This study presents a comprehensive life-cycle assessment of Vehicle-to-Grid (V2G) economic viability, explicitly integrating the costs of both battery cycling degradation and calendar aging. While V2G offers revenue through energy arbitrage, its net profitability is critically dependent on regional electricity price differentials and the associated battery degradation costs. We develop a dynamic cost–benefit model, validated over a 10-year horizon across five diverse regions (Shanghai, Chengdu, the U.S., the U.K., and Australia). The results reveal stark regional disparities: Chengdu (0.65 USD/kWh peak–valley gap) and Australia (0.53 USD/kWh) achieve substantial net revenues of up to USD 25,000 per vehicle, whereas Shanghai’s narrow price differential (0.03 USD/kWh) renders V2G unprofitable. Sensitivity analysis quantifies critical break-even price differentials, varying by EV model and annual mileage (e.g., 0.12 USD/kWh minimum for Tesla Model Y). Crucially, calendar aging emerged as the dominant degradation cost (67% at 10,000 km/year), indicating significant battery underutilization potential. Policy insights emphasize the necessity of targeted interventions, such as Chengdu’s discharge incentives (0.69 USD/kWh), to bridge profitability gaps. This research provides actionable guidance for policymakers, grid operators, and EV owners by quantifying the trade-offs between V2G revenue and battery longevity, enabling optimized deployment strategies.

Sea cucumber detection represents an important step in underwater environmental perception, which is an indispensable part of the intelligent subsea fishing system. However, water turbidity decreases the clarity of underwater images, presenting a challenge to vision-based underwater target detection. Therefore, accurate, real-time, and lightweight detection models are required. First of all, the development of subsea target detection is summarized in this present work. Object detection methods based on deep learning including YOLOv5 and DETR, which are, respectively, examples of one-stage and anchor-free object detection approaches, have been increasingly applied in underwater detection scenarios. Based on the state-of-the-art underwater sea cucumber detection methods and aiming to provide a reference for practical subsea identification, adjacent and overlapping sea cucumber detection based on YOLOv5 and DETR are investigated and compared in detail. For each approach, the detection experiment is carried out on the derived dataset, which consists of a wide variety of sea cucumber sample images. Experiments demonstrate that YOLOv5 surpasses DETR in low computing consumption and high precision, particularly in the detection of small and dense features. Nevertheless, DETR exhibits rapid development and holds promising prospects in underwater object detection applications, owing to its relatively simple architecture and ingenious attention mechanism.

As an innovative underwater propulsion technology, the rim-driven thruster (RDT) has garnered increasing attention due to its advantages over conventional diesel or gas turbine propulsion systems, including reduced noise, higher efficiency, and a compact structure. However, traditional hull–engine–propeller matching theories are not directly applicable to RDTs because of their unique shaftless and ducted characteristics. Based on conventional hull–engine–propeller matching theory and propeller design methodology, this study proposes a novel hull–engine–propeller matching approach tailored specifically to RDTs. The method enables rapid matching by using open-water characteristics for hull–engine–propeller matching. In the absence of open-water test data for shaftless propellers, key parameters derived from ducted propeller tests are used for matching based on open-water characteristics to design the shaftless propeller. The propeller is then optimized through computational fluid dynamics (CFD) simulations to achieve the required thrust performance, effectively enabling an equivalent replacement. The proposed method provides a practical framework for selecting and designing RDTs, improves overall propulsion efficiency, and offers specific guidelines for determining optimal motor design parameters.

Sea cucumber detection represents a significate step in underwater environmental perception, which is an indispensable part of the intelligent subsea fishing system. However, various complex factors such as water turbidity declines the clarity of underwater images, presenting a challenge to vision-based underwater target detection. Therefore, accurate, real-time and lightweight detection models are required. First of all, the development of subsea target detection is summarized in this presented work. Besides, since target detection methods based on deep learning such as YOLOv5 and DETR, which are respectively examples of one-stage and anchor-free deep leaning methods, have been increasingly applied in underwater detection scenarios. Based on the analysis of state-of-the-art underwater sea cucumber detection approaches and aiming to provide a reference for practical subsea identification, the sea cucumber detection based on the YOLOv5 and DETR are investigated and compared in detail. For each approach, the detection experiment is carried out on the derived dataset which contains a wide variety of sea cucumber sample images. The compared experiments demonstrate that the overall outperformance of YOLOv5 in terms of low computing consumption and high precision, particularly in detection of small and dense features. Nevertheless, the DETR exhibits rapid development and holds promising prospects in underwater object detection applications, owing to its relatively simple architecture and ingenious attention mechanism.

A novel linear permanent magnet vernier generator (LPMVG) for small-power off-grid wave power generation systems is proposed in this paper. Firstly, in order to reduce the cogging force and the inherent edge effect of the linear generator, a staggered tooth modular structure is proposed. Secondly, in order to improve the output power and efficiency of the LPMVG and reduce the fluctuation coefficient of electromagnetic force, the relationship between the parameters of the generator is studied, and a method combining multi-objective optimization and single parameter scanning based on the response surface model and particle swarm optimization algorithm is proposed to obtain the optimal structural parameters of the generator. Thirdly, the output power and efficiency of the optimized generator are calculated and analyzed based on the two-dimensional finite element method, and the effectiveness of the multi-objective optimization design method based on the response surface model and particle swarm optimization algorithm is verified. Finally, a prototype is developed, and the calculated results and the measured results are shown to be in good agreement.