Search Results (1,068)

To mitigate local congestion and address the adaptability limitations of traditional static routing under dynamic traffic, this paper proposes an end-to-end routing method based on a Graph Attention Network (GAT), termed Congestion-Aware Graph Attention Routing (CA-GAR). To alleviate the issue of local optima in traditional heuristic iterative optimization, we design a dynamic link cost optimization algorithm with multi-start parallel exploration. This algorithm employs a ”penalty–reselection–reward” closed-loop feedback mechanism, performing global searches from multiple random initial states to generate a high-quality, empirically near-optimal cost matrix as supervised labels. Building on this, CA-GAR leverages a multi-head attention mechanism to adaptively aggregate high-order topological features of nodes and edges, and incorporates a staged hierarchical hyperparameter optimization strategy to map real-time network states to link costs. Simulation results demonstrate that CA-GAR outperforms traditional static routing under light, medium, and heavy loads. Under high-load burst conditions, the method exhibits effective congestion avoidance capability, reducing end-to-end delay by approximately 50% and lowering the packet loss rate to as low as 2%. Compared with QLRA, CA-GAR shows promising performance in multi-path traffic splitting and possesses robust fast rerouting capabilities during node failures, thereby achieving intelligent traffic distribution and global load balancing. Full article

Hydrogen-enriched fuel injection in staged gas-turbine combustors is commonly achieved through jet-in-crossflow (JICF) configurations, where flame stabilization is governed by a local balance between flow-induced strain/mixing and chemical reaction rates. This work investigates turbulent reacting JICF relevant to staged combustion conditions using high-fidelity simulations with adaptive mesh refinement (AMR) and differential-diffusion effects together with Lagrangian particle statistics. Chemistry model uncertainties are incorporated by using a projection method that maps uncertainty estimates from detailed mechanisms into the model used in this work. Results show that the macroscopic flame topology remains in a stable two-branch regime (lee-stabilized and lifted) and is primarily controlled by the jet momentum–flux ratio J. Visualization of the normalized scalar dissipation rate reveals that the flame front resides on the low-dissipation side of intense mixing layers, occupying an intermediate region between over-strained and under-mixed regions. While hydrogen content does not significantly change the global stabilization mode for the cases studied, uncertainty analysis reveals composition-dependent differences that are not apparent in the mean behavior alone. In particular, visualization in Eulerian (χ, T) state-space analysis and particle statistics conditioned on the stoichiometric surface demonstrate that higher-hydrogen cases observe a lower scalar dissipation rate and exhibit substantially reduced variability in OH production under kinetic-parameter perturbations, whereas lower-hydrogen blends experience higher dissipation and amplified chemical sensitivity. These findings highlight that, even in globally similar JICF regimes, the hydrogen content can modify the local response of the flame to kinetic-parameter uncertainty, motivating uncertainty-aware interpretation and design for hydrogen-fueled staging systems. Full article

We introduce and study a new subclass of Janowski-type harmonic close-to-convex functions in the open unit disk defined via the Jackson q-derivative operator. The structure of the operator naturally reflects certain symmetric properties in the analytic representation of the considered harmonic mappings. By applying subordination techniques, we establish sufficient conditions for sense-preserving close-to-convexity and distortion estimates. The extreme points of the class are determined, and its topological properties are examined, showing that the class is convex and compact. We further obtain the radius of starlikeness and prove that the class is closed under convolution. Moreover, as $q\to 1^{-}$, the operator reduces to the classical derivative, and our results recover several known results in the existing literature, demonstrating that the present work extends and generalizes earlier findings. Full article

This paper aims to address the signal coupling problem of a shared-aperture dual-band dual-circularly polarized microstrip antenna. A decoupling optimization method that combined a convolutional neural network (CNN) with binary particle swarm optimization (BPSO) was proposed. The method introduced pixelated decoupling branches near the antenna feeds, constructed a surrogate model to capture the nonlinear mapping between the branch topology and the electromagnetic performance using a CNN, and adopted BPSO to perform global optimization on the binary pixel matrix, thereby alleviating the time-consuming optimization in a complex, high-dimensional parameter space. Simulation results showed that the optimized S₂₁ was reduced by an average of 20 dB over 1.607–1.620 GHz and by an average of 25 dB over 2.487–2.502 GHz, effectively improving the port isolation. These findings demonstrate that the proposed intelligent optimization strategy is effective and practically applicable for solving antenna decoupling problems. Full article

