Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are promising molecular biomarkers widely applied in paleoenvironmental reconstructions, including temperature and pH. However, knowledge of the microorganisms responsible for brGDGT production in marine environments remains limited, which constrains the further development and application of brGDGT-based proxies for reconstructing past marine conditions. In this study, both ‘living’ intact polar lipid-derived brGDGTs (IPL-brGDGTs) and ‘fossil’ core brGDGTs (CL-brGDGTs), together with bacterial community compositions, were analysed in multiple sediment cores collected along a nearshore-to-offshore transect in the East China Sea (ECS). The potential correlations between brGDGT distributions and bacterial community compositions at varying sediment depths across an environmental gradient were also explored. Results revealed that IPL-brGDGTs were predominantly biosynthesised in situ, whereas CL-brGDGTs reflected a mixture of marine autochthonous production and terrestrial inputs. Potential brGDGT-producing bacteria in nearshore environments were primarily composed of chemolithoautotrophic taxa (e.g., Gammaproteobacteria and Dehalococcoidia) and chemoheterotrophic taxa (e.g., Alphaproteobacteria, Bacilli, and Actinobacteria). In contrast, offshore regions were dominated by chemoheterotrophic hypoxic bacteria (e.g., Anaerolineae and Phycisphaerae) and facultatively anaerobic chemolithoautotrophic bacteria (e.g., Gammaproteobacteria and Desulfobacteria). A significant difference in bacterial community composition and IPL-brGDGT distribution was observed at a depth of 17 cm, likely due to physical disturbance in near-surface sediments, such as wave action, tidal forces, and storm events. Variance partitioning analysis (VPA) revealed that the bacterial community composition alone accounted for 14.1% of the variation in IPL-brGDGTs and 6.5% in CL-brGDGTs, further suggesting that the distribution of brGDGTs is primarily influenced by the composition of the bacterial community in the nearshore-to-offshore sedimentary ecosystems of the ECS. These findings regarding the potential biosynthesis of brGDGTs in coastal habitats advance our understanding of the microbial mechanisms that regulate brGDGT distribution in marine ecosystems. Moreover, they emphasise the importance of considering physical disturbance effects when interpreting sedimentary brGDGT records for paleoenvironmental reconstructions in marginal seas, such as the ECS.

Marginal seas are increasingly impacted by anthropogenic activities, leading to widespread eutrophication, yet the responses of marine microbial communities remain poorly understood. We compared sediments from the highly eutrophic Yangtze River Estuary (YRE) and the oligotrophic East China Sea (ECS) to examine how eutrophication alters microbial abundance, community structure, assembly processes, functional profiles, and life-history strategies. Our results showed that YRE sediments harbored significantly higher microbial abundance (1.3 × 108-1.1 × 109 cells g−1 vs. 8.0 × 107-7.1 × 108 cells g−1), Chao1 richness (9,782–18,129 vs. 9,366–14,903), and Shannon diversity (6.19–7.47 vs. 6.05–7.07). Functional profiling revealed an enrichment of nitrogen- and carbon-cycling genes, human pathogens, and antibiotic-resistance genes in YRE. Life-history traits in YRE microbial communities showed higher average 16S rRNA gene copy numbers (median 2.75 vs. 2.56), greater codon usage bias (0.0181 vs. 0.0178), higher maximum predicted growth rates (0.1054 vs. 0.0951 h−1), larger genome sizes (5.59 vs. 5.46 Mb), higher GC content (56.43 vs. 55.83%) and increased transposase abundance (3.46 vs. 1.71%), collectively indicating a shift from K-strategists to r-strategists in the eutrophic environment. Neutral and null model analyses, and statistical analyses revealed that human activities, especially those altering water quality and chemistry, drive significant shifts in microbial community structure, function, and assembly processes, which in turn reshape microbial life-history strategies in estuarine benthic ecosystems.

