Four-dimensional (4D) printed magnetoactive soft material (MASM) with a three-dimensional (3D) patterned magnetization profile possesses programmable shape transformation and controllable locomotion ability, showing promising applications in actuators and soft robotics. However, typical 4D printing strategies for MASM always introduced a printing magnetic field to orient the magneto-sensitive particles in polymers. Such strategies not only increase the cooperative control complexity of a 3D printer but may also induce local agglomeration of magneto-sensitive particles, which disturbs the magnetization of the already-printed structure. Herein, we proposed a novel 4D printing strategy that coupled the traditional 3D injection printing with the origami-based magnetization technique for easy fabrication of MASM objects with a 3D patterned magnetization profile. The 3D injection printing that can rapidly create complex 3D structures and the origami-based magnetization technique that can generate the spatial magnetization profile are combined for fabrication of 3D MASM objects to yield programmable transformation and controllable locomotion. A physics-based finite element model was also developed for the design guidance of origami-based magnetization and magnetic actuation transformation of MASM. We further demonstrated the diverse functions derived from the complex shape deformation of MASM-based robots, including a bionic human hand that played “rock-paper-scissors” game, a bionic butterfly that swung the wings on the flower, and a bionic turtle that crawled on the land and swam in the water.

A novel approach is developed in this paper for sensorless control of permanent magnet (PM) machines down to zero speed without signal injection or special pulsewidth modulation (PWM) techniques. Taking advantage of the low inductance in a PM machine, the phase current ripples under a conventional PWM excitation are measured to derive the rotor position and speed. The new approach can also be used to estimate the rotor position at standstill if a minor rotor saliency exists. Neither prior knowledge of machine parameters, nor any special signal injection, is needed for the rotor position detection. Sensorless control of a PM machine based on the proposed scheme has been investigated by comprehensive computer simulations. Experimental results are presented to verify the effectiveness of the approach.Index Terms-Current ripple, digital signal processor (DSP), permanent magnet (PM) machines, pulsewidth modulation (PWM), sensorless.

Ultrawideband (UWB) is well-suited for indoor positioning due to its high resolution and good penetration through objects. The observation model of UWB positioning is nonlinear. As one of nonlinear filter algorithms, extended Kalman filter (EKF) is widely used to estimate the position. In practical applications, the dynamic estimation is subject to the outliers caused by gross errors. However, the EKF cannot resist the effect of gross errors. The innovation will become abnormally large and the performance and the reliability of the filter algorithm are inevitably influenced. In this study, a robust EKF (REKF) method accompanied by hypothesis test and robust estimation is proposed. To judge the validity of model, the global test based on Mahalanobis distance is implemented to assess whether the test statistical term exceeds the threshold for outlier detection. To reduce and eliminate the effects of the individual outlier, the robust estimation using scheme III of the Institute of Geodesy and Geophysics of China (IGGIII) based on local test of the normalized residual is performed. Meanwhile, three kinds of stochastic models for outliers are expressed by modeling the contaminated distributions. Furthermore, the simulation and measurement experiments are performed to verify the effectiveness and feasibility of the proposed REKF for resisting the outliers. Simulation experiment results are given to demonstrate that the outliers following all the three kinds of contaminated distributions can be detected. The proposed REKF can effectively control the influences of the outliers being treated as systematic errors and large variance random errors. When the outliers come from the thick-tailed distribution, the robust estimation does not play a role, and the REKF are equivalent to the EKF method. The measured experiment results show that the outliers will be generated in the nonline-of-sight environment whose impact is abnormally serious. The robust estimation can provide relatively reliable optimized residuals and control the influences of the outliers caused by gross errors. We can believe that the proposed REKF is effective to resist the effects of outliers and improves the positioning accuracy compared with least-squares (LS) and EKF method. Moreover, the adaptive filter and ranging error model should be considered to compensate the state model errors and ranging systematic errors respectively. Then, the measurement outliers will be detected more correctly, and the robust estimation will be used effectively.

Pancreatic cancer (PC) is one of the most malignant tumors. Rapid progression and distant metastasis are the main causes of patient death. Hypoxia is a hallmark of multiple cancers and is involved in tumor biology. However, little is known about the roles of circRNAs in glycolysis and hypoxia-mediated progression of PC. Here, the expression pattern of hypoxia-related circRNAs was analyzed using RNA sequencing. A unique circRNA termed circRNF13 was found to be upregulated in PC tissues and may be a potential prognostic indicator. HIF-1α and EIF4A3 are involved in regulating the biogenesis of circRNF13. Furthermore, circRNF13 was validated to exert a stimulative effect on cell proliferation, angiogenesis, invasion and glycolysis. Importantly, we found that circRNF13 promoted PDK3 levels by acting as a miR-654-3p sponge, thus promoting the PC malignant process. Collectively, our results reveal that hypoxia-induced circRNF13 mediated by HIF-1α and EIF4A3 promotes tumor progression and glycolysis in PC, indicating the potential of circRNF13 as a prognostic biomarker and therapeutic target for PC.