the multi-contact sites and interfaces greatly limited the thermal conduction and enhanced the synergistic polarization. Moreover, the gold nanoparticles with low emissivity not only reduced the external thermal radiation, but also promoted the conduction loss. Optimized composites showed excellent thermal stealth and ultra-high electromagnetic wave absorption (EMA) performance. The difference between the surface and the ambient background temperature was less than 5 °C when the target temperature was 120 °C, thus the detection distance of the infrared system to the target would be effectively reduced. The products provided a minimum reflection loss of −55.9 dB with a thickness of only 1.8 mm, and an ultra-wide effective absorption bandwidth of 16 GHz within 1–5 mm. Surprisingly, in the far-field condition, the minimum radar cross section value can achieve −49 dB m2. This work provides valuable insights for synthesizing functional materials with excellent EMA and thermal stealth properties. [ABSTRACT FROM AUTHOR]

Object detection from UAV (unmanned aerial vehicle) images is a crucial and challenging task in the field of computer vision. The task suffers from the difficulties of small dense objects, low pixel occupation of objects, and features that are not easily extracted in images. In this paper, we proposed a multilayer feature fusion algorithm named SCA-YOLO (spatial and coordinate attention enhancement YOLO) for small object detection with hybrid attention mechanisms. It uses the single-stage detection algorithm YOLOv5 as the base framework. Firstly, a hybrid attention module with associated coordinate attention is designed to enhance the feature extraction of small objects. Secondly, to address the problem that small objects are vulnerable to being disturbed by the complex background information on UAV images, an improved SEB (simple and efficient bottleneck) module is designed to further distinguish foreground and background features. Thirdly, a multilayer feature fusion structure is built to perform channel stitching of shallow and deep feature maps, as well as to enrich the semantic information of shallow features by adding horizontal jump connections. Finally, experiments are conducted on the VisDrone2020 dataset, which involves a large number of small objects photographed by drones. In addition, we also conduct extended experiments on the DOTA dataset and PASCAL VOC dataset. Comparative experimental results indicate that the proposed method considerably improves the accuracy of small object detection on multiple benchmark datasets. [ABSTRACT FROM AUTHOR]

Human motion prediction aims to forecast future human poses given a historical motion. Current state-of-the-art approaches rely on deep learning architectures of arbitrary complexity, such as Recurrent Neural Networks (RNN), Graph Convolutional Networks (GCN), and typically requires multiple training stages and more parameters. In addition, existing learning-based methods fail to model the observation that human motion tends to repeat itself. In summary, to address the problem of the existing methods neglecting the repetitive nature of human motion, we first introduced a Multi-level Attention Mechanism (MAM) that explicitly leverages this observation to find relevant historical information for predicting future motion. Instead of modeling frame-wise attention via pose similarity, the motion attention was extracted to capture the similarity between the current motion context and the historical motion sub-sequences. In this context, the use of different types of attention, computed at joint, body part, and full pose levels was studied. Furthermore, to address the complexity of existing algorithms based on deep learning architectures, a Fully connected Transpose MLP (FTMLP) model was introduced. By combining a MLP network with a fully connected and transposed layer to process the aggregated relevant past movements, the patterns of motion from the long-term history can be quickly and efficiently used to predict the future poses. The experimental results on standard motion prediction benchmark datasets Human3.6 M and CMU motion capture dataset show that our model is able to make accurate short- and long-term predictions. [ABSTRACT FROM AUTHOR]

Abstract: Eco-city and low carbon city development enjoy high priority among a great many local governments in China. Underlying this is a host of serious environmental problems resulting from growing urbanization, industrialization and motorization. Still, political realities dictate that economic growth figures cannot be compromised. Consequently, strong eco-city agendas are often combined with the wish to become knowledge cities. To import science and technology from abroad, collaboration is established between Chinese and foreign governments, corporations and knowledge institutes to develop cities with both ecological and knowledge features. This article aims to identify conditions for robust Sino-foreign partnerships in eco-cities. It examines how Sino-foreign initiatives are organized with their strengths and weaknesses and then construes a typology of Sino-foreign eco knowledge city initiatives. A newcomer on this stage, Shenzhen Sino-Dutch Low Carbon City, in which the authors have been personally involved as initiators and advisors, is then described in depth and compared with other Sino-foreign eco-knowledge cities in terms of its organizational stability, mechanisms for political support and the role of knowledge institutes. We conclude that initiatives where collaboration is embedded in solid frameworks of bi-national political support, public and private investment, advice and exchange are the most likely to succeed. Sino-Singaporean collaboration rests on stronger foundations than Sino-European collaboration, while among European, Swedish and Germans players at their turn have been more effective in building structural long-term relations than others. [Copyright &y& Elsevier]

Postsurgical localized chemotherapy for breast cancer recurrence (BCR) still faces many problems which dampen researchers' enthusiasm and discounted prognosis. Simple strategies with controllable toxicities are expected to address these hurdles. Lentinan (LNT) has excellent biocompatibility and notable antitumor activity but rather low bioavailability after intravenous or oral administration. Here, a sponge-like LNT/chitosan composite (LNT/CS sponge) was prepared for efficient local delivery to prevent postoperative BCR. The obtained sponges exhibit uniform porosity and sustained release of LNT in vitro and in vivo. Furthermore, the sponges were implanted and showed significant reduction of postsurgical recurrence and suppression of long-term tumor regrowth with favorable biocompatibility in a subcutaneous postsurgical recurrence mouse model. Subsequent studies revealed that LNT can restrain the stemness of breast cancer cells, which may account for the long-term inhibition of tumor relapse. Therefore, LNT/CS sponge has a great potential as a promising alternative for postsurgical BCR. • Lentinan/chitosan composite for local delivery was prepared for the first time. • Sustained in vivo release of LNT from the composite was confirmed. • The composite showed good biosafety and long-term inhibition of cancer relapse. • LNT inhibited breast cancer relapse via restraining stemness of residual tumor. [ABSTRACT FROM AUTHOR]