Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess
development provides large amounts of heterogeneous experimental data, containing …

Abstract Machine learning (ML) is becoming increasingly crucial in many fields of
engineering but has not yet played out its full potential in bioprocess engineering. While …

Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical,
sequential setting of Bayesian Optimization does not translate well into laboratory …

With recent advances in digitisation and big data analytics, more pharmaceutical firms are
adopting digital tools to achieve modernisation. The biological phenomena within …

Biorefinery can be promoted by building accurate machine learning models. This work
proposed a strategy to enhance model's generalization ability and overcome insufficient …

While machine learning (ML) has made significant contributions to the biopharmaceutical
field, its applications are still in the early stages in terms of providing direct support for quality …

Background Studies have shown that magnetic resonance imaging (MRI)‐based deep
learning radiomics (DLR) has the potential to assess glioma grade; however, its role in …

The term hybrid modeling refers to the combination of parametric models (typically derived
from knowledge about the system) and nonparametric models (typically deduced from data) …

Clostridium carboxidivorans can use syngas to produce acids and alcohols. However,
simulating gas fermentation dynamics remains challenging. This study employed data …

Abstract γ-TiAl alloy is a typically difficult material to machine, with common machining
defects such as grain pull-out and material spalling during machining, resulting in …