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The new economic modelling framework developed for this analysis enables estimation of the effects of changes in fruit and vegetable consumption, physical activity, alcohol consumption and smoking into long-term costs and QALYs, and as such could be used to evaluate a broad range of public health interventions.
The predictive modelling framework developed is comprised of material and energy balances, constraint parameters, constitutive equations, design/optimization variables and possible polymer synthesis techniques.
We expect the conceptual modelling framework developed here to be applicable to many infectious disease with free-living pathogens in the environment beyond the specific disease system of human schistosomiasis considered here.
FLAME (Flexible Large-scale Agent Modelling Environment) [8] (http://www.flame.ac.uk) is an agent-based modelling framework developed at the University of Sheffield and has previously been used to model the emergent behaviour of various biological systems (e.g. keratinocyte colony formation [5] and urothelial cells [10]).
The modelling framework developed here is, to our knowledge, unique in its consideration of stochastic root system growth, maintenance and competition in heterogeneous environments.
The conceptual and mathematical modelling framework developed provides a valuable tool for the assessment and optimisation of the lignocellulosic bioethanol supply chain.
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This study builds on the Projected Land Use and Transportation (PLUTO) modeling framework developed by Stone et al. (2007).
Our spatiotemporal exposure assessment approach was based on a land use regression (LUR) modeling framework developed in the European Study of Cohorts for Air Pollution Effects (ESCAPE) framework (Beelen et al. 2013; Cyrys et al. 2012; Eeftens et al. 2012a, 2012b).
We utilize cointegration and error correction model framework developed by Engle Granger (1987) and Gregory Hansen (1996) in the analysis.
The basic framework used in this approach could be applied in other countries, and is similar in several respects to the Non-Energy use Emission Accounting Tables (NEAT) model framework developed independently and described by other authors in this issue.
We use the results from a coupled hydrological climate model framework developed by linking a global LSM that accounts for human land water management activities with its parent GCM to examine the extent of direct human influence on freshwater systems and climate over large scales.
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