Search Results (248)

Two-dimensional (2D) group-IV materials are promising for spintronics due to their silicon compatibility and tunable properties. In this work, we investigate the structural, electronic, magnetic, and optical properties of semi-hydrogenated 2D SiGe random alloys—where hydrogen atoms saturate only one side of the atomic plane—using density functional theory and many-body perturbation theory ($G{W}_0$). Substitutional disorder is modeled via representative high-symmetry configurations introduced by Baldereschi and co-workers to enable quasiparticle and optical simulations in large supercells. We demonstrate that these semi-hydrogenated alloys possess an intrinsic magnetic semiconducting ground state, arising from the electronic structure of the system, with an integer magnetic moment of $1{\mathsf{\mu }}_{\mathrm{B}}$ per primitive cell. The spin-resolved electronic structure features nearly flat frontier bands and a finite energy gap, which is significantly renormalized by quasiparticle corrections while maintaining robust spin polarization. These properties remain remarkably stable across different realizations of chemical disorder and over a wide range of alloy compositions considered in this work. Optical spectra calculated within the random phase approximation reveal a composition-dependent red-shift of the low-energy onset in the imaginary part of the dielectric function, consistent with the evolution of the quasiparticle electronic structure and the persistence of flat spin-polarized frontier bands. Our findings establish semi-hydrogenated 2D SiGe random alloys as a resilient model platform to explore interaction-driven magnetism in disordered two-dimensional systems, while simultaneously offering realistic prospects for spintronic and magneto-optoelectronic applications in the presence of chemical disorder. Full article

This paper presents the results of research on the influence of the post-weld treatment process on deformation and residual stresses in structural steel welded joints. For this purpose, T-joints were welded with 135 (MAG) methods from the following five steel grades: S235JR, S355J2+N, S460NL, S690QL and S960QL, maintaining a similar linear energy of the welding process. The welded joints in the post-weld heat treatment (stress relieving annealing) and High-Frequency Mechanical Impact (HFMI) condition were then measured for flatness and straightness deviations of the sheets to determine the effect of the post-weld treatment on their deformation. Based on the results of laser tracker tests, it was confirmed that HFMI processing effectively reduces the level of deformation, achieving better results than traditional post-weld heat treatment (PWHT). In symmetry, to determine the effect of post-weld machining on the level and distribution of stresses, a three-dimensional numerical model was made, and a numerical analysis was performed using the FEM method for the selected material variant (S355). The results of the numerical analyses confirmed that HFMI reduces stress in welded joints, although it is less effective in this respect than heat treatment process used. Full article

This paper presents a new analytical framework for studying a class of three-dimensional symmetric systems within the theory of difference equations, constituting a natural extension of structurally reducible models in one and two dimensions. Despite the classical focus on linear equations or low-dimensional systems, the problem of solving nonlinear systems with intertwined structures and interdependent delays remains largely unexplored. Drawing on recent structural developments, we define a periodic symmetric system based on three sequences interacting through fractional hyperbolic tangent-type forms and show how this structure reveals embedded linear recurrences governing the internal evolution. By exploiting symmetry and periodicity properties, we derive exact representation formulas and highlight the structural mechanisms that enable a deeper understanding of the system’s dynamic behavior, despite its nonlinear nature and the complex interlacing of its indices. This study thus contributes to the expansion of the theory of solvable nonlinear systems and provides a unified approach to high-dimensional symmetric structures. To illustrate the practical relevance of the proposed framework, a cyclic SIR-type interaction interpretation is presented, supported by numerical simulations that demonstrate the impact of parameter symmetry on the system’s dynamical behavior. Full article

Cooperative deployment of mobile agents under geometric and line-of-sight constraints gives rise to high-dimensional constrained optimization problems whose underlying physical configuration often exhibits exploitable structure. This paper develops a symmetry-driven inverse design framework that leverages two structural features of the engagement geometry—the ${\mathbb{Z}}_2\times {\mathbb{Z}}_2$ mirror symmetries of the extended target silhouette and a closed-form forward–inverse correspondence between line-of-sight-aligned burst locations and physical agent parameters—to construct low-dimensional seeds for subsequent physical parameter optimization. The framework is developed and validated on a representative naval defense instance in which a fleet of unmanned aerial vehicles (UAVs) releases spherical obscuration payloads to interrupt the line of sight between incoming mobile threats and a cylindrical extended target. Instead of searching only over the four-dimensional UAV parameter space (heading angle, speed, drop time, fuse delay), the method first specifies a desired burst location in a two-dimensional inverse space and analytically back-calculates feasible agent parameters, which are then refined by multi-start Nelder–Mead optimization in the physical parameter space. A conservative three-dimensional cylindrical line-of-sight obscuration model is developed by constructing four extreme tangent sightlines from the missile to the cylindrical target and verifying whether the spherical smoke cloud simultaneously blocks all of them. A hierarchical multi-agent task allocation framework combines a performance matrix, assignment enumeration, and joint multi-start refinement. Numerical experiments on five progressively complex sub-problems demonstrate obscuration durations of $1.362 \mathrm{s}$ (single fixed shot), $4.580 \mathrm{s}$ (optimized shot), $7.324 \mathrm{s}$ (three-shot relay), $11.140 \mathrm{s}$ (three-UAV cooperation), and $20.652 \mathrm{s}$ (full five-UAV three-missile assignment). Additional high-dimensional benchmarks, sensitivity tests, and error analyses clarify the reproducibility and limitations of the approach. Full article

