Results 151 to 160 of about 20,741 (306)

Association between urinary trace metals and multiple health outcomes. [PDF]

Sci Prog
Wu S, Yu B.
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

Raman spectrum of thallium bromoiodide

, 1974
Raman spectrum of thallium ...
Krishnamurthy, N
core  

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Effect of Exogenous Addition of Microplastics on the Ability of Plants and Soil to Accumulate Thallium. [PDF]

Toxics
Wang J, Che P, Zhou J, Luo J, Lan S, Meng X, Shi H, Hu J.   +7 more
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

A case of CD36 deficiency with multiple white matter lesions. [PDF]

BMC Neurol
Kizuka Y, Yamakawa H, Iwabuchi Y, Sakai J, Terayama C, Yatabe Y, Arai T, Matsushita H, Ieda M, Nakahara J, Izawa Y.   +10 more
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

The nature of thallium crystals in Brassica oleracea (kale): a synchrotron multi-technique investigation. [PDF]

Metallomics
Corzo-Remigio A, Harris HH, Jones MWM, Wang T, Brueckner D, Spiers KM, Garrevoet J, van der Ent A.   +7 more
europepmc   +1 more source

Thallium glasses. Part 2

, 2010
Część druga artykułu stanowi dalszy ciąg przeglądu dostępnych danych literaturowych, w tym artykułów, patentów i wyników prac doświadczalnych dotyczących szkieł zawierających tal (szkieł talowych), prowadzonych pod kątem uzyskania korzystnych właściwości
Marczewska, A.
core