International Journal of Modern Physics C

Optimal allocation of vaccine doses is a major challenge faced by the health authorities especially in the case of an ever-growing pandemic expansion and a limited supply availability. Based on a spatio-temporal compartmental virus propagation model applied to the case of SARS-CoV-2 virus, we investigate a layered vaccine allocation strategy for the subpopulations of a given country or a geographical region based on the prevalence of susceptible individuals as a prioritization metric. Our findings show that a relaxed layered allocation prioritization, where a maximum of regions benefit from vaccine doses is more effective in controlling the epidemic than a strict prioritization, focused only on the few most prioritized regions. These results are consistent among different vaccine rollout speeds for various limiting values of the priority list.

A thermal stability analysis method of meta-aramid insulating paper based on computer molecular dynamics technology is designed in this paper. First, the raw materials and equipment for preparing meta-aramid insulating paper were determined to prepare meta-aramid insulating paper. Then, the internal structure and morphology of meta-aramid insulating paper are analyzed for the subsequent stability analysis. Finally, the basic principle of computer molecular dynamics technology is analyzed, which is used to analyze the thermal stability of meta-aramid insulating paper, mainly from the stability of its crystal region. The experimental results show that the proposed method is effective and feasible in analyzing the thermal stability of aramid insulating paper between samples.

The gravitational search algorithm (GSA) is an eminent heuristic algorithm inspired by the laws of gravity and motion. It possesses an independent physical model in which the mass agents are guided by gravitational force to quickly achieve the convergence. Although the GSA is proven to be efficient for science and engineering problems, the mass agents can be trapped in premature convergence due to the heaviness of masses in the later iterations. The occurrence of premature convergence impedes the agents’ further exploration of the search space for a better solution. Here, the ant miner plus (AMP) variant of the ant colony optimization (ACO) algorithm is utilized to avoid the trapping of agents in local optima. The AMP algorithm extends the exploration ability of the GSA algorithm by using the attributes of pheromone updating rules generated by best ants and a problem-dependent heuristic function. The AMP variant adheres to the attributes of the ACO algorithm and is also a decision-making variant which determines the problem solution more efficiently by constructing a directed acyclic graph, considering class-specific heuristic values, and including weight parameters for the pheromone and heuristic values. In this research, this hybridization of GSA and AMP (GSAMP) algorithms is presented, and it is utilized for the decision-making application of fingerprint recognition. Here, fingerprint recognition is conducted for complete as well as latent fingerprints, which are poor quality partial fingerprints, mostly acquired from crime scenes by law enforcement agencies. The experiments are performed for the complete fingerprint dataset of FVC2004 and the latent fingerprint dataset of NIST SD27, using the proposed GSAMP approach and the individual algorithms of Ant Miner (AM) and AMP. The experimental evaluation indicates the superiority of the proposed approach compared to other methods.

The complex nature of tropical cyclones (TCs) has been recognized in a vast literature yet only few works perform complex systems diagnostics to understand their dynamics. This is especially important in order to study the effects of global warming on TC hazards. Here, such analysis is performed from a data-driven perspective using statistical and nonlinear dynamics diagnostics to the annual Accumulated Cyclonic Energy (ACE) data over the most active basin, the Northwest Pacific, from the years 1950 to 2021. The best quality data period, from 1984 to 2021, is also considered for a separate analysis in order to test the possible differences due to the data acquisition process. The following results are obtained: (i) The use of mobile windows shows a lack of trend. (ii) The closeness to a normal probability distribution indicates unpredictability, as confirmed by the return map and the autocorrelation function. As an explanation for such unpredictability, the ordering and ranking analyses reveal the presence of several processes governing the dynamics with fractal forcing dominating the larger ACE values. (iii) The Hurst exponent analysis shows a slight persistence for less than 3 years and a very slight antipersistence for longer periods of time suggesting the presence of negative feedback. (iv) Finally, the TC dynamical system dimensionality is studied. A discontinuity is inferred from a Katz fractal dimension value of 2.8, suggesting the system dynamics to be embedded by at least three independent variables, in agreement with statistical models for the TC season.

In this work, a detailed study of the structural, mechanical and thermodynamic changes in CaZn₂ under pressure range of 0–40 GPa is performed, and two different exchange correlations (XC) of GGA-PBE and LDA are used. It is found that the enthalpy values imply that CaZn₂ undergoes a phase transition from CeCu₂-type phase to Laves phase at 3.45 GPa with XC of GGA-PBE and 1.43 GPa with LDA, which is consistent with previous experiments and parts of theoretical conclusions. Furthermore, the elastic stability criterion is used to study the elastic constants of CaZn₂ under different pressures, confirming the stability of the CaZn₂ structure from 0 GPa to 40 GPa. Finally, the thermodynamic properties of CaZn₂ under pressures are comprehensively studied in detail.

