Search Results (1,772)

Weight reduction and dynamic performance optimization are critical for airborne direct-drive VICTS satellite communication antennas, which require lightweight, high-speed, and high-precision rotation. Traditional vibration suppression methods, such as uniform support layout and added damping, rely heavily on empirical trial and error, lack targeted modal control, and cannot balance lightweight design with dynamic stiffness. To address these issues, this paper proposes a wave-theory-based dynamic modeling and rapid optimization method for multi-layer rotating components in direct-drive VICTS antennas. The kinematic model of the rotating ring and ball revolution excitation are derived using the annular wave equation and bearing kinematics. A Modal Blocking Mechanism is established: placing support balls at positions satisfying the half-wavelength constraint suppresses target mode shapes via wave interference, achieving vibration attenuation at the source. A homogenization equivalent method based on RVE is developed for irregular cross-section rings, yielding analytical expressions for in-plane equivalent elastic modulus and out-of-plane equivalent shear modulus. These parameters are integrated into the wave equation to analytically solve vibration modes, avoiding iterative finite element computations. A rapid multi-objective optimization framework is then constructed, minimizing the structural weight and maximizing the modal separation interval under dynamic stiffness and excitation frequency constraints. Numerical simulations, FE analysis, and prototype tests validate the method: the maximum analytical error is only 3.1%. Compared with uniform support designs, the optimized structure achieves a 40% weight reduction, a 40% increase in minimum modal separation, and a 65% reduction in the RMS tracking error. This work provides an efficient, deterministic dynamic design method for large-diameter ring structures, transforming vibration control from empirical adjustment into a precise, physics-informed optimization. Full article

Transmission towers, as critical infrastructure in power systems, are frequently threatened by multiple hazards such as strong winds and flood scour. Traditional structural health monitoring methods face limitations in data feedback timeliness and mechanical interpretation, making real-time condition awareness and early warning under disaster scenarios challenging. To address these issues, this paper proposes a digital twin framework for transmission tower structures, integrating Building Information Modeling (BIM), Internet of Things (IoT) technology, and the Finite Element Method (FEM) for structural health monitoring and visual warning under wind loads and flood scour effects. The framework achieves cross-platform collaboration through the FEM Open Application Programming Interface (OAPI) and Python scripts. In the physical domain, fluctuating wind loads are simulated based on the Davenport spectrum, flood scour depth is modeled using the HEC-18 formulation, and foundation constraint degradation is represented through nonlinear spring stiffness reduction. In the FEM domain, dynamic time-history analyses are conducted to obtain structural responses. In the BIM domain, a three-level warning mechanism based on stress change rate (ΔR) is established to achieve intuitive rendering and dynamic feedback of structural damage. A 44.4 m high latticed angle steel tower is employed as the case study for validation. Results demonstrate that the simulated wind spectrum closely matches the theoretical target spectrum, confirming the validity of the load input. A critical scour evolution threshold of 40% is identified, beyond which the first two natural frequencies exhibit nonlinear decay with a maximum reduction of 80.9%. Non-uniform scour induces significant load transfer, with axial forces at leeside nodes increasing from 27 kN to 54 kN. During the 0–60 s wind loading process, BIM visualization accurately captures the full stress evolution from the tower base to the upper structure, showing excellent agreement with FEM results. The proposed framework establishes a closed-loop interaction mechanism of “physical sensing–digital simulation–visual warning”, effectively enhancing the timeliness and interpretability of structural health monitoring for transmission towers under multiple hazards, providing an innovative approach for intelligent disaster prevention in power infrastructure. Full article

