News / Highlights / Colloquia
- Published on 28 August 2017
Lessons from self-trapped electrons in crystal lattice offer better predictive power for transport model
Ever heard of polarons? They are a kind of quasi-particle resulting from electrons self-trapping in a vibrating crystal lattice. Polarons can be harnessed to transport energy under certain conditions related to the relative vibrations of the electrons and the lattice itself. The theory explaining how polarons carry energy in crystals can be applied to long molecules called polypeptides—which can fold into proteins. In a new study published in EPJ B, Jingxi Luo and Bernard Piette from Durham University, UK, present a new mathematical model describing how polarons can be displaced in a directed way with minimum energy loss in linear peptide chains—which were used as a proxy for the study of proteins. The model therefore accounts for the energy transport mechanism explaining how energy generated inside a biological cell moves along transmembrane proteins towards the cell's exterior.
- Published on 28 August 2017
Chinese scientists show how the network structure affects the accuracy of methods predicting the future evolution of a network, like those used to predict protein interactions
Nature and society are full of so-called real-world complex systems, such as protein interactions. Theoretical models, called complex networks, describe them and consist of nodes representing any basic element of that network, and links describing interactions or reactions between two nodes. In the case of protein-interaction studies, reconstruction of complex networks is key as the data available is often inaccurate and our knowledge of the exact nature of these interactions is limited. For reconstruction of networks, link predict -- the likelihood of the existence of a link between two nodes -- matters. Now, Chinese scientists have looked at the influence of the network structure to shed some light on the robustness of the latest methods used to predict the behaviour of such complex networks. Jin-Xuan Yang and Xiao-Dong Zhang from Shanghai Jiao Tong University in China have just published their work in EPJ B, providing a good reference for the choice of a suitable algorithm for link prediction depending on the chosen network structure. In this paper, the authors use two parameters of networks—the common neighbours index and the so-called Gini coefficient index—to reveal the relation between the structure of a network and the accuracy of methods used to predict future links.
- Published on 28 August 2017
In the study of phase transitions and critical phenomena, it is important to understand finite size corrections to thermodynamic quantities. Finite-size scaling concerns the critical behavior of systems in which one or more directions are finite. It is valuable in the analysis of experimental and numerical data in many situations, for example for films of finite thickness.
As soon as one has a finite system one must consider the question of boundary conditions on the outer surfaces or “walls” of the system because the critical behavior near boundaries normally differs from the bulk behavior.
The author of this EPJ B Colloquium investigates the effects of boundary conditions on finite-size corrections through the study of model systems, especially those which have exact results and can be analysed without numerical errors, such as the Ising model, the dimer model, the resistor network and the spanning tree model.
- Published on 11 July 2017
Lifetime simulation of biological populations reveals dramatic population fluctuations before extinction
Populations of endangered species reach a critical point in their life where they either survive or evolve towards extinction. Therefore, efforts to predict and even prevent the extinction of biological species require a thorough understanding of the underlying mechanisms. In a new study published in EPJ B, Hatem Barghathi and colleagues from Missouri University of Science and Technology, USA, have investigated how environmental disturbance at random times could cause strong fluctuations in the number of individuals in biological populations. This, in turn, makes extinction easier, even for large populations. They found that environmental disorder can lead to a period of slow population increase interrupted by sudden population collapses. These findings also have implications for solving the opposite problem when attempting to predict, control and eradicate population of viruses in epidemics.
- Published on 17 May 2017
Physicists are providing a greater level of autonomy for self-taught systems by combining how they respond to their learning as they evolve
Cars that can drive autonomously have recently made headlines. In the near future, machines that can learn autonomously will become increasingly present in our lives. The secret to efficient learning for these machines is to define an iterative process to map out the evolution of how key aspects of these systems change over time. In a study published in EPJ B, Agustín Bilen and Pablo Kaluza from Universidad Nacional de Cuyo, Mendoza, Argentina show that these smart systems can evolve autonomously to perform a specific and well-defined task over time. Applications range from nanotechnology to biological systems, such as biological signal transduction networks, genetic regulatory networks with adaptive responses, or genetic networks in which the expression level of certain genes in a network oscillates from one state to another.
- Published on 12 April 2017
Physicists prove important constraints for fermion gases with spin population imbalance
Fermions are ubiquitous elementary particles. They span from electrons in metals, to protons and neutrons in nuclei and to quarks at the sub-nuclear level. Further, they possess an intrinsic degree of freedom called spin with only two possible configurations, either up or down. In a new study published in EPJ B, theoretical physicists explore the possibility of separately controlling the up and down spin populations of a group of interacting fermions. Their detailed theory describing the spin population imbalance could be relevant, for instance, to the field of spintronics, which exploits polarised spin populations.
- Published on 17 March 2017
This Colloquium paper published in EPJ B by R. Kutner and J. Masoliver revisits the most significant achievements and future possibilities for continuous-time random walk (CTRW), a versatile and widely applied formalism.
- Published on 07 March 2017
Physicists define a smart way of inducing large-amplitude vibrations in graphene models, which could open the door for novel electronic applications
Graphene, the one-atom-thick material made of carbon atoms, still holds some unexplained qualities, which are important in connection with electronic applications where high-conductivity matters, ranging from smart materials that collectively respond to external stimuli in a coherent, tunable fashion, to light-induced, all-optical networks. Materials like graphene can exhibit a particular type of large-amplitude, stable vibrational modes that are localised, referred to as Discrete Breathers (DBs). The secret to enhancing conductivity by creating DBs lies in creating the external constraints to make atoms within the material oscillate perpendicular to the direction of the graphene sheet. Simulations-based models describing what happens at the atomic level are not straightforward, making it necessary to determine the initial conditions leading to the emergence of DBs. In a new paper published in EPJ B, Elham Barani from the Ferdowsi University of Mashhad, Iran, and colleagues from Russia, Iran and Singapore use a systematic approach to identify the initial conditions that lend themselves to exciting DBs in graphene, ultimately opening the door to understanding the keys to greater conductivity.
EPJ B Highlight - Tortoise electrons trying to catch up with hare photons give graphene its conductivity
- Published on 14 December 2016
Collective electron interaction, mediated by photons across space-time under a weak magnetic field, explains the special conductivity of the one-atom-thick material
How electrons interact with other electrons at quantum scale in graphene affects how quickly they travel in the material, leading to its high conductivity. Now, Natália Menezes and Cristiane Morais Smith from the Centre for Extreme Matter and Emergent Phenomena at Utrecht University, the Netherlands, and a Brazilian colleague, Van Sergio Alves, have developed a model attributing the greater conductivity in graphene to the accelerating effect of electrons interacting with photons under a weak magnetic field. Their findings have been published in EPJ B.
- Published on 09 November 2016
Tweaking equations to drive greater superconductivity-inducing crystal vibrations proves theoretical possibility of creating higher temperature superconductors
Superconductivity is like an Eldorado for electrons, as they flow without resistance through a conductor. However, it only occurs below a very low critical temperature. Physicists now believe they can enhance superconductivity - the idea is to externally drive its underlying physical phenomena by changing how ions vibrating in the crystal lattice of the conductor material, called phonons, interact with electron flowing in the material. Andreas Komnik from the University of Heidelberg and Michael Thorwart from the University of Hamburg, Germany, adapted the simplest theory of superconductivity to reflect the consequences of externally driving the occurrence of phonons. Their main result, published in EPJ B, is a simple formula explaining how it is theoretically possible to raise the critical temperature using phonon driving.