The European Physical Journal B最新文献

 The worldwide process of replacing fossil fuels with low-carbon energy sources is underway. Existing energy networks are expected to be deeply modified in nature and structure during this transition. This work uses graph-theoretical statistical physics tools to analyze topology and structural changes of power grids, with the French grid as a case study. We discuss the small-world model to define an optimality criterion, the construction of a graph model for the French high-voltage transmission grid, and the development of a growth model to study the dynamics of such networks. The main result of our project suggests that the high efficiency level in the current French network is due to a high-voltage mesh interconnecting thermal power plants. Since implementing low-power-density renewable energy sources would imply non-trivial adjustments to maintain features, such as efficiency and robustness, these considerations must be added to economic and energetic assessments of transition scenarios.

In a hybrid system of quantum dots coupled with microwave resonators, to address the large footprints challenge posed by conventional-material on-chip low-pass filters which are inserted to suppress resonator photon leakage, the utilization of filters with high kinetic inductance (HKI) materials has been demonstrated. However, the HKI film induces the distributed parasitic kinetic inductance to the capacitor structure, making the lumped circuit model which generally used to simulate the filter face failure, and hindering the superconducting filter performance. In our work, we fabricate a compact HKI planar filter and observe that the measured response curve exhibits a large deviation from the simulation result of the lumped circuit model. We propose a distributed circuit model to more accurately simulate transmission characteristics of the HKI filter. By analyzing the effect of parasitic inductance induced by the distributed kinetic inductance film, we explain the abnormal roll-off phenomenon observed in the transmission response curve of the HKI filter. Combining the Fano effect, the simulation result with the distributed model exhibits better correspondence with the experimental results than that of the lumped model. The developed circuit model will contribute to analyzing the adverse effects and optimizing the device design of the HKI film.

In this paper, we examine the performance characteristics at maximum cooling power for a parallelly connected two quantum dots refrigerator within the framework of ballistic electron transport between two reservoirs. The coefficient of performance (COP) at the maximum cooling power, which depends on the Carnot bound, was analyzed for a refrigerator of the quantum dot(QD) system and successfully compared with the Curzon–Ahlborn coefficient of performance. Besides, the coefficient of performance at the maximum cooling power of the model was demonstrated through numerical analysis. Our results indicate that the coefficient of performance at maximum cooling power differs from the Curzon–Ahlborn coefficient of performance in the limit of a small Carnot coefficient of performance. It is constrained by an upper bound of (varepsilon _C) and a lower bound of (varepsilon _{CA}).

This paper explores the problem of synchronous control of discrete complex network dynamics models. In view of the challenges such as the difficulty of modeling complex networks, the complexity of network structure and the difficulty of controller design, this paper proposes an improved model-free adaptive pinning control method. First, a method of entropy of the betweenness centrality and node strength is proposed to select the key nodes, construct the augmentation and generalization error system, and design the control strategy based on the node input and output data. Second, the synchronous stability is analyzed theoretically and the controller parameters are optimized by firefly optimization algorithm in order to overcome the parameter tuning difficulties. Finally, the effectiveness of the proposed pinning node selection strategy in this paper is verified by simulation, and it is verified by simulation experiments of BA scale-free network and ER stochastic network that the pinning control method in this paper only needs to control a few key nodes in the network to realize the synchronous state of the whole network. The method of this paper provides a new idea for synchronous control of complex networks.

This study introduces an extension of the classical Susceptible-Infectious-Recovered (SIR) model by incorporating individual self-protection awareness to more accurately reflect its impact on epidemic spread. We derive outbreak thresholds for both homogeneous and heterogeneous networks and validate the model through theoretical analysis. Key findings from numerical simulations on Erdős-Rényi (ER) and Barabási-Albert (BA) networks show that self-protective behaviors significantly reduce the epidemic spread. Notably, while heterogeneous networks demonstrate a more pronounced reduction in the peak of infected individuals, they still exhibit a larger final infection scale and a lower outbreak threshold. These results highlight the dual impact of network structure and self-protection awareness on epidemic dynamics, offering new insights for infectious disease control strategies.

The results of a new experiment on neutron diffraction by surface acoustic waves are presented. The measurements were carried out at a fixed incident angle in the time-of-flight mode, which made it possible to study the diffraction pattern in a wide range of neutron wavelengths at surface acoustic wave frequencies from 35 to 117 MHz. In a number of cases, diffracted waves of not only the first but also the second order were observed. The measurement results of both the angular distribution of diffracted waves and their amplitudes are in satisfactory agreement with the calculations. A new estimation has been obtained for the range of applicability of the dispersion law of neutron waves in matter moving with large acceleration.

In opinion dynamics, how individuals update their opinions has a profound impact on the final opinion distribution. Though extensive efforts have been made to explore opinion evolution rules, it still remains a challenging issue since opinions of individuals are usually shaped by complicated factors in the real world. In this paper, we introduce social learning theory (SLT) into opinion dynamics and study how the opinion evolution rule derived from SLT affects opinion evolution. Based on SLT, three factors are considered when individuals update their opinions, peer influence, role model influence and personal experience, and three parameters are introduced to regulate their weights of them. Numerical simulations on scale-free networks reveal that the opinion dynamics based on SLT could effectively promote consensus in a population. Especially, the role model influence from surroundings plays a significant role in the consensus of opinions. Whereas, consensus could not be realized through only the role model influence, and an appropriate combination with peer influence can facilitate consensus best. Meanwhile, we find that, holding personal experience to a certain extent is in favor of the final consensus, although it may extend the relaxation time. Besides, when the weight of personal experience is fixed, there exists an optimal weight combination of peer influence and role model influence that leads to the minimum relaxation time. These results may offer a new perspective on understanding the evolution of public opinions and the emergence of consensus.

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.