International Journal of Modern Physics C

Solitons and waves with memory effects are two examples of nonlinear wave phenomena that can be studied mathematically using the fractional nonlinear sine-Gordon equation. It is a modification of the traditional sine-Gordon equation that takes memory effects and nonlocal interactions into account by adding fractional derivatives. A more complex explanation of particle dynamics and interactions within relativistic quantum mechanics is made possible by the fractional Klein–Gordon model, a theoretical framework that expands the standard equation to include fractional derivatives. The study uses shifted Legendre–Gauss–Lobatto and shifted Legendre–Gauss–Radau collocation techniques to solve numerically two-dimensional sine-Gordon and Klein–Gordon models. The study handles two-dimensional sine-Gordon and Klein–Gordon models by extending a collocation approach using basis functions. It suggests a collocation technique that uses a suggested basis to automatically satisfy the conditions. The suggested methods’ spectral accuracy and efficiency are validated by numerical results.

In this work, a master–slave system composed by a pair of damped Duffing oscillators with variable coefficients and nonlinear coupling is investigated. An integral of motion for the system is obtained using a symmetry transformation and Noether’s theorem. Some numerical examples are presented for different cases of damping and oscillation frequency, for a varying coupling constant. The system dynamics is studied by means of space-time surfaces, time series and phase portraits. For a constant oscillation frequency, the slave presents envelopes that tend to become chaotic as the coupling constant increases. Meanwhile, as the frequency increases with time, the slave has higher amplitudes and speeds than the master oscillator.

In this paper, we study a kinetic modeling of opinion formation on social networks in which the distribution function depends on both the opinion and the connectivity of the agents. The opinion exchange process is governed by a Sznajd-type model with three opinions, $\pm 1$, 0, and the social network is represented statistically with connectivity denoting the number of contacts of a given individual. It is commonly accepted that, in social networks, the opinion of the agents with a higher connectivity, i.e. a larger number of followers, is more convincing than that of the agents with a lower number of followers. By the kinetic modeling approach, we derived the asymptotic mean opinion of a social network in terms of the initial opinion and the connectivity of the agents. The present work extends the result in [N. Loy, M. Raviola and A. Tosin, Opinion polarisation in social networks, Phil. Trans. R. Soc. A 380, 20210158 (2021)] where only binary opinions, $\pm 1$, were considered, that indecisive people who cannot make a clear choice between $\pm 1$ are allowed in this study.

Due to its unique properties such as superconductivity and superfluidity, high-pressure properties of hydrogen attract a lot of attention. However, the Liquid–Liquid Transition (LLT) of hydrogen under high-pressure and high-temperature is of particular significance for understanding its metallization. We propose a data-driven machine learning approach based on the density functional theory data to fit the potential energy surface into a deep neural network form. This method overcomes the simulation scale limitations of first-principles approaches to investigate the dissociation behavior of hydrogen molecules under high pressure. Our findings reveal an LLT curve exhibiting a first-order continuous change along with transition zone corresponding to hydrogen molecule dissociation. This study offers valuable insights into the LLT phenomenon and the metallization of hydrogen under high pressure.

The rapid expansion of artificial intelligence technologies has enabled machines to comprehend emotional intelligence. Among various indicators, facial expressions serve as an effective medium for understanding emotions. The concept of facial expression recognition (FER) relies heavily on the accurate and robust features available. Initially, the method of three-channel convolutional neural networks (TC-CNN) is adapted to extract facial features. However, only extracting the features is insufficient, the optimization of the extracted features is crucial to determining precise and robust features. This research work focuses on the optimization of the features using the quantum-inspired vortex search algorithm (QVSA). The QVSA integrates the attributes of Q-bits into the vortex search algorithm (VSA), optimizing the features by using the Q-bits to determine the vortex center on the Bloch sphere. The Q-bit attributes also improve the diversity of the features and help to avoid the premature convergence of the VSA. The final recognition of the facial expressions is performed using the deep neural network method of ResNet101v2. The experiments for facial expression recognition are performed on the datasets of RaFD and KDEF, which include different facial positions such as front pose, diagonal pose and profile pose. Performance comparisons demonstrate the effectiveness of the proposed system over state-of-the-art facial expression techniques.

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.

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.

In this paper, the application of the HAM-Padé technique for solving a system of differential equations is investigated. This method is a combination of the homotopy analysis method (HAM) and Padé approximation. The equations under consideration are the governing equations of an important physical problem that is also widely used in engineering. This study is a comparative research so that after solving the equations, the solutions obtained from the HAM-Padé method will be compared with the outcomes of the three previous studies. The comparison showed that the HAM-Padé solutions have an interesting agreement with the previous works and have good accuracy.

Between the years 2020 and 2022, the world was hit by the pandemic of COVID-19 giving rise to an extremely grave situation. Many people suffered and died from this disease. Also, the global economy was badly hurt due to the consequences of various intervention strategies (like social distancing, lockdown) which were applied by different countries to control this pandemic. There are multiple speculations that humanity will again face such pandemics in the future. Thus, it is very important to learn and gain knowledge about the spread of such infectious diseases and the various factors which are responsible for it. In this study, we have extended our previous work [S. Chowdhury, S. Roychowdhury and I. Chaudhuri, Eur. Phys. J. Spec. Top. 231, 3619 (2022), doi:10.1140/epjs/s11734-022-00619-1] on the probabilistic cellular automata (CA) model to reproduce the spread of COVID-19 in several countries by modifying its earlier used neighborhood criteria. This modification gives us the liberty to adopt the effect of different restrictions like lockdown and social distancing in our model. We have done some theoretical analysis for initial infection and simulations to gain insights into our model. We have also studied the data from eight countries for COVID-19 in a window of 876 days and compared it with our model. We have developed a proper framework to fit our model on the data for confirmed cases of COVID-19 and have also re-checked the goodness of the fit with the data of the deceased cases for this pandemic. Our model is compared with other well-known CA models and the ODE-based SEIR model. This model fits well with different peaks of COVID-19 data for all the eight countries and can be possibly generalized for a global prediction. Our study shows that the rate of disease spread depends both on infectivity of a disease and social restrictions. Also, it shows an overall decrement in mortality rate with time due to COVID-19 as more and more people get infected as well as vaccinated. Our minimal model with modified neighborhood condition can easily quantify the degree of social restrictions. It is statistically concluded that the overall degree of social restrictions is above the mean when we considered all eight countries. Finally to conclude, this study has given us various important insights about this pandemic which can help in preparing for combating epidemics in future situations.

