Supporting an NGO in Developing Mozambique

Many efforts are made to assist with the advancement of developing economies through the activities of Non Government Organizations (NGOs). There are many management l and engagement issues associated with any successful NGO in a developed economy. When the organization is operating in a developing country, where a lack of infrastructure and education, distrust and corruption are part of the operating environment, the issues multiply. This case study discusses the structure of a NGO started in 2008 and describes the development and interaction of its two main components: a community-based NGO in a developing country and a NGO with a voluntary committee in a developed economy, australia. Despertai Mozambique is the NGO set up in Beira, which provides the necessary support, training and funding to the local poor (defined by the UN as living below the internationally accepted poverty line of US$1.25 a day) to help them set up small, often informal, businesses, to enable them to become sustainable, and ultimately to help alleviate poverty. The partnering Australian organization, Awaken Mozambique, is responsible for providing the necessary intellectual and financial resources required by Despertai Mozambique to operate.

An efficient algorithm for the detection of Eden

In this paper, a polynomial time algorithm is presented for solving the Eden problem for graph cellular automata. The algorithm is based on our neighborhood elimination operation which removes local neighborhood configurations which cannot be used in a pre-image of a given configuration. This paper presents a detailed derivation of our algorithm from first principles, and a detailed complexity and accuracy analysis is also given. In the case of time complexity, it is shown that the average case time complexity of the algorithm is \Theta(n^2), and the best and worst cases are \Omega(n) and O(n^3) respectively. This represents a vast improvement in the upper bound over current methods, without compromising average case performance.

Exercising QS: Quantitative skills in an exercise science course

This study seeks to bring the discipline of exercise science into the discussion of Quantitative Skills (QS) in Science. The author’s experiences of providing learning support to students and working with educators in the field are described, demonstrating the difficulty of encouraging students to address their skills deficit. A survey of students’ perceptions of their own QS and of that required for their course, demonstrates the difficulties faced by students who do not have the prescribed assumed knowledge for the course. Limited results from academics suggest that their perceptions of students’ QS deficits are even more dire than those of the under-prepared students.

Understanding mild acid pretreatment of sugarcane bagasse through particle scale modeling

Sugarcane bagasse is an abundant and sustainable resource, generated as a by-product of sugarcane milling. The cellulosic material within bagasse can be broken down into glucose molecules and fermented to produce ethanol, making it a promising feedstock for biofuel production. Mild acid pretreatment hydrolyses the hemicellulosic component of biomass, thus allowing enzymes greater access to the cellulosic substrate during saccharification. A particle-scale mathematical model describing the mild acid pretreatment of sugarcane bagasse has been developed, using a volume averaged framework. Discrete population-balance equations are used to characterise the polymer degradation kinetics, and diffusive effects account for mass transport within the cell wall of the bagasse. As the fibrous material hydrolyses over time, variations in the porosity of the cell wall and the downstream effects on the reaction kinetics are accounted for using conservation of volume arguments. Non-dimensionalization of the model equations reduces the number of parameters in the system to a set of four dimensionless ratios that compare the timescales of different reaction and diffusion events. Theoretical yield curves are compared to macroscopic experimental observations from the literature and inferences are made as to constraints on these “unknown” parameters. These results enable connections to be made between experimental data and the underlying thermodynamics of acid pretreatment. Consequently, the results suggest that data-fitting techniques used to obtain kinetic parameters should be carefully applied, with prudent consideration given to the chemical and physiological processes being modeled.

A dynamic approach for evaluating earthwork hauls in construction

A graph theoretic approach is developed for accurately computing haulage costs in earthwork projects. This is vital as haulage is a predominant factor in the real cost of earthworks. A variety of metrics can be used in our approach, but a fuel consumption proxy is recommended. This approach is novel as it considers the constantly changing terrain that results from cutting and filling activities and replaces inaccurate “static” calculations that have been used previously. The approach is also capable of efficiently correcting the violation of top down cutting and bottom up filling conditions that can be found in existing earthwork assignments and sequences. This approach assumes that the project site is partitioned into uniform blocks. A directed graph is then utilised to describe the terrain surface. This digraph is altered after each cut and fill, in order to reflect the true state of the terrain. A shortest path algorithm is successively applied to calculate the cost of each haul and these costs are summed to provide a total cost of haulage

