Results 191 to 200 of about 923,263 (264)

Microstructural Evolution and Vacancy Defect Formation in Mn–Mo–Ni RPV Steel Under Low Cycle Fatigue: Insights From EBSD and PALS

Advanced Engineering Materials, EarlyView.
Low‐cycle fatigue damage in Mn–Mo–Ni reactor pressure vessel steel is examined using a combined electron backscatter diffraction and positron annihilation lifetime spectroscopy approach. The study correlates texture evolution, dislocation substructure development, and vacancy‐type defect formation across uniform, necked, and fracture regions, providing
Apu Sarkar, Dhanadeep Dutta, G. Bharat Reddy   +2 more
wiley   +1 more source

Dental age estimation in forensic odontology: a systematic review comparing traditional methods and artificial intelligence approaches. [PDF]

Front Dent Med
Lakhaouaja F, García Navarro A.
europepmc   +1 more source

Electrochemical Behavior of Flame‐Sprayed Sc‐Doped AlCoCrFeMo High‐Entropy Alloy Coatings in 3.5% Sodium Chloride Solution

Advanced Engineering Materials, EarlyView.
Scandium (Sc)‐doped AlCoCrFeMo HEA coatings are fabricated via flame spraying with 0.1, 0.3, and 0.5 wt% Sc additions. Among these, the HEA‐Sc0.3 coating exhibits the highest corrosion resistance, indicated by a more positive corrosion potential and lower current density.
Pankaj Kumar, Akash Vyas, Mohammad Aatif Qazi, Nashit Jalal, Maria Ophelia Jarligo, Mostafa Yakout, Jing Liu, André McDonald   +7 more
wiley   +1 more source

Absolute risk trade-offs of donor age, sex, and graft source in PTCy-based transplantation. [PDF]

Blood Adv
Aljawai Y, Nawas M, McCurdy S, Kanakry CG, Kanakry JA, Rimando JC, Saultz J, Al-Juhaishi T, Lazaryan A, Milano F, Mehta RS.   +10 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

Temporal trends, risk factors, and future projections of gastric cancer burden in China, India, and the United States: a GBD 2023 analysis. [PDF]

Discov Oncol
Lin F, Wei X, Song L, Liu L, Li J.
europepmc   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Infections in Infancy and Association with Long-Term Weight Gain and Obesity in Children, Southern California, USA. [PDF]

Emerg Infect Dis
Taylor ZW, Lin JC.
europepmc   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

Bias Characterization of Resting Energy Expenditure Prediction Equations in Japanese Subjects: A Pilot Exploratory Study. [PDF]

Nutrients
Matsuura H, Deguchi K, Iizuka K.
europepmc   +1 more source