International Journal of Modern Physics C

The development of novel Kinetic Energy (KE) functionals is an important topic in density functional theory (DFT). In particular, this happens by means of an analysis with newly developed benchmark sets. Here, I present a study of Laplacian-level kinetic energy functionals applied to metallic nanosystems. The nanoparticles are modeled using jellium sph eres of different sizes, background densities, and number of electrons. The ability of different functionals to reproduce the correct kinetic energy density and potential of various nanoparticles is investigated and analyzed in terms of semilocal descriptors. Most semilocal KE functionals are based on modifications of the second-order gradient expansion GE2 or GE4. I find that the Laplacian contribute is fundamental for the description of the energy and the potential of nanoparticles.

In recent years, the increasing human–computer interaction has spurred the interest of researchers towards facial expression recognition to determine the expressive changes in human beings. The detection of relevant features that describe the expressions of different individuals is vital to describe human expressions accurately. The present work has employed the integrated concept of Local Binary Pattern and Histogram of Gradient for facial feature extraction. The major contribution of the paper is the optimization of the extracted features using quantum-inspired meta-heuristic algorithms of QGA (Quantum-Inspired Genetic Algorithm), QGSA (Quantum-Inspired Gravitational Search Algorithm), QPSO (Quantum-Inspired Particle Swarm Optimization), and QFA (Quantum-Inspired Firefly Algorithm). These quantum-inspired meta-heuristic algorithms utilize the attributes of quantum computing that ensure the adequate control of facial feature diversity with quantum measures and Q-bit superstition states. The optimized features are fed to the deep learning (DL) variant deep convolutional neural network added with residual blocks (DCNN-R) for the classification of expressions. The facial expressions are detected for the KDEF and RaFD datasets under varying yaw angles of –90^∘, –45^∘, 0^∘, 45^∘, and 90^∘. The detection of facial expressions with varying angles is also a crucial contribution, as the features decrease with the increasing yaw angle movement of the face. The experimental evaluations demonstrate the superior performance of the QFA than other algorithms for feature optimization and hence the better classification of facial expressions.

We develop time-splitting finite-difference methods, using an implicit Backward–Euler and a semi-implicit Crank–Nicolson discretization schemes, to study the spin-orbit-coupled (SO-coupled) spinor Bose–Einstein condensates with coherent coupling in quasi-one- and quasi-two-dimensional traps. The split equations involving kinetic energy and spin-orbit-coupling operators are solved using either a time implicit Backward–Euler or a semi-implicit Crank–Nicolson method. We explicitly develop the methods for pseudospin-1/2, spin-1 and spin-2 condensates. The results for ground states obtained with time-splitting Backward–Euler and Crank–Nicolson methods are in excellent agreement with time-splitting Fourier spectral method, which is one of the popular methods to solve the mean-field models for SO-coupled spinor condensates. We confirm the emergence of different phases in SO-coupled pseudospin-1/2, spin-1 and spin-2 condensates with coherent coupling.

Aerodynamic improvements primarily result in decreased fuel usage and carbon dioxide emissions into the atmosphere. Numerous governments support ongoing aerodynamics development initiatives as a means of addressing the energy problem and reducing air pollution, Ahmed body investigation helps research to investigate versatile approaches and flexibility of design. This study is carried over a generic design of Ahmed body model. We attempted a passive arrangement system to reduce drag coefficient with a correlation of cases such as in primary objective varying parameter of slant angle from 20^∘ to 30^∘ proposed to monitor the behavior of drag coefficient. Once we finalized the optimum slant angle, which gives a lower drag coefficient, the next proposed configuration is to vary passive arrangement between lower and upper blend length to see the deflection of the boundary layer in correlation with the drag coefficient. The final topology is selected, which gives the lowest drag coefficient. The post-process correlation study was proposed by using an artificial neural network (ANN) scheme. The ANN model is developed between an achieved set of data from CFD investigation, ANN model indicates a strong correlation between the varying percentage of blend angle and increment percentage of the drag coefficient.

In complex networks, how topology structures influence the traffic dynamics on networks is a hot topic. The existence of structure within communities may affect the transmission of data packets in the network. In this paper, we investigate the impact of community structure on traffic dynamics in random networks which are homogeneous. It is found that three key factors influence traffic capacity in such community networks: community number, community strength, and average node degree. The increase of average node degree increases traffic capacity, while the increase of community number and community strength decreases traffic capacity.

