Search Results (717)

Ocean Acoustic Tomography (OAT) is an important marine remote sensing technique used for inverting large-scale ocean environmental parameters, but traditional methods face challenges in computational complexity and environmental interference. This paper proposes a causal analysis-driven AI FOR SCIENCE method for high-precision and rapid inversion of oceanic hydrological parameters in complex underwater environments. Based on the open-source VTUAD (Vessel Type Underwater Acoustic Data) dataset, the method first utilizes a fine-tuned Paraformer (a fast and accurate parallel transformer) model for precise classification of sound source targets. Then, using structural causal models (SCM) and potential outcome frameworks, causal embedding vectors with physical significance are constructed. Finally, a cross-modal Transformer network is employed to fuse acoustic features, sound source priors, and environmental variables, enabling inversion of temperature and salinity in the Georgia Strait of Canada. Experimental results show that the method achieves accuracies of 97.77% and 95.52% for temperature and salinity inversion tasks, respectively, significantly outperforming traditional methods. Additionally, with GPU acceleration, the inference speed is improved by over sixfold, aimed at enabling real-time Ocean Acoustic Tomography (OAT) on edge computing platforms as smart hardware, thereby validating the method’s practicality. By incorporating causal inference and cross-modal data fusion, this study not only enhances inversion accuracy and model interpretability but also provides new insights for real-time applications of OAT. Full article

Tubastraea coccinea and T. tagusensis, commonly known as sun corals, are two species of stony corals (Scleractinia, Dendrophylliidae) native to the Indo-Pacific region (T. coccinea) and the Galapagos Islands (T. tagusensis), respectively. They are considered highly invasive species, particularly in the Western Atlantic Ocean, due to high adaptability to various ecological conditions and notable resilience. Given their demonstrated invasiveness, it is important to delve into their physiology and the molecular bases supporting their resilience. However, to date, only a few molecular tools are available for the study of these organisms. The primary objective of the present study was the development of an efficient RNA extraction protocol for Tubastraea coccinea and T.a tagusensis samples collected off Ilha Grande Bay, Rio de Janeiro (Brazil). The quantity of isolated RNA was evaluated using NanoDrop, while its purity and quality were determined by evaluating the A260/A280 and A260/230 ratios. Subsequently, based on genes known for T. coccinea, two housekeeping genes and seven stress response-related genes were isolated and characterized, for the first time for both species, using a molecular approach. An interactomic analysis was also conducted, which revealed functional interactions among these genes. This study represents the first report on gene networks in Tubastraea spp., opening new perspectives for understanding the chemical ecology and the cellular mechanisms underlying the invasiveness of these species. The results obtained will be useful for ecological conservation purposes, contributing to the formulation of strategies to limit their further expansion. Full article

The Ouarzazate Group in the Anti-Atlas Belt of southern Morocco, part of the West African Craton (WAC), is a significant Proterozoic lithostratigraphic unit formed during the late Ediacaran period. It includes extensive volcanic rocks associated with the early stages of Iapetus Ocean opening. Zircon U-Pb dating and geochemical analyses of the Oued Dar’a Caldera (ODC) volcanic succession in the Saghro Massif reveal two major eruptive cycles corresponding to the lower and upper Ouarzazate Group. The 1st cycle (588–563 Ma) includes pre- and syn-caldera volcanic succession characterized by basaltic andesite to rhyolitic rocks, formed in a volcanic arc setting through lithospheric mantle-derived mafic magmatism and crustal melting. A major caldera-forming eruption occurred approximately 571–562 Ma, with associated rhyolitic dyke swarms indicating a larger caldera extent than previously known. The 2nd cycle (561–543 Ma) features post-caldera bimodal volcanism, with tholeiitic basalts and intraplate felsic magmas, signaling a shift to continental flood basalts and silicic volcanic systems. The entire volcanic activity spans approximately 23–40 million years. This succession is linked to late Ediacaran intracontinental super-eruptions tied to orogenic collapse and continental extension, likely in association with the Central Iapetus Magmatic Province (CIMP), marking a significant transition in the geodynamic evolution of the WAC. Full article

