Self-healing hydrogels, a novel class of “smart” hydrogels, possess the ability to autonomously restore their network structure and mechanical properties following damage through the reconnection of a fractured three-dimensional network via reversible interactions. This characteristic enhances their safety and durability, exhibiting significant potential in biomedicine. The key determinants of self-healing hydrogels are their mechanical strength and healing efficiency. Ideally, these hydrogels exhibit both high mechanical strength and good healing efficiency. Nevertheless, an inverse relationship between the mechanical strength and self-healing efficiency of self-healing hydrogels typically exists. Thus, research is currently focused on the development of self-healing hydrogels that combine good biocompatibility, high mechanical strength, and good self-healing efficiency. This review focuses on the research progress that is being made regarding the mechanical properties and self-healing capabilities of self-healing hydrogels, where we aim to achieve a balance between self-healing performance and mechanical strength. We outline the evaluation methods for assessing self-healing performance, followed by providing a summary of recent advancements in the mechanical strength and self-healing efficiency of external-stimulus-triggered self-healing hydrogels and autonomous self-healing hydrogels. Finally, we address the challenges and prospects for the future development of self-healing hydrogels.

Nighttime driving safety remains a critical challenge in modern transportation systems: insufficient ambient lighting significantly degrades visual perception quality, adversely affecting both human drivers and advanced driver-assistance systems (ADAS) and directly threatening road users’ safety. Traditional image enhancement methods often suffer from color distortion and visual artifacts, whereas existing deep learning approaches typically require paired training data and incur substantial computational overhead. To address these limitations, this paper presents BLEN (bio-inspired low-light enhancement network), a zero-reference deep learning framework that integrates biological vision principles with efficient convolutional architectures. Specifically, BLEN leverages Retinex theory for illumination–reflectance decomposition, is inspired by and functionally approximates lateral inhibition mechanisms for edge enhancement, and incorporates a Large-Kernel Convolution with Attention (LKCA) module that reduces the parameter count of the LKCA encoder block by 76% (0.56 M vs. 2.34 M for a standard 13 × 13 convolution) relative to standard large-kernel operations. Extensive experiments on the SICE and LOL benchmarks demonstrate that BLEN achieves state-of-the-art performance among real-time, edge-deployable zero-reference methods on the SICE benchmark, yielding a peak signal-to-noise ratio (PSNR) of 23.67 ± 0.14 dB and a structural similarity index measure (SSIM) of 0.891 ± 0.004 on SICE while maintaining 2.10 M parameters (2.1 MB in INT8, 8.4 MB in FP32). Furthermore, the proposed method enables real-time inference at 31 frames per second (FPS) on embedded platforms, including the HiSilicon SS928 and Jetson Nano, demonstrating that the proposed method is an efficient and effective front-end for camera-based ADAS perception on automotive-grade edge hardware.

Self-regulating magnetic induction hyperthermia (SMIH) represents a localized tumor therapeutic modality that utilizes the Curie temperature (Tc) of magnetic nanoparticles (MNPs) to realize the automatic control of the hyperthermia temperature, which has attracted extensive research attention. Although various low-Tc MNPs have been fabricated, three critical challenges hinder the clinical translation of SMIH. Regarding thermal performance parameters, the threshold values of Tc and heating efficiency for effective SMIH remain undetermined. In terms of intratumoral retention, rapid loss and redistribution of intratumoral MNPs severely weaken long-term thermal stability during repeated hyperthermia cycles. From the perspective of manufacturing, large-scale fabrication of well-dispersed low-Tc MNPs is still a technical challenge. This review systematically elaborates on the above three challenges and proposes future research directions to accelerate the clinical transformation of SMIH.