Variation in risk seeking behaviour following large losses : a natural experiment

This study explores people's risk taking behaviour after having suffered large real-world losses following a natural disaster. Using the margins of the 2011 Australian floods (Brisbane) as a natural experimental setting, we find that homeowners who were victims of the floods and face large losses in property values are 50% more likely to opt for a risky gamble -- a scratch card giving a small chance of a large gain ($500,000) -- than for a sure amount of comparable value ($10). This finding is consistent with prospect theory predictions regarding the adoption of a risk-seeking attitude after a loss.

Enhancing learning in large undergraduate classes using custom made audio-visual cases

This is an exploratory study into the effective use of embedding custom made audiovisual case studies (AVCS) in enhancing the student’s learning experience. This paper describes a project that used AVCS for a large divergent cohort of undergraduate students, enrolled in an International Business course. The study makes a number of key contributions to advancing learning and teaching within the discipline. AVCS provide first hand reporting of the case material, where the students have the ability to improve their understanding from both verbal and nonverbal cues. The paper demonstrates how AVCS can be embedded in a student-centred teaching approach to capture the students’ interest and to enhance a deep approach to learning by providing real-world authentic experience.

Reducing complexity of large EPCs

Business processes are an important instrument for understanding and improving how companies provide goods and services to customers. Therefore, many companies have documented their business processes well, often in the Event-driven Process Chains (EPC). Unfortunately, in many cases the resulting EPCs are rather complex, so that the overall process logic is hidden in low level process details. This paper proposes abstraction mechanisms for process models that aim to reduce their complexity, while keeping the overall process structure. We assume that functions are marked with efforts and splits are marked with probabilities. This information is used to separate important process parts from less important ones. Real world process models are used to validate the approach.

Improved healing of large segmental defects in the rat femur by reverse dynamization in the presence of bone morphogenetic protein-2

Background Large segmental defects in bone do not heal well and present clinical challenges. This study investigated modulation of the mechanical environment as a means of improving bone healing in the presence of bone morphogenetic protein (BMP)-2. Although the influence of mechanical forces on the healing of fractures is well established, no previous studies, to our knowledge, have described their influence on the healing of large segmental defects. We hypothesized that bone-healing would be improved by initial, low-stiffness fixation of the defect, followed by high-stiffness fixation during the healing process. We call this reverse dynamization. Methods A rat model of a critical-sized femoral defect was used. External fixators were constructed to provide different degrees of stiffness and, importantly, the ability to change stiffness during the healing process in vivo. Healing of the critical-sized defects was initiated by the implantation of 11 mg of recombinant human BMP (rhBMP)-2 on a collagen sponge. Groups of rats receiving BMP-2 were allowed to heal with low, medium, and high-stiffness fixators, as well as under conditions of reverse dynamization, in which the stiffness was changed from low to high at two weeks. Healing was assessed at eight weeks with use of radiographs, histological analysis, microcomputed tomography, dual x-ray absorptiometry, and mechanical testing. Results Under constant stiffness, the low-stiffness fixator produced the best healing after eight weeks. However, reverse dynamization provided considerable improvement, resulting in a marked acceleration of the healing process by all of the criteria of this study. The histological data suggest that this was the result of intramembranous, rather than endochondral, ossification. Conclusions Reverse dynamization accelerated healing in the presence of BMP-2 in the rat femur and is worthy of further investigation as a means of improving the healing of large segmental bone defects. Clinical Relevance These data provide the basis of a novel, simple, and inexpensive way to improve the healing of critical-sized defects in long bones. Reverse dynamization may also be applicable to other circumstances in which bonehealing is problematic.

