Sensor nodes in underwater wireless sensor networks (UWSNs) are in a three-dimensional space, and water fluidity continuously changes the positioning in water, the clock synchronization of underwater nodes is challenging, and ranging algorithms affected by water flow produce large errors. A three-dimensional UWSN positioning algorithm based on modified RSSI values is proposed to address the problem of UWSN positioning algorithms being susceptible to water influence and prone to unstable positioning and large positioning errors. An unlocated node screens the received anchor node signal strength and then makes a weighted correction to reduce the influence of the water environment and improve the ranging accuracy. A position estimation model is proposed and combined with a three-dimensional underwater model and least squares method to deduce the unlocated node’s position on the basis of the distance between the unlocated node and the anchor node. The proposed algorithm effectively reduces the influence of the water environment on the ranging algorithm’s accuracy and improves the performance of three-dimensional underwater positioning algorithms. Simulation results show that the proposed algorithm can effectively reduce the influence of the underwater environment on positioning algorithms.

As for recognizing Zhuang minority pattern symbols, current recognition models often cause high computational overhead and low accuracy since Zhuang minority pattern symbols have large feature vectors and some complex features. In this paper, we present the efficient attention receptive field you only look once (Earf-YOLO), a new scheme to address those problems. Firstly, a global-local-transformer (GLocalT) structure is proposed, through which other control systems are introduced into the axial self-attention module, and global-local training strategies are also designed. The structure can use other control systems to compensate for the lost feature information along the height, width, and channel axes. The global-local training strategy can encode long-term dependencies between features and reduce local information loss, fully illustrating that the structure has high feature expression ability. Besides, strength receptive field block (SRFB) is suggested to use the dilated convolution to control the receptive field’s eccentricity and enrich the feature information of the receptive field during its training. With more branches, it can better extract multiscale features, enrich the feature space of the convolution block, and reparametrize multibranch during prediction to fuse them into the main branch, all of which contribute to the improvement of the model performance. Finally, some advanced training techniques are adopted to enhance the detection effect further. In the end, comparative experiments are conducted on the datasets of Zhuang pattern symbols and PASCAL VOC, whose results indicate that the AP and FPS of the suggested model reach their highest values, manifesting its high efficiency.

Taking the literature on the quality assurance of higher education collected by Web of Science as the research object, CiteSpace is used to carry out visual analysis from the level of countries, keywords and so on. The research shows that the research hotspots mainly focus on quality assurance, sustainable development, medical education, prevention, involvement, management and so on. Research trends to mainly focus on Framework, knowledge and other aspects.