Report : stochastic analysis of road asset data for maintenance cost prediction

Reliable budget/cost estimates for road maintenance and rehabilitation are subjected to uncertainties and variability in road asset condition and characteristics of road users. The CRC CI research project 2003-029-C ‘Maintenance Cost Prediction for Road’ developed a method for assessing variation and reliability in budget/cost estimates for road maintenance and rehabilitation. The method is based on probability-based reliable theory and statistical method. The next stage of the current project is to apply the developed method to predict maintenance/rehabilitation budgets/costs of large networks for strategic investment. The first task is to assess the variability of road data. This report presents initial results of the analysis in assessing the variability of road data. A case study of the analysis for dry non reactive soil is presented to demonstrate the concept in analysing the variability of road data for large road networks. In assessing the variability of road data, large road networks were categorised into categories with common characteristics according to soil and climatic conditions, pavement conditions, pavement types, surface types and annual average daily traffic. The probability distributions, statistical means, and standard deviation values of asset conditions and annual average daily traffic for each type were quantified. The probability distributions and the statistical information obtained in this analysis will be used to asset the variation and reliability in budget/cost estimates in later stage. Generally, we usually used mean values of asset data of each category as input values for investment analysis. The variability of asset data in each category is not taken into account. This analysis method demonstrated that it can be used for practical application taking into account the variability of road data in analysing large road networks for maintenance/rehabilitation investment analysis.

Enhancing students' self-concepts and related constructs: A critical longitudinal analysis capitalising on and combining promising enhancement techniques for educational settings

Enhancing children's self-concepts is widely accepted as a critical educational outcome of schooling and is postulated as a mediating variable that facilitates the attainment of other desired outcomes such as improved academic achievement. Despite considerable advances in self-concept research, there has been limited progress in devising teacher-administered enhancement interventions. This is unfortunate as teachers are crucial change agents during important developmental periods when self-concept is formed. The primary aim of the present investigation is to build on the promising features of previous self-concept enhancement studies by: (a) combining two exciting research directions developed by Burnett and Craven to develop a potentially powerful cognitive-based intervention; (b) incorporating recent developments in theory and measurement to ensure that the multidimensionality of self-concept is accounted for in the research design; (c) fully investigating the effects of a potentially strong cognitive intervention on reading, mathematics, school and learning self-concepts by using a large sample size and a sophisticated research design; (d) evaluating the effects of the intervention on affective and cognitive subcomponents of reading, mathematics, school and learning self-concepts over time to test for differential effects of the intervention; (e) modifying and extending current procedures to maximise the successful implementation of a teacher-mediated intervention in a naturalistic setting by incorporating sophisticated teacher training as suggested by Hattie (1992) and including an assessment of the efficacy of implementation; and (f) examining the durability of effects associated with the intervention.

Stability analysis and control of multiple converter based autonomous microgrid

In this paper, the stability of an autonomous microgrid with multiple distributed generators (DG) is studied through eigenvalue analysis. It is assumed that all the DGs are connected through Voltage Source Converter (VSC) and all connected loads are passive. The VSCs are controlled by state feedback controller to achieve desired voltage and current outputs that are decided by a droop controller. The state space models of each of the converters with its associated feedback are derived. These are then connected with the state space models of the droop, network and loads to form a homogeneous model, through which the eigenvalues are evaluated. The system stability is then investigated as a function of the droop controller real and reac-tive power coefficients. These observations are then verified through simulation studies using PSCAD/EMTDC. It will be shown that the simulation results closely agree with stability be-havior predicted by the eigenvalue analysis.

Nonfatal heroin overdoses in Queensland, Australia : an analysis of ambulance data

In the past decade, the utilization of ambulance data to inform the prevalence of nonfatal heroin overdose has increased. These data can assist public health policymakers, law enforcement agencies, and health providers in planning and allocating resources. This study examined the 672 ambulance attendances at nonfatal heroin overdoses in Queensland, Australia, in 2000. Gender distribution showed a typical 70/30 male-to-female ratio. An equal number of persons with nonfatal heroin overdose were between 15 and 24 years of age and 25 and 34 years of age. Police were present in only 1 of 6 cases, and 28.1% of patients reported using drugs alone. Ambulance data are proving to be a valuable population-based resource for describing the incidence and characteristics of nonfatal heroin overdose episodes. Future studies could focus on the differences between nonfatal heroin overdose and fatal heroin overdose samples.

