Search Results (5,427)

Environmental stressors like low temperature, low relative humidity (RH), and particulate matter (PM2.5), promote tear film instability and dry eye disease. This study investigates how these conditions alter the interfacial behavior of meibomian gland secretion (MGS) films in vitro and evaluates the capacity of mucomimetic polymers (0.5% hyaluronic acid [HA], polyvinylpyrrolidone [PVP], and chondroitin sulfate [CHS]) to suppress these impacts. MGS films over polymer-containing aqueous subphases were analyzed using a Langmuir trough and Brewster angle microscopy under adverse conditions (20 °C subphase, 20% RH, PM2.5 exposure). A sophisticated analytical framework was developed to evaluate MGS duplex multilayers: (i) a Volmer equation-based 2D-VES model to probe interfacial molecular properties (limiting area, compressibility, cohesion pressure) and (ii) a combined Maxwell viscoelastic and diffusion-relaxation model to quantify the dilatational relaxation modulus. Results indicate that despite their distinct nature, environmental stressors similarly disrupt the multilayer structure, reorganization, and rheological properties of MGS layers during blink-like deformations. Polymer supplementation moderated these adverse effects, yielding partial recovery of film structure and isothermal reversibility. Distinct mechanisms of action for HA, PVP, and CHS at the film/aqueous interface are elucidated. Full article

Composite cylindrical shells suffer from thermal buckling in harsh thermal environments, impairing overall structural safety. This study aims to improve the thermal stability of such shells by investigating the thermal buckling behavior of graphene origami metamaterial-reinforced composite cylindrical shells. Four common thickness-wise distribution patterns (UD, FG-X, FG-O, and FG-A) are adopted, and temperature-dependent material properties are taken into account. Based on classical thin-shell theory with geometric nonlinearity, thermal buckling governing equations are derived. Analytical solutions of critical buckling temperature rises are obtained via an iterative procedure for both temperature-dependent and temperature-independent material models. Parametric studies are conducted to explore key influencing factors including reinforcement distribution, filler content, folding degree, tangential edge constraints, and shell geometric parameters. The results reveal that critical buckling temperature is strongly dependent on graphene origami distribution and structural features. Increasing filler content enhances thermal buckling resistance, while folding degree also dominates structural stability. Additionally, tangential constraints and geometric dimensions exert obvious effects. Significant discrepancies exist between two material models, verifying that temperature-dependent material properties are essential for precise thermal buckling analysis of the proposed composite shells. Full article

We study gravitational lensing by $k-n$ generalized black-bounce space-times both in regimes of weak and strong field approximations. These metrics interpolate between regular black holes and one-way or traversable wormholes. First, we investigate the light-like geodesic trajectories and derive an analytical expression for the deflection angle in terms of the bounce parameter in the weak-field gravitational regime. We then turn to the strong-field gravitational regime and display the behavior of the bending angle as a function of both the impact parameter and the bounce parameter. Next, using the lens equations, we analyze how the observables for Sagittarius A* behave concerning the bounce parameter. We obtain the shadow’s radii for some black-bounce metrics and plot the graph of their sizes, comparing them with the Schwarzschild one. Full article

Finite-dimensional closed-system gate synthesis is a geometric optimal-control problem on a compact Lie group, but global solutions require careful treatment of logarithm branches, determinant-one constraints, drift, anisotropic penalties, and amplitude limits. This paper assembles and extends a self-contained family of exactly solved benchmarks under explicit hypotheses. For a positive right-invariant quadratic metric, smooth stationary curves obey the Euler–Arnold equation and possess Lax invariants. Under fully actuated isotropic control on $\mathrm{SU}\left(n\right)$, the fixed-time minimum action is the squared Hilbert–Schmidt distance divided by twice the gate time and is generated by a minimum-norm skew-Hermitian logarithm. A finite eigenphase-unwrapping rule computes that logarithm, while the affine spectral-width cut wall and a branch-gap criterion distinguish stable selection from set-valued behavior. Drift is removed isometrically whenever the metric is invariant under the drift adjoint action. Weighted action and box-constrained time on maximal tori and arbitrary closed commuting subtori reduce to explicit period-lattice problems; the latter gives distinct strict determinant-one and projective period lattices for an ideal fSim sector. Numerical validation uses 1506 Haar-random targets through dimension 128, a complete cost-bounded dynamic-program cross-check for every target, 300 direct Cartesian branch enumerations, 120 random-conjugation tests with analytically prescribed spectra, 240 independently cross-checked random weighted-torus instances, a separate $\mathrm{SU}\left(256\right)$ stress target, large-cluster selector regressions, repeated timing trials, GRAPE–L-BFGS, a first-order sequential Krotov-type update with exact discrete propagation and Fréchet derivatives, randomized and drift-adapted CRAB–Powell bases, and cut-locus tests that exercise the implemented Schur/logarithm solver. When sparse actuation, decoherence, leakage, or uncertainty invalidates the ideal hypotheses, the exact results are positioned as ideal-model reference values, branch-aware seeds, and regression tests rather than as certificates for the enlarged objective. Full article

