Search Results (300)

Accurate forecasting of offshore wind power is essential for the stability of power systems, yet it remains challenging due to the strong non-stationarity and complex multivariate coupling of meteorological data. To address the tendency of error accumulation in medium- and long-term predictions, this paper proposes a novel framework, termed ISSAVMD-TCN-SOFTS, which integrates adaptive signal decomposition with lightweight deep temporal modeling. Specifically, an improved sparrow search algorithm, enhanced by Lévy flight and sine–cosine modulation mechanisms, is introduced to adaptively optimize the parameters of variational mode decomposition (VMD). This optimization ensures the robust decomposition of highly non-stationary power series. Furthermore, the framework combines the capability of temporal convolutional networks (TCN) to extract multiscale local temporal features with the efficiency of the STAR module in SOFTS for modeling global channel dependencies. Experiments on multi-site, multi-horizon SCADA data from real offshore wind farms show that the proposed model reduces MAE and RMSE by 10–45% compared with mainstream linear models, recurrent neural networks, and Transformer-based models, and maintains high stability over extended forecasting horizons. The results confirm that the integration of adaptive decomposition and collaborative temporal-channel modeling provides an effective solution for the accurate and stable forecasting of offshore wind power. Full article

Satellite object detection in space surveillance is challenged by sparse and weak targets in large-scale, structured backgrounds (e.g., star fields, clouds, and streak noise). Such interference is not random but exhibits spatial correlation and frequency regularity, causing target responses to be overwhelmed and difficult to separate within a single representation space. To address this issue, we propose a lightweight framework, termed DRSS-Net, based on the key observation that target–background separability can be enhanced across complementary representation coordinate systems. Specifically, spatial modeling captures local structural consistency, while frequency-domain processing characterizes global energy distribution and structured patterns. By alternating between these domains, the proposed method enables constraint propagation, where predictable background patterns are suppressed, and structurally inconsistent target responses are emphasized. In the spatial domain, a mutual conditioning mechanism with asymmetric channel allocation enhances the consistency between localization and semantic responses. In the frequency domain, a coupled refinement module models the interaction between energy distribution and structural configuration to distinguish structured background from anomalous targets. In addition, a scale selection strategy retains stable intermediate representations for efficient detection. Experiments on two independent space target datasets demonstrate that DRSS-Net consistently achieves superior detection performance with a compact model size under diverse observation conditions, including variations in target appearance, illumination, and structured background interference. Full article

Turnover is a major concern for hotel and hospitality industry on a global scale. This research focuses on Dubai 5-star hotels in terms of transactional leadership and how employees perceive its influence on climate and contract fulfillment. This research combines the premises of social exchange, organizational support, and psychological contract, and organizational climate theories to support the development of hypotheses. A total of 24 employee interviews from two 5-star hotels in Dubai were gathered using semi-structured interviews. The research used inductive qualitative approach via thematic network analysis using QSR NVivo software (version 14). Transactional leadership can stabilize the execution of services by clarifying roles, adequate monitoring, and contingent exchanges in the luxury hotel setting. The thematic qualitative evidence demonstrates that organizational climate is an immediate interpretive medium, while psychological contract fulfillment is an emergent factor that is formed through repetitive interactions between employees and leadership. The results provide an in-depth understanding of these dynamics in the luxury hotel context, which can be beneficial for both scholars and practitioners alike. Full article

Galaxy classification is essential for understanding the formation and evolution of cosmic structures. However, faced with the explosive growth of astronomical observation data, traditional single-modality classification methods relying solely on spectroscopy or imaging have struggled to meet high-precision demands due to insufficient feature utilization and limited generalization capability. Therefore, multimodal fusion has emerged as a promising direction by leveraging information complementarity to overcome the limitations of single data sources. Accordingly, this paper proposes a model named Galaxy CosineNet (GCSNet), which integrates imaging, spectroscopic, and tabular data for high-precision galaxy classification. Specifically, the model employs dedicated encoders to process the three modalities separately and utilizes skip connections to preserve raw features. Furthermore, it incorporates a multi-head self-attention mechanism to deeply mine global cross-modal complementary information. Finally, these features are concatenated and fed into a cosine similarity classification head. Experimental results demonstrate that GCSNet achieves 97.15% accuracy in classifying star-forming, composite, active galactic nuclei (AGNs), and normal galaxies. This performance outperforms the best single-modal baseline, GaSNet, by 0.76% and mainstream multi-modal models such as MB-ISTL and the Transformer by over 1.6%. Consequently, the proposed GCSNet offers an effective and novel approach for research on automatic galaxy classification. Full article

