Results 51 to 60 of about 198,734 (305)

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

The role of foresight in public policy: lessons from deliberative democracy and perspectival realism

European Journal of Futures Research
Foresight plays an important role in public policy development. The foresight learning process brings together various actors in the policy community and incentivizes them to think about the future.
Brian Galvin
doaj   +1 more source

High-School Students’ Topic-Specific Epistemic Beliefs about Climate Change: An Assessment-Related Study

Education Sciences, 2021
This study belongs to assessment-related research and aimed to investigate Finnish high-school students’ (n = 211) topic-specific epistemic beliefs about climate change and whether the Norwegian topic-specific epistemic beliefs questionnaire (TSEBQ) was ...
Eija Yli-Panula, Eero Laakkonen, Marja Vauras   +2 more
doaj   +1 more source

What's the Point of Understanding? [PDF]

, 2019
What is human understanding and why should we care about it? I propose a method of philosophical investigation called ‘function-first epistemology’ and use this method to investigate the nature and value of understanding-why.
Hannon, Michael
core   +1 more source

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

Epistemic Educational Futures in an Era of Deglobalization and the Polycrisis

Nordic Journal of Comparative and International Education
This conceptual paper explores what I term epistemic educational futures, utilizing a reparatory framework. It argues that this is possible because deglobalization and the polycrisis have created new geocentric spaces in which we can rethink the role of
tavis d. jules
doaj   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

African theology of reconstruction and queer epistemic violence: A theo-ethical discourse

Verbum et Ecclesia
The legacies of colonialism in South Africa and broadly in Africa have problematised discourses on violence and discrimination. To counter coloniality and its continued efforts to dehumanise Africa, many Africans have often uncritically embraced certain ...
Ayanda Mdokwana
doaj   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

Advanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua   +4 more
wiley   +1 more source