This study includes an interpretable machine learning (ML) framework for predicting the shear contribution of externally bonded fiber-reinforced polymer (FRP) composites in reinforced concrete beams. A database including total 313 experimental specimens was collected from previous experimental research. The data screening process has been conducted using the Isolation Forest algorithm, resulting in 268 cleaned specimens. The cleaned database was divided into a training subset containing 214 specimens and an independent test set containing 54 specimens. The trained subset was enlarged into 5204 synthetic data using two advanced generative models including Wasserstein generative adversarial network and conditional Variational autoencoder (CVAE). Separate constrained monotonic neural network (CMNN) models were then trained on both datasets and WGAN-based CMNN achieved R² = 0.9524 or the synthetic training dataset and R² = 0.9120 for the independent test set, whereas the CVAE-based CMNN achieved corresponding values of 0.9632 and 0.9011. To improve practical applicability, response functions were extracted from WGAN-based CMNN and fitted with analytical expressions to derive a closed-form prediction equation. The proposed equation was independently validated using separate unseen test specimens, which were not used in CMNN training and achieved R² = 0.79, RMSE = 24.98 kN, MAE = 19.65 kN, MAPE = 21.72%, VAF = 79.35%, U95 = ±54.94 kN, SI = 3.04, and PI = 0.11. Compared with ACI 440.2R-17, CSA-S806.12, CNR-DT200 R1.2013, TR-55, and JSCE, the proposed equation showed superior accuracy while maintaining a transparent and design-oriented format. Full article

The present article introduces and investigates a class of implicit fractional differential equations with time-varying state-dependent feedback, a setting that significantly extends existing fractional delay models. Existence and uniqueness results are established via the Banach fixed-point theorem, while sharp exponential growth estimates for the solutions are derived using a generalized Gronwall-type inequality. These results provide new analytical insights into the qualitative behavior of fractional systems with dynamically evolving feedback mechanisms. Full article

Accurate network traffic prediction is important for the autonomy, resilience and resource orchestration of 6G and AI-native communication infrastructures, while also supporting green networking and digital twin network applications. However, existing data-driven prediction models face several limitations: over-reliance on massive labeled data, physically implausible predictions, black-box non-interpretability and over-parameterization that impairs edge deployment. To address these issues, this paper proposes a Physics-Informed Network Traffic Prediction (PINTP) framework for graph topology network traffic prediction, which formalizes network traffic evolution as Graph-based Advection–Diffusion–Reaction (ADR) equations and embeds physical regularization into the neural architecture. The framework adopts a hybrid differentiation paradigm unifying automatic differentiation for temporal dynamics and spectral graph theory-derived operators for discrete spatial topologies, and designs a physics-constrained composite loss function with data-driven collocation to balance data fidelity and physical consistency. Experiments are conducted in two complementary settings: a 100-node synthetic random-graph benchmark that evaluates the full graph-topological formulation, and a topology-unavailable real-world telemetry proxy based on Alibaba Cluster Trace v2018 for evaluating sparse-label physics-informed temporal regularization. Comparative analysis with mainstream baselines, including Multilayer Perceptron (MLP), Spatio-Temporal Graph Convolutional Network (STGCN), Graph WaveNet, Transformer, Temporal Convolutional Network (TCN), and XGBoost, shows that the proposed PINTP/PINN implementation achieves a test $R^2$ of 0.898 and MSE of 0.000723 on the 100-node synthetic graph benchmark, close to the strongest Transformer result ($R^2=0.900$, MSE = 0.000710), while using substantially fewer trainable parameters. PINTP/PINN also outperforms Graph WaveNet, STGCN and TCN in this setting, indicating that physics-informed regularization can remain competitive as graph size increases. On the Alibaba proxy task, PINTP/PINN achieves the strongest result among the evaluated models with a test $R^2$ of 0.963. In an independent Alibaba ablation protocol, physical regularization (e.g., $\lambda =10.0$) reduces the mean squared error by 89.15% compared with pure data-driven models and helps mitigate overfitting. This work presents a systematic PINTP framework for graph topology network traffic prediction, achieving competitive prediction accuracy with high parameter efficiency and a degree of physical interpretability. It helps address several limitations of traditional data-driven models, indicates potential for future deployment-oriented studies on real-time network management and resource-constrained edge analytics, and provides an interpretable modeling route for physics-informed network analytics in next-generation communication systems. Full article

