Towards testing a new first-order decision making model for the procurement of public sector major infrastructure

Given global demand for new infrastructure, governments face substantial challenges in funding new infrastructure and simultaneously delivering Value for Money (VfM). As background to this challenge, a brief review is given of current practice in the selection of major public sector infrastructure in Australia, along with a review of the related literature concerning the Multi-Attribute Utility Approach (MAUA) and the effect of MAUA on the role of risk management in procurement selection. To contribute towards addressing the key weaknesses of MAUA, a new first-order procurement decision making model is mentioned. A brief summary is also given of the research method and hypothesis used to test and develop the new procurement model and which uses competition as the dependent variable and as a proxy for VfM. The hypothesis is given as follows: When the actual procurement mode matches the theoretical/predicted procurement mode (informed by the new procurement model), then actual competition is expected to match optimum competition (based on actual prevailing capacity vis-à-vis the theoretical/predicted procurement mode) and subject to efficient tendering. The aim of this paper is to report on progress towards testing this hypothesis in terms of an analysis of two of the four data components in the hypothesis. That is, actual procurement and actual competition across 87 road and health major public sector projects in Australia. In conclusion, it is noted that the Global Financial Crisis (GFC) has seen a significant increase in competition in public sector major road and health infrastructure and if any imperfections in procurement and/or tendering are discernible, then this would create the opportunity, through the deployment of economic principles embedded in the new procurement model and/or adjustments in tendering, to maintain some of this higher level post-GFC competition throughout the next business cycle/upturn in demand including private sector demand. Finally, the paper previews the next steps in the research with regard to collection and analysis of data concerning theoretical/predicted procurement and optimum competition.

Wound problems following hip arthroplasty before and after the introduction of a direct thrombin inhibitor for thromboprophylaxis

NICE guidelines have stated that patients undergoing elective hip surgery are at increased risk for venous thromboembolic events (VTE) following surgery and have recommended thromboprophylaxis for 28-35 days1, 2. However the studies looking at the new direct thrombin inhibitors have only looked at major bleeding. We prospectively looked at wound discharge in patients who underwent hip arthroplasty and were given dabigatran postoperatively between March 2010 and April 2010 (n=56). We retrospectively compared these results to a matched group of patients who underwent similar operations six months earlier when all patients were given dalteparin routinely postoperatively until discharge, and discharged home on 150mg aspirin daily for 6 weeks (n=67). Wound discharge after 5 days was significantly higher in the patients taking dabigatran (32% dabigatran n=18, 10% dalteparin n=17, p=0.003) and our rate of delayed discharges due to wound discharge significantly increased from 7% in the dalteparin group (n=5) to 27% for dabigatran (n=15, p=0.004). Patients who received dabigatran were more than five times as likely to return to theatre with a wound complication as those who received dalteparin (7% dabigatran n=4, vs. 1% dalteparin n=1), however, this was not statistically significant (p=0.18). The significantly higher wound discharge and return to theatre rates demonstrated in this study have meant that we have changed our practice to administering dalteparin until the wound is dry and then starting dabigatran. Our study demonstrates the need for further clinical studies regarding wound discharge and dabigatran.

Raising awareness of traffic pollution: the potential benefits and problems of using a warning smell

Exposure to traffic pollution is increasing worldwide as people move to cities, and as more vehicles join the roads, creating longer journeys and more traffic jams. Most traffic pollutants are odourless and invisible, which hides exposure from the public. If traffic pollution had a distinctive smell it would enable people to avoid exposure, and increase the political will for difficult policy changes. A smell may also instigate longer-term changes, such as switching to active transport for school pick-ups. A smell could be added using a fuel additive or a temporary device attached to vehicle exhausts.

