Roberto da Silva

Financial markets are complex systems whose return distributions exhibit heavy tails, traditionally described by power laws. An alternative framework based on Tsallis nonextensive statistical mechanics allows both the central region and the tails of the distribution to be represented by a single q-Gaussian functional form. In this work, we systematically compare these two descriptions across different time scales, considering stock markets from Brazil and the United States, as well as traditional assets and cryptocurrencies. The q-Gaussian parameters are estimated using the method of moments ratio, whereas the power-law parameters are obtained by maximum likelihood estimation in the asymptotic region. The quality of the fits is assessed using goodness-of-fit measures, bootstrap resampling, and the Vuong test. When the analysis is restricted exclusively to the extreme tails, the power law generally provides a better description, as expected from the asymptotic power-law behavior of the q-Gaussian itself. However, this superiority is not sufficiently pronounced to outweigh the main advantage of the q-Gaussian: its ability to provide a consistent description of the entire distribution, including the central peak, bulk, and tails, over all investigated time scales. Moreover, the evolution of the parameter q offers a simple and direct characterization of aggregational Gaussianity and of the transition between heavy- and short-tailed statistical regimes, without requiring separate fits for different regions of the distribution. These results indicate that, although the power law remains particularly suitable for describing extreme events, the q-Gaussian provides a broader and more practical framework for characterizing the statistical evolution of financial returns.

Visibility graphs are spatial interpretations of time series. When derived from the time evolution of physical systems, the graphs associated with such series may exhibit properties that can reflect aspects such as ergodicity, criticality, or other dynamical behaviors. It is important to describe how the criticality of a system is manifested in the structure of the corresponding graphs or, in a particular way, in the spectra of certain matrices constructed from them. In this paper, we show how the critical behavior of an Ising spin system manifests in the spectra of the adjacency and Laplacian matrices constructed from an ensemble of time evolutions simulated via Monte Carlo (MC) Markov chains, even for small systems and short MC steps. In particular, we show that the number of spanning trees-or its logarithm-which represents a kind of structural entropy or topological complexity here obtained from Kirchhoff's theorem, can, in an alternative way, describe the criticality of the spin system. These findings parallel those obtained from the spectra of correlation matrices, which similarly encode signatures of critical and chaotic behavior.

In this work, we investigated how the use of complex networks as catalytic surfaces can affect the phase diagram of the Yaldram–Khan model, as well as how the order of the phase transitions present in the seminal work behaves when randomness is added to the model. The study was conducted by taking into consideration two well‐known random networks, the Erdős‐Rényi network (ERN), with its long‐range randomness, and the random geometric graph (RGG), with its spatially constrained randomness. We perform extensive steady‐state Monte Carlo simulations for , the NO dissociation rate, and show the behavior of the reactive window as a function of the average degree of the networks. Our results also show that, different from the ERN, which preserves the nature of the phase transitions of the original model for all considered average degrees, the RGG seems to have two second‐order phase transitions for small values of average degree.

INTRODUCTION: Neuromonitoring enhances patient assessment in neuroscience nursing, yet its integration varies globally. At the 2025 International Neuroscience Nursing Research Symposium, nurses from 16 countries discussed the adoption of neuromonitoring devices. This proceedings article explores international perspectives on neuromonitoring use, barriers, and facilitators. METHODS: Participants summarized their country's neuromonitoring technology level, key facilitators and barriers to adoption, and devices with the greatest potential impact. RESULTS: Significant disparities exist in neuromonitoring accessibility. Developed countries frequently use continuous electroencephalography, intracranial pressure monitoring, and pupillometry, whereas lower-resource settings face financial and training limitations. Key facilitators include policy support, funding, and interdisciplinary training, whereas barriers include cost, limited resources, and competency gaps. CONCLUSION: Nurses worldwide recognize how neuromonitoring may improve outcomes. International collaboration, standardization of training, and technological advancements could help bridge disparities in access and implementation. Addressing systemic barriers and investing in education and policy support will enhance equitable neuromonitoring integration, ultimately improving neurological patient care across diverse healthcare settings.

The contact process is a nonequilibrium Hamiltonian model that, even in one dimension, lacks an exact solution and has been extensively studied via Monte Carlo simulations, both in steady-state and time-dependent scenarios. Although the effects of particle mobility and diffusion on criticality have been preliminarily explored, they remain poorly understood in many aspects. In this work, we examine how the critical rate of the model varies with the probability of particle mobility. By analyzing different stochastic evolutions of the system, we employ two modern approaches: (1) Random Matrix Theory (RMT): By building on the success of RMT, particularly Wishart-like matrices, in studying statistical physics of systems with up-down symmetry via magnetization dynamics [R. da Silva, IJMPC 2022], we demonstrate its applicability to models with an absorbing state; (2) Optimized Temporal Power Laws: By using short-time dynamics, we optimize power laws derived from ensemble-averaged evolutions of the system. Both methods consistently reveal that the critical rate decays with mobility according to a simple Belehradek function. Additionally, a straightforward mean-field analysis supports the decay of the critical parameter with mobility, although it predicts a simpler linear dependence. We also demonstrate that the more sophisticated pair approximation mean-field model developed by ben-Avraham and Köhler aligns closely with the Belehradek function, precisely matching our lattice simulation results.