Computers and Liquid State Statistical Mechanics

The advent of electronic computers has revolutionised the application of statistical mechanics to the liquid state. Computers have permitted, for example, the calculation of the phase diagram of water and ice and the folding of proteins. The behaviour of alkanes adsorbed in zeolites, the formation of liquid crystal phases and the process of nucleation. Computer simulations provide, on one hand, new insights into the physical processes in action, and on the other, quantitative results of greater and greater precision. Insights into physical processes facilitate the reductionist agenda of physics, whilst large scale simulations bring out emergent features that are inherent (although far from obvious) in complex systems consisting of many bodies. It is safe to say that computer simulations are now an indispensable tool for both the theorist and the experimentalist, and in the future their usefulness will only increase. This chapter presents a selective review of some of the incredible advances in condensed matter physics that could only have been achieved with the use of computers.Comment: 22 pages, 2 figures. Chapter for a boo

A guided tour of asynchronous cellular automata

Research on asynchronous cellular automata has received a great amount of attention these last years and has turned to a thriving field. We survey the recent research that has been carried out on this topic and present a wide state of the art where computing and modelling issues are both represented.Comment: To appear in the Journal of Cellular Automat

On the possible Computational Power of the Human Mind

The aim of this paper is to address the question: Can an artificial neural network (ANN) model be used as a possible characterization of the power of the human mind? We will discuss what might be the relationship between such a model and its natural counterpart. A possible characterization of the different power capabilities of the mind is suggested in terms of the information contained (in its computational complexity) or achievable by it. Such characterization takes advantage of recent results based on natural neural networks (NNN) and the computational power of arbitrary artificial neural networks (ANN). The possible acceptance of neural networks as the model of the human mind's operation makes the aforementioned quite relevant.Comment: Complexity, Science and Society Conference, 2005, University of Liverpool, UK. 23 page

Differences in intention to use educational RSS feeds between Lebanese and British students: A multi‑group analysis based on the technology acceptance model

Really Simple Syndication (RSS) offers a means for university students to receive timely updates from virtual learning environments. However, despite its utility, only 21% of home students surveyed at a university in Lebanon claim to have ever used the technology. To investigate whether national culture could be an influence on intention to use RSS, the survey was extended to British students in the UK. Using the Technology Adoption Model (TAM) as a research framework, 437 students responded to a questionnaire containing four constructs: behavioral intention to use; attitude towards benefit; perceived usefulness; and perceived ease of use. Principle components analysis and structural equation modelling were used to explore the psychometric qualities and utility of TAM in both contexts. The results show that adoption was significantly higher, but also modest, in the British context at 36%. Configural and metric invariance were fully supported, while scalar and factorial invariance were partially supported. Further analysis shows significant differences between perceived usefulness and perceived ease of use across the two contexts studied. Therefore, it is recommended that faculty demonstrate to students how educational RSS feeds can be used effectively to increase awareness and emphasize usefulness in both contexts