2021 Impact factor 0.870
Historical Perspectives on Contemporary Physics


EPJ E Highlight - Active Brownian particles have four distinct states of motion

Switching between locked and running states

Depending on the friction and external bias forces they experience, self-propelled Brownian particles will take on one of four possible states of motion. The discovery could help researchers to draw deeper insights into the behaviours of these unique systems in nature and technology.

Active Brownian motion describes particles which can propel themselves forwards, while still being subjected to random Brownian motions as they are jostled around by their neighbouring particles. Through new analysis published in EPJ E, Meng Su at Northwestern Polytechnical University in China, together with Benjamin Lindner at Humboldt University of Berlin, Germany, have discovered that these motions can be accurately described using four distinct mathematical patterns.


EPJ Data Science Highlight - Investigating gender equality in urban cycling

An overview of the gender gap in recreational cycling across cities included in the study according to Strava. Credit: A. Battison et al. (2023)

New research looks at why cycling has a low uptake among women in urban areas

Over recent years not only has cycling proved itself to be an outdoor activity with tremendous health benefits, but it has also presented itself as a useful tool in the quest to find an environmentally friendly method of urban transportation.

Despite the increasing popularity of cycling, many countries still have a negligible uptake in the pursuit and this is even more pronounced when considering how many women engage in cycling. To this day, a mostly unexplained gender gap exists in cycling.

A new paper in EPJ Data Science by the University of Turin Department of Computer Science researcher Alice Battiston and her co-authors attempts to understand the determinants behind the gender gap in cycling on a large scale.


EPJ E Highlight - Improving fluid simulations with embedded neural networks

Simulating flows in a complex fluid

While neural networks can help to improve the accuracy of fluid flow simulations, new research shows how their accuracy is limited unless the right approach is taken. By embedding fluid properties into neural networks, simulation accuracy can improve by orders of magnitude.

The Lattice Boltzmann Method (LBM) is a simulation technique used to describe the dynamics of fluids. Recently, there has been an increasing interest in employing neural networks for computational modelling of fluids. The results of a collaboration between researchers from Eindhoven University of Technology and Los Alamos National Laboratory, published in EPJ E, show how neural networks can be embedded into a LBM framework to model collisions between fluid particles. The team found that it is essential to embed the correct physical properties into the neural network architecture to preserve accuracy. These discoveries could deepen researchers’ understanding of how to model fluid flows.


EPJ D Highlight - Better understanding the bonds between carbon group elements

Experimental setup

Heating clusters of these elements reveals key differences

The bonds between clusters of elements in the fourteenth group of the periodic table are known to be fickle. Ranging from the nonmetal carbon, to the metalloids silicon and germanium, to the metals tin and lead, all these elements share the same configuration of valence electrons – electrons in their atoms’ outermost energy level. However, clusters formed from these elements respond differently to being excited with laser pulses. Studying the response of atomic clusters to photoexcitation as a function of the element they are composed of and their number of atoms reveals patterns that can be used to gain insight into their structure and binding mechanisms.


EPJ D Highlight - Predicting the composition of a steel alloy

Experimental setup

Austenitic steel is a potential material for nuclear fusion reactors

Producing energy on Earth through nuclear fusion, the type of reaction that powers the Sun, has proven to be a major challenge. The extreme conditions needed for such a reaction require the walls of a nuclear fusion device to be made of a material with a particular set of mechanical properties, including being able to withstand incredibly high temperatures and be shock- and corrosion-resistant. Austenitic steel, a non-magnetic steel with a crystalline structure, is one of the materials considered for use in nuclear fusion devices.


EPJ B Highlight - Statistical physics reveals how languages evolve

Charting the survival of linguistic structures

Models based on the principles of statistical physics can provide useful insights into how languages change through contact between speakers of different languages. In particular, the analysis reveals how unusual linguistic forms are more likely to be replaced by more regular ones over time.

