Fast unscented transformation-based transient stability margin estimation incorporating uncertainty of wind generation

Intermittent generation from wind farms leads to fluctuating power system operating conditions pushing the stability margin to its limits. The traditional way of determining the worst case generation dispatch for a system with several semi-scheduled wind generators yields a conservative solution. This paper proposes a fast estimation of the transient stability margin (TSM) incorporating the uncertainty of wind generation. First, the Kalman filter (KF) is used to provide linear estimation of system angle and then unscented transformation (UT) is used to estimate the distribution of the TSM. The proposed method is compared with the traditional Monte Carlo (MC) method and the effectiveness of the proposed approach is verified using Single Machine Infinite Bus (SMIB) and IEEE 14 generator Australian dynamic system. This method will aid grid operators to perform fast online calculations to estimate TSM distribution of a power system with high levels of intermittent wind generation.

Design and evaluation of decision and control strategies for autonomous vision-based see and avoid systems

This paper details the design and performance assessment of a unique collision avoidance decision and control strategy for autonomous vision-based See and Avoid systems. The general approach revolves around re-positioning a collision object in the image using image-based visual servoing, without estimating range or time to collision. The decision strategy thus involves determining where to move the collision object, to induce a safe avoidance manuever, and when to cease the avoidance behaviour. These tasks are accomplished by exploiting human navigation models, spiral motion properties, expected image feature uncertainty and the rules of the air. The result is a simple threshold based system that can be tuned and statistically evaluated by extending performance assessment techniques derived for alerting systems. Our results demonstrate how autonomous vision-only See and Avoid systems may be designed under realistic problem constraints, and then evaluated in a manner consistent to aviation expectations.

Feature overlap slows lexical selection: Evidence from the picture–word interference paradigm

How does the presence of a categorically related word influence picture naming latencies? In order to test competitive and noncompetitive accounts of lexical selection in spoken word production, we employed the picture–word interference (PWI) paradigm to investigate how conceptual feature overlap influences naming latencies when distractors are category coordinates of the target picture. Mahon et al. (2007. Lexical selection is not by competition: A reinterpretation of semantic interference and facilitation effects in the picture-word interference paradigm. Journal of Experimental Psychology. Learning, Memory, and Cognition, 33(3), 503–535. doi:10.1037/0278-7393.33.3.503) reported that semantically close distractors (e.g., zebra) facilitated target picture naming latencies (e.g., HORSE) compared to far distractors (e.g., whale). We failed to replicate a facilitation effect for within-category close versus far target–distractor pairings using near-identical materials based on feature production norms, instead obtaining reliably larger interference effects (Experiments 1 and 2). The interference effect did not show a monotonic increase across multiple levels of within-category semantic distance, although there was evidence of a linear trend when unrelated distractors were included in analyses (Experiment 2). Our results show that semantic interference in PWI is greater for semantically close than for far category coordinate relations, reflecting the extent of conceptual feature overlap between target and distractor. These findings are consistent with the assumptions of prominent competitive lexical selection models of speech production.

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.

Catholic teachers’ postgraduate qualifications and students’ end of schooling outcomes: A large scale Queensland based comparative study

A key feature of the current era of Australian schooling is the dominance of publically available student, school and teacher performance data. Our paper examines the intersection of data on teachers’ postgraduate qualifications and students’ end of schooling outcomes in 26 Catholic Systemic Secondary Schools and 18 Catholic Independent Secondary Schools throughout the State of Queensland. We introduce and justify taking up a new socially-just measurement model of students’ end of schooling outcomes, called the ‘Tracking and Academic Management Index’, otherwise known as ‘TAMI’. Additional analysis is focused on the outcomes of top-end students vis-à-vis all students who are encouraged to remain in institutionalised education of one form or another for the two final years of senior secondary schooling. These findings of the correlations between Catholic teachers’ postgraduate qualifications and students’ end of schooling outcomes are also compared with teachers’ postgraduate qualifications and students’ end of schooling outcomes across 174 Queensland Government Secondary Schools and 58 Queensland Independent Secondary Schools from the same data collection period. The findings raise important questions about the transference of teachers’ postgraduate qualifications for progressing students’ end of schooling outcomes as well as the performance of Queensland Catholic Systemic Secondary Schools and Queensland Catholic Independent Secondary Schools during a particular era of education.

