The European Physical Journal Special Topics最新文献

Time-varying and seasonal parameter inversion in the mathematical model of infectious diseases and uncertainty quantization based on actual data have great significance for real quantitative transmission process. In this study, the behavior-driven mathematical model of infectious diseases and the data-driven parameter identification method are combined to quantify the transmission law of tuberculosis (TB). To begin with, according to the characteristics of TB transmission, the TS-SID model with time-varying is established. Then, the improved identification algorithm is proposed to track the fluctuation of disease infection rate and mortality rate considering the seasonal influence. Meanwhile, focusing on the influence of noise on the spread of diseases, noise reduction and uncertain quantization are carried out on the data to identify the noise distribution. In addition, predict the denoised sequence and superimpose the noise distribution, which can improve the rationality of prediction. Finally, the numerical comparison shows that seasonal time-varying tracking is good for grasping and predicting the disease evolution.

The cerebrum and cerebellum play a crucial role in motion control and are crucial to perform a variety of fast, precise movements for humans and animals. Emotions are generated in the cerebral cortex, and activate the amygdala, which promotes the storage of information in various regions of the cerebrum. In this paper, cerebellar learning model, emotional learning model, and spinal cord calculation module are incorporated to complete the control of an arm musculoskeletal system, and the redundancy problem of the musculoskeletal system control is solved through the optimized calculation in the spinal cord module. The arm musculoskeletal system can thus complete the end trajectory execution task successfully. It is shown that compared with the cerebellar motion control scheme, the proposed scheme has the advantages of fast learning convergence, simplified synaptic adaptation of cerebellum and strong anti-disturbance ability. It is also verified that the proposed control scheme exhibits good robustness to random noise. The proposed arm musculoskeletal control scheme operates effectively and provides a theoretical reference for the application of biomimetic musculoskeletal system.

In view of the lack of an explicit expression for the stationary response probability density of generalized nonlinear systems subjected to combined harmonic and Gaussian white noise excitations, a data-driven method is proposed in this paper. The approach involves constructing an expansion expression with undetermined coefficients and determining these coefficients through solving an optimal problem. Initially, leveraging the principle of maximum entropy and the Buckingham Pi theorem, the stationary probability density of the system energy is represented in exponential form. The power of the exponential function is then expanded into a combination of basis functions of Pi groups with undetermined coefficients, constructed from system and excitation parameters, along with the system energy. Subsequently, the coefficients are determined by solving an optimal problem aimed at minimizing the residual between the expression and histogram-based estimations of the probability density of the system energy from random state data. Additionally, a sparse optimization algorithm is employed and then the explicit expression for the probability density of the system energy can be identified including system and excitation parameters. Two typical nonlinear systems, namely the Duffing oscillator and Coulomb friction system, are given to illustrate the effectiveness and accuracy of the proposed data-driven method. The identified expressions cover both resonant and non-resonant cases, showcasing the versatility and applicability of the proposed approach. Furthermore, the extensionality of the expression is thoroughly examined and discussed.

The prime objective of this work is to analyze a Caputo fractional order HIV model where infection occurs through various modes. A detailed investigation on existence, uniqueness, boundedness as well as non-negativity of solutions have been performed at first. Stability analysis of the fixed points has been carried out in the next section. Sensitivity analysis of the threshold parameter has also been performed. Finally, a series of numerical simulations are used to confirm theoretical findings.

A data-driven system identification method based on the Koopman operator with sparse optimization is proposed. Koopman theory provides insights into transforming nonlinear systems into a higher-dimensional measurement function space dominated by a linear Koopman operator, which enhances system identification. The effective data-driven approach of the eigenfunctions of the Koopman operator is becoming a challenging topic. Compared with the state-of-the-art methods, this paper introduces a sparse basis selection algorithm to enhance the implementation of the compressed Koopman operator. The validity and accuracy of the method are demonstrated in a 2D Duffing system and a 3D chaotic Lorenz system. The method is also robust to noisy data.

