The relaxation spectrum is an important tool for studying the behaviour of viscoelastic materials. The most popular procedure is to use data from a small-amplitude oscillatory shear experiment to determine the parameters in a multi-mode Maxwell model. However, the discrete relaxation times appear nonlinearly in the mathematical model for the relaxation modulus. The indirect calculation of the relaxation times is an ill-posed problem and its numerical solution is fraught with difficulties. The ill-posedness of the linear regression approach, in which the relaxation times are specified a priori and the minimization is performed with respect to the elastic moduli, is well documented. A nonlinear regression technique is described in this paper in which the minimization is performed with respect to both the discrete relaxation times and the elastic moduli. In this technique the number of discrete modes is increased dynamically and the procedure is terminated when the calculated values of the model parameters are dominated by a measure of their expected values. The sequence of nonlinear least-squares problems, solved using the Marquardt-Levenberg procedure, is shown to be robust and efficient. Numerical calculations on model and experimental data are presented and discussed.

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One of the research interests in educational technology has been in the automating the text assessment of the students' answer. The main reason for such interest is due to an overwhelming of marking scripts need to be graded within a time limit. Among many forms of answering format, an essay-type answer is the most difficult format for an automated system to assess. In this paper, we describe the architecture of our system which assigns a grade to an essay prepared by the students for the history subject. The system is built on modules that perform preprocessing of the document and query, prepare model answers and assign grades to the students' answer. We show the output of the system as the results of applying this system on the samples of data which are the students' answer for high school syllabus.

Intelligent Conversational Channel (ICC) is a community channel developed to facilitate knowledge sharing activities by utilizing multiple agents to create virtual community. The concept of social knowledge is emphasized as the major source of knowledge to be shared among communities. The social knowledge is extracted not only from the community talks but also from scientific documents. We show the architecture of the system to do this and methodology for social knowledge extraction. Agents are used as the communicator to the community members to deliver the knowledge in a conversational manner.