Transition metal oxides (TMOs) can provide high theoretical capacitance due to the change of multiple valence states of transition metals. However, their intrinsic drawbacks, including poor electrical conductivity, lower energy density, and huge volume expansion, will result in the pulverization of electrode materials and restricted electrochemical kinetics, thus leading to poor rate capability and rapid capacity fading. Composite electrodes based on transition metal oxides and carbon-based materials are considered to be promising candidates for overcoming these limitations. Herein, we reported a preparation method of hybrid ZIFs derived Zn-doped Co₃O₄/carbon (Zn-Co₃O₄/C-230) particles semi-embedded in wood-derived carbon skeleton for integrated electrodes. A large specific surface area, excellent conductivity, and electrochemical stability provide a larger electrochemical activity and potential window for the electrode. Prepared Zn-Co₃O₄@CW-230 electrode (0.6 mm thick) displays ultrahigh area specific capacitances of 7.83 and 6.46 F cm⁻² at the current densities of 5 and 30 mA cm⁻², respectively. Moreover, a symmetric supercapacitor assembled by two identical Zn-Co₃O₄@CW-230 electrodes delivers a superior area-specific capacitance of 2.61 F cm⁻² at the current densities of 5 mA cm⁻² and great energy densities of 0.36 mWh cm⁻² (6.0 mWh cm⁻³) at 2.5 mW cm⁻², while maintaining 97.3% of initial capacitance over 10,000 cycles. It notably outperforms those of most carbon-based metal oxides, endowing the Zn-Co₃O₄@CW-230 with extensive prospects for practical application.

High-voltage wiring harnesses in new energy vehicles (NEVs) are susceptible to thermal aging in high-temperature environments, whereas conventional assessment methods are difficult to deploy rapidly. This study combines laser-induced breakdown spectroscopy (LIBS) with random forest (RF) classification to assess thermal aging levels in cross-linked polyethylene (XLPE) insulation. Thirteen laboratory-aged XLPE wiring harness samples were prepared, and 100 single-shot spectra were acquired at fresh positions for each aging level. The specific methodological contribution is the use of class-wise median absolute deviation (MAD) at each wavelength as a variable-selection criterion before RF training. Three models were compared: RF, principal component analysis (PCA) combined with RF (PCA–RF), and MAD combined with RF (MAD–RF). RF achieved 100% internal hold-out accuracy for the non-aged versus 60-day comparison and 84.23% across all 13 aging levels. PCA–RF increased the multiclass accuracy to 87.31%, whereas MAD–RF reached 95.00% under the original exploratory hold-out workflow. These results indicate that wavelength-wise robust dispersion can provide a compact, discriminative representation of the present LIBS dataset.

To address the challenges of significant size variations, inconspicuous textures, weak edges, and background interference in wire harness insulation surface defect detection, this paper proposes WHD-YOLOv8n, an improved YOLOv8n-based defect detection algorithm that integrates ODConv, CBAM, BiFPN, and Inner-CIoU for adaptive feature extraction, defect-related feature enhancement, multi-scale feature fusion, and bounding-box regression, respectively. WHD-YOLOv8n achieves an mAP@0.5 of 90.15%, representing a 6.21 percentage-point improvement over the YOLOv8n baseline, with Precision, Recall, and F1-score of 92.05%, 89.87%, and 90.95%, respectively. The model has 4.83 M parameters and requires 11.9 GFLOPs, while achieving an inference speed of 135.6 FPS. Ablation experiments and comparisons with representative general-purpose and industrial defect detectors demonstrate the effectiveness of the proposed design and its favorable accuracy–efficiency trade-off. Further experiments under haze and fog degradation show that WHD-YOLOv8n consistently outperforms the baseline under the tested degraded imaging conditions, although severe degradation can still cause missed detections. The proposed method therefore provides an effective solution for wire harness insulation surface defect detection and offers a basis for further research on robust detection under complex industrial imaging conditions.