Fast radio bursts (FRBs) are brief, intense flashes of radio waves from unidentified extragalactic sources. Polarized FRBs originate in highly magnetized environments. We report observations of the repeating FRB 20190520B spanning 17 months, which show that the FRB’s Faraday rotation is highly variable and twice changes sign. The FRB also depolarizes below radio frequencies of about 1 to 3 gigahertz. We interpret these properties as being due to changes in the parallel component of the magnetic field integrated along the line of sight, including reversing direction of the field. This could result from propagation through a turbulent magnetized screen of plasma, located 10 ^–5 to 100 parsecs from the FRB source. This is consistent with the bursts passing through the stellar wind of a binary companion of the FRB source.

Multilateral tele-robotic system have potential applications in the industry environments with the advantages of cooperative manipulation for the remote and hazardous tasks, and its control design is quite challenging due to several coupling issues such as stability, position tracking, force feedback and cooperative manipulation under time delays, various uncertainties and external disturbance. In this paper, a novel RBFNN ( $Radial\ Basis\ Function\ Neural\ Network$ )-based adaptive sliding mode control design is proposed for nonlinear multilateral tele-robotic system with $n$ -master- $n$ -slave manipulators. The environment force is modeled with a general form via the RBFNN-based environment parameters estimation in the slave side. The estimated environment parameters (non-power signals) are transmitted to rebuild the environment dynamics in the master side and provide the good force feedback for the human operators. The RBFNN-based adaptive sliding mode controllers are designed separately for master and slave manipulators to achieve good position tracking under parameter variations and external disturbance. The coordinated force distribution algorithm is designed to achieve cooperative manipulation with the balance of force acting on the target object. The theoretical analysis is given and the comparative experiment for a nonlinear multilateral tele-robotic system with 2-master-2-slave manipulators is implemented. The results show the good performance of our design.

D ynamic W orkflow S cheduling in F og C omputing (DWSFC) is an important optimisation problem with many real-world applications. The current workflow scheduling problems only consider cloud servers but ignore the roles of mobile devices and edge servers. Some applications need to consider the mobile devices, edge, and cloud servers simultaneously, making them work together to generate an effective schedule. In this article, a new problem model for DWSFC is considered and a new simulator is designed for the new DWSFC problem model. The designed simulator takes the mobile devices, edge, and cloud servers as a whole system, where they all can execute tasks. In the designed simulator, two kinds of decision points are considered, which are the routing decision points and the sequencing decision points. To solve this problem, a new M ulti- T ree G enetic P rogramming (MTGP) method is developed to automatically evolve scheduling heuristics that can make effective real-time decisions on these decision points. The proposed MTGP method with a multi-tree representation can handle the routing decision points and sequencing decision points simultaneously. The experimental results show that the proposed MTGP can achieve significantly better test performance (reduce the makespan by up to 50%) on all the tested scenarios than existing state-of-the-art methods.

Underwater manipulators are important robotic tools in the exploration of the ocean environment. Up to now, most existing underwater manipulators are rigid and with fixed 5 or 7 degrees of freedom (DOF), which may not be very suitable for some complicated underwater scenarios (e.g., pipe networks, narrow deep cavities, etc.). The biomimetic concept of muscles and tendons is also considered as continuum manipulators, but load capacity and operation accuracy are their essential drawbacks and thus limit their practical applications. Recently, the cable-driven technique has been developed for manipulators, which can include numerous joints and hyper-redundant DOF to execute tasks with dexterity and adaptability and thus they have strong potential for these complex underwater applications. In this paper, the design of a novel cable-driven hyper-redundant manipulator (CDHRM) is introduced, which is driven by multiple cables passing through the tubular structure from the base to the end-effector, and the joint numbers can be extended and decided by the specific underwater task requirements. The kinematic analysis of the proposed CDHRM is given which includes two parts: the cable-joint kinematics and the joint-end kinematics. The geometric relationship between the cable length and the joint angles are derived via the established geometric model for the cable-joint kinematics, and the projection relationship between the joint angles and end-effector’s pose is established via the spatial coordinate transformation matrix for the joint-end kinematics. Thus, the complex mapping relationships among the cables, joints and end-effectors are clearly achieved. To implement precise control, the kinematic control scheme is developed for the CDHRM with series-parallel connections and hyper-redundancy to achieve good tracking performance. The experiment on a real CDHRM system with five joints is carried out and the results verify the accuracy of kinematics solution, and the effectiveness of the proposed control design. Particularly, three experiments are tested in the underwater environment, which verifies its good tracking performance, load carrying and grasping capacity.

The cooperative motion of multiple mobile robots has attracted wide attention due to its advantages in military, marine and aerospace fields, and formation control has become a significant technology in the realization of these tasks. However, most of the existing formation control designs of mobile robots do not consider the practical obstacles in the environment, and the maintenance of both formation and trajectory tracking while confronting the obstacles is still a challenging issue. Therefore, in this paper, a virtual-structure-based formation control approach is designed with obstacle avoidance for a system with multiple mobile robots. The basic trajectory is generated for each robot in the group and parameterized to keep the group in formation. A trajectory generator is then established regarding the obstacles, where a potential function is designed to adjust the basic trajectory and replan the reference trajectory to achieve obstacle avoidance. Then, a novel design for the path parameter is proposed to improve the performance of the robot group when encountering obstacles. Finally, a tracking controller is designed to achieve good tracking performance for robots, and the guaranteed performance is achieved via the Lyapunov theorem. A comparative simulation with three sets is carried out, where an objective function $F_{obj}$ is designed to evaluate the tracking performance in the presence of obstacles. Besides this, a real experiment is implemented to further verify the effectiveness. The simulation and experimental results verify the good formation and tracking performance of the proposed design for a system with multiple mobile robots with obstacle avoidance.

Brain–computer interface (BCI) technology based on motor imagery (MI) or motor attempt (MA) has shown promise in enhancing motor function recovery in stroke patients. This study aimed to evaluate the effectiveness of BCI-based rehabilitation in improving motor function through multimodal assessment, and to explore the potential neuroplastic changes resulting from this intervention. We conducted a randomized double-blind controlled clinical trial with multimodal assessment to evaluate the efficacy of a BCI system for enhancing motor recovery. A total of 48 ischemic stroke patients completed the study (25 BCI, 23 control). The BCI group used an 8-electrode electroencephalogram (EEG) system, a virtual reality training module, and a rehabilitation training robot for real-time motor intention-based feedback. The control group used identical BCI devices but without displaying real-time data and feedback. Participants underwent 20-minute upper and lower limb training sessions for two weeks. Motor function (Fugl-Meyer Extremity scale), electromyography (EMG), and functional near-infrared spectroscopy (fNIRS) were assessed pre- and post-intervention. The BCI group demonstrated significantly greater improvement in upper extremity motor function compared to the control group (ΔFMA-UE: 4.0 vs. 2.0, p = 0.046). EEG results of the BCI group showed a significant decrease in both DAR (p = 0.031) and DABR (p < 0.001) compared to baseline. EMG analysis revealed that BCI treatment resulted in significant increases in deltoid and bicipital muscle activity during both shoulder and elbow flexion movements compared to baseline (p < 0.01). fNIRS results indicated enhanced functional connectivity and activation in key motor-related brain regions, including the prefrontal cortex, supplementary motor area, and primary motor cortex in the BCI group. BCI-based rehabilitation using an attention-motor dual-task paradigm significantly improved upper limb motor function and enhanced motor and cognitive network activity in stroke patients. Multimodal assessment supports the potential of BCI rehabilitation as an effective tool for leveraging neuroplasticity and promoting motor recovery.