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To control the complex energy flow between neighboring microstructures for realizing the extreme acoustic functionality using a large-scale non-local metamaterial, here this paper shows that a collaborative non-local topology-optimization methodology is developed to construct gradient acoustic metasurfaces with smooth boundaries for the broadband large-angle reflection. An inverse-designed non-local acoustic metasurface is numerically and experimentally validated to support the large reflection angles within the low-frequency broadband range while maintaining high efficiency greater than 98.9%. It is further revealed that the strong non-local energy exchange between the multiple adjacent optimized microstructures can enable arbitrary surface impedances, thus obtaining efficient large-angle anomalous reflection from 52° to 90°. The present collaborative non-local topology-optimization methodology is not only applicable to airborne acoustic waves, but also can be extended to the customization of efficient wave functionalities of elastic and underwater acoustic metasurfaces.
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http://dx.doi.org/10.1121/10.0035880 | DOI Listing |
Materials (Basel)
August 2025
College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, China.
Blast-furnace staves serve as critical protective components in ironmaking, requiring synergistic optimization of slag-coating behavior and self-protection capability to extend furnace lifespan and reduce energy consumption. Traditional integer-order heat transfer models, constrained by assumptions of homogeneous materials and instantaneous heat conduction, fail to accurately capture the cross-scale thermal memory effects and non-local diffusion characteristics in multiphase heterogeneous blast-furnace systems, leading to substantial inaccuracies in predicting dynamic slag-layer evolution. This review synthesizes recent advancements across three interlinked dimensions: first, analyzing design principles of zonal staves and how refractory material properties influence slag-layer formation, proposing a "high thermal conductivity-low thermal expansion" material matching strategy to mitigate thermal stress cracks through optimized synergy; second, developing a mechanistic model by introducing the Caputo fractional derivative to construct a non-Fourier heat-transfer framework (i.
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June 2025
Architecture and Design College, Nanchang University, Nanchang 330031, China.
Fruit freshness monitoring represents one of the key research foci in the quality control of fruits and vegetables. Traditional manual inspection methods are characterized by subjectivity and inefficiency, which renders them unsuitable for large-scale and real-time detection demands. Automated detection methods based on deep learning have increasingly attracted attention.
View Article and Find Full Text PDFFront Plant Sci
May 2025
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China.
The early stage pathogens of plant diseases have the characteristic of low concentration and difficult detection, which exacerbates the difficulty of tracing the disease, leading to rapid spread and difficulty in effective control. Currently, common plant disease detection techniques such as imaging and spectroscopy can only be applied after the occurrence and manifestation of diseases, and it is difficult to accurately locate the source of disease outbreaks during spore germination or propagation stages. Therefore, this paper proposes a method for locating the source of airborne plant diseases based on the non-local-interpolation algorithm.
View Article and Find Full Text PDFThe non-locality of quantum correlations is a fundamental feature of quantum theory. The Bell inequality serves as a benchmark for distinguishing between predictions made by quantum theory and local hidden variable theory (LHVT). Recent advancements in photon-entanglement experiments have addressed potential loopholes and have observed significant violations of variants of Bell inequality.
View Article and Find Full Text PDFIEEE Trans Image Process
January 2025
Vision Transformer (ViT), known for capturing non-local features, is an effective tool for hyperspectral image classification (HSIC). However, ViT's multi-head self-attention (MHSA) mechanism often struggles to balance local details and long-range relationships for complex high-dimensional data, leading to a loss in spectral-spatial information representation. To address this issue, we propose a deformable convolution-enhanced hierarchical Transformer with spectral-spatial cluster attention (SClusterFormer) for HSIC.
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