Interest in deep learning in collider physics has been growing in recent years, specifically in applying these methods in jet classification, anomaly detection, particle identification etc. Among those, jet classification using neural networks is one of the well-established areas. In this review, we discuss different tagging frameworks available to tag boosted objects, especially boosted Higgs boson and top quark, at the Large Hadron Collider (LHC). Our aim is to study the interplay of traditional jet substructure-based methods with the state-of-the-art machine learning ones. In this methodology, we would gain some interpretability of those machine learning methods, and which in turn helps to propose hybrid taggers relevant for tagging of those boosted objects belonging to both Standard Model (SM) and physics beyond the SM.

The Poisson–Boltzmann equation characterizes the internal electric potential in electroosmotic and magnetohydrodynamic (MHD) processes, under the assumptions of thermodynamic equilibrium and negligible fluid flow effects. However, for significant convective ion transport, the Nernst–Planck equation is requisite. This study develops predictive models for electroosmotic and MHD flows between squeezing plates, where convective ion transport is minimal. The partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs) using similarity transformations and solved analytically via the homotopy analysis method (HAM). The HAM results, validated against the numerical solver BVP4c, exhibit strong concordance. Various physical effects are elucidated through graphical and tabular representations, revealing that squeezing the plates reduces electroosmotic flow profiles while increasing the magnetic Reynolds number in both homogeneous and heterogeneous reactions.

This article analyzes a fractional-order SEIR infection epidemic model, including time delays and vaccination strategies. Four differential equations describe the infection dynamics with non-integer derivative orders, which account for memory effects and non-local interactions in disease spread. The paper first establishes the existence and uniqueness of the solution and presents equilibrium points based on the basic reproduction number, (R_{0}). Using the Lyapunov direct method, the global stability of each equilibrium is proven to depend primarily on (R_{0}). Theoretical findings are validated through numerical simulations, exploring the impact of vaccination and fractional derivatives on the epidemic dynamics.

The flow of micropolar nanofluids affected by the phenomena of MHD and thermal radiation through a regular and irregular vertical plate is examined in the present study. The primary goal of the research is to investigate the influence of boundary irregularities on the flow and heat transfer phenomena while considering the phenomena of Brownian motion, MHD, thermal radiation, and thermophoresis. The similarity transformation is applied to the flow’s governing momentum and energy nonlinear coupled partial differential equations, converting them into linear coupled ordinary differential equations. The coupled ODEs are numerically solved using a quasilinearization technique and finite difference schemes. The physical effects of Brownian motion, MHD, heat radiation, and thermophoresis are explored through data and illustrations. The importance of MHD in controlling flow and boundary layer thickness is demonstrated in particular by showing the impact of crucial physical parameters such as the buoyancy force and the Brownian motion parameter. Also, more significant effects on velocity, temperature profiles, and heat transfer rate are observed in irregular boundaries than in regular boundaries.

The primary objective of the present study is to explore the novelty in the analysis of entropy generation introduced in the oscillatory flow of Jeffrey fluid through an asymmetric tapered wavy channel subjected to Lorentz force and thermal radiation. It has diverse applications in a range of disciplines: automotive elastomers in the material selection process, soft tissue mechanics modeling in biomechanics, extrusion and injection molding optimization in polymer processing, rheological test design and data interpretation in rheology. The unique nature of the tapered wavy shape in the channel and its influence on the velocity profile of MHD oscillatory Jeffrey fluid flow represents a novel element that has not been extensively explored previously. The governing equations are transformed into a system of nonlinear differential equations using non-similarity transformations. The transient system of dimensionless partial differential equations (PDEs) is solved using an implicit finite difference numerical scheme called the Crank-Nicolson method. Incorporating relevant parameters, the exact behavior of the flow with respect to velocity, temperature and volumetric rate of entropy generation is graphically depicted. The increase in entropy generation with a higher Brinkman number implies that the enhanced influence of the porous structure leads to greater irreversibility in the Jeffrey fluid flow. A comparative study is carried out to characterize Newtonian and Jeffrey fluid behavior by analyzing the velocity and temperature profiles. Finally, the findings of the current study have been compared to those of earlier studies. The comparison is seen to bear a good agreement with the existing literature.