Search Results (2,414)

With the rapid development of space laser communication and deep-space exploration, Deep Space Optical Sensor Networks (DSOSN) have become a vital support for deep-space information transmission. Conventional Directed Diffusion (DD) adopts fixed single-path reinforcement and blind flooding forwarding, which cannot adapt to high-dynamic topology and random link outages in deep space, resulting in high delay, unstable links and heavy network overhead. This paper puts forward a DSODD routing algorithm dedicated to DSOSN. It realizes selective packet forwarding in interest propagation and data return to reduce redundant relay nodes and transmission hops, thus prolonging network lifetime. Meanwhile, a gradient-based path reinforcement mechanism is adopted to strengthen reliable transmission paths. Under the simulation condition of solar scintillation and coronal fading interference, compared with DD, DSODD reduces end-to-end delay by 28% and delay jitter by 32%, and greatly improves link stability with 65% longer average link lifetime and 50% fewer route switches. Simulation results validate that DSODD achieves better comprehensive routing performance for deep-space optical sensor networks. Full article

The spatial pattern of building damage produced by an earthquake carries information that classical building-by-building vulnerability indices cannot capture. This study presents one of the first frameworks to use Topological Data Analysis (TDA), a set of methods that quantify the “shape” of data, to characterise the spatial topology of seismic damage across an urban building inventory. Using the geo-referenced centroids of buildings as a point cloud, a sequence of connectivity graphs (a Vietoris–Rips filtration) is built at increasing distance scales, and persistent homology is used to track which spatial features appear and disappear. From this we extract four interpretable descriptors: Betti numbers (the numbers of connected building clusters and of enclosed gaps), persistence entropy (a measure of how disordered the damage pattern is), total persistence (the combined lifespan of all topological features), and the Wasserstein-2 distance (how far the post-earthquake pattern has moved from the intact pre-earthquake pattern). These descriptors form a physics-informed feature vector that is used to predict the building-cluster damage state. The developed framework was trained, tested, and validated on 1490 buildings over seven post-earthquake scenarios. Lognormal fragility parameters were estimated with maximum likelihood estimation, and an Artificial Neural Network (ANN) and a Random Forest (RF) were retrained on the same 593-building training dataset for comparison. On the 847-building benchmark, the TDA framework reached 93.3% accuracy (95% CI: 91.4–94.9%), F1 = 0.921 (0.902–0.940), and AUC = 0.933, using a stratified 70/15/15 split (training = 593, validation = 127, test = 127). This is a 6.0-percentage-point gain over the retrained ANN and a 12.1-percentage-point gain over the HAZUS-MH index (McNemar p = 0.017). Damage was recorded on the six EMS-98 states DS0–DS5, with DS4 and DS5 merged into a single class to give a five-class taxonomy, and building-type-specific inter-storey drift ratio thresholds were validated against EN 1998-3 (Eurocode 8 Part 3). Exact Rips computation is practical only for clusters up to about 2000 buildings; for larger populations, a CGAL (Computational Geometry Algorithms Library)-based sparse approximation with O(N log N) cost is recommended. It seems that the topological descriptions of the damage field may provide predictive information above and beyond that given by density and ground motion intensity and offer a reproducible tool for post-earthquake screening. Full article

Redundant channels, sparse electrode topology, and insufficient cross-layer feature fusion limit electroencephalography (EEG)-based emotion recognition. This study proposes the Topology-Aware Dual-Bridge Fusion Network (TDBF-Net), a compact framework that integrates adaptive channel selection, topology-aware sparse convolution, and bidirectional bridge fusion. First, sample entropy, dispersion entropy, and fuzzy entropy are fused to estimate channel importance, while particle swarm optimization (PSO) learns the entropy weights and an elbow-based criterion determines the retained channel subset. Second, differential entropy (DE) features from the $\theta$, $\alpha$, $\beta$, and $\gamma$ bands are mapped to an $8\times 9$ sparse topological tensor according to electrode locations. A fixed spatial validity mask is applied before and after convolution to suppress invalid responses from zero-padded regions and preserve real electrode topology. Third, a dual-bridge fusion module recalibrates shallow and deep features in both directions through channel attention and gated fusion, and a bidirectional long short-term memory network (BiLSTM) further captures short-term temporal dependencies. Subject-dependent experiments on the SJTU Emotion EEG Dataset (SEED) and the Database for Emotion Analysis using Physiological Signals (DEAP) show that TDBF-Net achieves $97.62\%$ ± $1.59\%$ accuracy on SEED and $98.46\%$ ± $0.94\%$ and $98.14\%$ ± $0.77\%$ on DEAP valence and arousal, respectively. Paired DEAP ablations support topology and bridge contributions for valence, whereas the corresponding arousal differences are not significant. Selector controls, robustness tests, computational profiling, and held-out visualizations further characterize the method’s compression, cost, and interpretability. The evidence supports TDBF-Net as an effective subject-dependent framework while leaving subject-independent and cross-dataset generalization for future validation. Full article

