Search Results (1,545)

Multiple-Q magnetic states encompass a broad class of noncollinear and noncoplanar spin textures generated by the superposition of spin density waves. In this study, we theoretically explore the emergence of vortex crystals formed by multiple-Q spin density waves on a two-dimensional triangular lattice with $D_{3\mathrm{h}}$ point group symmetry. Using simulated annealing applied to an effective spin model, we demonstrate that the synergy among the easy-plane single-ion anisotropy, the biquadratic interaction, and the out-of-plane Dzyaloshinsky–Moriya interaction defined in momentum space can give rise to a variety of double-Q and triple-Q vortex crystals. We further examine the role of easy-plane single-ion anisotropy in triple-Q vortex crystals and show that weakening the anisotropy drives topological transitions into skyrmion crystals with skyrmion numbers $\pm 1$ and $\pm 2$. The influence of an external magnetic field is also analyzed, revealing a field-induced phase transition from vortex crystals to single-Q conical spirals. These findings highlight the crucial role of out-of-plane Dzyaloshinskii–Moriya interactions in stabilizing unconventional vortex crystals, which cannot be realized in systems with purely polar or chiral symmetries. Full article

When a city decides to undertake a certain urban project, is it modifying just the physical environment or the social fabric that dwells within? This work investigates the relationship between the geometric configuration of urban space (geometry–city) and the topology of the networks of encounters of its inhabitants (network–city) that form through daily interactions. The research departs from the hypothesis that changes in geometry–city would not significantly alter the topology of the network–city, testing this proposition conceptually through abstract computational simulations developed specifically for this study. In this simulator, abstract maps with buildings distributed over different primary geometries are generated and have activities (use: home or work) and a population assigned. Encounters of the “inhabitants” are registered while daily commute routines, enough to achieve differentiation and stability, are run. The initial results revealed that the geometry description was not enough, and definitions regarding activity attribution were also necessary. Thus, we could not confirm nor reject the original hypothesis exactly, but it had to be complemented, including the idea of an activity–city dimension. We found that despite the geometry–city per se not determining the structure of the network–city, the spatial (geometric) distribution of activities directly impacts the resulting topology. Urban geometry influences networks–city only insofar as it conforms to activity–city, defining areas for activities or restricting routing between them. But it is the geometry of localization of the activities that has a direct impact on the topology of the network–city. This conceptual discovery can have significant implications for urban planning if corroborated in real-world situations. It could suggest that land use policies may be more effective for intervening in network-based characteristics, like social cohesion and resilience, than purely morphological interventions. Full article

Wave propagation in periodic media is governed by energy–momentum relations and geometric phases characterizing band topology, such as Zak phase in one-dimensional lattices. We demonstrate that, in the off-diagonal trimer lattices, Zak phase carries quantized screw-type dislocations winding around degeneracies in parameter space. If the lattice evolves in time periodically, as in adiabatic Thouless pumps, the corresponding closed trajectory in parameter space is characterized by a Chern number equal to the negative total winding number of Zak phase dislocations enclosed by the trajectory. We discuss the correspondence between bulk Chern numbers and the edge states in a finite system evolving along various pumping cycles. Full article

Low-emissivity (Low-E) glass, crucial for thermal insulation, significantly attenuates wireless signals, hindering 5G communication. Metasurface technology offers a solution, but the existing designs often neglect the etching ratio constraint and lack physical interpretability. In this work, we propose a physically constrained search method that incorporates prior knowledge of the capacitive equivalent circuit to guide the design of metasurfaces on Low-E glass. First, the equivalent circuit type of the metasurface is determined as a capacitive structure through transmission line model analysis. Then, a random walk-based search is conducted within the solution space of topological patterns corresponding to capacitive structures, ensuring etching ratio constraints and maintaining structural continuity. Using this method, we design a metasurface pattern optimized for 5G communication, which demonstrates over 30 dB improvement in signal transmission compared with full-coating Low-E glass. A fabricated 300 mm × 300 mm prototype, etched with a ratio of 19.5%, demonstrates a minimum transmission loss of 2.509 dB across the 24–30 GHz band with a 3 dB bandwidth of 4.28 GHz, fully covering the 5G n258 band (24.25–27.5 GHz). Additionally, the prototype maintains a transmission coefficient reduction of no more than 3 dB under oblique incidence angles from 0° to 50°, enabling robust 5G connectivity. Full article

