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Underwater nodes are prone to drift under the water flow action, which makes the topological structure of underwater wireless sensor networks (UWSNs) have great uncertainty. It is bound to bring about the occurrence of node nonuniform distribution over time. Given the obstacles and boundaries constrained areas, the intention of this paper is to redeploy the drifted underwater nodes for regaining higher coverage rate. Hence, we propose a three-dimensional virtual force coverage algorithm (3D-VFCA). Inspired by the physical water flow action, the position evolution model of drifted underwater nodes is derived under the continuous gravity, buoyancy, propulsion and resistance forces, which can reveal the mechanism of UWSNs deformation and coverage holes caused by the underwater node drift. Then, the coverage problem of dense and sparse node distributions is transformed into the optimization problem of weighted distance between node positions and clustering centers in the pre-coverage process, which can reduce large moving distances caused by node blind movements and solve the problem of inaccurate clustering centers caused by outliers. Furthermore, the improved virtual force algorithm is designed in consideration of underwater nodes, obstacles and boundaries, which can drive the pre-covered underwater nodes to more optimal positions based on the adaptive moving distance per step. Finally, coverage performance evaluations of drifted underwater nodes are performed under different coverage algorithms, node numbers and obstacles. The experimental results indicate that the proposed 3D-VFCA can improve the coverage rates in UWSNs.

Due to the changeable sea current conditions and the uncertainty of the sediment situation, the piers in the offshore area are partially washed by seawater, resulting in the fragile parts of the piers being eroded by seawater. When erosion reaches a certain depth, it can threaten the safety of the bridge structure. In order to improve the accuracy of bridge pier crack identification, a bridge pier crack identification method based on an improved grasshopper optimization algorithm optimized support vector machines (SVM) is proposed. To improve the Grasshopper Optimisation Algorithm (GOA), first select piecewise chaotic functions for mapping. This will increase the diversity of GOA and improve its convergence speed. Secondly, the Cauchy mutation is added to avoid the GOA from falling into the local optimum, and the global optimum is optimized, and the adaptive update strategy is introduced at the local position update to continuously and automatically adjust the control parameter c during the iteration process, which improves the local search capability of the GOA. Finally, the improved Grasshopper Optimisation Algorithm (IGOA) is used to optimize the penalty factor and the kernel function parameter \(\sigma\) in the Support Vector Machine (SVM). IGOA is compared with various algorithms for fitness, and IGOA-SVM is compared with SVM and GOA-SVM experimentally, and the results show that IGOA has the best fitness, and IGOA-SVM is an efficient SVM optimization algorithm with obvious effects and the highest classification accuracy.

Based on the understanding of TCM and western medicine on diabetes mellitus (DM) and diabetic peripheral neuropathy (DPN), the relationship between DPN pathogenesis and blood stasis of TCM is discussed from the perspective of modern medicine. It is indicated blood stasis is the key pathogenesis to DPN, and a two-step acupuncture treatment of DPN from the theory of blood stasis is proposed. The first step is to analyze the pathogenesis of blood stasis, which could block the progress of the disease and diminish the symptoms. The second step is to apply acupuncture for pathological result of blood stasis by following the principle of