This study presents a comparison of Double Stator Single Rotor Axial Flux Inner Rotor North–South (DSSR AFIR NS) and Double Stator Single Rotor Axial Flux Inner Rotor North–North (DSSR AFIR NN) configurations based on the magnetic equivalent circuit (MEC) approach. In the first stage, a magnetic equivalent circuit model of the DSSR AFIR NS topology was developed, and the mathematical formulations of the air-gap reluctance, stator tooth reluctance, stator yoke reluctance, and rotor core reluctance constituting the magnetic flux path were derived. Considering the motor’s geometric and electromagnetic characteristics, the magnetic flux and flux density distributions in each region were analytically evaluated. The obtained results were then validated through finite element analysis (FEA). In the second stage, the DSSR AFIR NN topology was investigated using the same methodology, and the electromagnetic performances of the two machines with identical slot numbers, pole numbers, and physical dimensions were compared to determine their respective advantages and limitations. Numerical analyses show the AFIR-NN topology yields higher torque (26.53 Nm) than AFIR-NS (19.43 Nm). Conversely, AFIR-NS exhibits superior magnetic characteristics, with higher back-EMF (34.73 V vs. 19.37 V) and air-gap flux density (0.89 T vs. 0.46 T). Full article

Task decomposition is a fundamental challenge in multi-agent collaborative maintenance systems, where unstructured natural language instructions must be precisely translated into logically coherent, executable sub-task sequences. This paper formulates task decomposition as a constrained optimal path search problem on a heterogeneous knowledge graph that encodes coal mine equipment topology, fault causality, and maintenance procedures. We construct a composite cost function that systematically integrates semantic similarity from graph neural network embeddings, relation-type weights, and structural path length, transforming instruction parsing into a mathematically tractable combinatorial optimization. The cost function is derived from the principles of shortest-path reasoning in knowledge graphs: the relational weights capture domain-specific association strengths, the semantic similarity term promotes contextually coherent chains, and the path-length penalty prevents unnecessarily long derivations. A multi-hop reasoning algorithm coupling heterogeneous graph convolution with beam search is developed to solve this problem efficiently, achieving high-quality approximate solutions while ensuring computational tractability. The reasoning process is inherently interpretable, as the optimal path directly maps to a traceable atomic task sequence with explicit dependency relations. A formal complexity analysis shows the algorithm scales as $O$($b$·$K$·$d_{max}$), where b is the beam width and d_max is the maximum node degree. Several theoretical properties of the proposed framework are further derived: the cost function is non-negative and strictly monotonic, optimal paths satisfy the optimal substructure property, cycle-free optimal paths always exist, and beam search can yield globally optimal solutions given a sufficiently large beam width. These theoretical conclusions establish mathematical guarantees for the presented decomposition framework. Experiments on 200 composite maintenance instructions with gold-standard annotations (inter-annotator agreement Cohen’s $\kappa =0.88$) demonstrate that the proposed method achieves 94.3% task sequence accuracy (95% CI: 91.2–96.8%) and 96.4% dependency accuracy (95% CI: 93.5–98.1%), substantially outperforming both a rule-augmented baseline (58.6%, 62.1%) and a GPT-4o few-shot chain-of-thought baseline (73.2%, 70.5%); McNemar’s test yields p < 0.001 for both comparisons. The average inference time is 29.7 ms (SD 2.1 ms), meeting stringent industrial real-time constraints. Ablation studies quantify the contribution of each cost function component and confirm the robustness of the chosen beam width and hyperparameters. When integrated into a full multi-agent system, the framework delivers end-to-end response time within 3 s (P50: 1.87 s, P95: 2.83 s) and maintains an 86.7% task success rate even under dual agent failures, validating the robustness of the proposed mathematical formulation. This work establishes a rigorous graph-theoretic foundation for instruction decomposition in multi-agent systems, with direct applicability to safety-critical industrial environments. Full article

