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The development of multifunctional supramolecular nanocomposite hydrogels remains challenging. Here, the dynamic host-guest interactions involving the host molecule CB[8] and guest units were utilized to prepare [Fe.sub.3][O.sub.4] hybrid supramolecular nanocomposite hydrogels. The results showed that the hydrogels obtained possessed a porous structure. The CB[8]-modified [Fe.sub.3][O.sub.4] ([Fe.sub.3][O.sub.4]@CB[8]) nanoparticles served as cross-linkers in forming the network of hydrogels. By adjusting the [Fe.sub.3][O.sub.4]@CB[8] content, the mechanical properties of the hydrogels could be controlled. The tensile stress was measured at 160 kPa with a fracture strain of 1380%, while the compression stress was 230 kPa at 70% compression strain. The self-healing efficiency of the hydrogels at room temperature reached 95% after 24 h. The as-obtained hydrogels show strain sensitivity and have the potential for applications in detecting elbow and finger movements. Our supramolecular nanocomposite hydrogels exhibit multiple functions, including self-healing, injectability, photothermal responsiveness, and conductivity, making them suitable for integration into flexible electronics. Highlights * [Fe.sub.3][O.sub.4]@CB[8] nanoparticles serve as cross-linkers for the nanocomposite hydrogels. * CB[8] based host-guest interactions enable hydrogels to self-heal. * [Fe.sub.3][O.sub.4]@CB[8] endow hydrogels with stretchability and photothermal responsiveness. * Hydrogels exhibit injectability, NIR responsiveness, and conductive ability. KEYWORDS host-guest interactions, nanocomposite, self-healing, supramolecular hydrogels, 1 | INTRODUCTION Hydrogels are 3D crosslinked polymer networks formed by either chemical or physical crosslinking, which can hold a large amount of water. (1) Hydrogels have been extensively studied [...]

ObjectiveThe industrial robot industry has put forward higher requirements for RV reducers, and the precision life reflects the ability of the reducer to maintain transmission accuracy, which is one of the most important design criteria and usage indicators. To improve the precision performance of precision reducers, it is crucial to evaluate their reliability. Therefore, the degradation characteristics of precision reducers were analyzed.MethodsTaking the RV80E reducer as an example, a random degradation model based on Gamma process was proposed. Combined with the performance degradation data of the reducer transmission accuracy, the model parameters were estimated based on the matrix method and the maximum likelihood estimation method. A Gaussian process regression model optimized by genetic algorithm was established using vibration characteristic data to optimize the prediction of transmission accuracy.ResultsThe results show that the prediction accuracy based on Gaussian process regression model is significantly better than that of traditional regression model. The posterior distribution parameters of the random degradation model are updated by using the algorithm to predict the results, which can effectively evaluate the reliability of the accuracy life of RV reducer and lay the foundation for further reliability optimization design of accuracy life.