Hasil untuk "Physics"

B. Kadomtsev, M. Leontovich, H. Lashinsky et al.

S. Dong, Jun-ming Liu, S. Cheong et al.

Multiferroics are those materials with more than one ferroic order, and magnetoelectricity refers to the mutual coupling between magnetism (spins and/or magnetic field) and electricity (electric dipoles and/or electric field). In spite of the long research history in the whole twentieth century, the discipline of multiferroicity has never been so highly active as that in the first decade of the twenty-first century, and it has become one of the hottest disciplines of condensed matter physics and materials science. A series of milestones and steady progress in the past decade have enabled our understanding of multiferroic physics substantially comprehensive and profound, which is further pushing forward the research frontier of this exciting area. The availability of more multiferroic materials and improved magnetoelectric performance are approaching to make the applications within reach. While seminal review articles covering the major progress before 2010 are available, an updated review addressing the new achievements since that time becomes imperative. In this review, following a concise outline of the basic knowledge of multiferroicity and magnetoelectricity, we summarize the important research activities on multiferroics, especially magnetoelectricity and related physics in the last six years. We consider not only single-phase multiferroics but also multiferroic heterostructures. We address the physical mechanisms regarding magnetoelectric coupling so that the backbone of this divergent discipline can be highlighted. A series of issues on lattice symmetry, magnetic ordering, ferroelectricity generation, electromagnon excitations, multiferroic domain structure and domain wall dynamics, and interfacial coupling in multiferroic heterostructures, will be revisited in an updated framework of physics. In addition, several emergent phenomena and related physics, including magnetic skyrmions and generic topological structures associated with magnetoelectricity will be discussed. The review is ended with a set of prospectives and forward-looking conclusions, which may inevitably reflect the authors' biased opinions but are certainly critical.

T. Sjöstrand

E. M. Lifshitz, L. Landau

J. Lindl, P. Amendt, R. Berger et al.

E. Youngs, D. Hillel

S. Sze

M. Lewenstein, A. Sanpera, V. Ahufinger et al.

We review recent developments in the physics of ultracold atomic and molecular gases in optical lattices. Such systems are nearly perfect realisations of various kinds of Hubbard models, and as such may very well serve to mimic condensed matter phenomena. We show how these systems may be employed as quantum simulators to answer some challenging open questions of condensed matter, and even high energy physics. After a short presentation of the models and the methods of treatment of such systems, we discuss in detail, which challenges of condensed matter physics can be addressed with (i) disordered ultracold lattice gases, (ii) frustrated ultracold gases, (iii) spinor lattice gases, (iv) lattice gases in “artificial” magnetic fields, and, last but not least, (v) quantum information processing in lattice gases. For completeness, also some recent progress related to the above topics with trapped cold gases will be discussed. Motto: There are more things in heaven and earth, Horatio, Than are dreamt of in your philosophy 1

J. Larkin, J. McDermott, Dorothea P. Simon et al.

H. Sirringhaus

A. Barone, G. Paternò

J. Glimm, A. Jaffe

J. Prost, F. Jülicher, J. Joanny

R. Behringer, B. Chakraborty

Granular materials consist of macroscopic grains, interacting via contact forces, and unaffected by thermal fluctuations. They are one of a class systems that undergo jamming, i.e. a transition between fluid-like and disordered solid-like states. Roughly twenty years ago, proposals by Cates et al for the shear response of colloidal systems and by Liu and Nagel, for a universal jamming diagram in a parameter space of packing fraction, ϕ, shear stress, τ, and temperature, T raised key questions. Contemporaneously, experiments by Howell et al and numerical simulations by Radjai et al and by Luding et al helped provide a starting point to explore key insights into jamming for dry, cohesionless, granular materials. A recent experimental observation by Bi et al is that frictional granular materials have a a re-entrant region in their jamming diagram. In a range of ϕ, applying shear strain, γ, from an initially force/stress free state leads to fragile (in the sense of Cates et al), then anisotropic shear jammed states. Shear jamming at fixed ϕ is presumably conjugate to Reynolds dilatancy, involving dilation under shear against deformable boundaries. Numerical studies by Radjai and Roux showed that Reynolds dilatancy does not occur for frictionless systems. Recent numerical studies by several groups show that shear jamming occurs for finite, but not infinite, systems of frictionless grains. Shear jamming does not lead to known ordering in position space, but Sarkar et al showed that ordering occurs in a space of force tiles. Experimental studies seeking to understand random loose and random close packings (rlp and rcp) and dating back to Bernal have probed granular packings and their response to shear and intruder motion. These studies suggest that rlp’s are anisotropic and shear-jammed-like, whereas rcp’s are likely isotropically jammed states. Jammed states are inherently static, but the jamming diagram may provide a context for understanding rheology, i.e. dynamic shear in a variety of systems that include granular materials and suspensions.

