Excessive levels of Pb²⁺ and Cd²⁺ in seawater pose significant combined toxicity to marine organisms, resulting in harmful effects and further threatening human health through biomagnification in the food chain. Traditional methods for detecting marine Pb²⁺ and Cd²⁺ rely on laboratory analyses, which are hindered by limitations such as sample degradation during transport and complex operational procedures. In this study, we present an electrochemical sensor based on intelligent mobile detection devices. By combining G-COOH-MWCNTs/ZnO with differential pulse voltammetry, the sensor enables the efficient, simultaneous detection of Pb²⁺ and Cd²⁺ in seawater. The G-COOH-MWCNTs/ZnO composite film is prepared via drop-coating and is applied to a glassy carbon electrode. The film is characterized using cyclic voltammetry, electrochemical impedance spectroscopy, and scanning electron microscopy, while Pb²⁺ and Cd²⁺ are quantified using differential pulse voltammetry. Using a 0.1 mol/L sodium acetate buffer (pH 5.5), a deposition potential of −1.1 V, and an accumulation time of 300 s, a strong linear correlation was observed between the peak response currents of Pb²⁺ and Cd²⁺ and their concentrations in the range of 25–450 µg/L. The detection limits were 0.535 µg/L for Pb²⁺ and 0.354 µg/L for Cd²⁺. The sensor was applied for the analysis of seawater samples from Maowei Sea, achieving recovery rates for Pb²⁺ ranging from 97.7% to 103%, and for Cd²⁺ from 97% to 106.1%. These results demonstrate that the sensor exhibits high sensitivity and stability, offering a reliable solution for the on-site monitoring of heavy metal contamination in marine environments.

Hg(II), as an extremely hazardous heavy metal contaminant in the environment, poses a significant potential hazard to human health and ecosystems. A GR-MWCNTs-COOH/CeO₂/Nafion composite film-modified glassy carbon electrode was prepared using the drop-casting method in this study. The GR-MWCNTs-COOH/CeO₂/Nafion/GCE was electrochemically investigated through cyclic voltammetry (CV) and differential pulse stripping voltammetry (DPSV). Additionally, the surface morphologies of the composite film were evaluated using scanning electron microscopy (SEM). The conditions, such as buffer solution, pH, deposition potential, deposition time, modified film thickness, and Nafion content, were optimized. Under optimal experimental conditions, a good linear relationship between the peak current response of Hg(II) and its concentration in the range of 5–100 μg·L⁻¹ was observed, with a detection limit of 0.389 μg·L⁻¹. When it was used to detect Hg(II) in offshore seawater, the recovery rate ranged from 94.72% to 103.8%, with RSDs ≤ 5.79%.

This study combines an experimental semi-physical simulation model of an electric propulsion tugboat with four different neural networks to create a real-time simulation model for forecasting total power loads with small samples. The results of repeated experiments demonstrate that the BP neural network effectively forecasts the power load. Subsequently, addressing the limitations of traditional BP neural networks, an optimization approach employing an enhanced particle swarm algorithm and attention mechanism was developed, thereby improving the model’s prediction accuracy and robustness. The experiment shows that the improved prediction model achieves an R² value of 97.42%, demonstrating its effectiveness in forecasting changes in the short-term power load of ships as parameters change. In actual operation, ships can allocate power reasonably and in a timely manner according to the load forecast results, thereby improving the efficiency of the power grid.

7,12-Dimethylbenzo[a]anthracene (DMBA-7,12), a highly toxic and environmentally persistent polycyclic aromatic hydrocarbon (PAH), poses significant threats to marine biodiversity and human health due to its bioaccumulation through the food chain. Conventional chromatographic methods, while achieving comparable detection limits, are hindered by the need for expensive instrumentation and prolonged analysis times, rendering them unsuitable for rapid on-site monitoring of DMBA-7,12 in marine environments. Therefore, the development of novel, efficient detection techniques is imperative. In this study, we have successfully developed an electrochemical immunosensor based on a polydopamine (PDA)–chitosan (CTs) composite interface to overcome existing technical limitations. PDA provides a robust scaffold for antibody immobilization due to its strong adhesive properties, while CTs enhances signal amplification and biocompatibility. The synergistic integration of these materials combines the high efficiency of electrochemical detection with the specificity of antigen–antibody recognition, enabling precise qualitative and quantitative analysis of the target analyte through monitoring changes in the electrochemical properties at the electrode surface. By systematically optimizing key experimental parameters, including buffer pH, probe concentration, and antibody loading, we have constructed the first electrochemical immunosensor for detecting DMBA-7,12 in seawater. The sensor achieved a detection limit as low as 0.42 ng/mL. In spiked seawater samples, the recovery rates ranged from 95.53% to 99.44%, with relative standard deviations (RSDs) ≤ 4.6%, demonstrating excellent accuracy and reliability. This innovative approach offers a cost-effective and efficient solution for the in situ rapid monitoring of trace carcinogens in marine environments, potentially advancing the field of marine pollutant detection technologies.

The accurate extraction of navigational regions from images of navigational waters plays a key role in ensuring on-water safety and the automation of unmanned vessels. Nonetheless, current technological methods encounter significant challenges in addressing fluctuations in water surface illumination, reflective disturbances, and surface undulations, among other disruptions, in turn making it challenging to achieve rapid and precise boundary segmentation. To cope with these challenges, in this paper, we propose a coordinate-aware multi-scale feature network (GASF-ResNet) method for water segmentation. The method integrates the attention module Global Grouping Coordinate Attention (GGCA) in the four downsampling branches of ResNet-50, thus enhancing the model’s ability to capture target features and improving the feature representation. To expand the model’s receptive field and boost its capability in extracting features of multi-scale targets, the Avoidance Spatial Pyramid Pooling (ASPP) technique is used. Combined with multi-scale feature fusion, this effectively enhances the expression of semantic information at different scales and improves the segmentation accuracy of the model in complex water environments. The experimental results show that the average pixel accuracy (mPA) and average intersection and union ratio (mIoU) of the proposed method on the self-made dataset and on the USVInaland unmanned ship dataset are 99.31% and 98.61%, and 98.55% and 99.27%, respectively, significantly better results than those obtained for the existing mainstream models. These results are helpful in overcoming the background interference caused by water surface reflection and uneven lighting in the aquatic environment and in realizing the accurate segmentation of the water area for the safe navigation of unmanned vessels, which is of great value for the stable operation of unmanned vessels in complex environments.