Results 211 to 220 of about 1,551,868 (284)

Statistical shape modeling of MRI-based morphological response of lumbar intervertebral discs to a unilateral side lying spinal rotation mobilization. [PDF]

Osteoarthr Cartil Open
Putos J, Atkinson HF, Walton DM.
europepmc   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source

Biomechanical Effect of a Lumbar Interfacet Cage (FFX) Device When Combined With Pedicle Screw Constructs: A Finite Element Study. [PDF]

Int J Spine Surg
Saddiki R, Dhenin A, Montron L, Dufour T, Simon L, Buttin R, Srour R.   +6 more
europepmc   +1 more source

Structure and Spectroscopic Characterisation of Phenanthroline‐Based Iodobismuthate(III) Complexes Utilised for Raw Acoustic Signal Classification

Advanced Intelligent Discovery, EarlyView.
Memristors based on trimethylsulfonium (phenanthroline)tetraiodobismuthate have been utilised as a nonlinear node in a delayed feedback reservoir. This system allowed an efficient classification of acoustic signals, namely differentiation of vocalisation of the brushtail possum (Trichosurus vulpecula).
Ewelina Cechosz, Gisya Abdi, Andrzej Sławek, Marlena Gryl, Konrad Szaciłowski   +4 more
wiley   +1 more source

Comparison of Pleated and Rippled β-Sheet Assembly of Sequence Isomers of an Amphipathic Self-Assembling Peptide. [PDF]

Biochemistry
Jones CW, Chen J, Panda R, Jalali S, Cardani LP, Guo Y, Arnold IM, Dias CL, Nilsson BL.   +8 more
europepmc   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Improvement in spinopelvic parameters after laminectomy and complete factectomies above prior fusion facilitated by posterior arthroplasty: illustrative case. [PDF]

J Neurosurg Case Lessons
Pascual-Leone A, Joiner EF, Khan F, Agopyan-Miu A, Chou D.   +4 more
europepmc   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Tetranuclear Ag(i) and Au(i) nano-sized [L₂(R)₈ → M₄]⁴⁺ (M = Ag(i) and Au(i); R = C₂H₅, CH₃, H, F, Cl, Br, Ph and SiH₃) complexes: nature and cooperativity of metal-ligand bonds. [PDF]

Nanoscale Adv
Goodarzi S, Bayat M, Salehzadeh S, Alavipour E.   +3 more
europepmc   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source