Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers [PDF]
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
Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents [PDF]
Advanced Intelligent Discovery, EarlyView.This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer +3 more
wiley +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids [PDF]
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Prediction‐Guided Two‐Step Solid‐State Exploration of Unknown Pseudo‐Ternary Oxides [PDF]
Advanced Intelligent Discovery, EarlyView.Prediction‐guided selection combined with two‐step solid‐state exploration enables efficient search of unknown pseudo‐ternary oxides. Broad robotic slurry screening followed by manual single‐phase isolation leads to the discovery of a new oxide, Ba5SnV6O22, showing how data‐guided experiments connect unexplored composition regions to new materials ...
Hiroyuki Hayashi
wiley +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning [PDF]
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
Kategorizace u dětí na počátku školní docházky v kontextu různých socio-kulturních prostředí
Pedagogika, 2016 Studie z oblasti interkulturní psychologie zaměřené na otázku, jak sociální a kulturní prostředí ovlivňuje kognitivní procesy, obvykle srovnávají velmi odlišné kultury žijící ve vzdálených geografických lokalitách.
Denisa Denglerová
doaj
PDF/A-2: The New Part of PDF/A
Archiving Conference, 2010 Thomas Zellmann, Mark McKinney
openaire +1 more source
Thermometric Based‐Microswimmers with Chemical and Optical Engines [PDF]
Advanced Intelligent Systems, EarlyView.Temperature sensing at small scales is typically performed using passive luminescent particles. Here, an alternative approach is demonstrated by integrating upconversion thermometry into self‐propelled microswimmers powered by chemical fuels or light. This strategy offers a step toward dynamic thermal sensing at the microscale, relevant to both lab‐on ...
João M. Gonçalves, Katherine Villa
wiley +1 more source
“It Is Much Safer to Be Sparse than Connected”: Safe Control of Robotic Swarm Density Dynamics with PDE Optimization with State Constraints [PDF]
Advanced Intelligent Systems, EarlyView.This paper proposes a novel control framework to ensure safety of a robotic swarm. A feedback optimization controller is capable of driving the swarm toward a target density while keeping risk‐zone exposure below a safety threshold. Theory and experiments show how safety is more effectively achieved for sparsely connected swarms.
Longchen Niu, Gennaro Notomista
wiley +1 more source
Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics [PDF]
Advanced Intelligent Systems, EarlyView.Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez +4 more
wiley +1 more source