物理最新文献

Nonlinear wave phenomena play a central role in fluid dynamics and plasma physics. Soliton interaction is a key feature of such systems. The coupled Korteweg–de Vries (KdV) equations describe these nonlinear and dispersive waves. However, obtaining accurate and stable solutions remains difficult. This study presents a combined analytical and machine-learning approach. Analytical two-soliton solutions are derived using Hirota’s bilinear method. Dispersion relations and interaction properties are obtained to explain the evolution of wave profiles. Interaction times and peak positions are computed numerically to track soliton behavior before, during, and after collision. A modified physics-informed neural network (PINN) is then developed. Global physical constraints, such as energy conservation, are included directly in the loss function. This ensures that the learned solutions remain consistent with the underlying physics. Numerical experiments show that the PINN framework provides improved stability and accuracy. The model captures long-term nonlinear wave interactions with higher fidelity. The proposed method offers a reliable computational framework for analyzing coupled KdV systems and other nonlinear dispersive wave models.

This paper investigates the shadow and quasinormal modes (QNMs) of a Schwarzschild black hole (BH) embedded in a Hernquist dark matter (DM) halo, focusing on the influence of DM parameters – specifically the core radius (r_{s}) and core density (rho _{s}) – on BH observational signatures. We analyze the structure of the BH shadow, lensing ring, and photon ring, showing that the shadow radius increases with both (r_{s}) and (rho _{s}). Using the WKB approximation, Padé approximants, and time-domain integration, we compute the QNMs for scalar, electromagnetic, and axial gravitational perturbations. Our results reveal that the DM halo modifies the spacetime’s effective potential: larger values of (r_{s}) or (rho _{s}) reduce the peak of the potential barrier, leading to lower oscillation frequencies and slower wavefunction damping. These findings highlight the systematic impact of DM on strong-field observables and suggest that BH shadows and QNMs provide a viable means to probe DM profiles in galactic nuclei.

Developing stable and efficient electrode materials is requisite for improving energy-storage performance. In this study, we compare the charge retention behaviour of pristine NiO nanoparticles, Ni-MOFs prepared with single and dual organic linkers, and their corresponding derived and composite structures. The materials were synthesized through a solvothermal route and characterized to confirm structural and morphological features. Their electrochemical response was evaluated in alkaline medium using standard supercapacitor testing methods. The outcomes show a clear progression in retention performance linked to the choice and number of linkers. Pristine NiO exhibits a retention of 68%, while mono-linker and double-linker Ni-MOFs improve this to 74% and 84%, respectively. MOF-derived NiO provides 78% retention, and the corresponding composites show 72% for mono-linker Ni-MOF@NiO, 84% for the double-linker system, and a maximum of 86% for the MOF-derived NiO@NiO. These trends demonstrate that ligand multiplicity and structural integration with NiO significantly enhance charge stability. Inclusive, the work highlights a straightforward strategy for designing MOF-based hybrid electrodes with improved retention for next-generation supercapacitors.

In this work, the propagation of optical solitons in magneto-optic waveguides is investigated with the inclusion of self-phase modulation modeled by Kudryashov’s quintuple power-law. Further, generalized temporal evolution and nonlinear chromatic dispersion have been taken into consideration. Higher-order nonlinearities and dispersive effects are considered in the study. Using the Sardar sub-equation method, soliton solutions are obtained such as bright, kink, and double-hump profiles. Numerical simulations confirm theoretical findings and show stability for the optical soliton under varying parameter conditions. Bifurcation analysis and chaos analysis are performed using phase portraits, time-series data, Lyapunov exponents, and bifurcation maps, detailing the dynamical characteristics of the system. These results will add to the knowledge about soliton dynamics in magneto-optic waveguides and will indicate some possible applications in high-speed optical communication and photonic technologies.

