The European Physical Journal Plus最新文献

This study introduces a novel continuous probability distribution, the Zgamma distribution (ZGD), developed within a new methodological framework that derives a probability density function using the (r^{text {th}}) moment of a continuous random variable and its survival function. The ZGD is presented as a concrete application of this framework, and its statistical properties are investigated. These include the survival function, hazard rate function, moments, and measures of dispersion, which highlight the flexibility and potential applications of the distribution. An entropy analysis is also conducted to provide further insight into the uncertainty and information content of ZGD. Parameter estimation is performed using several classical methods, and a simulation study is carried out to evaluate the performance of the estimators. The simulation results show that the methods generally provide reliable estimates, with MLE and KE showing good overall performance under different parameter settings. Finally, the practical relevance of ZGD is demonstrated through its application to two real lifetime datasets.

Supernovae (SNe) are excellent probes of the physical, chemical, and cosmological context of galaxies across the history of the Universe. The James Webb Space Telescope (JWST) has a truly unique combination of wavelength, sensitivity, and resolution, providing a new window into SN physics, progenitors, dust production, redshift evolving stellar populations, and cosmology since its launch. Here we summarize the astounding observations JWST has made of SNe in its first few years of operations, as well as the corresponding advancements in our understanding of the Universe. This includes detailed views of nearby SNe, the first spectroscopic samples of high-z SNe, and a significant number of gravitationally lensed SNe valuable for both cosmological inference and high signal-to-noise (S/N) spectroscopy. While the upcoming Nancy Grace Roman Space Telescope is expected to revolutionize space-based time-domain astronomy, JWST and its unique capabilities will continue to play a complementary role by providing high-precision spectroscopic and photometric data for a broad range of SN science cases.

The Generalized Uncertainty Principle (GUP), motivated by various approaches to quantum gravity, predicts a fundamental minimal length that manifests as low-energy deformations of classical black hole spacetimes. We investigate the strong-field dynamics of spinning test particles in a spherically symmetric black hole geometry incorporating a GUP correction of strength (epsilon)–the S-GUP metric–using the Mathisson–Papapetrou–Dixon (MPD) formalism with the Tulczyjew spin supplementary condition. We derive the effective potential governing radial motion, establish the conditions for circular orbits, and determine the innermost stable circular-orbit (ISCO) radius, energy, and angular momentum as functions of (epsilon) and the particle’s specific spin s. The superluminal bound, which restricts the spin magnitude to physically admissible timelike trajectories, yields a maximum spin (s_{max }(epsilon )) that exhibits a non-monotonic behavior: it decreases from the Schwarzschild value to a shallow minimum near (epsilon approx 0.5)–0.7, then rises sharply as the extremal limit (epsilon rightarrow 1) is approached. We further compute the center-of-mass energy of head-on collisions between two spinning particles near the horizon, finding that spin misalignment and the GUP correction act constructively to significantly enhance the collision energy. Numerical integration of representative trajectories reveals that negative (epsilon) induces pronounced rosette-like precession, while positive (epsilon) yields more compact, nearly closed orbits. These results demonstrate that GUP-induced quantum corrections and spin–curvature coupling jointly modify the orbital structure, ISCO properties, and collision energetics in ways that are, in principle, distinguishable from classical general-relativistic predictions, thereby establishing spinning test particles as potential probes of minimal-length quantum-gravity effects in astrophysical environments.

We demonstrate that two-component photonic nonlinear lattices with opposite signs of the out-of-phase spatially modulated SPM and XPM coefficients can support two types of optical vector solitons (VSs), the fundamental and mixed ones. The fundamental VSs are composed of two Gaussian-like components, while the mixed ones combine Gaussian-like and dipole components. The structures and stability of both types of the VSs are investigated. The VSs are stable, except for narrow intervals at small values of the propagation constant.

Citizen science can be a powerful tool for empowering communities and building a more informed society. Furthermore, it can benefit scientific research itself, through new opportunities for learning and publication, addressing local and international issues, and influencing policy. However, its implementation can present critical issues, since the process requires, for example, ensuring data quality, fostering participant diversity, managing project logistics, and maintaining sustained engagement. For this reason, training the people involved in the initiatives is crucial. In this paper, we present a Citizen Science School that was organized by Italian National Institute for Nuclear Physics (INFN), in the framework of the Rome Technopole project and in collaboration with the European Citizen Science Association and Citizen Science Italia. The School, addressed to researchers, high school students and teachers, and University students, was held at INFN Frascati National Laboratory, and it offered participants the possibility of approaching citizen science in a direct, participatory, and immersive way through a full immersion in five real citizen science projects.

