Results 121 to 130 of about 5,428,738 (277)

Enabling deeper learning on big data for materials informatics applications. [PDF]

Sci Rep, 2021
Jha D, Gupta V, Ward L, Yang Z, Wolverton C, Foster I, Liao WK, Choudhary A, Agrawal A.   +8 more
europepmc   +1 more source

The Informatics Audit - A Collaborative Process [PDF]

The paper present issues regarding the audit in informatics field, the audit seen as a collaborative process and how the collaborative banking systems are audited. In this paper, the methodology and techniques for an effective audit process are described.
Cristian CIUREA
core  

Modulating Calcium Homeostasis via a Biomimetic Scaffold to Rescue Diabetic Ischemic Wounds

Advanced Science, EarlyView.
This strategy addresses impaired microcirculation and loss of extracellular matrix (ECM) guidance in diabetic wound healing. Musc@CP, a nanofibrous dressing combining an ECM‐mimetic chitosan‐pullulan scaffold with muscone, enhances perfusion by attenuating intracellular Ca2+ overload‐associated endothelial dysfunction.
Xiang Zheng, Mingmei Li, Jianjun Jiang, Yongchao Wang, Yaru Jia, Weilun Sun, Yi Yao, Pengli Gao, Linhua Zhang, Qi Guo, Guang Jia, Xing‐Jie Liang, Dunwan Zhu, Jinchao Zhang, Fangzhou Li   +14 more
wiley   +1 more source

AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management

SusMat
Artificial intelligence (AI) is revolutionizing sustainable materials science, yet a comprehensive and timely evaluation of the rapidly evolving AI techniques applied across the entire materials lifecycle remains lacking.
Yuehui Xian, Cheng Li, Yangyang Xu, Yumei Zhou, Dezhen Xue   +4 more
doaj   +1 more source

Benchmark datasets incorporating diverse tasks, sample sizes, material systems, and data heterogeneity for materials informatics. [PDF]

Data Brief, 2021
Henderson AN, Kauwe SK, Sparks TD.
europepmc   +1 more source

Informatics empowers healthcare transformation Studies in health technology and informatics ;, v. 238./ edited by John Mantas, Arie Hasman, Parisis Gallos and Mowafa S. Househ.

, 2017
Includes indexes.Includes bibliographical references and indexes.Title Page; Preface; ICIMTH 2017 Scientific Programme Committee and Reviewers; Contents; Keynotes; On Informatics Diagnostics and Informatics Therapeutics -- Good Medical Informatics ...
Househ Mowafa, Gallos Parisis, Mantas J., Hasman Arie   +3 more
core  

CoSP: Reconfigurable Metamaterial Inverse Design via Contrastive Pretrained Large Language Model

Advanced Science, EarlyView.
In this work, CoSP (contrastive multi‐state pretrain), an intelligent inverse design method for reconfigurable metamaterials based on a contrastive pretrained large language model, is proposed. Numerical experiments demonstrate that CoSP can design reconfigurable metamaterial structures for multi‐state, multi‐band optical responses, showing great ...
Shujie Yang, Yuqi Zhang, Xuzhe Zhao, Yansong Tang, Kaichen Dong   +4 more
wiley   +1 more source

Knowledge Distillation for Molecular Property Prediction: A Scalability Analysis

Advanced Science
Knowledge distillation (KD) is a powerful model compression technique that transfers knowledge from complex teacher models to compact student models, reducing computational costs while preserving predictive accuracy. This study investigated KD's efficacy
Rahul Sheshanarayana, Fengqi You
doaj   +1 more source

Bypassing Heteronuclear MRI: Metal‐Free Organosilicon Nanotracers for Background‐Free Hotspot 1H Imaging on Clinical Hardware

Advanced Science, EarlyView.
Organosilicon micelles enable background‐free molecular magnetic resonance imaging (MRI) while remaining fully compatible with standard proton (1H) hardware. By exploiting a spectrally silent 1H window at ∼0 ppm, the silicone nanotracer allows hotspot detection by 1H chemical‐shift imaging (CSI) without metals or heteronuclei, enabling direct overlay ...
Martin Orsagh, Dominik Havlicek, Andrea Galisova, Ondrej Sedlacek, Daniel Jirak   +4 more
wiley   +1 more source

On the Sufficiency of a Single Hidden Layer in Feed-Forward Neural Networks Used for Machine Learning of Materials Properties

Physchem
Feed-forward neural networks (NNs) are widely used for the machine learning of properties of materials and molecules from descriptors of their composition and structure (materials informatics) as well as in other physics and chemistry applications. Often,
Ye Min Thant, Sergei Manzhos, Manabu Ihara, Methawee Nukunudompanich   +3 more
doaj   +1 more source