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BACKGROUND: Unilateral neglect (UN) is a frequent cognitive disability following a stroke. Additional research is needed to determine the most effective cognitive rehabilitation techniques. OBJECTIVE: Based on the unilateral neglect neural network, we aim to explore the effect of a new model of transcranial direct current stimulation (tDCS) combined with cognitive training on stroke patients with unilateral neglect. METHODS: Thirty stroke patients with UN after stroke were randomly divided into three groups. All patients received cognitive training for UN and transcranial direct current stimulation with an anode placed on the corresponding part of the right hemisphere for 2 weeks. Treatment group A received multi-site tDCS from the inferior parietal lobule, middle temporal gyrus to prefrontal lobe. Group B received single-site tDCS of the inferior parietal lobule. The improvement of UN symptoms was evaluated by the scores of the Deviation index and Behavioral Inattention Test conventional tests. RESULTS: All groups showed improvements in all tests, and the scores of the treatment groups were statistically significant compared with the control group. CONCLUSION: Both single-site tDCS and multi-site tDCS have therapeutic effects on UN after stroke, and the difference in the therapeutic effects of the two modes still needs to be further explored.

The stability of formation flight is not only sensitive to external disturbances but also to data observed and transferred between sensors and unmanned aerial vehicles (UAVs). A multi-constrained model predictive control (MPC) strategy, combined with Kalman-consensus filter (KCF) and fixed-time disturbance observer (FTDOB) is developed for the formation control of multiple quadrotors here Firstly, KCF is used to effectively fuse the data shared in the formation with noise and uncertainty, which improves the applicability and robustness of the formation in complex environments. Secondly, FTDOB is able to estimate the external disturbances suffered by the quadrotor in a fixed time and provides real-time compensation for the controller. On this basis, an improved MPC (IMPC) is designed for each UAV of the formation, which improves the computational efficiency while ensuring the asymptotic stability of the system. Eventually, the capability and effectiveness of the proposed strategy are verified by simulation in terms of disturbance rejection and noise suppression, as well as good trajectory tracking of the formation.

The dynamic response of the rotor system in a ring die granulator is complex and difficult to solve when it operates under joint external, support and gear mesh forces. To solve this problem, a finite element method and extrusion theory was applied in this study to develop a dynamic coupling model for a hollow overhung rotor with external load excitation. A Newmark-β numerical integration method was used to solve for the dynamic response of the overhung rotor under multiple excitation forces. The results included time-domain response diagrams, frequency-domain response diagrams, phase diagrams, Poincaré section diagrams, and bifurcation diagrams. The model and the method were verified by testing a ring die granulator. On this basis, the dynamic response of the system is predicted according to the influence of different parameters. As the bearing support distance increased, the roller eccentricity decreased, the bearing clearance decreased, the response of the rotor system was significantly optimized, and the system tended to stabilize gradually. Therefore, this paper provides a theoretical basis and experimental verification for the optimization of a pelleting machine transmission structure.

Based on the material characteristics of buckwheat, the cyclone separation and cleaning test bench were designed. The cleaning rate and loss rate of the buckwheat separation and cleaning by the influencing factors of the investigation were studied through single factor and orthogonal tests. The single-factor test results show that the cyclone separation and cleaning performance is better when the speed range of the suction fan is 800~1200 r/min, the speed range of the feeding fan is 600~1200 r/min, and the length range of the suction pipe is 100~250 mm. The regression equation model of the cleaning rate and loss rate was constructed, and the response surface analysis of the test influencing factors and their interaction was carried out. The results show that the speed of the suction fan has the most significant influence on the cleaning rate and loss rate, followed by the speed of the feeding fan and the length of the suction pipe in the separation cylinder has the least effect. By optimizing and solving multi-objective parameters of the regression equation model, reasonable experimental parameters are obtained. The appropriate speed of the suction fan is 1055 r/min, the appropriate speed of the feeding fan is 600 r/min, and the proper length of the suction pipe in the separation cylinder is 175 mm. The cleaning rate and the loss rate of the cyclone separation and cleaning system were 94.78% and 1.67%, respectively.

