Characterization of VOCs from LPG and unleaded petroleum fuelled passenger cars

The study monitored the emissions of volatile organic compounds (VOCs) from the exhaust of cars fuelled by liquefied petroleum gas (LPG) and unleaded petrol (ULP). Six cars, four fuelled by LPG and two by ULP, were tested on a chassis dynamometer at two different cruising modes of operation (60 km h−1 and 80 km h−1) and idle. A total of 33 VOCs were identified in the exhaust of both types of fuels by the use of GC/MS. Due to the complexity of the dataset, Multi Criteria Decision Making (MCDM) software PROMETHEE and GAIA was used to rank the least polluting mode and fuel. The 60 km h−1 driving speed was identified as the cleaner mode of driving as was LPG fuel. The Ozone Formation Potential (OFP) of the VOCs was also calculated by using the incremental reactivity scale. Priority VOCs leading to ozone formation were identified according to the three incremental reactivity scales: MIR, MOIR and EBIR. PROMETHEE was applied to assess the most preferred scale of reactivity for predicting ozone formation potential under different scenarios. The results enhance the understanding of the environmental value of using LPG to power passenger cars.

Introduction to "Business innovation and disruption in the music industry"

Similar to most other creative industries, the evolution of the music industry is heavily shaped by media technologies. This was equally true in 1999, when the global recorded music industry had experienced two decades of continuous growth largely driven by the rapid transition from vinyl records to Compact Discs. The transition encouraged avid music listeners to purchase much of their music collections all over again in order to listen to their favourite music with ‘digital sound’. As a consequence of this successful product innovation, recorded music sales (unit measure) more than doubled between the early 1980s and the end of the 1990s. It was with this backdrop that the first peer-to-peer file sharing service was developed and released to the mainstream music market in 1999 by the college student Shawn Fanning. The service was named Napster and it marks the beginning of an era that is now a classic example of how an innovation is able to disrupt an entire industry and make large swathes of existing industry competences obsolete. File sharing services such as Napster, followed by a range of similar services in its path, reduced physical unit sales in the music industry to levels that had not been seen since the 1970s. The severe impact of the internet on physical sales shocked many music industry executives who spent much of the 2000s vigorously trying to reverse the decline and make the disruptive technologies go away. At the end, they learned that their efforts were to no avail and the impact on the music industry proved to be transformative, irreversible and, to many music industry professionals, also devastating. Thousands of people lost their livelihood, large and small music companies have folded or been forced into mergers or acquisitions. But as always during periods of disruption, the past 15 years have also been very innovative, spurring a plethora of new music business models. These new business models have mainly emerged outside the music industry and the innovators have been often been required to be both persuasive and persistent in order to get acceptance from the risk-averse and cash-poor music industry establishment. Apple was one such change agent that in 2003 was the first company to open up a functioning and legal market for online music. iTunes Music Store was the first online retail outlet that was able to offer the music catalogues from all the major music companies; it used an entirely novel pricing model, and it allowed consumers to de-bundle the music album and only buy the songs that they actually liked. Songs had previously been bundled by physical necessity as discs or cassettes, but with iTunes Music Store, the institutionalized album bundle slowly started to fall apart. The consequences had an immediate impact on music retailing and within just a few years, many brick and mortar record stores were forced out of business in markets across the world. The transformation also had disruptive consequences beyond music retailing and redefined music companies’ organizational structures, work processes and routines, as well as professional roles. iTunes Music Store in one sense was a disruptive innovation, but it was at the same time relatively incremental, since the major labels’ positions and power structures remained largely unscathed. The rights holders still controlled their intellectual properties and the structures that guided the royalties paid per song that was sold were predictable, transparent and in line with established music industry practices.

Response to: Electronic health records and healthcare identifiers : legislation discussion paper

Public submission # 029 to a Australian federal parliamentary committee considering proposed legislative changes to the Commonwealth's Healthcare Identifiers Act 2010 and the Personally Controlled Electronic Health Records Act 2012.

