Further notes on the application of zero-inflated models in highway safety

The intent of this note is to succinctly articulate additional points that were not provided in the original paper (Lord et al., 2005) and to help clarify a collective reluctance to adopt zero-inflated (ZI) models for modeling highway safety data. A dialogue on this important issue, just one of many important safety modeling issues, is healthy discourse on the path towards improved safety modeling. This note first provides a summary of prior findings and conclusions of the original paper. It then presents two critical and relevant issues: the maximizing statistical fit fallacy and logic problems with the ZI model in highway safety modeling. Finally, we provide brief conclusions.

Critical success factors of project management for Brunei construction projects : improving project performance

The problem of delays in the construction industry is a global phenomenon and the construction industry in Brunei Darussalam is no exception. The goal of all parties involved in construction projects – owners, contractors, engineers and consultants in either the public or private sector is to successfully complete the project on schedule, within planned budget, with the highest quality and in the safest manner. Construction projects are frequently influenced by either success factors that help project parties reach their goal as planned, or delay factors that stifle or postpone project completion. The purpose of this research is to identify success and delay factors which can help project parties reach their intended goals with greater efficiency. This research extracted seven of the most important success factors according to the literature and seven of the most important delay factors identified by project parties, and then examined correlations between them to determine which were the most influential in preventing project delays. This research uses a comprehensive literature review to design and conduct a survey to investigate success and delay factors and then obtain a consensus of expert opinion using the Delphi methodology to rank the most needed critical success factors for Brunei construction projects. A specific survey was distributed to owners, contractors and engineers to examine the most critical delay factors. A general survey was distributed to examine the correlation between the identified delay factors and the seven most important critical success factors selected. A consensus of expert opinion using the Delphi methodology was used to rank the most needed critical success factors for Brunei building construction. Data was collected and evaluated by statistical methods to identify the most significant causes of delay and to measure the strength and direction of the relationship between critical success factors and delay factors in order to examine project parties’ evaluation of projects’ critical success and delay factors, and to evaluate the influence of critical success factors on critical delay factors. A relative importance index has been used to determine the relative importance of the various causes of delays. A one and two-way analysis of variance (ANOVA) has been used to examine how the group or groups evaluated the influence of the critical success factors in avoiding or preventing each of the delay factors, and which success factors were perceived as most influential in avoiding or preventing critical delay factors. Finally the Delphi method, using consensus from an expert panel, was employed to identify the seven most critical success factors used to avoid the delay factors, and thereby improve project performance.

Near infrared for non-destructive testing of articular cartilage

The concept of non-destructive testing (NDT) of materials and structures is of immense importance in engineering and medicine. Several NDT methods including electromagnetic (EM)-based e.g. X-ray and Infrared; ultrasound; and S-waves have been proposed for medical applications. This paper evaluates the viability of near infrared (NIR) spectroscopy, an EM method for rapid non-destructive evaluation of articular cartilage. Specifically, we tested the hypothesis that there is a correlation between the NIR spectrum and the physical and mechanical characteristics of articular cartilage such as thickness, stress and stiffness. Intact, visually normal cartilage-on-bone plugs from 2-3yr old bovine patellae were exposed to NIR light from a diffuse reflectance fibre-optic probe and tested mechanically to obtain their thickness, stress, and stiffness. Multivariate statistical analysis-based predictive models relating articular cartilage NIR spectra to these characterising parameters were developed. Our results show that there is a varying degree of correlation between the different parameters and the NIR spectra of the samples with R2 varying between 65 and 93%. We therefore conclude that NIR can be used to determine, nondestructively, the physical and functional characteristics of articular cartilage.

Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

A Bayesian approach to assess interaction between known risk factors: The risk of lung cancer from exposure to asbestos and smoking

We review the literature on the combined effect of asbestos exposure and smoking on lung cancer, and explore a Bayesian approach to assess evidence of interaction. Previous approaches have focussed on separate tests for an additive or multiplicative relation. We extend these approaches by exploring the strength of evidence for either relation using approaches which allow the data to choose between both models. We then compare the different approaches.

Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

The impact of knowledge integration on enterprise system success

Despite promising benefits and advantages, there are reports of failures and low realisation of benefits in Enterprise System (ES) initiatives. Among the research on the factors that influence ES success, there is a dearth of studies on the knowledge implications of multiple end-user groups using the same ES application. An ES facilitates the work of several user groups, ranging from strategic management, management, to operational staff, all using the same system for multiple objectives. Given the fundamental characteristics of ES – integration of modules, business process views, and aspects of information transparency – it is necessary that all frequent end-users share a reasonable amount of common knowledge and integrate their knowledge to yield new knowledge. Recent literature on ES implementation highlights the importance of Knowledge Integration (KI) for implementation success. Unfortunately, the importance of KI is often overlooked and little about the role of KI in ES success is known. Many organisations do not achieve the potential benefits from their ES investment because they do not consider the need or their ability to integrate their employees’ knowledge. This study is designed to improve our understanding of the influence of KI among ES end-users on operational ES success. The three objectives of the study are: (I) to identify and validate the antecedents of KI effectiveness, (II) to investigate the impact of KI effectiveness on the goodness of individuals’ ES-knowledge base, and (III) to examine the impact of the goodness of individuals’ ES-knowledge base on the operational ES success. For this purpose, we employ the KI factors identified by Grant (1996) and an IS-impact measurement model from the work of Gable et al. (2008) to examine ES success. The study derives its findings from data gathered from six Malaysian companies in order to obtain the three-fold goal of this thesis as outlined above. The relationships between the antecedents of KI effectiveness and its consequences are tested using 188 responses to a survey representing the views of management and operational employment cohorts. Using statistical methods, we confirm three antecedents of KI effectiveness and the consequences of the antecedents on ES success are validated. The findings demonstrate a statistically positive impact of KI effectiveness of ES success, with KI effectiveness contributing to almost one-third of ES success. This research makes a number of contributions to the understanding of the influence of KI on ES success. First, based on the empirical work using a complete nomological net model, the role of KI effectiveness on ES success is evidenced. Second, the model provides a theoretical lens for a more comprehensive understanding of the impact of KI on the level of ES success. Third, restructuring the dimensions of the knowledge-based theory to fit the context of ES extends its applicability and generalisability to contemporary Information Systems. Fourth, the study develops and validates measures for the antecedents of KI effectiveness. Fifth, the study demonstrates the statistically significant positive influence of the goodness of KI on ES success. From a practical viewpoint, this study emphasises the importance of KI effectiveness as a direct antecedent of ES success. Practical lessons can be drawn from the work done in this study to empirically identify the critical factors among the antecedents of KI effectiveness that should be given attention.

The four principles: Can they be measured and do they predict ethical decision making?

Background The four principles of Beauchamp and Childress - autonomy, non-maleficence, beneficence and justice - have been extremely influential in the field of medical ethics, and are fundamental for understanding the current approach to ethical assessment in health care. This study tests whether these principles can be quantitatively measured on an individual level, and then subsequently if they are used in the decision making process when individuals are faced with ethical dilemmas. Methods The Analytic Hierarchy Process was used as a tool for the measurement of the principles. Four scenarios, which involved conflicts between the medical ethical principles, were presented to participants and they made judgments about the ethicality of the action in the scenario, and their intentions to act in the same manner if they were in the situation. Results Individual preferences for these medical ethical principles can be measured using the Analytic Hierarchy Process. This technique provides a useful tool in which to highlight individual medical ethical values. On average individuals have a significant preference for non-maleficence over the other principles, however, and perhaps counter-intuitively, this preference does not seem to relate to applied ethical judgements in specific ethical dilemmas. Conclusions People state they value these medical ethical principles but they do not actually seem to use them directly in the decision making process. The reasons for this are explained through the lack of a behavioural model to account for the relevant situational factors not captured by the principles. The limitations of the principles in predicting ethical decision making are discussed.

