Unsupervised online learning of condition-invariant images for place recognition

This paper presents an online, unsupervised training algorithm enabling vision-based place recognition across a wide range of changing environmental conditions such as those caused by weather, seasons, and day-night cycles. The technique applies principal component analysis to distinguish between aspects of a location’s appearance that are condition-dependent and those that are condition-invariant. Removing the dimensions associated with environmental conditions produces condition-invariant images that can be used by appearance-based place recognition methods. This approach has a unique benefit – it requires training images from only one type of environmental condition, unlike existing data-driven methods that require training images with labelled frame correspondences from two or more environmental conditions. The method is applied to two benchmark variable condition datasets. Performance is equivalent or superior to the current state of the art despite the lesser training requirements, and is demonstrated to generalise to previously unseen locations.

Hydrochemical evolution and groundwater flow processes in the Galilee and Eromanga basins, Great Artesian Basin, Australia: A multivariate statistical approach

The Galilee and Eromanga basins are sub-basins of the Great Artesian Basin (GAB). In this study, a multivariate statistical approach (hierarchical cluster analysis, principal component analysis and factor analysis) is carried out to identify hydrochemical patterns and assess the processes that control hydrochemical evolution within key aquifers of the GAB in these basins. The results of the hydrochemical assessment are integrated into a 3D geological model (previously developed) to support the analysis of spatial patterns of hydrochemistry, and to identify the hydrochemical and hydrological processes that control hydrochemical variability. In this area of the GAB, the hydrochemical evolution of groundwater is dominated by evapotranspiration near the recharge area resulting in a dominance of the Na–Cl water types. This is shown conceptually using two selected cross-sections which represent discrete groundwater flow paths from the recharge areas to the deeper parts of the basins. With increasing distance from the recharge area, a shift towards a dominance of carbonate (e.g. Na–HCO3 water type) has been observed. The assessment of hydrochemical changes along groundwater flow paths highlights how aquifers are separated in some areas, and how mixing between groundwater from different aquifers occurs elsewhere controlled by geological structures, including between GAB aquifers and coal bearing strata of the Galilee Basin. The results of this study suggest that distinct hydrochemical differences can be observed within the previously defined Early Cretaceous–Jurassic aquifer sequence of the GAB. A revision of the two previously recognised hydrochemical sequences is being proposed, resulting in three hydrochemical sequences based on systematic differences in hydrochemistry, salinity and dominant hydrochemical processes. The integrated approach presented in this study which combines different complementary multivariate statistical techniques with a detailed assessment of the geological framework of these sedimentary basins, can be adopted in other complex multi-aquifer systems to assess hydrochemical evolution and its geological controls.

Structural damage detection method using frequency response functions

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.

Weak acid extractable metals in Bramble Bay, Queensland, Australia : temporal behaviour, enrichment and source apportionment

Sediment samples were taken from six sampling sites in Bramble Bay, Queensland, Australia between February and November in 2012. They were analysed for a range of heavy metals including Al, Fe, Mn, Ti, Ce, Th, U, V, Cr, Co, Ni, Cu, Zn, As, Cd, Sb, Te, Hg, Tl and Pb. Fraction analysis, enrichment factors and Principal Component Analysis –Absolute Principal Component Scores (PCA-APCS) were carried out in order to assess metal pollution, potential bioavailability and source apportionment. Cr and Ni exceeded the Australian Interim Sediment Quality Guidelines at some sampling sites, while Hg was found to be the most enriched metal. Fraction analysis identified increased weak acid soluble Hg and Cd during the sampling period. Source apportionment via PCA-APCS found four sources of metals pollution, namely, marine sediments, shipping, antifouling coatings and a mixed source. These sources need to be considered in any metal pollution control measure within Bramble Bay.

Experimental and statistical investigation of Australian native plants for second-generation biodiesel production

This work explores the potential of Australian native plants as a source of second-generation biodiesel for internal combustion engines application. Biodiesels were evaluated from a number of non-edible oil seeds which are grow naturally in Queensland, Australia. The quality of the produced biodiesels has been investigated by several experimental and numerical methods. The research methodology and numerical model developed in this study can be used for a broad range of biodiesel feedstocks and for the future development of renewable native biodiesel in Australia.

