The European Physical Journal B最新文献

Dengue is an arthropod-borne viral disease prevalent in tropical and subtropical regions. Its adverse impact on human health and the global economy cannot be exaggerated. To improve the efficacy of vector control measures, there is a critical need for mechanisms that can forecast dengue cases with greater accuracy and urgency than before. So, we employ some deep learning techniques using the previous ten years of weekly dengue cases in Laos. A hybrid model combining CNN and stacked LSTM (BiLSTM) is applied along with CNN, LSTM, BiLSTM, and ConvLSTM in this work. Comparing all the outputs we have derived, hybrid CNN and 1 stacked BiLSTM outperform other deep learning models with the one-step-ahead prediction. Further, we have concluded that hybrid CNN and 1 stacked BiLSTM can considerably boost dengue prediction and can be applied in other dengue-prone regions.

Detection of SF(_6) decomposition gases is crucial in power equipment maintenance. This paper investigates the adsorption behavior of SO(_2) and H(_2)S on the intrinsic and Fe-modified net-Y surfaces using density functional theory. The adsorption parameters and electronic attributes of diverse configurations have been scrutinized. Calculations indicate that net-Y exhibits limited adsorption capacity for both gases. The doped substrate exhibits a localized magnetic moment around the Fe atom, indicating the possible occurrence of the Kondo effect in the system. The substrate chemisorbs with the target gas through the Fe 3d orbitals. Additionally, after the adsorption of gases, the system undergoes a transition from metallic to semiconductor properties, accompanied by a near-complete disappearance of magnetism. Specifically, in two adsorption configurations, the systems manifest the characteristics of half-semiconductor and half-metal, respectively. Our study provides evidence that the incorporation of Fe-modified net-Y shows potential as a disposable device for detecting and purifying the decomposition products of SF(_6), presenting a prospective application for net-Y in spintronic devices.

The investigation of site percolation on Sierpinski carpets is carried out through comprehensive numerical simulations. We utilize finite- size scaling theory, staying within the constraints of our computational resources, to determine critical exponents and percolation thresholds. Moreover, we employ an approach developed by Elliot et al. (Phys Rev C 6:3185, 1994; Phys Rev C 55:1319, 1997), which streamlines the process by eliminating the necessity of dealing with large lattices. This method facilitates the extraction of critical quantities that characterize the transition from a single generation within a given structure. By implementing this procedure, we enhance efficiency and accuracy in analyzing the percolation phenomenon on Sierpinski carpets. The obtained values of the percolation thresholds are plotted as a function of the fractal dimensions in order to determine the lower critical dimension of the site percolation problem which is calculated to be (d_c^L=1.52). In addition, the behavior of the critical exponents as a function of the fractal dimension is also shown and discussed.

Understanding boundary conditions is crucial for properly modeling interactions and constraints within a system. In particular, periodic boundary conditions play an important role, because they allow systems to be treated as if existing in a continuous, constraint-free space, with significant relevance across diverse scientific fields. Our study explores the effects of periodic boundary conditions on Small-World networks by comparing traditional and flat versions derived from Ring and Line networks, respectively, through comparisons of network metrics and disconnection assessments. Recognizing the critical role of network topology in the behavior of dynamical models, we use an epidemic model to show that the structure of a network can either facilitate or hinder the spread of disease, emphasizing the importance of boundary conditions on these dynamics. The faster spread of disease in Ring networks, with shorter Average Shortest Paths, as well as their resilience on keeping network connectivity under rewiring, illustrate the impact that periodic boundary conditions can have on epidemic scenarios.

We have studied the gradient-flow equations in information geometry from a point-particle perspective. Based on the motion of a null (or light-like) particle in a curved space, we have rederived the Hamiltonians which describe the gradient-flows in information geometry.

The appropriate amount of regulation to reconcile social stability and economic prosperity is a subject of intense study and debate. In practice, the degree of regulatory enforcement is at least as important as the regulation itself. In this work, we present a simulation model of society as a network of interacting agents subject to regulation of their economic activities, with varying degrees of enforcement. Each agent’s compliance with regulation is modeled as a string of bits, which also governs the probability of connection with other agents. Each agent has an initial share of the total wealth that changes over the course of transactions. At each simulation step, the poorest agent takes action to achieve a wealth change at the expense of its first neighbors in the network. Non-conformity to regulation is punished with a probability parameter representing the strength of regulatory enforcement. Our results suggest that regulation has the benefit of reducing wealth inequality but also the disadvantage of increasing job instability, which could lead to lower wealth production in the real world. Our analysis indicates a possible method for choosing an optimal level of regulatory enforcement.