Results 31 to 40 of about 26,939 (224)

Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-Informed Gaussian Processes via Kirchhoff–Love Theory

open access: yesJournal of Engineering Mechanics
25 pages, 11 ...
Igor Kavrakov   +2 more
openaire   +2 more sources

Inference of epidemiological parameters from household stratified data.

open access: yesPLoS ONE, 2017
We consider a continuous-time Markov chain model of SIR disease dynamics with two levels of mixing. For this so-called stochastic households model, we provide two methods for inferring the model parameters-governing within-household transmission ...
James N Walker   +2 more
doaj   +1 more source

PAK1 activation drives divergent resistance mechanisms to aromatase inhibition and tamoxifen in a luminal: A breast cancer model

open access: yesMolecular Oncology, EarlyView.
Breast cancer remains a major cause of cancer death in women, frequently developing endocrine therapy resistance. This study demonstrates that upregulated p21‐activated kinase 1 (PAK1) activity drives resistance to tamoxifen and long‐term estrogen deprivation in ER+ breast cancer models.
Luisa Schwarzmüller   +10 more
wiley   +1 more source

Neuromorphic Hebbian learning with magnetic tunnel junction synapses

open access: yesCommunications Engineering
Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency.
Peng Zhou   +5 more
doaj   +1 more source

Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data

open access: yesEarth and Space Science, 2021
Stochastic parameterizations are continuously providing promising simulations of unresolved atmospheric processes for global climate models (GCMs). One of the stochastic multi‐cloud model (SMCM) features is to mimic the life cycle of the three most ...
Kumar Roy   +4 more
doaj   +1 more source

Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig   +9 more
wiley   +1 more source

Population variability in the generation and selection of T-cell repertoires.

open access: yesPLoS Computational Biology, 2020
The diversity of T-cell receptor (TCR) repertoires is achieved by a combination of two intrinsically stochastic steps: random receptor generation by VDJ recombination, and selection based on the recognition of random self-peptides presented on the major ...
Zachary Sethna   +5 more
doaj   +1 more source

Periodic synchronization of isolated network elements facilitates simulating and inferring gene regulatory networks including stochastic molecular kinetics

open access: yesBMC Bioinformatics, 2022
Background The temporal progression of many fundamental processes in cells and organisms, including homeostasis, differentiation and development, are governed by gene regulatory networks (GRNs).
Johannes Hettich   +1 more
doaj   +1 more source

What Do Large Language Models Know About Materials?

open access: yesAdvanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer   +2 more
wiley   +1 more source

Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion.

open access: yesPLoS Computational Biology, 2016
Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems.
Fabian Fröhlich   +5 more
doaj   +1 more source

Home - About - Disclaimer - Privacy