Search Results (891)

The structure, relative stability, spin-state preference, and preliminary oxygen-release behavior of small nickel–oxygen clusters, (NiO₂)_n (n = 1–4), were investigated using density functional theory at the M06-2X/def2-TZVP level of theory. Several initial topologies and spin multiplicities were explored to distinguish between dissociated Ni···O₂ solutions, bonded dioxo-like arrangements, and side-on metal–dioxygen motifs. For the monomer, the lowest-energy solution of the fully explored set corresponds to a non-bonded Ni···O₂ arrangement; however, when the analysis is restricted to chemically bonded NiO₂ minima, the linear high-spin O–Ni–O structure is the most stable configuration. The side-on η²-O₂ motif was found as a higher-energy bonded minimum, retaining an elongated O–O bond and therefore representing an activated dioxygen-like species. ELF and LOL analyses were used as complementary localization descriptors to distinguish between the electronically separated oxo-like domains of the linear structure and the more coupled localization pattern of the side-on dioxygen adduct. Aggregation from n = 2 to n = 4 suggests a transition from compact bridged motifs to more open Ni–O frameworks. However, the size-dependent trend is discussed only within the explicitly explored conformational space. Preliminary analysis of O₂ release from the tetramer indicates that oxygen evolution is not a simple dissociation event but involves substantial structural reorganization. Overall, the results support the view that small (NiO₂)_n clusters may behave as metastable oxygen-rich intermediates, while also highlighting the strong sensitivity of their energetic ordering to spin state, topology, and structural relaxation. Full article

As the global energy transition accelerates, distribution systems are integrating increasing shares of inverter-interfaced renewables, making reliable voltage support a key operational requirement. In grid-connected microgrids, especially weak radial feeders in rural and remote areas, voltage-reactive power (Volt/Var) control must coordinate multiple inverters under uncertainty from photovoltaic (PV) intermittency, load volatility, and point-of-common-coupling (PCC) disturbances. Existing droop, model-based optimization, and non-graph reinforcement learning (RL) approaches often rely on fixed rules or do not explicitly exploit electrical topology, which limits adaptive coordination. To address this gap, we propose a topology-aware graph reinforcement learning framework for voltage-reactive power control in grid-connected microgrids under uncertainty. The method encodes node states with a graph convolutional network (GCN) and learns coordinated PV/storage reactive-power actions via proximal policy optimization (PPO) with a multi-objective reward balancing voltage quality, control effort, and action smoothness. In a controlled comparison against a multilayer perceptron (MLP)-PPO baseline with identical action space, reward, and PPO objective, our method reduces voltage violation rate (VVR) from 0.0316 ± 0.0086 to 0.0048 ± 0.0019. Additional validation on a modified IEEE 33-bus feeder further reduces VVR from 0.00726 for MLP-PPO and 0.02999 for Droop control to 0.00095, supporting the effectiveness of topology-aware state representation on a larger radial benchmark feeder. Full article

Meeting the rigorous performance standards of modern electrified transit necessitates the deployment of high-performance outer rotor PMSMs with elevated power-to-volume ratios. However, their unique internal heat source topology inherently restricts heat dissipation. This limitation risks permanent magnet demagnetization and winding insulation failure. To address these thermal bottlenecks, this paper proposes internal bio-inspired cooling channels. These channels feature micro-scale V-shaped ribs. This design targets a 60 kW outer rotor PMSM. The motor uses a fractional-slot concentrated winding. The analytical procedure commences with the formulation of a transient 2D numerical model utilizing the Time-Stepping Finite Element approach (TS-FEM). It is coupled with the Bertotti model to compute electromagnetic losses. This approach accurately determines losses under high-frequency rated conditions. Results reveal that stator iron loss constitutes the dominant heat source. It accounts for 76.4 percent of the total electromagnetic loss. Furthermore, these losses show severe spatial concentration at the stator teeth. Subsequently, a three-dimensional fluid-solid coupled CFD model is developed. This model evaluates the proposed internal cooling channels. The design integrates bio-inspired vein networks and V-shaped ribs. These internal ribs disrupt the near-wall thermal boundary layer. This disruption enhances the local convective heat transfer. Comparative multiphysics analyses indicate improved hydraulic and thermal performance of the bio-inspired design under the same numerical boundary conditions. The bio-inspired channel achieves a more uniform static pressure distribution and reduces severe fluid stagnation zones. In the numerical model, the maximum stator and permanent magnet temperatures are reduced to 48 °C and 42 °C, respectively. This work provides a numerical design reference for thermal management in high-performance electric aviation. Full article

