Search Results (350)

The species composition and host–parasite associations of Gyrodactylus parasitising freshwater fishes in Bulgaria were re-examined based on the revision of museum specimens. Revised data are provided for 28 species. There are 22 species confirmed as occurring in Bulgaria based on morphological examination (G. aphyae, G. cyprini, G. fossilis, G. gobii, G. gracilihamatus, G. katharineri, G. laevis, G. latus, G. leucisci, G. luciopercae, G. macrocornis, G. macronychus, G. malmbergi, G. markakulensis, G. medius, G. prostae, G. rhodei, G. shulmani, G. sprostonae, G. stankovici, G. truttae and G. vimbi). New records for the country are reported for G. cobitis, G. dykovae, G. gobiensis and G. papernai. The data about the occurrence of nine species could not be verified. The most species-rich region is the Danube Drainage (21 species), followed by the East-Aegean Sea Drainage (12 species). The smaller drainages (Black Sea Drainage—nine species; West-Aegean Sea Drainage—four species) are less studied. G. prostae (four host species from eight localities) and G. sprostonae (four host species, four localities) are revealed as the most frequent species; these euryxenous parasites infect a broad range of host species, often fishes of economic importance. Fish species of less commercial value are less studied. Full article

This study presents a belt sanding robot for large convex surfaces together with a proportional–integral force control method. Sanding belt tension strongly affects area coverage and spatial normal-force uniformity on large curved surfaces; existing approaches typically use fixed tool positions or lack active tension regulation, which limits coverage and makes force distribution difficult to control. The mechanism consists of two series elastic actuator arms and an active re-tensioner that adjusts belt tension during contact. In contrast to a conventional belt sander, the series elastic configuration enables indirect estimation of the reaction force without load cells and provides compliant interaction with contact transients. The system is evaluated on curved steel plates using vertical scans with a belt width of 50 mm and a drive wheel speed of 300 rpm. Performance is reported for two target curvature values, namely 0.47 and 1.37, with five trials for each condition. The control objective is a constant normal force along the contact, achieved through proportional–integral control of the arms for normal-force tracking and the re-tensioner for belt tension regulation. To quantify spatial force uniformity, the distribution rate is defined as the ratio of the difference between the maximum and minimum normal forces to the maximum normal force measured across the belt–workpiece contact region. Compared with a simple belt sander baseline, the proposed system increased the sanded area coverage by 31.85%, from 62.20% to 94.05%, at the curvature value of 0.47, and by 8.49%, from 81.21% to 89.70%, at the curvature value of 1.37. The distribution rate improved by 113% at the curvature value of 0.47 and by 16.7% at the curvature value of 1.37. Under identical operating conditions of 50 mm belt width, 300 rpm, and five repeated trials, these results indicate higher area coverage and more uniform force distribution relative to the baseline. Full article

Oceanic whitecaps are caused by wave breaking and are very important in air–sea interactions. Usually, whitecap coverage is considered a key factor in representing the role of whitecaps. However, the accurate identification of whitecap coverage in videos under dynamic marine conditions is a tough task. An EMA-SE-ResUNet deep learning model was proposed in this study to address this challenge. Based on a foundation of residual network (ResNet)-50 as the encoder and U-Net as the decoder, the model incorporated efficient multi-scale attention (EMA) module and squeeze-and-excitation network (SENet) module to improve its performance. By employing a dynamic weight allocation strategy and a channel attention mechanism, the model effectively strengthens the feature representation capability for whitecap edges while suppressing interference from wave textures and illumination noise. The model’s adaptability to complex sea surface scenarios was enhanced through the integration of data augmentation techniques and an optimized joint loss function. By applying the proposed model to a dataset collected by a shipborne camera system deployed during a comprehensive fishery resource survey in the northwest Pacific, the model results outperformed main segmentation algorithms, including U-Net, DeepLabv3+, HRNet, and PSPNet, in key metrics: whitecap intersection over union (IoU_W) = 73.32%, pixel absolute error (PAE) = 0.081%, and whitecap F1-score (F1_W) = 84.60. Compared to the traditional U-Net model, it achieved an absolute improvement of 2.1% in IoU_W while reducing computational load (GFLOPs) by 57.3% and achieving synergistic optimization of accuracy and real-time performance. This study can provide highly reliable technical support for studies on air–sea flux quantification and marine aerosol generation. Full article

