Performance measures for Australian laboratories reporting cervical cytology : a decade of data 1998–2008

Aim Performance measures for Australian laboratories reporting cervical cytology are a set of quantifiable measures relating to the profile and accuracy of reporting. This study reviews aggregate data collected over the ten years in which participation in the performance measures has been mandatory. Methods Laboratories submit annual data on performance measures relating to the profile of reporting, including reporting rates for technically unsatisfactory specimens, high grade or possible high grade abnormalities and abnormal reports. Cytology-histology correlation data and review findings of negative smears reported from women with histological high grade disease are also collected. Suggested acceptable standards are set for each measure. This study reviews the aggregate data submitted by all laboratories for the years 1998-2008 and examines trends in reporting and the performance of laboratories against the suggested standards. Results The performance of Australian laboratories has shown continued improvement over the study period. There has been a fall in the proportion of laboratories with data outside the acceptable standard range in all performance measures. Laboratories are reporting a greater proportion of specimens as definite or possible high grade abnormality. This is partly attributable to an increase in the proportion of abnormal results classified as high grade or possible high grade abnormality. Despite this, the positive predictive value for high grade and possible high grade abnormalities has continued to rise. Conclusion Performance measures for cervical cytology have provided a valuable addition to external quality assurance procedures in Australia. They have documented continued improvements in the aggregate performance, as well as providing benchmarking data and goals for acceptable performance for individual laboratories.

Compatibility of stormwater treatment performance data between different geographical areas

Using a case study approach, this paper presents a robust methodology for assessing the compatibility of stormwater treatment performance data between two geographical regions in relation to a treatment system. The desktop analysis compared data derived from a field study undertaken in Florida, USA, with South East Queensland (SEQ) rainfall and pollutant characteristics. The analysis was based on the hypothesis that when transposing treatment performance information from one geographical region to another, detailed assessment of specific rainfall and stormwater quality parameters is required. Accordingly, characteristics of measured rainfall events and stormwater quality in the Florida study were compared with typical characteristics for SEQ. Rainfall events monitored in the Florida study were found to be similar to events that occur in SEQ in terms of their primary characteristics of depth, duration and intensity. Similarities in total suspended solids (TSS) and total nitrogen (TN) concentration ranges for Florida and SEQ suggest that TSS and TN removal performances would not be very different if the treatment system is installed in SEQ. However, further investigations are needed to evaluate the treatment performance of total phosphorus (TP). The methodology presented also allows comparison of other water quality parameters.

Training needs research applied to the development of a standardised incident investigator training framework

In response to the rail industry lacking a consistently accepted standard of minimal training to perform incident investigations, the Australasian rail industry requested the development of a unified approach to investigator training. This paper details how the findings from a training needs analysis were applied to inform the development of a standardised training package for rail incident investigators. Data from job descriptions, training documents and subject matter experts sourced from 17 Australasian organisations were analysed and refined to yield a draft set of 10 critical competencies. Finally the draft of critical competencies was reviewed by industry experts to verify the accuracy and completeness of the competency list and to consider the most appropriate level of qualification for training development. The competencies identified and the processes described to translate research into an applied training framework in this paper, can be generalised to assist practitioners and researchers in developing industry approved standardised training packages.

A comparison of techniques for the reconstruction of leaf surfaces from scanned data

The foliage of a plant performs vital functions. As such, leaf models are required to be developed for modelling the plant architecture from a set of scattered data captured using a scanning device. The leaf model can be used for purely visual purposes or as part of a further model, such as a fluid movement model or biological process. For these reasons, an accurate mathematical representation of the surface and boundary is required. This paper compares three approaches for fitting a continuously differentiable surface through a set of scanned data points from a leaf surface, with a technique already used for reconstructing leaf surfaces. The techniques which will be considered are discrete smoothing D2-splines [R. Arcangeli, M. C. Lopez de Silanes, and J. J. Torrens, Multidimensional Minimising Splines, Springer, 2004.], the thin plate spline finite element smoother [S. Roberts, M. Hegland, and I. Altas, Approximation of a Thin Plate Spline Smoother using Continuous Piecewise Polynomial Functions, SIAM, 1 (2003), pp. 208--234] and the radial basis function Clough-Tocher method [M. Oqielat, I. Turner, and J. Belward, A hybrid Clough-Tocher method for surface fitting with application to leaf data., Appl. Math. Modelling, 33 (2009), pp. 2582-2595]. Numerical results show that discrete smoothing D2-splines produce reconstructed leaf surfaces which better represent the original physical leaf.

