Accurate traffic flow prediction can provide sufficient information for the formation of symmetric traffic flow. To overcome the problem that the basic fruit fly optimization algorithm (FOA) is easy to fall into local optimum and the search method is single, an improved fruit fly optimization algorithm (IFOA) based on parallel search strategy and group cooperation strategy is proposed. The multi-swarm mechanism is introduced in the parallel search strategy, in which each subswarm is independent and multiple center positions are determined in the iterative process, thereby avoiding the problems of reduced diversity and premature convergence. To increase communication between fruit fly subswarms, the informative fruit flies selected from subswarms are guided by the randomly generated binary fruit fly to achieve the crossover operation in the group cooperation strategy. Then a hybrid framework model based on wavelet neural network (WNN) with IFOA (IFOA-WNN) for traffic flow prediction is designed, in which IFOA is applied to explore appropriate structure parameters for WNN to achieve better prediction performance. The simulation results verify that the IFOA can provide high-quality structural parameters for WNN, and the hybrid IFOA-WNN prediction model can achieve higher prediction accuracy and stability than the compared methods.

An experimental system for dismantling waste printed circuit boards (WPCBs) is developed based on YOLOv5. The system integrates a non-contact hot-air convection heating module with a 4-degree-of-freedom SCARA robotic arm to support vision-guided selective component removal. An STM32-based control unit and a Jetson Nano–based vision module are employed to coordinate motion control, temperature regulation, and visual perception in a closed-loop workflow. YOLOv5 is used to detect, classify, and localize components on WPCBs, providing target positions for robotic operations. A host-computer interface supports online human–machine interaction and flexible task configuration during operation. Experimental results demonstrate accurate SCARA trajectory tracking and stable temperature control of the heating module, enabling repeatable component detection, classification, and separation under controlled conditions. Overall, the system exhibits convenient operation and promising disassembly performance in laboratory trials, offering a baseline reference for component-level recovery and secondary reuse.

To address dynamic positioning (DP) challenges encountered by ships navigating amid unknown model parameters, environmental disturbances, and input saturation, this study proposes a nonlinear gains recursive sliding mode (RSM) DP control law. Within this control framework, an RSM strategy is devised, leveraging variable-gain technology to enhance DP system control performance. A variable-gain adaptive radial basis function (RBF) neural network is employed for real-time online training to approximate the unknown ship model. Simultaneously, an auxiliary dynamic system is incorporated to deal with input saturation. Furthermore, a robust control item is implemented to counteract the influence of RBF neural network approximation errors and external disturbances on the DP system. By constructing an appropriate Lyapunov function, it is proven that all signals in the DP closed-loop control system are uniformly ultimately bounded. Finally, simulation results demonstrate the ship DP system’s rapid response and high accuracy under the proposed control law, along with an enhanced ability to reject environmental disturbances.