Shaking table testing is a common experimental method in earthquake engineering for performance assessment of structures subjected to dynamic excitations. As most shaking tables are driven by servo hydraulic actuators to meet the potentially significant force stroke demand, the review is restricted to hydraulic shaking tables. The purpose of the control systems of hydraulic shaking tables is to reproduce reference signals with low distortion. Accurate control of actuators is vital to the effectiveness of such apparatus. However, the system dynamics of a shaking table and the specimens to be tested on the shaking table are usually very complex and nonlinear. Achieving the control goal can prove to be challenging. A variety of closed- and open-loop control algorithms has been developed to solve different control problems. With the focus placed on the control schemes for hydraulic shaking tables, the paper reviews algorithms that are currently used in the testing industry, as well as those which are the subject of academic and industrial research. It is by no means a complete survey but provides key reference for further development.

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To improve the magnetorheological (MR) properties and dispersion stability of the carbonyl iron (CI) particles, bidisperse magnetorheological (BMR) fluids consisting of magnetic micron-sized CI and nanoparticles dual-coated with gelatin and multi-walled carbon nanotubes (MWCNTs) were synthesized for the first time. Gelatin was used as a grafting agent to improve the stability of bidisperse magnetic particles and restrict the oxidation of nanoparticles (Fe 3 O 4 ). And a dense network composed of MWCNTs on the surface of gelatin-coated bidisperse particles was fabricated based on the self-assembly of MWCNTs to produce considerably rough surfaces. The influence of functional dual-coated layer on rheological performance such as shear stress and yield stress behavior was investigated by a rotational rheometer upon various magnetic field applications. Additionally, the dispersion stability was measured through sedimentation tests. The results showed that CI-Fe 3 O 4 -Gelatin-MWCNTs (CI-Fe 3 O 4 -G-NT) magnetic microspheres possessed enhanced MR properties compared with those from CI-Fe 3 O 4 -Gelatin (CI-Fe 3 O 4 -G) microspheres, while the dispersion stability of CI-Fe 3 O 4 -G-NT microspheres was still maintained.

Immunotherapy has made remarkable strides in cancer therapy over the past decade. However, such emerging therapy still suffers from the low response rates and immune‐related adverse events. Various strategies have been developed to overcome these serious challenges. Therein, sonodynamic therapy (SDT), as a non‐invasive treatment, has received ever‐increasing attention especially in the treatment of deep‐seated tumors. Significantly, SDT can effectively induce immunogenic cell death to trigger systemic anti‐tumor immune response, termed sonodynamic immunotherapy. The rapid development of nanotechnology has revolutionized SDT effects with robust immune response induction. As a result, more and more innovative nanosonosensitizers and synergistic treatment modalities are established with superior efficacy and safe profile. In this review, the recent advances in cancer sonodynamic immunotherapy are summarized with a particular emphasis on how nanotechnology can be explored to harness SDT for amplifying anti‐tumor immune response. Moreover, the current challenges in this field and the prospects for its clinical translation are also presented. It is anticipated that this review can provide rational guidance and facilitate the development of nanomaterials‐assisted sonodynamic immunotherapy, helping to pave the way for next‐generation cancer therapy and eventually achieve a durable response in patients.

This paper focuses on an electro-hydraulic servo system, which is derived from a shaking table. It proposes a control scheme based on a back propagation (BP) neural network, whose weights are trained by the particle swarm optimization (PSO) according to the fitness, which is determined by the input and the feedback signals. Each particle of PSO includes weights and thresholds of BP. The movement of each particle is adjusted by its local best-known position and the global best-known position in the searching space. With the update, a satisfactory solution can be achieved. In order to show the performance of the proposed control scheme, the designed network is also trained and tested by BP only. The comparisons between the PSO-BP and BP networks demonstrate that the PSO-BP one has better performance than that of BP, both in convergence speed and in convergence accuracy.