title: Selective decay and coherent vortices in two-dimensional incompressible turbulence

authors: Matthaeus, William H.

subjects: turbulence

abstract: Numerical solution of two-dimensional incompressible hydrodynamics shows that states of a near-minimal ratio of enstrophy to energy can be attained in times short compared with the flow decay time, confirming the simplest turbulent selective decay conjecture, and suggesting that coherent vortex structures do not terminate nonlinear processes. After all possible vortex mergers occur, the vorticity attains a particlelike character, suggested by the late-time similarity of the streamlines to Ewald potential contours.

text: Selective decay and coherent vortices in two-dimensional incompressible turbulence Numerical solution of two-dimensional incompressible hydrodynamics shows that states of a near-minimal ratio of enstrophy to energy can be attained in times short compared with the flow decay time, confirming the simplest turbulent selective decay conjecture, and suggesting that coherent vortex structures do not terminate nonlinear processes. After all possible vortex mergers occur, the vorticity attains a particlelike character, suggested by the late-time similarity of the streamlines to Ewald potential contours.

year: 1991

title: Sensitivity of GIS-derived terrain variables at multiple scales for modelling stoat (Mustela erminea) activity

authors: Martin, R.D.

subjects: terrain

abstract: This research combines automated Geographical Information Systems (GIS) and iterative logistic regression scripting to data-mine for terrain variables that predict stoat (Mustela erminea) visitation to tracking-stations in a New Zealand indigenous forest and explore the impact of spatial analysis scale on modelling outcomes. Variables such as curvature and density of tracks are dependent on the scale of the analysis window used to compute the values. With automated GIS analysis it is possible to derive a large number of terrain parameters that vary in computational scale. It is common for analysts to make nominal choices regarding the size and type of analysis windows when calculating these derived variables, without testing the statistical validity of that choice. Stoats are a significant pest in New Zealand and threaten extinction for a number of vulnerable native species. Field data on stoat activity, based on footprint tracking tunnels, were used to develop an Akaike Information Criterion (AIC) optimised predictive model applying stepwise model selection. Once the optimal model was determined, the sensitivity of the model to different terrain parameters was tested by systematically substituting each variable and calculating the difference this made to the model equation. The most dominant terrain predictors influencing stoat visitation were proximity to tracks, altitude, northerly and easterly aspect, mean curvature, and topographical position and slope. Proximity to tracks and mean curvature were the most sensitive variables to analysis scale. This paper demonstrates the importance of considering scale in developing predictive models and the need to test many ecologically sensible analysis scales in order to find the best predictive variables. The paper concludes that GIS-based spatial data extraction, combined with automated statistical data mining methods, has an important role in developing accurate animal activity models.

text: Sensitivity of GIS-derived terrain variables at multiple scales for modelling stoat (Mustela erminea) activity This research combines automated Geographical Information Systems (GIS) and iterative logistic regression scripting to data-mine for terrain variables that predict stoat (Mustela erminea) visitation to tracking-stations in a New Zealand indigenous forest and explore the impact of spatial analysis scale on modelling outcomes. Variables such as curvature and density of tracks are dependent on the scale of the analysis window used to compute the values. With automated GIS analysis it is possible to derive a large number of terrain parameters that vary in computational scale. It is common for analysts to make nominal choices regarding the size and type of analysis windows when calculating these derived variables, without testing the statistical validity of that choice. Stoats are a significant pest in New Zealand and threaten extinction for a number of vulnerable native species. Field data on stoat activity, based on footprint tracking tunnels, were used to develop an Akaike Information Criterion (AIC) optimised predictive model applying stepwise model selection. Once the optimal model was determined, the sensitivity of the model to different terrain parameters was tested by systematically substituting each variable and calculating the difference this made to the model equation. The most dominant terrain predictors influencing stoat visitation were proximity to tracks, altitude, northerly and easterly aspect, mean curvature, and topographical position and slope. Proximity to tracks and mean curvature were the most sensitive variables to analysis scale. This paper demonstrates the importance of considering scale in developing predictive models and the need to test many ecologically sensible analysis scales in order to find the best predictive variables. The paper concludes that GIS-based spatial data extraction, combined with automated statistical data mining methods, has an important role in developing accurate animal activity models.

year: 2010

title: The relevance of discourse analysis to the teaching and learning of te reo Māori: A text-centred example

authors: Greensill, Hine-iti-moana

subjects: Māori language

abstract: In this paper, I analyse a single text written in te reo Māori in terms of two different approaches derived from discourse analysis (discourse macro-patterning and discourse relations and their signalling) in order to demonstrate the importance of deriving a discourse-centred methodology for teaching learners of Māori what is involved in interpreting and creating coherent discourses.

text: The relevance of discourse analysis to the teaching and learning of te reo Māori: A text-centred example In this paper, I analyse a single text written in te reo Māori in terms of two different approaches derived from discourse analysis (discourse macro-patterning and discourse relations and their signalling) in order to demonstrate the importance of deriving a discourse-centred methodology for teaching learners of Māori what is involved in interpreting and creating coherent discourses.

year: 2007