Grey-box radial basis function modelling: the art of incorporating prior knowledge

A basic principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process. We adopt this principle and consider grey-box radial basis function (RBF) modelling capable of incorporating prior knowledge. Specifically, we show how to explicitly incorporate the two types of prior knowledge: the underlying data generating mechanism exhibits known symmetric property and the underlying process obeys a set of given boundary value constraints. The class of orthogonal least squares regression algorithms can readily be applied to construct parsimonious grey-box RBF models with enhanced generalisation capability.

Post-normal science and the art of nature conservation

Nature conservation may be considered a post-normal science in that the loss of biodiversity and increasing environmental degradation require urgent action but are characterised by uncertainty at every level. An ‘extended peer community’ with varying skills, perceptions and values are involved in decision-making and implementation of conservation, and the uncertainty involved limits the effectiveness of practice. In this paper we briefly review the key ecological, philosophical and methodological uncertainties associated with conservation, and then highlight the uncertainties and gaps present within the structure and interactions of the conservation community, and which exist mainly between researchers and practitioners, in the context of nature conservation in the UK. We end by concluding that an openly post-normal science framework for conservation, which acknowledges this uncertainty but strives to minimise it, would be a useful progression for nature conservation, and recommend ways in which knowledge transfer between researchers and practitioners can be improved to support robust decision making and conservation enactment.

The art of shooting the moving goal - explorative study of EA pilot

Enterprise Architecture (EA) has been recognised as an important tool in modern business management for closing the gap between strategy and its execution. The current literature implies that for EA to be successful, it should have clearly defined goals. However, the goals of different stakeholders are found to be different, even contradictory. In our explorative research, we seek an answer to the questions: What kind of goals are set for the EA implementation? How do the goals evolve during the time? Are the goals different among stakeholders? How do they affect the success of EA? We analysed an EA pilot conducted among eleven Finnish Higher Education Institutions (HEIs) in 2011. The goals of the pilot were gathered from three different stages of the pilot: before the pilot, during the pilot, and after the pilot, by means of a project plan, interviews during the pilot and a questionnaire after the pilot. The data was analysed using qualitative and quantitative methods. Eight distinct goals were recognised by the coding: Adopt EA Method, Build Information Systems, Business Development, Improve Reporting, Process Improvement, Quality Assurance, Reduce Complexity, and Understand the Big Picture. The success of the pilot was analysed statistically using the scale 1-5. Results revealed that goals set before the pilot were very different from those mentioned during the pilot, or after the pilot. Goals before the pilot were mostly related to expected benefits from the pilot, whereas the most important result was to adopt the EA method. Results can be explained by possibly different roles of respondents, which in turn were most likely caused by poor communication. Interestingly, goals mentioned by different stakeholders were not limited to their traditional areas of responsibility. For example, in some cases Chief Information Officers' goals were Quality Assurance and Process Improvement, whereas managers’ goals were Build Information Systems and Adopt EA Method. This could be a result of a good understanding of the meaning of EA, or stakeholders do not regard EA as their concern at all. It is also interesting to notice that regardless of the different perceptions of goals among stakeholders, all HEIs felt the pilot to be successful. Thus the research does not provide support to confirm the link between clear goals and success.