This paper addresses a dynamic multi-period pricing problem that incorporates time-varying contextual information and inventory constraints. Sales are modeled as a function of both price and a multidimensional context vector, which may include factors such as customer location, income, loyalty, competitor prices, and promotional activity. This formulation captures complex market dynamics over a finite selling horizon. The problem is formulated as a quadratic programming model, and two alternative solution approaches are proposed. The first uses a multivariate regression model to approximate the sales function linearly, allowing an exact quadratic programming solution that serves as a benchmark. The second is a ‘learnheuristic’ algorithm that combines a nonlinear sales learning model with metaheuristic optimization to generate high-quality pricing strategies under realistic operational constraints. Computational experiments demonstrate the effectiveness of the proposed learnheuristic approach. Full article

This paper develops a rigorous algebraic framework for quasi-substructures in Sheffer-based Nelson algebras, extending the landscape of fuzzy algebraic theory. By systematically introducing $(\in ,\in \vee q)$-bipolar fuzzy quasi-subalgebras and ideals, we analyze their structural properties through generalized belongingness and quasi-coincidence relations. We formalise invariant threshold symmetry as the condition $g^{+}\left(\chi \right)+\left|g^{-}\left(\chi \right)\right|=c$ for a constant $c\in [0,2]$ and every $\chi \in \Omega$ (Definition 10) and prove its structural preservation within $(\in ,\in \vee q)$-bipolar fuzzy quasi-subalgebras (Theorem 4, supported by Theorems 3, 15 and 16). This enables a balanced dual evaluation of positive and negative information. Characterization theorems are established via level subsets, revealing how quasi-substructure properties are governed by bounds at critical membership values. Equivalence results unify classical and bipolar fuzzy perspectives, demonstrating that algebraic constraints preserve structural coherence across crisp and fuzzy environments. Algorithmic verification procedures are provided for practical validation in finite systems, and illustrative examples highlight applications in uncertainty modeling and decision support. Overall, the proposed theory formalizes bipolar fuzzy structures in Sheffer-based Nelson algebras, utilizing invariant threshold symmetry, level-set decomposition, and crisp equivalence to evaluate dual information. Full article

Structural Equation Modeling (SEM) is a key framework for analyzing complex economic relationships involving latent variables, mediation effects, and endogeneity, yet the choice between frequentist and Bayesian estimation remains theoretically and practically contested, especially in settings with non-stationary data and small samples. This study provides a formal comparison of the two approaches by formulating SEM as a probabilistic graphical model and deriving the corresponding estimation procedures, identifiability conditions, and uncertainty measures. We examine asymptotic properties of frequentist estimators and posterior consistency in Bayesian SEM, with particular attention to integrated time-series SEM applications such as shadow economy estimation. The analysis shows that while both approaches converge under large-sample conditions, important differences arise in finite samples. Bayesian methods exhibit more stable point estimates through coherent uncertainty quantification, particularly when prior information regularizes an otherwise ill-conditioned likelihood. Under model misspecification, Bayesian posteriors concentrate around the pseudo-true parameter defined by the Kullback-Leibler projection, providing a probabilistic representation of misspecification uncertainty through posterior spread—an advantage over frequentist inference, which typically conditions on the maintained model as exact. These findings carry direct implications for empirical economic modeling under realistic data constraints. In settings where sample sizes are small, identification is weak, and model uncertainty is substantial, conditions that routinely characterize macroeconomic research, the choice of inferential framework is not a matter of philosophical preference but a determinant of whether policy-relevant conclusions can be credibly defended. Bayesian SEM offers a principled and transparent path forward in precisely these conditions. Full article

This study focuses on the use of Physics-Informed Neural Networks (PINNs) to solve the 1D Advection–Diffusion–Reaction (ADR) equation. The performance of the PINN model is evaluated in comparison with the classical Crank–Nicolson Finite Difference Method (CNFDM) and validated against analytical solutions to assess improvements in accuracy, robustness, and flexibility. Quantitative analysis reveals that the PINN achieved a high level of accuracy with absolute errors ranging from approximately $2.13\times {10}^{-4}$ to $1.17\times {10}^{-3}$ across the spatial domain. The study utilizes a neural network architecture with two hidden layers of 80 neurons each, optimized through a two-stage training process involving Adam and L-BFGS optimizers. This work contributes to the growing field of physics-informed machine learning by demonstrating the strengths and quantitative reliability of the PINN technique for solving complex partial differential equations in transport phenomena. Full article