Efficient coordination under limited communication is a central challenge in multi-agent reinforcement learning (MARL). Existing approaches often focus on message exchange without explicitly modeling how communication affects policy learning, leading to redundant interactions and limited coordination gains. In this paper, we propose a threshold-gated sparse communication framework built upon QMIX, a monotonic value-decomposition method that mixes individual agent action values into a global team action value. In the proposed framework, communication is integrated into the agent utility function to directly influence policy learning. Each agent encodes local observations into structured representations and activates communication through a learned trigger mechanism. Messages are aggregated via neighbor-constrained attention and incorporated into utility estimation for decentralized decision-making. Experimental results on the StarCraft Multi-Agent Challenge (SMAC) benchmark show that the proposed method improves coordination quality and training stability while significantly reducing communication frequency. On MMM, the Marine–Marauder–Medivac heterogeneous scenario, the communication rate is reduced to approximately 30–38% while achieving up to 96.6% win rate, compared to 92.1% for QMIX. On 10m_vs_11m, a homogeneous scenario where ten allied Marines fight against eleven enemy Marines, communication remains within 28–37% while reaching 88.4% win rate, compared to 85.6% for QMIX. Moreover, on the same task, varying communication thresholds induce clearly differentiated policy behaviors, indicating that sparse communication not only reduces overhead but also plays a critical role in shaping coordination policies. These results demonstrate that selective communication enables efficient coordination while explicitly regulating policy formation. Full article

Existing methods for hypergraph node classification usually rely on local message passing and use a unified strategy for topological modeling across hyperedges of different sizes. However, they have two limitations in semi-supervised settings. First, representation learning mainly depends on local neighborhoods, making it difficult to incorporate global topological information. Second, a unified structural modeling strategy cannot effectively handle both small and large hyperedges. Small hyperedges require modeling fine-grained local relations, while large hyperedges need sparse group-level structure. To address these issues, we propose S2-HGNN, a scale-aware hypergraph node classification framework with spectral inductive bias for semi-supervised learning. S2-HGNN first injects global topological information into the input features using complementary hypergraph spectral operators. It then constructs different auxiliary topologies based on hyperedge size. For small hyperedges, it uses Top-k constrained clique expansion to preserve representative local relations. For large hyperedges, it uses star expansion to reduce redundant connections while preserving sparse group-level structure. Finally, node representations are jointly learned from the original hypergraph backbone and the two auxiliary branches, and final predictions are obtained through node-level adaptive fusion. Experiments on multiple public datasets show that the proposed method consistently outperforms strong baselines and exhibits superior robustness under feature perturbations. Full article

To address the challenges of dynamic obstacles, limited perception, and multi-UAV coordination constraints in complex urban environments, a hierarchical control framework based on a virtual leader-follower architecture is proposed, covering global planning, local obstacle avoidance, and formation coordination. In the global planning layer, a dynamic adaptive strategy rapidly exploring random tree star (DASRRT*) algorithm is proposed. To address the low sampling efficiency and limited path extension in dense environments that affect traditional RRT*, a hybrid guided sampling strategy, inefficient node optimization strategy, and perception-based adaptive step size strategy are designed. Additionally, a multi-objective cost function is introduced to provide smoother trajectories that better comply with dynamic constraints for trajectory tracking. In the local obstacle-avoidance layer, a distributed controller is constructed based on an improved artificial potential field method, integrating collision avoidance control laws derived from a spring-damper model, dynamic obstacle-avoidance laws that account for obstacle velocities, and formation coordination control laws grounded in consensus theory. In the coordination control layer, a real-time local target selection strategy is established to guide the virtual leader to precisely track the global path, and a dual-mode switching mechanism based on environmental complexity is constructed to dynamically adjust the priority between formation maintenance and autonomous obstacle-avoidance tasks. Comparative experimental results show that the proposed DASRRT* algorithm reduces path planning time by an average of 34.78% and shortens path length by 1.15%. Simulation results for formation flight demonstrate that the proposed hierarchical control framework can adaptively adjust control modes in response to changes in environmental complexity, exhibiting strong adaptability to complex environments and a good ability to generalize to various scenes. Full article

