Inner Mongolia University of Science and Technology

Flexible large-area monolithic organic solar cells suffer from electrical loss during up-scaling due to the limited conductivity of transparent electrodes. In this work, highly conductive silver grid fingers are integrated onto a roll-to-roll gravure-printed silver nanowire electrode via roll-to-roll screen printing, significantly reducing the composite sheet resistance from 15 to 1.5 Ω sq-1. A numerical model is established to optimize grid width and spacing, achieving an equivalent sheet resistance of 1 ~ 2 Ω sq-1 for higher-resistance electrodes. A self-masking strategy is developed to prevent shunting caused by uneven grid surfaces. As a result, monolithic flexible organic solar cells with areas of 4 and 16 cm² achieve power conversion efficiencies of 15.20% and 14.24%, respectively, demonstrating minimal efficiency loss with increased area. Additionally, the devices exhibit excellent mechanical flexibility and shelf stability, enabled by a robust photoresist passivation layer.

The arithmetic optimization algorithm (AOA) is a recently proposed swarm intelligence optimizer with a simple structure and few control parameters. However, the original AOA relies on a single update mechanism, which often leads to premature convergence and limited adaptability in complex optimization problems. To address these limitations, this paper proposes a multi-strategy improved arithmetic optimization algorithm (IAOA). The proposed algorithm constructs a heterogeneous strategy pool composed of six search strategies, including arithmetic update, differential evolution operators, competitive elite learning, interpolation-based acceleration, and curriculum education learning. Furthermore, an adaptive strategy regulation mechanism based on fitness improvement contribution is introduced to dynamically adjust the selection probability of each strategy. Extensive experiments conducted on the CEC2017 and CEC2022 benchmark suites demonstrate that IAOA achieves a superior optimization accuracy, convergence speed, and stability compared with several classical algorithms, recent metaheuristics, and AOA variants. Statistical tests including the Wilcoxon rank-sum test and Friedman mean rank test confirm the significance of the performance improvements. In addition, the algorithm is successfully applied to a three-dimensional path planning problem for amphibious unmanned aerial vehicles, demonstrating its effectiveness in solving complex engineering optimization problems.

This study investigates the synergistic effects of the O/S ratio and La–Ce mixed rare earths on the inclusion occurrence state and mechanical properties of U71Mn steel. Thermodynamic calculations based on the Wagner model and Factsage reveal the formation sequence and stability of rare‐earth inclusions, indicating that La exhibits higher activity than Ce and preferentially forms fine and stable (La, Ce) 2 O 2 S inclusions under low O/S conditions. When O/S ≤ 0.75 and the rare earth content ≥ 0.015%, MnS can be completely modified, and the inclusions transform from elongated or composite types into refined and spherical morphologies, significantly improving steel cleanliness. Microstructural observations show that rare earths promote grain refinement and reduce interfacial stress concentration. Mechanical tests demonstrate that rare earth modification increases hardness and reduces its fluctuation, enhances impact toughness by 22%–112%, and improves yield and tensile strengths by 7%–13% and 1%–8%, respectively. The friction coefficient decreases by 10%–40%, indicating improved wear resistance. These results confirm that regulating the O/S ratio and La/Ce proportion enables controlled inclusion evolution and comprehensive improvement of the strength, toughness, and wear performance of U71Mn steel.

Understanding the motion parameters of floating ice is very important for characterizing the ice water dynamics of rivers during freezing periods. Due to the low spatiotemporal resolution of satellite images, limited observation range of unmanned aerial vehicles, and deformation of shore-based camera images, it is difficult to simultaneously quantify the translational and rotational motion characteristics of floating ice and long-distance transportation. This study used the unmanned aerial vehicle GPS joint observation method to observe and obtain various motion parameters such as local translation, rotation, and long-distance transportation in the curved section of the upper reaches of the Yellow River and the straight section of the middle reaches of the Yellow River during the winter of 2024–2025 under conditions of ice density of 50–90%. The velocity field obtained by the drone shows an average ice velocity of 1.27 m/s at the bend and 1.18 m/s in the straight section, with lateral velocity gradients of −0.245 to 0.050 s−1 and −0.141 to 0.222 s−1, respectively. The angular velocity of a single floating ice block is 0.008–0.016 rad/s at bends and 0.010–0.036 rad/s in straight sections. The angular velocity is positively correlated with the local shear strength, and the rotation direction is consistent with the sign of the velocity gradient. GPS tracking provides long-distance transportation trajectories, and the average difference between the speeds obtained by GPS and drones is 0.10 m/s, confirming the reliability of speed estimation based on drones. These results indicate that integrated unmanned aerial vehicle GPS observation can quantitatively characterize local floating ice movement and long-distance floating ice transport behavior, providing on-site parameters for river ice water dynamics research and hazard assessment, and has the potential to be applied to rivers in other cold regions.