Constitutive model selection for unreinforced masonry cross sections based on best-fit analytical moment–curvature diagrams

2016 ◽  
Vol 111 ◽  
pp. 451-466 ◽  
Author(s):  
Fulvio Parisi ◽  
Giuseppe Sabella ◽  
Nicola Augenti
Keyword(s):  
Model Selection ◽  
Constitutive Model ◽  
Cross Sections ◽  
Unreinforced Masonry ◽  
Selection For ◽  
Best Fit

An exploratory framework for understanding electronic bill presentment and payment model selection

2000 ◽  
Vol 19 (4) ◽  
pp. 255-264
Author(s):  
Wenhong Luo ◽  
David Cook ◽  
Jimmie Joseph ◽  
Bopana Ganapathy
Keyword(s):  
Model Selection ◽  
Payment Model ◽  
Customer Interaction ◽  
Best Fit

Electronic bill presentment and payment (EBPP) provides an opportunity for firms to decrease their billing costs, while increasing their customer interaction. While many models exist, there is a dearth of information for determining which model would best fit customer characteristics and needs. This article examines the three primary models of EBPP, the characteristics of recurring bills, and customer concerns to develop an exploratory framework for determining which EBPP model a bill generating firm should deploy.

scholarly journals Optimization of Aerosol Model Selection for TROPOMI/S5P

2021 ◽  
Vol 13 (13) ◽  
pp. 2489
Author(s):  
Lanlan Rao ◽  
Jian Xu ◽  
Dmitry S. Efremenko ◽  
Diego G. Loyola ◽  
Adrian Doicu
Keyword(s):  
Model Selection ◽  
Insufficient Information ◽  
Satellite Measurements ◽  
Model Uncertainties ◽  
Bayesian Algorithm ◽  
Layer Height ◽  
Aerosol Model ◽  
Selection For ◽  
Temporal Inhomogeneity ◽  
Aerosol Properties

To retrieve aerosol properties from satellite measurements, micro-physical aerosol models have to be assumed. Due to the spatial and temporal inhomogeneity of aerosols, choosing an appropriate aerosol model is an important task. In this paper, we use a Bayesian algorithm that takes into account model uncertainties to retrieve the aerosol optical depth and layer height from synthetic and real TROPOMI O2A band measurements. The results show that in case of insufficient information for an appropriate micro-physical model selection, the Bayesian algorithm improves the accuracy of the solution.

Multiple order model migration and optimal model selection for online glucose prediction in Type 1 diabetes

AIChE Journal ◽  
2017 ◽  
Vol 64 (3) ◽  
pp. 822-834 ◽  
Author(s):  
Hong Zhao ◽  
Chunhui Zhao ◽  
Chengxia Yu ◽  
Eyal Dassau
Keyword(s):  
Type 1 Diabetes ◽  
Model Selection ◽  
Optimal Model ◽  
Order Model ◽  
Model Migration ◽  
Selection For ◽  
Glucose Prediction
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