Abstract A systematic numerical investigation is carried out to understand magnetohydrodynamic stability of the ideal infernal-kink instability in tokamak plasmas with both negative triangularity (neg-D) shaping and negative central shear for the equilibrium safety factor profile. The latter is motivated by the desire to form the internal transport barrier in the neg-D configuration, which is known to have difficulty in forming the edge transport barrier. The infernal-kink mode is generally found to be more unstable in neg-D plasmas as compared to their positive D-shaped (pos-D) counterpart. This is mainly due to less favorable (or even unfavorable) average magnetic curvature near the radial location of the minimum safety factor ( qmin ) as compared to the pos-D configuration. The larger Shafranov shift associated with the neg-D shape helps the mode stabilization but is not sufficient to overcome the destabilizing effect due to bad curvature. Strong poloidal mode coupling due to plasma shaping (toroidicity, elongation, triangularity, etc.) helps explain the slight shift with respect to that predicted by the analytic theory of the peak location of the computed mode growth versus qmin .
Traditional modular-based path planning methods have certain limitations, they have high latency and computational complexity, and cannot work in unknown environments. Current learning-based path planning methods show significant advantages in solving different planning problems in high-dimensional spaces and complex environments. Learning path planning from demonstrations is a hot research topic. We propose an end-to-end framework based on supervised learning. The end-to-end mapping of unmanned aerial vehicle (UAV) from perception to collision-free trajectories is achieved by learning demonstrations generated based on global path planning methods in a simulated environment. Replacing the traditional modular approach with neural networks can reduce computational latency and prevent error accumulation. Experimental results show that our method has a better performance than traditional path planner.
Purpose This paper aims to design a novel hybrid terrestrial-aerial robot, FlyingDog, including its modeling and implementation. By combining the complementary advantages of a quadrotor drone and a quadruped robot, FlyingDog demonstrates excellent maneuverability and high energy efficiency, showcasing great potential for applications in industrial inspection, field exploration, and search and rescue operations. Design/methodology/approach By integrating propellers and leg mechanisms, FlyingDog achieves hybrid motion, encompassing both aerial flight and ground movement. This paper first provides an overview of the robot’s structural design, emphasizing the minimization of interactions between the aerial and ground mechanisms while balancing the thrust-to-weight ratio and payload capacity. A distributed control framework is then proposed to achieve the hybrid motion, alongside the development of corresponding control strategies to ensure stability during various movements. Findings Experiments conducted in real-world conditions validated FlyingDog’s performance in terms of motion stability, energy efficiency, and obstacle-crossing ability. The results demonstrate that FlyingDog exhibits outstanding mobility by combining ground locomotion with aerial flight capabilities, allowing it to overcome challenging obstacles in purely ground-based mode. In ground mode, the robot achieved an energy efficiency of up to 93.5%. Originality/value The hybrid terrestrial-aerial robot presented in this paper features stable land and aerial mobility, a lightweight structure, high energy efficiency, and low manufacturing costs, making it a valuable innovation in the field of robotics.
The study presents a method to construct a brain-computer interaction (BCI) motion control system that uses human brainwave signals to control the motion of different kinds of individuals in a heterogeneous robot swarm. Through establishing the BCI system based on Stable State Visual Evoked Potential (SSVEP), the virtual reality (VR) twin scene for monitoring the operating state and the external environment of robots and the heterogeneous robot swarm composed of unmanned drones and vehicles, the direct motion control of human brain wave signals on heterogeneous robot swarms is realized. The experimental results present that the subjects can apply the system to achieve motion control of a heterogeneous robot swarm in a simulated complex obstacle avoidance scenario with a 90% obstacle avoidance success rate. The system provides a new control method for traditional heterogeneous robot swarm motion systems, and also provides a new solution for how to deal with sophisticated and versatile application scenarios, which is significant for improving the system's perception capability, decision-making ability, and intelligence level.