Bayesian indirect inference using a parametric auxiliary model

Indirect inference (II) is a methodology for estimating the parameters of an intractable (generative) model on the basis of an alternative parametric (auxiliary) model that is both analytically and computationally easier to deal with. Such an approach has been well explored in the classical literature but has received substantially less attention in the Bayesian paradigm. The purpose of this paper is to compare and contrast a collection of what we call parametric Bayesian indirect inference (pBII) methods. One class of pBII methods uses approximate Bayesian computation (referred to here as ABC II) where the summary statistic is formed on the basis of the auxiliary model, using ideas from II. Another approach proposed in the literature, referred to here as parametric Bayesian indirect likelihood (pBIL), we show to be a fundamentally different approach to ABC II. We devise new theoretical results for pBIL to give extra insights into its behaviour and also its differences with ABC II. Furthermore, we examine in more detail the assumptions required to use each pBII method. The results, insights and comparisons developed in this paper are illustrated on simple examples and two other substantive applications. The first of the substantive examples involves performing inference for complex quantile distributions based on simulated data while the second is for estimating the parameters of a trivariate stochastic process describing the evolution of macroparasites within a host based on real data. We create a novel framework called Bayesian indirect likelihood (BIL) which encompasses pBII as well as general ABC methods so that the connections between the methods can be established.

Fully Bayesian experimental design for pharmacokinetic studies

Utility functions in Bayesian experimental design are usually based on the posterior distribution. When the posterior is found by simulation, it must be sampled from for each future data set drawn from the prior predictive distribution. Many thousands of posterior distributions are often required. A popular technique in the Bayesian experimental design literature to rapidly obtain samples from the posterior is importance sampling, using the prior as the importance distribution. However, importance sampling will tend to break down if there is a reasonable number of experimental observations and/or the model parameter is high dimensional. In this paper we explore the use of Laplace approximations in the design setting to overcome this drawback. Furthermore, we consider using the Laplace approximation to form the importance distribution to obtain a more efficient importance distribution than the prior. The methodology is motivated by a pharmacokinetic study which investigates the effect of extracorporeal membrane oxygenation on the pharmacokinetics of antibiotics in sheep. The design problem is to find 10 near optimal plasma sampling times which produce precise estimates of pharmacokinetic model parameters/measures of interest. We consider several different utility functions of interest in these studies, which involve the posterior distribution of parameter functions.

Modeling the viability of the free-ranging cheetah population in Namibia : an object-oriented Bayesian network approach

Conservation of free-ranging cheetah (Acinonyx jubatus) populations is multi faceted and needs to be addressed from an ecological, biological and management perspective. There is a wealth of published research, each focusing on a particular aspect of cheetah conservation. Identifying the most important factors, making sense of various (and sometimes contrasting) findings, and taking decisions when little or no empirical data is available, are everyday challenges facing conservationists. Bayesian networks (BN) provide a statistical modeling framework that enables analysis and integration of information addressing different aspects of conservation. There has been an increased interest in the use of BNs to model conservation issues, however the development of more sophisticated BNs, utilizing object-oriented (OO) features, is still at the frontier of ecological research. We describe an integrated, parallel modeling process followed during a BN modeling workshop held in Namibia to combine expert knowledge and data about free-ranging cheetahs. The aim of the workshop was to obtain a more comprehensive view of the current viability of the free-ranging cheetah population in Namibia, and to predict the effect different scenarios may have on the future viability of this free-ranging cheetah population. Furthermore, a complementary aim was to identify influential parameters of the model to more effectively target those parameters having the greatest impact on population viability. The BN was developed by aggregating diverse perspectives from local and independent scientists, agents from the national ministry, conservation agency members and local fieldworkers. This integrated BN approach facilitates OO modeling in a multi-expert context which lends itself to a series of integrated, yet independent, subnetworks describing different scientific and management components. We created three subnetworks in parallel: a biological, ecological and human factors network, which were then combined to create a complete representation of free-ranging cheetah population viability. Such OOBNs have widespread relevance to the effective and targeted conservation management of vulnerable and endangered species.

