Learnings, case studies and guidelines for establishing shared and collaborative service delivery in the non-government sector: Evaluation of the Multi-Tenant Service Centre (MTSC) Pilots Project

This report presents learnings, case studies, guidelines and resources for non-government organisations that are planning to implement shared or collaborative arrangements with other agencies. It summarises results from an evaluation of the implementation phase of the Multi-Tenant Service Centre (MTSC) Pilots Project, which was completed in June 2008. This evaluation shows that developing and implementing shared and collaborative arrangements is a complex process that presents many risks, challenges and barriers to success, but can have many potential benefits for non government organisations. As this report makes clear, there is no ‘one size fits all’ approach to this process. The MTSC Pilots Project was conducted by the Department of Communities (DoC), Queensland Government, as part of its Strengthening Non-Government Organisations strategy. The objective of the MTSC Pilots initiative was to co-locate separate service providers in an appropriately located centre, operating with effective and transparent management, which enabled service providers to improve client services. Three MTSC consortiums in Mackay, Caboolture and Toowoomba were selected as the pilots over a four year period from 2006 – 2010.

Patient satisfaction from two studies of collaborative doctor : pharmacist prescribing in Australia

Background Pharmacist prescribing has been introduced in several countries and is a possible future role for pharmacy in Australia. Objective To assess whether patient satisfaction with the pharmacist as a prescriber, and patient experiences in two settings of collaborative doctor-pharmacist prescribing may be barriers to implementation of pharmacist prescribing. Design Surveys containing closed questions, and Likert scale responses, were completed in both settings to investigate patient satisfaction after each consultation. A further survey investigating attitudes towards pharmacist prescribing, after multiple consultations, was completed in the sexual health clinic. Setting and Participants A surgical pre-admission clinic (PAC) in a tertiary hospital and an outpatient sexual health clinic at a university hospital. Two hundred patients scheduled for elective surgery, and 17 patients diagnosed with HIV infection, respectively, recruited to the pharmacist prescribing arm of two collaborative doctor-pharmacist prescribing studies. Results Consultation satisfaction response rates in PAC and the sexual health clinic were 182/200 (91%) and 29/34 (85%), respectively. In the sexual health clinic, the attitudes towards pharmacist prescribing survey response rate were 14/17 (82%). Consultation satisfaction was high in both studies, most patients (98% and 97%, respectively) agreed they were satisfied with the consultation. In the sexual health clinic, all patients (14/14) agreed that they trusted the pharmacist’s ability to prescribe, care was as good as usual care, and they would recommend seeing a pharmacist prescriber to friends. Discussion and Conclusion Most of the patients had a high satisfaction with pharmacist prescriber consultations, and a positive outlook on the collaborative model of care in the sexual health clinic.

Eye-safe UV stand-off Raman spectroscopy for explosive detection in the field

This project focused on maximising the detection range of an eye-safe stand-off Raman system for use in detecting explosives. Investigation of the effect on detection range through differing laser parameters in this thesis provided optimal laser settings to achieve the largest possible detection range of explosives, while still remaining under the eye-safe limit.

The learning capability of construction organisations engaged in collaborative contracting - Literature Review [industry report]

