A multi-scale modelling framework to guide management of plant invasions in a transboundary context

Understanding the responses of materials of different sizes at various temperatures and strain rates is essential to evaluating the integrity and safety of microelectromechanical and nanoelectromechanical system devices under extreme loading conditions. Although material properties are size, rate, and temperature dependent in nature, little has been achieved in investigating the combined specimen size, loading rate, and temperature effects on the material properties. Based on the experimental and computational capabilities available, therefore, an attempt is made in this paper to formulate a hypersurface in spatial, temporal, and thermal domains to predict the combined size, rate, and temperature effects on the material properties of a tungsten crystalline block. It appears from the preliminary results that the proposed procedure might provide an effective means of bridging different spatial and temporal scales in a unified multi-scale modelling framework at different temperatures.

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A multi-scale modelling framework for anisotropy prediction in aluminium alloy sheet and its application in the optimisation of the deep-drawing process

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-07060-z ◽

2021 ◽

Author(s):

Wencheng Liu ◽

Yong Pang

Keyword(s):

Aluminium Alloy ◽

Deep Drawing ◽

Alloy Sheet ◽

Drawing Process ◽

Deep Drawing Process ◽

Multi Scale ◽

Modelling Framework ◽

Scale Modelling

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A theoretical framework for transitioning from patient-level to population-scale epidemiological dynamics: influenza A as a case study

Journal of The Royal Society Interface ◽

10.1098/rsif.2020.0230 ◽

2020 ◽

Vol 17 (166) ◽

pp. 20200230 ◽

Cited By ~ 4

Author(s):

W. S. Hart ◽

P. K. Maini ◽

C. A. Yates ◽

R. N. Thompson

Keyword(s):

Cohort Studies ◽

Influenza A ◽

Multi Scale ◽

Patient Level ◽

Modelling Framework ◽

Level Data ◽

Scale Models ◽

Scale Modelling ◽

Population Scale

Multi-scale epidemic forecasting models have been used to inform population-scale predictions with within-host models and/or infection data collected in longitudinal cohort studies. However, most multi-scale models are complex and require significant modelling expertise to run. We formulate an alternative multi-scale modelling framework using a compartmental model with multiple infected stages. In the large-compartment limit, our easy-to-use framework generates identical results compared to previous more complicated approaches. We apply our framework to the case study of influenza A in humans. By using a viral dynamics model to generate synthetic patient-level data, we explore the effects of limited and inaccurate patient data on the accuracy of population-scale forecasts. If infection data are collected daily, we find that a cohort of at least 40 patients is required for a mean population-scale forecasting error below 10%. Forecasting errors may be reduced by including more patients in future cohort studies or by increasing the frequency of observations for each patient. Our work, therefore, provides not only an accessible epidemiological modelling framework but also an insight into the data required for accurate forecasting using multi-scale models.

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