This study builds on a small body of work, all on Japanese, demonstrating how intonational phonology is critical for understanding prosodic modifications in infant-directed speech (IDS) relative to adult-directed speech. We performed similar analyses on simulated infant-directed speech vs. reading of a story in English and Bengali: two languages that – unlike Japanese – both have stress and do not use fundamental frequency (F0) to signal changes in word-level meaning, but that have two very different intonational grammars. These differences allowed us to disentangle previous hypotheses about intonational exaggeration in IDS being concentrated in a particular part of the melody. We tested hypotheses that state this locus of exaggeration is either at: the final position in the melody (final in the intonational phrase), the most unpredictable part of the melody, or in pragmatically informative tones. Our results support the first hypothesis. We found that the phonological choices of speakers to chunk the story into shorter, larger prosodic constituents drive intonational exaggeration in IDS. This is because the intonational phrase-final position in both languages is the site of greatest pre-boundary lengthening and F0 range expansion. We also demonstrate: (i) quantification of predictability in intonational melodies using probabilistic finite state automaton representations of intonational grammars and (ii) F0 statistical analyses that are robust and scalable to large, naturalistic IDS corpora. Full article

With the increasing density of transmission lines, line crossings and spans have become more common, and the electromagnetic environment of transmission lines has attracted increasing attention. Investigating the electromagnetic field distribution in transmission line crossing regions is therefore of great significance for line layout and preliminary design. In this study, the parameters of transmission lines in crossing regions are first obtained by parsing the GIM (Grid Information Model) file. A three-dimensional electromagnetic field model of a double-circuit transmission line on the same tower is then established using the finite element method, and the accuracy of the proposed approach is validated by comparison with field measurement data. Based on the developed model, the electric and magnetic field distributions of both the double-circuit transmission line and the crossing region are calculated. Furthermore, the effects of different crossing angles, phase sequence combinations, and voltage levels on the electromagnetic field distribution are systematically investigated. By comparing the electromagnetic field characteristics under different phase sequence schemes, an optimized phase sequence configuration for double-circuit transmission lines and crossing regions is proposed. The results provide a theoretical basis and technical reference for electromagnetic environment assessment and design optimization of transmission lines in crossing regions. Full article

We show that thermodynamic consistency in systems with finite excluded volume implies compact support of the grand canonical particle-number distribution. Understanding whether fundamental bounds on information and matter content can arise purely from statistical-mechanical principles—independent of gravitational dynamics—is of central interest in thermodynamics, information theory, and cosmology. For any nonzero excluded volume parameter b, the partition function vanishes identically beyond $N_{max}=V/b$, enforcing a strict upper bound on admissible macrostates. We demonstrate that this compactness induces bounded particle-number fluctuations and finite Fisher information with respect to the chemical potential, thereby rendering the associated statistical manifold effectively finite-dimensional. This informational compactness provides a structural mechanism limiting distinguishability of macrostates independently of gravitational considerations. We argue that such thermodynamically enforced bounds are compatible with entropy bounds and holographic scaling principles, suggesting that informational finiteness may arise from statistical-mechanical consistency alone. Cosmological implications are discussed cautiously: infinite matter content at fixed volume is incompatible with compact support induced by finite excluded volume. Accordingly, the Fisher metric and associated thermodynamic lengths remain bounded when particle-number fluctuations are restricted by excluded-volume constraints. These results show that excluded-volume constraints induce a natural information-geometric compactness of the thermodynamic manifold, providing a general mechanism by which statistical distinguishability and curvature remain finite in finite-occupancy systems. Full article