This paper is devoted to developing spectral solutions for the nonlinear fractional Klein–Gordon equation. The typical collocation method and the tau method are employed for obtaining the desired numerical solutions. In order to do this, a new operational matrix of fractional derivatives of Fibonacci polynomials is established. The idea behind the derivation of this matrix is based on utilizing the connection formula between the Fibonacci and Chebyshev polynomials. The introduced operational matrix is used along with the weighted residual quadrature spectral method and the collocation method to convert the nonlinear fractional Klein–Gordon equation into a system of algebraic equations. By solving the resulting system, we obtain a semi-analytic solution. The convergence and error analysis of the method are discussed. Some numerical results and discussions are presented aiming to illustrate the wide applicability and accuracy of the proposed algorithms.

In this paper, we present a simple discrete model of cascade behavior in an actual geographical space with built environments. By simultaneously triggering and relaxing random locations in a network of Voronoi cells interacting via the gravity model, we observe nontrivial statistics with heavy-tailed distributions of cells and actual area extents involved in the cascade. The distributions of these affected areas follow unimodal statistics, unlike the other externally-driven models operating over uniform neighborhoods that exhibit power-laws. Majority of the cascades are limited within the immediate neighborhoods of adjacent Voronoi cells, even for sufficiently large triggering magnitudes. The results are viewed from the perspective of inhomogeneous driving in sandpile-based models, and benchmarked with distributions obtained in other geographic datasets. The method offers a complexity perspective into the generation of large-scale events in physical and intangible flows, and explains their origins from cascaded accumulations of slow, random, and intermittent processes.

Electronic mails (emails) have been widely adapted by organizations and individuals as efficient communication means. Despite the pervasiveness of alternate means like social networks, mobile SMS, electronic messages, etc. email users are continuously growing. The higher user growth attracts more spammers who send unsolicited emails to anonymous users. These spam emails may contain malware, misleading information, phishing links, etc. that can imperil the privacy of benign users. The paper proposes a self-adaptive hybrid algorithm of big bang–big crunch (BB–BC) with ant colony optimization (ACO) for email spam detection. The BB–BC algorithm is based on the physics-inspired evolution theory of the universe, and the collective interaction behavior of ants is the inspiration for the ACO algorithm. Here, the ant miner plus (AMP) variant of the ACO algorithm is adapted, a data mining variant efficient for the classification. The proposed hybrid algorithm (HB³C-AMP) adapts the attributes of B³C (BB–BC) for local exploitation and AMP for global exploration. It evaluates the center of mass along with the consideration of pheromone value evaluated by the best ants to detect email spam efficiently. The experiments for the proposed HB³C-AMP algorithm are conducted with the Ling Spam and CSDMC2010 datasets. Different experiments are conducted to determine the significance of the pre-processing modules, iterations, and population size on the proposed algorithm. The results are also evaluated for the AM (ant miner), AM2 (ant miner2), AM3 (ant miner3), and AMP algorithms. The performance comparison demonstrates that the proposed HB³C-AMP algorithm is superior to the other techniques.

From the Rwandan genocide to the Arab Spring movement, it has been well known that social networks, offline or online, and mass media can collectively change and amplify public opinions, however there are few theoretical models to characterize the persuasion process. In this paper, we propose an opinion dynamics model based on invasion process with media effect and committed agents, and analytically obtain the fraction of each opinion at the steady state. We find that the relative proportion of committed agents plays a vital role in influencing corresponding opinion formation, and social networks can enhance the influence of committed agents through the interaction between individuals. Mass media can affect individuals not only directly due to exposure but indirectly due to social interactions. This paper reveals the influence of mass media and committed agents on the final distribution of opinions through a persuasion process, and lays the foundation for building more general models that consider individual heterogeneity and external influences.

As an important area of social networks, rumor spread has attracted the attention of many scholars. It aims to explore the rumor propagation, and to propose effective measures to curb the further spread of rumors. Different from some existing works, this paper believes that susceptible persons affected by rumor-refuting information will first enter the critical state, while ones who related to rumors will directly turn into the spread state. Therefore, this paper proposes a Susceptible-Infectious-Critical-Recovered (SICR) rumor model. In addition, considering that infectious persons with high levels of refuting rumors may cause emotional resonance among individuals, this model adds a connecting edge from the recovered to the infectious who are triggered by the information of refuting the rumors. First, the basic regeneration number $R(0)$ is obtained by using the next generation matrix method. Then, the global stability of the rumor-free equilibrium $E(0)$ and the persistence of rumor propagation are proved in detail in theoretical analysis. The simulation results show that the existence of a critical state can reduce the influence of rumors. Rumor refutation mechanism, as soon as possible to curb the spread of rumors, is an effective measure.