The current study examined the sociodemographic factors associated with perceived and experienced anti-Chinese discrimination and discrimination as a predictor of psychological distress and loneliness among Chinese Canadians. A cross-sectional online survey was conducted in early 2021 with a sample of 899 Chinese Canadians (i.e., immigrants, citizens, visitors, and international students) during the Wave 2 of the COVID-19 pandemic. Overall, anti-Chinese discrimination was generally associated with younger age and poor financial or health status. Christianity/Catholicism believers were less likely to report perceived discrimination, whereas being married/partnered and living with family reduced the incidences of experienced discrimination. Most importantly, hierarchical linear regression models showed that both perceived and experienced discrimination predicted higher psychological distress (

UAVs formation is a complex nonlinear system. Although quadrotor has many advantages, its dynamics model is nonlinear, coupled, and under driven. For model-based controller design, physical constraints, model uncertainties and external disturbances significantly deteriorate control performance. Therefore, a multi-constraints MPC strategy based on error model is proposed for the quadrotor formation. Firstly, a 3D model for quadrotor is established by using the Lagrange-Euler formulation, in which the model is divided into RS and TS. The corresponding multi-constraint MPC is designed for the two subsystems respectively. The tracking errors caused by external disturbances can be eliminated because of the integrator embedded in the augmented model. Comparing with RMPC, by making reasonable modifications to the cost function, the asymptotic stability of the open loop and the closed-loop subsystem can be ensured by MMPC. Moreover, the stability analysis of the above-mentioned algorithm is carried out. The simulation results show that the controller of the formation can not only achieve good path tracking, but also robustness to the multiple constraints and disturbances.

Considering the influence of the center of gravity (CG) and mass variations on flight dynamics, a nonlinear L1 adaptive control approach is presented in this paper and applied to design a flight controller to achieve the aim of improving the robustness of the flight system against sudden changes in the CG. Based on L1 adaptive control, this approach introduces a feedback linearization strategy to eliminate the known nonlinear dynamics of the aircraft and realize the rapid decoupling of the aircraft states. The nonlinear L1 adaptive control also addresses the problem of insufficient robustness in conventional L1 adaptive control due to overcoming its own known nonlinearities, and then improves the robustness to external disturbances. Additionally, the modified piecewise constant is employed to design the adaptive law to improve the estimation accuracy. Finally, the control performance and robustness of the designed flight control law are verified in the hardware in the loop (HIL) flight control semi-physical experimental platform. Experimental results indicate that the nonlinear L1 adaptive flight control law has strong robustness, which can effectively overcome the disturbance of the CG and mass sudden variations. Moreover, the designed controller takes into account the transient performance while ensuring the steady-state performance of the aircraft with CG change.

Aimed at improving the nonlinear integrated navigation solution performance of multiple low-cost sensors fusion, this paper presents a multilayer loosely-coupled, local-global, and step-optimized MF5DCKF (Multisensor Federated fifth-degree Cubature Kalman filter) state estimation algorithm for the small unmanned aerial vehicle (UAV). This method establishes a multilayer nonlinear integrated navigation model composed of the nonlinear attitude and heading reference system (AHRS) error model, strapdown inertial navigation system/global positioning system (SINS/GPS) error model, and strapdown inertial navigation system/barometer (SINS/BARO) error model to enhance the robustness and richness of the navigation module. Further, based on the above navigation models, a loosely-coupled error state fusion frame is designed to obtain the local convergent state vector. Simultaneously, a three-layer fifth-degree Cubature Kalman filter is proposed to improve the local state estimation accuracy. Subsequently, to optimize the estimated local state, this paper presents a novel distributed MF5DCKF scheme fusing the local state vector to calculate the global optimal state parameters in a step-optimized process. The experimental flight test results show that the proposed algorithm achieves a higher state solution accuracy and a better convergent performance compared with some conventional multisensor fusion algorithms. The new algorithm framework can provide applicability and reliability for the small UAV during the flight.

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.

N-doped carbon fibers (NCFs) were in situ prepared by Camellia sinensis branches waste through hydrothermal carbonization with urea/ZnCl