The transition toward renewable-dominated power systems introduces significant complexity and nonlinearity, rendering traditional mechanism-based modeling computationally prohibitive for real-time security assessment. While data-driven approaches offer computational efficiency, they fundamentally lack physical interpretability and often exhibit generalization failures under rare, large-signal disturbances due to the absence of intrinsic physical constraints. To bridge this gap, this paper proposes a Physics-Guided Symbolic Regression (PGSR) framework for constructing interpretable and robust dynamic equivalent models. The methodology embeds domain knowledge via topological masks and dimensional consistency rules to restrict the evolutionary search space to physically admissible manifolds. A multi-resolution extraction strategy based on the Pareto frontier is developed to autonomously identify both linear small-signal models and nonlinear large-signal formulations adaptable to varying analytical requirements. Furthermore, a post hoc verification stage based on Lyapunov stability theory ensures the dynamic validity and energy dissipation properties of the generated equations. A case study on the WSCC 9-bus system demonstrates that the proposed method accurately recovers the underlying Taylor-series structure of swing equations and significantly outperforms four data-driven baselines—including polynomial, kernel, and neural network models—in out-of-distribution generalization, achieving 12–42× lower trajectory error under unseen large perturbations. Full article

High-fidelity computational fluid dynamics (CFD) remains computationally expensive for steady aerodynamic prediction under multi-condition and variable-geometry configurations, which limits rapid design iteration. To address this issue, this study proposes a data-driven surrogate framework for aircraft surface flow-field prediction on irregular meshes. The framework combines a geometry-unification strategy for variable rudder-deflection configurations with KM-GAT, a hybrid neural architecture that integrates graph attention and KAN-based nonlinear feature transformation. Geometry unification maps the surface flow fields associated with different rudder-deflection states onto a common zero-deflection reference template, thereby establishing consistent mesh correspondence and fixed prediction locations across samples while retaining the rudder angle as an operating-condition variable. The KM-GAT model further combines topology-aware message passing with localized nonlinear refinement, while the Huber loss is adopted to improve training robustness for CFD-derived data. Experiments on the F-22 research model show that the proposed framework achieves lower prediction errors and more concentrated error distributions than baseline MLP and GNN-based models. Qualitative comparisons further indicate that KM-GAT better preserves localized high-gradient structures, including pressure transitions and vortex-dominated regions. These results suggest that the proposed framework provides an effective surrogate modeling strategy for variable-geometry aerodynamic flow field prediction. Full article

Traditional white canes offer a limited perception range, whereas end-to-end visual models face challenges in real-time deployment on edge devices. To address these limitations, this paper proposes a lightweight real-time assistive system that integrates geometric topology reconstruction with state-adaptive warning logic. The system utilizes YOLOv9 to extract discrete semantic primitives of tactile paving. It constructs a dual-branch perception framework based on Median Absolute Deviation and the Minimum Spanning Tree algorithm to analyze the topological structure of tactile paving. For complex intersections characterized by warning indicators, a one-dimensional connectivity clustering algorithm based on longitudinal topology is proposed. It generates accurate macroscopic feasible directional prompts under field-of-view boundary constraints. Additionally, a hierarchical scheduling framework dynamically orchestrates scenario-specific finite state machines to enable continuous dynamic interaction across typical high-risk scenarios. Evaluated on a custom real-world dataset, the system achieves a 95.21% frame-level comprehensive accuracy for straight-path deviation correction and intersection directional prompting. Dynamic temporal stress tests confirm the temporal stability and logical coherence of state transitions. Furthermore, latency evaluations demonstrate the logic layer’s minimal computational overhead, proving its theoretical feasibility for real-time edge deployment. This approach provides an effective, low-latency solution for delivering directional prompts and hazard warnings to visually impaired users. Full article

