Results 231 to 240 of about 5,203 (262)
Advanced Intelligent Discovery, EarlyView.This article establishes a Taguchi–Bayesian sampling strategy to reconstruct polymer processing–property landscape at minimal sampling cost, generically building the roadmap for materials database construction from sampling their vast design space. This sampling strategy is featured by an alternating lesson between uniformity and representativeness ...
Han Liu, Liantang Li
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley +1 more source
How to Live in the Moment: The Methodology and Limitations of Evolutionary Research on Consciousness. [PDF]
Cogn Scide Weerd CR, Dung L.
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.SuperResNET is a powerful integrated software that reconstructs network architecture and molecular distribution of subcellular structures from single molecule localization microscopy datasets. SuperResNET segments the nuclear pore complex and corners, extracts size, shape, and network features of all segmented nuclear pores and uses modularity analysis
Yahongyang Lydia Li +6 more
wiley +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 +8 more
wiley +1 more source
Arousal coherence, uncertainty, and well-being: an active inference account. [PDF]
Neurosci ConsciousBiddell H +3 more
europepmc +1 more source
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NEJM Evidence, 2023
In the Stats, STAT! video, "Bayesian Way", originally published April 25, 2023 (DOI: 10.1056/EVIDstat2300090), at the 5:19 mark, the video states "…there is a 95% probability that biking is faster than taking the train…". It should have said, "…there is a greater than 95% probability that biking is faster than taking the train…" A corrected version of ...
C Corey, Hardin +6 more
openaire +4 more sources
In the Stats, STAT! video, "Bayesian Way", originally published April 25, 2023 (DOI: 10.1056/EVIDstat2300090), at the 5:19 mark, the video states "…there is a 95% probability that biking is faster than taking the train…". It should have said, "…there is a greater than 95% probability that biking is faster than taking the train…" A corrected version of ...
C Corey, Hardin +6 more
openaire +4 more sources