International Journal of Modern Physics C最新文献

There has unavoidably been a problem for international grain cooperation in the Asia–Europe region as a result of the Russian–Ukrainian conflict. Analysis of the impact of Russian–Ukrainian conflict on the evolution of grain trade cooperation, identifying key influencing factors and formulating corresponding measures are of practical value for safeguarding grain security in the Asia–Europe region. In this paper, a grain trade network is constructed according to grain trade data from the Asian–European grain trade cooperation countries. A grain trade network game (GTNG) model of grain trade cooperation is established in order to investigate the elements that influence trade cooperation. The reciprocal preference is introduced. Through simulation experiments, the effects of cooperation costs, neighboring countries’ incentives, and punitive pressures, the degree of goodwill perception, the types of game, and the proportion of initial cooperators on trade cooperation are examined. This study may give useful policy recommendations for promoting Asia–Europe grain trade cooperation under the influence of the Russian–Ukrainian conflict.

To enhance the efficiency and stability of traffic flow in the vicinity of traffic signals under mixed traffic, a macroscopic model considering the characteristics of the vicinity of traffic signals is proposed from the perspective of cyber-physical systems (CPS). In this model, the effects of traffic lights and vehicle lane changing behavior are analyzed. Among them, the remaining time of the signal light, the distance between the vehicle and the signal light, and the urgency of lane change are considered to build a macro model. Finally, the simulation results show that compared with the traditional macroscopic model, the proposed model can improve the stability of traffic flow in the vicinity of traffic signals and effectively alleviate traffic congestion.

Online social media platforms have emerged as integral channels for facilitating social interactions, with celebrities utilizing these platforms to engage with their fan base and cultivate a substantial following. The group of engaged fans, commonly referred to as “active fans”, represents individuals who actively communicate with celebrities and actively participate in discussions pertaining to the celebrities’ endeavors. For celebrities, the task of retaining and augmenting the count of active fans holds immense significance, as it significantly amplifies their social impact and commercial value. Here, we construct dynamic weighted active fan networks by leveraging data from 2021 on Sina Weibo, which stands as China’s largest social media platform. Through a comparative analysis encompassing the network’s structure, the growth rate and the duration of active fans, we delve into the influence wielded by six distinct thematic categories, endorsement, variety, public welfare, sports, music and national affairs. This analysis covers a cohort of nine celebrities spanning five diverse domains, including actors, singers, online influencers, anchors and athletes. The growth trajectory and life cycle exhibited by celebrity fans exhibit notable variations, both within and across the aforementioned themes. These dynamics are further influenced by the inherent structural attributes of the personal fan network belonging to each celebrity. Employing the K-Shape time series clustering algorithm, we have undertaken an in-depth exploration of outburst growth patterns observed in active fans and determined the optimal value of the number of clusters to be $k=4$ through comparative analysis. Our findings underscore that the themes of endorsement and public welfare exhibit all four growth patterns, namely Double-Peak, Oscillatory, Single-Peak and Continuous Growth Patterns. In contrast, when considering all themes collectively, they collectively demonstrate a single-peaked decaying growth pattern the insights gleaned from this study not only serve as a valuable reference and guide for celebrities across diverse domains who aspire to bolster and augment their social influence but also contribute to the burgeoning fan economy. Moreover, this research introduces novel perspectives and insights for scrutinizing patterns of fan growth and their corresponding dynamics.

Studies have indicated that focusing solely on pairwise interactions between two nodes disregards the associativity among multi-nodes in the network’s local structure. This associativity can be seen as dependencies among nodes, where certain edges’ presence depends on the path leading to it. Examinations on diverse datasets have approved that the variable order of chained dependencies allows for the preservation of structure information, which enables the reconstruction of the original network into a Higher-Order Network (HON) with improved quality of network representation. This paper proposes a Density-based Higher-Order Network Embedding (DHONE) algorithm, which integrates the concept of higher-order density into the network-embedding process in order to classify the contribution of different orders of dependencies. Through the construction of a novel and effective higher-order adjacency matrix, DHONE steadily improves the accuracy of network representation learning. Experimental results demonstrate DHONEs proficiency in improving embedding accuracy and overall algorithm robustness. Furthermore, grounded in the concept of higher-order density proposed herein, numerous dependencies have been discerned within the network generated from trajectories, potentially indicating the role of multi-node structures in networks.

This paper presents a numerical procedure for handling delay fractional differential problems where the derivative is defined using the M-fractional approach. The proposed scheme modus operandi is based on the shifted Legendre–Galerkin procedure, which is a powerful tool for solving complex differential models of generalized fractional derivatives. The method involves constructing a series of Legendre polynomials that form the basis functions for approximating the solution of the required problem. The coefficients of the series are obtained after solving an algebraic system of linear types that results from the application of the Galerkin practice. The numerical accuracy and convergence assessment are also presented together with various results. Simulations-based analyses are realized to validate the truthfulness and exactness of the process. The results manifest that the M-derivatives and the Galerkin practice provide alternative innovative approaches for handling M-delay fractional problems. Several keynotes and future recommendations are exhibited at the last with some selected references.

The utilization of latent heat storage units with their high energy density and isothermal heat transfer behavior can enhance the performance of thermal energy systems. This study aims to investigate the effect of fin placement on the melting time of a wire and tube-based Phase Change Material (PCM) heat exchanger using numerical simulations. The study introduces a new complex geometry for the heat exchanger, and numerical analysis of heat transfer was conducted in Ansys Fluent software using an established solidification and melting model. The numerical results were validated against experimental data, and it was found that the position of the tubes and fins had a significant impact on heat transfer within the PCM. The model was able to predict temperature data with a maximum discrepancy of 3%.

Optimal transport aims to learn a mapping of sources to targets by minimizing the cost, which is typically defined as a function of distance. The solution to this problem consists of straight line segments optimally connecting sources to targets, and it does not exhibit branching. These optimal solutions are in stark contrast with both natural, and man-made transportation networks, where branching structures are prevalent. Here, we discuss a fast heuristic branching method for optimal transport in networks. We also provide several numerical applications to synthetic examples, a simplified cardiovascular network, and the “Santa Claus” distribution network which includes 141$$182 cities around the world, with known location and population.

We present a mapping between a Schrödinger equation with a shifted nonlinear potential and the Navier–Stokes equation. Following a generalization of the Madelung transformations, we show that the inclusion of the Bohm quantum potential plus the laplacian of the phase field in the nonlinear term leads to continuity and momentum equations for a dissipative incompressible Navier–Stokes fluid. An alternative solution, built using a complex quantum diffusion, is also discussed. The present models may capture dissipative effects in quantum fluids, such as Bose–Einstein condensates, as well as facilitate the formulation of quantum algorithms for classical dissipative fluids.

In this paper, a logistic oscillator model is presented to analyze the economic cycles of five selected economies: Mexico, Brazil, Canada, China and the United States. This selection was made taking as reference their level of economic development and their geographical position. The proposed model is an extension of the production Phillip’s model (1959), which considers autonomous expenses dependent on time. It should be noted that the logistic oscillator combines the dynamics of a forced damped oscillator, whose restoring force incorporates Verhulst’s logistic equation. The data used are the production levels of The Organization for Economic Cooperation and Development (OECD) at nominal prices of the mentioned nations. The results obtained show terms of no economic damping with explosive tendency. China shows greater nondamping with an explosive trend, as does Mexico. The countries with the greatest oscillatory behavior are Brazil and Canada. Additionally, those showing exponential dynamics are China and the USA. The fitting of the logistic oscillator to the data is significant given the level of the determination coefficient. Therefore, the results indicate that the model can be useful in formulating economic policy criteria, since it allows one to predict the evolution of the economic cycle in the future.