Understanding the composition of phytoplankton assemblages and monitoring changes in their diversity is a key factor in the comprehension of global biogeochemical cycles, climate regulation and marine ecosystem health, especially in the context of increasing global warming. Regional empirical algorithms for phytoplankton satellite estimates of size classes (PSCs) and functional types (PFTs) in the Mediterranean Sea have been developed and implemented in the EU Copernicus Marine Service since 2019. Here, we present an update of the PSC and PFT algorithms operational in the Copernicus catalogue since the end of 2024. Results show an overall improvement in the model performance, in line with Copernicus Marine Service requirements focused on the continuous enhancement of the accuracy of distributed biogeochemical variables. Finally, the new algorithms were applied to a time series of over 25 years of satellite data (1998–2024), enabling the identification of key changes in phytoplankton composition at both monthly and basin scales. These insights were made possible by an algorithm re-calibration based on updated and more comprehensive regional pigment ratios. Full article

This study examines the impact of macroeconomic and energy factors on the sustainable development of the logistics sector in eight Baltic Sea Region (BSR) countries from 2008 to 2023. A synthetic logistics sustainability index (SD), ranging from 0.54 (Lithuania, 2009) to 0.93 (Germany, 2023), was constructed to capture economic, social, and environmental dimensions. The analysis employed country-level regressions, fixed-effects panel models, and a one-step dynamic GMM estimator. Results show that higher GDP per capita (β ≈ +0.35, p < 0.05) significantly supports sustainable logistics, while higher energy intensity (β ≈ −0.41, p < 0.01) constrains it. Across the region, GDP per capita increased by 45% on average, and energy intensity (EI) declined by 18%, contributing to a steady rise in SDI, particularly in Finland, Germany, and Denmark. Renewable energy (RES) has heterogeneous effects: it promotes sustainability in Germany, Finland, and Latvia, but negatively affects Sweden, where rapid energy transition and high electricity costs temporarily reduce logistics efficiency. Electrification rate (RE) also shows a short-term adverse effect in Sweden and Finland, where investment speed exceeds infrastructure adaptability. Labour productivity (LP) and unemployment (UR) exhibit inconsistent effects. Overall, the findings confirm GDP per capita and energy efficiency as dominant drivers of sustainable logistics, while structural and policy differences explain cross-country heterogeneity in sustainability outcomes. These insights provide practical guidance for policymakers by emphasising the need to balance energy transition speed with infrastructure readiness and to tailor sustainability strategies to national economic and energy profiles. Full article

Understanding the seasonal dynamics of the fisheries and crustacean communities are of crucial ecological significance. To investigate the structural characteristics of these communities and their seasonal dynamics in the offshore of the Zhoushan Archipelago Seas, China, this study conducted a four seasons’ trawl survey to collect fisheries data in spring, summer, autumn, and winter of 2022. A normalized abundance size spectrum approach was applied to investigate the seasonal variation in regressed parameters (slope and intercept) for fish-only and fish-plus-crustacean communities. Our study found that average values of the slope of the size spectrum for fish and fish-plus-crustacean were −1.36 and −1.53, respectively; the overall adding effect with crustaceans in all seasons was more negative (a steeper slope). The results also showed that the adding effect of crustaceans in the fisheries communities were season-specific and region-specific. Temporally, adding crustaceans into fisheries communities contributed to more/less negative slopes in temperate/warm seasons, respectively. Regionally, the inclusion of crustaceans induced a reverse distribution pattern (nearshore–offshore) for fish abundance, as well as the re-scaled intercept, which could indicate the abundance in all seasons except in summer. It was assumed that although fish dominated the overall community structure, crustaceans contributed a compensatory effect by regulating the size distribution across trophic levels. This study provides valuable insights for the dynamic assessment and scientific management of fisheries and crustacean resources in the whole ecosystem. Full article