Genetic determinants of bone density and fracture risk: State of the art and future directions

Context: Osteoporosis is a common, highly heritable condition that causes substantial morbidity and mortality, the etiopathogenesis of which is poorly understood. Genetic studies are making increasingly rapid progress in identifying the genes involved. Evidence Acquisition and Synthesis: In this review, we will summarize the current understanding of the genetics of osteoporosis based on publications from PubMed from the year 1987 onward. Conclusions: Most genes involved in osteoporosis identified to date encode components of known pathways involved in bone synthesis or resorption, but as the field progresses, new pathways are being identified. Only a small proportion of the total genetic variation involved in osteoporosis has been identified, and new approaches will be required to identify most of the remaining genes.

Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture

Bone mineral density (BMD) is the most widely used predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and east Asian ancestry. We tested the top BMD-associated markers for replication in 50,933 independent subjects and for association with risk of low-trauma fracture in 31,016 individuals with a history of fracture (cases) and 102,444 controls. We identified 56 loci (32 new) associated with BMD at genome-wide significance (P < 5 × 10−8). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal stem cell differentiation, endochondral ossification and Wnt signaling pathways. However, we also discovered loci that were localized to genes not known to have a role in bone biology. Fourteen BMD-associated loci were also associated with fracture risk (P < 5 × 10−4, Bonferroni corrected), of which six reached P < 5 × 10−8, including at 18p11.21 (FAM210A), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.

Understanding file access mechanisms for embedded ubicomp collaboration interfaces

This paper explores the nature of interfaces to support people in accessing their files at tabletop displays embedded in the environment. To do this, we designed a study comparing people's interaction with two very different classes of file system access interface: Focus, explicitly designed for tabletops, and the familiar hierarchical Windows Explorer. In our within-subjects double-crossover study, participants collaborated on 4 planning tasks. Based on video, logs, questionnaires and interviews, we conclude that both classes of interface have a place. Notably, Focus contributed to improved collaboration and more efficient use of the workspace than with Explorer. Our results inform a set of recommendations for future interfaces enabling this important class of interaction -- supporting access to files for collaboration at tabletop devices embedded in an ubicomp environment.

Correlation of carotid atheromatous plaque inflammation with biomechanical stress: Utility of USPIO enhanced MR imaging and finite element analysis

Objective: The aim of this study was to explore whether there is a relationship between the degree of MR-defined inflammation using ultra small super-paramagnetic iron oxide (USPIO) particles, and biomechanical stress using finite element analysis (FEA) techniques, in carotid atheromatous plaques. Methods and Results: 18 patients with angiographically proven carotid stenoses underwent multi-sequence MR imaging before and 36 h after USPIO infusion. T2 * weighted images were manually segmented into quadrants and the signal change in each quadrant normalised to adjacent muscle was calculated after USPIO administration. Plaque geometry was obtained from the rest of the multi-sequence dataset and used within a FEA model to predict maximal stress concentration within each slice. Subsequently, a new statistical model was developed to explicitly investigate the form of the relationship between biomechanical stress and signal change. The Spearman's rank correlation coefficient for USPIO enhanced signal change and maximal biomechanical stress was -0.60 (p = 0.009). Conclusions: There is an association between biomechanical stress and USPIO enhanced MR-defined inflammation within carotid atheroma, both known risk factors for plaque vulnerability. This underlines the complex interaction between physiological processes and biomechanical mechanisms in the development of carotid atheroma. However, this is preliminary data that will need validation in a larger cohort of patients.

Smoothed rank-based procedure for censored data

A smoothed rank-based procedure is developed for the accelerated failure time model to overcome computational issues. The proposed estimator is based on an EM-type procedure coupled with the induced smoothing. "The proposed iterative approach converges provided the initial value is based on a consistent estimator, and the limiting covariance matrix can be obtained from a sandwich-type formula. The consistency and asymptotic normality of the proposed estimator are also established. Extensive simulations show that the new estimator is not only computationally less demanding but also more reliable than the other existing estimators.

