scholarly journals Addressing the need for just GeoHealth engagement: Evolving models for actionable research that transform communities

GeoHealth ◽  
2021 ◽  
Author(s):  
Claire M. Hayhow ◽  
Dan J. Brabander ◽  
Rebecca Jim ◽  
Martin Lively ◽  
Gabriel M. Filippelli
Keyword(s):  
Evolving Models

Evolving models of how housing can support recovery

2003 ◽  
Author(s):  
K. Scrimenti ◽  
B. Kloos
Keyword(s):  
Support Recovery ◽  
Evolving Models

scholarly journals Surveying the evolving models of digital publishing: where does Pharma fit?

2017 ◽  
Author(s):  
Amy Williams ◽  
Christopher Winchester ◽  
Lucy Robinson ◽  
Tim Koder ◽  
Christopher Rains ◽  
...  
Keyword(s):  
Digital Publishing ◽  
Evolving Models

STORM SURGE FORECAST MODEL USING GENETIC PROGRAMMING

2020 ◽  
pp. 75-89
Author(s):  
Quyen
Keyword(s):  
Genetic Programming ◽  
Storm Surge ◽  
Storm Surges ◽  
Forecast Model ◽  
Data Sets ◽  
Model Learning ◽  
Forecasting Models ◽  
Accurate Forecast ◽  
Evolving Models ◽  
Storm Surge Forecasting

Stormsurge is a typical genuine fiasco coming from the ocean. Therefore, an accurate forecast of surges is a vital assignment to dodge property misfortunes and decrease the chance of tropical storm surges. Genetic Programming (GP) is an evolution-based model learning technique that can simultaneously find the functional form and the numeric coefficients for the model. Moreover, GP has been widely applied to build models for predictive problems. However, GP has seldom been applied to the problem of storm surge forecasting. In this paper, a new method to use GP for evolving models for storm surge forecasting is proposed. Experimental results on data-sets collected from the Tottori coast of Japan show that GP can become more accurate storm surge forecasting models than other standard machine learning methods. Moreover, GP can automatically select relevant features when evolving storm surge forecasting models, and the models developed by GP are interpretable.

Some Salient Features of Evolving Models of Interstellar Clouds

1989 ◽  
pp. 345-355 ◽  
Author(s):  
S. P. Tarafdar ◽  
S. K. Ghosh ◽  
K. R. Heere ◽  
S. S. Prasad
Keyword(s):  
Interstellar Clouds ◽  
Salient Features ◽  
Evolving Models

scholarly journals Evolutionarily stable investments in recognition systems explain patterns of discrimination failure and success

2020 ◽  
Vol 375 (1802) ◽  
pp. 20190465 ◽  
Author(s):  
Michael J. Sheehan ◽  
H. Kern Reeve
Keyword(s):  
Signal Detection Theory ◽  
Social Insect ◽  
Experimental Tests ◽  
Theory Model ◽  
The Other ◽  
Theme Issue ◽  
Brood Parasites ◽  
Recognition Systems ◽  
Evolving Models ◽  
Evolutionarily Stable

Many animals are able to perform recognition feats that astound us—such as a rodent recognizing kin it has never met. Yet in other contexts, animals appear clueless as when reed warblers rear cuckoo chicks that bear no resemblance to their own species. Failures of recognition when it would seem adaptive have been especially puzzling. Here, we present a simple tug-of-war game theory model examining how individuals should optimally invest in affecting the accuracy of discrimination between desirable and undesirable recipients. In the game, discriminating individuals (operators) and desirable and undesirable recipients (targets and mimics, respectively) can all invest effort into their own preferred outcome. We demonstrate that stable inaccurate recognition will arise when undesirable recipients have large fitness gains from inaccurate recognition relative to the pay-offs that the other two parties receive from accurate recognition. The probability of accurate recognition is often determined by just the relative pay-offs to the desirable and undesirable recipients, rather than to the discriminator. Our results provide a new lens on long-standing puzzles including a lack of nepotism in social insect colonies, tolerance of brood parasites and male birds caring for extra-pair young in their nests, which our model suggests should often lack accurate discrimination. This article is part of the theme issue ‘Signal detection theory in recognition systems: from evolving models to experimental tests'.

scholarly journals Youth crime and justice: Key messages from the Edinburgh Study of Youth Transitions and Crime

2010 ◽  
Vol 10 (2) ◽  
pp. 179-209 ◽  
Author(s):  
Lesley McAra ◽  
Susan McVie
Keyword(s):  
Evidence Base ◽  
Exact Science ◽  
Youth Crime ◽  
Youth Justice ◽  
Policy Makers ◽  
At Risk Children ◽  
Social Adversity ◽  
The Uk ◽  
Evolving Models ◽  
Youth Transitions

Based on findings from the Edinburgh Study of Youth Transitions and Crime, this article challenges the evidence-base which policy-makers have drawn on to justify the evolving models of youth justice across the UK (both in Scotland and England/Wales). It argues that to deliver justice, systems need to address four key facts about youth crime: serious offending is linked to a broad range of vulnerabilities and social adversity; early identification of at-risk children is not an exact science and runs the risk of labelling and stigmatizing; pathways out of offending are facilitated or impeded by critical moments in the early teenage years, in particular school exclusion; and diversionary strategies facilitate the desistance process.The article concludes that the Scottish system should be better placed than most other western systems to deliver justice for children (due to its founding commitment to decriminalization and destigmatization). However, as currently implemented, it appears to be failing many young people.

Book Review: Evolving Models for Family Change

1989 ◽  
Vol 34 (6) ◽  
pp. 622-623
Author(s):  
Clark Falconer
Keyword(s):  
Family Change ◽  
Evolving Models

scholarly journals Finding a signal hidden among noise: how can predators overcome camouflage strategies?

2020 ◽  
Vol 375 (1802) ◽  
pp. 20190478 ◽  
Author(s):  
James A. M. Galloway ◽  
Samuel D. Green ◽  
Martin Stevens ◽  
Laura A. Kelley
Keyword(s):  
Visual Processing ◽  
Signal Detection Theory ◽  
Experimental Tests ◽  
The Past ◽  
Substantial Progress ◽  
Recognition Systems ◽  
The Relationship ◽  
Evolving Models ◽  
Made In ◽  
Detection And Recognition

Substantial progress has been made in the past 15 years regarding how prey use a variety of visual camouflage types to exploit both predator visual processing and cognition, including background matching, disruptive coloration, countershading and masquerade. By contrast, much less attention has been paid to how predators might overcome these defences. Such strategies include the evolution of more acute senses, the co-opting of other senses not targeted by camouflage, changes in cognition such as forming search images, and using behaviours that change the relationship between the cryptic individual and the environment or disturb prey and cause movement. Here, we evaluate the methods through which visual camouflage prevents detection and recognition, and discuss if and how predators might evolve, develop or learn counter-adaptations to overcome these. This article is part of the theme issue ‘Signal detection theory in recognition systems: from evolving models to experimental tests'.

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