High-precision wave data serve as a foundation for investigating the wave characteristics of the East China Sea (ECS) and wave energy development. Based on the simulating waves nearshore (SWAN) model, this study uses the ERA5 (ECMWF Reanalysis v5) reanalysis wind field data and ETOPO1 bathymetric data to perform high-precision simulations at a resolution of 0.05° × 0.05° for the waves in the area of 25–35° N and 120–130° E in the ECS from 2009 to 2023. The simulation results indicate that the application of the whitecapping dissipation parameter Komen and the bottom friction parameter Collins yields an average RMSE of 0.374 m and 0.369 m when compared to satellite-measured data, demonstrating its superior suitability for wave simulation in shallow waters such as the ESC over the other whitecapping dissipation parameter, Westhuysen, and the other two bottom friction parameters, Jonswap and Madsen, in the SWAN model. The monthly average significant wave height (SWH) ranges from 0 to 3 m, exhibiting a trend that it is more important in autumn and winter than in spring and summer and gradually increases from the northwest to the southeast. Due to the influence of the Kuroshio current, topography, and events such as typhoons, areas with significant wave heights are found in the northwest of the Ryukyu Islands and north of the Taiwan Strait. The wave energy flux density in most areas of the ECS is >2 kW/m, particularly in the north of the Ryukyu Islands, where the annual average value remains above 8 kW/m. Because of the influence of climate events such as El Niño and extreme heatwaves, the wave energy flux density decreased significantly in some years (a 21% decrease in 2015). The coefficient of variation of wave energy in the East China Sea exhibits pronounced regional heterogeneity, which can be categorized into four distinct patterns: high mean wave energy with high variation coefficient, high mean wave energy with low variation coefficient, low mean wave energy with high variation coefficient, and low mean wave energy with low variation coefficient. This classification fundamentally reflects the intrinsic differences in dynamic environments across various maritime regions. These high-precision numerical simulation results provide methodological and theoretical support for exploring the spatiotemporal variation laws of waves in the ECS region, the development and utilization of wave resources, and marine engineering construction.

Profiling floats are important platforms for oceanic profile observations, yet they are prone to positional drift and grounding when deployed in shallow-sea environments. In order to address these issues, an aluminum alloy-based propulsion-enabled station-keeping anchoring system (PESKAS) is designed in this paper. The PESKAS comprises anchor wings, thrusters, a steering connector, support frames, and an upper connection flange, which allows easy installation to the bottom of conventional profiling floats. Three anchor wings, with a cone angle of 40° and a length of 0.12 m, enable the attached profiling float to anchor to the seabed under ocean currents of up to 0.5 m/s when fully penetrating the sediment. Numerical simulation results show that achieving full penetration into clay, clayey silt, and silty sand requires thrust forces of 80–100 N, 100–120 N, and 160 N, respectively. To achieve full sediment penetration, the PESKAS employs a redundant quadruple-thruster configuration (total thrust 200 N) with an effective actuation duration of approximately 1 s. It ascends from the seabed via a thruster-generated upward force during the ascent of the profiling float, effectively avoiding grounding. Over a complete operational cycle (descent and ascent), the PESKAS consumes approximately 0.65–1.84 kJ of energy. Compared to the energy consumption of PROVOR profiling float motors (10.25 kJ) and sensors (8.33 kJ), the additional energy requirement for the PESKAS does not have a significant effect on the endurance of profiling floats. According to the results of the simulation experiment of the PESKAS, the system successfully achieves its design objectives of full penetration into and ascending from sediments. PESKAS is a cost-effective solution for the positional drift and grounding of profiling floats, which enables stable long-term profile observations in shallow-sea environments and has broad application prospects.

Complex underwater environmental factors, such as turbidity, turbulence, background light, and mobility, pose great challenges to the rapid and stable establishment of underwater wireless optical communication (UWOC) links. Therefore, it is crucial to accelerate the development of fast, high-precision, and robust acquisition, pointing, and tracking (APT) systems to promote the practical application of UWOC. In this work, the YOLOv5s algorithm with strong learning and adaptive abilities and a solar panel receiver with a large detection area are proposed to solve the link alignment issues caused by complex underwater environmental factors. In a 3-m water tank, the turbidity, bubbles-induced turbulence, and background light have less impact on the robust YOLOv5s-based APT system. The average pointing time is merely 5.97 ms, and the average tracking time is merely 229 ms for different underwater environments. When the concentration of Mg(OH) 2 reaches 3.39 mg/L and the scintillation index of bubbles reaches 0.019, the UWOC link can still be established quickly with a data rate of 1.76 Mbit/s, respectively. Moreover, the APT system can tolerate the illuminance of the background light as low as 0.2 lx. We also conduct a preliminary study on the performance of the system in mobile conditions, which proves the effectiveness of the system and lays a foundation for further research.