Non-indigenous species represent a significant threat to marine biodiversity, and accurate taxonomic identification is critical for effective management. This study revisits the long-standing record of the Australian sea anemone Oulactis muscosa in Argentina, which has been cited in numerous studies for nearly 50 years. We conducted a comprehensive taxonomic revision of specimens from Mar del Plata, Argentina, using both morphological and molecular analyses. Our findings reveal a persistent taxonomic error: the specimens belong to a different species. Detailed morphological comparisons and genetic sequencing of mitochondrial and nuclear markers re-identified the specimens as Anthopleura correae. This species is native to Brazil and is distributed from Ceará to Santa Catarina. This represents the first record of an Anthopleura species in Argentina, extending its known distribution. Genetic analyses confirmed the re-identification, showing no significant divergence between the Argentine and Brazilian specimens, while revealing notable differences from O. muscosa. We highlight the importance of rigorous taxonomic approaches integrating both morphological and molecular data to prevent misidentifications, which is particularly crucial when identifying potential invasive species. This study clarifies the taxonomic status of a regionally distributed species and contributes to the accurate inventory of sea anemones in Argentina. Full article

Despite the oligotrophic conditions of the southeastern Levantine Sea and northern Red Sea, six lobster species—five slipper lobsters (Scyllaridae) and one spiny lobster (Palinuridae)—maintain permanent, reproducing populations in the study area. Additionally, there are isolated records of four other sporadic lobster species. In the southeastern Mediterranean, permanent species include the Mediterranean slipper lobster,Scyllarides latus, small European locust lobster, Scyllarus arctus, and pygmy locust lobster, Scyllarus pygmaeus. In the northern Red Sea, they include the clamkiller slipper lobster, Scyllarides tridacnophaga, Lewinsohn locust slipper lobster, Eduarctus lewinsohni, and pronghorn spiny lobster, Panulirus penicillatus. This review synthesizes current knowledge of their biology and ecology, including distribution, habitat, reproduction and development, feeding, predators and anti-predatory adaptations, behavior, sensory modalities, environmental impacts, threats, and conservation. Recent advances focus mainly on larger, commercially valuable species (S. latus, S. tridacnophaga, P. penicillatus), while major gaps remain for oceanic post-embryonic stages and the nektonic nisto postlarva, as well as for smaller, often cryptic species (S. arctus, S. pygmaeus, E. lewinsohni). Addressing these gaps will require targeted research, using modern methodologies, in coastal, deep, and open waters, coupled with citizen-science surveys. While many Indo-Pacific decapods have been established in the Mediterranean, no immigrant lobster species have successfully colonized Levant waters, despite rare records of three non-indigenous species (NIS). However potential NIS predators and shifts in mollusk compositions, the main prey of some native lobsters, may affect the latter. Large lobsters remain targeted by fisheries despite protective regulations, which are not always effective or obeyed. No-take marine protected areas (MPAs) or nature reserves can be effective if sufficiently large and well-managed. Habitat loss from marine construction can be partly compensated by stable, environmentally safe artificial reefs tailored to lobster behavioral ecology. The categories of the studied lobsters’ species in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, last updated over fifteen years ago, should be re-evaluated. Full article

Collisions between ships and sea ice pose a significant threat to maritime safety, making it essential to detect sea ice and perform safety-oriented path planning for polar navigation. This paper utilizes an optimized You Only Look Once version 5 (YOLOv5) model, designated as YOLOv5-ICE, for the detection of sea ice in satellite imagery, with the resultant detection data being employed to input obstacle coordinates into a ship path planning system. The enhancements include the Squeeze-and-Excitation (SE) attention mechanism, improved spatial pyramid pooling, and the Flexible ReLU (FReLU) activation function. The improved YOLOv5-ICE shows enhanced performance, with its mAP increasing by 3.5% compared to the baseline YOLOv5 and also by 1.3% compared to YOLOv8. YOLOv5-ICE demonstrates robust performance in detecting small sea ice targets within large-scale satellite images and excels in high ice concentration regions. For path planning, the Any-Angle Path Planning on Grids algorithm is applied to simulate routes based on detected sea ice floes. The objective function incorporates the path length, number of ship turns, and sea ice risk value, enabling path planning under varying ice concentrations. By integrating detection and path planning, this work proposes a novel method to enhance navigational safety in polar regions. Full article