We consider first-order impulses for impulsive stochastic differential equations driven by fractional Brownian motion (fBm) with Hurst parameter $H\in ({\displaystyle \frac{1}{2}},1)$ involving a nonlinear $\varphi$-Laplacian operator. The system incorporates both state and derivative impulses at fixed time instants. First, we establish the existence of at least one mild solution under appropriate conditions in terms of nonlinearities, impulses, and diffusion coefficients. We achieve this by applying a nonlinear alternative of the Leray–Schauder fixed-point theorem in a generalized Banach space setting. The topological structure of the solution set is established, showing that the set of all solutions is compact, closed, and convex in the function space considered. Our results extend existing impulsive differential equation frameworks to include fractional stochastic perturbations (via fBm) and general $\varphi$-Laplacian dynamics, which have not been addressed previously in tandem. These contributions provide a new existence framework for impulsive systems with memory and hereditary properties, modeled in stochastic environments with long-range dependence. Full article

Border surveillance, as a critical component of national security, places increasingly stringent demands on the target perception capabilities of video monitoring systems, especially in wide-area and complex environments. To address the limitations of existing systems in low-confidence target detection and multi-camera collaboration, this paper proposes a novel visual enhancement method for cooperative control of multiple PTZ (Pan–Tilt–Zoom) cameras based on hierarchical reinforcement learning. The proposed approach establishes a hierarchical framework composed of a Global Planner Agent (GPA) and multiple Local Executor Agents (LEAs). The GPA is responsible for global target assignment, while the LEAs perform fine-grained visual enhancement operations based on the assigned targets. To effectively model the spatial relationships among multiple targets and the perceptual topology of the cameras, a graph-based joint state space is constructed. Furthermore, a graph neural network is employed to extract high-level features, enabling efficient information sharing and collaborative decision-making among cameras. Experimental results in simulation environments demonstrate the superiority of the proposed method in terms of target coverage and visual enhancement performance. Hardware experiments further validate the feasibility and robustness of the approach in real-world scenarios. This study provides an effective solution for multi-camera cooperative surveillance in complex environments. Full article

We present a unified framework for fuzzy statistical convergence of Grünwald–Letnikov (GL) fractional differences in Bag–Samanta fuzzy normed linear spaces, addressing memory effects and nonlocality inherent to fractional-order models. Theoretically, we establish the uniqueness, linearity, and invariance of fuzzy statistical limits and prove a Cauchy characterization: fuzzy statistical convergence implies fuzzy statistical Cauchyness, while the converse holds in fuzzy-complete spaces (and in the completion, otherwise). We further develop an inclusion theory linking fuzzy strong Cesàro summability—including weighted means—to fuzzy statistical convergence. Via the discrete Q-operator, all statements transfer verbatim between nabla-left and delta-right GL forms, clarifying the binomial GL↔discrete Riemann–Liouville correspondence. Beyond structure, we propose density-based residual diagnostics for GL discretizations of fractional initial-value problems: when GL residuals are fuzzy statistically negligible, trajectories exhibit Ulam–Hyers-type robustness in the fuzzy topology. We also formulate a fuzzy Korovkin-type approximation principle under GL smoothing: Cesàro control on the test set $\{1,x,x^2\}$ propagates to arbitrary targets, yielding fuzzy statistical convergence for positive-operator sequences. Worked examples and an engineering-style case study (thermal balance with memory and bursty disturbances) illustrate how the diagnostics certify robustness of GL numerical schemes under sparse spikes and imprecise data. Full article

Current cloud–edge collaboration collaboration architectures face challenges in security resource scheduling due to their mostly static nature, which cannot keep up with real-time attack patterns and dynamic security needs. To address this, this paper proposes a dynamic scheduling method using Deep Reinforcement Learning (DQN) and SRv6 technology. The method establishes a multi-dimensional feature space by collecting network threat indicators and security resource states; constructs a dynamic decision-making model with DQN to optimize scheduling strategies online by encoding security requirements, resource constraints, and network topology into a Markov Decision Process; and enables flexible security service chaining through SRv6 for precise policy implementation. Experimental results demonstrate that this approach significantly reduces security service deployment delays (by up to 56.8%), enhances resource utilization, and effectively balances the security load between edge and cloud. Full article