UAV aerial cinematography has become increasingly important in film production, surveying, and smart-city applications due to its efficiency and creative potential. However, existing UAV filming workflows still rely heavily on manual operation and professional piloting skills, resulting in complex mission design, limited planning autonomy, and inconsistent visual quality. To address these challenges, this paper proposes a unified aesthetics-aware trajectory planning framework for multi-region-of-interest (multi-ROI) UAV aerial cinematography that automatically generates safe, efficient, and visually coherent flight paths from user-specified ROIs. The proposed framework consists of three main components. First, for each ROI, candidate viewpoints are sampled using a spiral trajectory, and a learning-based aesthetic evaluation network is applied to select visually optimal viewpoints for local trajectory generation. Second, transition trajectories between ROIs are generated using a Goal-biased Bidirectional Rapidly exploring Random Tree Star (Goal-biased BiRRT*) planner and evaluated through a multi-objective cost function to determine the most suitable transition paths. Third, the global connection of multiple ROIs is formulated as a Set Traveling Salesman Problem (STSP) to obtain an efficient visiting sequence. By integrating learning-based aesthetic evaluation with hierarchical trajectory planning and coordinated multi-ROI route organization, the proposed framework jointly considers flight feasibility, planning efficiency, visual composition quality, and trajectory continuity within a unified planning pipeline. Experimental results demonstrate that the proposed method generates more visually appealing and coherent aerial trajectories than traditional manual or rule-based approaches, while significantly reducing operational complexity. The proposed system provides an effective solution for autonomous UAV aerial cinematography with improved global consistency, aesthetic performance, and practical planning capability in complex environments. Full article

Given the urgent need for corporate green transformation in the context of global climate governance, the sustainable development goals, and China’s dual carbon goals, this study examines the spillover effects of venture capital networks formed through common shareholder ties on green technology innovation from a complex network perspective. Based on regression analysis of panel data from Chinese A-share STAR and ChiNext Market listed companies between 2015 and 2023, we find the following: (1) Within venture capital networks, enterprises with higher centrality and structural hole positions exhibit more significant green technology innovation performance. (2) This facilitation effect varies across firm types. Private enterprises, foreign-invested enterprises and enterprises with weaker ESG performance rely more heavily on network advantage for innovation. (3) The mechanism analysis shows that occupying advantageous positions in venture capital networks enables firms to increase R&D personnel and R&D expenditure, thereby strengthening their ability to absorb external knowledge and transform innovation resources, which further enhances green technology innovation output. Full article

Infrared (IR) object detection on unmanned aerial vehicle (UAV) platforms is fundamentally challenged by low signal-to-noise ratios and extremely tight onboard computational budgets. Conventional CNNs lack sufficient global context, while Transformers suffer from quadratic complexity, hindering real-time deployment. To address these bottlenecks, we propose EfficientIR-Det, a lightweight end-to-end detector featuring a holistic optimization of the backbone, encoder, and sampling mechanisms. Specifically, we design a Partial Star Network (PSN) backbone that achieves implicit high-dimensional feature expansion via element-wise multiplication to amplify weak IR signals with minimal redundancy. Furthermore, a Hierarchical Mamba (HiMamba) encoder leverages selective state-space modeling to provide linear-complexity global enhancement with superior hardware efficiency. To refine cross-scale representations, we introduce an Adaptive Gated Sampling (AGS) module and a Hierarchical Sampling Strategy (HSS) to optimize feature fusion and sampling budget allocation toward dim-small targets. On HIT-UAV, EfficientIR-Det achieves 88.4% mAP@0.5, outperforming the RT-DETR-R18 baseline by 3.3 points while reducing FLOPs and parameters by 48.9% and 44.2%, respectively. On the larger-scale DroneVehicle dataset, it consistently leads with a 74.1% mAP@0.5 and a high inference speed of 140.8 FPS. Our results offer a promising research scheme for robust, real-time infrared perception on edge-constrained UAV platforms. Full article

Astronomical positioning of space targets is an important task in space situational awareness. In both ground-based and space-based optical observation scenarios, accurate positioning relies on the reliable matching of numerous stars in observational images. However, dense star scenes increase the ambiguity of local patterns and the computational burden of candidate retrieval. Building on established geometric voting and catalog-indexing strategies, this paper develops a two-stage star map matching method that specifically combines adaptive magnitude stratification with seed-guided residual-star diffusion for large-field dense star scenes. In the first stage, an adaptive magnitude-stratified bright-star subset is selected according to field density, and angular-distance voting is used to obtain reliable seed correspondences. In the second stage, residual-star candidates are retrieved from seed-centered dual-feature sub-libraries indexed by angular distance and magnitude difference, and are then refined through single-seed local diffusion and multi-seed global fusion. Experimental results from both simulated and real observational data demonstrate that the proposed method achieves a high matching success rate with low computational cost and performs effectively in large-field, dense star scenes. The proposed method provides a practical matching solution for astronomical positioning in dense star scenes. Full article

