Search Results (1,865)

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

Growing global supply chain uncertainties significantly threaten China’s forage import security. The evolving characteristics of the global forage trade network directly impact the stability of China’s supply. This study constructs a directed, weighted trade network based on global forage products trade data (2000–2024). Using complex network analysis methods, it systematically analyzes the network’s topological structure and evolutionary patterns, with a focus on their impact on China’s import security. The study addresses the following questions: What evolutionary patterns does the global forage trade network exhibit in terms of its topological structure? How does the evolution of this network impact the import security of forage products in China, specifically regarding supply chain stability and risk resilience? The research findings indicate the following: (1) From 2000 to 2024, the total volume of global forage products trade increased by 48.17%, primarily driven by forage products excluding alfalfa meal and pellets, which accounted for an average of 82.04% of volume annually. Additionally, the number of participating countries grew by 21.95%. (2) The global forage products trade network follows a power–law distribution, characterized by increasing network density, a clustering coefficient that initially declines and then rises, and a shortening of the average path length. (3) The core structure of the global forage products trade network shows an evolutionary trend of diffusion from core nodes in North America, Oceania, and Asia to multiple core nodes, including those in North America, Oceania, Europe, Africa, and Asia. (4) China’s forage products trade network displays distinct phase characteristics; however, imports face significant risks from high supply chain dependency and exposure to international price fluctuations. Based on these conclusions, it is recommended that China actively expands trade relations with potential product-exporting countries in Africa, encouraging enterprises to “go global.” Additionally, China should establish a three-dimensional supply chain security system, comprising maritime, land, and storage components, to enhance risk resistance and import safety. 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

We consider combinatorial multi-access coded caching with private caches, where users are connected to two types of caches: private caches and multi-access caches. Each user has its own private cache, while multi-access caches are connected in the same way as caches are connected in a combinatorial topology. A scheme is proposed that satisfies the following three requirements simultaneously: (a) Linear Function Retrieval (LFR), (b) content security against an eavesdropper, and (c) demand privacy against a colluding set of users. It is shown that the private caches included in this work enable the proposed scheme to provide privacy against colluding users. For the same rate, our scheme requires less total memory accessed by each user and less total system memory than the existing scheme for multi-access combinatorial topology (no private caches) in the literature. We derive a cut-set lower bound and prove optimality when $r\ge C-1$. For $r<C-1$, we show a constant gap of 5 under certain conditions. Finally, the proposed scheme is extended to a more general setup where different users are connected to different numbers of multi-access caches, and multiple users are connected to the same subset of multi-access caches. Full article

The subject of this paper is the computational homogenisation and identification of heterogeneous materials in the form of auxetic structures made of materials with nonlinear characteristics. It is assumed that some of the material and topological parameters of the auxetic structures are uncertain and are modelled as interval numbers. Directed interval arithmetic is used to minimise the width of the resulting intervals. The finite element method is employed to solve the boundary value problem, and artificial neural network response surfaces are utilised to reduce the computational effort. In order to solve the identification task, the Pareto approach is adopted, and a multi-objective evolutionary algorithm is used as the global optimisation method. The results obtained from computational homogenisation under uncertainty demonstrate the efficacy of the proposed methodology in capturing material behaviour, thereby underscoring the significance of incorporating uncertainty into material properties. The identification results demonstrate the successful identification of material parameters at the microscopic scale from macroscopic data involving the interval description of the process of deformation of auxetic structures in a nonlinear regime. Full article

This article explores the concept of low-level modeling of networks-on-chip (NoCs). A method for reducing the low-level NoC model by replacing the real IP blocks with a data packet generator module is proposed. This method is implemented in the low-level NoC modeling ECAD tool HDLNoCGen. This makes it possible to significantly increase the maximum number of nodes in the simulated NoC, as well as speed up the modeling and investigate the resource costs for network synthesis. A universal interface that can be used to connect new components to the network is also described. This interface has two main benefits: it reduces connection resource costs by eliminating the need to modify the connected component and shortens the time required to configure the connection interface itself. The proposed methodology of low-level NoC modeling is shown to be effective in analyzing the operation of routing algorithms of the NoC communication subsystem based on various topologies. Full article

