Urbanisation and its impact on building energy consumption and efficiency in China

The People's Republic of China and its 1.3 billion people have experienced a rapid economic growth in the past two decades. China's urbanisation ratio rose from around 20% in the early 1980s to 45% in 2007 [China Urban Research Committee. Green building. Beijing: Chinese Construction Industrial Publish House; 2008. ISBN 978-7-112-09925-2.]. The large volume and rapid speed of building construction rarely have been seen in global development and cause substantial pressure on resources and the environment. Government policy makers and building professionals, including architects, building engineers, project managers and property developers, should play an important role in enhancing the planning, design, construction, operation and maintenance of the building energy efficiency process in forming the sustainable urban development. This paper addresses the emerging issues relating to building energy consumption and building energy efficiency due to the fast urbanisation development in China.

Overview of an innovative EU-China collaboration in education and research in sustainable built environment

This paper introduces an international collaboration of EU and Asia in education, training and research in the field of sustainable built environment, which attempts to develop a network of practical and intellectual knowledge and training exchange between Chinese and European Universities in the field of sustainable building design and construction. The projects funded by the European Commission Asia Link program, UK Foreign & Commonwealth Office, British Council and the UK Engineering Physical Sciences Council (EPSRC) have been introduced. The projects have significant impacts on promoting sustainable development in built environment in China. The aim of this paper is to share the experiences with those who are interested and searching the ways to collaborate with China in education and research.

Stabilizing swarm intelligence search via positive feedback resource allocation

A novel Swarm Intelligence method for best-fit search, Stochastic Diffusion Search, is presented capable of rapid location of the optimal solution in the search space. Population based search mechanisms employed by Swarm Intelligence methods can suffer lack of convergence resulting in ill defined stopping criteria and loss of the best solution. Conversely, as a result of its resource allocation mechanism, the solutions SDS discovers enjoy excellent stability.

Second-order cybernetics and enactive perception

Purpose - To present an account of cognition integrating second-order cybernetics (SOC) together with enactive perception and dynamic systems theory. Design/methodology/approach - The paper presents a brief critique of classical models of cognition then outlines how integration of SOC, enactive perception and dynamic systems theory can overcome some weaknesses of the classical paradigm. Findings - Presents the critique of evolutionary robotics showing how the issues of teleology and autonomy are left unresolved by this paradigm although their solution fits within the proposed framework. Research limitations/implications - The paper highlights the importance of genuine autonomy in the development of artificial cognitive systems. It sets out a framework within which the robofic research of cognitive systems could succeed. Practical implications - There are no immediate practical implications but see research implications. Originality/value - It joins the discussion on the fundamental nature of cognitive systems and emphasise the importance of autonomy and embodiment.

Search space pruning and global optimisation of Multiple Gravity Assist spacecraft trajectories

We introduce and describe the Multiple Gravity Assist problem, a global optimisation problem that is of great interest in the design of spacecraft and their trajectories. We discuss its formalization and we show, in one particular problem instance, the performance of selected state of the art heuristic global optimisation algorithms. A deterministic search space pruning algorithm is then developed and its polynomial time and space complexity derived. The algorithm is shown to achieve search space reductions of greater than six orders of magnitude, thus reducing significantly the complexity of the subsequent optimisation.