Search Results (591)

Accurate pose estimation of non-cooperative space objects is crucial for applications such as satellite maintenance, space debris removal, and on-orbit assembly. However, monocular pose estimation methods face significant challenges in environments with limited visibility. Different from the traditional pose estimation methods that use images from a single band as input, we propose a novel deep learning-based pose estimation framework for non-cooperative space objects by fusing visible and infrared images. First, we introduce an image fusion subnetwork that integrates multi-scale features from visible and infrared images into a unified embedding space, preserving the detailed features of visible images and the intensity information of infrared images. Subsequently, we design a robust pose estimation subnetwork that leverages the rich information from the fused images to achieve accurate pose estimation. By combining these two subnetworks, we construct the Visible and Infrared Fused Pose Estimation Framework (VIPE) for non-cooperative space objects. Additionally, we present a Bimodal-Vision Pose Estimation (BVPE) dataset, comprising 3,630 visible-infrared image pairs, to facilitate research in this domain. Extensive experiments on the BVPE dataset demonstrate that VIPE significantly outperforms existing monocular pose estimation methods, particularly in complex space environments, providing more reliable and accurate pose estimation results. Full article

Cooperative navigation is a fundamental enabling technology for unlocking the full potential of Unmanned Underwater Vehicle (UUV) clusters in GNSS-denied environments. However, the severe constraints of the underwater acoustic channel, such as high latency, low bandwidth, and non-Gaussian noise, pose significant challenges to designing robust and efficient state estimation and information fusion algorithms. While numerous surveys have cataloged the available techniques, they have remained largely descriptive, lacking a rigorous, quantitative comparison of their performance trade-offs under realistic conditions. This paper provides a comprehensive and critical review that moves beyond qualitative descriptions to establish a novel quantitative comparison framework. Through a standardized benchmark scenario, we provide the first data-driven, comparative analysis of key frontier algorithms—from recursive filters like the Maximum Correntropy Kalman Filter (MCC-KF) to batch optimization methods like Factor Graph Optimization (FGO)—evaluating them across critical metrics including accuracy, computational complexity, communication load, and robustness. Our results empirically reveal the fundamental performance gaps and trade-offs, offering actionable insights for system design. Furthermore, this paper provides in-depth technical analyses of advanced topics, including distributed fusion architectures, intelligent strategies like Deep Reinforcement Learning (DRL), and the unique challenges of navigating in extreme environments such as the polar regions. Finally, leveraging the insights derived from our quantitative analysis, we propose a structured, data-driven research roadmap to systematically guide future investigations in this critical domain. Full article

As global Marine resource development continues to expand into deep-sea and ultra-deep-sea domains, the intelligent and green transformation of deep-sea aquaculture equipment has become a key direction for high-quality development of the Marine economy. Large deep-sea cages are considered essential equipment for deep-sea aquaculture. However, there are significant challenges associated with ensuring their structural integrity and long-term monitoring capabilities in the complex Marine environments characteristic of deep-sea aquaculture. The present study focuses on large deep-sea cages, addressing their dynamic response challenges and long-term monitoring power supply needs in complex Marine environments. The present study investigates the nonlinear vibration characteristics of flexible net structures under complex fluid loads. To this end, a multi-field coupled dynamic model is constructed to reveal vibration response patterns and instability mechanisms. A self-powered sensing system based on triboelectric nanogenerator (TENG) technology has been developed, featuring a curved surface adaptive TENG array for the real-time monitoring of net vibration states. This review aims to focus on the research of optimizing the design of curved surface adaptive TENG arrays and deep-sea cage monitoring. The present study will investigate the mechanisms of energy transfer and cooperative capture within multi-body coupled cage systems. In addition, the biomechanics of fish–cage flow field interactions and micro-energy capture technologies will be examined. By integrating different disciplinary perspectives and adopting innovative approaches, this work aims to break through key technical bottlenecks, thereby laying the necessary theoretical and technical foundations for optimizing the design and safe operation of large deep-sea cages. Full article

