The European Physical Journal B最新文献

This research utilizes a mean field theory (MFT) on spin-1/2 diatomic molecules for the first time to obtain the thermal changes in magnetizations for each molecule, thus generating the phase diagrams. The same model was also studied using the Bethe lattice (BL) approach in terms of the exact recursion relations (ERR) in order to compare its results with the MFT results. Different types of bilinear interaction parameters between the constituents of a molecule and nearest-neighbor (NN) molecules were assumed, and the coordination number is set to three, corresponding to the honeycomb lattice. It is found that the MFT only gives second-order phase transitions, but the ERR approach gives various phase transitions. The obtained results show that the ERR approach on the BL is superior over the MFT since it provides many more details of the model.

We study opinion formation in a society where agents interact on a modular network generated using a stochastic block model (SBM). Opinion dynamics is modelled through the Biswas–Chatterjee–Sen (BChS) kinetic exchange model, in which agents undergo pairwise interactions that could be positive or negative. By tuning the relative strength of intra- and inter-group connectivity inherent to the SBM, as well as the disagreement probability, we identify distinct collective phases. In particular, we observe a robust regime with strong intra-group ordering but no global consensus, in addition to fully ordered and disordered states. In the particular case of two modules, we observe an anti-ferromagnetic type ordering with the increase of negative interaction between the groups. We investigate these ordering regimes using approximate analytical calculations and numerical simulations. These results demonstrate how modular interaction structure can qualitatively alter collective opinion dynamics and hinder consensus formation.

To account for the inhomogeneity of optical fiber media, we investigate a variable-coefficient Kundu–Eckhaus equation for optical pulse propagation. Under the integrable coefficient constraints, dark one-, two-, and three-soliton solutions are derived by means of gauge transformation and Hirota bilinear method. The results show that the variable coefficient r(x) mainly modulates the soliton trajectories, while both overtaking and head-on collisions of dark solitons remain elastic.

Inhomogeneous optical fibers are described by a variable-coefficient Kundu–Eckhaus equation. Via gauge transformation and Hirota bilinear method, we derive dark one-, two- and three-soliton solutions. The dispersion coefficient r(x) modulates soliton trajectories without altering intrinsic soliton profiles. Both overtaking and head-on collisions of dark solitons are elastic under integrable coefficient constraints

We develop a polylogarithmic structure for Bragg diffraction based on a weighted multi-plane interference model. Within this kind of construction, the scattering amplitude is expressed as a polylogarithmic generating function. By introducing extra contributions with power-law and the usual exponential decay, it takes the form (F(q) = textrm{Li}_mleft( e^{i qd - epsilon }right) ), where (epsilon ) is a finite coherence length. In the infinite-coherence limit, Bragg enhancement is associated with the approach of the polylogarithmic argument to its boundary singular point (z=1). This formulation provides a compact analytical framework for describing diffraction line shapes within a generalized correlation model in which peak positions, widths, and line shapes arise from a single analytic structure. Although we are able to recover the standard Bragg law for ideal crystals, the polylogarithm model captures deviations due to finite correlation length, disorder and non-uniform lattice coherence. We show that if Bragg peaks correspond to boundary singularities of the polylogarithm, a connection between diffraction theory and complex analysis arises. The proposed theoretical model may be particularly relevant for disordered or partially coherent materials, where conventional diffraction models often require additional phenomenological broadening assumptions.

A person opens their news feed and finds, in rapid succession, a story that inspires hope and another that provokes outrage. This alternation between contradictory emotional valences is the ordinary texture of contemporary media environments. What are the long-term consequences of such contradictory stimulation on an individual’s core affective state—the continuous background feeling of being comfortable or uncomfortable, well or unwell, that colours all emotional experience? Could the long-term consequences depend less on the content of any single message than on the statistical structure of the stream’s inconsistency? We capture this structure in a minimal stochastic model that treats the strength of external influence as a fluctuating parameter, yielding a stochastic differential equation with multiplicative noise. Its stationary distribution reveals two regimes that defy straightforward intuition. Intense contradiction does not amplify engagement but instead drives the affective state toward neutrality—a form of apathy in which sustained affect is suppressed altogether; exact neutrality occurs only when the default bias vanishes. Conversely, moderate contradiction with strong influence produces bimodal affective instability, with random alternation between two well-separated emotional states that arise from intra-individual dynamics rather than social polarization. A systematic mapping of the parameter space reveals a region of robust bimodality for low regulatory capacity and a region of enhanced fragility for near-neutral default bias. We offer this model as a preliminary exploration rather than a definitive account, and we hope its analytical transparency will invite empirical testing and further theoretical development.

Generalized modeling provides a powerful framework for the local analysis of broad classes of nonlinear dynamical systems without fixing specific functional forms. A foundational step in this approach is the assumption that a steady state exists, followed by the normalization of the state variables and the construction of the Jacobian in terms of generalized parameters. In this paper, we provide a rigorous justification for this assumption across a broad class of finite-dimensional models. Under natural boundary sign conditions on a compact hyperrectangle, the Poincaré–Miranda theorem guarantees the existence of at least one steady state. We demonstrate how these conditions can be routinely verified in compartmental systems via donor-vanishing and upper-face dominance criteria and we derive a Brouwer-degree refinement that establishes topological robustness under admissible perturbations when the boundary inequalities are strict. The general theory is illustrated using representative examples from physics and biology, including a two-patch metapopulation model, an open SEIR system with immigration and a coupled-laser model. Together, these results provide a rigorous topological foundation for the steady-state hypothesis commonly used in generalized modeling, offering a highly practical criterion for verifying it in applications.