Femtosecond laser micromachining is a cornerstone of high-precision manufacturing, yet its multi-scale dynamics require a self-consistent bridging from atomic transitions to macroscopic morphology. This study establishes a multi-scale framework where Density Functional Theory (DFT) calculates temperature-dependent electronic thermal properties to inform both Two-Temperature Model-Molecular Dynamics (TTM-MD) and Finite Element Method (TTM-FEM) simulations. By comparing atomistic and macroscopic results, we systematically investigate the thermal-mechanical responses of copper ablation. The macroscopic TTM-FEM model, employing a removal criterion based on the enthalpy of vaporization, achieves high predictive accuracy for ablation depths in the low-to-medium power range up to 300 mW. However, a significant divergence at higher powers (>400 mW) highlights the physical transition from surface evaporation to phase explosion. Concurrently, the TTM-MD simulations provide microscopic insights into the transient temperature and stress evolution, establishing a physically synchronized link between atomic-scale dynamics and macroscopic results. This work defines the applicability boundaries of evaporation-based macroscopic models and provides a validated predictive tool for optimizing laser processing parameters in precision engineering. Full article

This study presents a method to retrieve the electron density structure of the transient C-region using very-low-frequency (VLF) Earth–ionosphere waveguide propagation. Here, we demonstrate the identification of the C-region from amplitude variations of a mid-range VLF propagation path that is nearly perpendicular to the solar terminator. Previous investigations have primarily relied on phase measurements along long-distance paths with small terminator angles, whereas the present approach utilizes amplitude information under conditions where modal interference is significant. The Faraday International Reference Ionosphere (FIRI-2018) provides an effective semi-empirical model of the lower-ionospheric electron density; however, discrepancies between simulations and observations are often observed at sunrise. To resolve this issue, we introduce Gaussian perturbations to the electron density profile output by FIRI-2018 and optimize their parameters so that finite-difference time-domain (FDTD) simulations reproduce the observed VLF amplitude. The analysis is performed for the 22.2 kHz JJI transmitter signal received in Chofu, Japan over a mid-range propagation path, ∼900 km. The optimized electron density profile successfully reproduces the characteristic features of the C-region, including a temporary enhancement near 65 km altitude during sunrise. These results demonstrate that mid-range VLF amplitude analysis provides a quantitative tool for identifying transient lower- ionospheric structures. Full article

To address the issues of load aggregation and traffic congestion in faulty electricity–transportation coupled networks (ETCNs), this paper proposes an electric vehicle (EV) charging guidance strategy based on Graph Reinforcement Learning (GRL). First, a graph-structured feature extraction model is developed. The GraphSAGE module is employed to capture the multi-scale spatiotemporal features of the ETCN. The topological changes and energy-information interaction characteristics under fault scenarios are analyzed. Second, a Finite Markov Decision Process (FMDP) framework is established to address the stochastic and dynamic nature of EV charging behavior. The charging station selection and route planning problem is transformed into an agent decision-making process. A reward function is designed by incorporating voltage constraints, traffic flow constraints, and state-of-charge margin penalties. This ensures a balanced consideration of power grid security and traffic efficiency. The FMDP model is then solved using a Deep Q-Network (DQN) to achieve optimal EV charging guidance under fault conditions. Finally, case studies are conducted on a coupled simulation scenario consisting of an IEEE 33-node power distribution system and a 23-node transportation network. Results show that the proposed method reduces the system operation cost to 218,000 CNY, controls the voltage deviation rate of the distribution network at 3.1% in line with the operation standard, and enables the model to achieve stable convergence after only 250 training episodes. It can effectively optimize the charging load distribution and maintain the voltage stability of the power grid under fault conditions. Full article

