Search Results (712)

Linear ultrasonic motors (LUSMs) occupy an important position in the field of high-precision actuation due to their advantages of simple structure, high control accuracy and direct linear motion generation. This review first classifies LUSMs according to wave modes into traveling wave linear ultrasonic motors (TWLUSMs) and standing wave linear ultrasonic motors (SWLUSMs). Among them, TWLUSMs include the straight beam type and the annular beam type, while SWLUSMs consist of the single-foot type and the multi-foot type. In addition, the working principles of TWLUSMs and SWLUSMs are elaborated. The structural characteristics and performance parameters of different types of ultrasonic motors (USMs) are sorted out, and the analysis shows that SWLUSMs are significantly superior to TWLUSMs in terms of output speed and output force. This review summarizes the application status of LUSMs in fields such as biomedicine, deep-sea exploration, aerospace and precision manufacturing, and finally outlines the development trends of LUSMs from the aspects of miniaturization and lightweighting, extreme environment adaptability and intelligent upgrade. This review provides a comprehensive reference for the structural design, performance improvement and application expansion of LUSMs. Full article

Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking. Full article

Five previously undescribed steroids, chrysogenones A–E (1–5), were isolated from the deep-sea-derived Penicillium sp. MCCC 3A00121. Their chemical structures were unambiguously established through comprehensive spectroscopic analyses, density functional theory (DFT)-based electronic circular dichroism (ECD) calculations, and X-ray crystallography. Chrysogenones represent a class of oxidatively modified ergosterone-type derivatives, with 1, 2, and 5 featuring an uncommon malonyl substitution at C-12 of the ergosterone skeleton. Biologically, 1–5 exhibited varying degrees of inhibitory activity against renal fibrosis, as evidenced by the downregulation of the key fibrotic markers α-smooth muscle actin (α-SMA) and collagen I (COL1A1). Among them, chrysogenone B (2) emerged as the most promising candidate, demonstrating superior potency and pronounced inhibition of activated NRK-49F cell proliferation. Integrated network pharmacology analysis and molecular docking studies further suggested that the anti-renal fibrotic effects of compound 2 may be mediated through its interaction with putative molecular targets, including AKT1, HSP90AA1, and MDM2. Full article

Sea ice segmentation based on Synthetic Aperture Radar (SAR) images has become an important technical means for polar climate change monitoring and navigation safety guarantee. However, the existing methods have limitations in the utilization of SAR polarization information and the modeling of local diversity details of sea ice, which leads to insufficient segmentation, especially in complex ice-water boundary regions. To address these issues, this paper proposes a novel Polarization-Fused Edge-Enhanced UNet (PFEE-UNet) designed specifically for sea ice segmentation from high-resolution SAR images. Specifically, we design the Cross-Polarization Channel Interaction (CPCI) module, which employs a dual interaction strategy of hierarchical inter-group cascading and symmetric cross-fusion. This approach effectively leverages the complementary features of the HH and HV polarization channels, significantly enhancing the distinction between sea ice and open water. Additionally, we present the Dense–Sparse Diversity Enhancement (DSDE) module, which combines a spatial-channel joint attention mechanism to strengthen the model’s ability to capture spatial relationships within complex ice–water structures, effectively alleviating misclassifications caused by abrupt local texture changes. Finally, we design the Selective Edge Fusion (SEF) module, which dynamically selects and integrates multi-level edge features, improving the continuity of sea ice boundaries and preserving its morphological integrity. The experimental results show that the proposed PFEE-UNet model outperforms mainstream segmentation methods on the AI4Arctic/ASIP sea ice dataset, achieving an average Intersection over Union (IoU) of 84.48%, which surpasses existing methods such as HRNet (82.52%) and DeepLabv3+ (82.40%). Additionally, PFEE-UNet was applied for end-to-end ice–water segmentation on real-world Sentinel-1 SAR scenes, demonstrating its effectiveness and robustness for practical sea ice monitoring. Full article

