All-environment visual place recognition with SMART

This paper presents Sequence Matching Across Route Traversals (SMART); a generally applicable sequence-based place recognition algorithm. SMART provides invariance to changes in illumination and vehicle speed while also providing moderate pose invariance and robustness to environmental aliasing. We evaluate SMART on vehicles travelling at highly variable speeds in two challenging environments; firstly, on an all-terrain vehicle in an off-road, forest track and secondly, using a passenger car traversing an urban environment across day and night. We provide comparative results to the current state-of-the-art SeqSLAM algorithm and investigate the effects of altering SMART’s image matching parameters. Additionally, we conduct an extensive study of the relationship between image sequence length and SMART’s matching performance. Our results show viable place recognition performance in both environments with short 10-metre sequences, and up to 96% recall at 100% precision across extreme day-night cycles when longer image sequences are used.

Conceptual framework for understanding the demand for emergency health services

Background & Objectives Emergency health services (EHS) throughout the world are increasingly congested. As more people use EHS, factors such as population growth and aging cannot fully explain this increase. Also, focus on patients’ clinical characteristics ignores the role that attitudinal and perceptual factors and motivations play in directing their decisions and actions. The aim of this study is to review and synthesize an integrated conceptual framework for understanding social psychological factors underpinning demand for EHS. Methodology A comprehensive search and review of empirical and theoretical studies about the utilization of EHS was conducted using major medical, health, social and behavioral sciences databases. Results A small number of studies used a relevant conceptual framework (e.g. Health Services Utilization Model or Health Belief Model) or their components to analyze patients’ decision to use EHS. The studies evidenced that demand was affected by perceived severity of the condition; perceived costs and benefits (e.g. availability, accessibility and affordability of alternative services); experience, preference and knowledge; perceived and actual social support; and demographic characteristics (e.g. age, sex, socioeconomic status, ethnicity, marital and living circumstances, place of residence). Conclusions Conceptual models that are commonly used in areas like social and behavioral sciences have rarely been applied in the EHS utilization field. Understanding patients’ decision-making and associated factors will lay the groundwork for identification of the evidence to inform improved policy responses and the development of demand management strategies. An integrated conceptual framework will be introduced as part of this study.

Duty in the time of epidemics : what China and Zimbabwe teach us

In November 2002, a man with ‘atypical pneumonia’ treated in Foshan hospital, Guangdong Province, in the People's Republic of China, was the first known case of Severe Acute Respiratory Syndrome (SARS). However, it was not until April 2003 that the Chinese government admitted to the full scale of ‘atypical pneumonia’ cases infected with SARS, two months after the disease had rapidly spread across the world with initial infections in Hong Kong and Vietnam sourced to Guangdong. In 2008, Zimbabwe experienced one of the biggest outbreaks of cholera ever recorded. By February 2009, the disease had spread across all of Zimbabwe's 10 provinces and to neighbouring countries—Botswana, South Africa, Zambia and Mozambique—causing thousands of infections amongst their populations. This article seeks to examine what duties the Chinese and Zimbabwe states had to protect their citizens and the international community from these outbreaks. The article refers to the findings of the International Law Commission's study into the role of states and international organisations in protecting persons in the event of a disaster to consider whether there is an international duty to protect persons from epidemics. The article concludes that both cases reveal a growing concept of protection that entails an international duty to assist individuals when an affected state proves unwilling or unable to assist its own population in the event of a disease outbreak.

Beyond point forecasting : evaluation of alternative prediction intervals for tourist arrivals

This paper evaluates the performances of prediction intervals generated from alternative time series models, in the context of tourism forecasting. The forecasting methods considered include the autoregressive (AR) model, the AR model using the bias-corrected bootstrap, seasonal ARIMA models, innovations state space models for exponential smoothing, and Harvey’s structural time series models. We use thirteen monthly time series for the number of tourist arrivals to Hong Kong and Australia. The mean coverage rates and widths of the alternative prediction intervals are evaluated in an empirical setting. It is found that all models produce satisfactory prediction intervals, except for the autoregressive model. In particular, those based on the biascorrected bootstrap perform best in general, providing tight intervals with accurate coverage rates, especially when the forecast horizon is long.

