Results 211 to 220 of about 5,876,040 (331)

Reinforcement Learning Approach to Advance Non‐Fullerene Acceptor‐Based Semi‐Transparent Organic Solar Cell

Advanced Theory and Simulations, EarlyView.
This study introduces a deep Q‐learning framework for semi‐transparent organic solar cells, combining AI and the Transfer Matrix Method to balance transparency/efficiency. Targeting PBDB‐T:ITIC‐based layers with asymmetric DMD contacts, the algorithm optimizes thickness/material properties, achieving 48.97% AVT and sustained photocurrent.
Erman Cokduygulular, Muhammed Yusuf Aykut, Caglar Cetinkaya   +2 more
wiley   +1 more source

Targeted molecular generation with latent reinforcement learning. [PDF]

Sci Rep
Haddad R, Litsa EE, Liu Z, Yu X, Burkhardt D, Bhisetti G.   +5 more
europepmc   +1 more source

Brain nicotinic receptors: structure and regulation, role in learning and reinforcement

, 1998
Jean‐Pierre Changeux, Daniel Bertrand, Pierre‐Jean Corringer, Stanislas Dehaene, Stuart J. Edelstein, Clément Léna, Nicolas Le Novère, Lisa M. Marubio, Marina R. Picciotto, Michèle Zoli   +9 more
openalex   +1 more source

Multivariate Screening and Automated Clustering of Macrophage Immunoreactome to Nanoparticles and Photothermal Therapy

Advanced Science, EarlyView.
Predicting immune responses to treatments, including nanomedicines, with high throughput test is challenging due to the immunoreactome's complexity and plasticity. A predictive screening platform integrating pathway activation and gene profiling in macrophages has been developed as a versatile tool for designing effective immunotherapies.
Sonia Becharef, Léa Jabbour, Nassima Bekaddour, Giulio Avveduto, Nathalie Luciani, Gautier Laurent, Rana Bazzi, Edouard Alphandery, Stéphane Roux, Amanda K. A. Silva, Kelly Aubertin, Jean‐Philippe Herbeuval, Florence Gazeau   +12 more
wiley   +1 more source

Constructing ancestral recombination graphs through reinforcement learning. [PDF]

Front Genet
Raymond M, Descary MH, Beaulac C, Larribe F.   +3 more
europepmc   +1 more source

Non‐Invasive Diagnosis of Chronic Myocardial Infarction via Composite In‐Silico‐Human Data Learning

Advanced Science, EarlyView.
This study presents a non‐invasive machine learning model to identify infarct regions in the left ventricle using cardiac strain data. By combining rodent‐based simulated data with limited human data, the model achieves high accuracy in predicting infarct size and location without the need for gadolinium contrast agents, offering a promising ...
Rana Raza Mehdi, Nikhil Kadivar, Tanmay Mukherjee, Emilio A. Mendiola, Akila Bersali, Dipan J. Shah, George Karniadakis, Reza Avazmohammadi   +7 more
wiley   +1 more source

Benchmarking reinforcement learning algorithms for autonomous mechanical thrombectomy. [PDF]

Int J Comput Assist Radiol Surg
Moosa F, Robertshaw H, Karstensen L, Booth TC, Granados A.   +4 more
europepmc   +1 more source

Robust reinforcement learning control with static and dynamic stability [PDF]

, 2001
R. Matthew Kretchmar, Peter M. Young, Charles W. Anderson, Douglas C. Hittle, Michael Anderson, Christopher C. Delnero   +5 more
openalex   +1 more source

The Morphological, Behavioral, and Transcriptomic Life Cycle of Anthrobots

Advanced Science, EarlyView.
DNA is thought to determine shape, behavior, and lifespan, with developmental plasticity reserved for stem cells, embryos, and species like amphibia. How much morphogenetic plasticity do adult somatic human cells exhibit? Anthrobots are a self‐organizing, motile synthetic living construct made of genetically normal human epithelial cells.
Gizem Gumuskaya, Nikolai Davey, Pranjal Srivastava, Andrew Bender, Léo Pio‐Lopez, Douglas Hazel, Michael Levin   +6 more
wiley   +1 more source

Behavior engineering using quantitative reinforcement learning models. [PDF]

Nat Commun
Dan O, Plonsky O, Loewenstein Y.
europepmc   +1 more source