Background Value for money (VfM) on collaborative construction projects is dependent on the learning capabilities of the organisations and people involved. Within the context of infrastructure delivery, there is little research about the impact of organisational learning capability on project value. The literature contains a multiplicity of often un-testable definitions about organisational learning abilities. This paper defines learning capability as a dynamic capability that participant organisations purposely develop to add value to collaborative projects. The paper reports on a literature review that proposes a framework that conceptualises learning capability to explore the topic. This work is the first phase of a large-scale national survey funded by the Alliancing Association of Australasia and the Australian Research Council. Methodology Desk-top review of leading journals in the areas of strategic management, strategic alliances and construction management, as well as recent government documents and industry guidelines, was undertaken to synthesise, conceptualise and operationalise the concept of learning capability. The study primarily draws on the theoretical perspectives of the resource-based view of the firm (e.g. Barney 1991; Wernerfelt 1984), absorptive capacity (e.g. Cohen and Levinthal 1990; Zahra and George 2002); and dynamic capabilities (e.g. Helfat et al. 2007; Teece et al. 1997; Winter 2003). Content analysis of the literature was undertaken to identify key learning routines. Content analysis is a commonly used methodology in the social sciences area. It provides rich data through the systematic and objective review of literature (Krippendorff 2004). NVivo 9, a qualitative data analysis software package, was used to assist in this process. Findings and Future Research The review process resulted in a framework for the conceptualisation of learning capability that shows three phases of learning: (1) exploratory learning, (2) transformative learning and (3) exploitative learning. These phases combine both internal and external learning routines to influence project performance outcomes and thus VfM delivered under collaborative contracts. Sitting within these phases are eight categories of learning capability comprising knowledge articulation, identification, acquisition, dissemination, codification, internationalisation, transformation and application. The learning routines sitting within each category will be disaggregated in future research as the basis for measureable items in a large-scale survey study. The survey will examine the extent to which various learning routines influence project outcomes, as well as the relationships between them. This will involve identifying the routines that exist within organisations in the construction industry, their resourcing and rate of renewal, together with the extent of use and perceived value within the organisation. The target population is currently estimated to be around 1,000 professionals with experience in relational contracting in Australia. This future research will build on the learning capability framework to provide data that will assist construction organisations seeking to maximise VfM on construction projects.

Binding properties of peptidic affinity ligands for plasmid DNA capture and detection

Peptides constructed from α-helical subunits of the Lac repressor protein (LacI) were designed then tailored to achieve particular binding kinetics and dissociation constants for plasmid DNA purification and detection. Surface plasmon resonance was employed for quantification and characterization of the binding of double stranded Escherichia coli plasmid DNA (pUC19) via the lac operon (lacO) to "biomimics" of the DNA binding domain of LacI. Equilibrium dissociation constants (K D), association (k a), and dissociation rates (k d) for the interaction between a suite of peptide sequences and pUC19 were determined. K D values measured for the binding of pUC19 to the 47mer, 27mer, 16mer, and 14mer peptides were 8.8 ± 1.3 × 10 -10 M, 7.2 ± 0.6 × 10 -10 M, 4.5 ± 0.5 × 10 -8 M, and 6.2 ± 0.9 × 10 -6 M, respectively. These findings show that affinity peptides, composed of subunits from a naturally occurring operon-repressor interaction, can be designed to achieve binding characteristics suitable for affinity chromatography and biosensor devices.

Course evidence matrix : a collaborative evaluation using multiple lines of evidence

Welcome to the Evaluation of course matrix. This matrix is designed for highly qualified discipline experts to evaluate their course, major or unit in a systemic manner. The primary purpose of the Evaluation of course matrix is to provide a tool that a group of academic staff at universities can collaboratively review the assessment within a course, major or unit annually. The annual review will result in you being ready for an external curricula review at any point in time. This tool is designed for use in a workshop format with one, two or more academic staff, and will lead to an action plan for implementation. I hope you find this tool useful in your assessment review.

Corner detection in images under different noise levels

Corner detection has shown its great importance in many computer vision tasks. However, in real-world applications, noise in the image strongly affects the performance of corner detectors. Few corner detectors have been designed to be robust to heavy noise by now, partly because the noise could be reduced by a denoising procedure. In this paper, we present a corner detector that could find discriminative corners in images contaminated by noise of different levels, without any denoising procedure. Candidate corners (i.e., features) are firstly detected by a modified SUSAN approach, and then false corners in noise are rejected based on their local characteristics. Features in flat regions are removed based on their intensity centroid, and features on edge structures are removed using the Harris response. The detector is self-adaptive to noise since the image signal-to-noise ratio (SNR) is automatically estimated to choose an appropriate threshold for refining features. Experimental results show that our detector has better performance at locating discriminative corners in images with strong noise than other widely used corner or keypoint detectors.

