Results 211 to 220 of about 73,753 (309)

Multivariate phase‐dependent optimization of bioprocesses boosts performance and quality—Why timing (of exposure) matters

Biotechnology Progress, EarlyView.
Abstract Applying a single parameter set to describe complex mammalian kinetics often is too simplistic, as it fails to capture sensitive cell‐to‐environment interactions that may be exploited to optimize production performance. To resolve this time dependency, intra‐experimental parameter shifts as part of design of dynamic experiments (DoDE) can be ...
Samuel Kienzle, Lisa Junghans, Anja Wittmann, Stefan Wieschalka, Ralf Takors, Nicole Erika Radde, Beate Presser, Verena Nold   +7 more
wiley   +1 more source

Enhancing IoT security through cloud-assisted post-quantum authentication: a case study with UOV signatures. [PDF]

Sci Rep
Maned VR, Rath S, Ramalingam J, Kuppusamy L.   +3 more
europepmc   +1 more source

In‐line Raman spectroscopy real‐time glucose prediction method for commercial pneumococcal vaccine drug substance fermentation manufacturing process control

Biotechnology Progress, EarlyView.
Abstract Traditional off‐line analysis methods to quantify residual metabolite concentrations in a drug substance fermentation process consume valuable time, require costly resources, and demand potentially hazardous manual operations when working with pathogenic biological organisms.
Griffin P. Thomas, Brian C. Marks, Barbara B. Alvarado‐Hernandez, Matthew Woodling, James O'Connor, Roberto Ortiz, John Higgins, Jon Shanter   +7 more
wiley   +1 more source

Mechanistic deconvolution of BSA size variants by constrained Raman pseudo‐Voigt hard modeling during anion‐exchange chromatography

Biotechnology Progress, EarlyView.
Abstract In biopharmaceutical manufacturing, protein aggregation is a critical quality attribute, necessitating rapid and reliable analytical strategies during downstream processes like anion‐exchange chromatography (AEX). While Raman spectroscopy enables continuous monitoring of protein secondary structure, standard data‐driven regression models ...
Jakob Heyer‐Müller, Robin Schiemer, Lars Robbel, Michael Schmitt, Jürgen Hubbuch   +4 more
wiley   +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

Matrix-Product State Skeletons in Onsager-Integrable Quantum Chains. [PDF]

J Stat Phys
Camp I, Jones NG.
europepmc   +1 more source

A [3]Rotaxane Containing {Ti7Ga} Rings Linking CuII: Synthesis, Structure, and Spectroscopic Studies

Chemistry – A European Journal, EarlyView.
Extended hybrid inorganic‐organic [2]‐ and [3]‐rotaxanes are reported based on heterometallic rings with threads that link CuII complexes; the crystal structures are reported, and the solution behavior is investigated by double electron electron resonance spectroscopy methods.
Selena J. Lockyer, Lubomir Loci, George F. S. Whitehead, Inigo J. Vitorica‐Yrezabal, Grigore A. Timco, Alice M. Bowen, Eric J. L. McInnes, Richard E. P. Winpenny   +7 more
wiley   +1 more source

Polynomial-time algorithms for the factorization of polynomials

Bull. EATCS, 1984
openaire   +2 more sources

3D Perception-Based Adaptive Point Cloud Simplification and Slicing for Soil Compaction Pit Volume Calculation. [PDF]

Sensors (Basel)
Han C, Wei J, Shen T, Guo C.
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