Quality by design is critical for cell and gene therapy manufacturing where process controls directly influence product ...

Abstract: We propose a robust data-driven model predictive control (MPC) scheme to control linear time-invariant systems. The scheme uses an implicit model description based on behavioral systems ...

Caption:Some users say predictive text is not as intuitive as before AI was brought into the mix.Photo credit:891 ABC ...

Researchers from the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, and Johns Hopkins University in ...

The PBMF (Publised in Cancer cell ) is an automated neural network framework based on contrastive learning. This general-purpose framework explores potential predictive biomarkers in a systematic and ...

Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints arXiv https://github.com/StavrosOrf ...

We describe a model of visual processing in which feedback connections from a higher- to a lower-order visual cortical area carry predictions of lower-level neural activities, whereas the feedforward ...

Abstract: The goal of this article is to provide a simple model-free solution to the loss problem of accuracy in system model inherent in the existing finite control-set (FCS) model predictive control ...

We are experiencing an extraordinary level of volatility in the global supply chain ecosystem right now. There are many factors contributing to the current volatility including, but not limited to, ...

Experts reveal how AI-driven models revolutionise credit assessments and fraud detection while balancing privacy concerns in the fintech ecosystem Predictive analytics is fundamentally changing how ...