Wind-energy based path planning for unmanned aerial vehicles using Markov decision processes

Exploiting wind-energy is one possible way to extend flight duration for Unmanned Arial Vehicles. Wind-energy can also be used to minimise energy consumption for a planned path. In this paper, we consider uncertain time-varying wind fields and plan a path through them. A Gaussian distribution is used to determine uncertainty in the Time-varying wind fields. We use Markov Decision Process to plan a path based upon the uncertainty of Gaussian distribution. Simulation results that compare the direct line of flight between start and target point and our planned path for energy consumption and time of travel are presented. The result is a robust path using the most visited cell while sampling the Gaussian distribution of the wind field in each cell.

Inherent errors in pollutant build-up estimation in considering urban land use as a lumped parameter

Stormwater quality modelling results is subject to uncertainty. The variability of input parameters is an important source of overall model error. An in-depth understanding of the variability associated with input parameters can provide knowledge on the uncertainty associated with these parameters and consequently assist in uncertainty analysis of stormwater quality models and the decision making based on modelling outcomes. This paper discusses the outcomes of a research study undertaken to analyse the variability related to pollutant build-up parameters in stormwater quality modelling. The study was based on the analysis of pollutant build-up samples collected from 12 road surfaces in residential, commercial and industrial land uses. It was found that build-up characteristics vary appreciably even within the same land use. Therefore, using land use as a lumped parameter would contribute significant uncertainties in stormwater quality modelling. Additionally, it was also found that the variability in pollutant build-up can also be significant depending on the pollutant type. This underlines the importance of taking into account specific land use characteristics and targeted pollutant species when undertaking uncertainty analysis of stormwater quality models or in interpreting the modelling outcomes.

Impact of prescribed burning on urban air quality : a case study

For fuel management and/or ecological reasons prescribed burnings are conducted each year across Australia. Smoke from prescribed burnings could be the major source of air pollution in urban environment during the period of intensive prescribed burning. To investigate the impact of prescribed burning on air quality and the characteristics of prescribed burning particles, field measurements were conducted during the end period of a prescribed burning event in September 2011, Brisbane, Australia.

Unstructured processes of strategic decision-making

The author aims at developing a better understanding of unstructured strategic decision making processes and the conditions for achieving successful decision outcomes. Specifically he investigates the processes used to make CRE (Corporate Real Estate) decisions. To reveal the fundamental differences between CRE decision-making in practice and the prescriptive ‘best practice’ advocated in the CRE literature, a study of seven leading Italian management consulting firms is undertaken addressing the aspects of content and process of decisions. This research makes its primary contribution by identifying the importance and difficulty of finding the right balance between problem complexity, process richness and cohesion to ensure a decision-making process that is sufficiently rich and yet quick enough to deliver a prompt outcome. While doing so, the study also provides more empirical evidence to some of the most established theories of decision-making, while reinterpreting their mono-dimensional arguments in a multi-dimensional model of successful decision-making.

Impact of the social networking applications for health information management for patients and physicians

Most social network users hold more than one social network account and utilize them in different ways depending on the digital context. For example, friendly chat on Facebook, professional discussion on LinkedIn, and health information exchange on PatientsLikeMe. Thus many web users need to manage many disparate profiles across many distributed online sources. Maintaining these profiles is cumbersome, time consuming, inefficient, and leads to lost opportunity. In this paper we propose a framework for multiple profile management of online social networks and showcase a demonstrator utilising an open source platform. The result of the research enables a user to create and manage an integrated profile and share/synchronise their profiles with their social networks. A number of use cases were created to capture the functional requirements and describe the interactions between users and the online services. An innovative application of this project is in public health informatics. We utilize the prototype to examine how the framework can benefit patients and physicians. The framework can greatly enhance health information management for patients and more importantly offer a more comprehensive personal health overview of patients to physicians.

Quality based frame selection for video face recognition

Quality based frame selection is a crucial task in video face recognition, to both improve the recognition rate and to reduce the computational cost. In this paper we present a framework that uses a variety of cues (face symmetry, sharpness, contrast, closeness of mouth, brightness and openness of the eye) to select the highest quality facial images available in a video sequence for recognition. Normalized feature scores are fused using a neural network and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face recognition system. Experiments on the Honda/UCSD database shows that the proposed method selects the best quality face images in the video sequence, resulting in improved recognition performance.