High-precision underwater object detection technology has important research value and broad application prospects in marine biological resources exploration and marine fisheries monitoring. However, blurry images pose great challenges to the detection of underwater objects of different sizes. The detection accuracy of underwater objects of different sizes in blurry images needs to be improved significantly. To this end, we propose a convolutional neural network based on YOLOv5 named YOLOv5-MobileOne-Attention (YOLOv5-MA) in this work. In YOLOv5-MA, we replace the backbone of YOLOv5 with MobileOne to improve the accuracy of the algorithm, and add the convolutional block attention module and the normalization-based attention module to enhance the feature extraction capability for underwater blurry images. Furthermore, we add the normalized gaussian wassernstein distance loss function based on the location loss function to reduce the sensitivity of the anchor frame of small objects to the intersection over union values. In the experiment, we investigate the performance of YOLOv5-MA using a dataset containing blurry images selected from the underwater robot professional contest 2020 dataset and the real-world underwater image enhancement dataset. Experimental results show that the mAP_0.5 value of YOLOv5-MA reaches 54.4%, which is 2.8% higher than that of YOLOv5s. Moreover, YOLOv5-MA can achieve a balance between accuracy and speed compared to YOLOv5, YOLOv8, and RE-DETR. This validates the advantages of YOLOv5-MA in underwater object detection for blurry images. It has great application prospect in future object detection based on underwater robots in complex underwater environments.

Tubular structures are widely used in steel constructions, such as offshore platforms, because of their superior mechanical qualities, efficient use of materials, lightweight and aesthetically pleasing appearance. The most common failure mode of offshore tubular joints is fatigue damage brought on by cyclic loading during the course of their service life, like wind, wave and equipment vibration. In this paper, the finite element model of the TK-type tubular joint at the stress concentration position is established by analyzing the load and response of the observation tower. The continuum damage mechanics method has been used to evaluate the fatigue crack initiation position and life of the tubular joint, and the crack initiation life of the key nodes has been predicted, which provides theoretical support for ensuring the service safety of the offshore observation tower.

Marine biological nitrogen fixation (BNF) is crucial for introducing “new nitrogen” into the oceans. Over the past 30 years, numerous laboratory and on-board culture experiments have been conducted studying the effects of nutrients such as total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), and dissolved iron (DFe) on marine diazotrophs such as Braarudosphaera bigelowii (B. bigelowii), Trichodesmium, Crocosphaera and noncyanobacterial diazotrophs (NCDs). Most studies concluded that elevated dissolved inorganic nitrogen levels inhibit nitrogen fixation in Trichodesmium, promote its growth, and have minimal effect on B. bigelowii. The impact on NCDs is unclear. Moreover, elevated dissolved inorganic phosphorus (DIP) levels can promote individual growth, population growth, and nitrogen fixation in most diazotrophs in P-limited marine environments. Dissolved organic phosphorus is a potential phosphorous source for diazotrophs in low-DIP environments. Elevated DFe can promote population growth and nitrogen fixation in diazotrophs in Fe-limited marine environments. At present, most diazotrophs have yet to achieve pure culture. Moreover, the effect of nutrients on diazotrophs is mainly limited to the study of a single nutrient, which cannot accurately reflect the actual Marine environment where diazotrophs live. As a result, our understanding of the effect of nutrients on diazotrophs is still insufficient. Future research focusing on the issues above and the development of innovative technologies and methodologies to investigate the impact of marine BNF is highly recommended, which will allow for a more precise assessment of the impact of marine BNF on global primary productivity while providing a scientific foundation for rational evaluation of ocean CO2 uptake and emissions.

To address challenges in outdoor multi-robot collaborative localization (MRCL) due to low GPS accuracy, we propose a system using three UGVs, each equipped with a shared camera and a laser rangefinder. Our trilateral localization algorithm combines least-squares matrix and gradient descent methods, resulting in an 80.4% improvement in accuracy compared to traditional methods. The system mitigates the limitations of GPS accuracy by utilizing accurate baseline measurements and optimizing the localization process. These advancements have potential applications in transportation, production, and logistics, enhancing MRCL performance in outdoor environments.