Volatile organic compounds: Characteristics, distribution and sources in urban schools

Long term exposure to organic pollutants, both inside and outside school buildings may affect children’s health and influence their learning performance. Since children spend significant amount of time in school, air quality, especially in classrooms plays a key role in determining the health risks associated with exposure at schools. Within this context, the present study investigated the ambient concentrations of Volatile Organic Compounds (VOCs) in 25 primary schools in Brisbane with the aim to quantify the indoor and outdoor VOCs concentrations, identify VOCs sources and their contribution, and based on these; propose mitigation measures to reduce VOCs exposure in schools. One of the most important findings is the occurrence of indoor sources, indicated by the I/O ratio >1 in 19 schools. Principal Component Analysis with Varimax rotation was used to identify common sources of VOCs and source contribution was calculated using an Absolute Principal Component Scores technique. The result showed that outdoor 47% of VOCs were contributed by petrol vehicle exhaust but the overall cleaning products had the highest contribution of 41% indoors followed by air fresheners and art and craft activities. These findings point to the need for a range of basic precautions during the selection, use and storage of cleaning products and materials to reduce the risk from these sources.

Exploring and validating the internal dimensions of occupational stress: Evidence from construction cost estimators in China

A recurring feature of modern practice is occupational stress of project professionals, with both debilitating effects on the people concerned and indirectly affecting project success. Previous research outside the construction industry has involved the use of a psychology perceived stress questionnaire (PSQ) to measure occupational stress, resulting in the identification of one stressor – demand - and three sub-dimensional emotional reactions in terms of worry, tension and joy. The PSQ is translated into Chinese with a back translation technique and used in a survey of young construction cost professionals in China. Principal component analysis and confirmatory factor analysis are used to test the divisibility of occupational stress - little mentioned in previous research on stress in the construction context. In addition, structural equation modelling is used to assess nomological validity by testing the effects of the three dimensions on organizational commitment, the main finding of which is that lack of joy has the sole significant effect. The three-dimensional measurement framework facilitates the standardizing measurement of occupational stress. Further research will establish if the findings are also applicable in other settings and explore the relations between stress dimensions and other managerial concepts.

Genetic architecture of subcortical brain regions: Common and region-specific genetic contributions

Understanding the aetiology of patterns of variation within and covariation across brain regions is key to advancing our understanding of the functional, anatomical and developmental networks of the brain. Here we applied multivariate twin modelling and principal component analysis (PCA) to investigate the genetic architecture of the size of seven subcortical regions (caudate nucleus, thalamus, putamen, pallidum, hippocampus, amygdala and nucleus accumbens) in a genetically informative sample of adolescents and young adults (N=1038; mean age=21.6±3.2years; including 148 monozygotic and 202 dizygotic twin pairs) from the Queensland Twin IMaging (QTIM) study. Our multivariate twin modelling identified a common genetic factor that accounts for all the heritability of intracranial volume (0.88) and a substantial proportion of the heritability of all subcortical structures, particularly those of the thalamus (0.71 out of 0.88), pallidum (0.52 out of 0.75) and putamen (0.43 out of 0.89). In addition, we also found substantial region-specific genetic contributions to the heritability of the hippocampus (0.39 out of 0.79), caudate nucleus (0.46 out of 0.78), amygdala (0.25 out of 0.45) and nucleus accumbens (0.28 out of 0.52). This provides further insight into the extent and organization of subcortical genetic architecture, which includes developmental and general growth pathways, as well as the functional specialization and maturation trajectories that influence each subcortical region. This multivariate twin study identifies a common genetic factor that accounts for all the heritability of intracranial volume (0.88) and a substantial proportion of the heritability of all subcortical structures, particularly those of the thalamus (0.71 out of 0.88), pallidum (0.52 out of 0.75) and putamen (0.43 out of 0.89). In parallel, it also describes substantial region-specific genetic contributions to the heritability of the hippocampus (0.39 out of 0.79), caudate nucleus (0.46 out of 0.78), amygdala (0.25 out of 0.45) and nucleus accumbens (0.28 out of 0.52).

