Dismantling the public sector bastion : evaluation capital works

Purpose: The determination of the most appropriate procurement system for a capital works project is a challenging task for public sector clients considering the array of assessment criteria that are considered and the procurement methods that are available. This is particularly pertinent to the Western Australian public sector where they have had a propensity to use traditional lump sum as the default procurement solution despite knowing that the selection of an inappropriate procurement method may lead to cost and time overruns, claims, and disputes’ on projects. This paper presents a six step procurement method evaluation approach that requires public sector agencies to consider in detail an array of options so as to obtain value for money. Design/methodology/approach: A procurement evaluation approach is developed and is examined using a focus group of 12 participants comprising of a public sector client, project team and key stakeholders. The focus group was used to examine the developed approach in the context of a real-life capital works project. Findings: The procurement method evaluation approach was deemed to be pragmatic and enabled decision-makers to re-evaluate outcomes from previous steps in the process. All focus group participants stated the six step process enabled a recommendation that was grounded in reflection and detailed evaluation. Practical implications: The developed procurement approach has enabled the public sector client evaluate the way in which they view procurement method selection and examine how they obtain ‘value for money’. Originality/value: The six step procurement approach makes use of quantitative and qualitative techniques and is reliant on discourse and reflection in making a procurement method recommendation. Consequently, the approach enables public sector clients to account for the complexities often associated with procurement selection.

Decision support system architecture for consultant selection

Decision Support System (DSS) has played a significant role in construction project management. This has been proven that a lot of DSS systems have been implemented throughout the whole construction project life cycle. However, most research only concentrated in model development and left few fundamental aspects in Information System development. As a result, the output of researches are complicated to be adopted by lay person particularly those whom come from a non-technical background. Hence, a DSS should hide the abstraction and complexity of DSS models by providing a more useful system which incorporated user oriented system. To demonstrate a desirable architecture of DSS particularly in public sector planning, we aim to propose a generic DSS framework for consultant selection. It will focus on the engagement of engineering consultant for irrigation and drainage infrastructure. The DSS framework comprise from operational decision to strategic decision level. The expected result of the research will provide a robust framework of DSS for consultant selection. In addition, the paper also discussed other issues that related to the existing DSS framework by integrating enabling technologies from computing. This paper is based on the preliminary case study conducted via literature review and archival documents at Department of Irrigation and Drainage (DID) Malaysia. The paper will directly affect to the enhancement of consultant pre-qualification assessment and selection tools. By the introduction of DSS in this area, the selection process will be more efficient in time, intuitively aided qualitative judgment, and transparent decision through aggregation of decision among stakeholders.

Building information modelling project decision support framework

Building Information Modelling (BIM) is an information technology [IT] enabled approach to managing design data in the AEC/FM (Architecture, Engineering and Construction/ Facilities Management) industry. BIM enables improved interdisciplinary collaboration across distributed teams, intelligent documentation and information retrieval, greater consistency in building data, better conflict detection and enhanced facilities management. Despite the apparent benefits the adoption of BIM in practice has been slow. Workshops with industry focus groups were conducted to identify the industry needs, concerns and expectations from participants who had implemented BIM or were BIM “ready”. Factors inhibiting BIM adoption include lack of training, low business incentives, perception of lack of rewards, technological concerns, industry fragmentation related to uneven ICT adoption practices, contractual matters and resistance to changing current work practice. Successful BIM usage depends on collective adoption of BIM across the different disciplines and support by the client. The relationship of current work practices to future BIM scenarios was identified as an important strategy as the participants believed that BIM cannot be efficiently used with traditional practices and methods. The key to successful implementation is to explore the extent to which current work practices must change. Currently there is a perception that all work practices and processes must adopt and change for effective usage of BIM. It is acknowledged that new roles and responsibilities are emerging and that different parties will lead BIM on different projects. A contingency based approach to the problem of implementation was taken which relies upon integration of BIM project champion, procurement strategy, team capability analysis, commercial software availability/applicability and phase decision making and event analysis. Organizations need to understand: (a) their own work processes and requirements; (b) the range of BIM applications available in the market and their capabilities (c) the potential benefits of different BIM applications and their roles in different phases of the project lifecycle, and (d) collective supply chain adoption capabilities. A framework is proposed to support organizations selection of BIM usage strategies that meet their project requirements. Case studies are being conducted to develop the framework. The results of the preliminary design management case study is presented for contractor led BIM specific to the design and construct procurement strategy.

