The European Physical Journal B最新文献

The Inspection Game is the canonical model for the strategic conflict between law enforcement (inspectors) and citizens (potential criminals). Its classical Mixed-Strategy Nash Equilibrium (MSNE) is afflicted by a paradox: the equilibrium crime rate is independent of both the penalty size (p) and the crime gain (g), undermining the efficacy of deterrence policy. We re-examine this challenge using evolutionary game theory, focusing on the long-term fixation probabilities of strategies in finite, asymmetric population sizes subject to demographic noise. The deterministic limit of our model exhibits stable limit cycles around the MSNE, which coincides with the neutral fixed point of the equilibrium analysis. Crucially, in finite populations, demographic noise drives the system away from this cycle and toward absorbing states. Our results demonstrate that high absolute penalties p are highly effective at suppressing crime by influencing the geometry of the deterministic dynamics, which in turn biases the fixation probability toward the criminal extinction absorbing state, thereby restoring the intuitive role of p. Furthermore, we reveal a U-shaped policy landscape where both high penalties and light penalties (where (p approx g)) are successful suppressors, maximizing criminal risk at intermediate penalty levels. Most critically, we analyze the realistic asymptotic limit of extreme population sizes asymmetry, where inspectors are exceedingly rare. In this limit, the system’s dynamic outcome is entirely decoupled from the citizen payoff parameters p and g, and is instead determined by the initial frequency of crime relative to the deterrence threshold (the ratio of inspection cost to reward for catching a criminal). This highlights that effective crime suppression requires managing the interaction between deterministic dynamics, demographic noise, and initial conditions.

This paper introduces a theoretical method for characterizing the quantum phase transitions (QPTs) of the anomalous Dicke model (ADM) by employing the Loschmidt echo (LE) to access the photon number variance. The ADM features a normal phase (NP) and two superradiant phases: electric (SEP) and magnetic (SMP), respectively. We further extend the ADM to one-dimensional (1D) chain-like and two-dimensional (2D) square-like lattice structures. Analysis of the energy bands in these lattice models reveals the emergence of unstable phases, which are primarily induced by photon–photon hopping interactions between adjacent cavities, namely the dispersion relation. We map the unstable phases onto the phase diagram and analyze the boundaries between different phases, along with the properties of the energy bands within each phase region. This work provides valuable insights into the applications of QPTs and quantum manipulations through the unconventional Dicke-lattice model.

Two-dimensional (2D) magnetic semiconductors have emerged as a vibrant frontier in condensed-matter physics, offering opportunities to probe fundamental magnetism at the atomic scale and to design novel spintronic and optospintronic devices. Theoretical progress, particularly through first-principles calculations, many-body approaches, and machine-learning frameworks, has provided predictive insights into exchange interactions, spin–orbit coupling, and magnetocrystalline anisotropy. On the experimental front, advances in exfoliation, chemical synthesis, and van der Waals assembly have enabled the realization of intrinsic 2D ferromagnets, gate-controlled magnetism, and magnetoresistance effects in heterostructures. Together, these developments have established a coherent platform for understanding and engineering reduced-dimensional magnetism. Beyond fundamental discoveries, device concepts such as spin valves, magnetic tunnel junctions, and optospintronic elements leverage proximity effects, interfacial engineering, and twist-angle control to achieve tunable functionalities. The integration of 2D magnets with electronic and photonic platforms promises energy-efficient information processing and multifunctional device architectures. The synergy between predictive theory, advanced synthesis, and high-throughput screening is expected to accelerate the discovery of materials with tailored magnetic properties, bridging the gap from fundamental science to transformative spintronic and quantum technologies.

The canonical-ensemble description of homogeneous reactive quantum gas mixtures is reformulated by incorporating a single global particle-number conservation constraint over the combined spectra of interconverting species. This constraint replaces the conventional equality of chemical potentials. Fermi–Dirac or Bose–Einstein correlations naturally emerge across one-particle energy eigenstates of species sharing identical spin statistics, which in ergodic single systems manifest as intrinsic features of the equilibrium state. By embedding all microstates linked by conversion pathways, the framework incorporates concentration fluctuations in the statistical description. The formalism offers fresh insights into quantum chemical equilibrium in reactive mixtures with composition fluctuations and smoothly reduces to the classical ideal gas limit via an extended partition function that generalizes classical chemical-equilibrium treatments.