Generalised features for bird vocalisation retrieval in acoustic recordings

Bioacoustic monitoring has become a significant research topic for species diversity conservation. Due to the development of sensing techniques, acoustic sensors are widely deployed in the field to record animal sounds over a large spatial and temporal scale. With large volumes of collected audio data, it is essential to develop semi-automatic or automatic techniques to analyse the data. This can help ecologists make decisions on how to protect and promote the species diversity. This paper presents generic features to characterize a range of bird species for vocalisation retrieval. In the implementation, audio recordings are first converted to spectrograms using short-time Fourier transform, then a ridge detection method is applied to the spectrogram for detecting points of interest. Based on the detected points, a new region representation are explored for describing various bird vocalisations and a local descriptor including temporal entropy, frequency bin entropy and histogram of counts of four ridge directions is calculated for each sub-region. To speed up the retrieval process, indexing is carried out and the retrieved results are ranked according to similarity scores. The experiment results show that our proposed feature set can achieve 0.71 in term of retrieval success rate which outperforms spectral ridge features alone (0.55) and Mel frequency cepstral coefficients (0.36).

Aspect-based opinion mining from product reviews using conditional random fields

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Natural language data is typically very sparse; the most common words are those that do not carry a lot of semantic content, and occurrences of any particular content-bearing word are rare, while co-occurrences of these words are rarer. Mining product aspects, along with corresponding opinions, is essential for Aspect-Based Opinion Mining (ABOM) as a result of the e-commerce revolution. Therefore, the need for automatic mining of reviews has reached a peak. In this work, we deal with ABOM as sequence labelling problem and propose a supervised extraction method to identify product aspects and corresponding opinions. We use Conditional Random Fields (CRFs) to solve the extraction problem and propose a feature function to enhance accuracy. The proposed method is evaluated using two different datasets. We also evaluate the effectiveness of feature function and the optimisation through multiple experiments.

Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning

Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

Identifying vertebral end plate lesions (schmorl's nodes) using a semi-automatic method

INTRODUCTION There is a large range in the reported prevalence of end plate lesions (EPLs), sometimes referred to as Schmorl's nodes in the general population (3.8-76%). One possible reason for this large range is the differences in definitions used by authors. Previous research has suggested that EPLs may potentially be a primary disturbance of growth plates that leads to the onset of scoliosis. The aim of this study was to develop a technique to measure the size, prevalence and location of EPLs on Computed Tomography (CT) images of scoliosis patients in a consistent manner. METHODS A detection algorithm was developed and applied to measure EPLs for five adolescent females with idiopathic scoliosis (average age 15.1 years, average major Cobb 60°). In this algorithm, the EPL definition was based on the lesion depth, the distance from the edge of the vertebral body and the gradient of the lesion edge. Existing low-dose, CT scans of the patients' spines were segmented semi-automatically to extract 3D vertebral endplate morphology. Manual sectioning of any attachments between posterior elements of adjacent vertebrae and, if necessary, endplates was carried out before the automatic algorithm was used to determine the presence and position of EPLs. RESULTS EPLs were identified in 15 of the 170 (8.8%) endplates analysed with an average depth of 3.1mm. 73% of the EPLs were seen in the lumbar spines (11/15). A sensitivity study demonstrated that the algorithm was most sensitive to changes in the minimum gradient required at the lesion edge. CONCLUSION An imaging analysis technique for consistent measurement of the prevalence, location and size of EPLs on CT images has been developed. Although the technique was tested on scoliosis patients, it can be used to analyse other populations without observer errors in EPL definitions.

Australian patients using a cardiac-diabetes web-based intervention program: Contributions to person-centered clinical practice

Rationale, aims and objectives: Patients with both cardiac disease and diabetes have poorer health outcomes than patients with only one chronic condition. While evidence indicates that internet based interventions may improve health outcomes for patients with a chronic disease, there is no literature on internet programs specific to cardiac patients with comorbid diabetes. Therefore this study aimed to develop a specific web-based program, then to explore patients’ perspectives on the usefulness of a new program. Methods: The interpretive approach using semi-structured interviews on a purposive sample of eligible patients with type 2 diabetes and a cardiac condition in a metropolitan hospital in Brisbane, Australia. Thematic analysis was undertaken to describe the perceived usefulness of a newly developed Heart2heart webpage. Results: Themes identified included confidence in hospital health professionals and reliance on doctors to manage conditions. Patients found the webpage useful for managing their conditions at home. Conclusions: The new Heart2heart webpage provided a positive and useful resource. Further research on to determine the potential influence of this resource on patients’ self-management behaviours is paramount. Implications for practice include using multimedia strategies for providing information to patients’ comorbidities of cardiac disease and type 2 diabetes, and further development on enhancement of such strategies

