2015 Impact factor 1.223
Condensed Matter and Complex Systems

EPJ D Highlight - Zig-zagging device focuses high-energy radiation emissions

Radiation spectra enhancements for the measurement performed with collimation.

Physicists have found a way to better control high-energy particle emissions in an undulator device that could potentially be used as a source of radiation for cancer treatment or nuclear waste processing

There’s no substitute for using the right tool for the job at hand. Using low-energy radiation sources simply isn’t suitable for certain tasks: equipment used in cancer treatment requires a strong, monochromatic source of radiation to produce hard X-rays. Other similar radiation sources find applications in nuclear waste processing. To design devices that steadily emit a specific type of radiation, physicists use a special kind of crystal, referred to as a crystalline undulator. In a recent study published in EPJ D, a team has demonstrated the ability to control radiation emissions from a particle travelling through such a device. Tobias Wistisen from Aarhus University, Denmark, and colleagues have shown how to manipulate the emitted radiation by selecting a combination of incoming particle charge and energy, oscillation amplitude and period of the undulator’s crystalline lattice.


EPJ E Colloquium - Quantum effects are hot in supercooled water

The importance of nuclear quantum effects is well known for in solid systems at very low temperatures (T<10K). At higher temperature (above ~20-50K) usually the contribution of these quantum effects to structural relaxation is considered minor. Traditionally, researchers who study the structural relaxation in liquids and the glass transition neglect to consider quantum effects. However, it is becoming increasingly evident when studying light molecules (such as water) at temperature of 100-200K that quantum effects might play an important role in structural dynamics, and provide non-negligible contributions at temperatures as high as ambient.


EPJ B Highlight - Autonomous machines edge towards greater independence

Mean error of self-taught system learning to recognise five vowels

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.


Executive Editors:
Eduardo Hernandez, Heiko Rieger, Bikas K. Chakrabarti, Wenhui Duan
I am naturally indebted to you and the referees who contributed to this success with your time and constructive advice.

Hamid Assadi

ISSN (Print Edition): 1434-6028
ISSN (Electronic Edition): 1434-6036

© EDP Sciences, Società Italiana di Fisica and Springer-Verlag

Conference announcements


Magnetic Island, Queensland, Australia, 22-24 July 2017