Self-healing elastomers refer to a class of synthetic polymers that possess the unique ability to autonomously repair from internal and external damages. In recent years, significant progress has been made in the field of self-healing elastomers. In particular, intrinsic self-healing elastomers have garnered a great deal of attention. This mini-review outlines recent advancements in the mechanisms, preparation methods, and properties of various intrinsic self-healing elastomers based on non-covalent bond systems, reversible covalent bond systems, and multiple dynamic bond composite systems. We hope that this review will prove valuable to researchers in order to facilitate the development of novel strategies and technologies for preparing high-performance self-healing elastomers for advanced applications.

The Controller Area Network with Flexible Data-Rate (CAN-FD) bus is the predominant in-vehicle network protocol, responsible for transmitting crucial application semantic signals. Due to the absence of security measures, CAN-FD is vulnerable to numerous cyber threats, particularly those altering its authentic physical values. This paper introduces Physical Semantics-Enhanced Anomaly Detection (PSEAD) for CAN-FD networks. Our framework effectively extracts and standardizes the genuine physical meaning features present in the message data fields. The implementation involves a Long Short-Term Memory (LSTM) network augmented with a self-attention mechanism, thereby enabling the unsupervised capture of temporal information within high-dimensional data. Consequently, this approach fully exploits contextual information within the physical meaning features. In contrast to the non-physical semantics-aware whole frame combination detection method, our approach is more adept at harnessing the physical significance inherent in each segment of the message. This enhancement results in improved accuracy and interpretability of anomaly detection. Experimental results demonstrate that our method achieves a mere 0.64% misclassification rate for challenging-to-detect replay attacks and zero misclassifications for DoS, fuzzing, and spoofing attacks. The accuracy has been enhanced by over 4% in comparison to existing methods that rely on byte-level data field characterization at the data link layer.

Non-pneumatic tires (NPTs) have garnered significant attention due to their advantages, such as energy efficiency, safety, versatile applications, and superior performance, compared to traditional rubber-based pneumatic tires (PTs). This mini review provides a concise overview of NPTs, beginning with their definition, structural design, and classification based on structural variations. The review then examines recent advancements in the materials used for NPTs, including those for the tread, elastic support structure, skeleton, and adhesives, with a focus on their specific properties. Furthermore, it summarizes various manufacturing techniques such as compression molding, centrifugal casting, injection molding, 3D printing, and mechanical assembly. Lastly, the review outlines the general manufacturing procedures of NPTs, discusses the challenges currently faced by the technology, and offers insights into future development directions. This mini review aims to support NPT researchers and practitioners, particularly in the fields of process and materials engineering, in advancing their work on NPTs.