Results 181 to 190 of about 1,044,682 (335)

Utilizing High-Resolution Mass Spectrometry Data Mining Strategy in R Programming Language for Rapid Annotation of Absorbed Prototypes and Metabolites of Gypenosides. [PDF]

Molecules
Li X, Zhang Q, Li Y, Qin L, Wu D, Tan D, Xie J, Wu J, Yang Q, Lu Y, Zhao Y, Fan Q, Wu X, He Y.   +13 more
europepmc   +1 more source

A survey of control structures in programming languages [PDF]

, 1972
David Fisher
openalex   +1 more source

Dynamic Control of Synaptic Plasticity by Competing Ferroelectric and Trap‐Assisted Switching in IGZO Transistors with Al2O3/HfO2 Dielectrics

Advanced Functional Materials, EarlyView.
A frequency‐tunable ferroelectric synaptic transistor based on a buried‐gate InGaZnO channel and Al2O3/HfO2 dielectric stack exhibits linear and reversible weight updates using single‐polarity pulses. By switching between ferroelectric and trap‐assisted modes depending on input frequency, the device simplifies neuromorphic circuit design and achieves ...
Ojun Kwon, Dong Ho Lee, Seyoung Oh, Jongwon Yoon, Hyo‐Bae Kim, Ji‐Hoon Ahn, Woojin Park, Han Seul Kim, Byungjin Cho   +8 more
wiley   +1 more source

Image-based quantification of Candida albicans filamentation and hyphal length using the open-source visual programming language JIPipe. [PDF]

FEMS Yeast Res
Praetorius JP, Hitzler SUJ, Gresnigt MS, Figge MT.   +3 more
europepmc   +1 more source

Visualizing Morphogenesis with the Processing Programming Language. [PDF]

J Biocommun, 2017
Patel A, Bains A, Millet R, Elul T.
europepmc   +1 more source

Language facilities for programmable backtracking [PDF]

, 1977
Ralph E. Griswold, David R. Hanson
openalex   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Correction: Automated code development based on genetic programming in graphical programming language: A pilot study. [PDF]

PLoS One
Kodytek P, Bodzas A, Zidek J.
europepmc   +1 more source

Stan: A Probabilistic Programming Language. [PDF]

J Stat Softw, 2017
Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, Brubaker MA, Guo J, Li P, Riddell A.   +9 more
europepmc   +1 more source

Report on the programming language Euclid [PDF]

, 1977
Butler Lampson, J. J. Horning, Ralph L. London, James G. Mitchell, Gerald J. Popek   +4 more
openalex   +1 more source