Results 221 to 230 of about 79,116 (291)

Characterization and Selection of Contractile Actuators for Soft Robotics Operating in the Quasistatic Regime

Advanced Intelligent Systems, EarlyView.
We developed methods to characterize soft contractile actuators using force–displacement measurements in the passive state and the total force in the active state and define the operational range of thermal, electrothermal, and pneumatic muscles. A graphical method enables the selection of an actuator based on the required mechanical force–displacement
Qiong Wang, Liuyang Cheng, Jeongmin Kim, Samuel Tsai, Devin Roach, Sameh Tawfick   +5 more
wiley   +1 more source

Response to a Skeptic

Quarterly Review, 1986
openaire   +3 more sources

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Advanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim, Yousung Jung, Silvana Botti, Miguel A. L. Marques, Tigany Zarrouk, Miguel A. Caro, Sanggyu Chong, Michele Ceriotti, Marivi Fernandez‐Serra, Sara Navarro‐Rodriguez, Aakash Ashok Naik, Janine George, Ömer Hasan Omar, Victor Trinquet, Gian‐Marco Rignanese, Andrey Ustyuzhanin, Sonia Conesa‐Boj, Juan Rojo, Minh‐Tuan Dau, Simon M.‐M. Dubois, Jean‐Christophe Charlier, Jose H. Garcia, Romain Gautier, Rachid Laref, Junfeng Qiao, Giovanni Pizzi, Nicola Marzari, Camilla Coletti, Antonio Rossi, Jan‐Lucas Uslu, Chiara Zanardi, Vincenzo Palermo, Wide Hogenhout, Natalia Konchakova, Peter Klein, Tejs Vegge, Stephan Roche   +38 more
wiley   +1 more source

Comparative Analysis of Model‐Agnostic Explanation Methods in Materials Science

Advanced Intelligent Systems, EarlyView.
To address the critical lack of explainable artificial intelligence (XAI) benchmarks in materials science, we present a quantitative and qualitative analysis of six XAI methods applied to molecular fingerprints. Our results reveal significant discrepancies in feature importance rankings, demonstrating that the chosen explanation approach introduces ...
Anna Przybyłowska, Joanna Neubauer, Monika Sztuder, Witold Taisner, Agnieszka Hryniewicka, Julia Kolodziejczyk, Marta Eliza Plonska‐Brzezinska, Dariusz Brzezinski   +7 more
wiley   +1 more source

Psychology of Eating the Future: Consumer Acceptance, Digital Influence and Behavioral Drivers of Novel Foods. [PDF]

Foods
Manzoor MF, Afraz MT, Waseem M, Ahmed Z.
europepmc   +1 more source

Bayesian Optimization–Reinforcement Learning Hybrid Framework for a‐IGZO Thin‐Film Transistor Compact Model Parameter Extraction in 2T0C Dynamic Random Access Memory Applications

Advanced Intelligent Systems, EarlyView.
A training‐free two‐stage BO–RL framework extracts compact model parameters for a‐IGZO TFTs, reaching BO‐level fitting quality at a constant per‐simulation optimization cost. Bayesian optimization performs exploration and provides an optimized starting point.
Seunghyun Son, Taejun Ha, Taeyoung Nam, Yoonyoung Chung, Sunmean Kim   +4 more
wiley   +1 more source

As the picture gets prettier the bar must rise higher. [PDF]

Am J Prev Cardiol
Nasir K.
europepmc   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Perceptions of Registered Dietitian Nutritionists (RDNs) on the Use of Artificial Intelligence (AI) in Clinical Nutrition Care: A Cross-Sectional Survey Within a Large U.S. Healthcare System. [PDF]

Nutrients
Johnson D, Hurt RT, Mundi MS, Salonen BR, Bonnes SL, Schroeder DR, Fokken SC, Croghan IT, Edakkanambeth Varayil J.   +8 more
europepmc   +1 more source

What is the problem and what can be done when data sharing challenges public legitimacy? [PDF]

Nat Commun
Hoeyer K, Skovgaard L.
europepmc   +1 more source