Results 201 to 210 of about 3,908,716 (347)

Challenges and enablers in fluidization technology

AIChE Journal, EarlyView.
Abstract Gas–solid fluidized beds provide excellent heat and mass transfer for high‐throughput operations from coating to catalytic conversion and underpin emerging low‐carbon technologies. Yet industrial reliability, scale‐up, and control lag scientific understanding, particularly as finer, stickier, and more variable feedstocks increasingly challenge
J. Ruud van Ommen, Jia Wei Chew
wiley   +1 more source

From Virtual Molecules to Clinical Trials: How AI Is Reshaping Preclinical Drug Discovery. [PDF]

J Med Internet Res
Cuffari B.
europepmc   +1 more source

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

AIChE Journal, EarlyView.
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim, Silabrata Pahari, Joseph Sang‐Il Kwon   +2 more
wiley   +1 more source

Kademlia hash snow ablation resource optimized stride scheduling for mobile computing services in healthcare sector. [PDF]

Sci Rep
Sivakumar NR.
europepmc   +1 more source

Algorithms for automated live migration of virtual machines

Journal of Systems and Software, 2015
Mattias Forsman, Andreas Glad, L. Lundberg, D. Ilie   +3 more
semanticscholar   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Energy-conscious scheduling in edge environments: hybridization of traditional control and DE algorithm. [PDF]

Front Robot AI
Ma K, Xu L.
europepmc   +1 more source

Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment

IEEE Transactions on Parallel and Distributed Systems, 2013
Zhen Xiao, Weijia Song, Qi Chen
semanticscholar   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Energy and makespan optimised task mapping in fog enabled IoT application: a hybrid approach. [PDF]

Sci Rep
Tripathy N, Sahoo S, Alghamdi NS, Viriyasitavat W, Dhiman G.   +4 more
europepmc   +1 more source