Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry
Advanced Intelligent Discovery, EarlyView.Artificial neural networks enable autonomous extraction of onset times from transient heating curves in luminescence thermometry. Using Ln3+‐doped upconverting nanoparticles as luminescent thermometers, we combine experimental transients with physically motivated synthetic curves to enhance data diversity and improve generalization.
David J. Sousa +3 more
wiley +1 more source
Long term effects of social stigma, career optimism, community disorder, and community ties on depressive symptoms among adolescent school dropouts. [PDF]
BMC PsycholKim YJ, Bae SM.
europepmc +1 more source
Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics
Advanced Intelligent Discovery, EarlyView.A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha +2 more
wiley +1 more source
Daily Negative Affect and Reaction Time Inconsistency in Emerging Adults: Ecological Momentary Assessment Study. [PDF]
JMIR Mhealth UhealthRutter LA +4 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design
Advanced Intelligent Discovery, EarlyView.MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa +2 more
wiley +1 more source
A study on identity conflict, self-concept clarity, and dual career adaptability among collegiate athletes. [PDF]
Front Sports Act LivingWang HC, Lu WC, Chen HC.
europepmc +1 more source
Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network
Advanced Intelligent Discovery, EarlyView.We introduce DeepBat, a deep learning framework featuring a 1D convolutional backbone designed to extract latent degradation patterns from a microstructurally diverse electrode dataset. By learning complex formulation–performance relationships, the model accurately predicts long‐term specific discharge capacity using limited early‐cycle data, providing
Tao Huang +16 more
wiley +1 more source
Visualizing the future: impact of holographic visualization on teaching minimally invasive veneer preparation: a randomized controlled trial. [PDF]
BMC Med EducJanson M +5 more
europepmc +1 more source
A 3D Multitask Graph Neural Network Framework for Ames Mutagenicity Prediction
Advanced Intelligent Discovery, EarlyView.Our proposed multitask graph neural network model has a high predictive accuracy for Ames mutagenicity. By operating directly on molecular graph representations instead of traditional molecular descriptors, and predicting strain‐specific results, the model outperforms most existing benchmarks.
Abigail E. Teitgen +3 more
wiley +1 more source