With advancements in Earth observation capabilities, the demand for large-scale mapping using remote sensing images has increased significantly. However, selecting an optimal image set for the area of interest (AOI) from a large collection of remote sensing images remains challenging. On the one hand, it is crucial to select images with minimal redundancy and low cloud cover to enhance production efficiency and the effective coverage of mapping products. On the other hand, adjacent selected images should transition naturally so that the resulting mapping products appear visually cohesive. Unfortunately, most existing remote sensing image selection algorithms focus only on the former, with little attention to visual consistency. Meanwhile, images from the same swath inherently offer advantages in both redundancy reduction and visual consistency. However, a larger coverage area also carries the potential for greater variation in cloud cover, and cloud distribution within a swath can be highly complex. Managing the relationships among swaths, images, and cloud cover is also challenging. To address these issues, this paper proposes a novel image selection model, SwathSel. Candidate images are grouped through a composite grouping strategy based on swaths, cloud cover, and topological connectivity, thereby expanding the fundamental unit for image selection from individual scenes to connected image subsets. A dynamic adjustment mechanism is introduced to enhance grouping flexibility. Additionally, local and global swath consistency constraints are designed to strengthen visual consistency among images, and a subset evaluation module is used to comprehensively assess swath consistency, coverage, cloud cover, and metadata information. Through a greedy strategy combined with a rapid refinement technique, the final selected image set is obtained. Experiments were conducted on four datasets, and four quantitative metrics were designed to evaluate the visual consistency of the results. Compared with baseline models, SwathSel achieves lower redundancy and cloud cover while delivering superior visual consistency. Full article

Under the background of the Rural Revitalization Strategy, Zhejiang Province is promoting “Zhejiang-style Vernacular Dwellings” as a crucial measure to enhance the rural living environment and architectural appearance. However, traditional stylistic control tools, such as standardized rural housing design atlases, exhibit limitations including weak responsiveness to villagers’ individualized needs and high professional thresholds. Consequently, they struggle to address the bottlenecks in grassroots governance efficiency caused by massive and personalized housing demands. Meanwhile, when applied to architectural design, general generative AI technologies often suffer from “structural hallucinations” and the weakening of regional characteristics due to a lack of physical tectonic constraints. Oriented towards the governance requirements of the Zhejiang Provincial Rural Housing Design Guidelines, this study proposes a compliance evaluation-driven “Contour-Semantic-Image” hierarchical generative control framework. This aims to construct a visual scheme generation and pre-screening workflow that deeply adapts to the logic of rural governance. At the data level, this research aggregates multi-source materials, including official standardized atlases, government stylistic guidelines, and real-world photographs. Through expert screening and standardized processing of 596 schemes, a dataset of 333 high-quality, finely annotated structured samples is constructed. Furthermore, a human-guided, machine-segmented workflow assisted by Segment Anything Model 2 (SAM 2) is employed to establish a semantic label system comprising 4 major categories and 13 subcategories of components, thereby achieving the structural deconstruction of architectural prior knowledge. At the generation level, a two-stage model is trained based on Stable Diffusion and ControlNet: Stage I utilizes contour conditions and “layout prompts” to generate semantic label maps, aiming to strengthen component topology and layout consistency; Stage II employs the semantic label maps and “style prompts” as conditions to generate photorealistic facade images. By utilizing explicit semantic constraints to guide the model from pixel synthesis to logical generation, it achieves the controllable rendering of stylistic details and material expressions. At the evaluation level, an automated verification system featuring “clause translation–metric calculation–comprehensive scoring” is proposed. It conducts scoring, re-ranking, and diagnostic feedback on the generated variants across three dimensions: Design Rationality (Q), General Compliance (G), and Jiangnan water-town Regional Characteristics (P-J), forming a closed-loop “Generation-Evaluation-Feedback” workflow. Overall, this framework provides a “visualizable, evaluable, and explainable” pathway for scheme generation and pre-screening in the digital governance of rural architectural appearance. Full article