The bus system plays an important role in the urban public transportation infrastructure system, providing a convenient way for the masses to travel. However, the operational resilience and functional stability of urban transit systems are frequently jeopardized by a variety of internal disruptions and external emergencies. Therefore, it is important to evaluate the robustness of urban bus networks. Based on the complex network theory, this research applies Space L and Space R methods to construct the bus stop network and bus line network models in Jinan, China. The topological characteristics of the two network models are studied, and the network robustness is analyzed using two attack strategies: random attack and deliberate attack. The robustness is optimized based on the network edge addition strategy. The results show that: (1) The bus stop network has a scale-free network property, but the bus stop network and the bus line network do not have the small-world network property. (2) The bus line network is more robust than the bus stop network when under attack, and the network under deliberate attack is more vulnerable than that under random attack. The maximum betweenness centrality node attack causes the most significant damage to the network. (3) Under random attack, both high betweenness centrality edge addition (HBA) and high degree edge addition (HDA) strategies are more effective at optimizing network robustness; under maximum degree node attack, both low betweenness centrality edge addition (LBA) and low degree edge addition (LDA) strategies are more effective on optimizing network robustness; under maximum betweenness centrality node attack, the LBA strategy has the best effect on optimizing network robustness. The research results can provide scientific guidance for the emergency scheduling and line optimization of urban public transportation system. Full article

Many machine-learning tasks involve structured data whose geometry, local feature distributions, and global organization interact in ways that are not well captured by existing methods based on vectorization, graph metrics, or homological signatures. We introduce Fiber Bundle Learning (FBL), a topological framework that represents each data sample as a discrete fiber bundle and extracts a classification signature combining persistent homology, local feature geometry, and gluing structure. FBL builds a base space from the coarse geometry of each object, models local feature patches as fibers, and estimates transition maps between neighboring fibers to construct a discrete connection. From this representation, FBL computes a set of invariants: persistent homology of the base, fibers, and total space; holonomy obtained by transporting fiber states along cycles; curvature-like quantities measuring transition inconsistency; and discrete analogues of characteristic classes. These components are assembled into a fixed-length feature vector that can be used with any standard classifier. We show that FBL yields a signature with three desirable theoretical properties: stability under perturbations of geometry and local features, invariance under isometries and global fiber reparameterizations, and robustness to sampling noise. Our synthetic experiments show that FBL distinguishes twisted from untwisted bundles with identical homology, a distinction classical topological methods fail to capture. Additional tests quantify the system’s resistance to noise, its invariance to geometric transformations, and the contribution of each signature component. Taken together, our results indicate that representing data through fiber bundle structure may provide an effective tool for classifying complex, multi-level objects. Full article

Amid the global transition toward sustainable energy systems, the hybrid energy storage system (HESS) plays a vital role due to its combined advantages of high energy density and high power density. However, distributed HESSs in islanded microgrids still lack effective management strategies for handling complex and abnormal operating conditions, which may compromise system stability. Therefore, this paper proposes a coordinated control and management strategy for distributed HESSs based on grid-forming (GFM) converters. First, a dynamic following decoupling algorithm based on actual power anchoring is proposed to eliminate the reverse active power regulation phenomenon during the initial transient period while enabling the frequency restoration process and the power transfer process to be completed independently. Second, to address communication interruptions in the multi-agent system, a communication weight update mechanism and a local degraded control strategy are designed to ensure that the system can still operate stably when communication is disconnected. Furthermore, through an information relay mechanism, a faulty converter is redefined as an information relay node to maintain the global communication topology of the multi-agent system under converter fault conditions. Finally, hardware-in-the-loop (HIL) experiments validate the effectiveness of the proposed control strategy, demonstrating its ability to enhance microgrid resilience and sustainability. Full article

