EPJ

About EPJ

The European Physical Journal (EPJ) is a series of peer-reviewed journals covering the whole spectrum of physics and related interdisciplinary subjects. EPJ is committed to high scientific quality in publishing and is indexed in all main citation databases.

Authors & Readers

Submission
- EPJ A
- EPJ B
- EPJ C
- EPJ D
- EPJ E
- EPJ AP
- EPJ ST
- EPJ H
- EPJ PLUS
- EPJ DS
- EPJ PV
- EPJ WOC
- EPJ NBP
- EPJ TI
- EPJ QT
- EPJ AM
LaTeX Macros
- EPJ A
- EPJ B
- EPJ C
- EPJ D
- EPJ E
- EPJ AP
- EPJ ST
- EPJ H
- EPJ PLUS
- EPJ DS
- EPJ WOC
- EPJ NBP
- EPJ TI
- EPJ QT
TOC alert
- EPJ A
- EPJ B
- EPJ C
- EPJ D
- EPJ E
- EPJ AP
- EPJ ST
- EPJ H
- EPJ PLUS
- EPJ DS
- EPJ PV
- EPJ WOC
- EPJ NBP
- EPJ TI
- EPJ QT
- EPJ AM
RSS feeds
Permission Requests
Policy on Publishing Integrity
FAQ

Tweets de @EPJscience

Latest news

EPJ Data Science - View featured video: How teams and players stack up and why

Details: Published on Tuesday, 14 March 2017 13:52

“Winning is not a sometime thing; it’s an all the time thing. Winning is a habit,” said legendary American football coach Vince Lombardi.

Human sports and games, with their rules of competition and measures of performance, serve as an ideal test-bed to look for universal features of hierarchy formation. In a recent article published in EPJ Data Science, José A. Morales and colleagues study the behaviour of performance rankings over time of players and teams for several sports and games, and find statistical regularities in the dynamics of ranks. This finding dispels the commonly held notion that rank changes are due to the intrinsic strengths or qualities of teams and players. The same phenomenon may apply to more complex competition settings with further examinations.

Read more in the highlight of this article.

EPJ Data Science Highlight - Predicting future sports rankings from evolving performance

Details: Published on Friday, 16 December 2016 17:01

Competitive sport ranking evolution over time is used to predict the future evolution of rankings

Competitive sports and games are all about the performance of players and teams, which results in performance-based hierarchies. Because such performance is measurable and is the result of varied rules, sports and games are considered a suitable model to help understand unrelated social or economic systems characterised by similar rules-based complexity. Now, a team of Mexican scientists have used the performance of national teams in tennis, chess, golf, poker and football as a test-bed for identifying universal features in the creation of hierarchies—such as the stratified structure found in the global hierarchical distribution of wealth. José Morales from the National Autonomous University of Mexico and his colleagues found they could, in principle, predict changes in rank occupancy over the course of a contender's lifetime, regardless of the particularities of the sports or activity. These findings, published in EPJ Data Science, enhance our ability to forecast how stratification occurs in competitive activities.

EPJ Data Science Highlight - Fiction-book narratives: only six emotional storylines

Details: Published on Friday, 18 November 2016 12:51

Annotated emotional arc of Harry Potter and the Deathly Hallows, by JK Rowling.

How scientists are using big data analysis to deconstruct the art of storytelling

Our most beloved works of fiction hide well-trodden narratives. And most fictions is based on far fewer storylines than you might have imagined. To come to this conclusion, big data scientists have worked with colleagues from natural language processing to analyse the narrative in more than a thousand works of fiction. By deconstructing some of the magic of narrative in fiction books, they have also confirmed that there are six different, common ways of telling a story that can be found time and time again in popular stories. They were inspired by the work of US fiction author Kurt Vonnegut, who originally proposed the similarity of emotional story lines in a Masters’s thesis rejected by the University of Chicago. These findings have just been published in EPJ Data Science by Andrew Reagan from the University of Vermont, USA, and colleagues.