Results 111 to 120 of about 151,418 (244)

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
wiley   +1 more source

Functional Materials for Environmental Energy Harvesting in Smart Agriculture via Triboelectric Nanogenerators

Advanced Functional Materials, EarlyView.
This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva, Giulio Fatti, Emanuel Carlos, Guilherme Ferreira, Sumita Goswami, Suman Nandy, Luiz H. C. Mattoso, Elvira Fortunato, Caio G. Otoni, Rodrigo Martins   +9 more
wiley   +1 more source

Liquid Metals as Initiators of Free‐Radical Polymerization of Hydrogels: A Perspective

Advanced Functional Materials, EarlyView.
Gallium‐based liquid metals initiate free radical polymerization to form hydrogels without the use of toxic molecular initiators. In addition to initiating polymerization, they can act as crosslinkers, yielding softer, more extensible, and tougher hydrogels than those formed with conventional initiators.
Syed Ahmed Jaseem, Pooria Rahmani, Tushar Sakorikar, Jinwoo Ma, Omar Almutairi, Maxim A. Voinov, Alex I. Smirnov, Baihang Chen, Michael D. Dickey   +8 more
wiley   +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

Methods and algorithms of swarm intelligence for the problems of nonlinear regression analysis and optimization of complex processes, objects, and systems: review and modification of methods and algorithms

System Research in Energy
The development of high-speed methods and algorithms for global multidimensional optimization and their modifications in various fields of science, technology, and economics is an urgent problem that involves reducing computing costs, accelerating, and effectively searching for solutions to such problems.
Vladyslav Khaidurov, Vadym Tatenko, Mykyta Lytovchenko, Tamara Tsiupii, Tetiana Zhovnovach   +4 more
openaire   +2 more sources

Average-case analysis of algorithms using Kolmogorov complexity

Journal of Computational Science and Technology, 2000
Tao Jiang, Ming Li, P. Vitányi
semanticscholar   +1 more source

On the analysis of average time complexity of estimation of distribution algorithms

IEEE Congress on Evolutionary Computation, 2007
Tianshi Chen, K. Tang, Guoliang Chen, X. Yao   +3 more
semanticscholar   +1 more source

Collaborative representation based on enhanced tensor robust PCA for hyperspectral anomaly detection. [PDF]

PLoS One
A R, Zhong CL, Wang S, Gao Q.
europepmc   +1 more source

Comparative analysis of algorithmic approaches in ensemble learning: bagging vs. boosting. [PDF]

Sci Rep
Zhao H, Liu W, Wang Y, Wu L.
europepmc   +1 more source

A novel swarm intelligence optimization method for efficient task allocation in industrial wireless sensor networks. [PDF]

Sci Rep
Wang C, Yu F, Cao Q, Pan Y.
europepmc   +1 more source