Tangled Webs : Using Bayesian Networks in the Fight Against Infection

Bayesian networks (BNs) provide a statistical modelling framework which is ideally suited for modelling the many factors and components of complex problems such as healthcare-acquired infections. The methicillin-resistant Staphylococcus aureus (MRSA) organism is particularly troublesome since it is resistant to standard treatments for Staph infections. Overcrowding and understa�ng are believed to increase infection transmission rates and also to inhibit the effectiveness of disease control measures. Clearly the mechanisms behind MRSA transmission and containment are very complicated and control strategies may only be e�ective when used in combination. BNs are growing in popularity in general and in medical sciences in particular. A recent Current Content search of the number of published BN journal articles showed a fi�ve fold increase in general and a six fold increase in medical and veterinary science from 2000 to 2009. This chapter introduces the reader to Bayesian network (BN) modelling and an iterative modelling approach to build and test the BN created to investigate the possible role of high bed occupancy on transmission of MRSA while simultaneously taking into account other risk factors.

Beyond compliance : project on an integrated systems approach for pest risk management in South East Asia

The Beyond Compliance project, which began in July 2011 with funding from the Standards and Trade Development Facility for 2 years, aims to enhance competency and confidence in the South East Asian sub-region by applying a Systems Approach for pest risk management. The Systems Approach involves the use of integrated measures, at least two of which are independent, that cumulatively reduce the risk of introducing exotic pests through trade. Although useful in circumstances where single measures are inappropriate or unavailable, the Systems Approach is inherently more complicated than single-measure approaches, which may inhibit its uptake. The project methodology is to take prototype decision-support tools, such as Control Point-Bayesian Networks (CP-BN), developed in recent plant health initiatives in other regions, including the European PRATIQUE project, and to refine them within this sub-regional context. Case studies of high-priority potential agricultural trade will be conducted by National Plant Protection Organizations of participating South East Asian countries in trials of the tools, before further modifications. Longer term outcomes may include: more robust pest risk management in the region (for exports and imports); greater inclusion of stakeholders in development of pest risk management plans; increased confidence in trade negotiations; and new opportunities for trade.

A Bayesian network approach to modelling temporal behaviour of Lyngbya majuscula bloom initiation

Lyngbya majuscula is a cyanobacterium (blue-green algae) occurring naturally in tropical and subtropical coastal areas worldwide. Deception Bay, in Northern Moreton Bay, Queensland, has a history of Lyngbya blooms, and forms a case study for this investigation. The South East Queensland (SEQ) Healthy Waterways Partnership, collaboration between government, industry, research and the community, was formed to address issues affecting the health of the river catchments and waterways of South East Queensland. The Partnership coordinated the Lyngbya Research and Management Program (2005-2007) which culminated in a Coastal Algal Blooms (CAB) Action Plan for harmful and nuisance algal blooms, such as Lyngbya majuscula. This first phase of the project was predominantly of a scientific nature and also facilitated the collection of additional data to better understand Lyngbya blooms. The second phase of this project, SEQ Healthy Waterways Strategy 2007-2012, is now underway to implement the CAB Action Plan and as such is more management focussed. As part of the first phase of the project, a Science model for the initiation of a Lyngbya bloom was built using Bayesian Networks (BN). The structure of the Science Bayesian Network was built by the Lyngbya Science Working Group (LSWG) which was drawn from diverse disciplines. The BN was then quantified with annual data and expert knowledge. Scenario testing confirmed the expected temporal nature of bloom initiation and it was recommended that the next version of the BN be extended to take this into account. Elicitation for this BN thus occurred at three levels: design, quantification and verification. The first level involved construction of the conceptual model itself, definition of the nodes within the model and identification of sources of information to quantify the nodes. The second level included elicitation of expert opinion and representation of this information in a form suitable for inclusion in the BN. The third and final level concerned the specification of scenarios used to verify the model. The second phase of the project provides the opportunity to update the network with the newly collected detailed data obtained during the previous phase of the project. Specifically the temporal nature of Lyngbya blooms is of interest. Management efforts need to be directed to the most vulnerable periods to bloom initiation in the Bay. To model the temporal aspects of Lyngbya we are using Object Oriented Bayesian networks (OOBN) to create ‘time slices’ for each of the periods of interest during the summer. OOBNs provide a framework to simplify knowledge representation and facilitate reuse of nodes and network fragments. An OOBN is more hierarchical than a traditional BN with any sub-network able to contain other sub-networks. Connectivity between OOBNs is an important feature and allows information flow between the time slices. This study demonstrates more sophisticated use of expert information within Bayesian networks, which combine expert knowledge with data (categorized using expert-defined thresholds) within an expert-defined model structure. Based on the results from the verification process the experts are able to target areas requiring greater precision and those exhibiting temporal behaviour. The time slices incorporate the data for that time period for each of the temporal nodes (instead of using the annual data from the previous static Science BN) and include lag effects to allow the effect from one time slice to flow to the next time slice. We demonstrate a concurrent steady increase in the probability of initiation of a Lyngbya bloom and conclude that the inclusion of temporal aspects in the BN model is consistent with the perceptions of Lyngbya behaviour held by the stakeholders. This extended model provides a more accurate representation of the increased risk of algal blooms in the summer months and show that the opinions elicited to inform a static BN can be readily extended to a dynamic OOBN, providing more comprehensive information for decision makers.

