The European Physical Journal Special Topics最新文献

We present the results of the theoretical analysis of remagnetization kinetics and magnetic hyperthermia effect in a single-domain ferromagnetic particle embedded in a soft elastic-viscous medium (gel or biological tissue for example). Unlike previous studies, we consider simultaneous action of both main mechanisms of particle remagnetization in an alternating magnetic field. Namely, (i) its body turn (rotation) with its magnetic moment and (ii) the Neel remagnetization through the potential barrier of the particle magnetic anisotropy. We suppose that the energy of the particle Zeeman interaction with the field is less than the energy of the anisotropy; no other restrictions on the field strength are assumed. In the linear, with respect to this field, approximation, the frequency dependences of the real and imaginary parts of the particle complex susceptibility are calculated in more details. The real part monotonic decreases with the field frequency. If the host medium is rigid enough, the imaginary part of the susceptibility has a maximum, corresponding to the Neel relaxation. In the soft system, it has maximum, reflecting the particle rotation in the field. In the intermediate case, our calculations demonstrate two maximums of the imaginary part. We hope the present results will be useful for the development of scientific basement of magnetic hyperthermia therapy of oncological diseases.

In this article, we review the application of modern machine learning (ML) techniques to boost the search for processes involving the top quarks at the LHC. We revisit the formalism of convolutional Neural networks (CNNs), graph neural networks (GNNs), and attention mechanisms. Based on recent studies, we explore their applications in designing improved top taggers, top reconstruction, and event classification tasks. We also examine the ML-based likelihood-free inference approach and generative unfolding models, focusing on their applications to scenarios involving top quarks.

In this paper, we consider the LSTM-Markov chain model, combining deep learning with statistical methods, to forecast greenhouse power consumption. By analyzing real-time data spanning two and a half years, the model captures temporal and sequential dependencies in seasonal energy usage patterns. Comparative analysis against CNN-LSTM, LSTM, and CNN models across different seasons highlights its superior accuracy and predictive capability. Particularly during seasonal transitions, the LSTM-Markov model demonstrates exceptional precision. Its effectiveness in optimizing resource allocation and enhancing energy efficiency in greenhouse operations offers valuable insights for stakeholders, enabling informed decision-making and sustainable agricultural practices.

The proposed investigation highlights the thermal variation and heat transmission behavior of a wetted porous fin under a local thermal non-equilibrium state (LTNE). The fluid–solid interaction is governed by the Darcy formulation. The two-equation model of LTNE is utilized to depict the energy transfer for both the solid and fluid phases. The pertinent thermal distribution problems are represented as highly nonlinear ordinary differential equations (ODEs) with boundary conditions for both solid and fluid phases. The governing heat equations have been transformed into a non-dimensional form by employing dimensionless variables. The application of the clique polynomial method with Laplace–Pade approximant (CPMLPA) for these modified governing equations is the unique objective of the present research endeavor. Furthermore, physics-informed Hermite neural network (PIHNN) is employed to solve the resulting non-dimensional heat equations of the wetted porous fin. An explanation and visual demonstration of the impact of embedded thermal variables on the temperature profiles are provided. As the values of the convection–conduction and surface-ambient radiation parameters rise, the thermal profile diminishes. Augmentation of the Rayleigh number diminishes temperature dispersion in the fin. The upsurge in values of the radiation parameter intensifies the temperature profile. This study compares the temperature values of PIHNN, CPMLPA, and the clique polynomial method and reveals a strong correlation.

This article investigates a (({mathscr {Q}},{mathscr {S}},{mathscr {R}}))-(upsilon )-dissipativity-based fuzzy memory sampled-data design control (MSDC) for stabilizing a nonlinear wind turbine system (WTS) against random packet losses that uses a permanent magnet synchronous generator (PMSG). To do this, the proposed control method employs Takagi–Sugeno (T–S) fuzzy approach to convert the nonlinear model of the system into linear sub-systems. Moreover, a fuzzy MSDC is designed, and a proper Lyapunov–Krasovskii functional (LKF) is formulated using the knowledge of the sampling information. Subsequently, the looped-type LKF facilitates the establishment of criteria for stabilizing the PMSG-based WTS, taking into account the sampling interval duration and a consistent communication delay. Besides, the conditions are described as linear matrix inequalities (LMIs), ensuring global asymptotic stability and (({mathscr {Q}},{mathscr {S}},{mathscr {R}})-upsilon -) dissipative of T–S fuzzy systems with the suggested control mechanism. At last, the efficacy and superiority of the proposed approach are illustrated through numerical validations of the PMSG-based WTS.