K. Albertsson, Piero Altoe, D. Anderson et al.

Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

Longfei Xu, Xuefeng Xu

This paper investigates the coupled relationship between solid-phase temperature fields and droplet evaporation, focusing on the effects of substrate thermal conduction properties on droplet evaporation behavior. A mathematical model is developed to analyze the impacts of substrate thermal conductivity, thickness, and lower-surface temperature on evaporation rate, surface temperature, and evaporation flux. A dimensionless relative evaporation rate (HCs) is introduced to characterize the influence of substrate thermal conduction. Results show that increasing substrate thermal conductivity enhances droplet surface temperature and evaporation flux, thereby monotonically increasing evaporation rate until it approaches the rate of the evaporative cooling model. Conversely, increasing substrate thickness lengthens the heat transfer path, reducing heat conducted to the solid–liquid interface and decreasing evaporation rate. Changes in substrate lower-surface temperature significantly affect evaporation rate, but HCs remains nearly unaffected. The concept of equivalent substrates is proposed and verified through dimensionless analysis and simulations. It is found that different combinations of substrate thickness and thermal conductivity exhibit consistent effects on droplet evaporation, with minimal relative errors in evaporation rate and total heat transfer at the solid–liquid interface. This confirms the existence of the equivalent substrate phenomenon. Additionally, the effects of droplet properties, such as contact angle and evaporative cooling coefficient (<i>Ec</i>), on the equivalent substrate phenomenon are explored, revealing negligible impacts. These findings provide theoretical guidance for optimizing droplet evaporation processes in practical applications, such as micro/nanoscale thermal management systems.

Hyuk Lee

As edge computing environments become increasingly dynamic, the need for efficient job scheduling and proactive fault prevention is becoming paramount. In such environments, minimizing machine downtime and maintaining productivity are critical challenges. In this paper, we propose an integrated approach to scheduling optimization that combines deep learning-based fault prediction with Satisfiability Modulo Theories (SMT)-based scheduling techniques. The proposed system predicts fault probabilities for machines in real time by leveraging operational state features such as temperature, vibration, tool wear, and operating hours. These fault predictions are then used as inputs to the SMT solver, which dynamically optimizes job scheduling. The system ensures task completion within deadlines while minimizing fault risks and optimizing resource utilization. To achieve this, the deep learning model continuously updates fault probabilities through a rolling prediction mechanism, allowing the scheduling system to proactively adapt to changing machine conditions. The SMT solver incorporates these predictions into its optimization process, ensuring that the schedule dynamically reflects the latest system state. The proposed method has been evaluated in simulated production line scenarios, demonstrating significant reductions in machine faults, improved scheduling efficiency, and enhanced overall system reliability. By integrating predictive maintenance with optimization techniques, this research contributes to the development of robust and adaptive scheduling systems for dynamic production environments.

Tatiana Pauletti, Paula Homem de Mello, Thereza Paiva et al.

Gender imbalance in Physics remains a persistent global challenge, and Brazil is no exception. While women account for only 24% of Physics faculty in the country, their representation in Quantum Physics is even smaller. In this work, we provide the first comprehensive overview of women working in Theoretical Quantum Physics in Brazil, here referred to as the SheQ (She + Quantum) community. Using data from the CNPq Lattes platform, we identify 93 researchers and analyze their geographic distribution, academic trajectories, scientific productivity, international experience, recognition through awards and fellowships, and engagement with initiatives promoting gender equity. Our results reveal both progress and persistent disparities: SheQ researchers have a strong scientific output, leadership roles, and international training; yet, their recognition through productivity fellowships remains modest, and their involvement in gender-related initiatives, although increasing among younger generations, remains limited. By combining quantitative indicators with institutional perspectives, we highlight structural barriers as well as opportunities for fostering a more inclusive environment in Quantum Physics. his study thus contributes to a broader reflection on how diversity not only promotes fairness but also strengthens creativity, innovation, and scientific progress.

T. Ullman, E. Spelke, P. Battaglia et al.