Risk-based approach to the management of on-site wastewater treatment facilities

Background On-site wastewater treatment system (OWTS) siting, design and management has traditionally been based on site specific conditions with little regard to the surrounding environment or the cumulative effect of other systems in the environment. The general approach has been to apply the same framework of standards and regulations to all sites equally, regardless of the sensitivity, or lack thereof, to the receiving environment. Consequently, this has led to the continuing poor performance and failure of on-site systems, resulting in environmental and public health consequences. As a result, there is increasing realisation that more scientifically robust evaluations in regard to site assessment and the underlying ground conditions are needed. Risk-based approaches to on-site system siting, design and management are considered the most appropriate means of improvement to the current standards and codes for on-site wastewater treatment systems. The Project Research in relation to this project was undertaken within the Gold Coast City Council region, the major focus being the semi-urban, rural residential and hinterland areas of the city that are not serviced by centralised treatment systems. The Gold Coast has over 15,000 on-site systems in use, with approximately 66% being common septic tank-subsurface dispersal systems. A recent study evaluating the performance of these systems within the Gold Coast area showed approximately 90% were not meeting the specified guidelines for effluent treatment and dispersal. The main focus of this research was to incorporate strong scientific knowledge into an integrated risk assessment process to allow suitable management practices to be set in place to mitigate the inherent risks. To achieve this, research was undertaken focusing on three main aspects involved with the performance and management of OWTS. Firstly, an investigation into the suitability of soil for providing appropriate effluent renovation was conducted. This involved detailed soil investigations, laboratory analysis and the use of multivariate statistical methods for analysing soil information. The outcomes of these investigations were developed into a framework for assessing soil suitability for effluent renovation. This formed the basis for the assessment of OWTS siting and design risks employed in the developed risk framework. Secondly, an assessment of the environmental and public health risks was performed specifically related the release of contaminants from OWTS. This involved detailed groundwater and surface water sampling and analysis to assess the current and potential risks of contamination throughout the Gold Coast region. Additionally, the assessment of public health risk incorporated the use of bacterial source tracking methods to identify the different sources of fecal contamination within monitored regions. Antibiotic resistance pattern analysis was utilised to determine the extent of human faecal contamination, with the outcomes utilised for providing a more indicative public health assessment. Finally, the outcomes of both the soil suitability assessment and ground and surface water monitoring was utilised for the development of the integrated risk framework. The research outcomes achieved through this project enabled the primary research aims and objects to be accomplished. This in turn would enable Gold Coast City Council to provide more appropriate assessment and management guidelines based on robust scientific knowledge which will ultimately ensure that the potential environmental and public health impacts resulting from on-site wastewater treatment is minimised. As part of the implementation of suitable management strategies, a critical point monitoring program (CPM) was formulated. This entailed the identification of the key critical parameters that contribute to the characterised risks at monitored locations within the study area. The CPM will allow more direct procedures to be implemented, targeting the specific hazards at sensitive areas throughout Gold Coast region.

Intelligent reliability analysis with incomplete covariates

The reliability analysis is crucial to reducing unexpected down time, severe failures and ever tightened maintenance budget of engineering assets. Hazard based reliability methods are of particular interest as hazard reflects the current health status of engineering assets and their imminent failure risks. Most existing hazard models were constructed using the statistical methods. However, these methods were established largely based on two assumptions: one is the assumption of baseline failure distributions being accurate to the population concerned and the other is the assumption of effects of covariates on hazards. These two assumptions may be difficult to achieve and therefore compromise the effectiveness of hazard models in the application. To address this issue, a non-linear hazard modelling approach is developed in this research using neural networks (NNs), resulting in neural network hazard models (NNHMs), to deal with limitations due to the two assumptions for statistical models. With the success of failure prevention effort, less failure history becomes available for reliability analysis. Involving condition data or covariates is a natural solution to this challenge. A critical issue for involving covariates in reliability analysis is that complete and consistent covariate data are often unavailable in reality due to inconsistent measuring frequencies of multiple covariates, sensor failure, and sparse intrusive measurements. This problem has not been studied adequately in current reliability applications. This research thus investigates such incomplete covariates problem in reliability analysis. Typical approaches to handling incomplete covariates have been studied to investigate their performance and effects on the reliability analysis results. Since these existing approaches could underestimate the variance in regressions and introduce extra uncertainties to reliability analysis, the developed NNHMs are extended to include handling incomplete covariates as an integral part. The extended versions of NNHMs have been validated using simulated bearing data and real data from a liquefied natural gas pump. The results demonstrate the new approach outperforms the typical incomplete covariates handling approaches. Another problem in reliability analysis is that future covariates of engineering assets are generally unavailable. In existing practices for multi-step reliability analysis, historical covariates were used to estimate the future covariates. Covariates of engineering assets, however, are often subject to substantial fluctuation due to the influence of both engineering degradation and changes in environmental settings. The commonly used covariate extrapolation methods thus would not be suitable because of the error accumulation and uncertainty propagation. To overcome this difficulty, instead of directly extrapolating covariate values, projection of covariate states is conducted in this research. The estimated covariate states and unknown covariate values in future running steps of assets constitute an incomplete covariate set which is then analysed by the extended NNHMs. A new assessment function is also proposed to evaluate risks of underestimated and overestimated reliability analysis results. A case study using field data from a paper and pulp mill has been conducted and it demonstrates that this new multi-step reliability analysis procedure is able to generate more accurate analysis results.

