scholarly journals RCaN: a software for Chance and Necessity modelling

2021 ◽  
Author(s):  
Hilaire Drouineau ◽  
Benjamin Planque ◽  
Christian Mullon
Keyword(s):  
Domain Knowledge ◽  
Computationally Efficient ◽  
Network Modelling ◽  
Participatory Modelling ◽  
Trophic Network ◽  
Biological Laws ◽  
Marine System ◽  
Meta Information ◽  
Fundamental Physical ◽  
Modelling Approach

Uncertainty is a challenge in modelling ecological systems and has been a source of misunderstandings between modelers and non-modelers. The "Chance and Necessity" (CaN) modelling approach has been proposed to address this issue, in the case of trophic network modelling. CaN modelling focuses exploring food-web trajectories that can satisfy fundamental physical and biological laws, while being compatible with observations and domain knowledge. This type of approach can facilitate discussion among actors as it promotes sharing of information and does not presuppose any knowledge of modelling practices. It is therefore suitable for participatory modelling , i.e. a modelling approach in which different actors can confront their knowledge and understanding of the marine system and of the associated uncertainties. One important ingredient to achieve participatory modelling is the availability of a modelling platform that is efficient, fast and transparent, so that all actors can understand and follow the modelling steps and choices, and can rapidly visualize and discuss the results. But, until now, there existed no software to easily perform CaN modelling. Here, we present RCaN and RCaNconstructor. Combined, these provide the first tool to build CaN models in an intuitive way that is 1) suitable within participatory frameworks, 2) transparent, 4) computationally efficient, 5) fully documented through the provision of meta-information and 6) supportive of exploratory analyses through predefined graphical functions.

scholarly journals Macro-Modelling of Laser Micro-Joints for Understanding Joint Strength in Electric Vehicle Battery Interconnects

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3552
Author(s):  
Abhishek Das ◽  
Richard Beaumont ◽  
Iain Masters ◽  
Paul Haney
Keyword(s):  
Joint Strength ◽  
Structural Model ◽  
Design Stage ◽  
Structural Modelling ◽  
Laser Weld ◽  
Computationally Efficient ◽  
Early Design Stage ◽  
Lap Shear ◽  
Battery Module ◽  
Modelling Approach

Laser micro-welding is increasingly being used to produce electrically conductive joints within a battery module of an automotive battery pack. To understand the joint strength of these laser welds at an early design stage, micro-joints are required to be modelled. Additionally, structural modelling of the battery module along with the electrical interconnects is important for understanding the crash safety of electric vehicles. Fusion zone based micro-modelling of laser welding is not a suitable approach for structural modelling due to the computational inefficiency and the difficulty of integrating with the module model. Instead, a macro-model which computationally efficient and easy to integrate with the structural model can be useful to replicate the behaviour of the laser weld. A macro-modelling approach was adopted in this paper to model the mechanical behaviour of laser micro-weld. The simulations were based on 5 mm diameter circular laser weld and developed from the experimental data for both the lap shear and T-peel tests. This modelling approach was extended to obtain the joint strengths for 3 mm diameter circular seams, 5 mm and 10 mm linear seams. The predicted load–displacement curves showed a close agreement with the test data.

scholarly journals A Participatory Modelling approach for enabling Nature-based Solutions implementation through Networking Interventions

2020 ◽  
Author(s):  
Raffaele Giordano ◽  
María Mañez Costa ◽  
Alessandro Pagano ◽  
Irene Pluchinotta ◽  
Pedro Zorrilla-Miras ◽  
...  
Keyword(s):  
Participatory Modelling ◽  
Modelling Approach

scholarly journals Distributed electrical network modelling approach for spatially resolved characterisation of photovoltaic modules

2014 ◽  
Vol 8 (5) ◽  
pp. 459-466 ◽  
Author(s):  
Xiaofeng Wu ◽  
Martin Bliss ◽  
Archana Sinha ◽  
Tom Betts ◽  
Rajesh Gupta ◽  
...  
Keyword(s):  
Electrical Network ◽  
Network Modelling ◽  
Photovoltaic Modules ◽  
Spatially Resolved ◽  
Modelling Approach

scholarly journals Voxel-wise and spatial modelling of binary lesion masks: A review and comparison of methods

2021 ◽  
Author(s):  
Petya Kindalova ◽  
Ioannis Kosmidis ◽  
Thomas E. Nichols
Keyword(s):  
Maximum Likelihood ◽  
Spatial Dependence ◽  
Reference Data ◽  
Spatial Modelling ◽  
Maximum Likelihood Estimates ◽  
Data Sets ◽  
Computationally Efficient ◽  
Data Set ◽  
Lesion Mapping ◽  
Modelling Approach

AbstractObjectivesWhite matter lesions are a very common finding on MRI in older adults and their presence increases the risk of stroke and dementia. Accurate and computationally efficient modelling methods are necessary to map the association of lesion incidence with risk factors, such as hypertension. However, there is no consensus in the brain mapping literature whether a voxel-wise modelling approach is better for binary lesion data than a more computationally intensive spatial modelling approach that accounts for voxel dependence.MethodsWe review three regression approaches for modelling binary lesion masks including massunivariate probit regression modelling with either maximum likelihood estimates, or mean bias-reduced estimates, and spatial Bayesian modelling, where the regression coefficients have a conditional autoregressive model prior to account for local spatial dependence. We design a novel simulation framework of artificial lesion maps to compare the three alternative lesion mapping methods. The age effect on lesion probability estimated from a reference data set (13,680 individuals from the UK Biobank) is used to simulate a realistic voxel-wise distribution of lesions across age. To mimic the real features of lesion masks, we suggest matching brain lesion summaries (total lesion volume, average lesion size and lesion count) across the reference data set and the simulated data sets. Thus, we allow for a fair comparison between the modelling approaches, under a realistic simulation setting.ResultsOur findings suggest that bias-reduced estimates for voxel-wise binary-response generalized linear models (GLMs) overcome the drawbacks of infinite and biased maximum likelihood estimates and scale well for large data sets because voxel-wise estimation can be performed in parallel across voxels. Contrary to the assumption of spatial dependence being key in lesion mapping, our results show that voxel-wise bias-reduction and spatial modelling result in largely similar estimates.ConclusionBias-reduced estimates for voxel-wise GLMs are not only accurate but also computationally efficient, which will become increasingly important as more biobank-scale neuroimaging data sets become available.

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