The European Physical Journal B最新文献

This study investigates the hidden dynamics of fractional-order discrete two-dimensional maps, focusing on the generation of hidden attractors and the impact of order on their size and boundaries. Three different nonlinear maps are used, and various measures, such as phase portraits, bifurcation diagrams, and basin of attraction, are presented. This study also observes changes in basin boundaries of hidden attractors with varying order. The rational memristive maps exhibit a well-defined basin of attraction for a broad range of system orders, with multistability and a riddled basin for some orders. The memristive Gauss map also shows well-defined and riddled basins, however, the quadratic chaotic map demonstrates a decreasing basin size and a riddled basin boundary for higher orders of the system.

In mathematical models of epidemic diffusion on networks based upon systems of differential equations, it is convenient to use the heterogeneous mean field approximation (HMF) because it allows to write one single equation for all nodes of a certain degree k, each one virtually present with a probability given by the degree distribution P(k). The two-point correlations between nodes are defined by the matrix P(h|k), which can typically be uncorrelated, assortative or disassortative. After a brief review of this approach and of the results obtained within this approximation for the Bass diffusion model, in this work, we look at the transition from the HMF approximation to the description of diffusion through the dynamics of single nodes, first still with differential equations, and then with agent-based models. For this purpose, one needs a method for the explicit construction of ensembles of random networks or scale-free networks having a pre-defined degree distribution (configuration model) and a method for rewiring these networks towards some desired or “target” degree correlations (Newman rewiring). We describe Python-NetworkX codes implemented for the two methods in our recent work and compare some of the results obtained in the HMF approximation with the new results obtained with statistical ensembles of real networks, including the case of signed networks.

We study the scattering of a single-photon pulse from quantum two-level system (qubit) coupled to a continuum spectrum of modes in a one-dimensional (1D) nanophotonic waveguide. We consider two different cases—single-excitation subspace, when photon is incident on a qubit in the ground state, and two-excitation subspace, when photon is incident on excited qubit. For both cases, we find the spatial distribution of photon fields inside a waveguide and derive a time-dependent analytical solution for qubit and photon amplitudes, which can be used to obtain probabilities to find excited qubit and forward or backward moving photons in different configurations. We find that incident wave has much higher probability to be transmitted through the excited qubit which is a signature of single-photon stimulated emission. This property together with a fact that a qubit in the ground state almost completely reflects resonant photons, which can be used to develop method to detect qubit state in an open waveguide. By analyzing two-photon amplitudes for photon incident on excited qubit, we show clear evidence of stimulated emission. Calculations are performed for two initial states of incident photon: plane wave and Gaussian pulse.

Charge states of polypropylene doped with zirconium oxide nanoparticles and their changes during heat treatment at different temperatures are studied by the method of thermally stimulated depolarization (TSD) current. It is shown that two peaks associated with the release of charge from different types of traps appear on the TSD thermograms for both pure polypropylene and composites. With an increase in the concentration of the filler, the intensity of the peaks increases up to 3 vol.%. A further increase in concentration leads to a decrease in the intensity of the peaks. The activation energy of charge release from traps, temperature of maximum of the peaks, and the magnitude of the accumulated charge in the traps have also maximal value at nanoparticle concentration of 3 vol.%. The study of charge characteristics of the nanocomposite with concentration of 3 vol.% after heat treatment shows that the intensity of TSD peaks, the temperature of peak maxima, and the activation energy of charge release increase with increasing pre-treatment temperature. The magnitude of the charge accumulated in traps at low treatment temperatures is practically unchanged but has a maximal value at treatment temperature of 140 °C owing to the increase in the number of traps.