Underwater object detection technology is crucial in many marine-related fields, including marine environmental monitoring, marine resource development, and marine ecological protection. However, this technology faces great challenges due to the poor quality of underwater optical images and the varying sizes of underwater objects. Therefore, we proposed an underwater optical detection network (UODN) based on the you only look once version 8 (YOLOv8) framework, which addresses these issues through the cross stage multi-branch (CSMB) module and large kernel spatial pyramid (LKSP) module. The aim of the CSMB module is to extract more features from underwater optical images to address the issue of poor image quality, while the LKSP module is designed to enhance the ability of the network to detect underwater objects of various scales. Furthermore, CSMBDarknet built by CSMB and LKSP can be used as the backbone of other underwater object detection algorithms for underwater feature extraction. Extensive experimental results on the underwater robot professional contest 2020 dataset revealed that the average precision (AP) of UODN increased by 1.0%, the AP50 of UODN increased by 1.1%, and the AP75 of UODN increased by 2.1% compared with those of the original YOLOv8s. Furthermore, UODN outperforms 12 state-of-the-art models on multiple underwater optical datasets, paving the way for future real-time and high-precision underwater object detection.

The rapid advancement of the next generation of communications and internet of things (IoT) technologies has made the provision of location-based services for diverse devices an increasingly pressing necessity. Localizing devices with/without intelligent computing abilities, including both active and passive devices is essential, especially in indoor scenarios. For traditional RF positioning systems, aligning transmission signals and dealing with signal interference in complex environments are inevitable challenges. Therefore, this paper proposed a new passive positioning system, the RF-band resonant beam positioning system (RF-RBPS), which achieves energy concentration and beam alignment by amplifying echoes between the base station (BS) and the passive target (PT), without the need for complex channel estimation and time-consuming beamforming and provides high-precision direction of arrival (DoA) estimation for battery-free targets using the resonant mechanism. The direction information of the PT is estimated using the multiple signal classification (MUSIC) algorithm at the end of BS. The feasibility of the proposed system is validated through theoretical analysis and simulations. Results indicate that the proposed RF-RBPS surpasses RF-band active positioning system (RF-APS) in precision, achieving millimeter-level precision at 2m within an elevation angle of 35 degrees, and an error of less than 3cm at 2.5m within an elevation angle of 35 degrees.

High-quality underwater optical images play a crucial role in marine resources exploration. However, the absorption and scattering of light by seawater can cause underwater optical images to appear blue-green and blurry. Therefore, we propose a generative adversarial network (GAN) with lightweight U-Net for underwater image enhancement, called LUUW-GAN. The adversarial structure of GAN allows the generation of vivid and realistic images, thus better solving the fading problem of underwater images. Moreover, we design the generator of GAN using the lightweight U-Net to preserve fine details of the original input images, thus effectively improving blurring caused by scattering. We incorporate the convolutional block attention module into the skip connections to enhance the feature representation capability. Furthermore, we introduce the top-k selection operator to facilitate self-attention and feature weighting to improve feature aggregation. The experimental results on the enhancement of underwater visual perception dataset show that the proposed method improves the peak signal-to-noise ratio by 1.24 and the structural similarity by 0.021 compared to the basic model.

In real-life marine environments, the composition and grain size of suspended sediments and the resuspension and sedimentation of sediments caused by turbulence may have a significant impact on underwater wireless optical communication (UWOC). However, to date, researchers have not conducted quantitative research on this issue. To this end, we innovatively study the effects of different compositions and grain sizes of suspended sediments on UWOC and the effects of turbulence-induced sediment resuspension and sedimentation on UWOC in this paper. Quartz and kaolin with different grain sizes are used to simulate sediments in seawater. An oscillating grid that can vary frequency and stroke is used to generate turbulence of different intensities. By comparing the turbidity and optical power density of different simulated sediments with different grain sizes, we find that the smaller the grain size of the simulated sediments, the higher the bit error rate (BER) under the same turbidity. But different simulated sediments with different grain sizes have similar effects on BER performance under the same optical power density. Therefore, turbidity can be used to characterize the changes of underwater channels, and optical power density can be used to evaluate the attenuation of light at the receiving end after transmission through the underwater channel. By continuously changing the frequency of the grid to cause the sediments to resuspend and sink, we prove that the process of turbulence-induced sediment resuspension and sedimentation can seriously affect the BER performance. The larger the frequency of the grid, the greater the turbulence intensity and the worse the BER performance. This study lays a foundation for the practical application of UWOC in mobile ocean observation networks.