Outbreak detection algorithms for seasonal disease data : a case study using Ross River virus disease

Background Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. Methods We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. Results The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. Conclusions This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

A runtime integrity monitoring framework for real-time relative positioning systems based on GPS and DSRC

This paper provides a three-layered framework to monitor the positioning performance requirements of Real-time Relative Positioning (RRP) systems of the Cooperative Intelligent Transport Systems (C-ITS) that support Cooperative Collision Warning (CCW) applications. These applications exploit state data of surrounding vehicles obtained solely from the Global Positioning System (GPS) and Dedicated Short-Range Communications (DSRC) units without using other sensors. To this end, the paper argues the need for the GPS/DSRC-based RRP systems to have an autonomous monitoring mechanism, since the operation of CCW applications is meant to augment safety on roads. The advantages of autonomous integrity monitoring are essential and integral to any safety-of-life system. The autonomous integrity monitoring framework proposed necessitates the RRP systems to detect/predict the unavailability of their sub-systems and of the integrity monitoring module itself, and, if available, to account for effects of data link delays and breakages of DSRC links, as well as of faulty measurement sources of GPS and/or integrated augmentation positioning systems, before the information used for safety warnings/alarms becomes unavailable, unreliable, inaccurate or misleading. Hence, a monitoring framework using a tight integration and correlation approach is proposed for instantaneous reliability assessment of the RRP systems. Ultimately, using the proposed framework, the RRP systems will provide timely alerts to users when the RRP solutions cannot be trusted or used for the intended operation.

The importance of good data, analysis and interpretation for showing the economics of reducing healthcare-associated infection

To the Editor—In a recent review article in Infection Control and Hospital Epidemiology, Umscheid et al1 summarized published data on incidence rates of catheter-associated bloodstream infection (CABSI), catheter-associated urinary tract infection (CAUTI), surgical site infection (SSI), and ventilator- associated pneumonia (VAP); estimated how many cases are preventable; and calculated the savings in hospital costs and lives that would result from preventing all preventable cases. Providing these estimates to policy makers, political leaders, and health officials helps to galvanize their support for infection prevention programs. Our concern is that important limitations of the published studies on which Umscheid and colleagues built their findings are incompletely addressed in this review. More attention needs to be drawn to the techniques applied to generate these estimates...

Nonvolatile multilevel data storage memory device from controlled ambipolar charge trapping mechanism

The capability of storing multi-bit information is one of the most important challenges in memory technologies. An ambipolar polymer which intrinsically has the ability to transport electrons and holes as a semiconducting layer provides an opportunity for the charge trapping layer to trap both electrons and holes efficiently. Here, we achieved large memory window and distinct multilevel data storage by utilizing the phenomena of ambipolar charge trapping mechanism. As fabricated flexible memory devices display five well-defined data levels with good endurance and retention properties showing potential application in printed electronics.

Surface reconstruction of wheat leaf morphology from three-dimensional scanned data

Realistic virtual models of leaf surfaces are important for a number of applications in the plant sciences, such as modelling agrichemical spray droplet movement and spreading on the surface. In this context, the virtual surfaces are required to be sufficiently smooth to facilitate the use of the mathematical equations that govern the motion of the droplet. While an effective approach is to apply discrete smoothing D2-spline algorithms to reconstruct the leaf surfaces from three-dimensional scanned data, difficulties arise when dealing with wheat leaves that tend to twist and bend. To overcome this topological difficulty, we develop a parameterisation technique that rotates and translates the original data, allowing the surface to be fitted using the discrete smoothing D2-spline methods in the new parameter space. Our algorithm uses finite element methods to represent the surface as a linear combination of compactly supported shape functions. Numerical results confirm that the parameterisation, along with the use of discrete smoothing D2-spline techniques, produces realistic virtual representations of wheat leaves.