This study investigates the environmental dimension of sustainable development across fifteen post-Soviet republics in 2022. While sustainability is generally understood as a triadic construct—economic, social, and environmental—this paper isolates the ecological pillar to highlight cross-country differences shaped by industrial legacies, institutional capacity, and governance models. A composite Environmental Performance Index (EPI) is developed using the Mazziotta–Pareto Index (MPI), which captures both average performance and internal consistency across three SDG-related domains: SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land). The study adds to existing literature as it includes a non-compensatory composite index and cluster analysis, and in policy terms, it provides a benchmarking system for facilitating ecological transition in the post-Soviet context. The results reveal strong divergence across the region: Baltic countries and Moldova achieve higher scores, reflecting policy convergence with the European Union and stronger environmental institutions, while Central Asian republics lag due to resource dependence, water scarcity, and weaker governance. Geographic cluster analysis corroborates these differences, showing clear spatial patterns of environmental convergence and divergence. Correlation analysis further demonstrates that environmental sustainability is positively associated with GDP per capita, HDI, and life expectancy, while negatively linked with inequality and fertility rates. These findings stress the need for context-sensitive and evidence-based policies, intra-regional cooperation, and integrated governance mechanisms to advance ecological transition in line with the 2030 Agenda for Sustainable Development. Full article

Mainlobe interference can severely degrade the performance of distributed phased-array radar systems in the presence of strong jamming or low-reflectivity targets. This paper introduces a signal–data dual-domain cooperative antijamming and localization (SDCAL) framework that integrates adaptive complete ensemble empirical mode decomposition with improved blind source separation and wavelet optimization (CEEMDAN-WOBSS) for signal-level denoising and separation. Following source separation, CFAR-based pulse compression is applied for precise range estimation, and multi-node data fusion is then used to achieve three-dimensional target localization. Under low signal-to-noise ratio (SNR) conditions, the adaptive CEEMDAN–WOBSS approach reconstructs the signal covariance matrix to preserve subspace rank, thereby accelerating convergence of the separation matrix. The subsequent pulse compression and CFAR detection steps provide reliable inter-node distance measurements for accurate fusion. The simulation results demonstrate that, compared to conventional blind-source-separation methods, the proposed framework markedly enhances interference suppression, detection probability, and localization accuracy—validating its effectiveness for robust collaborative sensing in challenging jamming scenarios. Full article

Background/Objectives: Sturge–Weber syndrome (SWS) is a rare neuro-oculocutaneous disorder characterized by leptomeningeal angioma, naevus flammeus, and ocular manifestations, including diffuse choroidal hemangioma (DCH). This study compares the diagnostic performance of near-infrared reflectance (NIR) imaging and enhanced depth imaging spectral-domain optical coherence tomography (EDI-SDOCT) with fundus photography in detecting DCH. Methods: Seventeen patients with SWS underwent comprehensive ophthalmologic evaluation, including fundus photography, NIR, and EDI-SDOCT imaging. Sensitivity, specificity, and accuracy of fundus photography, NIR, and EDI-SDOCT were calculated. Results: Sixteen patients had evaluable data. DCH was identified by fundus photography in five (31%), NIR in three (18.75%), and EDI-SDOCT in fourteen patients (87.50%). EDI-SDOCT alone demonstrated 100% sensitivity and 100% accuracy, outperforming both NIR (21.4% sensitivity; 31.6% accuracy) and fundus photography (35.7% sensitivity; 43.8% accuracy). When positive findings on NIR and/or SDOCT were combined, sensitivity and accuracy reached 100%. EDI-SDOCT provided detailed morphologic visualization of the choroid, allowing for early diagnosis of DCH even in pediatric cases with limited patient cooperation. Conclusions: EDI-SDOCT significantly improves the detection of DCH in SWS compared with fundus photography and NIR. Given its superior sensitivity and accuracy, incorporating EDI-SDOCT into routine clinical assessment may enable earlier diagnosis and reduce retinal complications in SWS. Full article