The relentless miniaturization of semiconductor components and Printed Circuit Boards (PCBs) has rendered Automated Optical Inspection (AOI) of micro-defects a critical bottleneck in modern manufacturing and metrology. While in-line inspection systems offer economically viable and scalable quality control solutions, they impose stringent constraints on both inference latency and detection robustness—particularly for diminutive, sparsely distributed defects (e.g., mouse bites, pinholes) amidst complex, repetitive circuit topologies. To bridge this gap, we present a semantic-enhanced FCOS framework specifically engineered for micro-defect inspection. Our approach introduces two synergistic innovations: (1) a Semantic-Guided Upsampling Unit (SGU) that adaptively reweights channel–spatial features to reconcile the semantic disparity between shallow textural details and deep contextual representations; and (2) a Sparse Center-ness Calibration (SCC) module that enforces high-confidence, spatially sparse supervision to sharpen localization precision and suppress false positives. The SGU is integrated within a Progressive Semantic-Enhanced Feature Pyramid Network (PSE-FPN) that extends multi-scale representations to stride-4 (P2) resolution, while the SCC module is embedded directly into the detection head. Comprehensive evaluations on MS COCO and the real-world DeepPCB dataset validate the efficacy of our design. On COCO, our model achieves 41.8% AP with real-time throughput of 28 FPS on a single NVIDIA 1080Ti GPU. A lightweight variant further attains 41.6% AP at 42 FPS, accommodating high-throughput production environments. For PCB defect detection, the framework delivers 98.7% mAP@0.5, substantially outperforming contemporary detectors. These results demonstrate that semantics-aware, lightweight architectures enable scalable, real-time quality assurance in semiconductor manufacturing. Full article

Electron correlation and quantum interference are pivotal in mesoscopic transport. We theoretically study the nonequilibrium transport dynamics of a triangular triple-quantum-dot (TTQD) molecule connected to fermionic reservoirs using the exact hierarchical equations of motion (HEOM) formalism. We demonstrate a counterintuitive transport signature in which the stationary current is significantly enhanced by increasing U, a behavior distinct from the suppression typically observed in linear quantum dot arrays. By analyzing the evolution of spectral functions, we attribute this enhancement to the interplay between Coulomb-interaction-induced energy shifts and quantum interference effects specific to the triangular topology. We also explore how the circulation of chiral currents and electrode coupling strength modulate these interaction effects. Finally, we present a three-dimensional map of the transport current as a function of inter-dot tunneling (t) and Coulomb interaction (U), illustrating their combined effect on the current magnitude and its applications. Full article

Chirality is a pervasive and functionally critical feature of biological macromolecules, yet its distributed and emergent forms remain poorly quantified in complex systems such as membrane proteins. We present Chirobiophore, a novel paradigm for capturing biochirality across scales—from atomic geometries to global structural asymmetries. Unlike traditional stereochemical metrics, Chirobiophore employs a multidimensional model-independent vector comprising Local Tetrahedral Asymmetry (LTA), Helical Path Curvature (HPC), Asymmetric Environment Score (AES), Directional Density Profile (DDP), Leaflet Asymmetry Index (LAI), and Orientation Twist Score (OTS). This framework enables coordinate-invariant comparisons of structurally diverse proteins in a continuous chirality space. We demonstrate its application to canonical, GPCR, and topologically complex membrane proteins, revealing distinct chirality signatures and functional clustering. Furthermore, we map Chirobiophore descriptors to tissue-level asymmetry indices, providing a bridge between molecular structure and morphogenetic patterning. Chirobiophore offers a unified, extensible platform for structural biology, synthetic design, and developmental modeling of chirality. Full article

