Harnessing Machine Learning to Understand and Design Disordered Solids
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
Disaster preparedness beliefs and response self-efficacy after Health Belief Model-based education in nursing students: a quasi-experimental study. [PDF]
BMC NursAvcı S, Ersin F, Kaplan B, Kılıç M.
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez +4 more
wiley +1 more source
Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology
Advanced Intelligent Discovery, EarlyView.Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh +3 more
wiley +1 more source
Comment On "The Effect Of Modular Training On Self-Management, Disease, And Treatment Adaptation In Individuals With Hypertension". [PDF]
J Clin Hypertens (Greenwich)Anushka, Saha M, Chatterjee S.
europepmc +1 more source
When Biology Meets Medicine: A Perspective on Foundation Models
Advanced Intelligent Discovery, EarlyView.Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
Identifying Key Factors in Adherence and Dropouts in Active Physiotherapy in Children with Acute Leukemia: A Systematic Review with Meta-Analysis and Meta-Regression. [PDF]
Healthcare (Basel)Ramírez-Pérez L, Moreno-Morales N.
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta +3 more
wiley +1 more source
Uncovering the Reasons Behind Maternal Care Dropout in Bangladesh: Cross-Sectional Study. [PDF]
JMIR Public Health SurveillAlam SS +7 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary +1 more
wiley +1 more source