International Journal of Modern Physics C

Short-time traffic flow prediction can not only help urban traffic management to complete the control and induce but also reduce the degree of urban congestion and improve the efficiency of urban operation. At the same time, improving the effect of short-time traffic flow prediction is one of the key points of traffic control and guidance development. Currently, there are many different models for short-term traffic flow prediction, and different prediction models have their different advantages and disadvantages. In this paper, we review the current status of research on short-time traffic flow prediction based on Long Short-Term Memory (LSTM) neural network: first, we analyze the research method in this paper; second, we briefly review the research and application effects of common prediction methods in traffic flow prediction; finally, the optimization method based on LSTM and its application effect are summarized and analyzed from the aspect of LSTM network and its combination model. In addition, short-time traffic flow prediction still faces challenges due to the problems of the data itself and the complexity of real-world traffic flow impact factors.

The coupling of numerical methods in different scales is of great significance in the investigation of intricate multiscale phenomena. This work develops a dynamic coupling approach based on domain decomposition by combining the mesoscale lattice Boltzmann method (LBM) and microscale molecular dynamics (MD) simulations. In the proposed scheme, a two-way concurrent exchange of information between different scales has been achieved. At the atomistic scale, the fluid dynamics are modeled through the particle-based MD method on the framework of the open large-scale atomic/molecular massively parallel simulator (LAMMPS). While at the coarse scale, the fluid system is simulated utilizing the LBM approach, which relies on the collision and streaming of the particles constrained in discretized lattices, adhering to the conservation laws of mass and momentum. The exchange of velocity distributions between the two scales was handled. The accuracy and efficiency of the proposed coupling scheme were validated through simulations of the classic Poiseuille and Couette flows. The obtained results show that satisfactory agreement against pure MD results has been achieved. Moreover, a notable improved efficiency as high as 92.8% has been observed for the coupling scheme in comparison to MD simulations. Due to the inherent parallelism of LBM and MD, the proposed coupling scheme exhibits great potential for extended application in studying complex multiscale phenomena with dynamic coupling between different scales.

The two-dimensional laminar flow over a simultaneously pitching-heaving teardrop airfoil is studied numerically. The influence of frequency and amplitude on the wake structure and aerodynamic performance is investigated at a constant Reynolds number, $\mathrm{\text{Re}}=2640$. The computational modeling accurately depicts the changes in the wake structure that occur between the von Karman, reverse von Karman and deflected conditions. Investigations at low flapping frequencies and amplitudes identified the presence of additional wake structures that had not been reported previously. The interaction between the flapping frequency and vortex shedding frequency appears to govern the formation of such multiple vortex wakes. The wake structures identified are presented in the form of a wake map where the transitions between the wake structures are visible. This study correlates the wake arrangement with the variation in force coefficients and explains why the presence of a reverse von Karman street is necessary but not a sufficient condition for thrust production. The time history of the drag coefficient and the phase relation between lift and drag show the dynamics of the wake. The variation of force coefficients and efficiency at different flapping conditions is evaluated, and the influence of flapping parameters is assessed. The underlying vortex interactions that influence the aerodynamic performance are identified. The development and distribution of stress fields developed due to periodic fluctuations behind the flapping airfoil have not been discussed previously. The velocity fluctuations in the wake due to the periodic flapping are presented, and regions of maximum stress distribution are identified.

Network public opinion is the reflection of social public opinion on the internet and an important part of social public opinion. There are various forms of network public opinion, including WeChat, Weibo, Twitter, etc., and the comments and interactions on these multiple platforms constitute the rich content of network public opinion. Real-time understanding of network public opinion propagation helps to grasp public feedback and emotions. When a public opinion crisis occurs, adopting effective control methods can quickly respond to and handle social hot events, avoid misunderstandings and the expansion of contradictions, and help maintain social harmony and stability. To address this problem, we propose a public opinion propagation model based on DI-SCIR for two-layer coupled social network. A strong coupled inter-layer connection method based on LBRank is proposed to establish a practical two-layer coupled social network. Theoretical analysis is conducted on the impact of different factors in the model on public opinion dissemination of coupled social network. A control method of direct immune SCIR is designed, which intervenes with real-time online users during the spreading of public opinion. This can guide public opinion to spread in a favorable direction. The simulation results show that the constructed public opinion propagation model for two-layer social network can better reflect the spreading of public opinion in real life. The public opinion control method can quickly guide and intervene in the spread of public opinion and reduce the impact of negative public opinion.