Painting anatase (TiO2) nanocrystals on long nanofibers to prepare photocatalysts with large active surface for dye degradation and organic synthesis

Anatase TiO2 nanocrystals were painted on H-titanate nanofibers by using an aqueous solution of titanyl sulfate. The anatase nanocrystals were bonded solidly onto the titanate fibers through formation of coherent interfaces at which the oxygen atoms were shared by the nanocrystals and the fiber. This approach allowed us to create large anatase surfaces on the nanofibers, which are active in photocatalytic reactions. This method was also applied successfully to coat anatase nanocrystals on surfaces of fly ash and layered clay. The painted nanofibers exhibited a much higher catalytic activity for the photocatalytic degradation of sulforhodamine B and the selective oxidation of benzylamine to the corresponding imine (with a product selectivity >99%) under UV irradiation than both the parent H-titanate nanofibers and a commercial TiO2 powder, P25. We found that gold nanoparticles supported on H-titanate nanofibers showed no catalytic activity for the reduction of nitrobenzene to azoxybenzene, whereas the gold nanoparticles supported on the painted nanofibers and P25 could efficiently reduce nitrobenzene to azoxybenzene as the sole product under visible light irradiation. These results were different from those from the reduction on the gold nanoparticles photocatalyst on ZrO2, in which the azoxybenzene was the intermediate and converted to azobenzene quickly. Evidently, the support materials significantly affect the product selectivity of the nitrobenzene reduction. Finally, the new photocatalysts could be easily dispersed into and separated from a liquid because of their fibril morphology, which is an important advantage for practical applications.

Evaluating the use of a very large-scale presentation and collaboration framework

In this paper we describe the use and evaluation of CubIT, a multi-user, very large-scale presentation and collaboration framework. CubIT is installed at the Queensland University of Technology’s (QUT) Cube facility. The “Cube” is an interactive visualisation facility made up of five very large-scale interactive multi-panel wall displays, each consisting of up to twelve 55-inch multi-touch screens (48 screens in total) and massive projected display screens situated above the display panels. The paper outlines the unique design challenges, features, use and evaluation of CubIT. The system was built to make the Cube facility accessible to QUT’s academic and student population. CubIT enables users to easily upload and share their own media content, and allows multiple users to simultaneously interact with the Cube’s wall displays. The features of CubIT are implemented via three user interfaces, a multi-touch interface working on the wall displays, a mobile phone and tablet application and a web-based content management system. The evaluation reveals issues around the public use and functional scope of the system.

Reason is too large : analogy and precedent in law

Hunter argues that cognitive science models of human thinking explain how analogical reasoning and precedential reasoning operate in law. He offers an explanation of why various legal theories are so limited and calls for greater attention to what is actually happening when lawyers and judges reason, by analogy, with precedent.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Joint 2D and 3D cues for image segmentation towards robotic applications

This thesis investigates the fusion of 3D visual information with 2D image cues to provide 3D semantic maps of large-scale environments in which a robot traverses for robotic applications. A major theme of this thesis was to exploit the availability of 3D information acquired from robot sensors to improve upon 2D object classification alone. The proposed methods have been evaluated on several indoor and outdoor datasets collected from mobile robotic platforms including a quadcopter and ground vehicle covering several kilometres of urban roads.

Spontaneous large volume adipose tissue generation from a vascularized pedicled fat flap inside a chamber space

A novel method of spontaneous generation of new adipose tissue from an existing fat flap is described. A defined volume of fat flap based on the superficial inferior epigastric vascular pedicle in the rat was elevated and inset into a hollow plastic chamber implanted subcutaneously in the groin of the rat. The chamber walls were either perforated or solid and the chambers either contained poly(D,L-lactic-co-glycolic acid) (PLGA) sponge matrix or not. The contents were analyzed after being in situ for 6 weeks. The total volume of the flap tissue in all groups except the control groups, where the flap was not inserted into the chambers, increased significantly, especially in the perforated chambers (0.08 ± 0.007 mL baseline compared to 1.2 ± 0.08 mL in the intact ones). Volume analysis of individual component tissues within the flaps revealed that the adipocyte volume increased and was at a maximum in the chambers without PLGA, where it expanded from 0.04 ± 0.003 mL at insertion to 0.5 ± 0.08 mL (1250% increase) in the perforated chambers and to 0.16 ± 0.03 mL (400% increase) in the intact chambers. Addition of PLGA scaffolds resulted in less fat growth. Histomorphometric analysis rather than simple hypertrophy documented an increased number of adipocytes. The new tissue was highly vascularized and no fat necrosis or atypical changes were observed.