Biomaterials in orthopedic bone plates : a review

This paper aims to review biomaterials used in manufacturing bone plates including advances in recent years and prospect in the future. It has found among all biomaterials, currently titanium and stainless steel alloys are the most common in production of bone plates. Other biomaterials such as Mg alloys, Ta alloys, SMAs, carbon fiber composites and bioceramics are potentially suitable for bone plates because of their advantages in biocompatibility, bioactivity and biodegradability. However, today either they are not used in bone plates or have limited applications in only some flexible small-size implants. This problem is mainly related to their poor mechanical properties. Additionally, production processes play an effective role. Therefore, in the future, further studies should be conducted to solve these problems and make them feasible for heavy-duty bone plates.

Perceptions of data quality dimensions and data roles

Several authors stress the importance of data’s crucial foundation for operational, tactical and strategic decisions (e.g., Redman 1998, Tee et al. 2007). Data provides the basis for decision making as data collection and processing is typically associated with reducing uncertainty in order to make more effective decisions (Daft and Lengel 1986). While the first series of investments of Information Systems/Information Technology (IS/IT) into organizations improved data collection, restricted computational capacity and limited processing power created challenges (Simon 1960). Fifty years on, capacity and processing problems are increasingly less relevant; in fact, the opposite exists. Determining data relevance and usefulness is complicated by increased data capture and storage capacity, as well as continual improvements in information processing capability. As the IT landscape changes, businesses are inundated with ever-increasing volumes of data from both internal and external sources available on both an ad-hoc and real-time basis. More data, however, does not necessarily translate into more effective and efficient organizations, nor does it increase the likelihood of better or timelier decisions. This raises questions about what data managers require to assist their decision making processes.

A burning issue : fashion and sun protection

The 'dick' tog, a briefs-style male swimsuit as it is colloquially referred to, is linked to Australia's national identity with overtly masculine bronzed 'Aussie' bodies clothed in this iconic apparel. However, the reality is, our hunger for worshiping the sun and the addiction to a beach culture is tempered by the pragmatic need to cover up and wear neck-to-knee, or more apt, head-to-toe sun protective clothing. Australia, in particular the state of Queensland, has one of the highest rates of skin cancer in the world; nevertheless, even after wide-ranging public programs for sun safety awareness many people still continue to wear designs that provide minimal sun protection. This paper will examine issues surrounding fashion and sun safe clothing. It will be proposed that in order to have effective community adoption of sun safe practices it is critical to understand the important role that fashion plays in determining sun protective behaviour.

Relevance feature discovery for text analysis

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term- based ones in describing user preferences, but many experiments do not support this hypothesis. This research presents a promising method, Relevance Feature Discovery (RFD), for solving this challenging issue. It discovers both positive and negative patterns in text documents as high-level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the high-level features. The thesis also introduces an adaptive model (called ARFD) to enhance the exibility of using RFD in adaptive environment. ARFD automatically updates the system's knowledge based on a sliding window over new incoming feedback documents. It can efficiently decide which incoming documents can bring in new knowledge into the system. Substantial experiments using the proposed models on Reuters Corpus Volume 1 and TREC topics show that the proposed models significantly outperform both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and other pattern-based methods.

Enriching XML documents clustering by using concise structure and content

With the growing number of XML documents on theWeb it becomes essential to effectively organise these XML documents in order to retrieve useful information from them. A possible solution is to apply clustering on the XML documents to discover knowledge that promotes effective data management, information retrieval and query processing. However, many issues arise in discovering knowledge from these types of semi-structured documents due to their heterogeneity and structural irregularity. Most of the existing research on clustering techniques focuses only on one feature of the XML documents, this being either their structure or their content due to scalability and complexity problems. The knowledge gained in the form of clusters based on the structure or the content is not suitable for reallife datasets. It therefore becomes essential to include both the structure and content of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both these kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. The overall objective of this thesis is to address these issues by: (1) proposing methods to utilise frequent pattern mining techniques to reduce the dimension; (2) developing models to effectively combine the structure and content of XML documents; and (3) utilising the proposed models in clustering. This research first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. A clustering framework with two types of models, implicit and explicit, is developed. The implicit model uses a Vector Space Model (VSM) to combine the structure and the content information. The explicit model uses a higher order model, namely a 3- order Tensor Space Model (TSM), to explicitly combine the structure and the content information. This thesis also proposes a novel incremental technique to decompose largesized tensor models to utilise the decomposed solution for clustering the XML documents. The proposed framework and its components were extensively evaluated on several real-life datasets exhibiting extreme characteristics to understand the usefulness of the proposed framework in real-life situations. Additionally, this research evaluates the outcome of the clustering process on the collection selection problem in the information retrieval on the Wikipedia dataset. The experimental results demonstrate that the proposed frequent pattern mining and clustering methods outperform the related state-of-the-art approaches. In particular, the proposed framework of utilising frequent structures for constraining the content shows an improvement in accuracy over content-only and structure-only clustering results. The scalability evaluation experiments conducted on large scaled datasets clearly show the strengths of the proposed methods over state-of-the-art methods. In particular, this thesis work contributes to effectively combining the structure and the content of XML documents for clustering, in order to improve the accuracy of the clustering solution. In addition, it also contributes by addressing the research gaps in frequent pattern mining to generate efficient and concise frequent subtrees with various node relationships that could be used in clustering.