Topology optimisation is a powerful methodology for identifying efficient material distributions within prescribed design domains. However, conventional approaches rely heavily on gradient-based optimisation and repeated numerical simulations, which impose significant computational cost and limit their use in early-stage design exploration. This work introduces a generative design framework, referred to as Large Language Model-Driven Symbolic Topology Optimisation (LLM-DSTO), in which large language models act as conceptual design generators. Engineering problems are formulated through structured textual descriptions defining the design domain, boundary conditions, loading scenarios, and material constraints. The language model interprets these inputs and produces symbolic representations of candidate structural topologies. The generated layouts are evaluated using physics-informed objective functions and refined iteratively through lightweight computational procedures. The resulting designs exhibit coherent load paths, strong structural connectivity, and material distributions that are consistent with practical manufacturing requirements, including additive manufacturing constraints. The proposed framework is validated across structural, thermal, thermofluid, and compliant mechanism design problems. Quantitative results show that the generated structures achieve approximately 87.5% of the stiffness obtained using the classical SIMP method for the cantilever benchmark, while reaching about 94.3% of the thermal performance in heat sink optimisation. These results are obtained without repeated finite element simulations, demonstrating a significant reduction in computational cost. In addition, the framework is extended to three-dimensional topology generation, producing volumetric structures under a 50% material volume constraint with coherent internal load paths. Full article

Non-parametric information geometry has long faced an “intractability barrier”: in the infinite-dimensional setting, the Fisher–Rao metric is a weak Riemannian metric functional that lacks a bounded inverse, rendering classical optimization and estimation techniques computationally inaccessible. This paper resolves this barrier by building the statistical manifold on the Orlicz space $L_0^{\mathsf{\Phi }}\left(P_f\right)$ (the Pistone–Sempi manifold), which provides the necessary exponential integrability for score functions and a rigorous Fréchet differentiability for the Kullback–Leibler divergence. We introduce a novel Structural Decomposition of the Tangent Space ($T_{f}M=S\oplus S^{\perp }$), where the infinite-dimensional space is split into a finite-dimensional covariate subspace (S)—representing the observable system—and its orthogonal complement ($S^{\perp }$). Through this decomposition, we derive the Covariate Fisher Information Matrix (cFIM), denoted as $G_f$, which acts as the computable “Hilbertian slice” of the otherwise intractable metric functional. Key theoretical contributions include proving the Trace Theorem ($H_G\left(f\right)=\mathrm{Tr}\left(G_f\right)$) to identify G-entropy as a fundamental geometric invariant; demonstrating the Geometric Invariance of the Covariate Fisher Information Matrix (cFIM) as a covariant $(0,2)$-tensor under reparameterization; establishing the cFIM as the local Hessian of the KL-divergence; and characterizing the Efficiency Standard through a generalized Cramer–Rao Lower Bound for semi-parametric inference within the Orlicz manifold. Furthermore, we demonstrate that this framework provides a formal mathematical justification for the Manifold Hypothesis, as the structural decomposition naturally identifies the low-dimensional subspace where information is concentrated. By shifting the focus from the intractable global manifold to the tractable covariate geometry, this framework proves that statistical information is not a property of data alone, but an active geometric interaction between the environment (data), the system (covariate subspace), and the mechanism (Fisher–Rao connection). Full article

The algebraic analysis of linear code parameters reveals deep connections with cryptographic constructions, including the Information Dispersal Algorithms (IDAs) and secret-sharing schemes. In this work, we propose an algebraic method for constructing bases of binary linear codes from subsets of codewords selected according to their generalized Hamming weights (GHWs). The approach employs a degree-compatible monomial ordering on the polynomial ring ${\mathbb{F}}_2[x_1,\dots ,x_n]$ and imposes the conditions $d_1\left(C\right)=1$ and $d_k\left(C\right)=n$. Under these assumptions, we prove the existence of a generator matrix containing an invertible $k\times k$ submatrix, which guarantees correct information reconstruction. This structural property enables the direct application of binary linear codes to information dispersal and recovery mechanisms without the need for larger finite fields. We validate the proposed framework through algebraic proofs and an explicit example illustrating both the dispersal and recovery procedures. These results provide a theoretical foundation for the design of information dispersal schemes relying exclusively on binary linear codes. Full article