Results 161 to 170 of about 1,043,746 (297)

The impact of machine learning on ethological neuroscience. [PDF]

Front Behav Neurosci
Hidalgo-Gadea G, Güntürkün O, Behroozi M.   +2 more
europepmc   +1 more source

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning

, 2023
Hongjun You, Mengya Dong
openalex   +1 more source

Complex Cryptographic and User‐Centric Physically Unclonable Functions Enabled by Strain‐Sensitive Nanocrystals via Selective Ligand Exchange

Advanced Functional Materials, EarlyView.
This study investigates electromechanical PUFs that improve on traditional electric PUFs. The electron transport materials are coated randomly through selective ligand exchange. It produces multiple keys and a key with motion dependent on percolation and strain, and approaches almost ideal inter‐ and intra‐hamming distances.
Seungshin Lim, Hanseok Seo, Taesung Park, Byung Ku Jung, Junhyeok Park, Young Kyun Choi, Dae Yang Oh, Soong Ju Oh   +7 more
wiley   +1 more source

Challenges and solutions to employing natural language processing and machine learning to measure patients’ health literacy and physician writing complexity: The ECLIPPSE study

, 2020
William Brown, Renu Balyan, Andrew J. Karter, Scott A. Crossley, Wagahta Semere, Nicholas D. Duran, Courtney R. Lyles, Jennifer Y. Liu, Howard H. Moffet, Ryane Daniels, Danielle S. McNamara, Dean Schillinger   +11 more
openalex   +2 more sources

Using In Situ TEM to Understand the Surfaces of Electrocatalysts at Reaction Conditions: Single‐Atoms to Nanoparticles

Advanced Functional Materials, EarlyView.
This review summarizes recent advances in closed‐cell in situ TEM strategies for accurate determination of the activity and stability of single‐atom catalyst systems during operation. Operando conditions causing dynamic changes of SAC systems are highlighted and we explain why ensemble average‐based optical techniques may benefit from the technological
Martin Ek, Soshan Cheong, Julie Cairney, Richard D. Tilley, Ingemar Persson   +4 more
wiley   +1 more source

Machine Learning in Transforming the Food Industry. [PDF]

Foods
Hussain MA, Khan MIH, Karim A.
europepmc   +1 more source

Ice Lithography: Recent Progress Opens a New Frontier of Opportunities

Advanced Functional Materials, EarlyView.
This review focuses on recent advancements in ice lithography, including breakthroughs in compatible precursors and substrates, processes and applications, hardware, and digital methods. Moreover, it offers a roadmap to uncover innovation opportunities for ice lithography in fields such as biological, nanoengineering and microsystems, biophysics and ...
Bingdong Chang, Gwendoline A. E. Anand, Rubaiyet Haque, Dylan A. Chiaro, Mona Sharma, Andrew R. Williams, Frank Niklaus, Daryl W. Yee, Gavin M. King, Anpan Han   +9 more
wiley   +1 more source

Machine learning-assisted screening for canine Cushing's syndrome. [PDF]

Vet Q
Yoo YJ, Jeong K, Seo H, Nam HS, Chae Y, Yun T, Kang BT, Lee E, Kim H.   +8 more
europepmc   +1 more source

Ultrahigh‐Yield, Multifunctional, and High‐Performance Organic Memory for Seamless In‐Sensor Computing Operation

Advanced Functional Materials, EarlyView.
Molecular engineering of a nonconjugated radical polymer enables a significant enhancement of the glass transition temperature. The amorphous nature and tunability of the polymer, arising from its nonconjugated backbone, facilitates the fabrication of organic memristive devices with an exceptionally high yield (>95%), as well as substantial ...
Daeun Kim, Jaehyoung Ko, Namju Kim, Quynh H. Nguyen, Hoyeon Lee, Joohwan Eo, Seongeon Jin, Ji Eon Kwon, Seung‐Yeol Jeon, Youngdo Jeong, Dongseok Kwon, Sung Gap Im, Sanghan Lee, Byung Chul Jang, Yongho Joo   +14 more
wiley   +1 more source

Machine learning in chemistry. [PDF]

Proc Natl Acad Sci U S A
Debenedetti PG, de Pablo JJ, Schatz GC.
europepmc   +1 more source