The application of Bowen’s family system theory (BFST) to the Malay population in Malaysia

The rapid economic development and social changes in Malaysia recently have led to many psychosocial problems in young people, such as drug addiction, child sexual abuse and mental illness. The Malaysian government is beginning to focus more attention on its social welfare and human service needs in order to alleviate these psychosocial problems. Although counselling is accepted and widespread in Malaysia, the practice of family therapy is not as accepted as it is still a widely held belief that family problems need to be kept within the family. However, changes are imminent and thus the theoretical basis of family therapy needs to be culturally relevant. Bowen‟s Family Systems Theory (BFST) is already one of the major theories taught to tertiary counselling students in Malaysian universities. The main tenet of Bowen‟s theory is that the family as a system may be unstable unless each member of the family is well differentiated. High differentiation levels in the family allow a person to both leave the family‟s boundaries in search of uniqueness and to continually return to the family fold in order to establish a more mature sense of belonging. The difficulty, however, is that while Bowen has claimed that his theory is universal nearly all of the research confirming the theory has been conducted in the United States of America. The only known study outside America, however, did show that Bowen‟s theory applied to a Filipino population but, one of the theory‟s propositions that differentiation is intergenerational was not supported in this non-American sample. The American sample that was compared to the Malay sample was taken from Skowron and Friedlander‟s (1998) study. One hundred and twenty-seven faculty staff in an American university completed the Differentiation of Self Inventory (DSI) to measure level of differentiation of self. This thesis therefore, set out to determine whether Bowen‟s theory applied to another non-American sample, the Malaysian community. The research also investigated if the intergenerational effect was present in the Malaysian sample as well as explored the role of socio-economic status on Bowen‟s theory of differentiation and gender effect. Three hundred and seventy-four families completed four measures to examine these research questions: the Differentiation of Self Inventory (DSI), the Family Inventory of Life Event (FILE), the Depression Anxiety and Stress Scale (DASS) and the Connor-Davidson Resilience Scale (CD-RISC). The results of the study showed that differentiation of self is a valid construct for the Malay population. However, all four subscales of the Differentiation of Self Inventory (DSI); emotional reactivity (ER), emotional cut-off (EC), fusion with other (FO) and I position (IP), showed significant differences compared to the American sample from Skowron and Friedlander‟s (1998) study. The Malay sample scored higher in emotional reaction (ER), fusion with other (FO), but lower on emotional cut-off (EC) and I position (IP) than the American sample. The intergenerational effect was found in the Malay population as the parent‟s level of differentiation correlated with their children‟s level of differentiation. It was found that stress as measured by the Family Inventory of Life Event (FILE) and as measured by the Depression Anxiety and Stress Scale (DASS) were not correlated with the level of differentiation of self in parents. However, gender had a significant effect in predicting the level of differentiation among parents in Malay population with females scores higher on emotional reactivity (ER) and fusion with other (FO) than males. An additional finding was that resilience can be predicted from the level of differentiation of self in children in the Malay sample. There was also a positive correlation between the level of differentiation of self in parents and resilience in their children. Findings from this study indicate that the concept of differentiation of self is applicable to a Malay sample; however, the implementation of the theory should be applied with cultural sensitivity.

