The European Physical Journal Special Topics最新文献

This research develops a comprehensive numerical model leveraging fuzzy fractional differential equations to analyze the transmission dynamics of the Monkeypox virus. Using Caputo’s fuzzy fractional differential equations, we construct a dynamical model for Monkeypox vaccination in humans. The importance of fuzzy fractional differential equations lies in their ability to provide a more accurate representation of the transmission dynamics due to their non-local properties, which capture memory and hereditary effects inherent in the spread of infectious diseases. Our numerical simulations highlight how vaccination significantly curbs disease spread, demonstrating the practical application of fuzzy fractional techniques in epidemiology. The study underscores the necessity of these advanced mathematical tools in capturing the complex dynamics of Monkeypox transmission, paving the way for more effective control strategies.

The conventional Big Bang model successfully anticipates the initial abundances of (^2)H(D), (^3)He, and (^4)He, aligning remarkably well with observational data. However, a persistent challenge arises in the case of (^7)Li, where the predicted abundance exceeds observations by a factor of approximately three. Despite numerous efforts employing traditional nuclear physics to address this incongruity over the years, the enigma surrounding the lithium anomaly endures. In this context, we embark on an exploration of Big Bang nucleosynthesis (BBN) of light element abundances with the application of Tsallis non-extensive statistics. A comparison is made between the outcomes obtained by varying the non-extensive parameter q away from its unity value and both observational data and abundance predictions derived from the conventional big bang model. A good agreement is found for the abundances of (^4)He, (^3)He and (^7)Li, implying that the lithium abundance puzzle might be due to a subtle fine-tuning of the physics ingredients used to determine the BBN. However, the deuterium abundance deviates from observations.

We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection tasks—machine learning models can be trained on background data to identify deviations if we model the background data precisely. The learning can also be partially unsupervised when we can provide some information about the anomalies at the data level. Generative models are useful in speeding up detector simulations—they can mimic the computationally intensive task without large resources. They can also efficiently map detector-level data to parton-level data (i.e., data unfolding). In this review, we focus on interesting ideas and connections and briefly overview the underlying techniques wherever necessary.

To explore the effect of memory, we first incorporate the fractional-order derivative in our defined model, which is a SIR-type epidemic model with logistic growth in susceptible and incubation delay in saturated incidence rate. Based on the value of a threshold parameter (R_0), called basic reproduction number, there exist two equilibria. Also, depending on that threshold value, stability and Hopf bifurcation analysis were performed in our formulated model. To study the effects of vaccination and treatment on Hopf bifurcation, we include these measures in our model and derive that these measures may increase the length of the critical delay. We also looked into a fractional-order optimal control problem to better understand the optimal role of treatment and vaccination in reducing disease prevalence and lowering associated costs. We have run simulations to verify the analytical results, considering the model’s feasible parameter values. Finally, to study the uncertainty analysis, we have used the partial rank correlation coefficient technique.

ATLAS collaboration uses machine learning (ML) algorithms in many different ways in its physics programme, starting from object reconstruction, simulation of calorimeter showers, signal to background discrimination in searches and measurements, tagging jets based on their origin and so on. Anomaly detection (AD) techniques are also gaining popularity where they are used to find hidden patterns in the data, with lesser dependence on simulated samples as in the case of supervised learning-based methods. ML methods used in detector simulation and in jet tagging in ATLAS will be discussed, along with four searches using ML/AD techniques.

It is evident that a tumor is a dangerous lump of tissue developed through the uncontrollable division of cells, replacing healthy tissue with abnormal tissue. It is cancerous and spreads through the lymphatic system or blood within the body of a host individual while the human immune system, consisting of interrelated special cells, tissues, and organs, is employed for the protection of the body from microorganisms, foreign diseases, infections, and toxins. Thus, the conceptualization and understanding of the intersections of tumor–immune cells are valuable. In this article, the natural process of tumor–immune cell interactions is formulated through a mathematical framework. The intricate dynamics of tumor–immune interactions are then represented by means of operators of fractional calculus involving nonlocal and nonsingular kernels. The definitions and basic properties of non-integer derivatives are introduced for the investigation of the proposed system. In addition, a new numerical scheme is introduced for the dynamics, showing the chaos and oscillation of the tumor–immune system. The proposed dynamics of tumor–immune interaction are highlighted with the effect of different input factors. Our findings not only contribute to a thorough comprehension of the complex interactions between input parameters and tumor dynamics, but critical factors that have a major impact on the dynamics are also identified. These outcomes are pivotal for refining and optimizing the proposed system to enhance its predictive accuracy and efficacy in modeling tumor behavior.