Measuring safety effectiveness : literature review

Cohen (1977) reviewed the then current research on occupational safety and stated that both strong company commitment to safety, and communication between all levels of a company are the most influential factors to improving safety. Other relevant factors included careful selection of staff, and early and continuous training throughout the lifetime with the company. These continue to be important factors in OHS today. There has been a continued decrease in the injury rates since Cohen’s review within the Australian construction industry, however, the construction industry has far more injuries and ill-health than the Australian average, with one fatality occurring on average per week in the Australian Construction Industry. The Fatality rate in the building and construction industry remains three times higher than the national average, and 15% of all industry fatalities are in the building and construction industry. In addition the construction industry pays one of the highest workers’ compensation premium rates – in 2001 alone approximately 0.5% ($267 million) of revenue would have to be allocated to the direct cost of 1998/99 compensations (Office of the Federal Safety Commissioner, 2006). Based on these statistics there is a need to measure and improve safety performance within the construction industry.

A comparative study : regulatory requirements on subdivisions for increasing passive cooling

A major project in the Sustainable Built Assets core area is the Sustainable Sub-divisions – Ventilation Project that is the second stage of a planned series of research projects focusing on sustainable sub-divisions. The initial project, Sustainable Sub-divisions: Energy focused on energy efficiency and examined the link between dwelling energy efficiency and sub-divisional layout. In addition, the potential for on site electricity generation, especially in medium and high-density developments, was also examined. That project recommended that an existing lot-rating methodology be adapted for use in SEQ through the inclusion of sub divisional appropriate ventilation data. Acquiring that data is the object of this project. The Sustainable Sub-divisions; Ventilation Project will produce a series of reports. The first report (Report 2002-077-B-01) summarised the results from an industry workshop and interviews that were conducted to ascertain the current attitudes and methodologies used in contemporary sub-division design in South East Queensland. The second report (Report 2002-077-B-02) described how the project is being delivered as outlined in the Project Agreement. It included the selection of the case study dwellings and monitoring equipment and data management process. This third report (Report 2002-077-B-03) provides an analysis and review of the approaches recommended by leading experts, government bodies and professional organizations throughout Australia that aim to increase the potential for passive cooling and heating at the subdivision stage. This data will inform issues discussed on the development of the enhanced lot-rating methodology in other reports of this series. The final report, due in June 2007, will detail the analysis of data for winter 2006 and summer 2007, leading to the development and delivery of the enhanced lot-rating methodology.

Improved GMM-based speaker verification using SVM-driven impostor dataset selection

The problem of impostor dataset selection for GMM-based speaker verification is addressed through the recently proposed data-driven background dataset refinement technique. The SVM-based refinement technique selects from a candidate impostor dataset those examples that are most frequently selected as support vectors when training a set of SVMs on a development corpus. This study demonstrates the versatility of dataset refinement in the task of selecting suitable impostor datasets for use in GMM-based speaker verification. The use of refined Z- and T-norm datasets provided performance gains of 15% in EER in the NIST 2006 SRE over the use of heuristically selected datasets. The refined datasets were shown to generalise well to the unseen data of the NIST 2008 SRE.

Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification

A data-driven background dataset refinement technique was recently proposed for SVM based speaker verification. This method selects a refined SVM background dataset from a set of candidate impostor examples after individually ranking examples by their relevance. This paper extends this technique to the refinement of the T-norm dataset for SVM-based speaker verification. The independent refinement of the background and T-norm datasets provides a means of investigating the sensitivity of SVM-based speaker verification performance to the selection of each of these datasets. Using refined datasets provided improvements of 13% in min. DCF and 9% in EER over the full set of impostor examples on the 2006 SRE corpus with the majority of these gains due to refinement of the T-norm dataset. Similar trends were observed for the unseen data of the NIST 2008 SRE.

Robust video stabilisation algorithm using feature point selection and delta optical flow

In this study, the authors propose a novel video stabilisation algorithm for mobile platforms with moving objects in the scene. The quality of videos obtained from mobile platforms, such as unmanned airborne vehicles, suffers from jitter caused by several factors. In order to remove this undesired jitter, the accurate estimation of global motion is essential. However it is difficult to estimate global motions accurately from mobile platforms due to increased estimation errors and noises. Additionally, large moving objects in the video scenes contribute to the estimation errors. Currently, only very few motion estimation algorithms have been developed for video scenes collected from mobile platforms, and this paper shows that these algorithms fail when there are large moving objects in the scene. In this study, a theoretical proof is provided which demonstrates that the use of delta optical flow can improve the robustness of video stabilisation in the presence of large moving objects in the scene. The authors also propose to use sorted arrays of local motions and the selection of feature points to separate outliers from inliers. The proposed algorithm is tested over six video sequences, collected from one fixed platform, four mobile platforms and one synthetic video, of which three contain large moving objects. Experiments show our proposed algorithm performs well to all these video sequences.

The applicability of biased estimation in model and model order selection

Biased estimation has the advantage of reducing the mean squared error (MSE) of an estimator. The question of interest is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model selection criteria. Specifically, we analyze the performance of the minimum description length (MDL) criterion based on biased and unbiased estimation and compare it against modern model selection criteria such as Kay's conditional model order estimator (CME), the bootstrap and the more recently proposed hook-and-loop resampling based model selection. The advantages and limitations of the considered techniques are discussed. The results indicate that, in some cases, biased estimators can slightly improve the selection of the correct model. We also give an example for which the CME with an unbiased estimator fails, but could regain its power when a biased estimator is used.