Algebraic Analysis of LEX

LEX is a stream cipher that progressed to Phase 3 of the eSTREAM stream cipher project. In this paper, we show that the security of LEX against algebraic attacks relies on a small equation system not being solvable faster than exhaustive search. We use the byte leakage in LEX to construct a system of 21 equa- tions in 17 variables. This is very close to the require- ment for an efficient attack, i.e. a system containing 16 variables. The system requires only 36 bytes of keystream, which is very low.

Comparison of 3D finite element analysis derived stiffness and BMD to determine the failure load of the excised proximal femur

Introduction: Bone mineral density (BMD) is currently the preferred surrogate for bone strength in clinical practice. Finite element analysis (FEA) is a computer simulation technique that can predict the deformation of a structure when a load is applied, providing a measure of stiffness (Nmm−1). Finite element analysis of X-ray images (3D-FEXI) is a FEA technique whose analysis is derived froma single 2D radiographic image. Methods: 18 excised human femora had previously been quantitative computed tomography scanned, from which 2D BMD-equivalent radiographic images were derived, and mechanically tested to failure in a stance-loading configuration. A 3D proximal femur shape was generated from each 2D radiographic image and used to construct 3D-FEA models. Results: The coefficient of determination (R2%) to predict failure load was 54.5% for BMD and 80.4% for 3D-FEXI. Conclusions: This ex vivo study demonstrates that 3D-FEXI derived from a conventional 2D radiographic image has the potential to significantly increase the accuracy of failure load assessment of the proximal femur compared with that currently achieved with BMD. This approach may be readily extended to routine clinical BMD images derived by dual energy X-ray absorptiometry. Crown Copyright © 2009 Published by Elsevier Ltd on behalf of IPEM. All rights reserved

How well are Australian children doing in the first year of school : a demographic analysis

Background: The transition to school is a sensitive period for children in relation to school success. In the early school years, children need to develop positive attitudes to school and have experiences that promote academic, behavioural and social competence. When children begin school there are higher expectations of responsibility and independence and in the year one class, there are more explicit academic goals for literacy and numeracy and more formal instruction. Most importantly, children’s early attitudes to learning and learning styles have an impact on later educational outcomes. Method: Data were drawn from The Longitudinal Study of Australian Children (LSAC). LSAC is a cross-sequential cohort study funded by the Australian Government. In these analyses, Wave 2 (2006) data for 2499 children in the Kindergarten Cohort were used. Children, at Wave 2, were in the first year of formal school. They had a mean age of 6.9 years (SD= 0.26). Measures included a 6-item measure of Approaches to Learning (task persistence, independence) and the Academic Rating Scales for language and literacy and mathematical thinking. Teachers rated their relationships with children on the short form of the STRS. Results: Girls were rated by their teachers as doing better than boys on Language and literacy, Approaches to learning; and they had a better relationship with their teacher. Children from an Aboriginal or Torres Strait Island (ATSI) background were rated as doing less well on Language and Literacy and Mathematical thinking and on their Approaches to learning. Children from high Socio Economic Position families are doing better on teacher rated Language and Literacy, Mathematical thinking, Approaches to learning and they had a better relationship with their teacher. Conclusions: Findings highlight the importance of key demographic variables in understanding children’s early school success.

Improvements in sustainable energy and water practice in the food processing industry : an in depth analysis of the manufacture of Ghee at the Butter Producers' Cooperative Federation Limited, Brisbane

This thesis is a documented energy audit and long term study of energy and water reduction in a ghee factory. Global production of ghee exceeds 4 million tonnes annually. The factory in this study refines dairy products by non-traditional centrifugal separation and produces 99.9% pure, canned, crystallised Anhydrous Milk Fat (Ghee). Ghee is traditionally made by batch processing methods. The traditional method is less efficient, than centrifugal separation. An in depth systematic investigation was conducted of each item of major equipment including; ammonia refrigeration, a steam boiler, canning equipment, pumps, heat exchangers and compressed air were all fine-tuned. Continuous monitoring of electrical usage showed that not every initiative worked, others had pay back periods of less than a year. In 1994-95 energy consumption was 6,582GJ and in 2003-04 it was 5,552GJ down 16% for a similar output. A significant reduction in water usage was achieved by reducing the airflow in the refrigeration evaporative condensers to match the refrigeration load. Water usage has fallen 68% from18ML in 1994-95 to 5.78ML in 2003-04. The methods reported in this thesis could be applied to other industries, which have similar equipment, and other ghee manufacturers.