This research presents a robust multivariate statistical framework for the non-destructive prediction of geomechanical state parameters in quartz-rich coastal sands through acoustic emission (AE) monitoring. Granular media under symmetrical compressive stress function as complex natural systems, where microscopic energy dissipation—arising from particle rearrangement and grain microcracking—radiates as transient elastic waves. To decode these stochastic processes, 24 confined uniaxial compression tests were conducted across diverse soil typologies and moisture contents (0–12%). A high-dimensional data matrix was constructed, integrating 13 geotechnical variables with 48 acoustic descriptors formulated through three distinct temporal aggregations: stage-specific, history average and weighted history average. The statistical results identify the logarithmic effective vertical stress (${\log }_{10}\left({{\mathsf{\sigma }}^{\prime }}_{\mathrm{v}}\right)$) and the cumulative axial strain ($\mathsf{\epsilon }$) as the most significant geomechanical drivers, exhibiting Pearson correlation coefficients $\left|p\right|$ ≥ 0.85 with acoustic activity. In the acoustic domain, the analysis reveals that Signal Strength (${\mathrm{S}}_{\mathrm{s}}$) and cumulative energy ($\mathrm{E}$) flux are the most reliable predictors for volumetric deformation, while the amplitude ($\mathrm{A}$), b-value ($\mathrm{b}$), and average frequency ($\mathrm{F}$) emerge as critical indicators for identifying the transition between spatial rearrangement and the onset of grain fragmentation. Furthermore, the inclusion of dimensionless parameters, particularly earliness ($\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{l}$), enhances model stability by standardising waveform symmetry across varying stress regimes. High-order polynomial regression models (up to the third degree) were derived, demonstrating that the statistical complexity of acoustic signatures allows for the high-fidelity inference of the soil matrix’s initial and state parameters. This methodology establishes a unified mathematical architecture for the in situ characterisation of granular skeletons, balancing computational efficiency with predictive power in intricate geological domains. Full article

The topological properties of vector optical fields are traditionally considered strictly conserved during continuous deformations and linear propagation. However, while structured light has been extended into nonlinear regimes, previous studies have predominantly focused on the intensity modulation of specific orbital angular momentum (OAM) components and the pure frequency conversion of structured light. The critical question of whether macroscopic topological invariants remain robust or experience fundamental breakdown during nonlinear light–matter interactions remains largely unexplored. To address this specific gap, we propose and generate multiple fractional vector optical fields (MF-VOFs), establishing their dynamic topological evolution and inherent conservation laws in free space. It should be noted that our experimental results are limited to free-space propagation. Strikingly, we report a significant departure from this paradigm during light–matter interactions: topological nonconservation anomalies manifest when these optical fields interact with nonlinear materials via second- and third-harmonic generation. Through a comprehensive quantitative analysis of the OAM spectrum, we confirm that the asymmetrical reconstruction and spatial transition of the total OAM along the propagation direction serve as the physical origins driving this topological symmetry breaking. These findings provide a fundamentally novel perspective on topological manipulation in nonlinear optical processes, offering advanced strategies for complex structured light generation and high-dimensional optical information processing. Full article