Productive inefficiency analysis and toxic chemical substances in US and Japanese manufacturing sectors

Corporate social responsibility is imperative for manufacturing companies to achieve sustainable development. Under a strong environmental information disclosure system, polluting companies are disadvantaged in terms of market competitiveness, because they lack an environmentally friendly image. The objective of this study is to analyze productive inefficiency change in relation to toxic chemical substance emissions for the United States and Japan and their corresponding policies. We apply the weighted Russell directional distance model to measure companies productive inefficiency, which represents their production technology. The data encompass 330 US manufacturing firms observed from 1999 to 2007, and 466 Japanese manufacturing firms observed from 2001 to 2008. The article focuses on nine high-pollution industries (rubber and plastics; chemicals and allied products; paper and pulp; steel and non-ferrous metal; fabricated metal; industrial machinery; electrical products; transportation equipment; precision instruments) categorized into two industry groups: basic materials industries and processing and assembly industries. The results show that productive inefficiency decreased in all industrial sectors in the United States and Japan from 2001 to 2007. In particular, that of the electrical products industry decreased rapidly after 2002 for both countries, possibly because of the enforcement of strict environmental regulations for electrical products exported to European markets.

Modal choice between air and rail : a social efficiency benchmarking analysis that considers CO2 emissions

The problem of modal choice between rail and air arises as public awareness of carbon dioxide (CO2) emissions by the transportation sector rises. In this paper, we answer this question quantitatively by performing an efficiency benchmarking analysis that takes into account life-cycle CO2 emission due to transport service provision. The paper employs nonparametric efficiency estimation methods, namely a slacks-based inefficiency measure, as well as a more conventional directional distance function approach. We apply them to a panel data set for three major railway companies and the aviation sector in Japan for the period from 1999 to 2007. Results shows that, contrary to the common argument, air transport can still be more socially efficient than rail transport, even when the environmental load due to CO2 emission is incorporated. This is due to the aviation sector's extremely low user cost, measured in terms of in-vehicle time. In other words, aviation is a necessary transportation mode for those with a very high willingness to pay for their time.

Improving the transparency, openness and efficiency of e-government in Qatar in the era of Open Government Data, and beyond

This thesis investigates how Open Government Data (OGD) concepts and practices might be implemented in the State of Qatar to achieve more transparent, effective and accountable government. The thesis concludes with recommendations as to how Qatar, as a developing country, might enhance the accessibility and usability of its OGD and implement successful and sustainable OGD systems and practices.

miRPlant : an integrated tool for identification of plant miRNA from RNA sequencing data

Background Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep’s probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction. Result We have developed a user-friendly plant miRNA prediction tool called miRPlant. We show using 16 plant miRNA datasets from four different plant species that miRPlant has at least a 10% improvement in accuracy compared to miRDeep-P, which is the most popular plant miRNA prediction tool. Furthermore, miRPlant uses a Graphical User Interface for data input and output, and identified miRNA are shown with all RNAseq reads in a hairpin diagram. Conclusions We have developed miRPlant which extends miRDeep* to various plant species by adopting suitable strategies to identify hairpin excision regions and hairpin structure filtering for plants. miRPlant does not require any third party tools such as mapping or RNA secondary structure prediction tools. miRPlant is also the first plant miRNA prediction tool that dynamically plots miRNA hairpin structure with small reads for identified novel miRNAs. This feature will enable biologists to visualize novel pre-miRNA structure and the location of small RNA reads relative to the hairpin. Moreover, miRPlant can be easily used by biologists with limited bioinformatics skills.

A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.