Control subarea division is essential for coordinated signal control, but methods based mainly on local correlation or static topology may not adequately capture traffic-state propagation under dynamic traffic loading. This study proposes a control subarea division method that explicitly models traffic-state propagation by integrating state-guided colored random walk and path entropy analysis. Intersection correlation degree and traffic state are used to construct a state-guided colored random walk process, in which transition probabilities are updated according to network connectivity and traffic-state consistency. Path entropy characterizes propagation uncertainty, and control subareas are identified by minimizing the distribution discrepancy between node-level and subarea-level path responses. To compare partitioning schemes, five complementary metrics were adopted: variance reduction rate of spatial delay, delay reduction rate, congestion mitigation index, stop reduction rate, and queue reduction rate. A VISSIM microsimulation model with dynamic traffic loading was developed to compare the proposed method with the Whitson and Fast Newman methods. The proposed method achieved the best performance across all five metrics, with values of 41.47%, 23.77%, 25.96%, 23.59%, and 15.08%, respectively. These results indicate that the proposed method improves spatial balance and network efficiency while mitigating bottlenecks, reducing stops, and suppressing queue accumulation. Full article

The study of conformational spaces and potential energy surface (PES) functions is fundamental for understanding the structural and dynamical properties of molecules with one or more rotational degrees of freedom. In this work, the topological characteristics of conformational spaces and PES functions are investigated for a set of molecules including ethane, butane, and butadiene, which possess one rotational degree of freedom, as well as n-pentane with two rotational degrees of freedom. Sublevel-set persistent homology was applied to the potential energy functions in order to characterize the topology of the associated energy landscapes. This approach allows for the identification of topological changes during the sublevel filtration process, which can be associated with the presence of critical points in the energy landscape, including minima (index 0), transition states (index-1), and maxima (index-2). Furthermore, the method provides information about the global connectivity and structural organization of the conformational landscape. The results show that sublevel-set persistent homology successfully reproduces the energy hierarchy and connectivity between molecular conformers, providing a coherent topological description of the molecular energy landscape. These findings demonstrate that persistent homology constitutes a useful framework for studying the topology of conformational spaces and potential energy surfaces in molecular systems. Full article

Preference cycles are prevalent in problems of decision-making, and they are contradictory when preferences are assumed to be transitive. This contradiction underlies Condorcet’s Paradox, a pioneering result of social choice theory, wherein intuitive and seemingly desirable constraints on decision-making necessarily lead to contradictory preference cycles. Topological methods have since broadened social choice theory and elucidated existing results. However, characterisations of preference cycles in topological social choice theory are lacking. In this paper, we address this gap by introducing a framework for topologically modelling preference cycles that generalises Baryshnikov’s existing topological model of strict, ordinal preferences on three alternatives. In our framework, the contradiction underlying Condorcet’s Paradox topologically corresponds to the non-orientability of a surface homeomorphic to either the Klein bottle or real projective plane depending on how preference cycles are represented. These findings allow us to reformulate Arrow’s Impossibility Theorem in terms of the orientability of a surface as well. Full article

