Hasil untuk "Physics"

B. Blocken

J. Elgeti, R. Winkler, G. Gompper

Locomotion and transport of microorganisms in fluids is an essential aspect of life. Search for food, orientation toward light, spreading of off-spring, and the formation of colonies are only possible due to locomotion. Swimming at the microscale occurs at low Reynolds numbers, where fluid friction and viscosity dominates over inertia. Here, evolution achieved propulsion mechanisms, which overcome and even exploit drag. Prominent propulsion mechanisms are rotating helical flagella, exploited by many bacteria, and snake-like or whip-like motion of eukaryotic flagella, utilized by sperm and algae. For artificial microswimmers, alternative concepts to convert chemical energy or heat into directed motion can be employed, which are potentially more efficient. The dynamics of microswimmers comprises many facets, which are all required to achieve locomotion. In this article, we review the physics of locomotion of biological and synthetic microswimmers, and the collective behavior of their assemblies. Starting from individual microswimmers, we describe the various propulsion mechanism of biological and synthetic systems and address the hydrodynamic aspects of swimming. This comprises synchronization and the concerted beating of flagella and cilia. In addition, the swimming behavior next to surfaces is examined. Finally, collective and cooperate phenomena of various types of isotropic and anisotropic swimmers with and without hydrodynamic interactions are discussed.

L. Mur, P. M. Harmans, W. G. van der Wiel

Competition between h/e and h/2e oscillations in a semiconductor Aharonov–Bohm interferometer Abstract. The magnetoresistance of a quasi-ballistic Aharonov–Bohm (AB) ring defined in the two-dimensional electron gas (2DEG) of an InP/In 0.8 Ga 0.2 As quantum well is studied. The ring is connected to an Al contact on one side and to a 2DEG reservoir at the other side. Two distinct magnetic field regimes can be identified. At magnetic field values where time-reversal symmetry (TRS) is broken, AB oscillations are observed. Besides oscillations with h/e periodicity, we also observe higher harmonics with h/2e and h/3e periods. In the low-magnetic field range, where TRS is preserved, the AB oscillations are alternately dominated by the h/e or h/2e component, depending on the bias voltage. Although Al is superconducting at these low magnetic fields, no evidence is found that the observed AB oscillations are related to the proximity of the superconductor. The bias voltage dependence is qualitatively described in terms of a 1D scattering model.

J. Antolak

P. Shukla, Abdullah Al Mamun

D. Hillel

K. Gschneidner, L. Eyring, G. Lander et al.

H. Heywood

Myron B. Salamon, M. Jaime

Y. Tokura, N. Nagaosa

Fang Wu, Bernardo A. Huberman

Abstract.We present a method that allows for the discovery of communities within graphs of arbitrary size in times that scale linearly with their size. This method avoids edge cutting and is based on notions of voltage drops across networks that are both intuitive and easy to solve regardless of the complexity of the graph involved. We additionally show how this algorithm allows for the swift discovery of the community surrounding a given node without having to extract all the communities out of a graph.

R. Bagnold

B. Berne, R. Pecora

A. Radovic, Mike Williams, D. Rousseau et al.

D. Guest, Kyle Cranmer, D. Whiteson

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

D. Buttazzo, A. Greljo, G. Isidori et al.

Motivated by additional experimental hints of Lepton Flavour Universality violation in B decays, both in charged- and in neutral-current processes, we analyse the ingredients necessary to provide a combined description of these phenomena. By means of an Effective Field Theory (EFT) approach, based on the hypothesis of New Physics coupled predominantly to the third generation of left-handed quarks and leptons, we show how this is possible. We demonstrate, in particular, how to solve the problems posed by electroweak precision tests and direct searches with a rather natural choice of model parameters, within the context of a U(2)q ×U(2)ℓ flavour symmetry. We further exemplify the general EFT findings by means of simplified models with explicit mediators in the TeV range: coloured scalar or vector leptoquarks and colour-less vectors. Among these, the case of an SU(2)L-singlet vector leptoquark emerges as a particularly simple and successful framework.

Christopher W. Lynn, D. Bassett

The brain is characterized by heterogeneous patterns of structural connections supporting unparalleled feats of cognition and a wide range of behaviours. New non-invasive imaging techniques now allow comprehensive mapping of these patterns. However, a fundamental challenge remains to understand how the brain’s structural wiring supports cognitive processes, with major implications for personalized mental health treatments. Here, we review recent efforts to meet this challenge, drawing on physics intuitions, models and theories, spanning the domains of statistical mechanics, information theory, dynamical systems and control. We first describe the organizing principles of brain network architecture instantiated in structural wiring under constraints of spatial embedding and energy minimization. We then survey models of brain network function that stipulate how neural activity propagates along structural connections. Finally, we discuss perturbative experiments and models for brain network control; these use the physics of signal transmission along structural connections to infer intrinsic control processes that support goal-directed behaviour and to inform stimulation-based therapies for neurological and psychiatric disease. Throughout, we highlight open questions that invite the creative efforts of pioneering physicists.The brain is the quintessential complex system, boasting incredible feats of cognition and supporting a wide range of behaviours. Physics has much to offer in the quest to distil the brain’s complexity to a number of cogent organizing principles.Key pointsFrom the first measurement of the nerve impulse by Hermann von Helmholtz in 1849 to the cutting-edge superconducting devices used in magnetoencephalography, physics and neuroscience have always been inextricably linked.Today, network neuroscience — the study of the brain as a complex web of interacting components — draws intuitions and techniques from nearly every branch of physics.The architecture of structural connections between neurons or brain regions is constrained by requirements of energy minimization and efficient information transfer.The materialization of long-range correlations and synchronization from the collective firing of individual neurons conjures notions of emergence and criticality from statistical mechanics.Together, these investigations of brain network structure and function guide targeted treatments for cognitive disorders using theories of network control.Now more than ever, understanding the complexities of the mind lies at the feet of curious and pioneering physicists.