Modeling fractional behaviors is of great importance in the characterization of complex phenomena governed by power-law dynamics, long-range correlations, memory effects, and fractal structures, which are frequently analyzed in scientific and engineering applications. In this work, we propose a fractional formulation to steady-state heat-conduction modeling. The problem is explored on a circular plate, and the adopted approach combines two techniques, namely separation of variables and fractional power-series expansion. Special emphasis is placed on clear and didactic analytical development, with each mathematical step carefully introduced and systematically detailed. Numerical results are also presented and discussed. Full article

Groundwater levels in gated agricultural drainage networks respond to canal-stage changes, rainfall, antecedent storage, and changing operating conditions. We examined groundwater and surface-water records from the Chezegou Watershed, Huaibei Plain, China (2019–2024), using analytical solutions of the linearized Boussinesq equation. Groundwater was measured mostly at intervals of about five days, and analyses used the original observation dates. Using the half-power criterion |Z|² = 1/2 and hydraulic diffusivities of 5.27 × 10³–1.05 × 10⁴ m² d⁻¹, cutoff periods were 56.1–111.7 d at 150 m and 399.1–794.1 d at 400 m; the half-power distance for a 30 d cycle was 77.7–109.7 m. The record also includes a 106 mm storm on 12 July 2020. Groundwater depth at J5 (490 m from the canal) decreased from 2.23 to 0.54 m in 48 h, a 1.69 m water-level rise, while J9 (1020 m) rose by 1.79 m over five days. These observations show a rapid shallow-groundwater head response, although water-level records alone do not separate vertical recharge from hydraulic-pressure transmission. Canal influence depends on forcing duration and aquifer properties, while rainfall responses also reflect lateral boundaries and the shrink-swell behavior of Shajiang black soil. The calculated time-distance relations provide site-specific reference values for canal operation. Full article

We investigate the dynamics of a driven quantum field coupled to a finite-temperature reservoir. The corresponding master equation is solved using superoperator techniques, yielding an analytical expression for the density operator. To obtain a compact and physically transparent description of the dynamics, we adopt a phase-space representation based on the Husimi Q-function. For an initially coherent state, we derive a closed-form Gaussian expression for the Husimi Q-function whose stationary limit corresponds to a displaced thermal state. This approach also enables an analytical study of quantum-interference dynamics for an initial superposition of coherent states. Furthermore, we derive the corresponding Fokker–Planck equation for the Husimi Q-function and obtain closed-form expressions for relevant statistical quantities, including the mean photon number, the photon-number standard deviation, and the Mandel parameter. We also investigate the Wehrl and linear entropies, which quantify the loss of phase-space information and purity induced by the thermal environment. The framework provides a complete analytical characterization of the phase-space dynamics, photon statistics, and entropic properties of driven–dissipative quantum fields while avoiding the explicit manipulation of the density operator. Full article

This study presents the Yang Transform Adomian Decomposition Method (YTADM), a semi-analytical framework for solving one-dimensional linear and nonlinear delay fractional partial differential equations involving the Caputo fractional derivative. The proposed method combines the Yang transform with the Adomian decomposition method to construct recursive solution series while efficiently handling delayed nonlinear terms. The applicability of the proposed framework is demonstrated through several examples, including proportional-delay Burgers-type equations, and its convergence properties are analyzed. The obtained results show that YTADM yields rapidly convergent semi-analytical approximations and provides an effective framework for solving one-dimensional delay fractional partial differential equations. Full article

This study presents an analytical elastic solution using the complex variable approach to determine the soil disturbance surrounding a shallow rectangular tunnel, taking into account the effect of gravity and considering the influence of grouting pressure applied at the tunnel boundary. A theoretical calculation model is first established by applying stress boundary conditions along the tunnel boundary. To handle the geometric complexity of the rectangular cavity, a conformal mapping function suitable for non-circular opening is adopted. This function maps the original half-infinite region with a rectangular cavity onto an annulus in the transformed plane, thereby converting the irregular cavity problem into a more tractable configuration. The boundary conditions at the ground surface and the tunnel interface are then addressed using Fourier series expansion. By solving these series-expanded equations, all unknown coefficients of the analytical functions are determined, leading to an analytical elastic solution for soil disturbance. Numerical verification validates the accuracy of the proposed approach. Moreover, parametric studies are conducted to evaluate the effects of soil mechanical properties and tunnel geometry on the soil disturbance. The findings offer a valuable reference for future investigations into construction-related issues associated with rectangular tunnels. Full article