A multidimensional collaborative filtering fusion approach with dimensionality reduction

Multidimensional data are getting increasing attention from researchers for creating better recommender systems in recent years. Additional metadata provides algorithms with more details for better understanding the interaction between users and items. While neighbourhood-based Collaborative Filtering (CF) approaches and latent factor models tackle this task in various ways effectively, they only utilize different partial structures of data. In this paper, we seek to delve into different types of relations in data and to understand the interaction between users and items more holistically. We propose a generic multidimensional CF fusion approach for top-N item recommendations. The proposed approach is capable of incorporating not only localized relations of user-user and item-item but also latent interaction between all dimensions of the data. Experimental results show significant improvements by the proposed approach in terms of recommendation accuracy.

On the use of optical flow for scene change detection and description

We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Green-fluorescent protein facilitates rapid in vivo detection of genetically transformed plant cells

Early detection of plant transformation events is necessary for the rapid establishment and optimization of plant transformation protocols. We have assessed modified versions of the green fluorescent protein (GFP) from Aequorea victoria as early reporters of plant transformation using a dissecting fluorescence microscope with appropriate filters. Gfp-expressing cells from four different plant species (sugarcane, maize, lettuce, and tobacco) were readily distinguished, following either Agrobacterium-mediated or particle bombardment-mediated transformation. The identification of gfp-expressing sugarcane cells allowed for the elimination of a high proportion of non-expressing explants and also enabled visual selection of dividing transgenic cells, an early step in the generation of transgenic organisms. The recovery of transgenic cell clusters was streamlined by the ability to visualize gfp-expressing tissues in vitro.

Application of anomaly technique in wind turbine bearing fault detection

Bearing faults are the most common cause of wind turbine failures. Unavailability and maintenance cost of wind turbines are becoming critically important, with their fast growing in electric networks. Early fault detection can reduce outage time and costs. This paper proposes Anomaly Detection (AD) machine learning algorithms for fault diagnosis of wind turbine bearings. The application of this method on a real data set was conducted and is presented in this paper. For validation and comparison purposes, a set of baseline results are produced using the popular one-class SVM methods to examine the ability of the proposed technique in detecting incipient faults.

Pan-Plasmodium band sensitivity for Plasmodium falciparum detection in combination malaria rapid diagnostic tests and implications for clinical management

Background: Malaria rapid diagnostic tests (RDTs) are appropriate for case management, but persistent antigenaemia is a concern for HRP2-detecting RDTs in endemic areas. It has been suggested that pan-pLDH test bands on combination RDTs could be used to distinguish persistent antigenaemia from active Plasmodium falciparum infection, however this assumes all active infections produce positive results on both bands of RDTs, an assertion that has not been demonstrated. Methods: In this study, data generated during the WHO-FIND product testing programme for malaria RDTs was reviewed to investigate the reactivity of individual test bands against P. falciparum in 18 combination RDTs. Each product was tested against multiple wild-type P. falciparum only samples. Antigen levels were measured by quantitative ELISA for HRP2, pLDH and aldolase. Results: When tested against P. falciparum samples at 200 parasites/μL, 92% of RDTs were positive; 57% of these on both the P. falciparum and pan bands, while 43% were positive on the P. falciparum band only. There was a relationship between antigen concentration and band positivity; ≥4 ng/mL of HRP2 produced positive results in more than 95% of P. falciparum bands, while ≥45 ng/mL of pLDH was required for at least 90% of pan bands to be positive. Conclusions: In active P. falciparum infections it is common for combination RDTs to return a positive HRP2 band combined with a negative pan-pLDH band, and when both bands are positive, often the pan band is faint. Thus active infections could be missed if the presence of a HRP2 band in the absence of a pan band is interpreted as being caused solely by persistent antigenaemia.