In contemporary power systems, harmonics as undesired electrical phenomena, have drawn considerable attention of the research community investigating in design and development of optimal electrical networks. Harmonics affect the power electronics devices with a number of negative impacts, including higher power losses, worse power quality, and interference with other delicate grid connected equipment. An objective function of inter- and sub-harmonics is constructed for joint estimation of amplitude and phase with known frequency. Accurate estimation of the harmonics model is carried out with stupendous knacks of raptor-inspired Harris Hawks optimization algorithm (HHOA), a revolutionary population-based, nature-motivated optimization approach. The primary sources of inspiration for HHOA are Harris Hawks cooperative demeanor and surprise pounce chasing style in nature. The HHOA’s performance is assessed for harmonics parameter estimation with several noise conditions, differing particle sizes and iterations. The proposed HHOA-based estimation technique provides accurate, convergent and robust performance for harmonics estimation. The reliable level of fitness is achieved consistently in the range of 10⁻³ to 10⁻¹⁰, an evidence of valuable precision, stability and robustness of the proposed algorithm.

Low-light images are challenging for both human observation and computer vision algorithms due to low visibility. To address this issue, various image enhancement techniques such as dehazing, histogram equalization, and neural network-based methods have been proposed. However, most existing methods often suffer from the problems of insufficient contrast and over-enhancement while enhancing the brightness, which not only affects the visual quality of images but also adversely impacts their subsequent analysis and processing. To tackle these problems, this paper proposes a low-light image enhancement method called LEFB. Specifically, the low-light image is first transformed into the LAB color space, and the L channel controlling brightness is enhanced using a local contrast enhancement algorithm. Then, the enhanced image is further enhanced using an exposure fusion-based contrast enhancement algorithm, and finally, a bilateral filtering function is applied to reduce image edge blurriness. Experimental evaluations are conducted on real datasets with four comparison algorithms. The results demonstrate that the proposed method has superior performance in enhancing low-light images, effectively addressing problems of insufficient contrast and over-enhancement, while preserving fine details and texture information, resulting in more natural and realistic enhanced images.

In interdependent networks, link addition strategies can enhance the connectivity of networks, thus improving robustness in the face of cascading failures. In this paper, first, interdependent networks models under various coupling methods and cascading failures model are proposed. Moreover, link addition strategies and node importance metrics are obtained. Finally, this paper analyzes the influence of link addition strategies and node importance metrics on robustness under three coupling methods. Besides, the effects of coupling ratio and link addition ratio on robustness in the interdependent network under different coupling methods are also analyzed. The simulation results show in partial coupled networks, intra-layer link addition strategy (Intra LAS) is more robust. In one-to-one coupled networks, inter-layer link addition strategy (Inter LAS) yields better performance in improving robustness when the number of initial attacked nodes is fewer, and intra-layer link addition strategy (Intra LAS) is more robust when more nodes are attacked initially. The effect of network size and the average degree of network on this result are also discussed. The variation trend of networks with multiple dependencies is similar to that of partial coupled network with high coupling rate, but it has less robust than partial coupled network. The conclusions can provide guidance on how to select link addition strategy and node importance metric under different coupling methods.

Considerable efforts have been made for link prediction by researchers from various disciplines because of its important value in a wide range of applications. Heuristics methods, which predict links based on some assumptions, can attain commendable accuracy when their assumptions are met. Otherwise, their effectiveness may be unsatisfactory. On the other hand, the methods that leverage Graph Neural Networks to learn the representations of node pairs have been confirmed to be effective for link prediction. However, they are usually very time-consuming. To circumvent these issues, we put forth the HELF method, a new link prediction technique built on fully connected neural networks (FCNNs) with fundamental heuristic elements. By investigating the formulas of a collection of heuristic methods, we extract a series of fundamental heuristic elements from them, which cover the core structural profiles of node pairs. Then, we encode target node pairs into feature vectors using these fundamental heuristic elements and feed the feature vectors to a FCNN to gauge the existence of links. Extensive experiments are conducted on several networks to evaluate the performance of our HELF method. The results demonstrate that HELF outperforms the well-known heuristic methods and state-of-the-art neural network-based methods subject to AUC and AP. Additionally, HELF runs much faster than these neural network-based methods.

This paper employs a numerical method for the numerical treatment of the time fractional Fokker–Planck equation. This method is based on applying the spectral Petrov–Galerkin method. We utilize suitable combinations for the shifted Chebyshev polynomial of the fifth-kind as basis functions. The key idea of the suggested strategy is to transform the governed boundary-value problem into a set of linear algebraic equations using the spectral Petrov–Galerkin method. Several approaches are available to solve the resultant linear system. An in-depth investigation is conducted on the convergence and error analysis of the expansion of the shifted Chebyshev polynomial function of the fifth kind. Many examples are provided to illustrate the precision of the suggested approach.