Brazilian Journal of Physics最新文献

We investigated daily variations in the particle count rate recorded by the ground-level Tanca Cherenkov detector, which contains 11,400 liters of water and is located in the central region of the South Atlantic Anomaly (SAA). The dataset analyzed corresponds to the year 2017 and reveals two distinct types of modulation in the daily count rate. In this study, we focus on the first and smaller modulation, which occurs during the early morning hours. We show that this modulation can be attributed to Earth’s orbital motion around the Sun, known as the Compton–Getting (C–G) effect. Seasonal variations in the Compton–Getting (C–G) effect were identified, with an annual mean amplitude of (0.049 ± 0.005)% and a phase at 03:37 LT, about 3.5 hours earlier than the expected 06:00 LT. This phase advance is attributed to the East–West asymmetry in the primary cosmic-ray flux, as (sim )90% of the secondary particles detected by Tanca originate from primaries with energies up to 2 TeV, still influenced by Earth’s magnetic field. A secondary modulation, observed in the afternoon, corresponds to the Solar Diurnal (SD) variation. These results provide a detailed characterization of the C–G modulation under the low-rigidity conditions of the South Atlantic Anomaly (SAA).

Computational Fluid Dynamics (CFD) is central to science and engineering, but faces severe scalability challenges, especially in high-dimensional, multiscale, and turbulent regimes. Traditional numerical methods often become prohibitively expensive under these conditions. Quantum computing and quantum-inspired methods have been investigated as promising alternatives. This review surveys advances at the intersection of quantum computing, quantum algorithms, machine learning, and tensor network techniques for CFD. We discuss the use of Variational Quantum Algorithms as hybrid quantum-classical solvers for PDEs, emphasizing their ability to incorporate nonlinearities through Quantum Nonlinear Processing Units. We further review Quantum Neural Networks and Quantum Physics-Informed Neural Networks, which extend classical machine learning frameworks to quantum hardware and have shown advantages in parameter efficiency and solution accuracy for certain CFD benchmarks. Beyond quantum computing, we examine tensor network methods, originally developed for quantum many-body systems and now adapted to CFD as efficient high-dimensional compression and solver tools. Reported studies include several orders of magnitude reductions in memory and runtime while preserving accuracy. Together, these approaches highlight quantum and quantum-inspired strategies that may enable more efficient CFD solvers. This review closes with perspectives: quantum CFD remains out of reach in the NISQ era, but quantum-inspired tensor networks already show practical benefits, with hybrid approaches offering the most promising near-term strategy. We emphasize that, in addition to being a review, this is also an introductory material to the topics covered.

The term cancer is generally used to refer to a large group of diseases. Neoplastic or cancerous cells grow and divide in an uncontrolled manner and, as a result, form tumours that progressively increase in size. There are different types of cancer which are categorised according to the type of tissue or fluid originated from inside the body. We incorporate chemotherapeutic agents into a mathematical model of non-small cell lung cancer interacting with immune cells. The immune system cells included in the model are composed of macrophages and cytotoxic lymphocytes. In our model, tumour infiltrating lymphocyte therapy is also considered. In this work, we show how pulsed chemotherapy combined with immunotherapy, known as chemoimmunotherapy, can improve survival outcomes in the treatment of non-small cell lung cancer.

In this study, we employ the recently developed recurrence microstate probabilities as features to improve accuracy of several well-established machine learning (ML) algorithms. These algorithms are applied to classify discrete and continuous dynamical systems, as well as colored noise. We demonstrate that the dynamical characteristics quantified by this method are effectively captured in recurrence microstate space, a space defined solely by the recurrence properties of the signal. This space change reduces dimensions, which also reduces the time needed to perform calculations and obtain relevant information about the underlying system. Here, we also demonstrate that a few optimal machine learning (ML) algorithms are particularly effective for classification when combined with recurrence microstates. Furthermore, these new machine learning vectors significantly reduce memory usage and computational complexity, outperforming the direct analysis of raw data.

Terahertz time-domain spectroscopy (THz-TDS) provides a powerful platform for investigating low-energy excitations in quantum materials. Because these materials are often limited in size, experimental setups typically rely on tightly focused beams and metallic holders with small apertures. In this work, we perform a systematic study of how aperture geometry influences THz signal transmission in a standard free-space configuration. By analyzing time- and frequency-domain data for circular apertures of varying diameters and thicknesses, we quantify the spatial and spectral filtering effects imposed by aperture size. We show that small apertures progressively attenuate low-frequency components of the transmitted signal, while higher-frequency content remains comparatively unaffected. These effects become especially significant for apertures smaller than typical THz beam waists, resulting in amplitude suppression and phase distortions that compromise the accuracy of frequency-domain analysis and optical parameter retrieval. To validate these observations, additional measurements were performed on a representative quantum material, confirming the practical relevance of the identified aperture effects. The transmitted intensity as a function of aperture diameter also provides a straightforward method for estimating the beam waist at the focus. In contrast, standard aperture thicknesses do not introduce measurable distortions, confirming the adequacy of treating thick, non-resonant apertures as dielectric slabs. These findings establish practical guidelines for aperture selection in THz-TDS and underscore the importance of preserving low-frequency response for reliable characterization of quantum materials.

This study investigates the influence of a non-ideal external force on the complexity of attraction basins in a system of coupled Van der Pol oscillators. By introducing a phase-modulated force (varvec{Phi (t) = f}_{varvec{0}}, varvec{cos [omega t + a}_{varvec{0}}, varvec{sin (b}_{varvec{0}} varvec{omega t)]}), we analyze how parameters (varvec{a}_{varvec{0}}), (varvec{b}_{varvec{0}}), and (varvec{omega }) affect the system’s dynamics, particularly the structure and fractal properties of attraction basins. Using numerical simulations, we compute the topological entropy ((varvec{h}_{varvec{top}})) and uncertainty coefficient ((varvec{alpha })) to quantify boundary complexity and sensitivity to initial conditions. Our results reveal that variations in (varvec{omega }) induce transitions between regular and chaotic regimes, with peak entropy values ((varvec{h}_{varvec{top}} varvec{approx 5.95}) for (varvec{a}_{varvec{0}} varvec{= b}_{varvec{0}} varvec{= 0.5})) corresponding to the emergence of multiple attractors and fractal basin boundaries. These findings highlight the critical role of external forcing in controlling synchronization and bifurcations, with direct implications for applications such as cardiac pacemakers and robust control systems. The proposed metrics ((varvec{h}_{varvec{top}}), (varvec{alpha })) provide a robust framework for predicting dynamical transitions in nonlinear coupled oscillators.

The notion of electron spin emerges from the problematic of the anomalous Zeemann effect. As a strictly quantum property of electrons, the development of this concept was only possible through severe conceptual constraints, since the mechanism for producing magnetic dipole moments cannot be conceived without considering rigid-body dynamics, which, in this case, implied attributing spatial dimensions to the electron. Some degree of relativistic kinematics was also required to explain the electron’s intrinsic magnetism, requiring, however, adjustments to the Lorentz transformations to be applied to the non-inertial frame of reference of a self-rotating electron. Despite the obvious contradictions - the electron is, after all, point-like - the proposed solution to the problem accounted for the experimental data, although it led to a new puzzle. Paradoxically, the concept of electron spin emerged by applying classical knowledge to a context in which it could not be employed, except metaphorically.