At present, using biomass materials to modify graphene oxide (GO) to enhance the anticorrosion performance of coatings meets the requirements of future sustainable development. In order to endow lignin/GO coatings with self-healing function to reduce maintenance costs and avoid accidents caused by material corrosion failure, this work utilized electrostatic interactions to load zinc ions onto lignin/GO (Zn@LGO). Experimental results indicated that the cured coating exhibited both self-healing anticorrosion and photothermal conversion properties. When the content of Zn@LGO was 0.4 wt%, the surface temperature of the cured coating rose to 139.2 °C after 180 s of near-infrared radiation. The cured coating was left for 100 days in salt water, the Z0.01Hz value of coating VER-3 was up to 1.3 × 109 Ω cm2, which was two orders of magnitude higher than that of pure resin coating, and fewer corrosion products were observed on the metal surface. The scratch test showed that the damaged coating was soaked in 3.5 wt% sodium chloride for 72 h; the charge transfer impedance of coating VER-3 was 2.61 × 104 Ω cm2, which was one order of magnitude higher than that of pure resin coating. This was mainly because during metal corrosion, the hydroxide generated by the combination of hydroxide ions at the cathode and zinc ions covered the damaged area of cured coating, hindering the penetration of the corrosive medium. All in all, this research promoted the application of lignin, and also provided a reference for the design of composite coatings with both photothermal conversion and self-healing anticorrosion properties.

Landslide hazard assessment increasingly demands the joint analysis of heterogeneous remote sensing data; however, automating this process remains difficult due to the pronounced resolution and texture discrepancies existing between satellite and aerial sensors. To address these limitations, this study proposes a robust segmentation framework capable of extracting sensor-robust representations. The framework leverages a DINOv3 transformer encoder and exploits representations from multiple transformer layers to capture complementary visual information, ranging from fine-grained surface textures to global semantic contexts, overcoming the receptive field constraints of conventional CNNs. Experiments on the Longxi satellite dataset achieve a Dice coefficient of 0.96 and an IoU of 0.938, and experiments on the Longxi UAV dataset achieve a Dice coefficient of 0.965 and an IoU of 0.941. These results show consistent segmentation performance on both the Longxi satellite and UAV datasets, despite differences in spatial resolution and surface appearance between acquisition platforms.

Old films suffer from scratches, dust, and brightness flicker caused by aging film stock and unstable analog exposure. Recurrent restoration frameworks suppress these artifacts under the guidance of degradation masks, yet pixel-domain frame differencing provides weak evidence for thin structural defects and confuses global brightness variation with content change. We present WaveletMask, a wavelet-domain degradation sensing framework that disentangles these two failure modes by construction: a high-frequency branch localizes transient structural defects from Haar detail-band differences between adjacent frames, a low-frequency branch isolates frame-level brightness deviations from coarse approximation responses, and a parameter-free maximum fusion rule passes the dominant cue to the recurrent gate. On the Synthetic and Real-World Old Video (SRWOV) benchmark, WaveletMask attains the best PSNR among ten re-trained methods (26.60 dB, $+0.61$ dB over the strongest competitor), and a paired comparison against the Recurrent Transformer Network (RTN) confirms a $+0.45$ dB gain while adding only 898 detector parameters. On real archival footage, WaveletMask removes scratches and flicker more cleanly while better preserving film texture and temporal stability. These results indicate that explicit wavelet-domain separation of structural and photometric cues offers a reliable, nearly cost-free upgrade for mask-guided recurrent restoration.