Automating human thought processes for a UAV forced landing

This paper describes the current status of a program to develop an automated forced landing system for a fixed-wing Unmanned Aerial Vehicle (UAV). This automated system seeks to emulate human pilot thought processes when planning for and conducting an engine-off emergency landing. Firstly, a path planning algorithm that extends Dubins curves to 3D space is presented. This planning element is then combined with a nonlinear guidance and control logic, and simulated test results demonstrate the robustness of this approach to strong winds during a glided descent. The average path deviation errors incurred are comparable to or even better than that of manned, powered aircraft. Secondly, a study into suitable multi-criteria decision making approaches and the problems that confront the decision-maker is presented. From this study, it is believed that decision processes that utilize human expert knowledge and fuzzy logic reasoning are most suited to the problem at hand, and further investigations will be conducted to identify the particular technique/s to be implemented in simulations and field tests. The automated UAV forced landing approach presented in this paper is promising, and will allow the progression of this technology from the development and simulation stages through to a prototype system

Urban network travel time estimation from stop-line loop detector data and signal controller data

This paper presents a model to estimate travel time using cumulative plots. Three different cases considered are i) case-Det, for only detector data; ii) case-DetSig, for detector data and signal controller data and iii) case-DetSigSFR: for detector data, signal controller data and saturation flow rate. The performance of the model for different detection intervals is evaluated. It is observed that detection interval is not critical if signal timings are available. Comparable accuracy can be obtained from larger detection interval with signal timings or from shorter detection interval without signal timings. The performance for case-DetSig and for case-DetSigSFR is consistent with accuracy generally more than 95% whereas, case-Det is highly sensitive to the signal phases in the detection interval and its performance is uncertain if detection interval is integral multiple of signal cycles.

Synthesis of 4-arm star poly(L-Lactide) oligomers using an in situ-generated calcium-based initiator

Using an in situ-generated calcium-based initiating species derived from pentaerythritol, the bulk synthesis of well-defined 4-arm star poly(L-lactide) oligomers has been studied in detail. The substitution of the traditional initiator, stannous octoate with calcium hydride allowed the synthesis of oligomers that had both low PDIs and a comparable number of polymeric arms (3.7 – 3.9) to oligomers of similar molecular weight. Investigations into the degree of control observed during the course of the polymerization found that the insolubility of pentaerythritol in molten L-lactide resulted in an uncontrolled polymerization only when the feed mole ratio of L-lactide to pentaerythritol was 13. At feed ratios of 40 and greater, a pseudo-living polymerization was observed. As part of this study, in situ FT-Raman spectroscopy was demonstrated to be a suitable method to monitor the kinetics of the ring-opening polymerization (ROP) of lactide. The advantages of using this technique rather than FT-IR-ATR and 1H NMR for monitoring L-lactide consumption during polymerization are discussed.

The impact of child sexual abuse on victims/survivors: Exploring posttraumatic outcomes as a function of childhood sexual abuse

There is sparse systematic examination of the potential for growth as well as distress that may occur for some adult survivors of childhood sexual abuse. The presented study explored posttraumatic growth and its relationship with negative posttrauma outcomes within the specific population of survivors of childhood sexual abuse (N = 40). Results showed that 95% of the participants experienced clinically significant post-traumatic stress disorder symptomatology related to their childhood sexual abuse. In conjunction with these high levels of negative symptoms, the population evidenced posttraumatic growth levels that were comparable to other trauma samples. This research has clinical relevance in terms of adding to the knowledge base on sexual abuse and the usefulness of this knowledge in therapeutic interventions and relationships.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Word Segmentation for Chinese Wikipedia Using N-Gram Mutual Information

In this paper, we propose an unsupervised segmentation approach, named "n-gram mutual information", or NGMI, which is used to segment Chinese documents into n-character words or phrases, using language statistics drawn from the Chinese Wikipedia corpus. The approach alleviates the tremendous effort that is required in preparing and maintaining the manually segmented Chinese text for training purposes, and manually maintaining ever expanding lexicons. Previously, mutual information was used to achieve automated segmentation into 2-character words. The NGMI approach extends the approach to handle longer n-character words. Experiments with heterogeneous documents from the Chinese Wikipedia collection show good results.

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.

What determines the health-related quality of life among regional and rural breast cancer survivors?

Objective: To assess the health-related quality of life (HRQoL) of regional and rural breast cancer survivors at 12 months post-diagnosis and to identify correlates of HRQoL. Methods: 323 (202 regional and 121 rural) Queensland women diagnosed with unilateral breast cancer in 2006/2007 participated in a population-based, cross-sectional study. HRQoL was measured using the Functional Assessment of Cancer Therapy, Breast plus arm morbidity (FACT-B+4) self-administered questionnaire. Results: In age-adjusted analyses, mean HRQoL scores of regional breast cancer survivors were comparable to their rural counterparts 12 months post-diagnosis (122.9, 95% CI: 119.8, 126.0 vs. 123.7, 95% CI: 119.7, 127.8; p>0.05). Irrespective of residence, younger (<50 years) women reported lower HRQoL than older (50+ years) women (113.5, 95% CI: 109.3, 117.8 vs. 128.2, 95%CI: 125.1, 131.2; p<0.05). Those women who received chemotherapy, reported two complications post-surgery, had poorer upper-body function than most, reported more stress, reduced coping, who were socially isolated, had no confidante for social-emotional support, had unmet healthcare needs, and low health self-efficacy reported lower HRQoL scores. Together, these factors explained 66% of the variance in overall HRQoL. The pattern of results remained similar for younger and older age groups. Conclusions and Implications: The results underscore the importance of supporting and promoting regional and rural breast cancer programs that are designed to improve physical functioning, reduce stress and provide psychosocial support following diagnosis. Further, the information can be used by general practitioners and other allied health professionals for identifying women at risk of poorer HRQoL.

