No abstract

No abstract

A membrane, or P system, is a biologically inspired computational modelling paradigm that simulates both the structure and dynamical processes of a cellular mechanism.The computational power of a membrane system is derived from the non-deterministic nature and the inherent parallelism of these structures and processes. Recently a number of researchers have tried to utilise this powerful computational paradigm to solve complex problems. Currently, parallelisation in practical implementations of this paradigm use a SIMD (Single Instruction Multiple Data) type approach, normally focusing on a specific aspect of the P system structure and applying this to the rest of the system in a parallel manner; in a few cases the rule selection algorithm has been parallelised for this purpose, the rules themselves being applied in a traditional sequential manner. In this paper we propose that a MIMD (Multiple InstructionMultiple Data) architecture is a closer representation of the biological membrane/P system structure and allows a degree of parallelism that is not possible using SIMD type approaches. We identify the elements of the membrane system that can be parallelised and also demonstrate how these elements can be parallelised using a MIMD approach. We examine how the XMOS XSI Simulator, which has an architecture suited to MIMD, can be used to implement a Numerical P system. Furthermore we suggest that the temporal aspects of cellular aging may be simulated by a simple extension to the standard P system model.

IMS Learning Design (IMS LD) is a specification for describing a range of pedagogic approaches. It allows the linking of pedagogical structure, content, and services, whilst keeping the three separate, thus providing the potential for reuse as well as forming the basis for interoperability between learning activities and services. As such, this specification promises unprecedented opportunities to build effective tutor support and presence into e-learning systems. The tools that implement the specification have primarily been used for research purposes and have not been targeted at teaching practitioners or learners working in teaching and learning situations. There is a perception amongst practitioners and tool developers that the specification and tools are too technical or difficult for practitioner use. This chapter examines practitioner use of current tools for creating IMS LD and the use of IMS LD units of learning (UoLs) with learners through projects being undertaken at Liverpool Hope University (LHU). It presents some of the experiences and findings gained from these projects. The chapter also examines current technologies and tools for creating and running IMS LD UoLs, and finally discusses the potential and future for IMS LD.

This paper builds on previous research into the development of the TMap3D system which allows for the creation of historical 3D spatiotemporal visualisations from natural language narratives [1]. This paper addresses the use of deductive 3D modelling techniques to generate the realism of the object behaviours, the environmental conditions and their interactions within the visualisation. The authors define deductive 3D modelling techniques as follows: (i) Given an object and its specification the Vist3D system (formally TMap3D) can deduce the object's visualised behaviour in response to a given "real world" instruction from the natural language narrative. (ii) The Vist3D system can deduce visualised environmental conditions from the natural language narrative. (iii) The Vist3D system can deduce the interactions between the objects (e.g. ship) and the environmental conditions (e.g. rough sea) to generate, for example, a ship rolling in rough seas.