Maritime decarbonization has shifted from a long-term aspiration to an engineering and systems-integrated problem under near-term compliance pressure. International regulatory bodies, governments, and a wide array of private-sector coalitions will tighten greenhouse-gas fuel-emission standards from 2028, translating climate targets into enforceable cost signals and accelerating interest in alternative-fuel and retrofit pathways. This review synthesizes the state of the art (SoA) of maritime decarbonization by mapping where technological bottlenecks concentrate along the well-to-wake (WtW) value chain for the main candidate pathways: biofuels, LNG/bio-LNG, hydrogen, ammonia, e-methanol, and electrification, and by benchmarking them side-by-side using a unified framework designed to compare their realizable well-to-wake GHG-reduction potential under maritime operating constraints. Building on that comparative lens, this work aims to connect pathway readiness to the near-term market and regulatory reality, while the alternative-fuel-capable fleet is projected to expand rapidly, creating a structural capability vs. supply gap, in which, for example, ship readiness can outpace low-GHG fuel availability and bunkering rollout. The merged evidence indicates that near-term abatement will be dominated by scalable drop-in biofuels, whereas deep-sea options (ammonia/hydrogen and e-fuels) remain gated by upstream low-GHG production, port infrastructure, and safety/regulatory maturation. Nevertheless, mid-term deployment of low-GHG fuels can act as a system “relief valve”, reducing infrastructure lock-in and accelerating emissions reductions while zero-carbon fuel supply chains scale up. Full article

The rolling seal is a pivotal sealing technology for marine equipment such as wet-mateable connectors, ensuring operational integrity in deep-sea environments during both static and mating phases. However, its working mechanisms remain inadequately understood, and the effects of sealing parameters and seawater pressure have yet to be systematically studied. To address these issues, a refined model for rolling seals operating in deep-sea pressure-balanced conditions was developed. The model’s accuracy was enhanced by incorporating two key inputs: experimentally measured boundary lubrication friction coefficients (replacing conventional dry friction values) for finite element simulation and torque calculation, and oil pressure under pressure-balanced conditions, derived from shell theory, as a boundary load. Through systematic parametric simulations, the effects of interference fit, rotational speed, and seawater pressure on sealing performance were elucidated. An experimental torque test setup under atmospheric pressure was constructed to validate the numerical model. The results indicate that, while ensuring reliable static sealing, higher rotational speeds and smaller interference fits help reduce rotational torque. Benefiting from the pressure-balanced design, increasing water depth significantly enhances hydrodynamic performance—accounting for over 90% of the total static contact pressure at 1500 m—while leakage shows a decreasing trend. These findings provide theoretical insights for optimizing deep-sea sealing structures. Full article

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing is pivotal for the Space–Air–Ground–Sea Integrated Network (SAGSIN) to support heterogeneous task offloading. However, the inherent resource constraints of UAVs limit their ability to support intensive and concurrent task processing in dynamic environments. In such complex scenarios, the dual requirements of discrete model partitioning and continuous bandwidth allocation make it difficult for traditional reinforcement learning algorithms to achieve optimal resource matching. Therefore, in this paper, we design a joint optimization framework based on Asynchronous Advantage Actor-Critic (A3C) and proximal policy optimization (PPO). Specifically, the model partitioning strategy is learned through PPO, which utilizes a clipped objective function to ensure training stability and generalization across complex Deep Neural Network (DNN) structures. Moreover, the framework leverages the asynchronous multi-threaded architecture of A3C to dynamically allocate bandwidth, effectively accommodating rapid fluctuations in terminal access. Finally, to prevent resource monopolization and ensure fairness, a weighted priority scheduling mechanism based on task urgency and computation time is introduced. Extensive simulations show that the proposed algorithm outperforms existing approaches in terms of task completion rate, task processing latency, and resource utilization under dynamic SAGSIN scenarios. Full article

As deep-sea exploration transitions from large-scale search to precision pinpoint operations, the inherent limitations of traditional “rigid-body and propeller” vehicles—specifically in low-speed maneuverability, environmental compliance, and acoustic stealth—are becoming increasingly apparent. Leveraging its unique integrated “gliding-flapping” locomotion and exceptional maneuverability, the manta ray serves as an ideal biological prototype for next-generation deep-sea operational platforms. From a systems engineering perspective, this paper provides a comprehensive review of the current research status and technical evolution of biomimetic manta ray submersibles. First, a technical pedigree centered on “operational depth” is established, illustrating how design paradigms transition from “mechanism replication” in shallow waters to “pressure adaptation” at full-ocean depths. Second, the mechanical challenges in structural design are explored, demonstrating that a “rigid-flexible” gradient distribution strategy is critical to resolving the conflict between pressure resistance and propulsive compliance. Regarding energy and propulsion, the synergistic effects of hybrid gliding-flapping drives and integrated structural batteries in enhancing long-range endurance and energy efficiency are analyzed. Finally, the evolution of motion control architectures—transitioning from spinal-cord-inspired Central Pattern Generator (CPG) rhythmic control to Deep Reinforcement Learning (DRL) featuring embodied intelligence—is outlined. Full article

