Results 151 to 160 of about 115,147 (262)

From AI-based image analysis to surgical decision support in prostate cancer: interdisciplinary application of the international radiomics platform. [PDF]

Front Oncol
Tollens F, Westhoff N, Moltz J, Hartenstein T, Michel AS, Naeimi M, Ludwig J, Kohlmann P, Herrmann J, Nikolaou K, Schoenberg SO, Nörenberg D.   +11 more
europepmc   +1 more source

Encyclopedia of 2D β′‐In2Se3 Growth Using Chemical Vapor Deposition: The Effects of Synthesis Parameters Onto Material Quality

Advanced Engineering Materials, EarlyView.
A distinct semi‐confined inner‐tube chemical vapor deposition geometry enables reproducible, large‐area growth of phase‐pure 2D β′‐In2Se3 from InI + Se precursors. Engineering local vapor transport and optimizing precursor delivery and temperature–time conditions yield uniform continuous films.
Dasun P. W. Guruge, P. Sanju S. Panawala, Joseph F. S. Fernando, Zhenhuan Chen, Chao Zhang, Dong‐Chen Qi, Liangzhi Kou, Konstantin L. Firestein, Dmitri V. Golberg   +8 more
wiley   +1 more source

Telemedicine documentation in neurology and telestroke: a global scoping review. [PDF]

Neurol Sci
de Andrade JBC, Novoa CG, Pisa IT, Carneiro TS, de Oliveira NS, Rodriguez CE, Mendes GNN.   +6 more
europepmc   +1 more source

Experimental Characterization and Constitutive Modeling of the Effect of Prior Inelastic Strain on the Viscoplastic Behavior of AD730 at 700 °C

Advanced Engineering Materials, EarlyView.
This study shows that superalloys used in aircraft engine disks become much more prone to deformation at high temperatures if they have been strained during manufacturing. This effect increases with the level of prior strain but eventually reaches a limit.
Fabio Machado Alves da Fonseca, Patrick Villechaise, Malik Durand, Arnaud Longuet, Florent Coudon, Loïc Signor, Julien De Jaeger, Dominique Eyidi, Nathalie Bozzolo, Jonathan Cormier   +9 more
wiley   +1 more source

A Comprehensive Digital Workflow for Enhancing Dental Restorations in Severe Structural Wear. [PDF]

Bioengineering (Basel)
Alshabib A, Berger J, Garcia E, Jurado CA, Cabral G, Baldotto A, Riquieri H, Alrabiah M, Floriani F.   +8 more
europepmc   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Conversion Techniques for Immediate-Loading Interim Implant-Supported Complete-Arch Fixed Dental Prostheses (ISCFDPs): Four Clinical Reports. [PDF]

Dent J (Basel)
Nagai T, Yang CC, Al-Faraj A, Thompson MG, Rubalcava E, Akarapattananukul A, Lin WS.   +6 more
europepmc   +1 more source

Precipitation Simulations of the O‐Phase in Ti2AlNb Alloys Processed by Laser Powder Bed Fusion

Advanced Engineering Materials, EarlyView.
Simulated and experimental evolution of the O‐phase volume fraction during postprocessing of a Ti‐21Al‐25Nb (at.%) alloy processed by laser powder bed fusion. With results of sensitivity to input parameters from a thorough and quantified analysis, the interfacial energy matrix/precipitate is the most relevant input parameter for the simulation of the O‐
Silvana Tumminello, Joachim Gussone, Suzana G. Fries, Marcus Rackel, Andreas Stark, Norbert Schell, Jan Haubrich, Guillermo Requena   +7 more
wiley   +1 more source

Artificial intelligence and the future of prosthodontics: a narrative review. [PDF]

Saudi Dent J
Jayasheelan JA, Manju V, Deepthy SS, Saranya SK.   +3 more
europepmc   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source