A piezoelectric-driven actuator based on coupling motion has been proposed and tested to achieve a large linear working stroke with high resolution. “Z-shaped” flexure hinges are exploited for the symmetric flexure hinge mechanism to reduce the structural stress. Coupling motion is obtained by placing this symmetric flexure hinge mechanism with an angle of θ = 20° to the slider. Experimental results indicate that linear motion with a large working stroke is effectively obtained by this coupling motion; the maximum motion speed is V _s = 6057 μm/s and the maximum output force is F _g = 350 g. Additionally, the influences of input frequency f and input voltage U _e are investigated, and the system kinetic model is established to better analyze the performance of this designed piezoelectric-driven linear actuator.

Nanoindentation techniques, with high spatial resolution and force sensitivity, have recently been moved into the center of the spotlight for measuring the mechanical properties of biomaterials, especially bridging the scales from the molecular via the cellular and tissue all the way to the organ level, whereas characterizing soft biomaterials, especially down to biomolecules, is fraught with more pitfalls compared with the hard biomaterials. In this review we detail the constitutive behavior of soft biomaterials under nanoindentation (including AFM) and present the characteristics of experimental aspects in detail, such as the adaption of instrumentation and indentation response of soft biomaterials. We further show some applications, and discuss the challenges and perspectives related to nanoindentation of soft biomaterials, a technique that can pinpoint the mechanical properties of soft biomaterials for the scale-span is far-reaching for understanding biomechanics and mechanobiology.

Remote sensing scene classification remains challenging due to the complexity and variety of scenes. With the development of attention-based methods, Convolutional Neural Networks (CNNs) have achieved competitive performance in remote sensing scene classification tasks. As an important method of the attention-based model, the Transformer has achieved great success in the field of natural language processing. Recently, the Transformer has been used for computer vision tasks. However, most existing methods divide the original image into multiple patches and encode the patches as the input of the Transformer, which limits the model’s ability to learn the overall features of the image. In this paper, we propose a new remote sensing scene classification method, Remote Sensing Transformer (TRS), a powerful “pure CNNs → Convolution + Transformer → pure Transformers” structure. First, we integrate self-attention into ResNet in a novel way, using our proposed Multi-Head Self-Attention layer instead of 3 × 3 spatial revolutions in the bottleneck. Then we connect multiple pure Transformer encoders to further improve the representation learning performance completely depending on attention. Finally, we use a linear classifier for classification. We train our model on four public remote sensing scene datasets: UC-Merced, AID, NWPU-RESISC45, and OPTIMAL-31. The experimental results show that TRS exceeds the state-of-the-art methods and achieves higher accuracy.

Piezo-driven precision positioning platforms with large stroke are greatly demanded in both scientific research and industrial applications. Although various principles have been proposed to design piezo-driven positioning platforms, the problem of backward motion commonly exists, greatly hindering their applications. To suppress the backward motion, in this paper, an idea by generating a forward frictional force to balance the reversed frictional force was proposed. Correspondingly, a specific arc-shape flexure hinge was designed to generate the forward frictional force via its elastic recovery. To verify the feasibility of the proposed idea, a positioning platform with alterable initial gap between contact surfaces was designed by the parasitic motion principle (PMP). By measuring the output displacement under various initial gaps, a critical initial gap was identified, at which the positioning platform could output stepping displacement without backward motion. Under this critical initial gap, effects of driving voltage and frequency on the backward motion were further investigated. In addition, the natural frequency and loading capacity of the positioning platform were tested. This study confirms that using PMP, it is feasible to design piezo-driven positioning platforms with completely suppressed backward motion, which would enhance the applications of the PMP and PMP positioning platforms.

Molecular dynamics simulations of nanoindentation tests on monocrystalline silicon (010) surface were conducted to investigate the mechanical properties and deformation mechanism from cryogenic temperature being 10 K to room temperature being 300 K. Furthermore, the load-displacement curves were obtained and the phase transformation was investigated at different temperatures. The results show that the phase transformation occurs both at cryogenic temperatures and at room temperature. By searching for the presence of the unique non-bonded fifth neighbour atom, the metastable phases (Si-III and Si-XII) with fourfold coordination could be distinguished from Si-I phase during the loading stage of nanoindentation process. The Si-II, Si-XIII and amorphous phase were also found in the region beneath the indenter. Moreover, through the degree of alignment of the metastable phases along specific crystal orientation at different temperatures, it was found that the temperature had effect on the anisotropy of the monocrystalline silicon and the simulation results indicate that the anisotropy of monocrystalline silicon is strengthened at low temperatures.