Results 81 to 90 of about 75,350 (240)
Adaptive wireless network multi-objective optimization algorithm based on image synthesis
EURASIP Journal on Image and Video Processing, 2018 Multi-objective optimization problems can be divided into continuous multi-objective optimization problems and discrete multi-objective optimization problems, and discrete multi-objective optimization is not universal.
Jianwei Zhang, Xueya Zhang
doaj +1 more source
Electroactive Metal–Organic Frameworks for Electrocatalysis
Advanced Functional Materials, EarlyView.Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska +7 more
wiley +1 more source
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
A Reference Point-Based Evolutionary Algorithm Solves Multi and Many-Objective Optimization Problems: Method and Validation. [PDF]
Comput Intell Neurosci, 2023 Jameel M, Abouhawwash M.
europepmc +1 more source
Timing the Decision Support for Real-World Many-Objective Optimization Problems [PDF]
, 2017 Lately, there is growing emphasis on improving the scalability of multi-objective evolutionary algorithms MOEAs so that many-objective problems characterized by more than three objectives can be effectively dealt with. Alternatively, the utility of integrating decision maker's DM's preferences into the optimization process so as to target some most ...
Duro, J.A., Saxena, D.K.
openaire +2 more sources
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
IEEE Access, 2019 This paper presents a tri-level many-objective optimization (TLMaO) approach to provide a final solution for many-objective optimization problems (MaOPs).
J. H. Zheng +3 more
doaj +1 more source
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
Advanced Functional Materials, EarlyView.This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
Advanced Functional Materials, EarlyView.In this study, the preparation techniques for silver‐based gas diffusion electrodes used for the electrochemical reduction of carbon dioxide (eCO2R) are systematically reviewed and compared with respect to their scalability. In addition, physics‐based and data‐driven modeling approaches are discussed, and a perspective is given on how modeling can aid ...
Simon Emken +6 more
wiley +1 more source