Biomimetic functional surfaces are attracting increasing attention for various technological applications, especially the superhydrophobic surfaces inspired by plant leaves. However, the replication of the complex hierarchical microstructures is limited by the traditional fabrication techniques. In this paper, superhydrophobic micro-scale artificial hairs with eggbeater heads inspired by Salvinia molesta leaf was fabricated by the Immersed surface accumulation three dimensional (3D) printing process. Multi-walled carbon nanotubes were added to the photocurable resins to enhance the surface roughness and mechanical strength of the microstructures. The 3D printed eggbeater surface reveals interesting properties in terms of superhydrophobilicity and petal effect. The results show that a hydrophilic material can macroscopically behave as hydrophobic if a surface has proper microstructured features. The controllable adhesive force (from 23 μN to 55 μN) can be easily tuned with different number of eggbeater arms for potential applications such as micro hand for droplet manipulation. Furthermore, a new energy-efficient oil/water separation solution based on our biomimetic structures was demonstrated. The results show that the 3D-printed eggbeater structure could have numerous applications, including water droplet manipulation, 3D cell culture, micro reactor, oil spill clean-up, and oil/water separation.

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A polyamide-66 ionised with 6.5 mol% of CaCl2, an optimum heterogeneous nucleator, maximally expedites poly(ethylene terephthalate) crystallisation by medium-concentration ion–dipole interactions.

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The recognition rate of SIFT algorithm in single hand vein database has been as high as 99.5%. But with the development of Internet-plus technology, the demand for distributed systems becomes more and more significant. However, the problem of picture quality caused by cross-device makes the intraclass variations larger. For example, when gathering the dorsal hand vein images, subtle changes in relative distance and orientation among the imaging camera, the illumination LED arrays and the different location of users’ hand, as well as shielding by the external housing box from ambient light sources and so on, these will make large difference to one person’s hand images. So, including the contrast, the lightness, the shifting, the angle of rotation, the size and so on, these differences make it possible to use some traditional methods to recognize dorsal hand vein with a low recognition rate of less than 50%. Therefore, based on the traditional SIFT, this paper optimized the scale factor [Formula: see text], extreme searching neighborhood structure and matching threshold [Formula: see text]. It can be seen that the cross-device hand vein feature is more robust, and the recognition rate reached an average of 88.5%.

Histograms are used in many ways in conventional databases and in data stream processing for summarizing massive data distributions. Previous work on constructing histograms on data streams with provable guarantees have not taken into account the workload characteristics of databases which show some parts of the distributions to be more frequently used than the others; on the other hand, previous work for constructing histograms that do make use of the workload characteristics-and have demonstrated the significant advantage of exploiting workload information-have not come with provable guarantees on the accuracy of the histograms or the time and space bounds needed to obtain reasonable accuracy. We study the algorithmic complexity of constructing workload-optimal histograms on data streams.We present an algorithm for constructing a nearly-optimal histogram in nearly linear time and polylogarithmic space, in one pass. In the more general cash register model where data is streamed as a series of updates, we can build a histogram using polylogarithmic space, polylogarithmic time to process each item, and polylogarithmic post-processing time to build the histogram. These are the first known algorithmic results with provable guarantees for workload-optimal histogram construction, and rely on a notion of linear robustness we introduce here. All these results need the workload to be explicitly stored since we show that if the workload is summarized in small space lossily, algorithmic results such as above do not exist. However, we show that our algorithmic results can be extended efficiently to the case when the workload is compressed without loss by using, for example, run-length encoding or a universal compression scheme of Lempel-Ziv.

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