Brain tumors have been proved challenging to treat. Here, we present a promising alternative by developing an implantable ultrasound-powered tumor treating device (UP-TTD) that electromagnetically disrupts the rapid division of cancer cells without any adverse effects on normal neurons, thereby safely inhibiting brain cancer recurrence. In vitro and in vivo experiments confirmed the significant therapeutic effect of the UP-TTD, with ~58% inhibition on growth rate of clinical tumor cells and ~78% reduction of cancer area in tumor-bearing rats. This UP-TTD is wireless ultrasound-powered, chip-sized, lightweight, and easy to operate on complex surfaces, with a largely boosting therapeutic efficiency and reducing energy consumption. Meanwhile, various treatment parameters could be tuned from the UP-TTD without increasing its size or adding circuits on the integrated chip. The tuning process was simulated and discussed, showing an excellent agreement with the experimental data. The encouraging results of the UP-TTD raise the possibility of a new modality for brain cancer treatment.

Capturing noncooperative targets in space has garnered continuous research interest in aerospace applications. This study addresses the demands of large-scale, multifaceted activities and varied working conditions for space capture missions by designing a space capture robot composed of multiple cable-driven manipulators operating in parallel. First, single- and multi-segment cable-driven robot models were designed, and a geometric model was subsequently built. The optimal number of segments was determined by analysing the condition number of a Jacobian matrix using the Monte Carlo method. Subsequently, based on the constant-curvature assumption, a kinematic model of the cable-driven space capture robot was formulated, and capture methods for different capture targets were designed using the Monte Carlo method. Finally, an eight-segment cable-driven robot prototype was developed, and compliance and driving experiments were conducted. This robot exhibits promising application potential for space noncooperative target capture and can be feasibly manufactured using on-orbit 3D machining technology.

Due to their high kinematic characteristics, six-bar tensegrities have great potential application value in the field of robotics, especially in the field of deep space exploration robots. In this paper, an ultralight six-bar tensegrity robot is designed, and a gait planning method for continuous motion is proposed. First, the equilibrium matrix of the tensegrity structure is constructed, and singular value decomposition (SVD) is performed to find the node coordinates and internal forces of the tensegrity structure. Two representative examples regarding tensegrity structures are presented to demonstrate the capability of the proposed method in the initial selfstress design that satisfies the stability of tensegrities. Furthermore, both the principal rolling analysis and gait planning are also addressed based on the offset of the center of gravity. A six-bar tensegrity robot prototype is developed, and the obstacle avoidance experiment is completed. Finally, the results show that the six-bar tensegrity robot has good kinematic performance. Moreover, this robot is expected to play a key role in future planetary exploration.

Cognitive workload is an internal factor that can be influenced by external factors. When drivers are distracted by these internal and external influences, their ability to drive safely can be compromised. Electroencephalogram (EEG) is a non-invasive neuroimaging technique that measures the electrical activity of the brain. and it can provide information about brain activity related to various cognitive and motor functions. In this study, on-road driving experiments were conducted to investigate the EEG characteristics of drivers under different levels of cognitive workload generated through mathematical calculations with varying difficulties. The collected EEG signals were processed using continuous wavelet transform to decompose them into delta, theta, alpha, beta, and gamma waveforms. Numerical values and power spectral characteristics of each waveband in the left and right frontal regions of the driver were then analyzed. Furthermore, the study utilized an improved multiscale sample entropy algorithm to analyze the EEG entropy characteristics of drivers in different states. The results indicate that under different degrees of cognitive workload, the amplitude of delta, theta, and alpha waves decreased, while beta and gamma waves showed significant changes. The improved multiscale sample entropy algorithm provided a more objective assessment of changes in entropy across different wavebands and stages, with smaller entropy fluctuation ranges and more accurate complexity changes. Overall, this research provides valuable insights into the characteristics of drivers under cognitive workload, contributing to the analysis of human factors in driving safety. These findings can inform the development of strategies to mitigate the adverse effects of cognitive workload on driving performance and enhance overall road safety.

Aviation resources in the post-pandemic era are still in short supply. The increasing air traffic flow aggravates flight delays and makes it difficult to ensure aviation safety. Instead of focusing on the economic benefits, this paper proposes a quantitative method for evaluating the safety of flight plans. A flight plan safety index system is constructed through airspace environment modelling and a conflict probability calculation. The proposed method provides a quantitative basis for the management and adjustment of flight plans at the strategic level. Improving the flight plan management strategy from the perspective of conflict avoidance is expected to fundamentally reduce the potential conflict and workload of controllers and pilots during flight and improve the safety level of the whole air transport system. Finally, the performance of the proposed flight plan safety evaluation method is demonstrated through an illustrative air traffic scenario.