Results 141 to 150 of about 126,742 (252)

Force–Displacement Characterization of PNIPAM Microactuators

Advanced Robotics Research, EarlyView.
A method to experimentally establish a force–displacement diagram of poly(N‐isopropyl acrylamide) (PNIPAM) microactuators, printed by 2PP, is presented herein. PNIPAM is a hydrogel whose volume and stiffness change with temperature due to the absorption or release of water.
Manon Cassignol, Esra Yalcinkaya, Adam Chafaï, Jérémy Odent, Pierre Lambert   +4 more
wiley   +1 more source

Readability of Epic's Obstetric Discharge Education. [PDF]

Pregnancy (Hoboken)
Ulanovsky K, Rouffiac AE, Roa J, Cudjoe E, Sahhar M, Lewkowitz AK, Thorsen MM.   +6 more
europepmc   +1 more source

Enhancing Amphibious Flipper‐Based Locomotion Through Terrain‐Dependent Bayesian Optimization of Central Pattern Generator Control

Advanced Robotics Research, EarlyView.
A sea turtle‐inspired amphibious robot uses simulation‐guided optimization to adapt its flipper coordination to different environments. Sensitivity analysis and multiobjective Bayesian optimization identify terrain‐dependent controllers that are validated on hardware.
Nnamdi C. Chikere, Yasemin Ozkan‐Aydin
wiley   +1 more source

Large Language Model-Generated Patient Education Materials in Pediatric Ophthalmology: A Scoping Review. [PDF]

Clin Ophthalmol
Mohamed AA, Armstrong TS, Srivastava O, Ing E, Pollock T, Solarte C.   +5 more
europepmc   +1 more source

Toward Prostate Cancer Early Warning with a Self‐Powered Wearable Biosensing Platform Integrated with Machine Learning

Advanced Science, EarlyView.
ABSTRACT Current prostate cancer detection methods remain limited in non‐invasiveness and specificity, prompting interest in urinary biomarkers such as sarcosine. Here, we report a urine‐powered wearable platform for non‐invasive sarcosine detection as a proof‐of‐concept for decentralized early warning.
Jing Xu, Hanxiao Chen, Qichen Yuan, Liucun Yin, Yifang Tao, Hong Wang, Yuquan Xue, Delai Fu, Huan Pang, Tie Chong, Li Xue   +10 more
wiley   +1 more source

Ethical Precision in Nanoscale Brain Interfacing

Advanced Science, EarlyView.
As brain interfaces approach the nanoscale, precision no longer only measures—it knows, predicts, and potentially reshapes the mind. This work argues that traditional ethics fails under such conditions and proposes a shift toward continuous, operation‐based governance using the recovery–discovery framework to track, constrain, and responsibly steer ...
Guilherme Wood
wiley   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +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

Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design

Advanced Science, EarlyView.
A large language model (LLM) based pipeline is developed to automatically extract a comprehensive and accurate multicomponent alloy database from literature corpus. The extracted dataset is integrated with sustainability indicators to identify potential alloys that outperform existing industrial benchmark materials in terms of both performance and ...
Aravindan Kamatchi Sundaram, Mohit Chakraborty, Sai Mani Kumar Devathi, B. Pabitramohan Prusty, Rohit Batra   +4 more
wiley   +1 more source

Automating Chemical Reasoning in High‐Throughput Phase Identification With a Probabilistic, LLM‐Guided Framework

Advanced Science, EarlyView.
Autonomous laboratories can now synthesize materials faster than experts can interpret the resulting diffraction data. A probabilistic framework combines refinement‐fit metrics with large language model‐derived chemical reasoning to rank competing phase interpretations and flag those unsuitable for autonomous use.
Olympia Dartsi, Lauren N. Walters, Amalie E. Trewartha, Steven B. Torrisi, Amanda Volk, Liam Joyce, Gerbrand Ceder, Anubhav Jain   +7 more
wiley   +1 more source