Accurate and robust state-of-health (SOH) estimation is essential for efficient battery management systems (BMS), especially in practical scenarios suffering from severe cell-to-cell inconsistencies and data distribution shifts. Although prevailing data-driven estimation methods can achieve satisfactory accuracy within specific domains, their generalization capability degrades drastically when applied to unseen battery cells. To fill this research gap, this paper develops an integrated physics-guided and domain-aware SOH estimation framework. The proposed framework combines redundancy-aware feature screening, cycle-aware soft covariance alignment specifically designed for physics-based health indicators, as well as monotonicity-constrained LightGBM regression embedded with split conformal uncertainty quantification. Experimental validations are conducted on the NASA B0005, B0006 and B0007 battery datasets under the leave-one-cell-out (LOCO) cross-cell evaluation strategy. Comparative results reveal that the presented method achieves prominent performance improvement on the most difficult B6 domain, where the root mean square error (RMSE) is reduced from 0.1068 to 0.0845 with a decline rate of 20.9%, and the coefficient of determination ($R^2$) rises from 0.2464 to 0.5286, while maintaining stable estimation accuracy on less challenging target domains. In terms of overall performance, the average RMSE across all tested cells drops from 0.0499 to 0.0425, corresponding to a 14.8% reduction, indicating improved cross-cell performance across the investigated cells. Furthermore, the adaptive alignment mechanism can be dynamically activated only when necessary, effectively avoiding redundant distortion of the original feature distribution. The research findings indicate that the integrated framework can alleviate cross-cell battery degradation discrepancies under the investigated conditions. The proposed strategy demonstrates promising potential for improving cross-cell SOH estimation under the investigated laboratory conditions; however, the uncertainty intervals are not fully calibrated under severe domain shift, and broader validation on larger and more heterogeneous battery datasets is required before practical deployment claims.

To address the design requirements of structural lightweighting and multi-performance coordination for UAV frames, an optimization method combining topology optimization and the NSGA-III multi-objective evolutionary algorithm is proposed. Twelve kinds of topological structures are obtained by setting different parameters through the Altair Inspire platform. A three-objective optimization model considering mass, stiffness, and strength was constructed to identify the Pareto-optimal solutions. Numerical analysis shows that the optimized design achieves approximately 59% mass reduction, with a maximum displacement of 0.2891 mm and a maximum stress of 4.449 MPa, meeting structural performance requirements. The results indicate that the proposed method effectively improves both the design efficiency and structural performance of UAV frames. The optimized models indicate favorable mechanical behavior and potential manufacturability; however, experimental manufacturing and structural testing are still required for full validation. The proposed workflow can be extended to the structural design of multirotor and fixed-wing UAV platforms.