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The main objective of this work is to develop a novel integrated experimental and computational approach, utilising a microalgal strain grown at bench-scale, with the aim to systematically identify the conditions that optimise growth and lipid production, in order to ultimately develop a cost-effective process to improve the system economic viability and overall sustainability.
Here we develop a novel integrated modeling approach with uniform design (UD), a machine learning approach of relevance vector machine (RVM) and a global searching algorithm of accelerating genetic algorithm (AGA) to optimize the operation of multi-variable MFCs after they are constructed.
Herein, we develop a novel integrated strategy for the preparation of theranostic chitosan microcapsules by encapsulating ion liquids (ILs) and Fe3O4 nanoparticles.
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To integrate the abnormal metabolite concentrations with the data available from previous studies, the authors [ 4] developed a novel integrated metabolome and interactome mapping (iMIM) approach.
To reduce the time and cost of moulds fabrication, a novel integrated developing and manufacturing system of rapid tooling (RT) based on rapid prototyping (RP) is proposed.
Accordingly, a novel integrated cellular and molecular approach of EpCAM-independent subtraction enrichment (SE) and immunostaining-FISH (iFISH®) has recently been successfully developed.
Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO.
In this paper, we develop a novel framework which integrates QFD and DSM to support product design planning, including product architecture/components deployment, design scheduling and costing.
To tackle these problems, this study develops a novel approach integrated with some graph-based heuristic working rules, robust back-propagation neural network (BPNN) engines via Taguchi method and design of experiment (DOE), and a knowledge-based engineering (KBE) system to assist the assembly engineers in promptly predicting a near-optimal assembly sequence.
The objective of this study is to develop a novel approach that integrates prior biological knowledge in the form of the Ontology Fingerprint to infer cell-type-specific signaling networks via data-driven Bayesian network learning; and to further use the trained model to predict cellular responses.
To this end, we develop a novel analytical framework that integrates our domain binding potentials with additional sources of data.
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