Analysis of the criticality in finance and economics is an important topic for scholars. The crises are conditions in which unpredictable behavior have been emerged. On the other hand, the contagion from one market to another one due to the complex nature of them is a crucial concept. Therefore, we should consider economies and financial markets as entangled systems that interact with each other, and studying one’s behavior without considering its interacting structures will not provide us with adequate information. In this paper, we have considered the critical states of some emerging and mature markets’ indices. S&P500 and Nikkei 225 as representatives of mature markets, and SSE Composite and TEDPIX as representatives of emerging markets have been analyzed. Coupled criticality emerges when two interacting systems reach their critical state due to their interactions. One of the efficient methods to study the coupled criticality is the bi-variate multifractal random walk model. This method is useful to assess the correlation of rare events and coupled criticality in entangled systems such as financial markets. The observations in this paper show that TEDPIX and S&P500 have higher critical states among others in most time lags by assessing criticality parameter explained in the model, and in longer time lags criticality parameter decreases in all indices. Coupled criticality parameter explained in the model is assessed between each index mentioned and TEDPIX. Results show that TEDPIX and S&P500 have the highest level of coupled criticality. To figure out how long-range time correlation between rare events in each index affect its critical behavior, the data of each index are shuffled, and the criticality parameter is calculated for the shuffled data. From the observations, it can be concluded that this matter has more substantial effect on mature markets. As it is known, shuffling removes the effect of the long-range time correlations. Therefore, the coupled criticality parameter is calculated for the shuffled S&P500 data and TEDPIX. Results show that the long-range correlation of S&P500 data has a significant effect on the coupled criticality behavior of S&P500 and TEDPIX.

This paper investigates the synchronizability of multi-layer small-world dynamical networks and discusses the significant factors affecting the synchronizability. First, we introduce the network model of multi-layer small-world dynamical networks with one-to-one connections in the inter-layer connections and each layer has the same topology. Second, we consider the topological parameters including adding probability, intra-layer coupling strength, inter-layer coupling strength, number of nodes per layer, initial node degree, and number of network layers which will influence the network’s synchronizability. Third, we adopt the master stability function method and numerical simulations to analyze the synchronizability of the network. We also examine how the topological parameters influence the synchronizability of multi-layer small-world dynamical networks and the relationships among these parameters. Finally, synchronization control experiments are conducted to verify our results. As a result, we find that only increasing the number of nodes per layer will weaken the synchronizability of multi-layer small-world dynamical networks, while increasing other topological parameters will enhance the synchronizability. The current findings enable us to gain a deeper understanding of the synchronization behavior and characteristics of multi-layer small-world dynamical networks.

Urban traffic emissions have significant environmental and health implications. Diverging from traditional research that primarily aims to improve traffic flow and efficiency, this study specifically focuses on the environmental impact on traffic emissions, conducting a comprehensive analysis within the Manhattan urban network through four route guidance strategies. The performance of these strategies is examined across various vehicle densities, and their impact on four traffic pollutant emissions (Carbon-dioxide, Nitrogen Oxides, Volatile Organic Compounds and Particulate Matter) is assessed. Moreover, our innovative approach analyzes emissions from the perspectives of both travel distance and trip frequency, placing special emphasis on trip frequency to provide practical insights with high real-world applicability. The results highlight the potential and limitation of the Congestion Coefficient Strategy. Under equal travel demands, the Congestion Coefficient Strategy showed promise in reducing carbon emissions. However, at lower vehicle densities, it led to a significant increase in emissions. This revelation pointed to the need for modifications to the strategy when applied in scenarios with lower traffic density. Recognizing this limitation, we introduced a modified strategy that achieved remarkable reductions in emissions across diverse vehicle densities, effectively overcoming the challenges posed by the original Congestion Coefficient Strategy. These findings offer valuable insights for policymakers and transportation planners in selecting optimal route guidance strategies to reduce pollutant emissions. Future studies will explore the efficacy of these strategies in road networks characterized by different topological configurations.

The significance of the hemodynamic analysis in the growth process of the micro saccular intracranial aneurysm is undeniable. This study has investigated the change of hemodynamic factors in the evolution process of the saccular aneurysm. The main goal is to estimate the rupture risk of aneurysm in the growth process. The numerical method is applied for the investigation of the hemodynamic factors in the evolution process of the saccular intracranial aneurysms. Wall shear stress, pressure and Oscillatory index are analyzed in this research. Two scale-down models and one scale-up geometry of the saccular aneurysm are studied in this paper. Presented results disclosed that mean wall shear stress increases more than two times when the intracranial aneurysm scale is 1/4 original size.