Preliminary results on the chemical composition of outdoor airborne particles at urban schools and possible implications for the air quality in classrooms

Vehicle emissions are a significant source of fine particles (Dp < 2.5 µm) in an urban environment. These fine particles have been shown to have detrimental health effects, with children thought to be more susceptible. Vehicle emissions are mainly carbonaceous in nature, and carbonaceous aerosols can be defined as either elemental carbon (EC) or organic carbon (OC). EC is a soot-like material emitted from primary sources while OC fraction is a complex mixture of hundreds of organic compounds from either primary or secondary sources (Cao et al., 2006). Therefore the ratio of OC/EC can aid in the identification of source. The purpose of this paper is to use the concentration of OC and EC in fine particles to determine the levels of vehicle emissions in schools. It is expected that this will improve the understanding of the potential exposure of children in a school environment to vehicle emissions.

Using advanced traits of passengers to facilitate route-choice decision-making

Research interest in pedestrian behaviour spans the retail industry, emergency services, urban planners and other agencies. Most models to simulate and model pedestrian movement can be distinguished on the basis of geographical scale, from the micro-scale movement of obstacle avoidance, through the meso-scale of individuals planning multi-stop shopping trips, up to the macro-scale of overall flow of masses of people between places. In this paper, route-choice decision-making model is devised for modelling passengers flow in airport terminal. A set of devised advanced traits of passengers is firstly proposed. Advanced traits take into account a passenger’s cognitive preferences and demonstrate underlying motivations of route-choice decisions. Although the activities of passengers are normally regarded as stochastic and sometimes unpredictable, real scenarios of passenger flows are basically feasible to be compared with virtual simulations in terms of tactical route-choice decision-making. Passengers in the model are as intelligent agents who possess a bunch of initial basic traits and are categorized into five distinguish groups in terms of routing preferences. Route choices are consecutively determined by inferring current advanced traits according to the utility matrix.

A decision support system for sustainable urban development : the integrated land use and transportation indexing model

Broad, early definitions of sustainable development have caused confusion and hesitation among local authorities and planning professionals. This confusion has arisen because loosely defined principles of sustainable development have been employed when setting policies and planning projects, and when gauging the efficiencies of these policies in the light of designated sustainability goals. The question of how this theory-rhetoric-practice gap can be filled is the main focus of this chapter. It examines the triple bottom line approach–one of the sustainability accounting approaches widely employed by governmental organisations–and the applicability of this approach to sustainable urban development. The chapter introduces the ‘Integrated Land Use and Transportation Indexing Model’ that incorporates triple bottom line considerations with environmental impact assessment techniques via a geographic, information systemsbased decision support system. This model helps decision-makers in selecting policy options according to their economic, environmental and social impacts. Its main purpose is to provide valuable knowledge about the spatial dimensions of sustainable development, and to provide fine detail outputs on the possible impacts of urban development proposals on sustainability levels. In order to embrace sustainable urban development policy considerations, the model is sensitive to the relationship between urban form, travel patterns and socio-economic attributes. Finally, the model is useful in picturing the holistic state of urban settings in terms of their sustainability levels, and in assessing the degree of compatibility of selected scenarios with the desired sustainable urban future.

The effect of exercise on sleep quality in heart failure

Purpose The primary objective of this study was to examine the effect of exercise on subjective sleep quality in heart failure patients. Methods This study used a randomised, controlled trial design with blinded end-point analysis. Participants were randomly assigned to a 12-week programme of education and self-management support (control) or to the same programme with the addition of a tailored physical activity programme designed and supervised by an exercise specialist (intervention). The intervention consisted of 1 hour of aerobic and resistance exercise twice a week. Participants included 108 patients referred to three hospital heart failure services in Queensland, Australia. Results Patients who participated in supervised exercise classes showed significant improvement in subjective sleep quality, sleep latency, sleep disturbance and global sleep quality scores after 12 weeks of supervised hospital based exercise. Secondary analysis showed that improvements in sleep quality were correlated with improvements in geriatric depression score (p=0.00) and exercise performance (p=0.03). General linear models were used to examine whether the changes in sleep quality following intervention occurred independently of changes in depression, exercise performance and weight. Separate models adjusting for each covariate were performed. Results suggest that exercise significantly improved sleep quality independent of changes in depression, exercise performance and weight. Conclusion This study supports the hypothesis that a 12 week program of aerobic and resistance exercise improves subjective sleep quality in patients with heart failure. This is the first randomised controlled trial to examine the role of exercise in the improvement of sleep quality for patients with this disease. While this study establishes exercise as a therapy for poor sleep quality, further research is needed to investigate exercise as a treatment for other parameters of sleep in this population. Study investigators plan to undertake a more in-depth examination within the next 12 months

