Results 161 to 170 of about 3,197,456 (294)

Interpretable Machine Learning for LPR Risk Estimation: A Single-Center Retrospective Case-Control Study. [PDF]

JGH Open
Long C, Li Y, Zhang X, Li J, Hao X, Hua H.   +5 more
europepmc   +1 more source

Advancing Electronic Application of Coordination Solids: Enhancing Electron Transport and Device Integration via Surface‐Mounted MOFs (SURMOFs)

Advanced Functional Materials, EarlyView.
The layer‐by‐layer (LbL) assembly of coordination solids, enabled by the surface‐mounted metal‐organic framework (SURMOF) platform, is on the cusp of generating the organic counterpart of the epitaxy of inorganics. The programmable and sequential SURMOF protocol, optimized by machine learning (ML), is suited for accessing high‐quality thin films of ...
Zhengtao Xu, Christof Wöll, Stefan Bräse   +2 more
wiley   +1 more source

Development and evaluation of machine learning models for predicting relapse in idiopathic nephrotic syndrome. [PDF]

Front Endocrinol (Lausanne)
Zhao M, Li P, Chen F, Wu Z.
europepmc   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Construction and validation of a machine learning-based prediction model for postoperative complications in patients with chronic otitis media. [PDF]

Front Med (Lausanne)
Wang J, Zhang F, Liu L, Ju Y, Zhan C.
europepmc   +1 more source

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +1 more source

Development and validation of multivariable prognostic machine learning models to identify patients at risk for inadequate bowel preparation when undergoing colonoscopy: a prospective, multicentre study. [PDF]

BMJ Open Gastroenterol
Schramm C, Hehn C, Curth H, Scheller I, Weingaertner JH, Reichert MC, Kasper P, Bozal D, Allo G, Casper M, Adams T, Jochheim L, Zeller A, Rahe G, Zeeh J, Neumann-Haefelin C, Lammert F, Schmidt H, Friedrich CM, Buerger M.   +19 more
europepmc   +1 more source

Slight Truncation Changes in Iron Oxide Nanocubes Strongly Affect Their Magnetic Properties

Advanced Functional Materials, EarlyView.
Subtle variations in nanoparticle morphology can lead to significant changes in functional properties. An automated shape‐fitting method captures minor differences in corner truncation between iron oxide nanocubes of similar sizes synthesized under identical conditions, revealing pronounced disparities in their magnetic and hyperthermia behavior ...
Kingsley Poon, Raúl López‐Martín, Verner Håkonsen, Nader Yaacoub, Francisco J. Terán, Alberto López Ortega, Jose A. De Toro, Gurvinder Singh   +7 more
wiley   +1 more source

Multimodal Deep Learning for Pulmonary Nodule Detection on Chest Radiography in High-Risk Adults, With Secondary Validation for All-Cause and Cause-Specific Mortality Prediction: A Multicenter Cohort Study. [PDF]

MedComm (2020)
Li J, Xing Y, Gao X, Liu Y, Zhang L, Huang Y, Zhang P, Ye Z, Wang M, Song F.   +9 more
europepmc   +1 more source

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source