The analysis of large scale data taken from the world groundnut (Arachis hypogaea L.) germplasm collection I. Two-way quantitative data

Data associated with germplasm collections are typically large and multivariate with a considerable number of descriptors measured on each of many accessions. Pattern analysis methods of clustering and ordination have been identified as techniques for statistically evaluating the available diversity in germplasm data. While used in many studies, the approaches have not dealt explicitly with the computational consequences of large data sets (i.e. greater than 5000 accessions). To consider the application of these techniques to germplasm evaluation data, 11328 accessions of groundnut (Arachis hypogaea L) from the International Research Institute for the Semi-Arid Tropics, Andhra Pradesh, India were examined. Data for nine quantitative descriptors measured in the rainy and post-rainy growing seasons were used. The ordination technique of principal component analysis was used to reduce the dimensionality of the germplasm data. The identification of phenotypically similar groups of accessions within large scale data via the computationally intensive hierarchical clustering techniques was not feasible and non-hierarchical techniques had to be used. Finite mixture models that maximise the likelihood of an accession belonging to a cluster were used to cluster the accessions in this collection. The patterns of response for the different growing seasons were found to be highly correlated. However, in relating the results to passport and other characterisation and evaluation descriptors, the observed patterns did not appear to be related to taxonomy or any other well known characteristics of groundnut.

The analysis of large scale data taken from the world groundnut (Arachis hypogaea L.) germplasm collection. II. Two-way data with mixed data types

As a sequel to a paper that dealt with the analysis of two-way quantitative data in large germplasm collections, this paper presents analytical methods appropriate for two-way data matrices consisting of mixed data types, namely, ordered multicategory and quantitative data types. While various pattern analysis techniques have been identified as suitable for analysis of the mixed data types which occur in germplasm collections, the clustering and ordination methods used often can not deal explicitly with the computational consequences of large data sets (i.e. greater than 5000 accessions) with incomplete information. However, it is shown that the ordination technique of principal component analysis and the mixture maximum likelihood method of clustering can be employed to achieve such analyses. Germplasm evaluation data for 11436 accessions of groundnut (Arachis hypogaea L.) from the International Research Institute of the Semi-Arid Tropics, Andhra Pradesh, India were examined. Data for nine quantitative descriptors measured in the post-rainy season and five ordered multicategory descriptors were used. Pattern analysis results generally indicated that the accessions could be distinguished into four regions along the continuum of growth habit (or plant erectness). Interpretation of accession membership in these regions was found to be consistent with taxonomic information, such as subspecies. Each growth habit region contained accessions from three of the most common groundnut botanical varieties. This implies that within each of the habit types there is the full range of expression for the other descriptors used in the analysis. Using these types of insights, the patterns of variability in germplasm collections can provide scientists with valuable information for their plant improvement programs.

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.

Computational science for undergraduate biologists via QUT.Bio.Excel

Molecular biology is a scientific discipline which has changed fundamentally in character over the past decade to rely on large scale datasets – public and locally generated - and their computational analysis and annotation. Undergraduate education of biologists must increasingly couple this domain context with a data-driven computational scientific method. Yet modern programming and scripting languages and rich computational environments such as R and MATLAB present significant barriers to those with limited exposure to computer science, and may require substantial tutorial assistance over an extended period if progress is to be made. In this paper we report our experience of undergraduate bioinformatics education using the familiar, ubiquitous spreadsheet environment of Microsoft Excel. We describe a configurable extension called QUT.Bio.Excel, a custom ribbon, supporting a rich set of data sources, external tools and interactive processing within the spreadsheet, and a range of problems to demonstrate its utility and success in addressing the needs of students over their studies.