G. Cimini, Tiziano Squartini, F. Saracco et al.

In the past 15 years, statistical physics has been successful as a framework for modelling complex networks. On the theoretical side, this approach has unveiled a variety of physical phenomena, such as the emergence of mixed distributions and ensemble non-equivalence, that are observed in heterogeneous networks but not in homogeneous systems. At the same time, thanks to the deep connection between the principle of maximum entropy and information theory, statistical physics has led to the definition of null models for networks that reproduce features of real-world systems but that are otherwise as random as possible. We review here the statistical physics approach and the null models for complex networks, focusing in particular on analytical frameworks that reproduce local network features. We show how these models have been used to detect statistically significant structural patterns in real-world networks and to reconstruct the network structure in cases of incomplete information. We further survey the statistical physics models that reproduce more complex, semilocal network features using Markov chain Monte Carlo sampling, as well as models of generalized network structures, such as multiplex networks, interacting networks and simplicial complexes. This Review describes advances in the statistical physics of complex networks and provides a reference for the state of the art in theoretical network modelling and applications to real-world systems for pattern detection and network reconstruction. Statistical physics is a powerful framework to explain properties of complex networks, modelled as systems of heterogeneous entities whose degrees of freedom are their interactions rather than their states. The statistical physics of complex networks has brought theoretical insights into physical phenomena that are different in heterogeneous networks than in homogeneous systems. From an applied perspective, statistical physics defines null models for real-world networks that reproduce local features but are otherwise as random as possible. These models have been used, on the one hand, to detect statistically significant patterns in real-world networks and, on the other, to infer the network structure when information is incomplete. These applications are particularly useful in the current information age to make consistent inference from huge streams of continuously produced, high-dimensional, noisy data. The statistical mechanics approach has also been extended using numerical techniques to reproduce semilocal network features and, more recently, to encompass structures such as multilayer networks and simplicial complexes. Statistical physics is a powerful framework to explain properties of complex networks, modelled as systems of heterogeneous entities whose degrees of freedom are their interactions rather than their states. The statistical physics of complex networks has brought theoretical insights into physical phenomena that are different in heterogeneous networks than in homogeneous systems. From an applied perspective, statistical physics defines null models for real-world networks that reproduce local features but are otherwise as random as possible. These models have been used, on the one hand, to detect statistically significant patterns in real-world networks and, on the other, to infer the network structure when information is incomplete. These applications are particularly useful in the current information age to make consistent inference from huge streams of continuously produced, high-dimensional, noisy data. The statistical mechanics approach has also been extended using numerical techniques to reproduce semilocal network features and, more recently, to encompass structures such as multilayer networks and simplicial complexes.

S. Chakraborty

For many systems in science and engineering, the governing differential equation is either not known or known in an approximate sense. Analyses and design of such systems are governed by data collected from the field and/or laboratory experiments. This challenging scenario is further worsened when data-collection is expensive and time-consuming. To address this issue, this paper presents a novel multi-fidelity physics informed deep neural network (MF-PIDNN). The framework proposed is particularly suitable when the physics of the problem is known in an approximate sense (low-fidelity physics) and only a few high-fidelity data are available. MF-PIDNN blends physics informed and data-driven deep learning techniques by using the concept of transfer learning. The approximate governing equation is first used to train a low-fidelity physics informed deep neural network. This is followed by transfer learning where the low-fidelity model is updated by using the available high-fidelity data. MF-PIDNN is able to encode useful information on the physics of the problem from the {\it approximate} governing differential equation and hence, provides accurate prediction even in zones with no data. Additionally, no low-fidelity data is required for training this model. Applicability and utility of MF-PIDNN are illustrated in solving four benchmark reliability analysis problems. Case studies to illustrate interesting features of the proposed approach are also presented.

Hidetoshi Taya

We provide a pedagogical review of the Schwinger effect, i.e., the non-perturbative production of particle and anti-particle pairs from the vacuum by strong fields, as well as related strong-field phenomena. Beginning with an overview of the Schwinger effect in quantum electrodynamics, we discuss its extensions to quantum chromodynamics and its applications in nuclear physics, including high-$Z$ nuclei, string breaking, relativistic heavy-ion collisions, and the chiral anomaly.