Results 171 to 180 of about 2,368,157 (325)

Machine Learning Approach for Predicting Older Adults' Responsiveness to Cognitive Training Interventions: Data from the ACTIVE Study. [PDF]

J Intell
Vargek P, Karakatič S, Bakračevič K.
europepmc   +1 more source

Carboxylic‐Acid Functionalized Multiwalled Carbon Nanotube‐Alkane‐Based Resistive Temperature Sensor for Cold Chain Applications

Advanced Engineering Materials, EarlyView.
This study presents a reversible temperature sensor with high switching ratio, ∼103. The device is fabricated using PET‐ITO and carbon nanotube dispersions in alkane. Considering its application in cold chain logistics, a proof‐of‐concept with LED is showcased. Thus, a temperature drop below the threshold temperature (crystallization temperature of the
Sunil Kumar Behera, Philippe Marrec, A. Jorge Parola, Madalena Dionísio, Hugo Águas, Andreia F. M. Santos, Hamed Pourkheirollah, Donald Lupo, César A. T. Laia   +8 more
wiley   +1 more source

Additional file 2 of Ferritin and transferrin predict common carotid intima-media thickness in females: a machine-learning informed individual participant data meta-analysis

Anand Ruban Agarvas, Richard Sparla, Janice L. Atkins, Claudia Altamura, Todd Anderson, Ebru Aşıcıoğlu, Judit Bassols, Abel López‐Bermejo, Hana Marie Dvořáková, José Manuel Fernández‐Real, Christoph Hochmayr, Michael Knoflach, Jovana Kusic Milicevic, Silvia Lai, José María Moreno‐Navarrete, Dariusz Pawlak, Krystyna Pawlak, Petr Syrovátka, Dorota Formanowicz, Pavel Kraml, José Manuel Valdivielso, L Valenti, Martina U. Muckenthaler   +22 more
openalex   +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

Additional file 6 of Ferritin and transferrin predict common carotid intima-media thickness in females: a machine-learning informed individual participant data meta-analysis

Anand Ruban Agarvas, Richard Sparla, Janice L Atkins, Claudia Altamura, Todd Anderson, Ebru Asicioglu, Judit Bassols, Abel López-Bermejo, Hana Marie Dvořáková, José Manuel Fernández-Real, Christoph Hochmayr, Michael Knoflach, Jovana Kusic Milicevic, Silvia Lai, José María Moreno-Navarrete, Dariusz Pawlak, Krystyna Pawlak, Petr Syrovátka, Dorota Formanowicz, Pavel Kraml, Jose M Valdivielso, L Valenti, Martina U. Muckenthaler   +22 more
openalex   +1 more source

Innovative Processing of Compacted Waste Aluminum Alloy Powders via Controlled Remelting and Solidification

Advanced Engineering Materials, EarlyView.
This study demonstrates an efficient recycling route for out‐of‐spec AlSi10Mg atomized powders through compaction and arc remelting followed by suction casting. By correlating compaction load, cooling rate, and resulting microstructure, we show that intermediate pressures (50–80 kN) and rapid cooling refine dendrites, reduce porosity, and enhance ...
Mila Christy de Oliveira, Marcella Gaute Cavalcante Xavier, Abdoul‐Aziz Bogno, Piter Gargarella, José Eduardo Spinelli   +4 more
wiley   +1 more source

Enhanced early detection of thyroid cancer using ensemble machine learning and serum proteomics. [PDF]

Front Oncol
Zhang D, Ding J, Zhou Z.
europepmc   +1 more source

Computational chromatography: A machine learning strategy for demixing individual chemical components in complex mixtures

, 2022
Mary M. Bajomo, Yilong Ju, Jingyi Zhou, Simina Elefterescu, Corbin Farr, Yiping Zhao, Oara Neumann, Peter Nordlander, Ankit Patel, Naomi J. Halas   +9 more
openalex   +2 more sources

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

Machine learning-based prediction model for fall risk among individuals with arthritis in China: an analysis using the China Health and Retirement Longitudinal Study (CHARLS) database

Minglun Xiao, Ting Yean Tan, Mao Liu
openalex   +1 more source