The editorial team would like to congratulate the winners of the 2018 Entropy Best Paper Award. The winner nominations were made by a selection committee, which was chaired by the Editor-in-Chief, Prof. Dr. Kevin H. Knuth, and supported by thirteen Editorial Board Members. The three top-voted papers, in no particular order, have won the 2018 Entropy Best Paper Award:
Levitated Nanoparticles for Microscopic Thermodynamics—A Review
Jan Gieseler and James Millen
Entropy 2018, 20(5), 326; https://doi.org/10.3390/e20050326
Transductive Feature Selection Using Clustering-Based Sample Entropy for Temperature Prediction in Weather Forecasting
Zahra Karevan and Johan A.K. Suykens
Entropy 2018, 20(4), 264; https://doi.org/10.3390/e20040264
Pointwise Partial Information Decomposition Using the Specificity and Ambiguity Lattices
Conor Finn and Joseph T. Lizier
Entropy 2018, 20(4), 297; https://doi.org/10.3390/e20040297
We congratulate and thank the authors for having chosen Entropy to publish their work.
In recognition of their accomplishment, the authors will collectively receive a cash award of 500 CHF, and the privilege of publishing an additional research article or review paper free of charge in open access format in Entropy after the usual peer-review procedure.
Eligibility and Requirements
The debate about what causes the generation of form and structure in embryological development goes back to antiquity. Most recently, it has focused on the divergent views as to whether the generation of patterns and form in development is a largely...
The transdisciplinary nature of science as a whole became evident as the necessity for the complex nature of phenomena to explain social and life science, along with the physical sciences, blossomed into complexity theory and most recently into compl...
Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience. The axioms are translated into a set of postulates about the substrate of consciousness (c...
Time series (TS) and multiple time series (MTS) predictions have historically paved the way for distinct families of deep learning models. The temporal dimension, distinguished by its evolutionary sequential aspect, is usually modeled by decompositio...
Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance o...
Kevin Knuth
Antonio Maria Scarfone
Carlo Cafaro
Dawn Holmes
GuiLu Long
José Mendes
Lamberto Rondoni
Luca Faes
Matteo Convertino
Miguel Rubi
Nikolay K Vitanov
Peter Harremoës
Syed Jafar
Udo Von Toussaint
Yasar DemirelGet Alerted
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