A simple energy balance model which simulates the thermal regime of urban and rural surfaces under calm, cloudless conditions at night is used to assess the relative importance of the commonly stated causes of urban heat islands. Results show that the effects of street canyon geometry on radiation and of thermal properties on heat storage release, are the primary and almost equal causes on most occasions. In very cold conditions, space heating of buildings can become a dominant cause but this depends on wall insulation. The effects of the urban 'greenhouse' and surface emissivity are relatively minor. The model confirms the importance of local control especially the relation between street geometry and the heat island and highlights the importance of rural thermal properties and their ability to produce seasonal variation in the heat island. A possible explanation for the small heat islands observed in some tropical and Asian settlements is proposed.

No abstract

Case-Based Reasoning (CBR) is a relatively recent problem solving technique that is attracting increasing attention. However, the number of people with first-hand theoretical or practical experience of CBR is still small. The main objective of this review is to provide a comprehensive overview of the subject to people new to CBR. The paper outlines the development of CBR in the US in the 1980s. It describes the fundamental techniques of CBR and contrasts its approach to that of model-based reasoning systems.1A critical review of currently available CBR software tools is followed by descriptions of CBR applications both from academic research and, in more detail, three CBR systems that are presently being used commercially. Each of the three commercial case studies highlights features that made CBR particularly suitable for the application. Moreover, the last case study describes a development methodology for implementing CBR systems. The paper concludes with a research agenda for CBR. A detailed categorized bibliography of CBR research is provided in a companion paper (Marir & Watson, 1994).

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention. R. LÓPEZ DE MÁNTARAS ET AL.

The Manchester project has developed a powerful dataflow processor based on dynamic tagging. This processor is large enough to tackle realistic applications and exhibits impressive speedup for programs with sufficient parallelism.