Studying temperature effects on defect behaviors during thermal annealing is significant for understanding the performance degradation and recovery of semiconductor devices under irradiation. We systematically studied temperature effects on annealing crucial deep-level defects in neutron-irradiated silicon, by developing a multiscale modeling approach. The temperature-dependent concentrations and electron occupation ratios of crucial defects of divacancies (V₂) and tri-vacancies (V₃) were given for dynamic and post-irradiation annealing. Besides the common direct dissociation, we found a new approach to eliminating V₂ and V₃ by their recombination with interstitials dissociated from interstitial-relative defects at relatively low temperatures. To effectively eliminate V₂ and V₃ by post-irradiation annealing, we further determined the activation energies of 1.98eV and 1.71eV for V₂ and V₃, respectively. We also found that, within the operation temperature range of devices, the higher the temperature, the better the radiation resistance. It is thus recommended that the optimal temperature of post-irradiation annealing for device performance recovery is near 600K.

Fractional ordinary differential equations are crucial for modeling various phenomena in engineering and physics. This study aims to enhance solution methodologies by combining the Laplace transform with the reproducing kernel Hilbert space method (RKHSM). This integration leads to a more effective approach compared to the classical RKHSM. We apply the Laplace-reproducing kernel Hilbert space method (L-RKHSM) to develop novel numerical solutions for nonlinear fractional Riccati differential equations. The L-RKHSM systematically produces both approximate and analytic solutions in series form. We present detailed results for four illustrative examples, showcasing the superior performance of the L-RKHSM over traditional methods. This innovative approach not only advances our understanding of nonlinear fractional ordinary differential equations but also demonstrates its effectiveness through significantly improved outcomes in various applications.

Real networks often exhibit community structures, while community networks often have heterogeneity. Group interactions beyond pairwise interactions can adequately characterize the high-order features in the propagation process. In this paper, we construct a community network with heterogeneity which can reflect the heterogeneity among communities. According to the proposed model, high-order interactions are introduced to study the impact of heterogeneity among communities and higher-order interactions on epidemic dynamics. It is shown that heterogeneity among communities and high-order interactions will have an impact on the propagation speed, propagation threshold and final propagation scale.

Given a finite collection of two-dimensional tile types, we study the tiling of a rectangular region of the plane when the available tile types are all rectangular. Unlike the case of tiling the whole, unbounded plane, the additional complications imposed by the boundary conditions tend to constrain progress to mostly indirect results, such as recurrence relations. The tile types we use are squares, dominoes and straight tetraminoes, in which case not even recurrence relations are available. We seek to characterize this complex system through some fundamental physical quantities and follow two parallel tracks: One fully analytical for what seems to be the most complex special case still amenable to such approach, the other based on the Wang–Landau method for state-density estimation. Given a simple energy function based solely on tile contacts, we have found either approach to lead to illuminating depictions of entropy, temperature, and above all partitions of the configuration space. A configuration, in this context, refers to how many tiles of each type are used. We have found that certain partitions help bind together different aspects of the system in question and conjecture that future applications will benefit from the possibilities they afford.

Fermat–Wiener index based on topological Wiener index is the total sum of Fermat distance over all the triplets for vertices. In this paper, we construct a class of hierarchical graphs based on hierarchical product generalized from Cartesian product. We study some critical properties of the hierarchical networks by investigating its topological indices. Applying the finite pattern method, we analytically deduce the dominant term of average Fermat distance and obtain its asymptotic formula, which implies small-world property. We finally exhibit a close connection between Fermat–Wiener index and related graph invariants like average geodesic distance, Wiener index and eigenvalues of Laplacian matrix.

From five parameters at the state level: percentage of Catholic adherents (CATH-ADH), average number of adherents per congregation (ADH/CON), number of congregations per capita (CON/POP) and voting percentages from the 2020 election for Trump (TRUMP) and Biden (BIDEN), we obtain six Pearson correlation coefficients at the national and regional levels. These yield a significant positive correlation between CATH-ADH and BIDEN, as well as for ADH/CON and BIDEN, while the opposite is true for TRUMP. Conversely, there is a negative correlation between CON/POP and BIDEN and a positive one for TRUMP. The interplay between these, as well as important geographical and confessional spatial autocorrelations measured using Moran’s index, show the influence of religion on the 2020 election among two main lines: Catholic support for Biden and Trump support from atomized yet smaller congregations.