Results 191 to 200 of about 8,740,437 (317)

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Machine learning-assisted abstract screening on learning analytics: a step-by-step tutorial. [PDF]

Syst Rev
Xu Z, Ma S, Zhuang X, Adeyemi AE, Kogut A.   +4 more
europepmc   +1 more source

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology. [PDF]

Sci Rep, 2023
MacDonald S, Foley H, Yap M, Johnston RL, Steven K, Koufariotis LT, Sharma S, Wood S, Addala V, Pearson JV, Roosta F, Waddell N, Kondrashova O, Trzaskowski M.   +13 more
europepmc   +1 more source

A Concept of a Digital and Traceable Manufacturing Documentation Based on Formalized Process Description Applied on Composite Aircraft Moveable

Advanced Engineering Materials, EarlyView.
The documentation of component manufacture has become an essential part of today's production processes, especially for the analysis and optimization of production or component design with regard to structural performance, economic efficiency, and sustainability.
Björn Denker, Dominik Delisle, Christian Krombholz, Martin Rädel, Frank Dressel   +4 more
wiley   +1 more source

Survey data on students' perceptions, knowledge, and use of learning analytics (LA) and generative artificial intelligence (GenAI) for the personalization of learning. [PDF]

Data Brief
Calderón-Garrido D, Lindín C, Parcerisa L.   +2 more
europepmc   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Students' Perceptions of Learning Analytics for Mental Health Support: Qualitative Study. [PDF]

JMIR Form Res
Freccero A, Onwunle M, Elliott J, Podder N, Purrinos De Oliveira J, Dewa LH.   +5 more
europepmc   +1 more source

Untangling connections between challenges in the adoption of learning analytics in higher education. [PDF]

Educ Inf Technol (Dordr), 2023
Alzahrani AS, Tsai YS, Iqbal S, Marcos PMM, Scheffel M, Drachsler H, Kloos CD, Aljohani N, Gasevic D.   +8 more
europepmc   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

AI-enabled learning analytics use relates to physical literacy and engagement in university PE via smart teaching and personalised feedback. [PDF]

Sci Rep
Chen Y, Xian D, Zhao Y, Sun Y, Ren Y, Wang C.   +5 more
europepmc   +1 more source