Single-walled carbon nanotube-based polymer monoliths for the enantioselective nano-liquid chromatographic separation of racemic pharmaceuticals

Many protocols have been used for extraction of DNA from Thraustochytrids. These generally involve the use of CTAB, phenol/chloroform and ethanol. They also feature mechanical grinding, sonication, N2 freezing or bead beating. However, the resulting chemical and physical damage to extracted DNA reduces its quality. The methods are also unsuitable for large numbers of samples. Commercially-available DNA extraction kits give better quality and yields but are expensive. Therefore, an optimized DNA extraction protocol was developed which is suitable for Thraustochytrids to both minimise expensive and time-consuming steps prior to DNA extraction and also to improve the yield. The most effective method is a combination of single bead in TissueLyser (Qiagen) and Proteinase K. Results were conclusive: both the quality and the yield of extracted DNA were higher than with any other method giving an average yield of 8.5 µg/100 mg biomass.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

The prevalence, predictors and associations of hypertension in Sri Lanka: A cross-sectional population based national survey

We studied the community prevalence, patterns and predictors of hypertension in a large sub-population of South Asian adults with a view of identifying differential risk factors. Data were collected between years 2005-2006 and 5000 adults were invited for the study. The sample size was 4485, and about 39.5% were males. Mean systolic and diastolic blood pressures were 127.1 ± 19.8 mmHg and 75.4 ± 11.3 mmHg, respectively. Age-adjusted prevalence in all adults, males and females was 23.7%, 23.4% and 23.8%, respectively. Urban adults had a significantly higher prevalence of hypertension than rural adults. In the binary logistic-regression analysis, male gender (OR: 1.2), increasing age, Sri Lankan Moor ethnicity (OR: 1.6), physical inactivity (OR: 1.7), presence of diabetes (OR: 2.2) and central obesity (OR: 2.3) all were significantly associated with hypertension. In conclusion, nearly one-third of the Sri Lankan adult population is hypertensive. Hence, public health initiatives should encourage healthier lifestyles with emphasis on preventing obesity and increasing physical activity.

Evidence-based recommendations for the diagnosis of ankylosing spondylitis: Results from the Australian 3E initiative in rheumatology

• As part of the 3E program, we conducted a systematic literature review and gathered consensus from 23 practising Australian rheumatologists to develop guidelines for early identification of ankylosing spondylitis and specialist referral. • In three rounds of break-out sessions followed by discussion and voting, the specialist panel addressed three questions related to diagnosis of ankylosing spondylitis: In individuals with back pain, what are the early clinical features that suggest ankylosing spondylitis? How useful is imaging in identifying early ankylosing spondylitis? Based on which clinical features should a general practitioner refer a patient to a rheumatologist for further evaluation? • The panel agreed on six recommendations related to the three questions: 1a. Early clinical features to suggest ankylosing spondylitis include inflammatory back pain and age at symptom onset < 45 years. 1b. The absence of symptomatic response to an appropriate course of non-steroidal anti-inflammatory drugs makes the diagnosis of ankylosing spondylitis less likely. 1c. Raised inflammatory markers are supportive, but their absence does not rule out the diagnosis of ankylosing spondylitis. 2a. Despite low sensitivity to detect changes of early ankylosing spondylitis, plain radiographs of the pelvis and spine are appropriate initial imaging techniques. 2b. Magnetic resonance imaging is a useful imaging modality for detecting early changes of ankylosing spondylitis. 3. Individuals with inflammatory back pain should be referred to a rheumatologist for further evaluation. • Effective dissemination and implementation of these recommendations are important to standardise the approach to early diagnosis of ankylosing spondylitis.

A novel human leucocyte antigen-DRB1 genotyping method based on multiplex primer extension reactions

We have developed and validated a semi-automated fluorescent method of genotyping human leucocyte antigen (HLA)-DRB1 alleles, HLA-DRB1*01-16, by multiplex primer extension reactions. This method is based on the extension of a primer that anneals immediately adjacent to the single-nucleotide polymorphism with fluorescent dideoxynucleotide triphosphates (minisequencing), followed by analysis on an ABI Prism 3700 capillary electrophoresis instrument. The validity of the method was confirmed by genotyping 261 individuals using both this method and polymerase chain reaction with sequence-specific primer (PCR-SSP) or sequencing and by demonstrating Mendelian inheritance of HLA-DRB1 alleles in families. Our method provides a rapid means of performing high-throughput HLA-DRB1 genotyping using only two PCR reactions followed by four multiplex primer extension reactions and PCR-SSP for some allele groups. In this article, we describe the method and discuss its advantages and limitations.