This study is concerned with the dynamics of a polydisperse ensemble of crystals in a single-component metastable solution/melt. A new theory based on the kinetic and balance equations is developed for the description of initial and intermediate stages of bulk crystallization. Such phenomena as unsteady growth rates of individual crystals with fluctuations, diffusion of the crystal-size distribution function in the space of particle radii, the Gibbs–Thomson and atomic kinetics effects, various crystal nucleation mechanisms are taken into account. The analytical solution is constructed in a parametric form with the modified time being the decision variable. Namely, the metastability degree, particle-radius distribution function, crystallization time, total number of crystals and their mean size are found as the functions of decision variable. The analytical solutions show that the metastability degree decreases with time as a result of liquid desupersaturation/desupercooling. As this takes place, the particle-radius distribution function moves to greater particle radii, becomes wider and lower with increasing the crystallization time. The theory is tested against experiments on the growth of such polypeptide hormones as porcine and bovine insulins. We show that the theory is in good agreement with the experimental data.

Combination antiretroviral Therapy (cART) is the standard treatment approach for human immunodeficiency virus (HIV), involving the use of various antiretroviral drugs to effectively suppress the virus’s replication in the body. The objective of cART is to decrease the viral load in the blood to undetectable levels, enhance the immune system’s function, and ultimately prolong the patient’s life by preventing the progression to AIDS and associated opportunistic infections. In this work, we formulated the dynamics of HIV infection, including the effects of cART, within a fractional framework. This paper presents a numerical study that investigates the complex dynamics of HIV infection in (text {CD4}^{+}) T cells. The proposed HIV model incorporates the impact of antiretroviral medication via the Caputo–Fabrizio derivative. To comprehend the dynamics of the proposed HIV infection model, a numerical approach is employed. The dynamic behavior of the system is illustrated by examining the influence of various input parameters, aiming to capture the system’s sensitivity to these factors. Furthermore, this modeling approach highlights the interaction between the immune system and the virus. Through numerical simulations utilizing specific input values, we explore the chaotic and periodic behavior of HIV infection and provide insights into its intricate dynamics.

A stochastic nonlinear model for Rab5 and Rab7 proteins describing the transformation of early endosomes into late endosomes was formulated. This model consists of two stochastic nonlinear differential equations for Rab5 and Rab7 protein levels on the endosome surface. The primary goal of this paper is to understand the impact of multiplicative noise on the nonlinear dynamics of Rab5 and Rab7. The main idea is to introduce the stochastic variable T, which defines the random time when the conversion from Rab5 to Rab7 occurs. It follows from the dynamics of pH level that T can also be considered as the escape time of pH-sensitive nanoparticles and viruses from endosomes. The probability density function for T was obtained numerically. It was shown that the average conversion time T is shifted to the right when compared to the deterministic one, potentially influencing the pH distribution function and, consequently, the average escape time of viruses and nanoparticles.

This study is concerned with the influence of astronomical forcing and stochastic disturbances on non-linear dynamics of the Earth’s climate. As a starting point, we take the system of climate equations derived by Saltzman and Maasch for late Cenozoic climate changes. This system contains variations of three prognostic variables: the global ice mass, carbon dioxide concentration, and deep ocean temperature. The bifurcation diagram of deterministic system shows possible existence/coexistence of stable equilibria and limit cycle leading either to monostability or bistability. Fitting the astronomical forcing by an oscillatory function and representing the deep ocean temperature deviations by means of white Gaussian noise of various intensities, we analyze the corresponding stochastic system of Saltzman and Maasch equations for the deviations of prognostic variables from their average values (equilibrium state). The main conclusions of our study are as follows: (i) astronomical forcing causes the climate system transitions from large-amplitude oscillations to small-amplitude ones and vice versa; (ii) astronomical and stochastic forcings together cause the mixed-mode climate oscillations with intermittent large and small amplitudes. In this case, the Earth’s climate would be shifting from one stable equilibrium with a warmer climate to another stable equilibrium with a colder climate and back again.