Multi-omnidirectional mobile robot formations offer significant advantages for applications in unstructured environments. However, under constraints such as limited field of view and high operator cognitive load, existing teleoperation frameworks struggle to guarantee formation safety and stability. In this study, a bilateral shared control framework for multi-robot formation that integrates intent perception and vortex-field modulation is proposed. First, an Intent-Mediated Asymmetric Vortex Modulation (IM-AVM) strategy is developed, where the operator’s micro-intentions are mapped to determine the topological orientation of a vortex field. By constructing a dynamic asymmetric modulation matrix, saddle points in the potential field are geometrically eliminated, enabling deadlock-free obstacle avoidance while maintaining a rigid formation. Second, a multi-dimensional perception-based dynamic authority arbitration and topological deadlock escape mechanism is constructed, facilitating a seamless transition from assisted deadlock to autonomous escape. Finally, a formation coordination system based on anisotropic flow field modulation and adaptive sliding mode control is designed. Rigid formation constraints are transformed into a tangential safe flow field, and robust tracking is subsequently achieved through an Adaptive Nonsingular Fast Terminal Sliding Mode Controller (ANFTSMC). Theoretical analysis and experimental results demonstrate that the proposed framework achieves collision-free navigation for the formation in simulated environments. Full article

To unveil the underlying risk structures in complex industrial systems, this paper proposes a hybrid analytical framework that integrates BERTopic modeling, a large language model (LLM), and social network analysis (SNA). This framework aims to extract systemic safety intelligence from unstructured accident reports. It first employs BERTopic to identify latent causal topics based on 745 Chinese accident investigation reports and utilizes DeepSeek-V3.1 (LLM) for semantic refinement and causal mapping of these topics. Subsequently, a semantic network of causal keywords based on positive pointwise mutual information (PPMI) is constructed, and its topological structure is analyzed using SNA methods. The study identifies and analyzes five major risk communities: confined spaces, fire, mining, construction, and road traffic. It reveals that accident causation exhibits the small-world characteristics of multi-factor coupling and non-linearity, with core risk nodes concentrated in systemic inducements such as organizational management and compliance deficiencies. The results demonstrate that this framework effectively identifies the latent systemic risk patterns embedded within the texts, providing methodological support for developing sustainable safety management mechanisms based on design for safety. Full article

The accurate and timely identification of urban building footprints is critical for sustainable urban planning and disaster management. Traditional remote sensing methods for this task often face limitations in scalability, accuracy, and adaptability to complex urban morphologies. This paper addresses these challenges by developing and evaluating a novel data-centric framework that synergistically integrates Graph Neural Networks (GNNs) with zero-shot superpixel segmentation derived from the Segment Anything Model (SAM) applied to Sentinel-2 imagery. A cornerstone of our methodology is a rigorous assessment of OpenStreetMap (OSM) data, refined through temporal NDVI stability analysis to generate high-quality ground truth. We propose an optimized UrbanGraphSAGE model, enhanced with spectral data augmentation and trained using a robust loss function with label smoothing to mitigate label noise. In the complex urban landscape of Algiers, Algeria, our approach achieves a Test F1-Score of 0.7131, demonstrating highly competitive performance with standard pixel-based baselines like U-Net while offering significant topological and computational advantages. Specifically, our model operates with merely 19,585 parameters—orders of magnitude fewer than pixel-based CNNs. A rigorous Gold Standard evaluation against manually labeled imagery confirms the model’s high recall (0.8484) and reliability for automated urban monitoring. Full article

LiDAR point cloud semantic segmentation is essential for autonomous driving, yet LiDAR-only methods remain constrained by sparsity and limited texture cues. We propose Cross-Modal Collaborative Manifold Distillation (CMCMD), which transfers open-world semantic priors from the DINOv3 Vision Foundation Model to a LiDAR student network. The framework combines an Adaptive Relation Convolution (ARConv) backbone with geometry-conditioned aggregation, a Unified Bidirectional Mapping Module (UBMM) for explicit 2D–3D interaction, and Manifold-Aware Topological Distillation (MATD), which aligns inter-sample affinity structures in a shared latent manifold rather than enforcing pointwise feature matching. By preserving relational topology instead of absolute feature coordinates, CMCMD mitigates negative transfer across heterogeneous modalities. Experiments on SemanticKITTI and nuScenes yield mIoU values of 72.9% and 81.2%, respectively, surpassing the compared distillation baselines and approaching the performance of multimodal fusion methods at lower inference cost. Additional evaluation on real-world campus scenes further supports the cross-domain robustness of the proposed framework. Full article