Differentiation of cultivars of flos chrysanthemum with the use of high-performance liquid chromatography fingerprints and chemometrics

Flos Chrysanthemum is a generic name for a particular group of edible plants, which also have medicinal properties. There are, in fact, twenty to thirty different cultivars, which are commonly used in beverages and for medicinal purposes. In this work, four Flos Chrysanthemum cultivars, Hangju, Taiju, Gongju, and Boju, were collected and chromatographic fingerprints were used to distinguish and assess these cultivars for quality control purposes. Chromatography fingerprints contain chemical information but also often have baseline drifts and peak shifts, which complicate data processing, and adaptive iteratively reweighted, penalized least squares, and correlation optimized warping were applied to correct the fingerprint peaks. The adjusted data were submitted to unsupervised and supervised pattern recognition methods. Principal component analysis was used to qualitatively differentiate the Flos Chrysanthemum cultivars. Partial least squares, continuum power regression, and K-nearest neighbors were used to predict the unknown samples. Finally, the elliptic joint confidence region method was used to evaluate the prediction ability of these models. The partial least squares and continuum power regression methods were shown to best represent the experimental results.

Runs of homozygosity and a cluster of vulvar cancer in young Australian Aboriginal women

Objective A cluster of vulvar cancer exists in young Aboriginal women living in remote communities in Arnhem Land, Australia. A genetic case–control study was undertaken involving 30 cases of invasive vulvar cancer and its precursor lesion, high-grade vulvar intraepithelial neoplasia (VIN), and 61 controls, matched for age and community of residence. It was hypothesized that this small, isolated population may exhibit increased autozygosity, implicating recessive effects as a possible mechanism for increased susceptibility to vulvar cancer. Methods Genotyping data from saliva samples were used to identify runs of homozygosity (ROH) in order to calculate estimates of genome-wide homozygosity. Results No evidence of an effect of genome-wide homozygosity on vulvar cancer and VIN in East Arnhem women was found, nor was any individual ROH found to be significantly associated with case status. This study found further evidence supporting an association between previous diagnosis of CIN and diagnosis of vulvar cancer or VIN, but found no association with any other medical history variable. Conclusions These findings do not eliminate the possibility of genetic risk factors being involved in this cancer cluster, but rather suggest that alternative analytical strategies and genetic models should be explored.

Acoustic classification of Australian anurans using syllable features

Acoustic classification of anurans (frogs) has received increasing attention for its promising application in biological and environment studies. In this study, a novel feature extraction method for frog call classification is presented based on the analysis of spectrograms. The frog calls are first automatically segmented into syllables. Then, spectral peak tracks are extracted to separate desired signal (frog calls) from background noise. The spectral peak tracks are used to extract various syllable features, including: syllable duration, dominant frequency, oscillation rate, frequency modulation, and energy modulation. Finally, a k-nearest neighbor classifier is used for classifying frog calls based on the results of principal component analysis. The experiment results show that syllable features can achieve an average classification accuracy of 90.5% which outperforms Mel-frequency cepstral coefficients features (79.0%).

Use of physicochemical signatures to assess the sources of metals in urban road dust

Road deposited dust is a complex mixture of pollutants derived from a wide range of sources. Accurate identification of these sources is seminal for effective source-oriented control measures. A range of techniques such as enrichment factor analysis (EF), principal component analysis (PCA) and hierarchical cluster analysis (HCA) are available for identifying sources of complex mixtures. However, they have multiple deficiencies when applied individually. This study presents an approach for the effective utilisation of EF, PCA and HCA for source identification, so that their specific deficiencies on an individual basis are eliminated. EF analysis confirmed the non-soil origin of metals such as Na, Cu, Cd, Zn, Sn, K, Ca, Sb, Ba, Ti, Ni and Mo providing guidance in the identification of anthropogenic sources. PCA and HCA identified four sources, with soil and asphalt wear in combination being the most prominent sources. Other sources were tyre wear, brake wear and sea salt.

Speeding up AdaBoost Classifier with Random Projection

The development of techniques for scaling up classifiers so that they can be applied to problems with large datasets of training examples is one of the objectives of data mining. Recently, AdaBoost has become popular among machine learning community thanks to its promising results across a variety of applications. However, training AdaBoost on large datasets is a major problem, especially when the dimensionality of the data is very high. This paper discusses the effect of high dimensionality on the training process of AdaBoost. Two preprocessing options to reduce dimensionality, namely the principal component analysis and random projection are briefly examined. Random projection subject to a probabilistic length preserving transformation is explored further as a computationally light preprocessing step. The experimental results obtained demonstrate the effectiveness of the proposed training process for handling high dimensional large datasets.