Understanding the spatial interactions of human mobility is crucial for urban planning, traffic engineering, as well as for the prevention and control of infectious diseases. Although many models have been developed to model human mobility, it is not clear whether such models could also capture the traveling mechanisms across different time periods (e.g. workdays, weekends or holidays). With one-year long nationwide location-based service (LBS) data in China, we investigate the spatiotemporal characteristics of population movements during different time periods, and make thorough comparisons for the applicability of five state-of-the-art human mobility models. We find that population flows show significant periodicity and strong inequality across temporal and spatial distribution. A strong “backflow” effect is found for cross-city movements before and after holidays. Parameter fitting of gravity models reveals that travels in different type of days consider the attractiveness of destinations and cost of distance differently. Surprisingly, the comparison indicates that the parameter-free opportunity priority selection (OPS) model outperforms other models and is the best to characterize human mobility in China across all six different types of days. However, there is still an urgent need for development of more dedicated models for human mobility on weekends and different types of holidays.

The purpose of this paper is to debate the influences of variable thermal conductivity as well as viscosity on a hybrid nanofluid flow through a non-Darcy porous medium along a wavy surface. Two kinds of base fluids are considered; the first is water (H₂O) with $\mathrm{\text{Pr}}=6.785$ and the second is ethylene glycol–water mixture (C₂H₆O${}_2-$H₂O) with $\mathrm{\text{Pr}}=7.33$. Furthermore, the physical properties of the base fluid have been modified via the addition of two kinds of suspended nanoparticles. The suspended nanoparticles of alumina Al₂O₃ were firstly added to the base fluid. Then, titania TiO₂ suspended nanoparticles are immersed into this nanofluid to form two hybrid nanofluids (TiO${}_2-$Al₂O₃/H₂O) and (TiO₂-Al₂O₃/C₂H₆O₂–H₂O). Dupuit Forchheimer model as a non-Darcy porous medium is utilized. The mathematical model for the present physical problem with convective boundary condition is studied numerically via the Chebyshev pseudospectral (CPS) technique. Nusselt number, velocity and temperature profiles are investigated under different parameters. The present outcomes clarify that adding the alumina nanoparticle supports and improves the efficiency of the titania nanoparticle. Also, taking both of the hybrid nanofluid viscosity/thermal conductivity as variable functions of temperature has an important role to support the studies of heat transfer.

Using an inner complex blockage within a square cavity is spreading massively for the cooling process. This study adopts the time-fractional derivative of the incompressible smoothed particle hydrodynamics (ISPH) method for studying the magnetic field, diffusion-thermo, and thermo-diffusion impacts on the double diffusion of a nanofluid in a porous annulus between a square cavity and an astroid shape. The alterations of the pertinent parameters, fractional derivative order $\alpha$ between 0.9 and 1, dimensionless time parameter $\tau$ between 0 and 0.6, the radius of an astroid $R_a$ between 0.1 and 0.45, solid volume fraction $\varphi$ between 0 and 0.06, Hartman parameter Ha between 0 and 100, Darcy parameter Da between $10^{-2}$ and $10^{-5}$, and Soret number Sr between 0.1 and 2 supplemented by Dufour number Du between 0.6 and 0.03 on the velocity field, temperature, concentration, and mean of Nusselt and Sherwood numbers are discussed. The main findings of the ISPH numerical simulations showed that a decrease in a fractional derivative order $\alpha$ delivers the sooner steady-state of the double diffusion which suppresses the performed calculations. The velocity field’s maximum powers by 19.23% as $R_a$ increases from 0.1 to 0.45 and it decreases by 16.67%, 28.89%, and 97.99% as $\varphi$ powers from 0 to 0.06, Ha powers from 0 to 100, and Da decreases from 10${}^{-2}$ to 10${}^{-5}$, respectively. The outlines of $\overline{\mathrm{\text{Nu}}}$ and $\overline{\mathrm{\text{Sh}}}$ are increasing as $R_a$ and $\varphi$ are increased. A growth in Sr supplemented by a reduction in Du is diminishing the distributed concentration and nanofluid velocity within an annulus.

This brief paper studies the consensus problems for networked Euler–Lagrange (EL) agents in which only partial EL agents’ velocity measurements are available. Based on partial EL agents’ velocity measurements, the consensus protocols which indicate the communication structure among EL agents are derived in detail. Furthermore, the networked EL agents can achieve consensus with derived protocols. The obtained results show that the consensus protocol can always be designed under the condition that at least one EL agent can measure velocity measurements for any networked EL agents. Finally, numerical simulations are given to illustrate the effectiveness of the proposed protocols.