Since EEG signals encode an individual’s intent of executing an action, scientists have extensively focused on this topic. Motor Imagery (MI) signals have been used by researchers to assistance disabled persons, for autonomous driving and even control devices such as wheelchairs. Therefore, accurate decoding of these signals is essential to develop a Brain–Computer interface (BCI) systems. Due to dynamic nature, low signal-to-noise ratio and complexity of EEG signals, EEG decoding is not simple task. Extracting temporal and spatial features from EEG is accessible via Convolution neural network (CNN). However, enhanced CNN models are required to learn the dynamic correlations existing in MI signals. It is found that good features are extracted via CNN in both deep and shallow models, which indicate that various levels related features can be mined. In this case, spatial patterns from multi-scaled data in different frequency bands are learnt at first and then the temporal and frequency band information from projected signals is extracted. Here, to make use of neural activity phenomena, the feature extraction process employed is based on Multi-scale FBCSP (MSFBCSP). In CNN, the envelope of each spatially filtered signal is extracted in time dimension by performing Hilbert transform. However, to access common morphologies, the convolutional operation across time is performed first and then another convolution layer across channels in the frequency band is used to represent the carried information in a more compact form. Moreover, Bayesian approach is used for mapping hyperparameters to a probability of score on the objective function. The prominent feature of the proposed network is the high capacity of preserving and utilizing the information encoded in frequency bands. Our proposed method significantly improves the efficiency of current classification method in specific dataset of the physionet. According to empirical evaluations, strong robustness and high decoding classification are two distinctive characteristics of our proposed work.

Social inequalities are ubiquitous, and here we show that the values of the Gini ($g$) and Kolkata ($k$) indices, two generic inequality indices, approach each other (starting from $g=0$ and $k=0.5$ for equality) as the competitions grow in various social institutions like markets, universities and elections. It is further shown that these two indices become equal and stabilize at a value (at $g=k\simeq 0.87$) under unrestricted competitions. We propose to view this coincidence of inequality indices as a generalized version of the (more than a) century old 80-20 law of Pareto. Furthermore, the coincidence of the inequality indices noted here is very similar to the ones seen before for self-organized critical (SOC) systems. The observations here, therefore, stand as a quantitative support toward viewing interacting socio-economic systems in the framework of SOC, an idea conjectured for years.

This paper investigates numerical solutions for the unperturbed and general perturbed Newell–Whitehead–Segel-type equations with the help of the local discontinuous Galerkin method. The stability analysis and error estimations of the proposed local discontinuous Galerkin algorithm are extensively examined. First, the spatial variables are discretized to provide a semidiscrete method of lines scheme. This generates an ordinary differential equation system in the temporal variable, which is subsequently solved using the total variation diminishing Runge–Kutta method of higher order. The generated numerical results are compared to the exact results and a few other existing numerical methods via various tables and figures to illustrate the efficiency and accuracy of the proposed method. The numerical results show that the proposed method is an effective numerical scheme for solving the Newell–Whitehead–Segel equation since the solutions obtained using the local discontinuous Galerkin method are highly close to the exact solutions with significantly less error.

Exposed to the constant light, the master clock located in the bilaterally paired suprachiasmatic nucleus (SCN) above the optic chiasma exhibits three rhythmic behaviors in hamsters. Some hamsters remain or lose circadian rhythms due to synchronization or desynchronization between the SCN neurons, respectively. Interestingly, the other hamsters show a phenomenon called “split”, in which the left SCN and right SCN oscillate with a stable anti-phase. In this paper, a modified Kuramoto model is built to explain these three rhythmic behaviors, where the phase-shift of cellular coupling is taken into account. Three cases of phase-shifts are considered, including that first case exists in all the SCN neurons, second case exists between the left and right SCN, and the last case exists within each group. We found that the phase-shift is able to induce the anti-phase synchronization between the left SCN and right SCN in the former two cases, but eliminate this anti-phase synchronization in the latter case. Our findings provide an alternative explanation for the emergency of the split and shed light on the collective behaviors of the SCN neurons.