Interactions of phenyldithioesters with gold nanoparticles (AuNPs): Implications for AuNP functionalization and molecular barcoding of AuNP assemblies

The interactions of phenyldithioesters with gold nanoparticles (AuNPs) have been studied by monitoring changes in the surface plasmon resonance (SPR), depolarised light scattering, and surface enhanced Raman spectroscopy (SERS). Changes in the SPR indicated that an AuNP-phenyldithioester charge transfer complex forms in equilibrium with free AuNPs and phenyldithioester. Analysis of the Langmuir binding isotherms indicated that the equilibrium adsorption constant, Kads, was 2.3 ± 0.1 × 106 M−1, which corresponded to a free energy of adsorption of 36 ± 1 kJ mol−1. These values are comparable to those reported for interactions of aryl thiols with gold and are of a similar order of magnitude to moderate hydrogen bonding interactions. This has significant implications in the application of phenyldithioesters for the functionalization of AuNPs. The SERS results indicated that the phenyldithioesters interact with AuNPs through the C═S bond, and the molecules do not disassociate upon adsorption to the AuNPs. The SERS spectra are dominated by the portions of the molecule that dominate the charge transfer complex with the AuNPs. The significance of this in relation to the use of phenyldithioesters for molecular barcoding of nanoparticle assemblies is discussed.

Within-session variability modelling for factor analysis speaker verification

This work presents an extended Joint Factor Analysis model including explicit modelling of unwanted within-session variability. The goals of the proposed extended JFA model are to improve verification performance with short utterances by compensating for the effects of limited or imbalanced phonetic coverage, and to produce a flexible JFA model that is effective over a wide range of utterance lengths without adjusting model parameters such as retraining session subspaces. Experimental results on the 2006 NIST SRE corpus demonstrate the flexibility of the proposed model by providing competitive results over a wide range of utterance lengths without retraining and also yielding modest improvements in a number of conditions over current state-of-the-art.

Recruiting older adults to health research studies : a systematic review

Objective: To provide a systematic review of papers comparing the effectiveness of different strategies to recruit older adults (aged 50 years and over) to participate in health research studies, to guide successful recruitment in future research. Methods: Four major databases were searched for papers published between 1995 and 2008 with: target group aged 50 years or over; participants allocated to receive one of two or more recruitment strategies; and an outcome measure of response rate or enrolment in study. Results: Twelve papers were included in the review. Conclusion: For postal questionnaires, recruitment strategies used with older adults had comparable outcomes to those used to recruit from the general population. For other types of studies, strategies involving face-to-face contact may be more effective than indirect methods, but this needs to be balanced against feasibility. Overall, little evidence on the topic exists and more rigorous investigation is necessary.

Inducing shades of meaning by matrix methods : a first step towards thematic analysis of opinion

This article explores two matrix methods to induce the ``shades of meaning" (SoM) of a word. A matrix representation of a word is computed from a corpus of traces based on the given word. Non-negative Matrix Factorisation (NMF) and Singular Value Decomposition (SVD) compute a set of vectors corresponding to a potential shade of meaning. The two methods were evaluated based on loss of conditional entropy with respect to two sets of manually tagged data. One set reflects concepts generally appearing in text, and the second set comprises words used for investigations into word sense disambiguation. Results show that for NMF consistently outperforms SVD for inducing both SoM of general concepts as well as word senses. The problem of inducing the shades of meaning of a word is more subtle than that of word sense induction and hence relevant to thematic analysis of opinion where nuances of opinion can arise.

Ticagrelor, a platelet aggregation inhibitor for the potential prevention and treatment of arterial thrombosis and acute coronary syndromes

Ticagrelor is an orally active ADP P2Y12 receptor antagonist in development by AstraZeneca plc for the reduction of recurrent ischemic events in patients with acute coronary syndromes (ACS). Prior to the development of ticagrelor, thienopyridine compounds, such as clopidogrel, were the focus of research into therapies for ACS. Although the thienopyridines are effective platelet aggregation inhibitors, they are prodrugs and, consequently, exert a slow onset of action. In addition, the variability in inter-individual metabolism of thienopyridine prodrugs has been associated with reduced efficacy in some patients. Ticagrelor is not a prodrug and exhibits a more rapid onset of action than the thienopyridine prodrugs. In clinical trials conducted to date, ticagrelor was a potent inhibitor of ADP-induced platelet aggregation and demonstrated effects that were comparable to clopidogrel. In a phase II, short-term trial, the bleeding profile of participants treated with ticagrelor was similar to that obtained with clopidogrel; however, an increased incidence of dyspnea was observed - an effect that has not been reported with the thienopyridines. Considering the occurrence of dyspnea, and the apparent non-superiority of ticagrelor to clopidogrel, it is difficult to justify a clear benefit to the continued development of ticagrelor. Outcomes from an ongoing phase III trial comparing ticagrelor with clopidogrel in 18,000 patients with ACS are likely to impact on the future development of ticagrelor.

The language of neofeudal corporatism and the war on Iraq

In this paper we argue that the term “capitalism” is no longer useful for understanding the current system of political economic relations in which we live. Rather, we argue that the system can be more usefully characterised as neofeudal corporatism. Using examples drawn from a 300,000 word corpus of public utterances by three political leaders from the “coalition of the willing”— George W. Bush, Tony Blair, and John Howard—we show some defining characteristics of this relatively new system and how they are manifest in political language about the invasion of Iraq.