Ultra-high-performance concrete (UHPC) typically contains high cement and steel-fiber contents, leading to high cost and carbon emissions. This study developed a low-carbon UHPC by partially replacing cement with industrial solid waste (ISW) composed of ground granulated blast-furnace slag and calcium carbide slag, and by partially replacing steel fibers with recycled plastic fibers (RPF). The effects of ISW and RPF on flowability, mechanical properties, hydration behavior, microstructure, carbon emissions, and raw-material cost were investigated. ISW had a limited influence on flowability, whereas RPF markedly reduced flowability. Appropriate ISW and RPF contents increased flexural and compressive strengths by up to 41.02% and 14.93%, respectively. The 30% ISW-50% RPF mixture provided the highest flexural strength, while 30% ISW-30% RPF achieved the highest compressive strength with acceptable flowability. Hydration heat, XRD, SEM, and FTIR analyses showed that moderate ISW promoted early hydration and C-S-H/C-A-S-H gel formation, whereas excessive ISW caused dilution and reduced matrix compactness. Therefore, 30% ISW-30% RPF is recommended as the balanced formulation, whereas 50% ISW-50% RPF is more suitable for carbon- and cost-sensitive applications and maintains approximately 150 MPa compressive strength.

Low-altitude wind fields significantly increase the complexity of multi-UAV cooperative path planning (MUAVCP) by affecting flight time, energy consumption, trajectory feasibility, and arrival coordination. To address this problem, this study establishes a three-dimensional MUAVCP model considering kinematic constraints, no-fly zones, temporal coordination, inter-UAV separation, and multiple mission-related costs. An improved hybrid Rime–Snow Ablation Optimizer (IHRSO) is proposed. IHRSO employs the previously developed IRIME as the primary search component and a normal-cloud-model-enhanced SAOHTC (NCM-SAOHTC) as the auxiliary component. The two components are coordinated through an adapted stagnation-triggered mechanism that activates auxiliary search after insufficient primary-search improvement and subsequently returns the shared elite to IRIME for further refinement. IHRSO is evaluated on the CEC2017 and CEC2022 benchmark suites and several multi-UAV path-planning scenarios under low-altitude wind conditions. Ablation experiments show that IHRSO achieves the best Friedman mean rank of 1.375 and reduces optimization error by an average of 37.30% compared with fixed-period switching. In multi-UAV simulations, IHRSO consistently generates high-quality coordinated trajectories with lower mission delays and planning costs. In the large-fleet experiments, IHRSO maintains favorable solution quality and mission-delay performance as the fleet size increases to 20 UAVs, although the computational cost rises substantially with problem dimensionality.

Rapid and reliable flood segmentation utilizing optical remote-sensing imagery is critical for effective flood disaster response and risk assessment. Nevertheless, current models frequently struggle with imprecise boundary delineation and fragmented predictions in complex environments, especially where floodwater displays high spectral variability and closely resembles shadows, dark pavements, or wet soil. To overcome these challenges, we introduce FloodSeg, an innovative Mamba-CNN encoder–decoder network incorporating two lightweight yet highly effective components: a Shift module and a sequence-shuffle module. The spatial Shift module leverages spatially shifted feature aggregation to fortify boundary-aware representations, thereby ensuring the continuity of inundation contours even under varying illumination and cluttered backgrounds. Meanwhile, the sequence-shuffle module reorganizes multi-scale features via sequence-wise mixing and cross-regional interaction, significantly enhancing long-range dependency modeling. This facilitates the generation of globally consistent flood masks while mitigating local overfitting to dataset-specific textures. Evaluated on the Kaggle and FloodNet benchmark datasets, FloodSeg achieves outstanding mIoU scores of 81.85% and 91.21%, respectively. By outperforming various state-of-the-art CNN-, Transformer-, and Mamba-based baselines, our model demonstrates a superior accuracy-efficiency trade-off. These results substantiate that FloodSeg significantly advances boundary recognition and overall segmentation completeness, establishing it as a robust and practical solution for real-world remote-sensing flood mapping applications.