Data-driven background dataset selection for SVM-based speaker verification

The recently proposed data-driven background dataset refinement technique provides a means of selecting an informative background for support vector machine (SVM)-based speaker verification systems. This paper investigates the characteristics of the impostor examples in such highly-informative background datasets. Data-driven dataset refinement individually evaluates the suitability of candidate impostor examples for the SVM background prior to selecting the highest-ranking examples as a refined background dataset. Further, the characteristics of the refined dataset were analysed to investigate the desired traits of an informative SVM background. The most informative examples of the refined dataset were found to consist of large amounts of active speech and distinctive language characteristics. The data-driven refinement technique was shown to filter the set of candidate impostor examples to produce a more disperse representation of the impostor population in the SVM kernel space, thereby reducing the number of redundant and less-informative examples in the background dataset. Furthermore, data-driven refinement was shown to provide performance gains when applied to the difficult task of refining a small candidate dataset that was mis-matched to the evaluation conditions.

Exploiting multiple feature sets in data-driven impostor dataset selection for speaker verification

This study assesses the recently proposed data-driven background dataset refinement technique for speaker verification using alternate SVM feature sets to the GMM supervector features for which it was originally designed. The performance improvements brought about in each trialled SVM configuration demonstrate the versatility of background dataset refinement. This work also extends on the originally proposed technique to exploit support vector coefficients as an impostor suitability metric in the data-driven selection process. Using support vector coefficients improved the performance of the refined datasets in the evaluation of unseen data. Further, attempts are made to exploit the differences in impostor example suitability measures from varying features spaces to provide added robustness.

The home stigma : adverse selection in ADRs and the home capital market environment

We investigate whether characteristics of the home country capital environment, such as information disclosure and investor rights protection continue to affect ADRs cross-listed in the U.S. Using microstructure measures as proxies for adverse selection, we find that characteristics of the home markets continue to be relevant, especially for emerging market firms. Less transparent disclosure, poorer protection of investor rights and weaker legal institutions are associated with higher levels of information asymmetry. Developed market firms appear to be affected by whether or not home business laws are common law or civil law legal origin. Our finding contributes to the bonding literature. It suggests that cross-listing in the U.S. should not be viewed as a substitute for improvement in the quality of local institutions, and attention must be paid to improve investor protection in order to achieve the full benefits of improved disclosure. Improvement in the domestic capital market environment can attract more investors even for U.S. cross-listed firms.

Selection and optimization of piezoelectric polyvinylidene fluoride polymers for adaptive optics in space environments

Various piezoelectric polymers based on polyvinylidene fluoride (PVDF) are of interest for large aperture space-based telescopes. Dimensional adjustments of adaptive polymer films depend on charge deposition and require a detailed understanding of the piezoelectric material responses which are expected to deteriorate owing to strong vacuum UV, � -, X-ray, energetic particles and atomic oxygen exposure. We have investigated the degradation of PVDF and its copolymers under various stress environments detrimental to reliable operation in space. Initial radiation aging studies have shown complex material changes with lowered Curie temperatures, complex material changes with lowered melting points, morphological transformations and significant crosslinking, but little influence on piezoelectric d33 constants. Complex aging processes have also been observed in accelerated temperature environments inducing annealing phenomena and cyclic stresses. The results suggest that poling and chain orientation are negatively affected by radiation and temperature exposure. A framework for dealing with these complex material qualification issues and overall system survivability predictions in low earth orbit conditions has been established. It allows for improved material selection, feedback for manufacturing and processing, material optimization/stabilization strategies and provides guidance on any alternative materials.

A hybrid genetic algorithm for the optimal constrained web service selection problem in web service composition

Web service composition is an important problem in web service based systems. It is about how to build a new value-added web service using existing web services. A web service may have many implementations, all of which have the same functionality, but may have different QoS values. Thus, a significant research problem in web service composition is how to select a web service implementation for each of the web services such that the composite web service gives the best overall performance. This is so-called optimal web service selection problem. There may be mutual constraints between some web service implementations. Sometimes when an implementation is selected for one web service, a particular implementation for another web service must be selected. This is so called dependency constraint. Sometimes when an implementation for one web service is selected, a set of implementations for another web service must be excluded in the web service composition. This is so called conflict constraint. Thus, the optimal web service selection is a typical constrained ombinatorial optimization problem from the computational point of view. This paper proposes a new hybrid genetic algorithm for the optimal web service selection problem. The hybrid genetic algorithm has been implemented and evaluated. The evaluation results have shown that the hybrid genetic algorithm outperforms other two existing genetic algorithms when the number of web services and the number of constraints are large.