Electrical machine design faces growing constraints from power density, wide operating ranges, thermal and mechanical limits, acoustics, manufacturability, cost, and critical material availability. While finite element and multi-physics simulations remain essential, their direct use in population-based or multi-objective optimization is often computationally prohibitive. AI, spanning surrogate modeling, machine learning, deep learning, physics-informed networks, Bayesian optimization, and emerging generative methods, is increasingly used to accelerate analysis, enlarge design spaces, and support inverse or multi-objective tasks. This review examines AI-assisted electrical machine design from a workflow perspective, distinguishing functional approximation, performance prediction, topology-aware learning, physics-informed modeling, active learning, and robust optimization under uncertainty. It highlights current limitations, including narrow topology coverage, reliance on FEM-generated data, weak extrapolation, limited uncertainty reporting, scarce experimental validation, and insufficient attention to manufacturability and sustainability. A design-readiness framework and minimum reporting checklist are proposed to improve trustworthiness and reusability. The review concludes that AI should serve as a physics-aware, validation-dependent accelerator, complementing, not replacing, electromagnetic expertise, multi-physics simulation, and prototype testing. Full article

Modern power transmission grids are increasingly operated under volatile load, variable generation, and near-limit line-flow conditions. In such environments, deterministic line-flow forecasting is insufficient for operational decision support because operators require calibrated risk probabilities, uncertainty intervals, and reliable early warning signals. This article proposes B-TGPRF, a Bayesian Temporal Graph Probabilistic Risk Forecaster for calibrated overload-risk forecasting in power transmission grids. The proposed framework is positioned as a hybrid probabilistic graph-temporal forecasting system rather than as a new end-to-end graph neural network; its novelty lies in the leakage-controlled sequential combination of temporal forecasting, electrical graph descriptors, calibrated Bayesian risk estimation, residual correction, and compact interval uncertainty assessment. The model integrates temporal line-flow, load, and generation features with graph-topological descriptors, operating-regime indicators, residual correction, conformal interval estimation, probability calibration, and a Bayesian risk layer. A leakage-controlled data preparation pipeline was built using an open large-scale benchmark for machine learning applications in transmission grids. The final modeling dataset contains more than 3.48 million observations, 100 selected critical lines, train-only risk thresholds, and chronological train, validation, test, and external-like scenario splits. B-TGPRF was compared with persistence baselines, linear models, Bayesian baselines, tree ensembles, boosting models, neural temporal models, and compact state-of-the-art-style temporal and graph-temporal architectures. On the strict external-like test, B-TGPRF achieved MAE = 0.3650, RMSE = 0.5478, R² = 0.9962, Brier Score = 0.0147, and ECE = 0.0064. The results show that the proposed model provides a strong overall balance between line-flow forecasting accuracy, calibrated risk estimation, compact interval prediction, and low false-positive risk-signaling, while boosting models remain highly competitive for pure risk-class detection. Full article

Triply periodic minimal surface (TPMS) lattices are an emerging class of cellular architectures whose smooth, mathematically defined surfaces enable highly tailorable mechanical performance. This study quantifies the compressive elastic response of three sheet-based TPMS topologies, i.e., Diamond, Gyroid, and Lidinoid, through finite element (FE) analysis and experimental validation. For each topology, the effective Young’s modulus was computed by single-cell finite element analysis under uniaxial compression boundary conditions on a 2 mm unit cell across five wall thicknesses (0.35, 0.45, 0.65, 0.95, and 1.30 mm), corresponding to relative densities from approximately 25% to 95%. Experimentally, cylindrical specimens of 316L stainless steel at 70% relative density were fabricated by laser powder bed fusion (LPBF) and tested in uniaxial compression with a strain rate of 10⁻³ s⁻¹ per ISO 13314:2011 (i.e., 0.02 mm/s). The Diamond topology exhibited the highest effective modulus across the full density range, followed by Lidinoid and Gyroid. FE predictions agreed with experimental moduli within 4.7% for Diamond (100.2 GPa vs. 95.5 ± 3.9 GPa), 0.04% for Gyroid (79.8 GPa vs. 79.8 ± 0.88 GPa), and 1.5% for Lidinoid (85.6 GPa vs. 86.9 ± 3.5 GPa). Moreover, the Gibson–Ashby exponents determined span the range from stretching- to bending-dominated deformation with n = 1.69 for Diamond, 1.74 for Lidinoid, and 2.08 for Gyroid. Single-cell FE analysis accurately captured the effective elastic response of LPBF 316L stainless steel TPMS lattices examined. Full article