Reliable short-term wind power forecasting is crucial for smart grid stability. However, high-dimensional noise and stochastic fluctuations in wind sequences often degrade the accuracy of traditional forecasting models. Moreover, wind power time series typically exhibit asymmetric rising and decaying patterns, which further complicate accurate modeling. To address these challenges, this study proposes a hybrid intelligent system that integrates three components: data preprocessing, heterogeneous ensemble learning, and probabilistic interval forecasting. First, we build a multi-stage preprocessing workflow. Adaptive DBSCAN and Local Outlier Factor (LOF) remove spatial and density anomalies. Then multivariate variational mode decomposition (MVMD) synchronously separates multi-scale oscillatory patterns while preserving cross-channel correlations and frequency-domain symmetry across input variables. SHAP analysis quantifies feature importance, ensuring interpretability. The selected features are fed into a heterogeneous ensemble model consisting of Transformer, BPNN, ELM, XGBoost, and QRLSTM, which collectively capture multi-scale temporal dependencies and diverse data patterns. The ensemble weights are dynamically optimized by a modified multi-objective dragonfly algorithm (MMODA) that balances forecast accuracy and stability. Based on this ensemble, we apply MMODA to tune kernel density estimation for generating high-quality forecast intervals, maximizing coverage while minimizing interval width. Experiments on two wind farms in Shandong show that our MMODA-optimized ensemble reduces mean absolute percentage error by about 44.7% compared to single models, and ablations confirm that MVMD preprocessing adds a further 10.7% reduction. The proposed system provides an interpretable and reliable decision-support tool for sustainable grid operations. Full article

The space–air–ground integrated network (SAGIN) is a key 6G architecture that provides seamless three-dimensional connectivity, exhibiting hierarchical structural symmetry between LEO satellite and HAP layers. Integrating information-centric networking (ICN) with caching on Low Earth Orbit (LEO) satellites and high-altitude platforms (HAPs) significantly enhances content distribution efficiency. Existing studies on caching mechanisms have made progress but lack optimized cache resource allocation and accurate popular content identification. Thus, an interest-aware caching scheme (ICRL) based on reinforcement learning is proposed to optimize the SAGIN’s popular content caching decisions, aiming to achieve rational symmetric allocation of cache resources across LEO and HAP layers. Different from existing RL-based caching methods, the proposed ICRL scheme considers the LEO-HAP hierarchical architecture and designs an improved reinforcement learning mechanism to adapt to the dynamic characteristics of the SAGIN. First, an air–space two-tier caching architecture is constructed to enable collaborative caching between LEO satellites and HAPs. Second, to select high-value nodes intelligently, the proposed scheme leverages a comprehensive importance model that quantitatively analyzes HAP and LEO indicators such as topology, transmission capacity, and location. Finally, a reinforcement learning-based dynamic cache mechanism is developed. It captures real-time network requests and cache states to select optimal actions and adapt to network dynamics for better content popularity matching. Extensive evaluations based on NDNSIM demonstrate that ICRL outperforms baseline schemes in terms of cache hit ratio, server load, and request latency and achieves a symmetric balance of network load and service performance in the whole SAGIN. Full article

Wireless sensor network (WSN) coverage optimization is a challenging high-dimensional and nonlinear problem that directly affects network performance, including sensing quality, energy efficiency, and system reliability. Although metaheuristic algorithms have been widely applied to this problem, many existing methods still suffer from premature convergence, insufficient population diversity, and an imbalance between exploration and exploitation. To address these issues, this paper proposes a multi-strategy enhanced enterprise development optimization algorithm (MEEDOA) inspired by business management mechanisms. The proposed method integrates a hybrid population initialization strategy, an adaptive activity switching mechanism based on performance feedback, a multi-elite collaborative learning strategy, and a Lévy flight-based stagnation escape mechanism. These strategies are tightly coupled within a unified adaptive framework to improve global search capability, convergence speed, and robustness. Furthermore, a mathematical model for WSN deployment is constructed based on a binary sensing model and discrete coverage evaluation. From the perspective of symmetry, the sensing regions of sensor nodes exhibit significant geometric symmetry in both two-dimensional and three-dimensional deployment spaces. In the two-dimensional case, the sensing and communication regions are modeled as concentric circular structures, while in the three-dimensional case, the sensing regions are represented by isotropic spheres with symmetric spatial distributions. Such symmetry properties provide an effective basis for describing coverage behavior, reducing redundant overlap, and improving the uniformity of node deployment. Meanwhile, the proposed MEEDOA preserves population diversity and enhances search balance, enabling the algorithm to better capture symmetric coverage patterns and more effectively explore complex spatial deployment configurations. Extensive experiments on CEC2014, CEC2017, CEC2020, and CEC2022 benchmark functions demonstrate that MEEDOA achieves superior convergence accuracy, faster convergence speed, and stronger robustness compared with several state-of-the-art algorithms. Additional simulation results in WSN deployment applications verify its effectiveness in improving coverage performance under both symmetric and irregular spatial deployment scenarios. The results indicate that the proposed MEEDOA provides a reliable and efficient solution for complex global optimization problems and practical engineering applications. Full article

