Public-oriented interactive environmental decision support system

With the application of GIS methodologies to spatial data, researchers can now identify patterns of occurrence for many social problems including health-issues and crime. Further more, since this type of data also contains clues as to the underlying causes of social problems, it can be used to make well-educated and consequently, more effective policy decisions.

A review of non-traditional occupational therapy practice placement education : a focus on role-emerging and project placements

Practice placement education has been recognised as an integral and critical component of the training of occupational therapy students. Although there is an extensive body of literature on clinical education and traditional practice placement education models, there has been limited research on alternative placements.-------- This paper reviews the literature on various practice placement education models and presents a contemporary view on how it is currently delivered. The literature is examined with a particular focus on the increasing range of practice placement education opportunities, such as project and role-emerging placements. The drivers for non-traditional practice placement education include shortages of traditional placement options, health reform and changing work practices, potential for role development and influence on practice choice. The benefits and challenges of non-traditional practice placement education are discussed, including supervision issues, student evaluation, professional and personal development and the opportunity to practise clinical skills.--------- Further research is recommended to investigate occupational therapy graduates' perceptions of role-emerging and project placements in order to identify the benefits or otherwise of these placements and to contribute to the limited body of knowledge of emerging education opportunities.

Prognosis of bearing failure based on health state estimation

This paper proposes a new prognosis model based on the technique for health state estimation of machines for accurate assessment of the remnant life. For the evaluation of health stages of machines, the Support Vector Machine (SVM) classifier was employed to obtain the probability of each health state. Two case studies involving bearing failures were used to validate the proposed model. Simulated bearing failure data and experimental data from an accelerated bearing test rig were used to train and test the model. The result obtained is very encouraging and shows that the proposed prognostic model produces promising results and has the potential to be used as an estimation tool for machine remnant life prediction.