Results 21 to 30 of about 47,553 (125)

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

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, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Routine physics work efficacy and efficiency improvement through a shared-duty physicist-of-the-day model in a multi-hospital radiation oncology network. [PDF]

J Appl Clin Med Phys
Abstract Background Large multi‐hospital radiation oncology networks often rely on site‐exclusive physicist coverage models that can lead to imbalanced workload distribution, inconsistent task execution, communication fragmentation, and vulnerability to delayed or missed quality checks.
Chen GP, Prah DE, Paulson ES.
europepmc   +2 more sources

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Advanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim, Yousung Jung, Silvana Botti, Miguel A. L. Marques, Tigany Zarrouk, Miguel A. Caro, Sanggyu Chong, Michele Ceriotti, Marivi Fernandez‐Serra, Sara Navarro‐Rodriguez, Aakash Ashok Naik, Janine George, Ömer Hasan Omar, Victor Trinquet, Gian‐Marco Rignanese, Andrey Ustyuzhanin, Sonia Conesa‐Boj, Juan Rojo, Minh‐Tuan Dau, Simon M.‐M. Dubois, Jean‐Christophe Charlier, Jose H. Garcia, Romain Gautier, Rachid Laref, Junfeng Qiao, Giovanni Pizzi, Nicola Marzari, Camilla Coletti, Antonio Rossi, Jan‐Lucas Uslu, Chiara Zanardi, Vincenzo Palermo, Wide Hogenhout, Natalia Konchakova, Peter Klein, Tejs Vegge, Stephan Roche   +38 more
wiley   +1 more source

Intra‐articular Orthobiologics Show Statistically but Not Clinically Meaningful Improvements Compared With Viscosupplementation in Knee Osteoarthritis: A Network Meta‐analysis of Randomized Controlled Trials

Arthroscopy, EarlyView.
Purpose To evaluate the comparative effectiveness of intra‐articular platelet‐rich plasma (PRP), stromal vascular fraction (SVF), bone marrow aspirate concentrate (BMAC), umbilical cord‐derived mesenchymal stem cell (UC‐MSC), and hyaluronic acid (HA) for pain relief and functional improvement in patients with knee osteoarthritis through network meta ...
Joo Hyung Han, Min Jung, Kwangho Chung, Hyun‐Soo Moon, Se‐Han Jung, Sung‐Hwan Kim   +5 more
wiley   +1 more source

Prediction of experimental excited‐state absorption spectra by using vibronic transition calculations: Its practical application to a π‐conjugated molecule

Bulletin of the Korean Chemical Society, EarlyView.
Excited‐state absorption (ESA) spectra are notoriously hard to compute. We show that routine DFT and TDDFT vibronic transition calculations reproduce the vibronic band shapes and positions of azulene's experimental ESA spectrum, providing a practical route to assign ESA bands to specific electronic transitions and vibrational modes.
Joonyoung F. Joung, Sungnam Park
wiley   +1 more source

From Text to Value: Measuring and Pricing Firm Climate Risk Exposure

Business Strategy and the Environment, EarlyView.
ABSTRACT We examine how the prominence and tone of climate risk disclosures affect firm value and strategic climate positioning for large European nonfinancial companies. We developed a firm‐level climate risk exposure (CRE) index that assesses climate risks within corporate narratives across four EU categories: transition risk, physical risk ...
Stefano Dell'Atti, Matteo Foglia, Grazia Onorato   +2 more
wiley   +1 more source

Issue Information [PDF]

J Appl Clin Med Phys
No abstract is available for this article.
europepmc   +4 more sources

Climate Stress Testing on European SME Securitised Loans Under Climate Mitigation Scenarios

Business Strategy and the Environment, EarlyView.
ABSTRACT Assessing the future impact of climate risks on the probability of default (PD) of small and medium enterprises (SMEs) is challenging due to limited disclosure, policy uncertainty and exposure to physical risks. This paper addresses this gap by integrating macroeconomic variables from the Network for Greening the Financial System (NGFS ...
Luca Zanin, Raffaella Calabrese
wiley   +1 more source