Wireless sensor networks (WSNs) are deployed in terrestrial environments. However, on the sea surface, sensor nodes can drift due to ocean currents and wind; thus, network topologies continuously evolve, and the communication between nodes is frequently disrupted. These unstable connections significantly degrade data transmission stability and overall network performance. These problems are particularly significant in maritime regions where the sea state changes rapidly, thus imposing stringent technical requirements on the design of long-range, reliable, low-latency, and persistent sensing systems. This study proposes a wireless sensor network architecture for sea surface drifting nodes, which is termed Drift-Aware Routing and Clustering with Recovery (DARCR). The proposed system consists of three major components: (1) an enhanced dynamic drift model that more accurately predicts node movement for realistic ocean conditions; (2) a cluster-based framework that prevents disconnection and minimizes delay, which improves cluster stability and adaptability to dynamic environments through refined clustering and route setup mechanisms; and (3) a self-recovery routing strategy for re-establishing communication after disconnection. The proposed method is evaluated using ocean current data from the Copernicus Ocean Data Center simulating a 60-h drifting scenario around the central Taiwan Strait. The experimental results show that the average hourly disconnection rate is maintained at 6.2%, with a variance of 0.31%, and the transmission of newly sensed data is completed within 3 to 5 s, with a maximum delay of approximately 10 s. These findings demonstrate the feasibility of maintaining communication stability and low-latency data transmission for sea surface WSNs that operate in highly dynamic marine conditions. Full article

The East China Sea Shelf Basin (ECSSB) and its adjacent areas, as key regions of the ocean–continent transition zone, have been affected by multiple complex plate collisions, subduction, and back-arc tension since the Mesozoic Era. The structural deformation provides a large amount of geological information on the ocean–continent transition zone. There are significant spatiotemporal differences in the structural deformation within the basin. However, the research remains insufficient and understanding is inconsistent, especially regarding the systematic study of the differences and dynamic mechanisms of north–south structural deformation, which is relatively lacking. This study is based on two-dimensional multi-channel deep reflection seismic profiles spanning the southern and northern basin. Through an integrated re-analysis of gravity, magnetic, and OBS data, the deformation characteristics and processes of the Meso-Cenozoic structures in the basin are analyzed. The differences in structural deformation between the southern and northern basin are summarized, and the controlling effects of deep crust–mantle activity and the influencing factors of shallow structural deformation are explored. Based on deep reflection seismic profiles, the structural deformation characteristics of the Yushan–Kume fault are revealed for the first time, and it is proposed that NW faults, represented by the Yushan–Kume fault, have important tuning effects on the north–south structural differential deformation in the ECSSB. The thermal subsidence of the lithosphere is the direct cause of the development of the Mesozoic ECSSB, while the subduction of the Paleo-Pacific plate is one of the important factors contributing to it. The combined effect of the two has led to significant differences between the northern and southern Mesozoic basin. During the Cenozoic Era, the alternating subduction and changes in the direction of subduction of the Pacific Plate led to spatiotemporal differences in structural deformation within the ECSSB. The development of NW faults was a key factor in the differences in structural deformation between the northern and southern basin. The study of structural deformation differences in the ECSSB not only deepens our understanding of the tectonic evolution in the East Asian continental margin region, but also has important significance for the exploration and evaluation of deep hydrocarbon resources in the ECSSB. Full article

ClimateHub, the National Collaboration Programme (NCP) in Greece aims at delivering innovative services to national authorities regulating the energy sector by developing climate-based tools and services building on the C3S experience. As a service provider, ClimateHub fills the knowledge and service gap on climate information at time scales exceeding the typical weather forecast. Through a co-design approach, ClimateHub has identified three applications where public authorities have virtually no access to climate-related impacts on the renewable energy sources (RES) sector at seasonal and decadal time scales, (a) energy demand, (b) solar power and (c) wind power. This study addresses the performance of ECWMF SEAS5 seasonal and the CMCC-CM2-SR5 decadal prediction systems over Greece, for near-surface temperature. Full article

