Polymer/clay composites are an innovative class of materials. In this study, we present a facile method for the preparation of biodegradable and robust PLA/organomodified montmorillonite (OMMT) composite films with excellent gas barrier performance. When the design of PLA/OMMT composite films, in addition to making OMMT have good intercalation effect in the matrix, the compatibility of intercalating polymer and matrix should also be considered. In this work, two polymers with high gas barrier properties, namely poly(vinyl alcohol) (PVA) and ethylene vinyl alcohol copolymer (EVOH), were selected to intercalate OMMT. The morphology and microstructures of the prepared PLA/PVA/OMMT and PLA/EVOH/OMMT composites were characterized by the X-ray diffraction measurement, scanning electron microscopy, and differential scanning calorimetry. It was shown that the good dispersibility of PVA in the PLA matrix, rather than the intercalation effect, was responsible for the improved gas barrier and mechanical properties of PLA/PVA/OMMT composite. The elongation at break increases from 4.5% to 22.7% when 1 wt % PVA is added to PLA/OMMT. Moreover, gas barrier of PLA/PVA1/OMMT measured as O₂ permeability is 52.8% higher than that of neat PLA. This work provides a route to intercalate OMMT interlayer with high gas barrier polymers and thus can be a useful reference to fabricate PLA/OMMT composites with improved gas barrier and mechanical properties. A comparison of oxygen permeabilities with existing commercial packaging films indicates that the biodegradable PLA/PVA/OMMT may serve as a viable substitute for packaging film applications.

There are many bars in grid structures, and their topological relationships are complex, so the workload of calculations involved in researching their reliability is large. In this paper, based on the geometric topological relationship within a grid structure, a series–parallel system analysis of the structure is carried out by means of node analysis, constraint grade division, and hierarchical combination analysis, and the series–parallel analysis results for the structure are used to judge the failure of the structure. In order to avoid the problem of missing important components due to unreasonable selection of the bounding threshold when identifying failed components, an adaptive dynamic bounding threshold method with high accuracy is introduced. The failure probability value of the failure path is calculated using the direct numerical integration method, and the reliability of the whole structure is obtained. Finally, the validity of this method is verified using an example of a square pyramid grid structure. The results show that the method is feasible for series–parallel system analysis, structural failure judgment, and reliability analysis of a grid structure.

Accurately estimating the road adhesion coefficient is essential for ensuring vehicle driving safety. This study proposes a method that integrates a seven-degree-of-freedom vehicle model and the magic formula tire model with the eXtreme Gradient Boosting algorithm, leveraging Shapley Additive exPlanations values to determine feature importance. Based on this, a road adhesion coefficient estimation model is constructed, incorporating a Bayesian neural network and a Support vector regression to enhance interpretability. To address potential issues such as the model predicting road adhesion coefficients beyond the range present in the training data, a data fusion strategy is designed to improve generalization. Simulation results demonstrate that the fusion estimator outperforms individual models in terms of robustness, real-time performance, and prediction accuracy, especially under varying vehicle speeds and road conditions. Compared to a single estimator, the data fusion model reduced the mean square error by an average of 16.01% and 18.89%, respectively, while the root mean square error was reduced by an average of 11.33% and 16.04%, respectively. This approach provides valuable theoretical support to advance the development of vehicle active safety systems.