The story of Somersetshire : with a new map of the county, and upwards of ninety illustrations of abbeys and churches, castles and manor houses, and famous natives of Somersetshire /

Mode of access: Internet.

The history of Wakefield, in the county of York. The rectory manor, with biographical and other notices of some of the persons connected therewith.

Mode of access: Internet.

The Court leet records of the manor of Manchester, from the year 1552 to the year 1686, and from the year 1731 to the year 1846.

Includes index.

Air Gondwana and Mosswood Manor : blended learning environments utilising second life machinima to teach negotiation skills

Seminal reports into higher education in Australia and overseas have recognised negotiation as an essential skill of a practising lawyer and have recommended that all law schools include instruction in negotiation theory and practice in their curricula. Effective negotiation training includes the elements of instruction, modelling, practice and feedback. Ideally such training takes place in the context of small groups. However, this does not necessarily mean that negotiation cannot be taught effectively in the context of large groups. This paper discusses two related blended learning environments that provide instruction in negotiation theory and practice as part of the graduate capabilities program of the undergraduate law degree in the School of Law at the Queensland University of Technology in Brisbane, Australia. Air Gondwana, which forms part of the curriculum of the two first year Contract Law subjects, and Mosswood Manor, which forms part of the curriculum of the second year Trusts subject, utilise a common narrative concerning the family of a wealthy industrialist to facilitate learning of negotiation skills. The programs both combine online and in-class components, the online components utilising machinima (computer graphics created without the need for professional software) to depict the narrative. This strategy has enabled the creation of effective, engaging and challenging learning experiences for large cohorts of students studying by different modes (full-time, part-time and distance external). The use of a common narrative, including the same characters and settings, in the two programs also provides a familiar environment in which students advance their learning from one level of attainment to the next.

Integrated structure selection and parameter optimisation for eng-genes neural models

The eng-genes concept involves the use of fundamental known system functions as activation functions in a neural model to create a 'grey-box' neural network. One of the main issues in eng-genes modelling is to produce a parsimonious model given a model construction criterion. The challenges are that (1) the eng-genes model in most cases is a heterogenous network consisting of more than one type of nonlinear basis functions, and each basis function may have different set of parameters to be optimised; (2) the number of hidden nodes has to be chosen based on a model selection criterion. This is a mixed integer hard problem and this paper investigates the use of a forward selection algorithm to optimise both the network structure and the parameters of the system-derived activation functions. Results are included from case studies performed on a simulated continuously stirred tank reactor process, and using actual data from a pH neutralisation plant. The resulting eng-genes networks demonstrate superior simulation performance and transparency over a range of network sizes when compared to conventional neural models. (c) 2007 Elsevier B.V. All rights reserved.