This paper establishes a functorial algebraic isomorphism between the moduli space ${\mathcal{B}}_{\mathcal{C}}^{\mathrm{ps}}(\Sigma ,G)$ of polystable principal G-bundles with prescribed monodromy on a punctured Riemann surface $\Sigma$ of genus $g\ge 2$, for a complex reductive Lie group G, and the character variety ${\mathcal{M}}_{\mathcal{C}}^K({\Sigma }^{*},G)$ of representations of its fundamental group with relatively compact image. The dimension formula $dim{\mathcal{B}}_{\mathcal{C}}^{\mathrm{ps}}(\Sigma ,G)=2(g-1){dim}_{\mathbb{C}}\left(G\right)+{\sum }_{i=1}^k{dim}_{\mathbb{R}}\left({\mathcal{C}}_i\right)$, where ${\mathcal{C}}_1,\dots ,{\mathcal{C}}_k$ are conjugacy classes in a maximal compact subgroup $K\subset G$, is derived for complex reductive Lie groups, and singularities are characterized as polystable bundles with non-trivial automorphism groups. As applications of the above geometric results to control theory, it is proved that topologically distinct polystable robotic navigation strategies around obstacles are classified by this character variety. The geometry of singular points in families of polystable control strategies is further investigated, revealing enhanced stability properties characterized by reduced tangent space dimensions arising from non-trivial automorphism groups. Full article

The extraction of architectural structural line features can simplify the 3D spatial representation of built environments, reduce the storage and processing burden of large-scale point clouds, and provide essential geometric primitives for downstream modeling tasks. However, existing 3D line extraction methods suffer from incomplete and fragmented contours, with missing or misaligned intersections. To overcome these limitations, this study proposes a patch-level framework for 3D line extraction and structural optimization from building point clouds. The proposed method first partitions point clouds into planar patches and establishes local image planes for each patch, enabling a structured 2D representation of unstructured 3D data. Then, graph-cut segmentation is proposed to extract compact boundary contours, which are vectorized into closed lines and back-projected into 3D space to form the initial line segments. To improve geometric consistency, regularized geometric constraints, including adjacency, collinearity, and orthogonality constraints, are further designed to merge homogeneous segments, refine topology, and strengthen structural outlines. Finally, we evaluated the approach on three indoor building environments and four outdoor scenes, and experimental results show that it reduces noise and redundancy while significantly improving the completeness, closure, and alignment of 3D line features in various complex architectural structures. Full article

With the rapid growth in satellite types and numbers in space information networks, accurate and fast inter-satellite link prediction has become a core requirement for topology modeling and capability evaluation. However, the current space information networks are characterized by large scales and the coexistence of multi-orbit satellites, posing dual challenges to inter-satellite link prediction. Link state prediction demands higher accuracy with limited computing power, while diverse satellite communication antenna loads require stronger generalization to adapt to different scenarios. To address these issues, this paper proposes a fully connected neural network model based on Bayesian optimization. By introducing a weighted loss function, the model effectively handles data imbalance in the link states. Combined with Bayesian optimization, the neural network hyperparameters and weighted loss function coefficients are fine-tuned, significantly improving the prediction accuracy and scene adaptability. Experimental results show that the BO-FCNN model exhibited an excellent performance on the test dataset, with an F1 score of 0.91 and an average accuracy of 93%. In addition, validation with actual satellite data from CelesTrak confirms the model’s real-world performance and its potential as a reliable solution for inter-satellite link prediction. Full article

The suspension arm is a crucial connecting component in the automotive powertrain system, required to withstand various working condition loads, thus necessitating high mechanical performance. With the continuous development of forming processes, the forming method of suspension arms has gradually shifted from traditional gravity casting to squeeze casting. Along with the demand for automotive lightweighting, there is an urgent need for lightweight requirements in suspension arm components. This study employs a multi-condition topology optimization method, incorporating the forming requirements of the squeeze casting process, to conduct lightweight design of a certain mounting bracket. The filling and solidification processes were numerically simulated using Anycasting, followed by mechanical property testing and microstructure analysis of the product. The results revealed that the topology-optimized suspension arm met the strength and stiffness requirements under all working conditions, with a mass reduction of approximately 54.7% compared to the pre-optimized version. Based on the forming process analysis of the suspension arm, the design of its squeeze casting mold was completed. Using AnyCasting software (AnyCasting 6.7), numerical simulations of the filling and solidification processes of the suspension arm were conducted. Combined with theoretical calculations, the forming process parameters for the suspension arm were finally determined as follows: extrusion speed of 15 cm/s-10 cm/s-5 cm/s (multi-stage speed), pouring temperature of 690 °C, mold temperature of 250 °C, extrusion pressure of 81.4 MPa, and holding time of 45 s. Through T6 heat treatment, the tensile strength, yield strength, and elongation after fracture of the suspension arm reached 326.05 MPa, 276.87 MPa, and 9.68%, respectively. Metallographic analysis showed that the eutectic silicon in the T6 heat-treated specimens was primarily spherical in shape, uniformly distributed without significant clustering. The reason for this difference may be that heat treatment affects the boundary dissolution degree of alloying elements. For eutectic Al-Si alloys, the boundary dissolution and diffusion of alloying elements are accelerated, which is beneficial for improving the mechanical properties of the alloy. Finally, in order to quantitatively analyze the microstructural properties of the material after heat treatment, analyses of secondary dendrite arm spacing and porosity were conducted, leading to the conclusion that the microstructure after heat treatment is more uniform and dense. Full article

