Developing spatial infrastructures for the Q.U.T. Samford Ecological Research Facility.

Staff and students of the Surveying and Spatial Sciences discipline at QUT have worked collaboratively with the Institute of Sustainable Resources in the creation and development of spatial information layers and infrastructure to support multi-disciplinary research efforts at the Samford Ecological Research Facility (SERF). The SERF property is unique in that it provides staff and students with a semi-rural controlled research base for multiple users. This paper aims to describe the development of a number of spatial information layers and network of survey monuments that assist and support research infrastructure at SERF. A brief historical background about the facility is presented along with descriptions of the surveying and mapping activities undertaken. These broad ranging activities include introducing monument infrastructure and a geodetic control network; surveying activities for aerial photography ground-control targets including precise levelling with barcode instruments; development of an ortho-rectified image spatial information layer; Real-Time-Kinematic Global Positioning Systems (RTK-GPS) surveying for constructing 100metre confluence points/monuments to support science-based disciplines to undertake environmental research transects and long-term ecological sampling; and real-world learning initiative to assist with water engineering projects and student experiential learning. The spatial information layers and physical infrastructure have been adopted by two specific yet diverse user groups with an interest in the long-term research focus of SERF.

Beyond statistical procedures for predictive modelling: Data mining algorithms and support for university research at QUT

In a seminal data mining article, Leo Breiman [1] argued that to develop effective predictive classification and regression models, we need to move away from the sole dependency on statistical algorithms and embrace a wider toolkit of modeling algorithms that include data mining procedures. Nevertheless, many researchers still rely solely on statistical procedures when undertaking data modeling tasks; the sole reliance on these procedures has lead to the development of irrelevant theory and questionable research conclusions ([1], p.199). We will outline initiatives that the HPC & Research Support group is undertaking to engage researchers with data mining tools and techniques; including a new range of seminars, workshops, and one-on-one consultations covering data mining algorithms, the relationship between data mining and the research cycle, and limitations and problems with these new algorithms. Organisational limitations and restrictions to these initiatives are also discussed.

Review of railway research in China

Railways in Hong Kong have been one of the few successful stories in the major metropolitan cities around the world, not only for their profit-making operation but also the efficiency in dealing with the astonishingly high traffic demands every day. While railway operations require a chain of delicate systems working in harmony all the time, there are numerous engineering problems arising and jeopardising the quality of services. Research in various railway engineering problems is therefore a must to tackle these problems. This paper highlights the railway research works in Hong Kong and discusses their relevance to Mainland China.