Results 61 to 70 of about 299,863 (342)
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 +3 more
wiley +1 more source
Interactive Reference Region Based Multi-Objective Evolutionary Algorithm Through Decomposition
IEEE Access, 2016 Many evolutionary multi-objective optimization (EMOs) methodologies have been proposed and shown a great potential in approximating the entire Pareto front. While in real world, what decision makers (DMs) want is one or several solutions to satisfy their
Ruochen Liu +4 more
doaj +1 more source
Improving a multi-objective evolutionary algorithm to discover quantitative association rules [PDF]
, 2015 This work aims at correcting flaws existing in multi-objective evolutionary schemes to discover quantitative association rules, specifically those based on the wellknown non-dominated sorting genetic algorithm-II (NSGA-II).
Martínez Ballesteros, María del Mar +3 more
core +1 more source
A Novel Multi-objective Evolutionary Algorithm [PDF]
, 2007 Evolutionary Algorithms are recognized to be efficient to deal with Multi-objective Optimization Problems(MOPs) which are difficult to be solved with traditional methods. Here a new Multi-objective Optimization Evolutionary Algorithm named DGPS which is compound with Geometrical Pareto Selection Method (GPS), Weighted Sum Method (WSM) and Dynamical ...
Bojin Zheng, Ting Hu
openaire +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 +7 more
wiley +1 more source
AIAccurately forecasting power consumption is crucial important for efficient energy management. Machine learning (ML) models are often employed for this purpose. However, tuning their hyperparameters is a complex and time-consuming task.
Aleksei Vakhnin +3 more
doaj +1 more source
Frontiers in Energy Research, 2022 In order to achieve good adaptability, medical bone implants for clinical applications need to have porous characteristics. From a biological and mechanical point of view, the design of porous structures requires both suitable porosities to facilitate ...
Rui Liu +3 more
doaj +1 more source
Generalized decomposition and cross entropy methods for many-objective optimization [PDF]
, 2012 Decomposition-based algorithms for multi-objective optimization problems have increased in popularity in the past decade. Although their convergence to the Pareto optimal front (PF) is in several instances superior to that of Pareto-based algorithms ...
Fleming, P.J. +2 more
core
Advanced Functional Materials, EarlyView.This research presents a novel implantable bio‐battery, GF‐OsG, tailored for diabetic bone repair. GF‐OsG generates microcurrents in high‐glucose conditions to enhance vascularization, shift macrophages to the M2 phenotype, and regulate immune responses.
Nanning Lv +10 more
wiley +1 more source
An optimization method for nacelle design [PDF]
, 2017 A multi-objective optimiZation method is demonstrated using an evolutionary genetic algorithm. The applicability of this method to preliminary nacelle design is demonstrated by coupling it with a response surface model of a wide range of nacelle designs.
Heidebrecht, A. +2 more
core +1 more source