Reliable three-dimensional (3D) object detection is critical for intelligent vehicles to ensure safety in complex traffic environments, and recent progress in multi-modal sensor fusion, particularly between LiDAR and camera, has advanced environment perception in urban driving. However, existing approaches remain vulnerable to occlusions and dense traffic, where depth estimation errors, calibration deviations, and cross-modal misalignment are often exacerbated. To overcome these limitations, we propose BEVAlign, a local–global feature alignment framework designed to generate unified BEV representations from heterogeneous sensor modalities. The framework incorporates a Local Alignment (LA) module that enhances camera-to-BEV view transformation through graph-based neighbor modeling and dual-depth encoding, mitigating local misalignment from depth estimation errors. To further address global misalignment in BEV representations, we present the Global Alignment (GA) module comprising a bidirectional deformable cross-attention (BDCA) mechanism and CBR blocks. BDCA employs dual queries from LiDAR and camera to jointly predict spatial sampling offsets and aggregate features, enabling bidirectional alignment within the BEV domain. The stacked CBR blocks then refine and integrate the aligned features into unified BEV representations. Experiment on the nuScenes benchmark highlights the effectiveness of BEVAlign, which achieves 71.7% mAP, outperforming BEVFusion by 1.5%. Notably, it achieves strong performance on small and occluded objects, particularly in dense traffic scenarios. These findings provide a basis for advancing cooperative environment perception in next-generation intelligent vehicle systems. Full article

Comprehensive maritime domain awareness is crucial for navigation safety, traffic management, and security surveillance. In the context of an increasingly complex modern electromagnetic environment, the disadvantages of traditional active single-station radars, such as their high cost and susceptibility to interference, have started to surface. Due to their unique advantages, such as low cost, environmental sustainability (by reusing existing signals), and resilience in congested spectral environments, non-cooperative passive multistatic radar (PMR) systems have gained significant interest in maritime monitoring. This paper presents the research background of non-cooperative passive multistatic radar systems, performs a fundamental analysis of the detection performance of multistatic radar systems, and suggests an optimization method for the transceiver configuration of non-cooperative passive multistatic radar systems based on geometric coverage theory and a signal-to-noise ratio model. A multi-objective optimization model is developed, considering both detection coverage and positioning error, and is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The optimization aims to find the optimal receiver location relative to a fixed configuration of four transmitters, representing common maritime traffic patterns. According to the simulation results, the multi-target genetic algorithm can be utilized to optimize the receiver position under the S-band radar settings used in this work. Compared to a random placement baseline, this can reduce the positioning error by about 8.9% and extend the detection range by about 15.8%. Furthermore, for the specific four-transmitter configuration and S-band radar parameters considered in this study, it is found that the best detection performance is more likely to be obtained when the receiver is placed within 15 km of the transmitters’ geometric center. Full article

The use of JAK inhibitors (JAKi) represents a promising therapeutic approach for patients with lymphoid leukemias (Lym-L). Clinical trials are ongoing to evaluate the safety and efficacy of JAK inhibitors. Over the last years, there have been reports of preclinical Lym-L models that developed JAKi resistance, and reports of patients treated with JAKi who experienced treatment failure. Although evidence shows that there are diverse JAKi mechanisms, no review studies have been performed that summarize and discuss this information. This scoping review aimed to provide an updated overview of the mechanisms underlying JAKi molecular resistance in Lym-L. According to a scoping review PRISMA guidelines, a search was conducted in the PubMed and Europe PMC databases for studies published from 2010 to 2024. We included articles that described the molecular resistance to JAKi in Lym-L preclinical models or patients. The search was complemented by a review of laboratory-engineered resistant mutations in genomic datasets to obtain more information about their presence in patients with Lym-L. Twenty-two articles were eligible for this review, and six different mechanisms of molecular resistance were identified: (1) point mutations in the kinase domain, (2) cooperation between double-JAK mutants, (3) inactivation of phosphatases, (4) evasion of JAK inhibition due to trans-phosphorylation of JAK family proteins, (5) upregulation of pro-survival proteins, and (6) activation of kinase cross-signaling pathways. The integrated evidence enabled the identification of specific mechanisms of molecular resistance to JAKi in Lym-L, as well as promising therapeutic approaches to prevent them. These include selecting a sensitive JAKi, choosing an effective dosage regimen, and combining inhibitory molecules. Full article