This paper introduces weak variants of level convergence (L-convergence) and epigraph convergence (E-convergence) for nets of level functions on general topological spaces, extending the classical metric and real-valued frameworks to ordered codomains and generalized minima. We show that L-convergence implies E-convergence and that the two notions coincide when the limit function is level-continuous, mirroring the relationship between strong and weak variational convergence. In Hausdorff topological groups, we define robust level functions and prove that every level function can be approximated by robust ones via convolution-type operations, enabling perturbation-resilient modeling. These results both generalize and connect to $\Gamma$-convergence: they recover the classical metric, lower semicontinuous case, and extend the scope for optimization on Lie groups, fuzzy systems, and mechanics in non-Euclidean spaces. An explicit nonmetrizable example demonstrates the relevance of our theory beyond the reach of $\Gamma$-convergence. Full article

For the issue of excessive mass in the chain drive system of long-distance scraper conveyors, this paper proposes a method to optimize the scraper chains by integrating discrete element simulation (DEM) with topological optimization. The aim is to reduce the system’s mass while maintaining its transportation capacity and structural integrity. The SGZ1000 model scraper conveyor with a length of 400 m was selected as the research object. Studies have demonstrated that for 56 × 187 mm scraper chains, a non-equally spaced configuration (6p-8p-6p, where p represents the chain link pitch) outperforms an equally spaced configuration (6p). While ensuring the conveying capacity of the scraper chains, the optimized configuration reduces the number of scrapers in chains of equal length by 11.11%. For a 400 m scraper conveyor, adopting the 6p-8p-6p scraper spacing reduces the number of scrapers by 72 and decreases the mass by 6357.6 kg. Additionally, utilizing topologically optimized scrapers further reduces the total mass by 10,131.4 kg. Compared to the original chain drive system, the optimized scraper chains have reduced the mass by 26.2%, significantly lowering the no-load energy consumption of the long-distance scraper conveyor. Full article

Power converters play an important role in incorporating renewable energy sources into power systems. Among different converter designs, Buck and Boost converters are popular, as they use fewer components and deliver cost savings and high efficiency. However, Boost converters are known as non–minimum phase systems, imposing harder constraints for designing a robust converter. Developing an efficient controller for these topologies can be difficult since they exhibit nonlinearity and distortion in high frequency modes. The Lyapunov-based Adaptive Backstepping Control (ABSC) technology is used to regulate suitable outputs for these structures. This approach is an updated version of the technique that uses the stability Lyapunov function to produce increased stability and resistance to fluctuations in real-world circumstances. However, in real-time situations, disturbances with larger ranges such as supply voltage changes, parameter variations, and noise may have a negative impact on the operation of this strategy. To increase the controller’s flexibility under more difficult working settings, the most appropriate first gains must be established. To solve these concerns, the ABSC’s performance is optimized using the Reinforcement Learning (RL) adaptive technique. RL has several advantages, including lower susceptibility to error, more trustworthy findings obtained from data gathering from the environment, perfect model behavior within a certain context, and better frequency matching in real-time applications. Random exploration, on the other hand, can have disastrous effects and produce unexpected results in real-world situations. As a result, we choose the Deep Deterministic Policy Gradient (DDPG) approach, which uses a deterministic action function rather than a stochastic one. Its key advantages include effective handling of continuous action spaces, improved sample efficiency through off-policy learning, and faster convergence via its actor–critic architecture that balances value estimation and policy optimization. Furthermore, this technique uses the Grey Wolf Optimization (GWO) algorithm to improve the initial set of gains, resulting in more reliable outcomes and quicker dynamics. The GWO technique is notable for its disciplined and nature-inspired approach, which leads to faster decision-making and greater accuracy than other optimization methods. This method considers the system as a black box without its exact mathematical modeling, leading to lower complexity and computational burden. The effectiveness of this strategy is tested in both modeling and experimental scenarios utilizing the Hardware-In-Loop (HIL) framework, with considerable results and decreased error sensitivity. Full article