To investigate the relationship between the Erk1/2 signal pathway and neuronal apoptosis in ischemic stroke rats. Male SD(Sprague Dawley) rats (n = 24) were randomly divided into three groups, each containing 8 rats: sham-operated group, MCAO(Midle cerebral artery oclusion) group, and MCAO + U0126 intervention group (U0126 group). In in vitro trial, primary cortical nerve cells were divided into three groups: control group, OGD(Oxygen and glucose deprivation) group, and U0126 intervention group (U0126 group). In vivo protein expression levels of Erk1/2, p-Erk1/2 and Bcl-2 were determined using western blot. The expressions of Bcl-2, Bcl-xl and Bax were assayed using immunohistochemical staining. Nerve cell mortality in cerebral tissue was detected using TUNEL staining. In in vitro trials, cell apoptosis was assayed with flow cytometry and LDH release. The activity of caspase-3 was determined. Nerve cell apoptosis was determined using Hoechst33258 staining method. In in vivo trial, it was found that the protein expression level of p-ERK1/2 in cerebral tissue in the MCAO group was significantly increased, when compared with that of the sham-operated group, while the protein expression level of p-Erk1/2 in the U0126 group was significantly lower than that in the MCAO group. The expression levels of Bcl-2 and Bcl-xl in the MCAO group were significantly lower than the corresponding expression levels in the sham-operated group, while the expressions of Bcl-2 and Bcl-xl in the U0126 group were significantly lower than those in MCAO group. In MCAO group, the expression of Bax was significantly higher than that in the sham-operated group, while Bax expression was higher in U0126 than in MCAO group. There were significantly higher number of dead nerve cells in MCAO group than in the sham-operated group, while nerve cell mortality in U0126 group was significantly lower than in MCAO group. In in vitro trials, flow cytometry revealed significantly higher apoptosis of OGD-treated nerve cells, relative to the control group. Nerve cells exposed to U0126 and treated with ODR (Oxygen-dependent repressor) were significantly decreased in population, when compared with single OGD treatment group. The LDH release level of nerve cells treated OGD was significantly increased, when compared with that of the control group. However, LDH release level of nerve cells treated with OGD after U0126 intervention was significantly decreased, relative to the single OGD treatment group. The dilution of nerve cell nucleus after OGD treatment was significantly increased, when compared with that of the control group. For nerve cells treated with ODR after U0126 intervention, the nuclear dilution was significantly decreased, relative to that of nerve cell nucleus in the single OGD treatment group. The OGD treatment led to significant increase in nerve cell caspase-3 activity, relative the control group. However, the caspase-3 activity of nerve cells treated with ODR after U0126 intervention was significantly decreased, when compared with single OGD treatment group. The activation of Erk1/2 signal pathway during ischemic stroke promotes apoptosis of nerve cells. Based on these findings, it can be reasonably inferred that the ERK1/2 signal pathway may be an important target for treating ischemic stroke.
As the Internet of Things devices are deployed on a large scale, location-based services are being increasingly utilized. Among these services, kNN (k-nearest neighbor) queries based on road network constraints have gained importance. This study focuses on the CkNN (continuous k-nearest neighbor) queries for non-uniformly distributed moving objects with large-scale dynamic road network constraints, where CkNN objects are continuously and periodically queried based on their motion evolution. The present CkNN high-concurrency query under the constraints of a super-large road network faces problems, such as high computational cost and low query efficiency. The aim of this study is to ensure high concurrency nearest neighbor query requests while shortening the query response time and reducing global computation costs. To address this issue, we propose the DVTG-Index (Dynamic V-Tree Double-Layer Grid Index), which intelligently adjusts the index granularity by continuously merging and splitting subgraphs as the objects move, thereby filtering unnecessary vertices. Based on DVTG-Index, we further propose the DVTG-CkNN algorithm to calculate the initial kNN query and utilize the existing results to speed up the CkNN query. Finally, extensive experiments on real road networks confirm the superior performance of our proposed method, which has significant practical applications in large-scale dynamic road network constraints with non-uniformly distributed moving objects.
This article presented an experiment to determine the effect of dynamic 3D vision-evoked modules on the performance of the system we designed to steer a four-rotor unmanned aerial vehicle (UAV). Nowadays, UAV system is widely used in various industries, and UAV control has always been a focus of research. The Brain-Computer Interface (BCI) system was based on the well-known steady-state visually evoked potentials (SSVEP). The specificity of the interface was the integration of dynamic 3D paradigms in virtual reality. In order to achieve a more immersive and interactive scene, VR scenes were moving synchronously with the UAV. Users can achieve remote control of four-rotor UAV through virtual scenarios. Ten subjects participated in the experiment and operated the UAV to complete a specific track. The dynamic paradigm based on the experimenter's feedback may indeed affect their attention, we proposed an effective solution to overcome this problem. The detailed results of these experiments were reported in this paper and we discussed the possible causes of performance loss under such conditions.
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