Bisphenol A (BPA) aggregates on Ag(111) shows a polymorphism between two supramolecular motifs leading to formation of distinct networks depending on thermal energy. With rising temperature a dimeric pairing scheme reversibly converts into a trimeric motif, which forms a hexagonal superstructure with complex dynamic characteristics. The trimeric arrangements notably organize spontaneously into a self-assembled one-component array with supramolecular BPA rotors embedded in a two-dimensional stator sublattice. By varying the temperature, the speed of the rotors can be controlled as monitored by direct visualization. A combination of scanning tunneling microscopy and dispersion-corrected density-functional tight-binding (DFTB-vdW(surf)) based molecular modeling reveals the exact atomistic position of each molecule within the assembly as well as the driving force for the formation of the supramolecular rotors.
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http://dx.doi.org/10.1021/acs.nanolett.5b05026 | DOI Listing |
Sensors (Basel)
December 2024
Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510630, China.
Real-time online monitoring of track deformation during railway construction is crucial for ensuring the safe operation of trains. However, existing monitoring technologies struggle to effectively monitor both static and dynamic events, often resulting in high false alarm rates. This paper presents a monitoring technology for track deformation during railway construction based on dynamic Brillouin optical time-domain reflectometry (Dy-BOTDR), which effectively meets requirements in the monitoring of both static and dynamic events of track deformation.
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December 2024
Shanghai Research Institute of Microelectronics, Peking University, Shanghai 201203, China.
Despite the accuracy and robustness attained in the field of object tracking, algorithms based on Siamese neural networks often over-rely on information from the initial frame, neglecting necessary updates to the template; furthermore, in prolonged tracking situations, such methodologies encounter challenges in efficiently addressing issues such as complete occlusion or instances where the target exits the frame. To tackle these issues, this study enhances the SiamRPN algorithm by integrating the convolutional block attention module (CBAM), which enhances spatial channel attention. Additionally, it integrates the kernelized correlation filters (KCFs) for enhanced feature template representation.
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December 2024
Faculty of Physics, University of Warsaw, Pasteura 5, 02-093 Warsaw, Poland.
We demonstrate high-resolution single-pixel imaging (SPI) in the visible and near-infrared wavelength ranges using an SPI framework that incorporates a novel, dedicated sampling scheme and a reconstruction algorithm optimized for the rapid imaging of highly sparse scenes at the native digital micromirror device (DMD) resolution of 1024 × 768. The reconstruction algorithm consists of two stages. In the first stage, the vector of SPI measurements is multiplied by the generalized inverse of the measurement matrix.
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December 2024
School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China.
In intelligent transportation systems, accurate vehicle target recognition within road scenarios is crucial for achieving intelligent traffic management. Addressing the challenges posed by complex environments and severe vehicle occlusion in such scenarios, this paper proposes a novel vehicle-detection method, YOLO-BOS. First, to bolster the feature-extraction capabilities of the backbone network, we propose a novel Bi-level Routing Spatial Attention (BRSA) mechanism, which selectively filters features based on task requirements and adjusts the importance of spatial locations to more accurately enhance relevant features.
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December 2024
School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China.
With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module.
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