Liquid Chromatography-Mass Spectrometry in Studies of Drug Metabolism and Permeability

Poor pharmacokinetics is one of the reasons for the withdrawal of drug candidates from clinical trials. There is an urgent need for investigating in vitro ADME (absorption, distribution, metabolism and excretion) properties and recognising unsuitable drug candidates as early as possible in the drug development process. Current throughput of in vitro ADME profiling is insufficient because effective new synthesis techniques, such as drug design in silico and combinatorial synthesis, have vastly increased the number of drug candidates. Assay technologies for larger sets of compounds than are currently feasible are critically needed. The first part of this work focused on the evaluation of cocktail strategy in studies of drug permeability and metabolic stability. N-in-one liquid chromatography-tandem mass spectrometry (LC/MS/MS) methods were developed and validated for the multiple component analysis of samples in cocktail experiments. Together, cocktail dosing and LC/MS/MS were found to form an effective tool for increasing throughput. First, cocktail dosing, i.e. the use of a mixture of many test compounds, was applied in permeability experiments with Caco-2 cell culture, which is a widely used in vitro model for small intestinal absorption. A cocktail of 7-10 reference compounds was successfully evaluated for standardization and routine testing of the performance of Caco-2 cell cultures. Secondly, cocktail strategy was used in metabolic stability studies of drugs with UGT isoenzymes, which are one of the most important phase II drug metabolizing enzymes. The study confirmed that the determination of intrinsic clearance (Clint) as a cocktail of seven substrates is possible. The LC/MS/MS methods that were developed were fast and reliable for the quantitative analysis of a heterogenous set of drugs from Caco-2 permeability experiments and the set of glucuronides from in vitro stability experiments. The performance of a new ionization technique, atmospheric pressure photoionization (APPI), was evaluated through comparison with electrospray ionization (ESI), where both techniques were used for the analysis of Caco-2 samples. Like ESI, also APPI proved to be a reliable technique for the analysis of Caco-2 samples and even more flexible than ESI because of the wider dynamic linear range. The second part of the experimental study focused on metabolite profiling. Different mass spectrometric instruments and commercially available software tools were investigated for profiling metabolites in urine and hepatocyte samples. All the instruments tested (triple quadrupole, quadrupole time-of-flight, ion trap) exhibited some good and some bad features in searching for and identifying of expected and non-expected metabolites. Although, current profiling software is helpful, it is still insufficient. Thus a time-consuming largely manual approach is still required for metabolite profiling from complex biological matrices.

Investigating physical properties of solid dosage forms during pharmaceutical processing : Process analytical applications of vibrational spectroscopy

In order to improve and continuously develop the quality of pharmaceutical products, the process analytical technology (PAT) framework has been adopted by the US Food and Drug Administration. One of the aims of PAT is to identify critical process parameters and their effect on the quality of the final product. Real time analysis of the process data enables better control of the processes to obtain a high quality product. The main purpose of this work was to monitor crucial pharmaceutical unit operations (from blending to coating) and to examine the effect of processing on solid-state transformations and physical properties. The tools used were near-infrared (NIR) and Raman spectroscopy combined with multivariate data analysis, as well as X-ray powder diffraction (XRPD) and terahertz pulsed imaging (TPI). To detect process-induced transformations in active pharmaceutical ingredients (APIs), samples were taken after blending, granulation, extrusion, spheronisation, and drying. These samples were monitored by XRPD, Raman, and NIR spectroscopy showing hydrate formation in the case of theophylline and nitrofurantoin. For erythromycin dihydrate formation of the isomorphic dehydrate was critical. Thus, the main focus was on the drying process. NIR spectroscopy was applied in-line during a fluid-bed drying process. Multivariate data analysis (principal component analysis) enabled detection of the dehydrate formation at temperatures above 45°C. Furthermore, a small-scale rotating plate device was tested to provide an insight into film coating. The process was monitored using NIR spectroscopy. A calibration model, using partial least squares regression, was set up and applied to data obtained by in-line NIR measurements of a coating drum process. The predicted coating thickness agreed with the measured coating thickness. For investigating the quality of film coatings TPI was used to create a 3-D image of a coated tablet. With this technique it was possible to determine coating layer thickness, distribution, reproducibility, and uniformity. In addition, it was possible to localise defects of either the coating or the tablet. It can be concluded from this work that the applied techniques increased the understanding of physico-chemical properties of drugs and drug products during and after processing. They additionally provided useful information to improve and verify the quality of pharmaceutical dosage forms