Results 81 to 90 of about 427,183 (276)

In Situ Micromechanical Study of Bimodal γ′–γ″ Precipitate Assemblies in Ni–Cr–Al–Nb Superalloy

Advanced Engineering Materials, EarlyView.
A Ni–Cr–Al–Nb superalloy with a bimodal γ′–γ″ precipitate distribution is developed. Composite precipitate assemblies form through heterogeneous nucleation, effectively impeding dislocation motion. Micropillar compression reveals high strength at room and elevated temperatures, governed by precipitate shearing, with coupled faulting mechanisms ...
Ujjval Bansal, Tae‐Hyeok Kang, Pyuck‐Pa Choi, Subin Lee, Christoph Kirchlechner   +4 more
wiley   +1 more source

Lipid Nanoparticles for the Delivery of CRISPR/Cas9 Machinery to Enable Site‐Specific Integration of CFTR and Mutation‐Agnostic Disease Rescue

Advanced Functional Materials, EarlyView.
Lipid nanoparticles (LNPs) are optimized to co‐deliver Cas9‐encoding messenger RNA (mRNA), a single guide RNA (sgRNA) targeting the endogenous cystic fibrosis transmembrane conductance regulator (CFTR) gene, and homologous linear double‐stranded donor DNA (ldsDNA) templates encoding CFTR.
Ruth A. Foley, Paul G. Ayoub, Vrishti Sinha, Colin Juett, Alicia Sanoyca, Emily C. Duggan, Lindsay E. Lathrop, Priyanka Bhatt, Kevin Coote, Beate Illek, Brigitte N. Gomperts, Donald B. Kohn, Steven J. Jonas   +12 more
wiley   +1 more source

Complex and Hypercomplex-Valued Support Vector Machines: A Survey

Applied Sciences, 2019
In recent years, the field of complex, hypercomplex-valued and geometric Support Vector Machines (SVM) has undergone immense progress due to the compatibility of complex and hypercomplex number representations with analytic signals, as well as the power ...
Nancy Arana-Daniel
doaj   +1 more source

Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites.

PLoS ONE, 2020
Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy ...
Maeri Yamamoto, Epifanio Bagarinao, Itaru Kushima, Tsutomu Takahashi, Daiki Sasabayashi, Toshiya Inada, Michio Suzuki, Tetsuya Iidaka, Norio Ozaki   +8 more
doaj   +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

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source

Strain‐Programmable Luminescent Adhesive Patch With Tartrazine‐Mediated Optical Skin Clearing for Photochemical Tissue Bonding

Advanced Functional Materials, EarlyView.
We propose a suture‐complementary approach that integrates optical skin clearing with a strain‐programmable luminescent adhesive patch. Hyaluronic acid promotes transdermal delivery of tartrazine to improve optical clearing and stabilizes its interaction with a photosensitizer. Optical clearing increases the penetration depth of visible light into skin,
Seong‐Jong Kim, Kyungjoo Jo, Kwanghyeon Park, Jun Hee Kim, Jong Hyeong Cho, Inhoo Choi, Sei Kwang Hahn   +6 more
wiley   +1 more source

Support Vector Machines [PDF]

, 2002
This chapter gives a short introduction to support vector machines, the basic learning method used, extended, and analyzed for text classification throughout this work. Support vector machines [Cortes and Vapnik, 1995][Vapnik, 1998] were developed by Vapnik et al.
openaire   +2 more sources

Electrocatalytic Reduction of CO2 to Ethylene: Catalyst Design and Synchrotron‐Based Characterizations

Advanced Functional Materials, Volume 36, Issue 35, 30 April 2026.
This review evaluates strategies for electrochemical CO2 reduction to ethylene, focusing on copper‐based catalyst design and microenvironment modulation to achieve industrial‐grade performance. By leveraging operando synchrotron‐based characterizations, we provide a multiscale understanding of dynamic structural transformations and key reaction ...
Meng Zhang, Zuolong Chen, Yimin A. Wu
wiley   +1 more source

Facial expression recognition using three-stage support vector machines

Visual Computing for Industry, Biomedicine, and Art, 2019
Herein, a three-stage support vector machine (SVM) for facial expression recognition is proposed. The first stage comprises 21 SVMs, which are all the binary combinations of seven expressions.
Issam Dagher, Elio Dahdah, Morshed Al Shakik   +2 more
doaj   +1 more source