With the advancement of scientific research, understanding the physical parameters governing acoustic wave propagation in sea ice has become increasingly important. Among these parameters, shear wave velocity plays a crucial role. However, as measurements progressed, it became apparent that there was a large discrepancy between measured values of shear waves and predictions based on empirical formulas or existing models. These inconsistencies stem primarily from the complex internal structure of natural sea ice, which significantly influences its physical behavior. Research reveals that shear wave velocity is not only influenced by bulk properties such as density, temperature, and stress state but is also sensitive to microstructural features, including air bubbles, inclusions, and ice crystal orientation. Compared to longitudinal wave velocity, the characterization of shear wave velocity is far more challenging due to these inherent complexities, underscoring the need for more precise measurement and modeling techniques. To address the challenges posed by the complex internal structure of natural sea ice and improve prediction accuracy, this study introduces a novel, integrated approach combining simulation, measurement, and inversion intelligent learning model. First, a laboratory-based method for generating sea ice layers under controlled formation conditions is developed. The produced sea ice layers align closely with measured values for Poisson’s ratio, multi-year sea ice density, and uniaxial compression modulus, particularly in the high-temperature range. Second, enhancements to shear wave velocity measurement equipment have been implemented. The improved device achieves measurement accuracy exceeding 1%, offers portability, and meets the demands of high-precision experiments conducted in harsh polar environments. Finally, according to the characteristics of small sample data. The ANN neural network was improved to a deep residual neural network with the addition of Squeeze-and-Excitation Attention (SE-ResNet) to predict longitudinal and transverse wave velocities. This prediction method improves the accuracy of shear and longitudinal wave velocity prediction by 24.87% and 39.59%, respectively, compared to the ANN neural network. Full article

Geological data of the region indicate that the Canary Islands have not been connected to the mainland before. However, fossil evidence suggests some kind of faunal exchange with Africa during the late Neogene. After extensive field work during past years, a re-evaluation of the fossil remains of the first terrestrial vertebrates that settled and thrived on the Canary Islands is presented, with special attention to the long-debated identity of birds that laid large-sized eggs, reported some decades ago on Lanzarote Island. The age of the eggshell-bearing deposits has been recently updated as Early Pliocene (ca. 4 Ma). The dispersal mode of these terrestrial birds to reach the island was an unsolvable challenge in previous studies because the regional geography of the sea bottom was neglected, as well as the chronological succession of events in the formation of the Canary Eastern Ridge, which increased attention to a unique case of arrival of ratites on an island never before united with the mainland. The few animals found in northern Lanzarote (ratites, snakes, turtles, terrestrial snails and bite marks on eggshells pointing to a jagged and unknown large predator) probably made the sea crossing from the mainland in different ways. Two scenarios are contemplated. In both, the circumstances facilitating the faunal transit from Africa to the Canaries ceased after the early Pliocene, around 4 Ma, since these animals have never managed to cross the Canary Channel again. Full article

In maritime blurred image recognition, ship collision accidents frequently result from three primary blur types: (1) motion blur from vessel movement in complex sea conditions, (2) defocus blur due to water vapor refraction, and (3) scattering blur caused by sea fog interference. This paper proposes a dual-branch recognition method specifically designed for motion blur, which represents the most prevalent blur type in maritime scenarios. Conventional approaches exhibit constrained computational efficiency and limited adaptability across different modalities. To overcome these limitations, we propose a hybrid CNN–Transformer architecture: the CNN branch captures local blur characteristics, while the enhanced Transformer module models long-range dependencies via attention mechanisms. The CNN branch employs a lightweight ResNet variant, in which conventional residual blocks are substituted with Multi-Scale Gradient-Aware Residual Block (MSG-ARB). This architecture employs learnable gradient convolution for explicit local gradient feature extraction and utilizes gradient content gating to strengthen blur-sensitive region representation, significantly improving computational efficiency compared to conventional CNNs. The Transformer branch incorporates a Hierarchical Swin Transformer (HST) framework with Shifted Window-based Multi-head Self-Attention for global context modeling. The proposed method incorporates blur invariant Positional Encoding (PE) to enhance blur spectrum modeling capability, while employing DyT (Dynamic Tanh) module with learnable α parameters to replace traditional normalization layers. This architecture achieves a significant reduction in computational costs while preserving feature representation quality. Moreover, it efficiently computes long-range image dependencies using a compact 16 × 16 window configuration. The proposed feature fusion module synergistically integrates CNN-based local feature extraction with Transformer-enabled global representation learning, achieving comprehensive feature modeling across different scales. To evaluate the model’s performance and generalization ability, we conducted comprehensive experiments on four benchmark datasets: VAIS, GoPro, Mini-ImageNet, and Open Images V4. Experimental results show that our method achieves superior classification accuracy compared to state-of-the-art approaches, while simultaneously enhancing inference speed and reducing GPU memory consumption. Ablation studies confirm that the DyT module effectively suppresses outliers and improves computational efficiency, particularly when processing low-quality input data. Full article