Future directions in course quality assurance

The Course Quality Assurance System at Queensland University of Technology (QUT) has as its centrepiece an exemplar of data visualisation known as the Individual Course Report (ICR). This report provides every course coordinator with an annual snapshot of their performance data evaluated against QUT and national benchmarks. In this article, the impact of the ICR is explored through the case study of one undergraduate course identified as underperforming. The case study features an innovative, ethnographic approach to working with course teams and highlights the importance of context, collaboration and appropriate support in creating evidence-based action plans for course improvement.

Evaluation of the quality of diverse teaching practice in higher education research methods

The purpose of this paper is to report on a methods research project investigating the evaluation of diverse teaching practice in Higher Education. The research method is a single site case study of an Australian university with data collected through published documents, surveys, interviews and focus groups. This project provides evidence of the wide variety of evaluation practice and diverse teaching practice across the university. This breadth identifies the need for greater flexibility of evaluation processes, tools and support to assist teaching staff to evaluate their diverse teaching practice. The employment opportunities for academics benchmark the university nationally and position the case study in the field. Finally this reaffirms the institutional responsibility for services to support teaching staff in an ongoing manner.

Exercise for health : a randomized, controlled trial evaluating impact of a pragmatic, translational exercise intervention on quality of life, function and treatment-related side effects following breast cancer

Purpose Exercise for Health was a randomized, controlled trial designed to evaluate two modes of delivering (face-to-face [FtF] and over-the-telephone [Tel]) an 8-month translational exercise intervention, commencing 6-weeks post-breast cancer surgery (PS). Methods Outcomes included quality of life (QoL), function (fitness and upper-body) and treatment-related side effects (fatigue, lymphoedema, body mass index, menopausal symptoms, anxiety, depression and pain). Generalised estimating equation modelling determined time (baseline [5-weeks PS], mid-intervention [6-months PS], post-intervention [12-months PS]), group (FtF, Tel, Usual Care [UC]) and time-by-group effects. 194 women representative of the breast cancer population were randomised to the FtF (n=67), Tel (n=67) and UC (n=60) groups. Results: There were significant (p<0.05) interaction effects on QoL, fitness and fatigue, with differences being observed between the treatment groups and the UC group. Trends observed for the treatment groups were similar. The treatment groups reported improved QoL, fitness and fatigue over time and changes observed between baseline and post-intervention were clinically relevant. In contrast, the UC group experienced no change, or worsening QoL, fitness and fatigue, mid-intervention. Although improvements in the UC group occurred by 12-months post-surgery, the change did not meet the clinically relevant threshold. There were no differences in other treatment-related side-effects between groups. Conclusion This translational intervention trial, delivered either face-to-face or over-the-telephone, supports exercise as a form of adjuvant breast cancer therapy that can prevent declines in fitness and function during treatment and optimise recovery post-treatment.

Reputation modelling in citizen science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, as the gathered information is from the crowd, the data quality is always hard to manage. There are many ways to manage data quality, and reputation management is one of the common approaches. In recent year, many research teams have deployed many audio or image sensors in natural environment in order to monitor the status of animals or plants. The collected data will be analysed by ecologists. However, as the amount of collected data is exceedingly huge and the number of ecologists is very limited, it is impossible for scientists to manually analyse all these data. The functions of existing automated tools to process the data are still very limited and the results are still not very accurate. Therefore, researchers have turned to recruiting general citizens who are interested in helping scientific research to do the pre-processing tasks such as species tagging. Although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Therefore, this research aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we aim to investigate how to use reputation management to enhance data reliability. Reputation systems have been used to solve the uncertainty and improve data quality in many marketing and E-Commerce domains. The commercial organizations which have chosen to embrace the reputation management and implement the technology have gained many benefits. Data quality issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. However, research on reputation management in this area is relatively new. We therefore start our investigation by examining existing reputation systems in different domains. Then we design novel reputation management approaches for Citizen Science projects to categorise participants and data. We have investigated some critical elements which may influence data reliability in Citizen Science projects. These elements include personal information such as location and education and performance information such as the ability to recognise certain bird calls. The designed reputation framework is evaluated by a series of experiments involving many participants for collecting and interpreting data, in particular, environmental acoustic data. Our research in exploring the advantages of reputation management in Citizen Science (or crowdsourcing in general) will help increase awareness among organizations that are unacquainted with its potential benefits.