Under natural boundary sign conditions on a compact hyper rectangle, the Poincaré–Miranda theorem guarantees the existence of at least one steady state (x^*) in generalized gain-loss dynamical systems without requiring specific functional forms. The inward-pointing components of the vector field along opposite boundary faces ((f_i ge 0) on lower faces (F_i^-) and (f_i le 0) on upper faces (F_i^+)) provide a rigorous, model-independent topological foundation and degree-theoretic robustness for the steady-state hypothesis across physical, ecological, and epidemiological networks

Against the backdrop of infectious disease outbreaks, individual-level opinions, contact behaviors, and disease transmission processes exhibit strong dynamic coupling. To clarify how opinion-driven contact formation shapes this coupled process, we propose a coevolution model on a two-layer network consisting of an opinion layer and a disease layer. The opinion layer describes information diffusion and opinion evolution, whereas the disease layer represents physical contacts and serves as the substrate for disease transmission. Different from existing models that assume static contact structures or neglect adaptive behavioral responses, our model establishes a bidirectional coupling between opinion evolution and disease transmission. This coupling is realized by an opinion-dependent contact formation mechanism that considers both individual opinions and pairwise similarity. Based on Monte Carlo simulations, we find that opinion-driven contact behavior suppresses and delays the epidemic peak, while reshaping the evolution of collective opinions. The infection rate primarily determines epidemic intensity, whereas the bounded confidence threshold controls the effective interaction range among individuals and thereby influences both opinion clustering and disease propagation. More importantly, the contact formation parameter governs the balance between opinion similarity and individual propensity in contact decisions. These findings reveal how opinion-driven contact formation links social cognition and disease transmission and shapes their coevolution in complex social systems.

Using density functional theory combined with dynamical mean-field theory (DFT+DMFT), we explore the correlated electronic structure of the (text {KNi}_2text {Se}_2) superconductor and analyze how its paramagnetic normal state spectra is modified by electron–electron interactions and electron/hole doping. We demonstrate the emergence of orbital selectivity, characterized by the coexistence of Mott-localized states, pseudogapped spectral features, and narrow Kondo-like quasiparticle excitations. In addition, we investigate the interplay between on-site Coulomb interactions and electron–phonon coupling in both pristine and doped (text {KNi}_2text {Se}_2). Our results reveal that the combination of these interactions gives rise to low-energy electron bound states. These findings provide microscopic insight into the heavy-electron normal state of (text {KNi}_2text {Se}_2) and contribute to a deeper understanding of the coupled roles of dynamical electronic correlations, electron–phonon interactions, and band filling in shaping the normal state properties of unconventional multi-orbital superconductors.

We study localization control in a one-dimensional tight-binding chain with diagonal disorder whose spatial correlations are governed by one or two exponential length scales. The main objective is not only to characterize localization, but to develop an interpretable machine-learning framework for inverse design: given a desired transport or localization behavior, the method identifies disorder profiles that realize it. We compare three disorder ensembles: uncorrelated disorder, single-scale exponentially correlated disorder, and a two-scale correlated ensemble with correlation function (C(r)=a_1 e^{-r/xi _1}+a_2 e^{-r/xi _2}), where (a_1+a_2=1). Exact diagonalization is used to compute central-window eigenstate observables, including the participation ratio normalized by system size, (textrm{PR}/N), and the localization length normalized by system size, (ell _{textrm{loc}}/N). Even with a second, independently tunable correlation scale, we find no signature of a genuine localization-delocalization transition: dedicated finite-size scaling up to (N=1024) shows no size-independent crossing point, so the correlation-length and weight dependence remains a smooth, monotonic crossover throughout the exponential-correlation family rather than a critical phenomenon. A central outcome of the work is a machine-learning surrogate and inverse-design workflow. An ensemble ExtraTrees regressor learns the mapping from disorder parameters and system size to the normalized logarithmic observables, achieving (R^2approx 0.98) on held-out data for the two targets ((R^2approx 0.980) for the normalized participation ratio and (R^2approx 0.979) for the normalized localization length; Sect. 3). The surrogate provides smooth interpolation of the high-dimensional parameter space, uncertainty estimates, SHAP-based feature attribution, and a fast inverse-design search for disorder profiles that maximize localization (the framework is equally applicable to a delocalization objective, though only the localization-maximizing search is reported here). The analysis shows that the scaled correlation lengths and the disorder-weight asymmetry are the dominant controls on transport, and that the strongest localization is realized by short-range-dominated disorder at the edge of the sampled correlation-length range, consistent with the general expectation that weakly correlated disorder localizes most strongly. This combination of exact diagonalization, interpretable machine learning, and inverse design establishes a practical framework for designing and controlling localization in low-dimensional quantum systems.