The typical strategy for the phase-shifted full-bridge (PSFB) converter is to design a linear compensator based on a small-signal model. However, this approach cannot provide a strongly robust output under changing operating conditions or with perturbations in the converter parameters. In this article, a fixed-frequency pulse width modulation (PWM) based full-order sliding mode controller is proposed to improve the performance of a PSFB converter with diodes in series on the lagging leg. Firstly, to facilitate the application of sliding control laws, we construct a simplified equivalent control model by analyzing the phase-shifted modulation and operating waveforms of the converter. Secondly, we derive strict existence conditions for the sliding phases to ensure that the state trajectories fulfill the requirements. Thirdly, the perturbations of both the external and internal variables are considered in the equivalent control function to improve the stability of the controller. Finally, simulation and experimental results verify that the proposed sliding mode controller provides better dynamic regulation performance and robustness than does a linear controller.

Multi-Robot Cooperative Localization (MRCL) is a promising technology for applications, e.g., shipping, production, logistics, etc. However, MRCL for outdoor applications faces challenges in localization accuracy due to the low-precision baseline measurement between every two robots by Global Positioning System (GPS). In this paper, we focus on target localization using three mobile robots with ordinary cameras in the same horizontal space. Taking the cloud server as the basic computing platform, we propose a localization system using 3-robots with the laser rangefinder for the baseline measurement between robots. We also propose a trilateral positioning algorithm, which combines the advantages of both least squares matrix method and gradient descent method. The simulation results demonstrate that the proposed system can improve the accuracy by 80.4% compared with the traditional method.

Underwater target detection aims to locate and identify targets in underwater scenes, which plays a significant role in the development of the marine economy and environmental protection. However, existing target detection algorithms often cannot meet people’s needs in underwater environments. In this paper, we propose an underwater target classification convolutional neural network (UTC-CNN) based on You Only Look Once Version 8. This network builds a C2f_Squeeze and Excitation module to enhance the feature extraction ability of blurred underwater targets, and adopts Bidirectional Feature Pyramid Network to replace Feature Pyramid Network to further enhance the feature fusion capability. The experiments show this model outperforms other target detection models on Underwater Robot Professional Contest 2020 dataset, with mAP0.5 reaching 84.4%, demonstrating the advantages of UTC-CNN in underwater target detection tasks.

Rye (Secale cereale L., 2n=2x=14, RR) is a significant genetic resource for improving common wheat because of its resistance to multiple diseases and abiotic-stress tolerant traits. The 1RS chromosome from the German cultivated rye variety Petkus is critical in wheat breeding. However, its weakened disease resistance highlights the need to identify new resources. In the present study, a novel derived line called D27 was developed from common wheat and Mexico Rye. Cytological observations characterized the karyotype of D27 as 2n=42=21 II. Genomic in situ hybridization indicated that a pair of whole-arm translocated Mexico Rye chromosomes were inherited typically in the mitotic and meiosis stages of D27. Experiments using fluorescence in situ hybridization (FISH) and gliadin electrophoresis showed that D27 lacked wheat 1DS chromosomes. They were replaced by 1RS chromosomes of Mexico Rye, supported by wheat simple-sequence repeat markers, rye sequence characterized amplified region markers, and wheat 40K SNP array analysis. The wheat 1DS chromosomes could not be detected by molecular markers and wheat SNP array, but the presence of rye 1RS chromosomes was confirmed. Agronomic trait assessments indicated that D27 had a higher tiller number and enhanced stripe rust and powdery mildew resistance. In addition, dough properties analysis showed that replacing 1DS led to higher viscosity and lower dough elasticity in D27, which was beneficial for cake making. In conclusion, the novel cytogenetically stable common wheat–Mexico Rye T1DL·1RS translocation line D27 offers excellent potential as outstanding germplasm in wheat breeding programs focusing on disease resistance and yield improvement. Additionally, it can be valuable for researching the rye 1RS chromosome's genetic diversity.

We develop an optical wireless communication-based 2K real-time video surveillance system prototype with fieldprogrammable gate arrays.Using a 3-W blue light-emitting diode and an avalanche photo-diode, 20-m and 1.5-m realtime picture/video transmission with a high resolution of 1920 × 1080 pixels is implemented in free space and pure water channel, respectively.It indicates the good performance of the prototype, which is the first step to realize underwater visual monitoring in future human-robot interaction applications.