An Fe₆AlCoCrNi medium-entropy (MEA) coating was coated on a steel substrate by laser cladding. The micro-structure, crystal structure, phases, and wear properties of the coating were investigated. The coating was mainly composed of a dendritic face-center cubic (FCC) phase, which showed preferred crystal orientation of , normal to the coating surface, and a body-center cubic (BCC) phase. The MEA coating exhibited satisfactory rigidity with superior wear resistance at different loads and temperatures, much higher than that of the steel substrate. When the test temperature increased from 293 K to 573 K, the coefficient of friction (COF) of the coating markedly decreased from about 0.75 to 0.35; a large decrease in wear was also observed. The wear mechanism of the MEA coating was abrasion wear at room temperature, while the wear of the coating at high temperatures involved considerable oxidation, which enhanced the wear resistance of the coating.

The carapace size of the Chinese mitten crab (Eriocheir sinensis) is a vital indicator for assessing the growth performance of crabs. However, measuring the carapace sizes of Chinese mitten crabs remains challenging due to environmental complexity, species-specific behavioral patterns, and the current limitations of data acquisition methods characterized by labor-intensive manual measurements and subjective empirical judgments. Our study proposes an automated carapace dimension-measuring method integrating enhanced computer vision techniques to address the above challenges. Specifically, we used the YOLOv8 algorithm combined with the pose keypoint detection algorithm to process Chinese mitten crab images to acquire carapace sizes. We redesigned the YOLOv8l-pose architecture by incorporating Swin Transformer as the backbone network to improve feature representation for multikeypoint detection on crab carapaces, significantly enhancing global contextual feature extraction capabilities. Furthermore, we refined the loss function to model spatial correlations between keypoint locations accurately and thus improve detection accuracy for dorsal carapace dimension key points in Chinese mitten crabs. Our system enabled noncontact size measurement to leverage the proportional relationship between precalibrated background markers and detected carapace keypoints. Experimental results demonstrated that our enhanced model achieved a mean average precision of 95.88%, representing a 2.61% improvement over the baseline. The overall object keypoint similarity reached 91.32%, with maximum and mean dimensional measurement errors of 4.8% and 2.34%, respectively, validating our method’s reliability for aquaculture applications.

In recent years, the RRT* algorithm has been widely applied in industrial fields because of its asymptotic optimality. However, the traditional RRT* algorithm exhibits limitations in terms of convergence speed and quality of generated paths, and its path exploration capability in complex environments remains inadequate. To address these issues, this study proposes a self-adaptive bidirectional APF-RRT* (SA-Bi-APF-RRT*) algorithm. Specifically, a hierarchical node expansion mechanism is established, enabling dynamic adjustment of the new node expansion strategy. Furthermore, a bidirectional artificial potential field (APF) guidance strategy is introduced to enhance obstacle avoidance performance. An obstacle range density evaluation module, which autonomously adjusts APF parameters according to the density distribution of surrounding obstacles, is then incorporated. Additionally, the algorithm integrates a segmented greedy approach with Bézier curve fitting techniques to achieve simultaneous optimization of path length and smoothness, while ensuring path safety. Finally, the proposed algorithm is compared against RRT*, GB-RRT*, Bi-RRT*, APF-RRT*, and Bi-APF-RRT*, demonstrating superior adaptability and efficiency in environments characterized by low iteration counts and high obstacle density. Results indicate that the SA-Bi-APF-RRT* algorithm constitutes a promising optimization solution for USVs path planning tasks.

Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications.