International Journal of Modern Physics C

The behavior of nonlinear systems can be analyzed by artificial neural networks. Air temperature change is one example of the nonlinear systems. In this work, a new neural network method is proposed for forecasting maximum air temperature in two cities. In this method, the regular graph concept is used to construct some partially connected neural networks that have regular structures. The learning results of fully connected ANN and networks with proposed method are compared. In some case, the proposed method has the better result than conventional ANN. After specifying the best network, the effect of input pattern numbers on the prediction is studied and the results show that the increase of input patterns has a direct effect on the prediction accuracy.

Complex networks have been proven useful in characterizing written texts. Here, we use networks to probe if there exist a similarity within, and difference across, era as reflected within the poem's structure. In literary history, boundary lines are set to distinguish the change in writing styles through time. We obtain the network parameters and motif frequencies of 845 poems published from 1522 to 1931 and relate this to the writing of the Elizabethan, 17th Century, Augustan, Romantic and Victorian eras. Analysis of the different network parameters shows a significant difference of the Augustan era (1667–1780) with the rest. The network parameters and the convex hull and centroids of the motif frequencies reflect the adjectival sequence pattern of the poems of the Augustan era.

In real traffic, the right-turn vehicles at intersections are not controlled by signal lights and their effects are neglected. In this paper, we develop a cellular automaton model to formulate the complicated turning behaviors of vehicles at intersections. Simulation results are quite in accord with the observation on the Beijing's 4th ring road. It is found that the right-turn vehicles may produce queue near the intersection, a short lane designed for right-turn has prominent effect in improving traffic flow, but, a too long lane for right-turn cannot further decrease the stop ratio as expected. These findings deepen our understanding on the effects of right-turn vehicles and may help the design and management of intersections.

The clustering coefficient of user–object bipartite networks is presented to evaluate the overlap percentage of neighbors rating lists, which could be used to measure interest correlations among neighbor sets. The collaborative filtering (CF) information filtering algorithm evaluates a given user's interests in terms of his/her friends' opinions, which has become one of the most successful technologies for recommender systems. In this paper, different from the object clustering coefficient, users' clustering coefficients of user–object bipartite networks are introduced to improve the user similarity measurement. Numerical results for MovieLens and Netflix data sets show that users' clustering effects could enhance the algorithm performance. For MovieLens data set, the algorithmic accuracy, measured by the average ranking score, can be improved by 12.0% and the diversity could be improved by 18.2% and reach 0.649 when the recommendation list equals to 50. For Netflix data set, the accuracy could be improved by 14.5% at the optimal case and the popularity could be reduced by 13.4% comparing with the standard CF algorithm. Finally, we investigate the sparsity effect on the performance. This work indicates the user clustering coefficients is an effective factor to measure the user similarity, meanwhile statistical properties of user–object bipartite networks should be investigated to estimate users' tastes.

In this paper, we introduce an age-related preferential selection mechanism into the prisoner's dilemma game. Under this mechanism, players can select a source of strategy imitation from their neighbors in a biased way proportional to their ages. Tuned by a selection parameter α, the impact of aging on the evolution of cooperation can be very different. The numerical results show that the cooperation will be effectively promoted by adjusting the value of α. By examining the evolution process and spatial patterns for different values of α, it is found that larger α will effectively promote the formation of large scale of cooperator clusters. In addition, the impact of noise K on the spreading process of cooperation strategy is studied. We find that small K can make cooperation monotonously increase with α, while large K guarantees the optimal existence of cooperation for an intermediate α.

In this work, we study a generic squared symmetric Hamiltonian of two degrees of freedom. Our aim is to show a global methodology to analyze the evolution of the families of periodic orbits and their bifurcations. To achieve it, we use several numerical techniques such as a systematic grid search algorithm in sequential and parallel, a fast chaos indicator and a tool for the continuation of periodic orbits. Using them, we are able to study the special and generic bifurcations of multiplicity one that allow us to understand the dynamics of the system and we show in detail the evolution of some symmetric breaking periodic orbits.

Graphics processing unit (GPU) is becoming a powerful computational tool in scientific and engineering fields. In this paper, for the purpose of the full employment of computing capability, a novel mode for parallel molecular dynamics (MD) simulation is presented and implemented on basis of multiple GPUs and hybrids with central processing units (CPUs). Taking into account the interactions between CPUs, GPUs, and the threads on GPU in a multi-scale and multilevel computational architecture, several cases, such as polycrystalline silicon and heat transfer on the surface of silicon crystals, are provided and taken as model systems to verify the feasibility and validity of the mode. Furthermore, the mode can be extended to MD simulation of other areas such as biology, chemistry and so forth.

Since the betweenness of nodes in complex networks can theoretically represent the traffic load of nodes under the currently used routing strategy, we propose an improved efficient (IE) routing strategy to enhance to the network traffic capacity based on the betweenness centrality. Any node with the highest betweenness is susceptible to traffic congestion. An efficient way to improve the network traffic capacity is to redistribute the heavy traffic load from these central nodes to non-central nodes, so in this paper, we firstly give a path cost function by considering the sum of node betweenness with a tunable parameter β along the actual path. Then, by minimizing the path cost, our IE routing strategy achieved obvious improvement on the network transport efficiency. Simulations on scale-free Barabási–Albert (BA) networks confirmed the effectiveness of our strategy, when compared with the efficient routing (ER) and the shortest path (SP) routing.