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Sport plays a crucial role in human society, fostering physical health, collaboration, and the spirit of rivalry. The importance of sports in promoting a sense of well-being, self-control, and cognitive sharpness should be considered. The efficacy of sports and athletic performance often relies on the caliber of clothing used. Traditional sportswear encounters many issues, including but not limited to restricted breathability, inefficient moisture management, and insufficient ultraviolet (UV) protection. To examine these concerns, the present study investigates the Intelligent Sportswear Design (ISWD), an innovative advancement rooted in conjugated nanomaterials. The field of ISWD covers the development of sports apparel and footwear specifically intended to boost performance and provide optimal comfort. Coatings of graphene, silver nanoparticles, and environmentally friendly green nanomaterials are employed in these multipurpose PES fabrics. This research looks at how we might better protect athletes from the sun by developing fabrics with UV-blocking qualities. Results show increased moisture-wicking efficiency, better UV-radiation protection, and enhanced electrical conductivity. The study found that fabric moisture management could be improved by 25%, UV-blocking performance could be increased by 30%, fabric conductivity could be increased by 15%, and heat retention could be decreased by 20%. The experimental results show that the proposed ISWD exhibits notable performance in several necessary measures, including moisture-wicking effectiveness (23.22%), UV-blocking performance (34.22%), fabric conductivity (20.88%), heat retention reduction (26.94%), and UV radiation shielding (35.68%).

In order to solve the problem of selecting missile health state evaluation indexes and the mapping relationship between the selected indexes and missile health state, a missile health state prediction method based on CA-RBF neural network is proposed. Firstly, the influencing factors of missile health state are analyzed through missile life profile, and the quantitative method is given. Then, the corresponding analysis (CA) method is used to screen the influencing factors of missile health state. Taking the selected factors and the evaluation results of missile health state as the input and output of neural network, the training samples of RBF neural network are established to predict the missile health state. Finally, an example is given to illustrate the practicability and effectiveness of the proposed method. This method can provide a new idea for the selection of missile health state indexes and missile health state prediction, and provide a basis for missile preventive maintenance decision-making.

Aiming at the problems of long search time and local optimal solution of ant colony algorithm (ACA) in the path planning of unmanned aerial vehicle (UAV), an improved ant colony algorithm (IACA) was proposed from the aspects of simplicity and effectiveness. The flight performance constraints of fixed wing UAVs were treated as conditions of judging whether the candidate expanded nodes are feasible, thus the feasible nodes’ number was reduced and the search efficiency was effectively raised. In order to overcome the problem of local optimal solution, the pheromone update rule is improved by combining local pheromone update and global pheromone update. The heuristic function was improved by integrating the distance heuristic factor with the safety heuristic factor, and it enhanced the UAV flight safety performance. The transfer probability was improved to increase the IACA search speed. Simulation results show that the proposed IACA possesses stronger global search ability and higher practicability than the former IACA.