This study focused on the carbonate system dynamics and air–sea CO₂ fluxes in the open-ocean waters of the Madeira–Tore Seamount Complex during August 2024. Surface water properties revealed pronounced latitudinal gradients in sea surface temperature (21.9–23.1 °C), salinity (36.2–36.7), and dissolved oxygen (228–251 µmol Kg⁻¹), influenced by mesoscale eddies and topographically driven upwelling. Despite oligotrophic conditions, distinct phytoplankton assemblages were observed, with coccolithophores dominating southern seamounts and open-ocean stations, and green algae and diatoms indicating episodic nutrient input. Surface total alkalinity (TA: 2236–2467 µmol Kg⁻¹), dissolved inorganic carbon (DIC: 2006–2183 µmol Kg⁻¹), and pCO₂ (467–515 µatm) showed spatial variability aligned with water mass characteristics and biological activity. All stations exhibited positive air–sea CO₂ fluxes (2.8–11.5 mmol m⁻² d⁻¹), indicating the region is a CO₂ source during summer. Calcite and aragonite saturation states were highest in stratified, warmer waters. Principal Component Analysis highlighted the role of physical mixing, carbonate chemistry, and biological uptake in structuring regional variability. Our findings emphasize and contribute to the complex interplay of physical and biogeochemical drivers in modulating carbon cycling and ecosystem structure across Atlantic seamounts. Full article

Reinforcement learning (RL), a branch of artificial intelligence (AI), is becoming more popular in a variety of application fields such as games, workplaces, and behavioral analysis, due to its ability to model complex decision-making through interaction and feedback. Traditional systems for personality and behavior assessment often rely on self-reported questionnaires, which are prone to bias and manipulation. RL offers a compelling alternative by generating diverse, objective behavioral data through agent–environment interactions. In this paper, we propose a Reinforcement Learning-based framework in a game environment, where agents simulate personality-driven behavior using context-aware policies and exhibit a wide range of realistic actions. Our method, which is based on the OCEAN Five personality model—openness, conscientiousness, extroversion, agreeableness, and neuroticism—relates psychological profiles to in-game decision-making patterns. The agents are allowed to operate in numerous environments, observe behaviors that were modeled using another simulation system (HiDAC) and develop the skills needed to navigate and complete tasks. As a result, we are able to identify the personality types and team configurations that have the greatest effects on task performance and collaboration effectiveness. Using interaction data derived from self-play, we investigate the relationships between behaviors motivated by the personalities of the agents, communication styles, and team outcomes. The results demonstrate that in addition to having an effect on performance, personality-aware agents provide a solid methodology for producing realistic behavioral data, developing adaptive NPCs, and evaluating team-based scenarios in challenging settings. Full article

With the natural evolution of the Arctic route and advancements in related technologies, the development of new green ice-class ships is becoming a key technological breakthrough for the global shipbuilding industry. As a special vessel form that must perform icebreaking operations and undertake long-distance ocean voyages, an ice-class ship requires sufficient icebreaking capacity to navigate ice-covered water areas. However, since such ships operate for most of their time under open water conditions, it is also crucial to consider their resistance characteristics in these environments. Firstly, this paper employs linear interpolation to extract wind, wave, and sea ice data along the route and calculates the proportion of ice-covered and open water area in the overall voyage. This provides data support for hull form optimization based on real sea state conditions. Then, a resistance optimization platform for ice-class ships is established by integrating hull surface mixed deformation control within a scenario analysis framework. Based on the optimization results, comparative analysis is conducted between the parent hull and the optimized hull under various environmental resistance scenarios. Finally, the optimization results are evaluated in terms of energy consumption using a fuel consumption model of the ship’s main engine. The optimized hull achieves a 16.921% reduction in total resistance, with calm water resistance and wave-added resistance reduced by 5.92% and 27.6%, respectively. Additionally, the optimized hull shows significant resistance reductions under multiple wave and floating ice conditions. At the design speed, calm water power and hourly fuel consumption are reduced by 7.1% and 7.02%, respectively. The experimental results show that the hull form optimization process in this paper can take into account both ice-region navigation and ice-free navigation. The design ideas and solution methods can provide a reference for the design of ice-class ships. Full article

