The European Physical Journal B最新文献

The atomistic structure of clusters dictates their chemical activity, and its understanding is crucial when synthesizing and using them, particularly when working at often needed finite temperatures. The structural evolution becomes more intricate when the system contains more than one type of atom, such as in the case of bimetallic nanoparticles. We analyze the thermal evolution of the clusters through a simple approach, using molecular dynamics simulations to calculate the centrosymmetry parameter of each of the atoms. The atoms are then grouped into inequivalent sites according to the values of the parameter. This method allows us to map distinctive regions of the cluster structure and analytically track the structural evolution of individual sites and constituent regions in relation to the temperature in clusters with arbitrary spatial and chemical order. We show its application in Au-Cu nanoclusters, where we found partial melting temperatures and segregation temperatures for a range of morphologies and chemical compositions. This method reveals, through a simple calculation, the internal structure of nanoclusters at select temperatures as way of analyzing their behavior, facilitating their design and use on thermal applications.

Coevolution on social models couples the time evolution of the network with the time evolution of the states of the agents. This paper presents a new coevolution dynamic allowing more than one rewiring on the network. We explore how this coevolution can be employed as an optimization strategy for problem-solving capability of task-forces. We use an agent-based model to study how this new coevolution dynamic can help a group of agents whose task is to find the global maxima of NK fitness landscapes. Each agent can replace more than one neighbor, and this quantity is a tunable parameter in the model. These rewirings are a way for the agent to obtain information from individuals that were not previously part of its neighborhood. Our results showed that this tunable coevolution can indeed produce gain on the computational cost under certain circumstances. At high average degree network and difficult landscape, the effect is complex. If the agent has a low fitness, 3 or 4 rewirings can bring some improvement.

In this study, the effects of the arm width of the Co 2D-nanoring structure, along with the amplitude and frequency of the external dynamic sinusoidal magnetic field on nonequilibrium phase transitions have been comprehensively investigated. Dynamic magnetic hysteresis curves have been obtained through the numerical solution of the nonlinear Landau–Lifshitz–Gilbert (LLG) equation using OOMMF software. Dynamic hysteresis characteristics, including hysteresis loop area, remanent magnetization, and coercive field have been calculated as functions of frequency. It has been found that nonequilibrium phase transitions are significantly influenced by the arm width, in addition to the frequency and amplitude of the applied external dynamic field. Numerical results highlight that increasing the arm width of the nanoring system causes the transition frequency of the dynamically ordered phase to shift to lower values.

Recently, we proposed polycontextural networks as a model of evolving systems of interacting beliefs. Here, we present an analysis of the phase transition as well as the scaling properties. The model contains interacting agents that strive for consensus, each with only subjective perception. Depending on a parameter that governs how responsive the agents are to changing their belief systems the model exhibits a phase transition that mediates between an active phase where the agents constantly change their beliefs and a frozen phase, where almost no changes appear. We observe the build-up of convention-aligned clusters only in the intermediate regime of diverging susceptibility. Here, we analyze in detail the behavior of polycontextural networks close to this transition. We provide an analytical estimate of the critical point and show that the scaling properties and the space–time structure of these clusters show self-similar behavior. Our results not only contribute to a better understanding of the emergence of consensus in systems of distributed beliefs but also show that polycontextural networks are models, motivated by social systems, where susceptibility—the sensitivity to change own beliefs—drives the growth of consensus clusters.

Static and dynamic properties of magnetically soft amorphous ferromagnets have been studied analytically within the random-field model and numerically within the random-anisotropy model. External field and coherent anisotropy that are weak compared to their random counterparts are sufficient to bring the magnet close to saturation. The scaling of spin–spin correlations in this regime is computed, and its dependence on parameters is confirmed by Monte Carlo simulation. We show that near the ferromagnetic resonance, the spin excitations are damped and spatially localized due to randomness even close to saturation. Uniform precession is not an eigenmode of the system which causes its damping. On increasing the strength of randomness, the localization length goes down in accordance with theoretical expectations, while the damping of the uniform precession goes up.

Tribute to Prof. Amit Dutta