Ischemic stroke is a common cause of death and disability worldwide, which leads to serious neurological and physical dysfunction and results in heavy economic and social burdens. For now, timely and effective dissolution of thrombus, and ultimately improvement in the recovery of neurological functions, is the treatment strategy focus. Recently, many studies have reported that transcranial ultrasound stimulation (TUS), as a non-invasive method, can dissolve thrombus, improve cerebral blood circulation, and exert a neuroprotective effect post-stroke. TUS can promote functional recovery and improve rehabilitation efficacy among patients with ischemic stroke. This mini-review summarizes the potential mechanism and limitation of TUS in stroke aims to provide a new strategy for the future treatment of patients with ischemic stroke.

With the development of science and technology, a variety of computer technologies have emerged. The disadvantages of traditional education appear one after another. It is difficult for teachers and students to interact synchronously, and classroom efficiency is not high. Therefore, it is urgent to improve classroom efficiency and the interaction between teachers and students. In this paper, the hardware and software modules are analyzed and studied using wireless network technology, and deep learning convolutional neural network architecture is constructed. The neural network is trained until the optimal model is obtained. The system consists of many scientific front-end technologies. It is a system structure that integrates various information technologies such as computer vision, network communication, wireless sensors, analog electronic technology, and digital electronic technology. The results show that the model generates the most network structures irregularly when the loss rate of hidden nodes is 0.5. Besides, the training effect of the model is the best. In addition, the recognition accuracy of the training dataset can reach 0.98 after the model iteration round is ten. The recognition accuracy of the validation dataset is 0.6. After model iteration, the recognition accuracy can be improved by 8.6%. The performance of the system can be further optimized. In addition, the intelligent assistance system has completed multiple data iterative updates, and the performance of the system can be optimal. The system can ensure the quality of teachers’ teaching, improve the quality of students’ classroom learning, and adjust the classroom atmosphere. This paper has important reference value for enhancing the interaction between teachers and students and improving learning efficiency.