SimuMap: a computational system for spatial modelling

The paper presents a computational system based upon formal principles to run spatial models for environmental processes. The simulator is named SimuMap because it is typically used to simulate spatial processes over a mapped representation of terrain. A model is formally represented in SimuMap as a set of coupled sub-models. The paper considers the situation where spatial processes operate at different time levels, but are still integrated. An example of such a situation commonly occurs in watershed hydrology where overland flow and stream channel flow have very different flow rates but are highly related as they are subject to the same terrain runoff processes. SimuMap is able to run a network of sub-models that express different time-space derivatives for water flow processes. Sub-models may be coded generically with a map algebra programming language that uses a surface data model. To address the problem of differing time levels in simulation, the paper: (i) reviews general approaches for numerical solvers, (ii) considers the constraints that need to be enforced to use more adaptive time steps in discrete time specified simulations, and (iii) scaling transfer rates in equations that use different time bases for time-space derivatives. A multistep scheme is proposed for SimuMap. This is presented along with a description of its visual programming interface, its modelling formalisms and future plans. (C) 2003 Elsevier Ltd. All rights reserved.

Neuromuscular-skeletal constraints on the acquisition of skill in a discrete torque production task

The organisation of the human neuromuscular-skeletal system allows an extremely wide variety of actions to be performed, often with great dexterity. Adaptations associated with skill acquisition occur at all levels of the neuromuscular-skeletal system although all neural adaptations are inevitably constrained by the organisation of the actuating apparatus (muscles and bones). We quantified the extent to which skill acquisition in an isometric task set is influenced by the mechanical properties of the muscles used to produce the required actions. Initial performance was greatly dependent upon the specific combination of torques required in each variant of the experimental task. Five consecutive days of practice improved the performance to a similar degree across eight actions despite differences in the torques required about the elbow and forearm. The proportional improvement in performance was also similar when the actions were performed at either 20 or 40% of participants' maximum voluntary torque capacity. The skill acquired during practice was successfully extrapolated to variants of the task requiring more torque than that required during practice. We conclude that while the extent to which skill can be acquired in isometric actions is independent of the specific combination of joint torques required for target acquisition, the nature of the kinetic adaptations leading to the performance improvement in isometric actions is influenced by the neural and mechanical properties of the actuating muscles.

Anisotropy-based robust performance analysis of finite horizon linear discrete time varying systems

We consider a problem of robust performance analysis of linear discrete time varying systems on a bounded time interval. The system is represented in the state-space form. It is driven by a random input disturbance with imprecisely known probability distribution; this distributional uncertainty is described in terms of entropy. The worst-case performance of the system is quantified by its a-anisotropic norm. Computing the anisotropic norm is reduced to solving a set of difference Riccati and Lyapunov equations and a special form equation.

Continuous action system refinement

Action systems are a framework for reasoning about discrete reactive systems. Back, Petre and Porres have extended these action systems to continuous action systems, which can be. used to model hybrid systems. In this paper we define a refinement relation, and develop practical data refinement rules for continuous action systems. The meaning of continuous action systems is expressed in terms of a mapping from continuous action systems to action systems. First, we present a new mapping from continuous act ion systems to action systems, such that Back's definition of trace refinement is correct with respect to it. Second, we present a stream semantics that is compatible with the trace semantics, but is preferable to it because it is more general. Although action system trace refinement rules are applicable to continuous action systems with a stream semantics, they are not complete. Finally, we introduce a new data refinement rule that is valid with respect to the stream semantics and can be used to prove refinements that are not possible in the trace semantics, and we analyse the completeness of our new rule in conjunction with the existing trace refinement rules.