The Visualisation of Spatial Structures with Computer Techniques

2009 ◽  
Author(s):  
P. Huybers
Keyword(s):  
Spatial Structures

Discrimination of Hidden Spatial Structures by Pigeons

2010 ◽  
Author(s):  
Allison A. Cook ◽  
Robert G. Cook
Keyword(s):  
Spatial Structures

RECONSTRUCTION OF NON-STATIONARY COMPLEX SPATIAL STRUCTURES BY A NOVEL FILTER-BASED MULTI SCALE MPS ALGORITHM

2014 ◽  
Vol 2014 (2) ◽  
pp. 60-71
Author(s):  
Peyman Mohammadmoradi ◽  
◽  
Mohammad Rasaeii ◽  
Keyword(s):  
Spatial Structures ◽  
Multi Scale

Exploring the Molecular Mechanism of Action Between Drug and RNA Polymerase Based on Partially-Resolved Spatial Structures

2013 ◽  
Vol 9 (4) ◽  
pp. 547-555 ◽  
Author(s):  
Yajun Sheng ◽  
Chen Zhang ◽  
Xingye Qiu ◽  
Wei Zheng ◽  
Jishou Ruan ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Molecular Mechanism ◽  
Mechanism Of Action ◽  
Spatial Structures ◽  
Molecular Mechanism Of Action

From cities to networks: Power rules

2021 ◽  
pp. 1468795X2110220
Author(s):  
Manuel Castells
Keyword(s):  
Social Organization ◽  
Communication Networks ◽  
Human Activities ◽  
Social Life ◽  
Power Relations ◽  
Digital Communication ◽  
Spatial Structures ◽  
Digital Communication Networks

Power relations are the source of social organization and institutions. This has been observed and theorized by the author in relation to various realms of social life, such as the formation of spatial structures and the networking of human activities around digital communication networks.

scholarly journals Competing risks joint models using R-INLA

2021 ◽  
Vol 21 (1-2) ◽  
pp. 56-71
Author(s):  
Janet van Niekerk ◽  
Haakon Bakka ◽  
Håvard Rue
Keyword(s):  
Competing Risks ◽  
Count Data ◽  
Joint Model ◽  
Point Of View ◽  
Spatial Structures ◽  
Joint Models ◽  
Hazard Functions ◽  
Longitudinal Count Data ◽  
Non Gaussian ◽  
Made In

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this article, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, times-dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model as specific examples.

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