The influence of atmospheric conditions on the leakage current of ceramic insulators on the Colombian Caribbean coast

The contamination of electrical insulators is one of the major contributors to the risk of operation outages in electrical substations, especially in coastal zones with high salinity levels and atmospheric pollution. By using the measurement of leakage-currents, which is one of the main indicators of contamination in insulators, this work seeks to the determine the correlation with climatic variables, such as ambient temperature, relative humidity, solar irradiance, atmospheric pressure, and wind speed and direction. The results obtained provide an input to the behaviour of the leakage current under atmospheric conditions that are particular to the Caribbean coast of Colombia. Spearman’s rank correlation coefficients and principal component analysis are utilised to determine the significant relationships among the different variables under consideration. The necessary information for the study was obtained via historical databases of both atmospheric variables and the leakage current measured in over a period of one year in a 220-kV potential transformer insulator. We identified the influencing factors of temperature, humidity, radiation, wind speed and direction on the magnitude of the leakage current as the most relevant.

Construction of volcanic records from marine sediment cores : a review and case study (Montserrat, West Indies)

Detailed knowledge of the past history of an active volcano is crucial for the prediction of the timing, frequency and style of future eruptions, and for the identification of potentially at-risk areas. Subaerial volcanic stratigraphies are often incomplete, due to a lack of exposure, or burial and erosion from subsequent eruptions. However, many volcanic eruptions produce widely-dispersed explosive products that are frequently deposited as tephra layers in the sea. Cores of marine sediment therefore have the potential to provide more complete volcanic stratigraphies, at least for explosive eruptions. Nevertheless, problems such as bioturbation and dispersal by currents affect the preservation and subsequent detection of marine tephra deposits. Consequently, cryptotephras, in which tephra grains are not sufficiently concentrated to form layers that are visible to the naked eye, may be the only record of many explosive eruptions. Additionally, thin, reworked deposits of volcanic clasts transported by floods and landslides, or during pyroclastic density currents may be incorrectly interpreted as tephra fallout layers, leading to the construction of inaccurate records of volcanism. This work uses samples from the volcanic island of Montserrat as a case study to test different techniques for generating volcanic eruption records from marine sediment cores, with a particular relevance to cores sampled in relatively proximal settings (i.e. tens of kilometres from the volcanic source) where volcaniclastic material may form a pervasive component of the sedimentary sequence. Visible volcaniclastic deposits identified by sedimentological logging were used to test the effectiveness of potential alternative volcaniclastic-deposit detection techniques, including point counting of grain types (component analysis), glass or mineral chemistry, colour spectrophotometry, grain size measurements, XRF core scanning, magnetic susceptibility and X-radiography. This study demonstrates that a set of time-efficient, non-destructive and high-spatial-resolution analyses (e.g. XRF core-scanning and magnetic susceptibility) can be used effectively to detect potential cryptotephra horizons in marine sediment cores. Once these horizons have been sampled, microscope image analysis of volcaniclastic grains can be used successfully to discriminate between tephra fallout deposits and other volcaniclastic deposits, by using specific criteria related to clast morphology and sorting. Standard practice should be employed when analysing marine sediment cores to accurately identify both visible tephra and cryptotephra deposits, and to distinguish fallout deposits from other volcaniclastic deposits.

Are empowered women more likely to deliver in facilities? An explorative study using the Nepal Demographic and Health Survey 2011

Development literature has argued that empowering women can effectively increase the utilisation of maternal health care. This study examines this hypothesis in the context of Nepal where only 28% of women delivered in facilities. The two-level random intercept logit models were fitted for data from the Nepal Demographic and Health Surveys 2011. Women‟s empowerment was quantified with a single index constructed from many variables. These variables captured different aspects of women‟s lives and decision-making in their households, and were combined using the principal component analysis method. The results confirmed a positive relationship between women‟s as an inevitable product of the economic development process.

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.