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JournalISSN: 1099-4300

Entropy 

Multidisciplinary Digital Publishing Institute
About: Entropy is an academic journal published by Multidisciplinary Digital Publishing Institute. The journal publishes majorly in the area(s): Medicine & Computer science. It has an ISSN identifier of 1099-4300. It is also open access. Over the lifetime, 10735 publications have been published receiving 132032 citations. The journal is also known as: thermodynamic entropy.


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Journal ArticleDOI
29 May 2013-Entropy
TL;DR: The emergence of the 0-law, I- law, II-law and III-law of thermodynamics from quantum considerations is presented and it is claimed that inconsistency is the result of faulty analysis, pointing to flaws in approximations.
Abstract: Quantum thermodynamics addresses the emergence of thermodynamic laws from quantum mechanics. The viewpoint advocated is based on the intimate connection of quantum thermodynamics with the theory of open quantum systems. Quantum mechanics inserts dynamics into thermodynamics, giving a sound foundation to finite-time-thermodynamics. The emergence of the 0-law, I-law, II-law and III-law of thermodynamics from quantum considerations is presented. The emphasis is on consistency between the two theories, which address the same subject from different foundations. We claim that inconsistency is the result of faulty analysis, pointing to flaws in approximations.

815 citations

Journal ArticleDOI
10 Jan 2014-Entropy
TL;DR: It is shown that intermetallic phases are consistent with HEA definitions, and the strategy developed here includes both single-phase, solid solution HEAs and HEAs with intentional addition of a 2nd phase for particulate hardening.
Abstract: We develop a strategy to design and evaluate high-entropy alloys (HEAs) for structural use in the transportation and energy industries. We give HEA goal properties for low (≤150 °C), medium (≤450 °C) and high (≥1,100 °C) use temperatures. A systematic design approach uses palettes of elements chosen to meet target properties of each HEA family and gives methods to build HEAs from these palettes. We show that intermetallic phases are consistent with HEA definitions, and the strategy developed here includes both single-phase, solid solution HEAs and HEAs with intentional addition of a 2nd phase for particulate hardening. A thermodynamic estimate of the effectiveness of configurational entropy to suppress or delay compound formation is given. A 3-stage approach is given to systematically screen and evaluate a vast number of HEAs by integrating high-throughput computations and experiments. CALPHAD methods are used to predict phase equilibria, and high-throughput experiments on materials libraries with controlled composition and microstructure gradients are suggested. Much of this evaluation can be done now, but key components (materials libraries with microstructure gradients and high-throughput tensile testing) are currently missing. Suggestions for future HEA efforts are given.

651 citations

Journal ArticleDOI
16 Nov 2009-Entropy
TL;DR: Further advances are needed to better define model thresholds, to test model significance, and to address model selection to strengthen the utility of Maxent for wildlife research and management.
Abstract: Maximum entropy (Maxent) modeling has great potential for identifying distributions and habitat selection of wildlife given its reliance on only presence locations. Recent studies indicate Maxent is relatively insensitive to spatial errors associated with location data, requires few locations to construct useful models, and performs better than other presence-only modeling approaches. Further advances are needed to better define model thresholds, to test model significance, and to address model selection. Additionally, development of modeling approaches is needed when using repeated sampling of known individuals to assess habitat selection. These advancements would strengthen the utility of Maxent for wildlife research and management.

595 citations

Journal ArticleDOI
01 Dec 1999-Entropy
TL;DR: For readers interested in the Prigogine school of thermodynamics, this book is the first choice because it is a textbook.
Abstract: For readers interested in the Prigogine school of thermodynamics, this book is the first choice because it is a textbook.[...]

570 citations

Journal ArticleDOI
25 Dec 2020-Entropy
TL;DR: In this paper, a literature review and taxonomy of machine learning interpretability methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.
Abstract: Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

543 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
2023937
20221,946
20211,497
20201,436
20191,270
2018996