The field of historical linguistics explores how languages change over time, with a particular focus on the evolution of sounds, meanings, and structures in words and sentences. So far, however, it hasn’t been widely studied from the viewpoint of statistical physics – which uses mathematical models to explain patterns and behaviours in complex, evolving systems. Through a series of models described in EPJ B, Jean-Marc Luck at Université Paris-Saclay, together with Anita Mehta at the Clarendon Institute in Oxford, use statistical physics to show how exceptions to well-established grammatical rules are linked to the influence of neighbouring languages.


EPJ E Highlight - Training models with a structured data curriculum

Building a structured curriculum of data

By carefully structuring the data used to train models of complex systems by leveraging physics and information theory, researchers can significantly improve the quality of their predictions, without relying on additional principles from machine learning in situations where less information about the system is available.

Researchers are now increasingly driven to identify and model the intricate mathematical patterns found in complex natural systems, where the interactions of many simple parts and subsystems can give rise to deeply intricate mathematical patterns. Today, machine learning is the most widely used technique to model these systems. Through new analysis in EPJ E, a research team at Université Paris-Saclay shows how a ‘curriculum learning’ approach, which carefully structures the data used to train models, can significantly improve their results, without relying on additional machine learning principles.


EPJ Plus Focus Point Issue: Breakthrough Optics- and Complex Systems-based Technologies of Modulation of Drainage and Clearing Functions of the Brain

Guest Editors: Jürgen Kurths, Thomas Penzel, Valery Tuchin, Teemu Myllylä, Ruikang Wang, Oxana Semyachkina-Glushkovskaya

The treatment of brain diseases during sleep is a pioneering trend in modern medicine. This is due to new discoveries in the science of lymphatic "vessels-vacuums" that clean the brain during deep sleep. Today, sleep is considered as a novel biomarker and a promising therapeutic target for brain diseases associated with the drainage system injuries and the blood-brain barrier (BBB) leakage, including Alzheimer's and Parkinson's diseases, depression, brain trauma and intracranial hemorrhages. This issue presents multi-disciplinary approaches, including nonlinear signal processing analysis, maсhine learning technologies, modeling of the brain drainage system, optical methods, brave and innovative ideas and very promising experimental and clinical results focusing on the study of therapeutic and diagnostic properties of sleep as well as the development of novel strategies for the modulation of restorative sleep functions.

All articles are available here and are freely accessible until 13 May 2023. For further information, read the Editorial.

EPJ Web of Conferences Highlight - ESSENA11: 11th European Summer School on Experimental Nuclear Astrophysics

More than 70 participants from 15 countries attended ESSENA11, in Catania, in June 2022.

The European Summer School on Experimental Nuclear Astrophysics has run for more than 2 decades and brings together nuclear physicists and astrophysicists from major universities, laboratories and research facilities. It has been organized jointly by the INFN Laboratori Nazionali del Sud (Catania) and the Dipartimento di Fisica e Astronomia “E. Majorana” of the Catania University.

It is an opportunity to present novel work across the full range of both theoretical and experimental activities covering all novel aspects ranging from cosmology to stellar physics as well as nuclear aspects, methods and instruments related to investigations of nuclear reactions important for nuclear astrophysics.


Quentin Glorieux joins the EPJ Scientific Advisory Committee (SAC)

Quentin Glorieux

The Scientific Advisory Committee of EPJ is delighted to welcome Professor Quentin Glorieux, as the new representative for the French Physical Society.

Quentin Glorieux is Associate Professor at Sorbonne Université, Laboratoire Kastler Brossel, and fellow member of Institut Universitaire de France (IUF). His expertise covers a broad range of topics from nanooptics to quantum gases and superfluidity. In the last years, his experimental work focus on Quantum Fluids of Light to simulate many-body physics and analogue gravity with light in various platforms (from exciton-polaritons in microcavities to non-linear propagation of light in atomic vapors.)

M. Eckert and J.D. Wells
ISSN (Print Edition): 2102-6459
ISSN (Electronic Edition): 2102-6467

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