Evaluation of the cost-effectiveness of strategies claiming to reduce the risk of surgical site infections following primary total hip arthroplasty

Background Total hip arthroplasty (THA) is a commonly performed procedure and numbers are increasing with ageing populations. One of the most serious complications in THA are surgical site infections (SSIs), caused by pathogens entering the wound during the procedure. SSIs are associated with a substantial burden for health services, increased mortality and reduced functional outcomes in patients. Numerous approaches to preventing these infections exist but there is no gold standard in practice and the cost-effectiveness of alternate strategies is largely unknown. Objectives The aim of this project was to evaluate the cost-effectiveness of strategies claiming to reduce deep surgical site infections following total hip arthroplasty in Australia. The objectives were: 1. Identification of competing strategies or combinations of strategies that are clinically relevant to the control of SSI related to hip arthroplasty 2. Evidence synthesis and pooling of results to assess the volume and quality of evidence claiming to reduce the risk of SSI following total hip arthroplasty 3. Construction of an economic decision model incorporating cost and health outcomes for each of the identified strategies 4. Quantification of the effect of uncertainty in the model 5. Assessment of the value of perfect information among model parameters to inform future data collection Methods The literature relating to SSI in THA was reviewed, in particular to establish definitions of these concepts, understand mechanisms of aetiology and microbiology, risk factors, diagnosis and consequences as well as to give an overview of existing infection prevention measures. Published economic evaluations on this topic were also reviewed and limitations for Australian decision-makers identified. A Markov state-transition model was developed for the Australian context and subsequently validated by clinicians. The model was designed to capture key events related to deep SSI occurring within the first 12 months following primary THA. Relevant infection prevention measures were selected by reviewing clinical guideline recommendations combined with expert elicitation. Strategies selected for evaluation were the routine use of pre-operative antibiotic prophylaxis (AP) versus no use of antibiotic prophylaxis (No AP) or in combination with antibiotic-impregnated cement (AP & ABC) or laminar air operating rooms (AP & LOR). The best available evidence for clinical effect size and utility parameters was harvested from the medical literature using reproducible methods. Queensland hospital data were extracted to inform patients’ transitions between model health states and related costs captured in assigned treatment codes. Costs related to infection prevention were derived from reliable hospital records and expert opinion. Uncertainty of model input parameters was explored in probabilistic sensitivity analyses and scenario analyses and the value of perfect information was estimated. Results The cost-effectiveness analysis was performed from a health services perspective using a hypothetical cohort of 30,000 THA patients aged 65 years. The baseline rate of deep SSI was 0.96% within one year of a primary THA. The routine use of antibiotic prophylaxis (AP) was highly cost-effective and resulted in cost savings of over $1.6m whilst generating an extra 163 QALYs (without consideration of uncertainty). Deterministic and probabilistic analysis (considering uncertainty) identified antibiotic prophylaxis combined with antibiotic-impregnated cement (AP & ABC) to be the most cost-effective strategy. Using AP & ABC generated the highest net monetary benefit (NMB) and an incremental $3.1m NMB compared to only using antibiotic prophylaxis. There was a very low error probability that this strategy might not have the largest NMB (<5%). Not using antibiotic prophylaxis (No AP) or using both antibiotic prophylaxis combined with laminar air operating rooms (AP & LOR) resulted in worse health outcomes and higher costs. Sensitivity analyses showed that the model was sensitive to the initial cohort starting age and the additional costs of ABC but the best strategy did not change, even for extreme values. The cost-effectiveness improved for a higher proportion of cemented primary THAs and higher baseline rates of deep SSI. The value of perfect information indicated that no additional research is required to support the model conclusions. Conclusions Preventing deep SSI with antibiotic prophylaxis and antibiotic-impregnated cement has shown to improve health outcomes among hospitalised patients, save lives and enhance resource allocation. By implementing a more beneficial infection control strategy, scarce health care resources can be used more efficiently to the benefit of all members of society. The results of this project provide Australian policy makers with key information about how to efficiently manage risks of infection in THA.