Nenestatins (NENs) belong to benzo[b]fluorene-containing atypical angucyclines, a structurally diverse class of microbial natural products. Bioinformatic analysis of the NEN biosynthetic gene cluster (nes BGC) from the deep-sea sediment-derived Micromonospora echinospora SCSIO 04089 implicated Nes5 as an α/β hydrolase. The targeted inactivation of the nes5 gene led to the accumulation of five new analogs, NENs E–I (1–5), together with the known monomer homo-dehydrorabelomycin E (6). Their structures were elucidated by comprehensive spectroscopic analysis and electronic circular dichroism calculations. Notably, both NEN A and NEN B were absent in the Δnes5 mutant, indicating that Nes5 is essential for their biosynthesis; however, the exact function of Nes5 requires further exploration. Full article

Mediterranean cyclones over North Africa were analyzed using CyTRACK and ERA5 reanalysis data for the period 2015–2025. Cyclones were classified by minimum sea level pressure into Very Deep, Deep, Moderate, and Weak categories, and their structural characteristics—including spatial extent, lifetime, and associated synoptic-scale systems—were examined. The relationship between cyclone activity and monthly precipitation was assessed for Morocco, Algeria, Tunisia, Libya, and Egypt, revealing substantial spatial variability in rainfall response. Egypt exhibited the strongest correspondence between cyclone frequency and precipitation, while other countries showed weaker or inconsistent associations, highlighting the role of cyclone intensity and moisture availability in driving regional hydroclimatic impacts. This intensity-resolved, region-specific analysis provides a comprehensive view of Mediterranean cyclone behavior and its influence on rainfall extremes, offering a valuable framework for improved forecasting, risk assessment, and climate resilience planning in the southern Mediterranean. Full article

Deep learning has become a key approach for automated sea ice mapping in the AI4Arctic Sea Ice Challenge dataset, yet most studies focus on accuracy metrics and rarely evaluate whether predicted probabilities are reliable for operational use. This paper investigates calibration-aware training for multi-task sea ice segmentation of sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE) using the U-Net model. We train the network with cross-entropy (CE) and augment the objective with focal loss, Brier loss, and an entropy-regularization term to reduce overconfidence and improve calibration. Experiments follow a scene-level Monte Carlo cross-validation protocol on the ready-to-train AI4Arctic Sea Ice Challenge dataset (AI4Arctic) dataset and are evaluated using $R^2$ for SIC, F1 for SOD and FLOE, a weighted combined score, and expected calibration error (ECE) and reliability diagrams. Results show that calibration-aware loss functions improve test performance relative to the CE loss, and the full objective (CE + Brier + focal + entropy) achieves the highest combined score of $84.73\%$ and reduces FLOE ECE to 0.044. Qualitative comparisons further indicate cleaner spatial structures and fewer scattered errors, particularly for FLOE. Overall, the proposed loss design improves both segmentation quality and confidence reliability, supporting more trustworthy sea ice products for decision-making. Full article

For addressing difficult detail extraction and low operating efficiency in monitoring sea ice in a large area with wide-field-of-view images from the Chinese Gaofen-1 satellite, a lightweight, high-precision sea ice segmentation network adaptive multistatistic fusion attention (AMFA) module using DeepLabV3+ as the base architecture (AMFA-DeepLab) is proposed. First, the module replaces the backbone network with a lightweight MobileNetV2 to ensure feature extraction capability and greatly reduce model computational complexity using inverted residuals and depthwise separable convolution. Second, to solve the problems of fragmented ice texture blurring and speckle noise interference in optical images, an AMFA is designed and introduced into the decoder side. This module innovatively integrates the global median pooling branch and adapts the recalibrated feature weight through a dynamic channel mixing mechanism, effectively enhancing the model’s capability of capturing fine sea ice edge features and its antinoise robustness in complex backgrounds. Experimental results based on the dataset from Liaodong Bay in the Bohai Sea of China show that the intersection over union of AMFA-DeepLab reaches 92.15% and the F1-score reaches 95.91%, increases of 3.06%, and 1.68%, respectively, compared with those of the baseline model. In addition, only 5.85 million model parameters are needed, the training time is shortened to 4.42 h, and the inference speed is 281.76 frames per second. Visualized analysis and generalization test further demonstrates that this model can accurately eliminate clutter interference from coastal land and seawater and extract the fine filamentous structure of drift ice in the scene of complex melting ice. This research overcomes the precision bottleneck while achieving an ultimate lightweight model, providing efficient technical support for operational dynamic monitoring of sea ice disasters based on Chinese GaoFen-1 satellites. Full article