The epidemiology of invasive group A streptococcal disease in Victoria, Australia

Objective To estimate the incidence and severity of invasive group A streptococcal infection in Victoria, Australia. Design Prospective active surveillance study. Setting Public and private laboratories, hospitals and general practitioners throughout Victoria. Patients eople in Victoria diagnosed with group A streptococcal disease notified to the surveillance system between 1 March 2002 and 31 August 2004. Main outcome measure Confirmed invasive group A streptococcal disease. Results We identified 333 confirmed cases: an average annualised incidence rate of 2.7 (95% CI, 2.3-3.2) per 100000 population per year. Rates were highest in people aged 65 years and older and those younger than 5 years. The case-fatality rate was 7.8%. Streptococcal toxic shock syndrome occurred in 48 patients (14.4%), with a case-fatality rate of 23%. Thirty cases of necrotising fasciitis were reported; five (17%) of these patients died. Type 1 (23%) was the most frequently identified emm sequence type in all, age groups. All tested isolates were susceptible to penicillin and clindamycin. Two isolates (4%) were resistant to erythromycin. Conclusion The incidence of invasive group A streptococcal disease in temperate Australia is greater than previously appreciated and warrants greater public health attention, including its designation as a notifiable disease.

What is an expert? A systems perspective on expertise

1. Expert knowledge continues to gain recognition as a valuable source of information in a wide range of research applications. Despite recent advances in defining expert knowledge, comparatively little attention has been given to how to view expertise as a system of interacting contributory factors, and thereby, to quantify an individual’s expertise. 2. We present a systems approach to describing expertise that accounts for many contributing factors and their interrelationships, and allows quantification of an individual’s expertise. A Bayesian network (BN) was chosen for this purpose. For the purpose of illustration, we focused on taxonomic expertise. The model structure was developed in consultation with professional taxonomists. The relative importance of the factors within the network were determined by a second set of senior taxonomists. This second set of experts (i.e. supra-experts) also provided validation of the model structure. Model performance was then assessed by applying the model to hypothetical career states in the discipline of taxonomy. Hypothetical career states were used to incorporate the greatest possible differences in career states and provide an opportunity to test the model against known inputs. 3. The resulting BN model consisted of 18 primary nodes feeding through one to three higher-order nodes before converging on the target node (Taxonomic Expert). There was strong consistency among node weights provided by the supra-experts for some nodes, but not others. The higher order nodes, “Quality of work” and “Total productivity”, had the greatest weights. Sensitivity analysis indicated that although some factors had stronger influence in the outer nodes of the network, there was relatively equal influence of the factors leading directly into the target node. Despite differences in the node weights provided by our supra-experts, there was remarkably good agreement among assessments of our hypothetical experts that accurately reflected differences we had built into them. 4. This systems approach provides a novel way of assessing the overall level of expertise of individuals, accounting for multiple contributory factors, and their interactions. Our approach is adaptable to other situations where it is desirable to understand components of expertise.

Complex Modelling in the Primary and Middle School Years : An Interdisciplinary Approach

The world’s increasing complexity, competitiveness, interconnectivity, and dependence on technology generate new challenges for nations and individuals that cannot be met by continuing education as usual. With the proliferation of complex systems have come new technologies for communication, collaboration, and conceptualisation. These technologies have led to signifi cant changes in the forms of mathematical and scientifi c thinking required beyond the classroom. Modelling, in its various forms, can develop and broaden students’ mathematical and scientific thinking beyond the standard curriculum. This chapter first considers future competencies in the mathematical sciences within an increasingly complex world. Consideration is then given to interdisciplinary problem solving and models and modelling, as one means of addressing these competencies. Illustrative case studies involving complex, interdisciplinary modelling activities in Years 1 and 7 are presented.