Modelling: promoting creativity whilst forging links between science education and design and technology education

Educational reforms in many countries currently call for the development of knowledge-based societies. In particular, emphasis is placed on the promotion of creativity, especially in the areas of science education and of design and technology education. In this paper, perceptions of the nature of creativity and of the conditions for its realization are discussed. The notion of modelling as a creative act is outlined and the scope for using modelling as a bridge between science education and design and technology education explored. A model for the creative act of modelling is proposed and its major aspects elaborated upon. Finally, strategies for forging links between the two subjects are outlined.

Generalized matching networks and their properties

In this paper, we introduce two kinds of graphs: the generalized matching networks (GMNs) and the recursive generalized matching networks (RGMNs). The former generalize the hypercube-like networks (HLNs), while the latter include the generalized cubes and the star graphs. We prove that a GMN on a family of k-connected building graphs is -connected. We then prove that a GMN on a family of Hamiltonian-connected building graphs having at least three vertices each is Hamiltonian-connected. Our conclusions generalize some previously known results.

Developments in block copolymer science and technology

Focuses on recent advances in research on block copolymers, covering chemistry (synthesis), physics (phase behaviors, rheology, modeling), and applications (melts and solutions). Written by a team of internationally respected scientists from industry and academia, this text compiles and reviews the expanse of research that has taken place over the last five years into one accessible resource. Ian Hamley is the world-leading scientist in the field of block copolymer research Presents the recent advances in the area, covering chemistry, physics and applications. Provides a broad coverage from synthesis to fundamental physics through to applications Examines the potential of block copolymers in nanotechnology as self-assembling soft materials

Computer generation and quantitative morphometric analysis of virtual neurons

An important goal in computational neuroanatomy is the complete and accurate simulation of neuronal morphology. We are developing computational tools to model three-dimensional dendritic structures based on sets of stochastic rules. This paper reports an extensive, quantitative anatomical characterization of simulated motoneurons and Purkinje cells. We used several local and global algorithms implemented in the L-Neuron and ArborVitae programs to generate sets of virtual neurons. Parameters statistics for all algorithms were measured from experimental data, thus providing a compact and consistent description of these morphological classes. We compared the emergent anatomical features of each group of virtual neurons with those of the experimental database in order to gain insights on the plausibility of the model assumptions, potential improvements to the algorithms, and non-trivial relations among morphological parameters. Algorithms mainly based on local constraints (e.g., branch diameter) were successful in reproducing many morphological properties of both motoneurons and Purkinje cells (e.g. total length, asymmetry, number of bifurcations). The addition of global constraints (e.g., trophic factors) improved the angle-dependent emergent characteristics (average Euclidean distance from the soma to the dendritic terminations, dendritic spread). Virtual neurons systematically displayed greater anatomical variability than real cells, suggesting the need for additional constraints in the models. For several emergent anatomical properties, a specific algorithm reproduced the experimental statistics better than the others did. However, relative performances were often reversed for different anatomical properties and/or morphological classes. Thus, combining the strengths of alternative generative models could lead to comprehensive algorithms for the complete and accurate simulation of dendritic morphology.

PID autotuning using neural networks and model reference adaptive control

This paper describes the application of artificial neural networks for automatic tuning of PID controllers using the Model Reference Adaptive Control approach. The effectiveness of the proposed method is shown through a simulated application.