The observations of atmospheric, oceanic, and sea ice data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition were used to analyze the influence of snow redistribution and melt-pond processes on the evolution of sea ice thickness (SIT) in 2019 and 2020. To mitigate the effect of missing atmospheric observations from the time of the expedition, we used ERA5 atmospheric reanalysis along the MOSAiC drift trajectory to force the single-column sea ice model Icepack. SIT simulations from six combinations of two melt-pond schemes and three snow-redistribution configurations of Icepack were compared with observations and analyzed to investigate the sources of model–observation discrepancies. The three snow-redistribution configurations are the bulk scheme, the snwITDrdg scheme, and one simulation conducted without snow redistribution. The bulk scheme describes snow loss from level ice to leads and open water, and snwITDrdg describes wind-driven snow redistribution and compaction. The two melt-pond schemes are the TOPO scheme and the LVL scheme, which differ in the distribution of melt water. The results show that Icepack without snow redistribution simulates excessive snow–ice formation, resulting in an SIT thicker than that observed in spring. Applying snow-redistribution schemes in Icepack reduces snow–ice formation while enhancing the congelation rate. The bulk snow-redistribution scheme improves the SIT simulation for winter and spring, while the bias is large in simulations using the snwITDrdg scheme. During the summer, Icepack underestimates the sea ice surface albedo, resulting in an underestimation of SIT at the end of simulation. The simulations using the TOPO scheme are characterized by a more realistic melt-pond evolution compared to those using the LVL scheme, resulting in a smaller bias in SIT simulation. Full article

Underwater acoustic target recognition (UATR) is challenged by complex marine noise, scarce labeled data, and inadequate multi-scale feature extraction in conventional methods. This study proposes DART-MT, a semi-supervised framework that integrates a Dual Attention Parallel Residual Network Transformer with a mean teacher paradigm, enhanced by domain-specific prior knowledge. The architecture employs a Convolutional Block Attention Module (CBAM) for localized feature refinement, a lightweight New Transformer Encoder for global context modeling, and a novel TriFusion Block to synergize spectral–temporal–spatial features through parallel multi-branch fusion, addressing the limitations of single-modality extraction. Leveraging the mean teacher framework, DART-MT optimizes consistency regularization to exploit unlabeled data, effectively mitigating class imbalance and annotation scarcity. Evaluations on the DeepShip and ShipsEar datasets demonstrate state-of-the-art accuracy: with 10% labeled data, DART-MT achieves 96.20% (DeepShip) and 94.86% (ShipsEar), surpassing baseline models by 7.2–9.8% in low-data regimes, while reaching 98.80% (DeepShip) and 98.85% (ShipsEar) with 90% labeled data. Under varying noise conditions (−20 dB to 20 dB), the model maintained a robust performance (F1-score: 92.4–97.1%) with 40% lower variance than its competitors, and ablation studies validated each module’s contribution (TriFusion Block alone improved accuracy by 6.9%). This research advances UATR by (1) resolving multi-scale feature fusion bottlenecks, (2) demonstrating the efficacy of semi-supervised learning in marine acoustics, and (3) providing an open-source implementation for reproducibility. In future work, we will extend cross-domain adaptation to diverse oceanic environments. Full article

Sea lice have been a persistent pest of the salmon farming industry for more than 50 years. In this study, we aimed to identify if different strains of Atlantic salmon with discrete long-term lice exposure histories had variable resistance to copepodid attachment and/or different attachment-specific transcriptome patterns. We additionally sought to characterize lice distributions on fins, head, and skin and identify if attachment location influenced transcriptomic profiles of lice. Lice counts were correlated with body size and highest on St. John River (SJR; open ocean-run) relative to Grand Lakes Stream (GLS; 200-year restricted ocean-run) or Sebago Lake (CAS; ~11,000 years landlocked) Atlantic salmon. However, lice density was similar between strains. Skin and fins had expectedly different transcriptomic profiles; however, notable differences were not observed between salmon strains. Variance in lice transcriptomes was minimally affected by attachment location even though lice strongly preferred fins relative to head or body. Attached lice did have different transcriptomic profiles on GLS relative to CAS or SJR. This study cumulatively identified a minimal host evolutionary component for sea lice attachment resistance, although lice behavior post-attachment appeared somewhat affected by strain. Non-uniform settlement distributions and tank-specific variability in lice attachment were observed across populations. Full article