The cooperative airdrop of UAV swarms by multiple transport aircraft creates a large-scale multi-agent planning problem. The mission involves heterogeneous aircraft, multi-visit airdrop areas, strict time windows, and threat-aware flight paths. To address these challenges, this work develops an integrated framework for both global task allocation and real-time replanning in complex three-dimensional operational environments. First, for the combinatorial optimization of task execution sequences across multiple aircraft, a static task assignment method is proposed. This method employs a Hybrid-encoding Constrained Black-winged Kite Algorithm (HCBKA), which incorporates optimization metrics such as mission execution time, completion rate, and load-balancing symmetry among aircraft. The HCBKA aims to find a task assignment scheme that achieves a comprehensive optimum across multiple objectives through efficient model solving. Second, to handle potential real-time dynamic changes during mission execution, a rapid-response and generalizable replanning mechanism is developed. This mechanism utilizes an event-triggered strategy based on a Time-window aware Dynamic Auction Algorithm (TDAA). It ensures that the system can promptly initiate and execute online task reallocation in response to contingencies such as changing mission requirements or losses within its own drone swarm, thus maintaining the adaptability and robustness of the overall plan. Simulation results show that the proposed framework produces high-quality global solutions and maintains strong robustness under dynamic changes. The approach provides an effective and scalable solution for coordinated multi-aircraft swarm airdrop missions. Full article

To enhance the performance of the Social Network Search (SNS) algorithm in solving complex numerical optimization problems, this paper proposes a Multi-strategy Enhanced Social Network Search (MESNS) algorithm. The original SNS simulates human social behaviors through four decision-making emotions—imitation, conversation, disputation, and innovation—to perform population-based search. However, its uniform emotion selection mechanism and purely random interaction strategy may reduce convergence efficiency and weaken exploitation capability, particularly in the later stages of optimization. To overcome these limitations, MESNS incorporates three improvement strategies. First, an adaptive decision-making emotion selection mechanism is developed to dynamically adjust the probabilities of exploration and exploitation behaviors according to the iteration progress, thereby promoting a more symmetric and coordinated search transition over time. Second, an elite-guided communication strategy is introduced to enhance information propagation by integrating high-quality individuals into the interaction process, which improves convergence while maintaining population diversity. Third, a dynamic interaction radius adjustment mechanism is designed to adaptively regulate the search step size, achieving a better balance and dynamic symmetry between global exploration and local refinement. Extensive experiments are conducted on the IEEE CEC2014, CEC2017, and CEC2022 benchmark suites under multiple dimensional settings. The results demonstrate that MESNS achieves superior optimization accuracy, faster convergence speed, and improved solution stability compared with several state-of-the-art metaheuristic algorithms. Furthermore, the proposed algorithm is successfully applied to the three-dimensional wireless sensor network deployment optimization problem, where it produces a more uniformly distributed and spatially balanced sensor layout, reduces coverage holes and redundant overlaps, and thus exhibits desirable symmetry in deployment structure and sensing coverage. These findings indicate that MESNS is an effective and competitive optimization framework for complex global optimization tasks with both theoretical significance and practical value from the perspective of symmetry. Full article

Realizing high-quality-factor (high-Q) plasmonic resonances in the visible regime is critical for enhancing light-matter interactions and advancing biochemical sensing. However, traditional localized surface plasmon resonances (LSPRs) typically suffer from broad spectral linewidths due to severe radiative damping. In this work, we propose a simple two-dimensional symmetric gold nanohole-array metasurface that supports a symmetry-protected bound state in the continuum (SP-BIC) at normal incidence. By introducing extrinsic symmetry breaking via oblique incidence, this non-radiative dark state is successfully transformed into an observable high-Q quasi-BIC Fano resonance. Cartesian multipole decomposition reveals that this sharp mode ($\lambda \approx 688$ nm) is predominantly driven by a tightly confined Magnetic Dipole (MD) excitation, which drastically suppresses radiative leakage compared to the highly damped Electric Dipole (ED)-dominated LSPR. Consequently, the quasi-BIC mode exhibits an ultra-narrow spectral linewidth ($\mathrm{FWHM}\approx 17.4$ nm). While its bulk sensitivity ($236.9$ nm/RIU) is slightly lower than that of the LSPR mode, the exceptionally sharp resonance yields a remarkably low Limit of Detection (LOD) of $7.35\times {10}^{-3}$ RIU, achieving a nearly five-fold improvement over the traditional LSPR. Furthermore, the quasi-BIC mode maintains an outstanding Figure of Merit (FOM up to ∼19.7 ${\mathrm{RIU}}^{-1}$) across the entire sensing range. By eliminating the need for complex asymmetric nanofabrication, this robust angle-tuned design strategy provides a highly promising platform for the development of high-resolution, low-cost optical biosensors. Full article

