A framework for state-based time-series analysis and prediction

Time-series analysis and prediction play an important role in state-based systems that involve dealing with varying situations in terms of states of the world evolving with time. Generally speaking, the world in the discourse persists in a given state until something occurs to it into another state. This paper introduces a framework for prediction and analysis based on time-series of states. It takes a time theory that addresses both points and intervals as primitive time elements as the temporal basis. A state of the world under consideration is defined as a set of time-varying propositions with Boolean truth-values that are dependent on time, including properties, facts, actions, events and processes, etc. A time-series of states is then formalized as a list of states that are temporally ordered one after another. The framework supports explicit expression of both absolute and relative temporal knowledge. A formal schema for expressing general time-series of states to be incomplete in various ways, while the concept of complete time-series of states is also formally defined. As applications of the formalism in time-series analysis and prediction, we present two illustrating examples.

[Book review] Chance encounters: a first course in data analysis and inference

Book review of: Chance Encounters: A First Course in Data Analysis and Inference by Christopher J. Wild and George A.F. Seber 2000, John Wiley & Sons Inc. Hard-bound, xviii + 612 pp ISBN 0-471-32936-3

Analysis and modeling of heaping behavior of granular mixtures within a computational mechanics framework

This paper presents a continuum model of the flow of granular material during filling of a silo, using a viscoplastic constitutive relation based on the Drucker-Prager plasticity yield function. The performed simulations demonstrate the ability of the model to realistically represent complex features of granular flows during filling processes, such as heap formation and non-zero inclination angle of the bulk material-air interface. In addition, micro-mechanical parametrizations which account for particle size segregation are incorporated into the model. It is found that numerical predictions of segregation phenomena during filling of a binary granular mixture agree well with experimental results. Further numerical tests indicate the capability of the model to cope successfully with complex operations involving granular mixtures.

Can the use of supplementary video and audio materials support student understanding of 3D animation? Some results, analysis and conclusions

A project within a computing department at the University of Greenwich, has been carried out to identify whether podcasting can be used to help understanding and learning of a subject (3D Animation). We know that the benefits of podcasting in education (HE) can be justified, [1]; [2]; [3]; [4]; [5]; [6] and that some success has been proven, but this paper aims to report the results of a term-long project that provided podcast materials for students to help support their learning using Xserve and Podcast Producer technology. Findings in a previous study [6] identified podcasting as a way to diversify learning and provde a more personalised learning experience for students, as well as being able to provide access to a greater mix of learning styles [7]. Finally this paper aims to present the method of capture and distribution, the methodologies of the study, analysis of results, and conclusions that relate to podcasting and enhanced supported learning.