In low-voltage distribution networks, load switching, induction motor start-up, photovoltaic output variations, and short-circuit faults may produce highly overlapping electrical characteristics, which can lead to maloperation or failure to operate in conventional protection. To address this problem, this paper proposes an adaptive protection method integrating physically guided and cost-sensitive learning. First, an incremental topology-constraint deviation and a voltage-current trajectory curvature are constructed based on the fault-superimposed network constraint and the variation characteristics of system equivalent impedance, enabling the discrimination of short-circuit faults from non-fault transient disturbances. Then, a cost-sensitive physically guided extreme gradient boosting (XGBoost) model is developed, in which a fault-current-increment-based weight is introduced into the objective function to enhance the learning capability for weak-fault samples. Furthermore, a temporal-consistency-based protection operation logic is designed using sliding-window confirmation and majority voting to suppress isolated abnormal predictions. Simulation and RTDS-based real-time validation results on a 0.4-kV low-voltage distribution network with distributed photovoltaic generation show that the proposed method improves weak-fault detection sensitivity and reduces maloperation under complex source–load disturbances. The method relies only on local measurements and has potential for deployment in low-voltage intelligent protection terminals. Full article

Myocardial ischemia and reperfusion initiate spatially organized injury–repair programs that subsequently shape ventricular remodeling. Although cardiac fibroblasts are indispensable for scar formation, single-cell and spatial transcriptomic studies reveal temporally dynamic and regionally distinct fibroblast-lineage states. This critical narrative review integrates direct evidence from myocardial ischemia–reperfusion (I/R) with model-labeled evidence from permanent myocardial infarction, clinically heterogeneous human infarction, fibroblast-specific ubiquitin biology, cell-cycle regulation, and cardiac fibroblast atlases. A direct fibroblast I/R study identifies an HSP47–USP10–SMAD4 deubiquitination axis, whereas most other fibroblast ubiquitin–proteasome system (UPS) mechanisms derive from permanent infarction, non-ischemic cardiac stress, or in vitro systems. We propose that CCNB1-associated, G2/M-enriched cycling fibroblast-lineage states may impose heightened proteostatic demands within defined post-ischemic niches. The conceptual novelty is not that CCNB1 turnover or UPS activity is cardiac-specific; both are general features of proliferating cells. Rather, the framework asks whether fibroblast lineage, injury-model provenance, anatomical niche, temporal window, cell state, and substrate-specific UPS nodes jointly define proteostatic dependencies during post-ischemic remodeling. RNA-based ubiquitin-related signatures remain transcriptional proxies and do not directly quantify ubiquitinated substrates, ubiquitin-chain topology, enzyme activity, or proteasome flux. Resolving the proposed relationships will require spatial colocalization, protein-level and ubiquitin-remnant profiling, proteasome and ribosome assays, fibroblast-specific perturbation, and validation in human infarct tissue. Full article

This paper presents a novel method for designing manufacturable porous infill structures using a Voronoi-based topology optimization framework. By integrating discrete Voronoi representations into density-based topology optimization in a differentiable manner, the method enables variable-thickness edge structures, with Euclidean distance fields generated from seed points. The material distribution and structural shape are determined by the seed point locations and the distance tensor, which serve as the design variables in this work. As the seed points are directly associated with the dual graph of the Voronoi diagram (VD), namely the Delaunay triangulation (DT), a constraint is formulated based on the DT to ensure the manufacturability of the infill structure. This is achieved by constraining all edge angles of the DT to satisfy the overhang requirement. Since 3D printers can fabricate overhanging structures up to a certain length, VD edges shorter than this threshold are exempt from the self-supporting constraint. To reduce the number of design variables and simplify the manufacturability constraint, a merging strategy is introduced to combine seed points that are sufficiently close during the optimization process. To ensure manufacturability of the outer surface, a set of seed points is additionally sampled on the outer boundary and kept fixed throughout optimization. The proposed method is validated on both regular and irregular 2D design domains, and the results demonstrate its capability to generate manufacturable porous infill structures with satisfactory mechanical performance. Full article