This work investigated the ionizing radiation shielding performance of four lead-free alloys by combining first principal density function theory (DFT), XGBoost machine learning (ML) model, and Geant4 Monte Carlo simulations over the photon energy range of 0.015–15 MeV. For the four target formulas, the XGBoost estimates were numerically closer to selected Materials Project-computed density labels than the present DFT results; this comparison is not validation against experiment. On the validation subset used for early stopping, XGBoost achieved R² = 0.991 and MAE = 0.186 g/cm³; the threefold cross-validated MAE was 0.277 ± 0.018 g/cm³. The Geant4-simulated linear attenuation coefficients showed very close agreement with Phy-X across the full energy range, with percentage differences generally remaining below 3%. Among the investigated alloys, VW showed the strongest overall shielding performance, with the highest MAC and effective atomic number values, while Ni₄W showed the weakest attenuation. These findings highlight VW as a promising lead-free shielding material and show that integrating ML, DFT, and Monte Carlo simulation is an efficient strategy for screening advanced radiation shielding alloys.

The sensing-storage-computing architecture integrated with a convolutional neural network (CNN) leverages memristor crossbar arrays to implement parallel matrix operations and incorporates high-efficiency artificial synaptic and neuronal circuits. These features can markedly improve the computational speed and energy efficiency of neural networks, thereby helping overcome the limitations of the traditional von Neumann architecture in power transformer fault diagnosis. In this paper, an adaptive voltage-threshold memristor model was first developed, and a 1R1M memristive synaptic and neuronal circuit with flexible weight adjustment was designed and optimized to achieve high-precision nonlinear mapping by combining the Tanh and sigmoid activation functions. A double-layer memristive neural network circuit was then constructed based on a backpropagation (BP) neural network, and a memristive CNN architecture was further developed to optimize high-efficiency memristor-based compute-in-memory and activation circuits. By employing a hybrid kernel and variable-dimensional optimization strategy, we drastically reduced the model size while maintaining high recognition accuracy. Furthermore, a vertically integrated sensing-storage-computing system was established by integrating a gas-sensitive sensor array with the memristive CNN and cascading it with the memristive BP neural network, enabling real-time qualitative classification of transformer fault types and quantitative detection of fault gas concentrations. The proposed architecture maintains excellent classification accuracy while significantly reducing model size and circuit power consumption. These advantages provide a promising solution for power system fault monitoring and lay a solid hardware and algorithmic foundation for the practical application of memristors in complex industrial scenarios.

Measuring the Higgs potential represents one of the main goals of the physics programs of future colliders. At center-of-mass energies of (sqrt{s} ge {500,mathrm{text {G}text {eV}}}), direct access to the self-coupling (lambda) is enabled through the ZHH process. The ongoing update of the ILD ZHH analysis focuses on the final state (text {H}text {H}rightarrow text {b}bar{text {b}}text {b}bar{text {b}}). In our contribution, we discuss recent advancements in event reconstruction, namely, in the identification of heavy-quark jets as well as the kinematic reconstruction using corrections of semileptonic decays, kinematic fits, and observables based on matrix elements. By basing our analysis on the modern distributed pipelining system Luigi, we achieve a highly scalable, automated, and fast workflow. Composed of the full chain from event generation, fast simulation, reconstruction, and analysis, the workflow can be executed in a single step. Through columnar analysis, the event selection is greatly accelerated. We can process (mathcal {O}(10^8)) events in a few hours with (mathcal {O}(10^3)) available nodes.

The present paper deals with the thermal-wave propagation in a bounded elastic medium. In this context, hyperbolic type heat conduction model is considered subjected to a moving heat source. The analysis incorporates memory-dependent derivatives (MDD) within the framework of generalized thermoelasticity to account for thermal memory effects in the hyperbolic heat conduction model. Including a sequence of delayed temperature gradient, the concept of a memory-dependent adjoint operator is introduced. Finally, the Green’s function method is applied to derive analytical solutions, and the salient features of the temperature profile are analyzed in detail.

We test a phenomenological extension of the Richardson–Dushman equation for thermionic emission based on Tsallis’ nonextensive statistics. A temperature-dependent effective entropic index is represented by a polynomial centered on the observed temperature interval, and its parameters are inferred from two datasets for self–Joule–heated individual carbon nanotubes. To avoid comparing a flexible nonlinear model with an artificially restrictive baseline, we analyze seven alternatives: Richardson–Dushman emission with a free effective prefactor, a one-dimensional thermionic law, a free temperature exponent, a temperature-dependent work function, and nonextensive models with constant, linear, and quadratic q(T). Identical broad bounded uniform priors and nested sampling are used for both samples to obtain posterior distributions, Bayesian evidences, and information criteria. For the (15,textrm{V}) collection-voltage dataset, the quadratic q(T) model is strongly preferred over the classical alternatives. For the (0,textrm{V}) dataset, quadratic q(T) also ranks first, although its evidence and BIC differences relative to the temperature-dependent work function model are inconclusive. Thus, the data support temperature-dependent corrections to ideal thermionic emission, but do not establish a unique nonextensive interpretation across both configurations.