Results 241 to 250 of about 62,078 (294)

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Scheduling optimization of ship plane block flow line considering dual resource constraints. [PDF]

Sci Rep
Li J, Lin P, Wu X, Song D, Yang B, Zhou L.   +5 more
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

A hybrid evolution strategies algorithm for non-permutation flow shop scheduling problems. [PDF]

Sci Rep
Khurshid B, Maqsood S, Habib MS, Omair M, Hwang SJ.   +4 more
europepmc   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Schedule optimization for chemical library synthesis. [PDF]

Digit Discov
Ai Q, Meng F, Wang R, Klein JC, Godfrey AG, Coley CW.   +5 more
europepmc   +1 more source

Reinforcement learning based multi objective task scheduling for energy efficient and cost effective cloud edge computing. [PDF]

Sci Rep
Zhang W, Ou H.
europepmc   +1 more source

Dynamic job shop scheduling under multiple order disturbances using deep reinforcement learning. [PDF]

Sci Prog
Sun Z, Han W, Gao L, Zhu C, Lyu Q.
europepmc   +1 more source

A multi-objective brain storm optimization for integrated distributed flexible job shop and distribution problems. [PDF]

Heliyon
Jia Y, Zhou Y, Fu Y.
europepmc   +1 more source

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A genetic algorithm for the Flexible Job-shop Scheduling Problem

Computers & Operations Research, 2008
In this paper a genetic algorithm for the flexible job-shop scheduling problem is presented. Given are a set of machines and a set of jobs consisting of operations which have to be sequenced in a fixed order. Each operation can be processed by a subset of the machines and its processing time depends on the assigned machine.
Pezzella, F., Morganti, G., Ciaschetti, G.   +2 more
openaire   +4 more sources