Motor imagery, as a paradigm of brain-computer interface, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-computer interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.

Experiments were performed on Al6082-T6 using friction plug welding to investigate and resolve keyhole faults. The process required using friction as supplemental heating which was from between the shoulder and base metal. Friction plug welded connections were successfully created at rotational speeds of 1600 rpm, 1800 rpm, and 2000 rpm. The weld joints have no apparent structural flaws, and the joints demonstrate distinct partition features. During them, the thermomechanically affected zone of joints displays a distinct boundary between the base metal and plug, accompanied by a pronounced, preferred orientation. It can be deduced that augmenting the plug's rotational speed, the size of grain and the composition of high-angle grain boundaries will facilitate dynamic recrystallization and lead to a more pronounced recrystallization texture composition, assuming all other parameters remain constant.

In this paper, we study the (Gorenstein) flat dimension of modules over triangular matrix rings, and give estimations of the (Gorenstein) weak global dimension and the supremum of flat lengths of injective modules over triangular matrix rings. As applications, some new Ding–Chen rings are constructed.

Abstract The purpose of this study is to discuss the effect of defects on the mechanical properties of halbach-type guideway surfaces in high-temperature superconducting permanent magnetic levitation systems with single-surface superconductor distribution versus multi-surface superconductor distribution. The study models the high-temperature superconducting permanent magnetic levitation system based on Maxwell’s system of equations and compares it with the experimental data to obtain the variation curve of the levitation force of the levitation system under ideal conditions. Through numerical simulation and data analysis, it is found that the levitation force and guiding force of the high-temperature superconducting permanent magnetic levitation system will regularly decrease with the expansion of the defects, which will lead to the reduction of the system’s load-carrying capacity and stability. These findings are of great significance for the design and application of high-temperature superconducting magnetic levitation systems, and provide theoretical support and technical guidance for the practical application of high-temperature superconducting magnetic levitation systems.

Background: Implanted titanium skull plates (TSPs) in cranioplasty are used to replace or reconstruct areas of the skull that have been damaged or removed due to trauma, surgery, or other medical conditions. However, the presence of a TSP in the head may influence the distribution of the electric field induced during transcranial magnetic stimulation (TMS) procedures. The purpose of this study was to determine how the presence of TSP would interfere with TMS-induced cortical electric fields. Methods: The TMS with a figure-of-eight coil was applied to a realistic head model with TSPs. The distribution of the induced electric field in head tissues was calculated by employing the impedance method, and the results were compared with that of a normal head without TSP. Results: Simulation results show that the distribution of the induced electric field has changed greatly for the head model with TSP. The maximum value of the induced electric field in head tissues was present under one of the circular coil wings rather than in the tissues beneath the junction of the two wings of the Fo8 coil. Conclusions: The induced electric field in deep brain regions was increased for the head model with TSP, which could potentially lead to deep brain stimulation. Since the presence of metallic TSP can greatly influence the distribution of the induced electric field in TMS applications, it is important to adjust the treatment scheme when considering TMS for individuals with cranial titanium plates.