Results 161 to 170 of about 776,504 (298)
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
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 +7 more
wiley +1 more source
Fuzzy granulation-based wind speed prediction with multi-objective optimization. [PDF]
Zhang C, Wang J, Li Z.
europepmc +1 more source
Multi-objective Design Optimization of the Robot Grippers with SPEA2
Ayşenur Avder +2 more
openalex +2 more sources
We introduce a nucleic acid nanoparticle (NANP) platform designed to be rrecognized by the human innate immune system in a regulated manner. By changing chemical composition while maintaining constant architectural parameters, we identify key determinants of immunorecognition enabling the rational design of NANPs with tunable immune activation profiles
Martin Panigaj +21 more
wiley +1 more source
Intelligent multi-objective optimization of thermal comfort and ventilation performance in stratum ventilation design. [PDF]
Hammouda NG +7 more
europepmc +1 more source
A Multiple Surrogate Assisted Decomposition-Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization [PDF]
Ahsanul Habib +4 more
openalex +1 more source
Chemoselective Sequential Polymerization: An Approach Toward Mixed Plastic Waste Recycling
Inspired by biological protein metabolism, this study demonstrates the closed‐loop recycling of mixed synthetic polymers via ring‐closing depolymerization followed by a chemoselective sequential polymerizations process. The approach recovers pure polymers from mixed feedstocks, even in multilayer formats, highlighting a promising strategy to overcome a
Gadi Slor +5 more
wiley +1 more source
Multi-Objective Optimization of the Crashworthiness of Aluminum Circular Tubes with Graded Thicknesses. [PDF]
Ren J, Liu S, Dong X, Zhao C.
europepmc +1 more source