We examine the growth of Internet Protocol version 6 (IPv6) routing state from 2010 to 2025. The global IPv4 address space has been exhausted, and the transition to IPv6 is ongoing. Using publicly accessible data from the RIPE Route Collectors (RRCs), we show that growth in the number of globally visible IPv6 routing prefixes follows different models over time, reflecting different growth patterns: exponential, power-law, and stretched-exponential. In addition to building models using publicly available RIPE data, we use this data source to demonstrate that our analysis holds across different Internet Exchange Points (IXPs) around the world and has predictive value. We provide in-depth analyses of IPv6 routing state growth, and we believe these are the first such analyses. Additionally, we highlight previous similar analyses of other aspects of network characteristics (such as topology and network traffic), and show that our analyses provide new insights. Specifically, we show the following: (1) previous models that have worked well for other network characteristics do not work well for routing state; (2) growth patterns for IPv6 routing state have changed significantly over time; (3) growth patterns cannot be described by a single model, and need to be analysed in a piecewise fashion; (4) fitting of previous data might not necessarily result in good predictive quality, and we identify the factors that may affect the predictive quality of a model and the predictive models that are suitable at the current time. Our analyses include metrics for assessing model fit. Overall, we observe a decrease in the rate of growth of IPv6 routing state, while the overall use of IPv6 continues to grow. We provide a critical evaluation of our approach, and also discuss possible factors affecting the growth of global IPv6 routing state. Full article

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) Union Hospital Station as an example, this study constructs a “topology-climate” dual quantitative assessment framework that integrates space syntax and parametric universal thermal climate index (UTCI) simulation. In response to the current problems of mixed pedestrian and vehicular traffic and high-intensity heat radiation, a comprehensive intervention strategy combining three-dimensional stitching and spatial optimization is proposed. The results show that: (1) The implantation of three-dimensional corridors improved the spatial integration of the core area of the site by 67.0%, significantly optimizing network connectivity. (2) During the extreme high-temperature period of daytime (9:00–18:00) in summer and autumn, the intervention strategy precisely opened up a continuous low-heat-stress linear shade zone through the synergistic mechanism of building projection shadows, physical shading of connecting corridors, (landscape shading effect, original evaporation removed). (3) The study confirms that landscape-coupled shading layout is the most effective method, reducing potential pedestrian heat exposure across the entire area, while the three-dimensional connecting corridors precisely control the thermal environment of core walkways. Together, these two elements construct a “topology-climate” optimization framework, achieving a synergistic improvement in spatial accessibility and simulated thermal comfort performance under standard meteorological input and quantitatively verifying the optimization effectiveness of the tiered intervention scheme. This study provides a data-driven decision-making basis for optimizing potential walking thermal conditions for vulnerable groups and reshaping the space’s potential to improve microclimate via shading design of medical hub areas and also provides a scientific paradigm for TOD microclimate planning focused on shading-based thermal environment optimization. Full article

Additively manufactured cellular architectures frequently exhibit brittle failure under impact due to layer-induced stress concentrations. Through the programming of architectural and material design, specifically combining Fused Deposition Modeling (FDM) lattice topology with hyperelastic polyurea infiltration, this study achieves active control over the macroscopic transition from catastrophic structural fragmentation to stable progressive collapse. To evaluate this, auxetic and honeycomb specimens printed with ABS and glass-fiber-reinforced polyamide (PA-GF) were evaluated in unreinforced and polyurea-infiltrated states under quasi-static compression, three-point bending, and Charpy impact loading. Results show that the compressive response depends primarily on cellular topology; the pure auxetic (A-A) configuration provided the highest stiffness and energy absorption. Polyurea infiltration did not significantly alter elastic stiffness but increased post-yield stability, leading to a 96.6% elastic recovery in PA-GF A-A structures. In flexure, the base polymer governed stiffness, with ABS structures measuring 68% stiffer than PA-GF. Unreinforced ABS achieved 34% higher specific energy absorption (SEA) than PA-GF under compression, with the A-H topology maximizing SEA. Under dynamic impact, PA-GF absorbed an average of 70% more energy than ABS, and the H-A configuration recorded the highest impact resistance. The addition of polyurea shifted the failure mode from brittle fragmentation to stable elastomeric deformation, increasing absorbed impact energy by 52% for ABS and over 30% for PA-GF, preventing catastrophic structural failure. Integrating topological sequencing with elastomeric confinement provides a direct method to control energy dissipation and damage tolerance in 3D-printed cellular composites. Full article