Coastal and maritime regions and their entities face accelerated degradation due to the combined effects of environmental stressors and anthropogenic activities. Coastal degradation can be identified, visualized and estimated through periodic monitoring over a region of interest using earth observation, climate, meteorological, seasonal, waves, sea level rising, and other ocean- and maritime-related datasets. Usually, these datasets are provided through different sources, in different structures or data types; in many cases, a complete dataset can be large in size and needs some kind of preprocessing (information filtering) before use in the intended application. Recently, the term data cube introduced in the scientific community and frameworks like Google Earth Engine and Open Data Cubes have emerged as a solution to earth observation data harmonization, federation, and exchange framework; however, these sources either completely lack the ability to process climate, meteorological, waves, sea lever rising, etc., data from open sources, like CORDEX and WCRP, or preprocessing is required. This study describes and utilizes the Ocean-DC framework for modular earth observation and other data types to resolve major big data challenges. Compared to the already existing approaches, the Ocean-DC framework harmonizes several types of data and generates ready-to-use data cubes products, which can be merged together to produce high-dimensionality visualization products. To prove the efficiency of the Ocean-DC framework, a case study at Crete Island, emphasizing the Port of Heraklion, demonstrates the practical utility by revealing degradation trends via time-series analysis of several related remote sensing indices calculated using the Ocean-DC framework. The results show a significant reduction in processing time (up to 89%) compared to traditional remote sensing approaches and optimized data storage management, proving its value as a scalable solution for environmental resilience, highlighting its potential use in early warning systems and decision support systems for sustainable coastal infrastructure management. Full article

Closed-circuit rebreather diving has opened new opportunities for direct observation of the rich biodiversity of unexplored mesophotic coral ecosystems. We recently encountered four specimens of a rare benthic ctenophore genus, Lyrocteis. The specimens were found on the east coast of Réunion Island off the harbor of Sainte Rose at ~95 m. This finding contributes to the limited number of records of the genus in the Indian Ocean. Continued exploration of mesophotic zones remains essential, as each dive has the potential to reveal remarkable and unexpected discoveries. Full article

Offshore synthetic fuel production and marine carbon dioxide removal can be enabled by direct ocean capture, which extracts carbon dioxide from the ocean that then can be used as a feedstock for fuel production or sequestered underground. To maximize carbon capture, plants require a variety of low-carbon energy sources to operate, such as variable renewable energy. However, the impacts of variable power on direct ocean capture have not yet been thoroughly investigated. To facilitate future deployments, a generalizable model for electrodialysis-based direct ocean capture plants is created to evaluate plant performance and electricity costs under intermittent power availability. This open-source Python-based model captures key aspects of the electrochemistry, ocean chemistry, post-processing, and operation scenarios under various conditions. To incorporate realistic energy supply dynamics and cost estimates, the model is coupled with the National Renewable Energy Laboratory’s H2Integrate tool, which simulates hybrid energy system performance profiles and costs. This integrated framework is designed to provide system-level insights while maintaining computational efficiency and flexibility for scenario exploration. Initial evaluations show similar results to those predicted by the industry, and demonstrate how a given plant could function with variable power in different deployment locations, such as with wind energy off the coast of Texas and with wind and wave energy off the coast of Oregon. The results suggest that electrochemical systems with greater tolerances for power variability and low minimum power requirements may offer operational advantages in variable-energy contexts. However, further research is needed to quantify these benefits and evaluate their implications across different deployment scenarios. Full article

