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The surface of lotus leaves has a hierarchical micro–nano-rough structure. We determined that the papillary structure also possesses hierarchical features on the microscale. We used alumina particles as rough structure building units to construct a Hierarchical Papillary microrough Structure (HPS) on a ceramic surface. The effects of the spatial distribution of HPS on the abrasion resistance and mechanical stability of hydrophobic coatings were investigated. Furthermore, for each HPS, the falling sand abrasion process was analyzed using finite element fluid mechanics analysis. A denser or more two-dimensional HPS implied that more area was impacted by the falling sand and that the abrasion amount and rate were higher. This is contrary to the common belief that when there are more wear-resistant substances on the surface, the abrasion resistance is better; thus, abrasion resistance does not necessarily depend entirely on the concentration of wear-resistant substances on the surface, but it is also influenced by the abrasion mode and the spatial distribution structure of the wear-resistant substances. The 3D stacked HPS (3D-HPS) with excellent abrasion resistance and rich pore structure considerably enhanced the mechanical stability of the hydrophobic coatings. These findings provide novel insights and a theoretical basis for designing spatial structures on high abrasion-resistant superhydrophobic ceramic surfaces.

Radar is widely used in human activity recognition (HAR). The radar-based HAR algorithms can be roughly categorized into feature map-based and point cloud-based. Between the two, the point cloud-based algorithms are more suitable for multiperson activity recognition (MPAR) tasks due to the spatial properties of point clouds and are therefore increasingly attracting attention. However, most existing point cloud-based algorithms make decisions by utilizing either Doppler or coordinate features of point clouds, which are not sufficient to characterize the activity; the remaining algorithms have large randomness in extracting both features. Neither of these algorithms can fully exploit the advantage of feature fusion, which is the key to recognition performance. To address this shortcoming, this article proposes a novel MPAR algorithm. First, a feature mapping approach is proposed and defined in equation form, the Doppler, range, azimuth, and elevation features of the point clouds are calculated and accumulated sequentially, so we can obtain the four time-domain feature maps. Second, with the four feature maps as inputs, a four-channel convolutional neural network (CNN) classification model with channel attention is trained for MPAR tasks. Datasets of multi-person activities are collected respectively under indoor circumstances and aquatic circumstances using the millimeter-wave multiple-input–multiple-output (MIMO) radar platform. The dataset-based evaluation performance shows that the proposed algorithm achieves accuracy results of 96.09% for the indoor-MPAR task and 93.97% for the aquatic-MPAR task, and outperforms three conventional point cloud-based algorithms in terms of the overall MPAR accuracy, the generalization ability to the aquatic activity recognition, and the robustness of distance and headcount.

Deep learning (DL) frameworks are widely used in various applications due to their superiority over conventional handcrafted feature-based algorithms. However, applying DL to time-range feature map-based people flow detection (PFD) with frequency-modulated continuous wave (FMCW) radar still faces several challenges: 1) simultaneously achieving people counting and motion direction recognition requires a unified framework; 2) existing mainstream network backbones designed for semantic information-rich optical images or natural language suffer performance loss in time-range feature maps with weak semantic information; and 3) the construction of labeled datasets in PFD scenes is usually costly, and limited data lead to performance loss due to overfitting. Therefore, this article proposes novel solutions from various aspects to efficiently apply DL to time-range feature map-based PFD. First, new preprocessing pipelines with Doppler spectrum analysis-based feature map truncation (DSAFMT) are proposed for the first time to simultaneously achieve people counting and direction recognition using a single radar in the radar PFD field. Second, a novel lightweight multiscale feature space fusion-based convolutional neural network backbone (MFSNet) is designed to efficiently extract multichannel differentiated representative features from time-range feature maps. Finally, a multiencoder-classifier cotraining architecture based on embedded features with two data synthesis methods and a newly designed loss function is proposed to improve the generalization ability of the algorithm. Using the test dataset collected from real scenes, the performance comparison results show that the proposed PFD algorithm outperforms the state-of-the-art algorithms and ablation studies demonstrate the effectiveness of each component of the proposed algorithm in PFD.

Tumor cells can activate platelets, which in turn facilitate tumor cell survival and dissemination. Platelets inhibition or blocking platelet–tumor cell interactions has become a strategy to suppress tumor progression. In this study, we investigated the effect of ticagrelor, a new antiplatelet drug, on tumor cell proliferation and metastasis. Our results show that ticagrelor not only inhibits the proliferation, migration, and invasion of B16F10 and Lewis lung carcinoma cells but also induces platelet apoptosis. In addition, we find that apoptosis of the platelet cells is dose dependent. Further, the result of in-vivo experiments proved that ticagrelor treatment decreased the tumor metastasis. The results of this study demonstrate that ticagrelor may be a potential anti-tumor agent for tumor metastasis.

We propose a remotely pumped Er-doped fiber amplifier (EDFA)-based 40-Gb/s downstream and 10-Gb/s upstream transmission long-reach wavelength division multiplexing passive optical network (WDMPON) scheme with downstream and upstream transmission implemented by quadrature phase-shift keying (QPSK) transceiver and reflective semiconductor optical amplifier (RSOA)-based intensity modulation direct detection (IM-DD). Transmissions of the 40-Gb/s QPSK downstream, using a Mach-Zehnder modulator, and the 10-Gb/s non-return-to-zero on-off keying (NRZ-OOK) upstream, using a 1.2-GHz RSOA equalized by fiber Bragg grating (FBG), are demonstrated with 40-km fiber transmission.