Multi-scale bio-inspired place recognition

This paper presents a novel place recognition algorithm inspired by the recent discovery of overlapping and multi-scale spatial maps in the rodent brain. We mimic this hierarchical framework by training arrays of Support Vector Machines to recognize places at multiple spatial scales. Place match hypotheses are then cross-validated across all spatial scales, a process which combines the spatial specificity of the finest spatial map with the consensus provided by broader mapping scales. Experiments on three real-world datasets including a large robotics benchmark demonstrate that mapping over multiple scales uniformly improves place recognition performance over a single scale approach without sacrificing localization accuracy. We present analysis that illustrates how matching over multiple scales leads to better place recognition performance and discuss several promising areas for future investigation.

Using the Alliance Form for Operation and Maintenance of Privatized Infrastructures

A significant number of privatizations utilized to operate and maintain critical networked infrastructures have failed to meet contractual expectations and the expectations of the community. The author carried out empirical research ex-ploring four urban water systems. This research revealed that of the four forms of privatization the alliance form was particularly suited to the stewardship of an ur-ban water system. The question then is whether these findings from urban water can be generalised to O&M of infrastructure generally. The answer is increasingly important as governments seek financial sustainability through reapplying the contestability strategy and outsource and privatise further services and activities. This paper first examines the issues encountered with O & M privatisations. Second the findings as to the stewardship achieved by the four case study water systems are unpacked with particular focus upon the alliance form. Third the key variables which were found to have distinct causal links to the stewardship-like behaviour of the private participants in the Alliance case study are described. Fourth the variables which may be crucial to the successful application of the alliance form to the broader range of infrastructures are separated out. Fifth this paper then sets the path for research into these crucial features of the alliance form.

The nurse's role in managing chemotherapy-induced nausea and vomiting

Background Nurses play a substantial role in the prevention and management of chemotherapy-induced nausea and vomiting (CINV). Objectives This study set out to describe nurses’ roles in the prevention and management of CINV and to identify any gaps that exist across countries. Methods A self-reported survey was completed by 458 registered nurses who administered chemotherapy to cancer patients in Australia, China, Hong Kong, and 9 Latin American countries. Results More than one-third of participants regarded their own knowledge of CINV as fair to poor. Most participants (>65%) agreed that chemotherapy-induced nausea and chemotherapy-induced vomiting should be considered separately (79%), but only 35% were confident in their ability to manage chemotherapy-induced nausea (53%) or chemotherapy-induced vomiting (59%). Only one-fifth reported frequent use of a standardized CINV assessment tool and only a quarter used international clinical guidelines to manage CINV. Conclusions Participants perceived their own knowledge of CINV management to be insufficient. They recognized the need to develop and use a standardized CINV assessment tool and the importance of adopting international guidelines to inform the management of CINV. Implications for Practice: Findings indicate that international guidelines should be made available to nurses in clinically relevant and easily accessible formats, that a review of chemotherapy assessment tools should be undertaken to identify reliable and valid measures amenable to use in a clinical settings, and that a CINV risk screening tool should be developed as a prompt for nurses to enable timely identification of and intervention for patients at high risk of CINV.

Vision based guidance for robot navigation in agriculture

This paper describes a novel vision based texture tracking method to guide autonomous vehicles in agricultural fields where the crop rows are challenging to detect. Existing methods require sufficient visual difference between the crop and soil for segmentation, or explicit knowledge of the structure of the crop rows. This method works by extracting and tracking the direction and lateral offset of the dominant parallel texture in a simulated overhead view of the scene and hence abstracts away crop-specific details such as colour, spacing and periodicity. The results demonstrate that the method is able to track crop rows across fields with extremely varied appearance during day and night. We demonstrate this method can autonomously guide a robot along the crop rows.

Long-term exploration and tours for energy constrained robots with online proprioceptive traversability estimation

This paper is concerned with how a localised and energy-constrained robot can maximise its time in the field by taking paths and tours that minimise its energy expenditure. A significant component of a robot's energy is expended on mobility and is a function of terrain traversability. We estimate traversability online from data sensed by the robot as it moves, and use this to generate maps, explore and ultimately converge on minimum energy tours of the environment. We provide results of detailed simulations and parameter studies that show the efficacy of this approach for a robot moving over terrain with unknown traversability as well as a number of a priori unknown hard obstacles.

Empirical modelling of rolling shutter effect

We propose and evaluate a novel methodology to identify the rolling shutter parameters of a real camera. We also present a model for the geometric distortion introduced when a moving camera with a rolling shutter views a scene. Unlike previous work this model allows for arbitrary camera motion, including accelerations, is exact rather than a linearization and allows for arbitrary camera projection models, for example fisheye or panoramic. We show the significance of the errors introduced by a rolling shutter for typical robot vision problems such as structure from motion, visual odometry and pose estimation.