Growing evidence from population studies and clinical observations strongly suggests a complex connection between major proteinopathies, to know, Type 2 diabetes (T2D) and Alzheimer’s disease (AD). People with T2D face a significantly higher risk of developing AD compared to those without diabetes. These two conditions share several key features, such as inflammation, oxidative stress, metabolic dysfunction, and the buildup of certain proteins known as beta-amyloid (A(upbeta )42) and pancreatic islet amyloid polypeptide (IAPP or amylin). In AD, the brain develops characteristic senile plaques mainly composed of A(upbeta )42. Similarly, in individuals with T2D, the pancreas shows deposits of a protein called human amylin (hIAPP). The accumulation of these aggregated proteins in both the brain and pancreas has been associated with impaired cell function and even cell death. To gain molecular insight in the outset of such complex scenario, we perform microcanonical thermostatistics analysis of Monte Carlo simulations of A(upbeta )42 cross-seeded by hIAPP, or its biotechnological therapeutic analogous as Pramlintide. We find that while A(upbeta )42 is quite prone to cross-seed with hIAPP, and then to aggregate around that critical nuclei, this can be abolished for some of its analogues, which may possibly lead to alternative therapeutic approaches to T2D and AD.

Based on the spin-generalized Bogoliubov–de Gennes theory, we investigate the existence of first- and second-order topological phases in Rashba superconducting ribbon and loop systems. The topological phase transitions driven by the strength of spin-orbit coupling and the spin correlation as well as the temperature are demonstrated. The formation of zero-energy Majorana edge or corner states is highly sensitive to these effects. Due to the modification of the relative strength of competing s- and d-wave pairing symmetries, the number and location of Majorana zero modes in the square loop may be tuned by the introduced spin correlation. Meanwhile, the second-order topological phase and emergent Majorana corner modes can remain robust for finite temperatures. We expect that our theoretical predictions may provide useful information for future experiments.

Heterogeneous graph representation learning gains popularity due to its powerful capabilities of feature extraction and numerous related algorithms have emerged for various downstream tasks in graph structural datasets. However, the interactions among nodes for the heterogeneous graphs in the real world often extend beyond individual pairs, excessive attention is payed on isolated pairwise connections. In this paper, we propose a novel framework of Heterogeneous Hypergraph Representation Learning method (HHRL) to capture high-order interactions for learning effective node representations of heterogeneous graphs. The method firstly organizes the heterogeneous connections as different hypergraphs. By modeling the heterogeneous connections, HHRL captures the rich structural and semantic information present in the graphs. Then, the graph neural network (GNN) is applied for each hypergraph to capture the interdependencies between nodes and their associated features. By utilizing GNN, HHRL can effectively learn expressive node representations that encode both the structural and feature information of the network. Finally, we concatenate the vectors from different hypergraphs to obtain the link representations. The experiments are conducted on five real dataset for link prediction and the results demonstrate the well performance of the proposed framework comparing to the existing baselines

Rainfall constitutes an important climatic variable as its lack can lead to severe droughts while its excess can trigger catastrophic events. The Mediterranean basin is one of the European regions most affected by climate change’s effects and Sicily island, for its location in the middle of the Mediterranean sea, represents a very interesting place to study climatic variables. During the last decade, Sicily displayed unusual rainfall behavior and unexpected extreme events. In this paper, we depict how seasonal rainfall characteristics and trends changed since the beginning of twenty-first century. Using an empirical approach, we are able to confirm with a higher resolution previous results obtained through less granular data and detect the emergence of new anomalous behaviors. From our study we see that, after the heavy summer drought during 2009–2012, all seasons recorded a sudden volume variation: increasing during autumn–summer and decreasing in winter–spring. This coupled behavior emerges as an anomaly for the period 2013–2023: the similarity between summer–autumn and winter–spring highlights the passage from a 4 to a 2 season-like cycle,with the occurrence of severe droughts and frequent flash floods. The present contribution adds further evidence that a deep change is occurring and that new strategies for managing risks and resources are urgently needed to reduce human and economic losses.

We study a neural-level model (Das and Levina Phys Rev X 9:021062, 2019) that exhibits critical avalanches satisfying power-law distribution. The model recently explained a change in the scaling exponent from 3/2 to 5/4, accounting for a change in the drive condition from no input to moderate strength, along with a relaxed separation of time scale between drive and dissipation. We examine the effect of a class of noisy stimuli in the moderate input regime. Applying the scaling method, we compute scaling functions associated with the avalanche size distribution, revealing striking finite-size scaling. For a class of noisy drives, we find that the scaling exponent can take a value different from 5/4, with an explicit system size dependence.