This paper presents a comprehensive review of control techniques, simulation mechanisms, and validation methods applied to DC-DC converters, with a focus on high-step-up topologies used in renewable energy systems such as photovoltaic and wind power applications. Control strategies including classical PID, fuzzy logic, sliding mode, and model predictive control (MPC) are analyzed in terms of performance, robustness, and implementation complexity. Simulation platforms and hardware-in-the-loop (HIL) validation frameworks are also discussed as key enablers for rapid prototyping. The findings reveal a clear trend toward intelligent and hybrid control schemes that combine nonlinear techniques with artificial intelligence to address the inherent nonlinearities and parametric uncertainties of DC-DC converters. However, challenges remain in real-time implementation due to computational demands, which drives the need for future developments focused on the (i) integration of AI-based controllers with low-cost embedded platforms, (ii) standardization of HIL-based validation workflows, and (iii) optimization of converter topologies for specific applications such as electric vehicle charging and photovoltaic grid integration. Looking forward, the convergence of advanced control algorithms, real-time validation platforms, and application-specific converter design is expected to define the next generation of power electronics systems, enabling more efficient, reliable, and scalable renewable energy integration. Full article

AI technologies are becoming operational components of urban infrastructure systems, including transport networks, structural health monitoring platforms, water utilities, energy systems, and public facilities. These systems support prediction, diagnosis, control, maintenance planning, and asset-management decisions across long service periods. However, much of the existing literature still evaluates infrastructure AI at the model-design or deployment-performance stage, with limited attention to post-deployment validity, operational degradation, update control, and end-of-life management. This survey examines AI applications in urban infrastructure from a lifecycle-management perspective, covering deployment, runtime monitoring, maintenance and adaptation, governance, and retirement. The review applies a PRISMA-guided search and screening protocol to classify retained studies by lifecycle phase, infrastructure domain, deployment evidence, monitoring strategy, adaptation mechanism, governance control, and benchmark support. The cross-domain analysis indicates that deployment-stage accuracy alone is not sufficient for long-term reliability assessment, because sensor wear, environmental variation, asset aging, data drift, maintenance intervention, topology change, and operating-regime shifts can alter model behavior after deployment. The findings further show that current research provides limited support for linking model outputs to maintenance actions, validating model updates under operational constraints, documenting governance evidence, estimating lifecycle cost, defining retirement criteria, and building shared lifecycle benchmarks. The survey concludes that urban infrastructure AI should be managed as a long-term socio-technical asset, with continuous validation, model-health monitoring, controlled adaptation, audit-ready governance, and retirement planning integrated into infrastructure operations. Full article

This paper presents a microwave sensing platform based on a 32-element dipole array designed for near-field dielectric contrast mapping. The sensor utilizes an $8\times 8$ tightly coupled dipole array (TCDA) topology, where pairs of dipoles form unit cells that exploit electromagnetic coupling variations. A 32-way equal power divider network ensures uniform excitation across the aperture. Operating at 830 MHz, the dipole array exhibits high absorption (>90%), which enhances near-field intensity and sensitivity to surface perturbations. Experimental validation with dielectric samples, saline liquids of varying concentrations (${ϵ}_r\approx$ 70–78), and biological tissues demonstrates the array’s capability to map spatial variations in electromagnetic properties through rectified DC voltage shifts. When compared to a state-of-the-art multi-port Vector Network Analyzer (VNA) configurations, the proposed architecture offers a robust, low-complexity, proof-of-concept alternative by eliminating complex RF routing networks and multi-port switches. Full article

Optical remote sensing object detection faces challenges such as large variations in scale, slender and direction-sensitive targets, complex backgrounds, and limited deployment resources. This paper proposes a lightweight geometrically decoupled student network with a dynamic multi-teacher distillation framework. Based on YOLO11n, the student detector keeps the original classification branch while redesigning the regression branches at different scales. Lightweight regression towers are used for the shallow and deep branches, whereas a geometrically decoupled regression tower is introduced only at the intermediate branch to enhance localization for slender and orientation-sensitive objects with limited extra cost. A geometry-adaptive box loss is further employed to stabilize localization training. For knowledge transfer, three specialized teachers are constructed for semantic classification, geometric regression, and structural topology supervision. A branch-decoupled adaptive weighting strategy dynamically integrates their complementary knowledge for classification and regression distillation. Experiments on DIOR show that the proposed model reduces parameters by 6.8% and GFLOPs by 13.9%, while improving mAP50 by 0.23 percentage points over YOLO11n. Validation on NWPU VHR-10 and deployment tests using PT, ONNX, and TensorRT further demonstrate improved accuracy–efficiency trade-offs and practical inference acceleration. Full article