Decarbonizing the industrial sector is essential to achieving net-zero targets and ensuring a sustainable future. Carbon–Hydrogen–Oxygen Symbiosis Networks (CHOSYN) are a set of interconnected hydrocarbon-processing plants that optimize the synergistic use of mass and energy resources in pursuit of both environmental objectives and profitability enhancement. However, this interconnectedness also introduces fragility, arising from technical and administrative dependencies among the participating facilities. In this work, a systematic framework is introduced to incorporate resilience assessment and sustainability enhancement within CHOSYNs. A CHOSYN representation is developed for a proposed industrial cluster, where processes are linked through interceptor units, which facilitate the exchange and conversion of carbon-, hydrogen-, and oxygen-based streams to meet demands. A multi-objective optimization framework is formulated with four competing goals: minimizing cost, minimizing net CO₂ emissions, maximizing internal CO₂ utilization, and minimizing the number of interceptors’ processing steps. The augmented $\epsilon$-constraint method is used to generate a Pareto front that captures the trade-offs among these objectives. To complement the synthesis, a resilience assessment framework is applied to evaluate network performance under disruption by incorporating inter-plant dependencies and modeling disruption propagation. The results show that even under worst-case scenarios, integration through CHOSYN can achieve significant gains in CO₂ utilization and reductions in raw material procurement requirements. Resilience analysis adds an important dimension by quantifying the economic impacts of disruptions to both highly connected and sparsely connected yet critical nodes, revealing vulnerabilities not evident from topology alone. Full article

This study provides an in-depth analysis of the learning dynamics of multichannel optical neurons based on spatial solitons generated in lithium niobate crystals. Single-node and multi-node configurations with different topological complexities (3 × 3, 4 × 4, and 5 × 5) were compared, assessing how the number of channels, geometry, and optical parameters affect the speed and efficiency of learning. The simulations indicate that single-node neurons achieve the desired imbalance more rapidly and with lower energy expenditure, whereas multi-node structures require higher intensities and longer timescales, yet yield a greater variety of responses, more accurately reproducing the functional diversity of biological neural tissues. The results highlight how the plasticity of these devices can be entirely modulated through optical parameters, paving the way for fully optical photonic neuromorphic networks in which memory and computation are co-localized, with potential applications in on-chip learning, adaptive routing, and distributed decision-making. Full article

The rapid growth of Electric Vehicles (EVs) risks causing grid congestion. Smart charging strategies can help to prevent overload while ensuring timely charging, thereby reducing the need for costly infrastructure upgrades. We study EV charging from a scheduling perspective, assuming an aggregator manages charging while respecting network cable capacities. In our model, vehicles depart only after charging is complete, so delays are possible. Our aim is to minimize these delays. We consider a network of parking lots, some of which are equipped with solar panels, where the demand that can be served is limited by the cables connecting them to the grid. We propose novel scheduling strategies that combine an online variant of well-known schedule generation schemes with a destroy-and-repair heuristic. We evaluate their effectiveness in a case study with data from the city of Utrecht. Without control, network cables would be overloaded 60–70% of the time. Our strategies completely eliminate overload, introducing an average delay of just over 1.5 min per EV in high-occupancy scenarios. This demonstrates that scheduling can significantly increase the number of EVs charged without compromising grid safety at the cost of a rather small delay. We also highlight the importance of accounting for grid topology and show the benefits of using flexible charging rates. Full article