We study the unsteady mechanical response of an incompressible viscoelastic polymeric fluid in a flat channel, governed by the Vinogradov–Pokrovskii rheological model. The motion arises from an electrohydrodynamic reduction of Poiseuille type, after which the mechanical subsystem decouples from the electric field; the velocity then depends on time and on the transverse coordinate only. Treating the rheological parameter as small, we reduce the governing system in the leading-order approximation to a non-autonomous second-order evolution equation whose stiffness coefficient relaxes exponentially in time, so that the nonstationarity is driven by the internal relaxation of the normal stress rather than by an external force. For spatially homogeneous initial normal stress, we diagonalize the Galerkin system in the sine basis and obtain an explicit modal representation in which each mode satisfies a Bessel equation whose order depends on the mode number. This yields a critical index that splits the modes into three regimes—real order, zero order, and purely imaginary order—a structure absent from the classical UCM and Oldroyd-B solutions. Using the explicit representation, we prove convergence of the modal series and show that the solution decays in the long-time limit, so that the rest state is asymptotically stable in the natural energy phase space. The analytical solution is confirmed numerically. Full article

Information asymmetry in the unobservable sustainability attributes of food products, together with widespread consumer scepticism toward unverifiable green claims, continues to constrain the translation of pro-environmental attitudes into actual purchases. Blockchain technology has been positioned as a structural response to this asymmetry because its tamper-evident and independently verifiable records lower the cost of substantiating credence claims. However, the bulk of the consumer-facing evidence has examined blockchain as an object of adoption rather than as a conduit of information whose quality determines its persuasive force. Drawing on Signalling Theory within a Stimulus–Organism–Response framework, this study advances and tests a model in which blockchain-enabled information quality (stimulus) influences sustainable purchase decision-making (response) through perceived credibility (organism), with consumer trust serving as a boundary condition on the stimulus-to-organism path. A cross-sectional survey of 412 agri-food consumers was analysed using partial least squares structural equation modelling to test direct, mediated, moderated, and conditional indirect effects. The contributions are threefold: blockchain is re-specified as an information signal whose quality, rather than the technology itself, drives downstream sustainability behaviour; perceived credibility is empirically isolated as the psychological mechanism that carries that effect; and message-level credibility is analytically separated from dispositional trust, addressing a recurring conflation in the digital transparency literature. Full article

The generalized Burgers–Huxley (gBH) equation arises as a canonical model in nerve-pulse propagation (generalizing the Hodgkin–Huxley/FitzHugh–Nagumo excitable-media framework), in population dynamics with Allee-threshold reaction kinetics, and in nonlinear wave propagation in dispersive media; accurate benchmark solutions are essential for quantitative predictions in these domains. We couple the fourth-order exponential time differencing scheme ETDRK4 with a Chebyshev collocation spatial discretization and a linear boundary-lifting procedure to solve the gBH equation on a bounded interval with non-homogeneous Dirichlet data. On the canonical Ismail–Raslan–Rabboh travelling-wave benchmark the scheme attains $L^{\infty }$ errors at the level of floating-point round-off (∼10⁻¹⁹ absolute, ∼10⁻¹⁵ relative) with as few as $N=2$ collocation points and a single time step of size $\Delta t=1.0$—that is, three total nodes and one ETDRK4 advance. In strongly nonlinear regimes ($\gamma =0.1, 0.3, 0.5, 0.9$) the scheme exhibits approximately $\mathcal{O}(\Delta t^{2.45})$ temporal convergence across all four parameter values, consistent with the classical Hochbruck–Ostermann order reduction for exponential integrators on parabolic PDEs with non-homogeneous Dirichlet data. Used as a high-accuracy probe, the scheme provides a diagnostic of independent interest: the wave-speed formula of Wang, Zhu and Lu, still appearing as the exact-solution benchmark in numerical studies as recently as 2020, does not satisfy the partial differential equation. The corrected formula stated by Deng and verified symbolically by Appadu and Tijani is the unique value that makes the travelling-wave ansatz a genuine solution. We derive the residual associated with Wang’s formula in closed form, $R=\frac{\gamma A_1}{2}(A_2-A_2^{\mathrm{W}})(1-v^2)$, and show both analytically and numerically that reported errors for schemes benchmarked against Wang’s formula coincide with the analytical wave-profile gap $\frac{\gamma A_1}{2}|A_2-A_2^{\mathrm{W}}|$ rather than with true scheme accuracy. At the Ismail benchmark this gap equals $3.748\times {10}^{-7}$, which matches the N- and $\Delta t$-independent plateau observed when the scheme is measured against Wang’s profile. In the nerve-pulse and excitable-media interpretation, the two formulas correspond to action-potential propagation speeds of opposite sign at the Ismail benchmark, underscoring that the correction is not a mere algebraic curiosity but changes the qualitative physical prediction of the model. Full article