Combination strategies for a factor analysis phone-conditioned speaker verification system

This work aims to take advantage of recent developments in joint factor analysis (JFA) in the context of a phonetically conditioned GMM speaker verification system. Previous work has shown performance advantages through phonetic conditioning, but this has not been shown to date with the JFA framework. Our focus is particularly on strategies for combining the phone-conditioned systems. We show that the classic fusion of the scores is suboptimal when using multiple GMM systems. We investigate several combination strategies in the model space, and demonstrate improvement over score-level combination as well as over a non-phonetic baseline system. This work was conducted during the 2008 CLSP Workshop at Johns Hopkins University.

Site survey and analysis of highway trucks overloading status quo in Anhui

In 2004, with the increasing overloading restriction requirements of society in Anhui, a provincial comprehensive overloading transportation survey has been developed to take evaluations on overloading actuality and enforcement efficiency with the support of the World Bank. A total of six site surveys were conducted at Hefei, Fuyang, Luan, Wuhu, Huainan and Huangshan Areas with four main contents respectively: traffic volume, axle load, freight information and registration information. Via statistical analysis on the survey data, conclusions were gained that: vehicle overloading are very universal and serious problems at arterial highways in Anhui now. The traffic loads have far exceeded the designed endure capacity of highways and have caused prevalent premature pavement damage, especially for rigid pavement. The overloading trucks are unimpeded engaged in highway freight transportation actually due to the disordered overloading enforcement strategies and the deficient inspecting technologies.

Development of a colorimetric assay for heparanase activity suitable for kinetic analysis and inhibitor screening

The role that heparanase plays during metastasis and angiogenesis in tumors makes it an attractive target for cancer therapeutics. Despite this enzyme’s significance, most of the assays developed to measure its activity are complex. Moreover, they usually rely on labeling variable preparations of the natural substrate heparan sulfate, making comparisons across studies precarious. To overcome these problems, we have developed a convenient assay based on the cleavage of the synthetic heparin oligosaccharide fondaparinux. The assay measures the appearance of the disaccharide product of heparanase-catalyzed fondaparinux cleavage colorimetrically using the tetrazolium salt WST-1. Because this assay has a homogeneous substrate with a single point of cleavage, the kinetics of the enzyme can be reliably characterized, giving a Km of 46 μM and a kcat of 3.5 s−1 with fondaparinux as substrate. The inhibition of heparanase by the published inhibitor, PI-88, was also studied, and a Ki of 7.9 nM was determined. The simplicity and robustness of this method, should, not only greatly assist routine assay of heparanase activity but also could be adapted for high-throughput screening of compound libraries, with the data generated being directly comparable across studies.

The bereavement response : a cluster analysis

BACKGROUND: Literature and clinical experience suggest that some people experience atypical, complicated or pathological bereavement reactions in response to a major loss. METHOD: Three groups of community-based bereaved subjects--spouses (n = 44), adult children (n = 40), and parents (n = 36)--were followed up four times in the 13 months after a loss. A 17-item scale of core bereavement times was developed and used to investigate the intensity of the bereavement response over time. RESULTS: Cluster analysis revealed a pattern of bereavement-related symptoms approximating a syndrome of chronic grief in 11 (9.2%) of the 120 subjects. None of the respondents displayed a pattern consistent with delayed or absent grief. CONCLUSIONS: In a non-clinical community sample of bereaved people, delayed or absent grief is infrequently seen, unlike chronic grief, which is demonstrated in a minority.

A review on degradation models in reliability analysis

With increasingly complex engineering assets and tight economic requirements, asset reliability becomes more crucial in Engineering Asset Management (EAM). Improving the reliability of systems has always been a major aim of EAM. Reliability assessment using degradation data has become a significant approach to evaluate the reliability and safety of critical systems. Degradation data often provide more information than failure time data for assessing reliability and predicting the remnant life of systems. In general, degradation is the reduction in performance, reliability, and life span of assets. Many failure mechanisms can be traced to an underlying degradation process. Degradation phenomenon is a kind of stochastic process; therefore, it could be modelled in several approaches. Degradation modelling techniques have generated a great amount of research in reliability field. While degradation models play a significant role in reliability analysis, there are few review papers on that. This paper presents a review of the existing literature on commonly used degradation models in reliability analysis. The current research and developments in degradation models are reviewed and summarised in this paper. This study synthesises these models and classifies them in certain groups. Additionally, it attempts to identify the merits, limitations, and applications of each model. It provides potential applications of these degradation models in asset health and reliability prediction.