Adoption of low-cost rail level crossing warning devices : an Australian case study

The objective of this chapter is to provide rail practitioners with a practical approach for determining safety requirements of low-cost level crossing warning devices (LCLCWDs) on an Australian railway by way of a case study. LCLCWDs, in theory, allow railway operators to improve the safety of passively controlled crossing by upgrading a larger number of level crossings with the same budget that would otherwise be used to upgrade these using the conventional active level crossing control technologies, e.g. track circuit initiated flashing light systems. The chapter discusses the experience and obstacles of adopting LCLCWDs in Australia, and demonstrates how the risk-based approach may be used to make the case for LCLCWDs.

The Wood Royal Commission : a case study in the slippery slope from unethical conduct to criminal acts

Over the last two and a half decades, corruption in the police service in Australia has come under increased official and public scrutiny. Numerous scandals involving police officers has caused concerned about the integrity and ethics within the Police Service. This paper examines the Wood Royal Commission, specifically looking at testimony from Trevor Haken. This paper provides insights into the nature of police corruption as well as the process or ‘slippery slope’ corrupt officers go through. This paper also contributed to the existing literature by providing knowledge into the types of corruption used by police officers in real-life situations, and deepening understanding of how corruption emerges and why. It specifically confirms the literature on slippery slope arguments about police corruption and the role of trust in building a corrupt career. The paper contributes to the existing literature by providing insights into the nature of corruption used by police officers in real-life situations, and deepens the understanding of the process of corruption. The findings also contribute to our understanding that corruption is not just an individual incident but rather a result of reoccurring incidents that are generated by the nature of work, organizational structure and society in relation to corruption.

Challenges in mass spectrometry-based quantification of bioactive peptides: A case study exploring the neuropeptide Y family

The study of biologically active peptides is critical to the understanding of physiological pathways, especially those involved in the development of disease. Historically, the measurement of biologically active endogenous peptides has been undertaken by radioimmunoassay, a highly sensitive and robust technique that permits the detection of physiological concentrations in different biofluid and tissue extracts. Over recent years, a range of mass spectrometric approaches have been applied to peptide quantification with limited degrees of success. Neuropeptide Y (NPY), peptide YY (PYY), and pancreatic polypeptide (PP) belong to the NPY family exhibiting regulatory effects on appetite and feeding behavior. The physiological significance of these peptides depends on their molecular forms and in vivo concentrations systemically and at local sites within tissues. In this report, we describe an approach for quantification of individual peptides within mixtures using high-performance liquid chromatography electrospray ionization tandem mass spectrometry analysis of the NPY family peptides. Aspects of quantification including sample preparation, the use of matrix-matched calibration curves, and internal standards will be discussed. This method for the simultaneous determination of NPY, PYY, and PP was accurate and reproducible but lacks the sensitivity required for measurement of their endogenous concentration in plasma. The advantages of mass spectrometric quantification will be discussed alongside the current obstacles and challenges. © 2012 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 98: 357–366, 2012.

What health problems overcrowd hospital emergency departments in Queensland and has this changed over time? A case study of two hospitals in Queensland 2001 - 2009

Aim: to describe what health problems patients attending emergency department with and whether this changed over time. Methods: Electronic data was retrieved from EDIS (Emergency Department Information System) and HBCIS (Hospital Based Clinical Information System) in two hospitals in Queensland in the period 2001-2009. The ICD-10 code of patient's diagnosis was then extrapolated and then group into ICD-10 chapters, such that the health problem can be presented. Results: Among the specific health problems, Chapter XIX 'Injury and poisoning' ranked number one consistently (ranging from 22.1% to 31.2% of the total presentations) in both the urban and remote hospitals in Queensland. The top ten specific presenting health problems in both the urban and remote hospital include Chapter XI 'Digestive system', Chapter XIV 'Genitourinary system', Chapter IX 'Circulatory system', and Chapter XIII 'Musculoskeletal system and connective tissue'. Chapter X 'Respiratory system' made the top ten presenting Chapters in both hospitals, but ranked much higher (number four consistently for the eight years, ranging from 6.8% to 8.3%) in the remote hospital. Chapter XV 'Pregnancy childbirth and puerperium' made to the top ten in the urban hospital only while Chapter XII 'Skin and subcutaneous tissue', Chapter I 'Infectious and parasitic diseases' made the top ten in the remote hospital only. Conclusion: The number one health problem presenting to both the urban and remote hospitals in Queensland is Chapter XIX 'Injury and poisoning', and it did not change in the period 211 - 2009.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.