The primary objective of this study is a comprehensive investigation of the self-bound properties of strange quark matter (SQM), which is hypothesized to represent the absolute ground state of superdense strongly interacting matter. An analysis is performed within the framework of the MIT bag model, including first-order perturbative QCD corrections and the finite strange quark mass. By systematically varying the vacuum pressure (bag constant, $B$) and the strong coupling constant (${\alpha }_c$) over a broad parameter space, while assuming a finite strange quark mass ($m_s\ne 0$), we explicitly compute the thermodynamic characteristics of the system including pressure, energy density, baryon number density, and the chemical potentials of quarks and charge-neutralizing electrons under conditions of $\beta$-equilibrium and global charge neutrality. Particular emphasis is placed on determining the minimum energy per baryon, which serves as the criterion for absolute stability. For parameter sets satisfying the self-binding condition, the integral properties of strange stars are derived via the numerical integration of the Tolman–Oppenheimer–Volkoff equations. The resulting mass–radius and mass–central density relations are analyzed, yielding the maximum stellar masses in the range $(1.9-2.4)M_{\odot }\hspace{0.17em}$. This study identifies the regions in the space of phenomenological parameters that allow for pure self-bound strange stars and demonstrates the sensitivity of stability and stellar properties to the underlying bag model parameters. Full article

In order to enable an autonomous navigation capability in environments where global navigation satellite systems (GNSSs) are either denied (e.g., areas with intentional jamming or spoofing) or not available yet (Moon, Mars), Sodern is currently developing star trackers for Earth-based aircrafts and space rovers. This system is designed to compensate for inertial sensor (IMU)-induced drifts by providing an absolute attitude reference. The resulting celestial navigation system (CNS) aims at providing a position evaluation with a 100 m class precision, independent of the mission duration. In this paper, we present the star tracker design with a specific focus on daytime capabilities and the hybridization strategy to implement the retrieved celestial attitude in the CNS. Additionally, we present two application cases currently under development at Sodern, for space rovers and aircrafts. We evaluate the typical performances that can be reached depending on the IMU and star tracker class in harsh environments (luminance, dynamics, radiations…). We conclude with a brief presentation of future developments in this field. Full article

Strong magnetic fields and anisotropic stresses can substantially modify the structure and observable properties of compact stars. In this review, we present a unified treatment of magnetically induced anisotropy across neutron stars, hybrid stars, and white dwarfs, connecting the microphysical equation of state effects to macroscopic structure and multimessenger observables. We demonstrate that magnetic-field geometry plays a decisive role: toroidally oriented (transverse) fields enhance the maximum mass by providing additional perpendicular pressure support, whereas radially oriented fields primarily increase central compression with comparatively small mass gain. In neutron stars, anisotropy and magnetic stresses can shift phase-transition thresholds in hybrid models and enable configurations in the lower mass gap with significantly smaller magnetic energy compared to the gravitational binding energy. We further show that continuous gravitational wave emission from magnetically deformed neutron stars provides a complementary probe of internal field geometry through ellipticity-driven strain evolution. In magnetized white dwarfs, super-Chandrasekhar masses arise from the spatial redistribution of magnetic stresses rather than from globally strong magnetic energy. Taken together, these results highlight that magnetic-field geometry and matter anisotropy are as important as field strength in determining mass–radius relations, tidal deformability, gravitational wave detectability, and the emergence of extreme compact-star configurations. Full article

Senecavirus A (SVA) has rapidly emerged as a globally distributed swine pathogen, with clinical signs mimicking vesicular diseases such as Foot-and-Mouth Disease, posing challenges for timely detection and control. Here, we analyzed 329 complete SVA genomes spanning multiple continents to provide a comprehensive view of its evolutionary dynamics, recombination patterns, haplotype diversity, and global dissemination. Phylogenetic analyses revealed two major lineages: Lineage 1, consisting mainly of early strains from the United States before 2007, and Lineage 2, which emerged post-2007 and subsequently spread across the Americas and East Asia. Recombination was confined to Lineage 2 and concentrated in nonstructural regions, particularly 2C, highlighting intra-lineage genetic exchange as a driver of recent diversification. Haplotype analysis of the 3AB gene identified 170 distinct haplotypes, revealing a star-like network structure consistent with rapid population expansion from a central ancestral variant, while secondary branches reflect ongoing regional diversification. Despite this high genetic variation, genome-wide dN/dS ratios remained below one, and purifying selection was strongest in the N-terminal domains of structural and nonstructural proteins, indicating functional constraints that maintain viral fitness. Time-scaled phylogenetic reconstruction and Bayesian Skyline analysis revealed rapid lineage diversification and a marked increase in effective population size in the early 2010s. Phylogeographic inference further identified repeated introductions from the Americas into East Asia, likely facilitated by swine trade and other anthropogenic factors. Collectively, SVA evolution is driven by frequent mutation and intra-lineage recombination yet constrained by pervasive purifying selection, generating extensive genetic diversity while maintaining functional integrity, with implications for genomic surveillance and targeted control. Full article