Rank regression for analyzing ordinal qualitative data for treatment comparison

Ordinal qualitative data are often collected for phenotypical measurements in plant pathology and other biological sciences. Statistical methods, such as t tests or analysis of variance, are usually used to analyze ordinal data when comparing two groups or multiple groups. However, the underlying assumptions such as normality and homogeneous variances are often violated for qualitative data. To this end, we investigated an alternative methodology, rank regression, for analyzing the ordinal data. The rank-based methods are essentially based on pairwise comparisons and, therefore, can deal with qualitative data naturally. They require neither normality assumption nor data transformation. Apart from robustness against outliers and high efficiency, the rank regression can also incorporate covariate effects in the same way as the ordinary regression. By reanalyzing a data set from a wheat Fusarium crown rot study, we illustrated the use of the rank regression methodology and demonstrated that the rank regression models appear to be more appropriate and sensible for analyzing nonnormal data and data with outliers.

Efficient estimation for rank-based regression with clustered data

Rank-based inference is widely used because of its robustness. This article provides optimal rank-based estimating functions in analysis of clustered data with random cluster effects. The extensive simulation studies carried out to evaluate the performance of the proposed method demonstrate that it is robust to outliers and is highly efficient given the existence of strong cluster correlations. The performance of the proposed method is satisfactory even when the correlation structure is misspecified, or when heteroscedasticity in variance is present. Finally, a real dataset is analyzed for illustration.

Rank regression analysis of correlated water quality data from South East Queensland

With growing population and fast urbanization in Australia, it is a challenging task to maintain our water quality. It is essential to develop an appropriate statistical methodology in analyzing water quality data in order to draw valid conclusions and hence provide useful advices in water management. This paper is to develop robust rank-based procedures for analyzing nonnormally distributed data collected over time at different sites. To take account of temporal correlations of the observations within sites, we consider the optimally combined estimating functions proposed by Wang and Zhu (Biometrika, 93:459-464, 2006) which leads to more efficient parameter estimation. Furthermore, we apply the induced smoothing method to reduce the computational burden. Smoothing leads to easy calculation of the parameter estimates and their variance-covariance matrix. Analysis of water quality data from Total Iron and Total Cyanophytes shows the differences between the traditional generalized linear mixed models and rank regression models. Our analysis also demonstrates the advantages of the rank regression models for analyzing nonnormal data.

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.

Rank regression for analysis of clustered data: A natural induced smoothing approach

We consider rank regression for clustered data analysis and investigate the induced smoothing method for obtaining the asymptotic covariance matrices of the parameter estimators. We prove that the induced estimating functions are asymptotically unbiased and the resulting estimators are strongly consistent and asymptotically normal. The induced smoothing approach provides an effective way for obtaining asymptotic covariance matrices for between- and within-cluster estimators and for a combined estimator to take account of within-cluster correlations. We also carry out extensive simulation studies to assess the performance of different estimators. The proposed methodology is substantially Much faster in computation and more stable in numerical results than the existing methods. We apply the proposed methodology to a dataset from a randomized clinical trial.

Weighted rank regression for clustered data analysis

We consider ranked-based regression models for clustered data analysis. A weighted Wilcoxon rank method is proposed to take account of within-cluster correlations and varying cluster sizes. The asymptotic normality of the resulting estimators is established. A method to estimate covariance of the estimators is also given, which can bypass estimation of the density function. Simulation studies are carried out to compare different estimators for a number of scenarios on the correlation structure, presence/absence of outliers and different correlation values. The proposed methods appear to perform well, in particular, the one incorporating the correlation in the weighting achieves the highest efficiency and robustness against misspecification of correlation structure and outliers. A real example is provided for illustration.

Rank-based regression for analysis of repeated measures

We consider rank-based regression models for repeated measures. To account for possible withinsubject correlations, we decompose the total ranks into between- and within-subject ranks and obtain two different estimators based on between- and within-subject ranks. A simple perturbation method is then introduced to generate bootstrap replicates of the estimating functions and the parameter estimates. This provides a convenient way for combining the corresponding two types of estimating function for more efficient estimation.