物理最新文献

This paper investigates the propagation characteristics of SH-type waves originating from a point source situated at the interface of a unique structure comprising a functionally graded viscoelastic (FGV) layer of finite depth overlying a functionally graded monoclinic (FGM) half-space. The upper viscoelastic layer exhibits a hyperbolic gradient property in its material constants, while an exponential gradient property characterizes the lower monoclinic half-space. Employing the Fourier transform and Green’s function method to account for surface and interfacial boundary conditions, a dispersion relation for the SH-type waves is derived. The obtained dispersion relation for the gradient layered structures reveals a complex interplay between wave phenomena and material properties. Numerical analysis is performed to illustrate the theoretical results for various gradient parameter values, demonstrating a significant influence on dispersion curves, phase velocity, group velocity, and wave number. This understanding holds paramount importance for seismic imaging, geological resource exploration, and the design of resilient infrastructure, thereby fostering innovation in geophysics and engineering.

The rapid advancements in machine learning have made its application to anomalous diffusion analysis both essential and inevitable. This review systematically introduces the integration of machine learning techniques for enhanced analysis of anomalous diffusion, focusing on two pivotal aspects: single trajectory characterization via machine learning and representation learning of anomalous diffusion. We extensively compare various machine learning methods, including both classical machine learning and deep learning, used for the inference of diffusion parameters and trajectory segmentation. Additionally, platforms such as the anomalous diffusion challenge that serve as benchmarks for evaluating these methods are highlighted. On the other hand, we outline three primary strategies for representing anomalous diffusion: the combination of predefined features, the feature vector from the penultimate layer of neural network, and the latent representation from the autoencoder, analyzing their applicability across various scenarios. This investigation paves the way for future research, offering valuable perspectives that can further enrich the study of anomalous diffusion and advance the application of artificial intelligence in statistical physics and biophysics.

We present our analysis of the identified hadrons ((pi ^+), (K^+), and p) kinetic freeze-out properties in relativistic collisions at (sqrt{s_{NN}} = 200) GeV. We analyze the transverse momentum spectra of these hadrons across various collision systems, such as copper–copper ((Cu-Cu)), zirconium–zirconium ((Zr-Zr)), ruthenium–ruthenium ((Ru-Ru)), uranium–uranium ((U-U)), and gold–gold ((Au-Au)) collisions, in distinct centrality intervals at the same center-of-mass energy using the modified Hagedorn model with embedded flow. The freeze-out parameters, namely the kinetic freeze-out temperature ((T_0)), transverse flow velocity ((beta _T)), and the entropy-related parameter (n), are extracted. Taking (T_0) and (beta _T) as common, it is observed that in all the above collisions, (T_0), (beta _T), and the parameter n diminish toward the periphery and are greater in central collisions. However, (T_0) in central collisions across all the systems remains unchanged, indicating a phase transition from hadronic matter to quark–gluon plasma. Furthermore, the temperature required for the phase transition across various systems is different. Large systems exhibit a shift in the potential start of the phase transition in peripheral collisions, which is intriguing. We also observe a direct relation between the extracted parameters and the system’s size.

In the last three decades, numerical stochastic perturbation theory (NSPT) has proven to be an excellent tool for calculating perturbative expansions in theories such as Lattice QCD, for which standard, diagrammatic perturbation theory is known to be cumbersome. Despite the significant success of this stochastic method and the improvements made in recent years, NSPT apparently cannot be successfully implemented in low-dimensional models due to the emergence of huge statistical fluctuations: as the perturbative order gets higher, the signal to noise ratio is simply not good enough. This does not come as a surprise, but on very general grounds, one would expect that the larger the number of degrees of freedom, the less severe the fluctuations will be. By simulating 2D O(N) non-linear sigma models for different values of N, we show that indeed the fluctuations are tamed in the large N limit, meeting our expectations: for a large number of internal degrees of freedom (i.e. for large enough N), NSPT perturbative computation can be pushed to large perturbative orders n. By re-expressing our perturbative expansions as power series in the gN (’t Hooft) coupling, we show some evidence that at any given order n there is a tendency to gaussianity for the stochastic process distributions at large N. By summing our series, we can verify leading order results for the energy and its (field theoretic) variance in the large N limit. We finally establish general relationships between the various perturbative orders in the expansion of the (field theoretic) variance of a given observable and combinations of variances and covariances of given orders NSPT stochastic processes. Having established all this, we conclude discussing interesting applications of NSPT computations in the context of theories similar to O(N) (i.e. (CP(N-1)) models).