Factors influencing research engagement : research interest, confidence and experience in an Australian speech-language pathology workforce

Background Recent initiatives within an Australia public healthcare service have seen a focus on increasing the research capacity of their workforce. One of the key initiatives involves encouraging clinicians to be research generators rather than solely research consumers. As a result, baseline data of current research capacity are essential to determine whether initiatives encouraging clinicians to undertake research have been effective. Speech pathologists have previously been shown to be interested in conducting research within their clinical role; therefore they are well positioned to benefit from such initiatives. The present study examined the current research interest, confidence and experience of speech language pathologists (SLPs) in a public healthcare workforce, as well as factors that predicted clinician research engagement. Methods Data were collected via an online survey emailed to an estimated 330 SLPs working within Queensland, Australia. The survey consisted of 30 questions relating to current levels of interest, confidence and experience performing specific research tasks, as well as how frequently SLPs had performed these tasks in the last 5 years. Results Although 158 SLPs responded to the survey, complete data were available for only 137. Respondents were more confident and experienced with basic research tasks (e.g., finding literature) and less confident and experienced with complex research tasks (e.g., analysing and interpreting results, publishing results). For most tasks, SLPs displayed higher levels of interest in the task than confidence and experience. Research engagement was predicted by highest qualification obtained, current job classification level and overall interest in research. Conclusions Respondents generally reported levels of interest in research higher than their confidence and experience, with many respondents reporting limited experience in most research tasks. Therefore SLPs have potential to benefit from research capacity building activities to increase their research skills in order to meet organisational research engagement objectives. However, these findings must be interpreted with the caveats that a relatively low response rate occurred and participants were recruited from a single state-wide health service, and therefore may not be representative of the wider SLP workforce.

Metabolic urinary profiling of alcohol hepatotoxicity and intervention effects of Yin Chen Hao Tang in rats using ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry

This paper was designed to study metabonomic characters of the hepatotoxicity induced by alcohol and the intervention effects of Yin Chen Hao Tang (YCHT), a classic traditional Chinese medicine formula for treatment of jaundice and liver disorders in China. Urinary samples from control, alcohol- and YCHT-treated rats were analyzed by ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) in positive ionization mode. The total ion chromatograms obtained from the control, alcohol- and YCHT-treated rats were easily distinguishable using a multivariate statistical analysis method such as the principal components analysis (PCA). The greatest difference in metabolic profiling was observed from alcohol-treated rats compared with the control and YCHT-treated rats. The positive ions m/z 664.3126 (9.00 min) was elevated in urine of alcohol-treated rats, whereas, ions m/z 155.3547 (10.96 min) and 708.2932 (9.01 min) were at a lower concentration compared with that in urine of control rats, however, these ions did not indicate a statistical difference between control rats and YCHT-treated rats. The ion m/z 664.3126 was found to correspond to ceramide (d18:1/25:0), providing further support for an involvement of the sphingomyelin signaling pathway in alcohol hepatotoxicity and the intervention effects of YCHT.