With the rapid expansion of digital knowledge platforms and intelligent information systems, organizations and communities are producing a vast number of unstructured knowledge data, including annotated corpora, technical diagrams, collaborative whiteboard content, and domain-specific multimedia archives. However, knowledge sharing across institutions is hindered by privacy risks, high communication overhead, and fragmented ownership of data. Federated learning promises to overcome these barriers by enabling collaborative model training without exchanging raw knowledge artifacts, but its success depends on motivating data holders to undertake the additional computational and communication costs. Most existing incentive schemes, which are based on non-cooperative game formulations, neglect unstructured interactions and communication efficiency, thereby limiting their applicability in knowledge-driven scenarios. To address these challenges, we introduce SC-NBTI, a smart contract and Nash bargaining-based incentive framework for federated learning in knowledge collaboration environments. We cast the reward allocation problem as a cooperative game, devise a heuristic algorithm to approximate the NP-hard Nash bargaining solution, and integrate a probabilistic gradient sparsification method to trim communication costs while safeguarding privacy. Experiments on the FMNIST image classification task show that SC-NBTI requires fewer training rounds while achieving 5.89% higher accuracy than the DRL-Incentive baseline. Full article

This study conducted a systematic review of 222 research articles (2014–2024) from the Web of Science Core Collection database to investigate the ecological and environmental impacts of pumped hydro storage (PHS). Utilizing CiteSpace 6.1R software for visual analysis, the research hotspots and evolutionary trends over the past decade were comprehensively examined. Key findings include the following: (1) Annual publication output exhibited sustained growth, with China contributing 29.7% of total publications, ranking first globally. (2) Research institutions demonstrated broad geographical distribution but weak collaborative networks, as the top 10 institutions accounted for only 21.6% of total publications, highlighting untapped potential for cross-regional cooperation. (3) Current research focuses on three domains: ecological–environmental benefit assessment, renewable energy synergistic integration, and power grid regulation optimization. Emerging trends emphasize multi-objective planning (e.g., economic–ecological trade-offs) and hybrid system design (e.g., solar–wind–PHS coordinated dispatch), providing critical support for green energy transitions. (4) Post-2020 research has witnessed novel thematic directions, including deepened studies on wind–PHS coupling and life-cycle assessment (LCA). Policy-driven renewable energy integration research entered an explosive growth phase, with PHS–photovoltaic–wind complementary technologies emerging as a core innovation pathway. Future research should prioritize strengthening institutional collaboration networks, exploring region-specific ecological impact mechanisms, and advancing policy–technology–environment multi-dimensional frameworks for practical applications. Full article

Elucidating the evolutionary origins of social behavior remains a major challenge due to the complexity of social systems across taxa. Here, we examine social behavior through the lens of competition and cooperation across the three domains of life—Bacteria, Archaea, and Eukarya. By focusing on gene-based mechanisms, we propose that sociality arises from conserved molecular pathways shaped by similar selective pressures, even in phylogenetically distant organisms. This cross-domain perspective highlights the potential for convergent evolutionary solutions and offers a foundation for identifying invariant principles underlying the emergence and maintenance of social behavior. Full article