This paper proposes and designs a dynamic radiative cooling smart window based on a metal-dielectric multilayer structure. An improved genetic algorithm is employed to optimize the number of layers, their thickness, and material type to achieve different target visible transmittance levels (from 30 to 60%). The optimal structure identified is a 10-layer stack of VO₂ and BaF₂ on a 1-μm-thick BaF₂ substrate, achieving an average visible transmittance of 50%. This design provides significant spectral control capabilities. Below its phase-transition temperature, the window is in an insulating state with a high near-infrared (NIR, 0.78–2.5 μm) transmittance of 80.8% and a low mid-infrared (MIR, 8–13 μm) emissivity of 6.7%, facilitating passive solar heating. Above the phase-transition temperature, it switches to a metallic state with a low NIR transmittance of 10.6% and a high MIR emissivity of 88.3%, enabling effective radiative cooling. The window exhibits substantial modulation abilities, with |ΔT_NIR| and Δε values of 0.70 and 0.82, respectively. This energy-free, self-adaptive technology, which dynamically regulates thermal radiation in response to ambient temperature, is ideally suited for enhancing energy efficiency in green buildings and smart windows under varying weather conditions.

The paper proposes the analysis of a rather general reaction–diffusion equation in ((2+1))-dimensions with time-dependent coefficients. The novelty brought by our approach refers to the analysis of the equation with the diffusion coefficient as an explicit function of time and the dependent variable. The free derivative term called source will be considered here to be both linear and quadratic in the main variable. The reported results are of twofold importance: (i) for modeling real diffusion phenomena, such as the spread of tumor cells throughout brain tissue; (ii) for investigating mathematical models described by PDEs with variable coefficients in ((2+1))-dimensions, insufficiently studied until now.

This work examines the coupled flow–acoustic behaviour of a compact hybrid motorcycle muffler incorporating half-elliptical baffles, dual Helmholtz resonators, and ceramic-wool absorptive packing for a high-speed single-cylinder engine. Baseline spectral analysis of the untreated exhaust identified a dominant tonal component at 511 Hz, which guided the temperature-corrected tuning of the resonator cavities. One-dimensional acoustic simulations using Ricardo Wave predicted frequency-dependent transmission loss characterized by resonator-induced attenuation in the mid-frequency range and a progressive broadband increase associated with dissipative mechanisms. Three-dimensional CFD analysis revealed that the half-elliptical baffles condition the internal flow by redirecting the exhaust stream towards the absorptive region while limiting large-scale recirculation, thereby promoting stable acoustic coupling with the resonators under varying operating conditions. Experimental validation was carried out using ISO 5130:2019–compliant, dB(C)-weighted sound pressure level measurements, which indicated reductions of approximately 10–11 dB(C) at idle and 14–15 dB (C) at 7500 rpm, achieving compliance with Formula Bharat noise limits. The combined numerical and experimental results indicate that curvature-conditioned flow management, when integrated with reactive and dissipative elements, provides an effective and compact approach for broadband and tonal noise reduction in small-displacement engine exhaust systems.

This study presents a novel non-invasive dual wavelength photoacoustic imaging (DWPAI) system developed to detect distinct spectral signatures of historical ink families with high sensitivity. The recorded images were processed through unsupervised machine learning algorithms, to provide a quantitative indicator of the ink type based on the ratio of photoacoustic signals following excitation in the visible (532 nm) and near-infrared (1064 nm). The performance of the system was demonstrated using three inks commonly employed in 19th-century Europe and Greece, specifically Chinese ink, sepia ink, and iron gall ink. Chinese ink provided signal ratios around 0.9, sepia ink yielded consistent ratios at 0.6, whereas iron gall ink exhibited considerably higher ratios in the order of 1.7. Our findings demonstrate the ability of DWPAI to reliably and non-destructively discriminate between major ink families independent of the writer or the nib type used. Apart from guiding conservators in safe and effective restoration interventions, DWPAI may provide archaeologists and art historians with complementary insights into the dating, origin, and material characterization of various types of artifacts. Its demonstrated accuracy and versatility establish DWPAI as a powerful diagnostic tool for cultural heritage research, archaeology, and conservation science.

We consider a rational scalar field model in (1+1)-dimensions where the long-range character of the kinks is controllable. We show via numerical simulations that kinks with long-range tails on both sides can exhibit resonance windows. The resonant energy exchange mechanism occurs via the excitation of quasinormal modes, which we obtain via a spectral analysis. Additionally, we locate a resonance window in a family of (phi ^{10}) models with long-range tails on both sides. Moreover, we propose a new algorithm for initializing long-range kink collisions, based on convection–diffusion dynamics.