Results 241 to 250 of about 4,837,596 (345)

Hybrid experimental and machine learning approach for optimizing abrasive wear of microcrystalline cellulose modified hemp/bamboo fiber composites. [PDF]

Sci Rep
Davis Hans SJ, Muthukumaran M, Kumaresan K, Pradeep Kumar VG, Goudar DM, Bhat SK.   +5 more
europepmc   +1 more source

Harnessing Non‐Covalent Protein–Protein Interaction Domains for Production of Biocatalytic Materials Systems

Advanced Functional Materials, EarlyView.
Non‐covalent protein–protein interactions mediated by SH3, PDZ, or GBD domains enable the self‐assembly of stable and biocatalytically active hydrogel materials. These soft materials can be processed into monodisperse foams that, once dried, exhibit enhanced mechanical stability and activity and are easily integrated into microstructured flow ...
Julian S. Hertel, Maria Alessandra Martini, Marius Stoeckle, Annika J. Weber, Kersten S. Rabe, Christof M. Niemeyer   +5 more
wiley   +1 more source

Analysis of Inbreeding Coefficient and Genetic Diversity in Xinjiang Brown Cattle Based on Pedigree and ROH Evaluation. [PDF]

Animals (Basel)
Ma K, Li X, Shang Y, Wei J, Zhang M, Wang D, Huang X, Chen Q, Xu L.   +8 more
europepmc   +1 more source

Device Integration Technology for Practical Flexible Electronics Systems

Advanced Functional Materials, EarlyView.
Flexible device integration technologies are essential for realizing practical flexible electronic systems. In this review paper, wiring and bonding techniques critical for the industrial‐scale manufacturing of wearable devices are emphasized based on flexible electronics.
Masahito Takakuwa, Kento Yamagishi, Sunghoon Lee, Yusaku Tagawa, Takao Someya, Tomoyuki Yokota   +5 more
wiley   +1 more source

A compressive sensing-driven data collection scheme for UAV-assisted wireless sensor networks. [PDF]

Sci Rep
Han Z, Zheng G, Li C, Wang G, Mu Y.
europepmc   +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

Sparse Bayesian multidimensional scaling(s). [PDF]

Comput Stat
Sheth A, Smith A, Holbrook AJ.
europepmc   +1 more source

Communities clustered using Principal Coordinates Analysis (PCoA) of the unweighted UniFrac distance matrix.

, 2015
S. Wills Edgar, M. A. E. Jonkers Daisy, Hadassah Paul, Aliev Ad, J. Pierik Marieke, Penders John   +5 more
openalex   +2 more sources

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

Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from ESMFold-Predicted Tertiary Structures. [PDF]

Antibiotics (Basel)
Cordoves-Delgado G, García-Jacas CR, Marrero-Ponce Y, Aguila SA, Lizama-Uc G.   +4 more
europepmc   +1 more source