Sophorolipids (SLs) constitute a group of unique biosurfactants in light of their unique properties, among which their physicochemical characteristics and antimicrobial activity stand out. SLs can exist mainly in acidic and lactonic forms, both of which display inhibitory activity. This study explores the interaction of non-acetylated acidic SL with bovine serum albumin (BSA). SL significantly enhances BSA’s thermal stability, increasing its midpoint unfolding temperature from 61.9 °C to approximately 76.0 °C and ΔH from 727 to 1054 kJ mol⁻¹ at high concentrations, indicating cooperative binding. Fourier-Transform Infrared Spectroscopy (FTIR) analysis confirms SL’s protective effect against thermal unfolding, enabling BSA to maintain its helical structure at 70 °C, distinguishing it from other surfactants that cause denaturation. Furthermore, SL fundamentally alters the sequence of thermal unfolding events; β-aggregation precedes helical domain unfolding, suggesting protective binding to BSA’s helical regions. Computational docking reveals high-affinity binding (Kd = 14.5 μM). Uniquely, SL binds between BSA domains IB and IIIA, establishing hydrophobic interactions, salt bridges, and hydrogen bonds, thus stabilizing the protein’s 3D structure. This distinct binding site is attributed to SL’s amphipathic character. This work deepens the understanding of the molecular characteristics of SL–protein interactions and contributes to improving the general knowledge of this outstanding biosurfactant. Full article

This study proposes a real-time path generation method called the Gradient-based Time-extended Potential Field (GT-PF) for cooperative autonomous driving environments. The proposed approach models the road environment and dynamic obstacles as a time-variant potential field and generates safe and feasible paths by tracing the negative gradient of the field, which corresponds to the direction of steepest descent. In contrast to conventional sampling-based or optimization-based methods, the proposed PF framework enables lightweight computation and continuous trajectory generation in spatiotemporal domains. Furthermore, a velocity-oriented bias is introduced in the PF formulation to ensure that the generated paths satisfy the vehicle’s kinematic constraints and desired cruising behavior. The effectiveness of the proposed method is verified through comparative simulations against a sampling-based Rapidly exploring Random Tree (RRT) planner. Results demonstrate that the GT-PF approach exhibits superior performance in terms of runtime efficiency and safety. The system is particularly suitable for RSU (Roadside Unit)-based infrastructure control in real-time traffic environments. Future work includes the extension to complex urban scenarios, integration with multi-agent planning frameworks, and deployment in sensor-fused cooperative perception systems. Full article

As an essential technology for precision airdrop missions, parafoil systems have gained widespread adoption in military and civilian applications due to their superior glide performance and maneuverability compared to conventional parachutes. Addressing the trajectory-tracking control challenges of the parafoil system under significant wind disturbances, characterized by wind uncertainty and system underactuation, this paper proposes a stochastic model predictive control (SMPC) framework based on Markov-based multi-scenario optimization. Traditional deterministic model predictive control (MPC) methods often exhibit excessive conservatism due to reliance on worst-case assumptions and fail to capture the time-varying nature of real-world wind fields. To address these limitations, a high-fidelity dynamic model is developed to accurately characterize aerodynamic coupling effects, overcoming the oversimplifications of conventional three-degree-of-freedom point-mass models. Leveraging Markov state transitions, multiple wind-disturbance scenarios are dynamically generated, effectively overcoming the limitations of independent and identically distributed hypotheses in modeling realistic wind variations. A probabilistic constraint-reconstruction strategy combined with a rolling time-domain covariance update mechanism mitigates uncertainties and enables cooperative optimization of inner-loop attitude stabilization and outer-loop trajectory tracking. The simulation results demonstrate that the SMPC framework achieves superior comprehensive performance compared to deterministic MPC, evidenced by significant reductions in maximum position error, average position error, and control effort variation rate, along with a 94% tracking success rate. By balancing robustness, tracking precision, and computational efficiency, the method provides a theoretical foundation and a promising simulation-validated solution for airdrop missions. Full article