Soft open point (SOP) offers a viable alternative to traditional tie switches for optimizing power flow distribution between connected feeders, thereby improving power quality and enhancing the reliability of distribution networks (DNs). Among existing medium-voltage (MV) SOP demonstration projects, the modular multilevel converter (MMC) back-to-back voltage source converter (BTB-VSC) is the most commonly adopted configuration. However, MMC BTB-VSC suffers from high cost and significant volume, with device requirements increasing substantially as the number of feeders grows. To address these challenges, this paper proposes a novel star-connected cascaded H-bridge (CHB) STATCOM SOP (SCS-SOP). The SCS-SOP integrates the static synchronous compensator (STATCOM) and low-voltage (LV) BTB-VSC into a single device, enabling reactive power support within feeders and active power exchange between feeders, while achieving reduced component cost and volume, simplified power decoupling control, and increasing power quality management capabilities. The topology derivation, configuration, operational principles, and control strategies of the SCS-SOP are elaborated. Finally, simulation and experimental models of a two-port 3 Mvar/300 kW SCS-SOP are developed, with results validating the theoretical analysis. Full article

In structural topology optimisation, intermediate densities are typically interpreted as local distributions of heterogeneous materials, bridging the gap between a solid and a void through optimised arrangements of cellular or composite microstructures. These multiscale configurations, governed by interactions between micro- and macroscales, are commonly addressed via hierarchical approaches. However, such methods often suffer from high computational cost and limited practical applicability. This work proposes an alternative strategy that reformulates the hierarchical problem by replacing pointwise microscale variations with a subdomain-based formulation. Each subdomain is associated with a periodic microstructure, reducing the number of local problems and significantly decreasing computational demands. A multiscale topology optimisation framework is developed using Asymptotic Expansion Homogenisation, enabling effective macrostructural properties and supporting inverse homogenisation for microscale design. The proposed method is implemented in a user-developed code and validated through several benchmark problems. The results show that the subdomain approach yields discrete and manufacturable microstructures that better reflect real-world composite applications, while also achieving substantial improvements in computational efficiency. Full article

This study focuses on improving the efficiency of interior permanent magnet synchronous motors (IPMSMs) for electric vehicle (EV) compressors. Seven rotor topologies (B, dB, V, dV, D, U, and UV) were first compared, among which the U-type rotor demonstrated the highest efficiency and the lowest total loss. Subsequently, the influence of the turn number and rotor outer diameter (ROD) on the shift of the high-efficiency region was analyzed, and six key design variables were identified through Pearson correlation-based sensitivity analysis. Using these variables, a multi-objective optimization was performed in Ansys OptiSLang, which improved the integrated part load value (IPLV)-weighted efficiency from 91.05% to 92.29% and shifted the high-efficiency region closer to the main operating point. Experimental validation of the reference model confirmed the reliability of the FEM analysis, and the proposed optimal design is expected to enhance low-speed efficiency and reduce battery energy consumption in EV compressor applications. 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

Transformerless inverters have received significant attention in solar photovoltaic (PV) applications. The absence of low-frequency transformers contributes to improved efficiency and reduced size compared to other topologies; however, there are concerns about leakage currents. The common ground (CG) connection in PV inverters is an attractive solution to this issue, as it generates a constant common-mode voltage and theoretically eliminates the leakage current. In this context, multilevel CG inverters can eliminate the leakage current while achieving high-quality output voltages. Nonetheless, achieving simultaneous control of the grid current and inner capacitor voltages can be challenging. Furthermore, controlling the capacitor voltages in multilevel inverters requires feedback from measurement sensors, which can increase the cost and may affect the overall reliability. To address these issues, this paper proposes a model predictive controller (MPC) for a CG multilevel inverter with a reduced number of sensors. While conventional MPC uses a classical multi-objective technique with a single cost function, the proposed method avoids the use of weighting factors in the cost function. Additionally, a sliding-mode observer is developed to estimate the capacitor voltages, and an incremental conductance-based maximum power point tracking (MPPT) algorithm is used to generate the current reference. Simulation and experimental results confirm the effectiveness of the proposed observer and MPC strategy. 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