Roadway work zone forms as a result of traffic accidents or road maintenance. In this paper, to analyze roadway work zone effects on vehicular flow in a freeway ring, a macroscopic multi-lane traffic model is proposed, the model describes the mandatory net lane-changing rate just upstream the roadway work zone with a random number model based on golden section analysis, and the net-changing rate in normal road segment with a lane-changing time model. Assuming that the free flow speeds on lanes I–III are 120, 100 and 90km/h, respectively, with a numerical method of third-order accuracy, the model equations are solved numerically to simulate vehicular flows in a three-lane freeway ring with a roadway work zone with a total length of 160m and lane III completely banned. Numerical results show that the roadway work zone generates traffic shock when initial density normalized by jam density is around 0.18. While the ramp flows have little effect on mean travel time through the freeway ring road with a total length of 100km, but can cause observable deviation of the root mean square value of travel time.

This paper considers linear and nonlinear fractional delay Volterra integrodifferential equation of order $\rho$ in the Atangana–Beleanu–Caputo (ABC) sense. We used continuous Laplace transform (CLT) to find equivalent Volterra integral equations that have been used together with the Arzela–Ascoli theorem and Schauder’s fixed point theorem to prove the local existence solution. Moreover, the obtained Volterra integral equations and the contraction mapping theorem have been successfully applied to construct and prove the global existence and uniqueness of the solution for the considered fractional delay integrodifferential equation (FDIDE). The Galerkin algorithm instituted within shifted Legendre polynomials (SLPs) is applied in the approximation procedure for the corresponding delay equation. Indeed, by this algorithm, we get algebraic system models and by solving this system we gained the approximated nodal solution. The reliability of the method and reduction in the size of the computational work give the algorithm wider applicability. Linear and nonlinear examples are included with some tables and figures to show the effectiveness of the method in comparison with the exact solutions. Finally, some valuable notes and details extracted from the presented results were presented in the last part, with the sign to some of our future works.

Label propagation algorithm is widely used for community detection in a network due to its linear time complexity. It also does not need any predefined information such as the number of communities. However, the results of this algorithm are not stable because of the randomness strategy used in its propagation process. In this paper, a modification on label propagation strategy is proposed in which labels are propagated based on nodes importance defined by their positions and popularity among neighbors. The proposed strategy is an updating process which reduces the instability of the label propagation algorithm. Experiments on real-world and synthetic networks show that the proposed method improves accuracy in terms of modularity, NMI and ARI. Also, the method has an acceptable execution time.

Natural rivers are one of the main sources of water and energy for humans. The design and management of exploitation of natural rivers systems will require a complete understanding of flow and sediment transport mechanics. Meanders are one of the most common types of rivers in nature, which have a very complex flow. The three-dimensional (3D) and complex characteristics of flow in meanderings bring up the necessity of investigating the flow pattern in this channel with 3D numerical models. This study aimed to investigate the flow pattern and predict the erosion and sedimentation site in the meandering. Also, the effect of the Froude number (Fr), the ratio of width-to-depth ($B$/$h)$ and meander angle parameters on the flow field are investigated. In this study, the turbulent flow pattern in a sine-generated channel and the angle of 50^∘ with a rectangular section with width = 40 cm and the rigid bed is studied using the 3D mathematical model SSIIM1.1. The primary and secondary flow patterns were studied using the turbulence models of $k$-$\epsilon$ and SST $k$-$\omega$. Comparison of numerical models and experimental results showed that the flow pattern is predicted well by both turbulence models of turbulence. But in some cases, the SST $k$-$\omega$ model has provided closer results to the experimental results. The results show that the shear stress pattern depends on the changes in the Fr. But, the secondary flow pattern does not change much by changing the Fr. Also, changes in the $B$/$h$ ratio affect the secondary flow pattern and shear stress pattern, so that for $B$/$h<8$, a small rotary cell is made in addition to the main rotary cell near the outer coast. The results also show that the flow pattern and shear stress depend on the channel angle.

This paper aims to describe the spatiotemporal transmission of COVID-19 and examine how various factors influence the global spread of COVID-19 using a modified gravity model. Log-linearizing the model, we run a negative binomial regression with observational data from 22 January 2020 to 31 December 2020. In the first model, population size and GDP per capita are positively related to the sum of newly confirmed COVID-19 cases within a 10-day window; the values for both variables are statistically significant throughout the study period. However, the significance of geographic distance varies. When a single geographic source exits in the early stage, the value is statistically significant. In the intermediate stage, when disease transmission is explosive between countries, the distance loses its statistical significance due to the emergence of multiple geographic transmission sources. In the containment stage, when the spread of disease is more likely to occur within a country, distance becomes statistically significant. According to the second model, the government’s internal movement control and nonpharmaceutical intervention policy, percentage of the population over 70 years old, and population-weighted density are statistically significant and are positively related to the incidence of COVID-19. By contrast, average monthly temperature, international travel restriction policies, and political regimes are statistically significant and negatively associated with the dependent variable.