Achieving transparency in carbon labelling for construction materials – Lessons from current assessment standards and carbon labels

The construction industry is one of the largest sources of carbon emissions. Manufacturing of raw materials, such as cement, steel and aluminium, is energy intensive and has considerable impact on carbon emissions level. Due to the rising recognition of global climate change, the industry is under pressure to reduce carbon emissions. Carbon labelling schemes are therefore developed as meaningful yardsticks to measure and compare carbon emissions. Carbon labelling schemes can help switch consumer-purchasing habits to low-carbon alternatives. However, such switch is dependent on a transparent scheme. The principle of transparency is highlighted in all international greenhouse gas (GHG) standards, including the newly published ISO 14067: Carbon footprint of products – requirements and guidelines for quantification and communication. However, there are few studies which systematically investigate the transparency requirements in carbon labelling schemes. A comparison of five established carbon labelling schemes, namely the Singapore Green Labelling Scheme, the CarbonFree (the U.S.), the CO2 Measured Label and the Reducing CO2 Label (UK), the CarbonCounted (Canada), and the Hong Kong Carbon Labelling Scheme is therefore conducted to identify and investigate the transparency requirements. The results suggest that the design of current carbon labels have transparency issues relating but not limited to the use of a single sign to represent the comprehensiveness of the carbon footprint. These transparency issues are partially caused by the flexibility given to select system boundary in the life cycle assessment (LCA) methodology to measure GHG emissions. The primary contribution of this study to the construction industry is to reveal the transparency requirements from international GHG standards and carbon labels for construction products. The findings also offer five key strategies as practical implications for the global community to improve the performance of current carbon labelling schemes on transparency.

Making optimal and justifiable asset maintenance decisions

Maintenance decisions for large-scale asset systems are often beyond an asset manager's capacity to handle. The presence of a number of possibly conflicting decision criteria, the large number of possible maintenance policies, and the reality of budget constraints often produce complex problems, where the underlying trade-offs are not apparent to the asset manager. This paper presents the decision support tool "JOB" (Justification and Optimisation of Budgets), which has been designed to help asset managers of large systems assess, select, interpret and optimise the effects of their maintenance policies in the presence of limited budgets. This decision support capability is realized through an efficient, scalable backtracking- based algorithm for the optimisation of maintenance policies, while enabling the user to view a number of solutions near this optimum and explore tradeoffs with other decision criteria. To assist the asset manager in selecting between various policies, JOB also provides the capability of Multiple Criteria Decision Making. In this paper, the JOB tool is presented and its applicability for the maintenance of a complex power plant system.

Pipeline renewal decision support : a case study using LinEAR in a water pipeline network

This paper presents a case study for the application of a Linear Engineering Asset Renewal decision support software tool (LinEAR) at a water distribution network in Australia. This case study examines how the LinEAR can assist water utilities to minimise their total pipeline management cost, to make a long-term budget based on mathematically predicted expenditure, and to present calculated evidence for supporting their expenditure requirements. The outcomes from the study on pipeline renewal decision support demonstrate that LinEAR can help water utilities to improve the decision process and save renewal costs over a long-term by providing an optimum renewal schedules. This software can help organisation to accumulate technical knowledge and prediction future impact of the decision using what-if analysis.

Reforming the procurement of construction and financing of Australian infrastructure : advancing capacity, competition and investment

Funded by an Australian Research Council (ARC) Linkage grant over four years (2009–13), the Major Infrastructure Procurement project sought to find more effective and efficient ways of procuring and delivering the nation’s social and economic infrastructure by investigating constraints relating to construction capacity, competition, and finance in new public sector major infrastructure.1 The research team comprised researchers in construction economics and finance from Queensland University of Technology (QUT), Griffith University (GU), The University of Hong Kong (UHK), and The University of Newcastle (UoN). Project partners included state government departments and agencies responsible for infrastructure procurement and delivery from all Australian mainland states, and private sector companies and peak bodies in the infrastructure sector (see “Introduction” for complete list). There are a number of major outcomes from this research project. The first of these is a scientifically developed decisionmaking model for procurement of infrastructure that deploys a novel and state-of-the-art integration of dominant microeconomic theory (including theories developed by two Nobel Prize winners). The model has been established through empirical testing and substantial experiential evidence as a valid and reliable guide to configuring procurement of new major and mega infrastructure projects in pursuance of superior Valuefor- Money (VfM). The model specifically addresses issues of project size, bundling of contracts, and exchange relationships. In so doing, the model determines the suitability of adopting a Public-Private Partnership (PPP) mode.