Redundant robot manipulators are widely used in constrained operations and tasks in complex environments. However, when multiple task constraints and inequality constraints coexist, motion planning becomes significantly more difficult. In high-dimensional configuration spaces, conventional planners are prone to local minima and may generate trajectories that are difficult to execute in real time. To address these issues, this paper proposes a hierarchical, redundancy-driven real-time replanning framework. First, we perform Cartesian sampling on the task-constraint manifold to reduce the search dimension and generate multiple candidate joint configurations for each Cartesian sample via a redundancy mapping. During connection, manipulability and executability margin are used as evaluation metrics, so that redundant degrees of freedom are explicitly exploited in tree expansion and configuration selection. Second, at the local execution layer, we employ a null-space manipulability optimization strategy to continuously improve dexterity while keeping the primary task unchanged and combine it with a priority-based hard inequality constraint filtering mechanism to project the nominal motion onto the feasible set under joint limits, velocity bounds, and safety-distance constraints in real time. Unlike existing approaches that treat global planning and local control as loosely coupled modules, the proposed framework unifies redundancy reconfiguration, feasibility maintenance, and topological replanning within a single closed-loop structure, thereby reinterpreting local minima as event-triggered topology-switching conditions. To handle the mismatch between dynamic environments and real-time perception, we further introduce a feasibility-margin monitoring mechanism that triggers event-based replanning based on changes in manipulability, constraint scaling, and safety distance, enabling fast topology-level switching and escape from local minima. Simulation and experimental results show that the proposed method effectively restores manipulability through redundancy-driven configuration adjustment and achieves a higher success rate of local recovery under dynamic obstacle intrusion. In forced replanning scenarios, the framework further demonstrates faster environmental response and lower replanning overhead while maintaining better task-constraint stability compared with existing approaches. Full article

Mesoscale modeling provides an efficient and cost-effective approach for investigating the damage mechanisms of fiber-reinforced concrete. To address the physical distortion in conventional models that arises from neglecting the volumetric effect of steel fibers and to construct a more realistic random mesoscale model of steel fiber-reinforced concrete (SFRC), this study proposes an efficient modeling method based on geometric entity expansion and point–line topology. First, polygonal aggregates with diverse morphologies are generated using a polar-coordinate perturbation scheme combined with a convex-hull correction algorithm. Next, abandoning the traditional zero-thickness line-segment assumption, steel fibers are expanded into rectangular entities via rigid-body kinematics to explicitly represent their excluded volume. Furthermore, a vector-cross-product-based Point–Line Method is developed to replace conventional circumscribed-circle screening, enabling accurate discrimination of interference interactions between fiber–aggregate and fiber–fiber pairs. An automated framework—consisting of skeleton placement, entity generation, topological discrimination, and mesh mapping—is implemented through a Python 3.13.9 scripting interface, allowing efficient batch generation of high-content mesoscale models with aggregate area fractions up to 70%. The proposed model is then used to simulate the failure process of SFRC specimens under uniaxial compression and benchmarked against experimental results. The results show that the developed mesoscale model accurately reproduces the nonlinear mechanical response and the strengthening–toughening effects of SFRC, achieving a relative error of only 0.31% in peak stress and a root mean square error (RMSE) as low as 1.70 MPa over the full stress–strain curve. The simulations not only confirm the pronounced strength gain due to steel fiber incorporation (~19.7%), but also reveal, at the mesoscale, the mechanism by which fiber bridging suppresses damage localization, thereby demonstrating the reliability and practical effectiveness of the proposed modeling approach. Full article