Planktonic organisms, despite their passive drift in the ocean, exhibit complex responses to fluid flow, including escape behaviors and larval settlement detection. But what flow signals can they perceive? This paper addresses this question by considering an organism covered with sensitive cilia and immersed in a background flow. The organism is modeled as a spherical particle in Stokes flow, with cilia assumed to measure the local shear at the particle surface. This study reveals that, while these organisms can always measure certain components of the flow strain, bottom-heaviness is necessary to measure the horizontal component of vorticity. These findings shed light on flow sensing by plankton, contributing to a better understanding of their behavior.

Motions and collisions of topological defects produced during symmetry breaking transitions is a crucial issue in cosmology and in condensed matter physics. Here, we deal with topological defects in nematics and cholesterics. Nematics may contain linear defects i.e. disclinations and point defects i.e. monopoles while cholesterics may contain linear defects of their 1D periodic order parameter, i.e. dislocations. The dowser texture appears as a natural universe of the nematic monopoles. They can be generated in it, set into motion and brought to collisions that may result in annihilation of pairs of monopoles. We show how to generate pairs of disclinations in twisted nematic cells by the isotropic-nematic transition in the presence of magnetic fields. When two such disclinations collide, i.e. enter into a contact at one point, an intercommutation or rewiring into a new pair of disclination can occur. We show how to bring these disclinations to collisions by means of an electric field and how to steer the rewiring by magnetic fields. For generation of dislocations in cholesteric we use a Grandjean–Cano wedge made of crossed cylindrical mica sheets. After their nucleation upon dilation, dislocation loops are growing and collide. Collision of dislocation loops can result in a trivial crossing or may produce a stable configuration called Lehmann cluster. Subsequently, upon application of a high enough tensile strain, the Lehmann splits into a pair of dislocations that can be entangled.

In this article, the Haar wavelet collocation method (HWCM) is proposed for the numerical solution of a first-order nonlinear differential equation with a two-point integral condition. A nonlinear ordinary differential equation with an initial condition, an integral condition, or a two-point integral condition can be solved using the proposed technique in a straightforward manner. Two nonlinear test problems have been solved: one with an integral condition and the other with a two-point integral condition. The accuracy of the proposed method is significantly higher than that of the traditional Haar wavelet technique.

This paper examines the issue of designing an extended dissipative state estimator for a class of neural networks with multiple time-varying delays. The novelty of this problem lies in assuming distinct time-varying delays for each node, demonstrating its generalizability and complexity. An event-triggered state estimator with a known output measurement is proposed to facilitate these targeted network responses by saving limited communication resources. Consequently, sufficient conditions for an extended dissipative estimator have been achieved by constructing an augmented Lyapunov–Krasovskii functional (LKF) and finding its derivative. A generalized free-weighting matrix inequality (GFWMI) is utilized to achieve a tighter upper bound of the derivative, leading to a less conservative result in linear matrix inequalities (LMIs). Ultimately, a numerical example is shown to verify the advantages and efficacy of the main findings.

The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable growth and innovation in the past decade. This review provides a detailed examination of current and past ML techniques in this domain. It starts by exploring various data representation methods and ML architectures, encompassing traditional ML algorithms and advanced deep learning techniques. Subsequent sections discuss specific instances of successful ML applications in jet flavor tagging in the ATLAS and CMS experiments at the LHC, ranging from basic fully-connected layers to graph neural networks employing attention mechanisms. To systematically categorize the advancements over the LHC’s three runs, the paper classifies jet tagging algorithms into three generations, each characterized by specific data representation techniques and ML architectures. This classification aims to provide an overview of the chronological evolution in this field. Finally, a brief discussion about anticipated future developments and potential research directions in the field is presented.