The atmospheric boundary layer (ABL) and tropopause play critical roles in weather formation and climate change. This study initially focuses on the ABL height (ABLH), tropopause height (TPH), and temperature (TPT) retrieved from the integrated radio occultation (RO) profiles from FY-3E, FY-3F, and FY-3G satellites during September 2022 to August 2024. All three FY-3 series satellites are equipped with the RO payload of Global Navigation Satellite System Radio Occultation Sounder-II (GNOS-II), which includes open-loop tracking RO observations from the BeiDou navigation satellite system (BDS) and the Global Positioning System (GPS). The wavelet covariance transform method was used to determine the ABL top, and the temperature lapse rate was applied to judge the tropopause. Results show that the maximum ABL detection rate of FY-3/GNOS-II RO can reach up to 76% in the subtropical eastern Pacific, southern hemisphere Atlantic, and eastern Indian Ocean. The ABLH is highly consistent with the collocated radiosonde observations and presents distinct seasonal variations. The TPH retrieved from FY-3/GNOS-II RO profiles is in agreement with the radiosonde-derived TPH, and both TPH and TPT from RO profiles display well-defined spatial structures. From 45°S to 45°N and south of 55°S, the annual cycle of the TPT is negatively correlated with the TPH. This study substantiates the promising performance of FY-3/GNOS-II RO measurements in observing the ABL and tropopause, which can be incorporated into the weather and climate systems. Full article

This study presents a global assessment of the climatological seasonal variability of river discharge into the oceans, based on an expanded dataset comprising 958 gauging stations across 136 countries. Monthly discharges were compiled for 145 major rivers and tributaries, with a focus on improving the accuracy and spatial coverage of global freshwater flux estimates. Compared to previous datasets, this updated compilation includes a broader set of rivers, explicitly integrates tributary inflows, and quantifies both the absolute and relative seasonal amplitudes of discharge variability. The results reveal substantial differences among ocean basins. The Atlantic Ocean, although receiving the highest total runoff, shows relatively weak seasonal variability, with a coefficient of variation of CV = 12.6% due to asynchronous peak discharge from its major rivers (Amazon, Congo, Orinoco). In contrast, the Indian Ocean exhibits the most pronounced seasonal cycle (CV = 88.3%), driven by monsoonal rivers. The Pacific Ocean shows intermediate variability (CV = 62.1%), influenced by a combination of monsoon rains and snowmelt. At the river scale, Orinoco and Changjiang display high seasonal amplitudes, exceeding 89% of their mean flows, whereas more stable regimes are found in equatorial and temperate rivers like the Amazon and Saint Lawrence. In addition, the critical role of tributaries in altering discharge magnitude and seasonal variability is well established. This study provides high-resolution monthly discharge climatologies at global and basin scales, enhancing freshwater forcing in OGCMs. By improving the representation of land–ocean exchanges, it enables more accurate simulations of salinity, circulation, biogeochemical cycles, and climate-sensitive processes in coastal and open-ocean regions. Full article

This study aimed to accurately simulate the main tidal characteristics in a regional domain featuring four open boundaries, with a primary focus on baroclinic tides. Such understanding is crucial for improving the representation of oceanic energy transfer and mixing processes in numerical models. To this end, the astronomical potential, load tide effects, and a wavelet-based analysis method were implemented in the three-dimensional ROMS model. The inclusion of the astronomical tidal and load tide aimed to enhance the accuracy of tidal simulations, while the wavelet method was employed to analyze the generation and propagation of internal tides from their source regions and to characterize their main features. Twin simulations with and without astronomical potential forcing were conducted to evaluate its influence on tidal elevations and currents. Model performance was assessed through comparison with tide gauge observations. Incorporating the potential forcing improves simulation accuracy, as the model fields successfully reproduced the main features of the barotropic tide and showed good agreement with observed amplitude and phase data. A complex principal component analysis was then applied to a matrix of normalized wavelet coefficients derived from the enhanced model outputs, enabling the characterization of horizontal modal propagation and vertical mode decomposition of both $M_2$ and nonlinear $M_4$ internal tides. Full article