Optimized dispatch of microgrids is crucial for improving the economic performance and long-term sustainability of modern low-carbon power systems. In particular, accurate economic dispatch modeling for battery energy storage systems (BESSs) is essential for properly evaluating the operational benefits and lifetime costs of microgrids. However, when both battery cycle aging and calendar aging are considered, the resulting scheduling model becomes highly nonlinear, high-dimensional, non-convex, and multimodal, which poses substantial challenges to conventional optimization methods. To alleviate the above problem, a symmetry-guided multi-strategy differential hybrid slime mold algorithm (MDHSMA) is introduced for the day-ahead economic dispatch of microgrids under a refined battery degradation framework. First, a chaotic bimodal mirrored Latin hypercube sampling strategy is designed to exploit symmetry during population initialization, thereby enhancing diversity and improving structured coverage of the search space. Second, a history-driven adaptive differential evolution mechanism is integrated to balance global exploration and local exploitation more effectively during the iterative search process. Third, a state-aware stagnation handling framework is incorporated to maintain population vitality and further improve convergence accuracy and robustness. MDHSMA is evaluated against 12 state-of-the-art optimizers on the CEC2017 and CEC2022 benchmark suites and two representative engineering optimization problems to verify its overall performance. In addition, it is applied to a microgrid case study with refined BESS degradation modeling. The results show that MDHSMA achieves the lowest comprehensive operating cost by effectively coordinating electricity arbitrage and battery life consumption. Moreover, it guides the energy storage system toward shallow charge–-discharge patterns, thereby mitigating accelerated degradation caused by excessive cycling. These results confirm the effectiveness and practical value of the proposed method for sustainable microgrid dispatch in complex nonconvex optimization scenarios. Full article

Metaheuristic optimization algorithms often suffer from structural imbalance between exploration and exploitation, leading to premature convergence and performance degradation in high-dimensional or constrained problems. To address this issue, a symmetry-enhanced Improved Enterprise Development Optimization Algorithm (IEDOA) is proposed. The algorithm establishes a dynamic symmetry between global exploration and local exploitation through three coordinated strategies: a performance-feedback-based adaptive activity selection mechanism, a multi-elite-guided structural evolution strategy, and a lifecycle-aware exploration mechanism inspired by technological scheduling dynamics. The proposed symmetric regulation framework improves population diversity while preserving convergence stability, thereby enhancing search efficiency in complex landscapes. To validate its performance, IEDOA is evaluated on CEC2017 (30/50 dimensions) and CEC2022 (10/20 dimensions) benchmark suites and compared with several advanced metaheuristic algorithms. Experimental results demonstrate superior convergence accuracy, robustness, and scalability. Statistical analyses using the Wilcoxon signed-rank and Friedman tests further confirm its significant performance advantages. To demonstrate practical applicability, IEDOA is applied to a grid-connected microgrid economic dispatch problem involving renewable generation units, controllable generators, and energy storage systems under 24 h operational constraints. Simulation results show that the proposed method achieves lower operational costs and smaller performance variance across independent runs. Overall, IEDOA provides an effective symmetric optimization framework for complex engineering systems characterized by nonlinearity, multi-constraints, and high dimensionality. Full article

Integral-form nonlocal elasticity provides a mechanically meaningful approach to describing size effects, yet it leads to Volterra-type integro-differential equations that are difficult to solve analytically and numerically challenging for boundary layers and high-order modes. In this work, we developed a symplectic numerical integration framework for Eringen’s two-phase (local/nonlocal mixture) integral model by embedding the constitutive operator into a Hamiltonian formulation and discretizing the influence domain in a belt-wise manner. A step-increase strategy was incorporated to allow flexible spatial marching while preserving the geometric (symplectic) structure of the transfer operation. In addition, a symmetry-explicit, element-level stiffness representation was derived for the discretized integral operator; it exposes a mirrored long-range coupling pattern and enables symmetric, energy-consistent assembly. The resulting kernel-agnostic algorithm accommodates both smooth and finite-range kernels. Static benchmarks and longitudinal vibrations are investigated for exponential, Gaussian, and triangular kernels over representative length ratios and mixture parameters. Comparisons with available analytical and asymptotic solutions show good agreement within their validity ranges, and the method yields stable higher-order eigenfrequencies when asymptotic expansions may be unreliable. The current study is limited to a linear one-dimensional rod setting, and validation is restricted to published analytical/asymptotic solutions rather than experimental calibration. Full article