The Tinjar–West Baram Fault in the southern South China Sea is a major NW-trending strike-slip fault that has remained tectonically active since the Oligocene. It forms a key structural boundary between the Zengmu, Beikang, and Nansha Trough basins. Multi-phase strike-slip movements have strongly controlled sediment provenance dispersal pathways, and reservoir development in the Zengmu Basin, yet the sedimentary response to these tectonic processes remains poorly understood. This study integrates 2D seismic profiles to analyze the fault geometry, kinematics, and impact on deep-water sedimentary systems. Results indicate that Oligocene right-lateral motion directed sediment supply from the southwest, mainly sourced from Kalimantan, forming fluvial–deltaic systems with depocenters in the southern basin. Since the Late Miocene, a transition to left-lateral motion reoriented sediment provenance toward the southeast, leading to delta-front complexes and northward migration of depocenters. Strike-slip activity deformation enhanced rock fragmentation and sediment supply, producing fan delta, fluvial, and shallow lacustrine facies near the fault. Associated uplift and subsidence induced relative sea-level fluctuations, resulting in alternating transgressive–regressive sequences. From the Late Eocene to Miocene, the basin evolved from a land–sea transitional system to a deltaic–carbonate complex controlled by the paleo-Sunda River. During the Pliocene–Quaternary, sedimentation was dominated by shallow-marine shelf and semi-deep-marine deposits. Fault-related fracturing significantly enhanced porosity and permeability, creating favorable conditions for hydrocarbon migration and entrapment in both sandstone and carbonate reservoirs. These findings demonstrate a strong coupling between strike-slip fault activity and sedimentary system evolution, providing important insights into sedimentary processes and hydrocarbon potential in strike-slip fault-bounded basins globally. Full article

Studies of deep-sea hydrothermal vent systems have generated a spectrum of hypotheses concerning the origin of life on Earth. The present paper integrates recent literature surrounding three separate hydrothermal vent systems (Lost City in the mid-Atlantic, Guaymas Basin in the Gulf of California, and 9°50′ N on the East Pacific Rise) to provide biological, chemical, and geophysical support for these origin-of-life hypotheses. Comparisons between deep-sea hydrothermal vents and impact-generated hydrothermal vent systems may provide further insights into the origin of life. Impact-generated hydrothermal vent systems may have cradled early life. A comprehensive review of studies conducted at Lonar Lake, the Haughton impact structure, and the Chicxulub impact crater provide evidence of long-term hydrothermal activity conducive to the formation of early life, as well as potentially unique DNA structures found in sediment samples—opening the discussion for further investigations into the possible origin (or origins) of life both on Earth and other planetary bodies. Full article

We present a high-resolution 3-D shear-wave velocity model of the Indonesian lithosphere and upper mantle, constructed through a weighted joint inversion of complementary surface wave datasets. Our model integrates teleseismic Rayleigh waves from 387 earthquakes recorded at 31 stations, analyzed using a modified two-plane-wave tomography method, with two years of ambient noise data from 30 stations processed via image transformation techniques. Our results provide new structural constraints on the four principal subduction systems in Indonesia. Along the Sunda–Java Trench, the slab exhibits a systematic along-strike transition from a continuous and well-defined geometry in the west to increasingly disrupted and thickened structures toward the east. This evolution correlates with the subduction of progressively older lithosphere. Beneath the Banda Arc, we image a continuous slab whose dramatic 180° curvature and deep coalescence of distinct segments provide direct evidence for a single-slab rollback and folding origin. In the Molucca Sea region, tomography reveals a shallow low-velocity zone and resolves the complex geometry of an active double-sided subduction system associated with arc–arc collision. Collectively, these findings provide unprecedented constraints on slab segmentation and deformation, highlighting the dominant control of lithospheric age and complex plate interactions on the geodynamic evolution of this exceptional convergent boundary. Full article