Austenitic stainless steel is a very promising metal bipolar plate for proton exchange membrane fuel cells (PEMFC), which has advantages such as high mechanical strength, easy processing, and low cost. However, it is prone to corrosion in the acidic environment of PEMFC, which can have adverse effects on its lifespan and conductivity. By adopting a composite modification method to generate a dense nitride structure on the surface of 304 stainless steel bipolar plates, the corrosion resistance of the bipolar plate substrate can be improved. In this paper, shot peening pretreatment and plasma nitriding hybrid modification methods were employed to improve nitriding efficiency and reduce nitriding critical temperature, which generated a modified layer with good corrosion resistance on the surface of 304 austenitic stainless steel.

This study proposes a lightweight real-time fishing vessel tracking and size measurement model based on a UAV. In view of the problems faced by the current fishing port management department, such as low efficiency of fishing vessel size measurement methods and difficulty in updating the size information of large quantities of fishing vessels in time, this paper proposes a lightweight real-time fishing vessel tracking and size measurement model based on a UAV. (YOLO-LFVM). The model incorporates lightweight modules, such as MobileNetV3, AKConv, and C2f, and utilizes Python scripts in conjunction with OpenCV to measure vessel size in pixels. The findings indicate that, compared to the original model, the YOLO-LFVM model’s accuracy rate, recall rate, and mAP@0.5 decrease by only 0.7%, 0.2%, and 0.3%, respectively, while mAP@0.95 increases by 1.7%. Additionally, the model’s parameters decrease by 65%, and GFLOPs decrease by 69%. When comparing the model’s output with actual vessel data, the average relative error for total length is 2.67%, and for width, it is 3.28%. The research shows that the YOLO-LFVM model is effective in ship identification, ship tracking statistics, and measurement. Through the integration with UAV remote sensing technology, it is conducive to the timely updating of large-scale fishing vessel size information. Finally, the model can assist the daily management and law enforcement of the fishing port management department and can be applied to other equipment with limited computing power to perform target detection and object size measurement tasks.

Discrete floe ice increases the propulsion resistance of small unmanned surface vehicles (USVs) and generates impulsive bow and side loads. For high-density polyethylene (HDPE) USVs, these loads govern propulsion demand and provide inputs for structural assessment. Existing studies address floe-ice resistance of conventional ships and HDPE material behaviour separately. Using the geometry of an HDPE USV, this study combined the Reynolds-averaged Navier–Stokes (RANS) equations, volume-of-fluid (VOF) method, and discrete element method (DEM). The model computed open-water resistance and longitudinal loads under varying speed, ice concentration, and ice thickness. The numerical method was assessed against KRISO Container Ship (KCS) experiments in open water and floe ice. Open-water resistance increased from 49.2 to 230.7 N between 2 and 4 kn. At a 60% ice concentration and 0.02 m ice thickness, the mean total load increased from 125.6 to 428.9 N over this range. Increasing speed raised both fluid resistance and contact force, whereas higher concentration and thickness primarily increased contact force; the peak load reached 929.2 N at 0.04 m thickness. These fixed-attitude predictions provide comparative load estimates for propulsion assessment and subsequent structural analysis of the HDPE USV.