Results 91 to 100 of about 945,610 (300)

Revisiting Stability Criteria in Ball‐Milled High‐Entropy Alloys: Do Hume–Rothery and Thermodynamic Rules Equally Apply?

open access: yesAdvanced Engineering Materials, Volume 27, Issue 6, March 2025.
The stability criteria affecting the formation of high‐entropy alloys, particularly focusing in supersaturated solid solutions produced by mechanical alloying, are analyzed. Criteria based on Hume–Rothery rules are distinguished from those derived from thermodynamic relations. The formers are generally applicable to mechanically alloyed samples.
Javier S. Blázquez   +5 more
wiley   +1 more source

Overfitting Reduction of Text Classification Based on AdaBELM

open access: yesEntropy, 2017
Overfitting is an important problem in machine learning. Several algorithms, such as the extreme learning machine (ELM), suffer from this issue when facing high-dimensional sparse data, e.g., in text classification. One common issue is that the extent of
Xiaoyue Feng   +5 more
doaj   +1 more source

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

open access: yesAdvanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi   +4 more
wiley   +1 more source

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

open access: yesAdvanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier   +17 more
wiley   +1 more source

Symbolic model checking of logics with actions [PDF]

open access: yes, 2006
Reasoning about agents and modalities such as knowledge and belief leads to models where different relations over states co-exist, or equivalently, where information (labels, actions) is associated to state transitions.
Raimondi, F.   +4 more
core  

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

open access: yesAdvanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang   +2 more
wiley   +1 more source

A Parallel Extension Rule-Based Algorithm for #SAT Problem Using Model-Counting Tree

open access: yesIEEE Access, 2018
The #SAT problem, which is also called model counting, is one of the most important problems in artificial intelligence and is challenging to the researchers.
Naiyu Tian   +4 more
doaj   +1 more source

Towards Diagrammatic Specifications of Symbolic Computation Systems.

open access: yes, 2005
The aim of this work is to present an ongoing project to formalize,in the framework of diagrammatic logic (due to Dominique Duvaland Christian Lair) some data structures appearing in Sergeraerts symbolic computation systems Kenzo and EAT. More precisely,
Rubio García, Julio   +3 more
core  

Optimization of the Production of Rubber Compounds Using Mathematical Models

open access: yesAdvanced Engineering Materials, EarlyView.
Rubber compounds were mixed in a batch internal mixer, and symbolic regression was used to derive mathematical models linking recipe and process parameters to ram path, torque, and mixing quality (incorporation, dispersion, distribution). Subsequent optimization with evolutionary algorithms identified operating conditions that reduce specific energy ...
Anke Bardehle   +7 more
wiley   +1 more source

Deep Residual Convolutional Neural Network for Protein-Protein Interaction Extraction

open access: yesIEEE Access, 2019
Knowledge extracted from the protein-protein interaction (PPI) network can help researchers reveal the molecular mechanisms of biological processes. With the rapid growth in the volume of the biomedical literature, manually detecting and annotating PPIs ...
Hao Zhang   +6 more
doaj   +1 more source

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