Practice and Procedure. Self-executing order - construction - whether further judicial at required for self-executing order to take effect

In Mango Boulevard Pty Ltd v Spencer [2010] QCA 207, a self-executing order had been made in consequence of continuing default by parties to the proceedings in meeting their disclosure obligations. The case involved several questions about the construction and implications of the self-executing order. This note focuses on the aspects of the case relating to that order.

M-ary trees for combinatorial asset management decision problems

A novel m-ary tree based approach is presented to solve asset management decisions which are combinatorial in nature. The approach introduces a new dynamic constraint based control mechanism which is capable of excluding infeasible solutions from the solution space. The approach also provides a solution to the challenges with ordering of assets decisions.

Scheduling Trains with Priorities : a no-wait blocking parallel-machine job-shop scheduling model

The paper investigates train scheduling problems when prioritised trains and non-prioritised trains are simultaneously traversed in a single-line rail network. In this case, no-wait conditions arise because the prioritised trains such as express passenger trains should traverse continuously without any interruption. In comparison, non-prioritised trains such as freight trains are allowed to enter the next section immediately if possible or to remain in a section until the next section on the routing becomes available, which is thought of as a relaxation of no-wait conditions. With thorough analysis of the structural properties of the No-Wait Blocking Parallel-Machine Job-Shop-Scheduling (NWBPMJSS) problem that is originated in this research, an innovative generic constructive algorithm (called NWBPMJSS_Liu-Kozan) is proposed to construct the feasible train timetable in terms of a given order of trains. In particular, the proposed NWBPMJSS_Liu-Kozan constructive algorithm comprises several recursively-used sub-algorithms (i.e. Best-Starting-Time-Determination Procedure, Blocking-Time-Determination Procedure, Conflict-Checking Procedure, Conflict-Eliminating Procedure, Tune-up Procedure and Fine-tune Procedure) to guarantee feasibility by satisfying the blocking, no-wait, deadlock-free and conflict-free constraints. A two-stage hybrid heuristic algorithm (NWBPMJSS_Liu-Kozan-BIH) is developed by combining the NWBPMJSS_Liu-Kozan constructive algorithm and the Best-Insertion-Heuristic (BIH) algorithm to find the preferable train schedule in an efficient and economical way. Extensive computational experiments show that the proposed methodology is promising because it can be applied as a standard and fundamental toolbox for identifying, analysing, modelling and solving real-world scheduling problems.

Towards human and social innovation: not wanting to know in order to create

The formation of a venture relies, in part, upon the participants reaching a shared understanding of purpose and process. Yet in circumstances of great complexity and uncertainty how can such a shared understanding be created? If the response to complexity and uncertainty is to seek simplicity in order to find commonality then what is lost and what is at risk? Can shared understandings of purpose and process be arrived at by embracing complexity and uncertainty and if so how? These questions led us to explore the process of dialogue and communication of a team in its formative stages. Our interests were not centred upon the behavioural characteristics of the individuals in the 'forming' stage of group dynamics but rather the process of cognitive and linguistic turns, the wax and wan of ideas and, the formation of shared meaning. This process of cognitive and linguistic turns was focused thematically on the areas of foresight, innovation, entrepreneurship, and public policy. This cross disciplinary exploration sought to explore potential synergies between these domains, in particular in developing a conceptual basis for long term thinking that can inform wiser public policy.

Microstrip decoupling networks for low-order multi-port arrays with reduced element spacing

An array of monopole elements with reduced element spacing of λ/6 to λ/20 is considered for application in digital beam-forming and direction-finding. The small element spacing introduces strong mutual coupling between the array elements. This paper discusses that decoupling can be achieved analytically for arrays with three elements and describes Kuroda’s identities to realize the lumped elements of the derived decoupling network. Design procedures and equations are proposed. Experimental results are presented. The decoupled array has a bandwidth of 1% and a superdirective radiation pattern.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model