Results 191 to 200 of about 36,988 (280)

Machine learning based prediction of platelet concentration from ROTEM measurements. [PDF]

Sci Rep
Brooks R, Noitz M, Mahečić TT, Mühl D, Kerekes Á, Nardai G, Kubasta C, Kasalo M, Ulamec M, Maletzky A, Fazakas J, Meier J.   +11 more
europepmc   +1 more source

Machine learning‐assisted clone selection for intensified cell culture processes

Biotechnology Progress, EarlyView.
Abstract Intensified fed‐batch processes are becoming increasingly prevalent among biomanufacturers due to their superior space–time yields relative to traditional, non‐intensified fed‐batch processes. However, the shift towards intensified manufacturing has unexpectedly made optimal clone selection more challenging.
Nicolas Wolnick, John Schmitt, Christian Siltanen, Julia Armstrong, Shannon Hendrick, Ariel Atchley, Brandon Downey   +6 more
wiley   +1 more source

Examining the experimental effects of concentration and temperature on the viscosity of nanofluid containing graphene oxide, suggesting a correlation, and developing a neural network. [PDF]

Sci Rep
Aghayari R, Keyvani B, Toghraie D, Salahshour S.   +3 more
europepmc   +1 more source

Advanced glucose control strategies leveraging Raman spectroscopy for optimized mammalian cell culture manufacturing

Biotechnology Progress, EarlyView.
Abstract Maintaining consistent quality in the manufacturing of biotherapeutic proteins in mammalian cell culture is challenging, with unplanned deviations causing inconsistencies and potential batch failure. Current methods for monitoring and controlling critical process parameters (CPPs) rely on slow, labor‐intensive offline analyses.
Matthew Banner, Daniel Ray, Irfan Ahanger, Fabio Zurlo, Noah Hitchcock, Rajesh Mistry, Kasia Kozakowska‐McDonnell, Matthew Cheeks, Richard Turner, Jonathan Welsh, Suzanne S. Farid, Michael Thomas, Stephen Goldrick   +12 more
wiley   +1 more source

Developing a custom loss function for regulating underestimation and overestimation of concrete mechanical properties predictions in neural network models. [PDF]

Sci Rep
Habib A, Habib M, Junaid MT, Altoubat S, Maalej M, Alibrahim B.   +5 more
europepmc   +1 more source

Implementation of Machine Learning Models to Predict Functionality of Pea Flour From Its Composition

Cereal Chemistry, EarlyView.
ABSTRACT Background and Objectives The goal of this research was to examine the relationship between the composition and functionality of pea flour using the following machine learning algorithms: linear regression, partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression, gradient‐boosted decision trees ...
Colten N. Nickerson, Sarah Hogarth, Andrea K. Stone, Gene C. Arganosa, Pankaj Bhowmik, Tom D. Warkentin, Jordan Ubbens, Michael T. Nickerson   +7 more
wiley   +1 more source

Random forest regression for catalyst performance prediction and validation of tri‐reforming of methane (TRM)

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Carbon dioxide‐reduced hydrogen can be synthesized through various methods such as dry‐reforming (DRM), steam reforming (SMR), and partial oxidation (POX). Tri‐reforming of methane (TRM) is a promising technology which combines all the above‐mentioned processes for the simultaneous production of hydrogen and syngas with high energy efficiency.
Paulo A. L. de Souza, Raja Muhammad Afzal, Felipe Gomes Camacho, Nader Mahinpey   +3 more
wiley   +1 more source

A TLS-Motivated Non-Iterative Robust Square-Root Cubature Kalman Filter for Bearings-Only Tracking. [PDF]

Sensors (Basel)
Li C, Wu H, Zeng G, Yang M, Zhao Y, Mehmood A.   +5 more
europepmc   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

Longitudinal Sparse Single-Omics Factor Analysis for High-Dimensional Blood Biomarkers in Alzheimer's Disease. [PDF]

Stat Med
Zou H, Kaddurah-Daouk R, Luo S, Alzheimer's Disease Neuroimaging Initiative.   +3 more
europepmc   +1 more source