Osteoporosis medication dispensing for older Australian women from 2002 to 2010: Influences of publications, guidelines, marketing activities and policy

Purpose Developments in anti-osteoporosis medications (AOMs) have led to changes in guidelines and policy, which, along with media and marketing strategies, have had an impact upon the prescribing of AOM. The aim was to examine patterns of AOM dispensing in older women (aged 76–81 years at baseline) from 2002 to 2010. Methods Administrative claims data were used to describe AOM dispensing in 4649 participants (born in 1921–1926 and still alive in 2011) in the Australian Longitudinal Study on Women's Health. The patterns were interpreted in the context of changes in guidelines, indications for subsidy, publications (scholarly and general media), and marketing activities. Results Total use of AOM increased from 134 DDD/1000/day in 2002 to 216 DDD/1000/day in 2007 but then decreased to 184 DDD/1000/day in 2010. Alendronate was the most commonly dispensed AOM but decreased from 2007, while use of risedronate (2002 onward), strontium ranelate (2007 onward) and zoledronic acid (2008 onward) increased. Etidronate and hormone replacement therapy (HRT) prescriptions gradually decreased over time. The decline in alendronate dispensing coincided with increases of other bisphosphonates and publicity about potential adverse effects of bisphosphonates, despite relaxing indications for bone density testing and subsidy for AOM. Conclusions Overall dispense of AOM from 2002 reached a peak in 2007 and thereafter declined despite increases in therapeutic options and improved subsidised access. The recent decline in overall AOM dispensing seems to be explained largely by negative publicity rather than specific changes in guidelines and policy.

Universalization of access to modern energy services in Indian households-Economic and policy analysis

Provision of modern energy services for cooking (with gaseous fuels)and lighting (with electricity) is an essential component of any policy aiming to address health, education or welfare issues; yet it gets little attention from policy-makers. Secure, adequate, low-cost energy of quality and convenience is core to the delivery of these services. The present study analyses the energy consumption pattern of Indian domestic sector and examines the urban-rural divide and income energy linkage. A comprehensive analysis is done to estimate the cost for providing modern energy services to everyone by 2030. A public-private partnership-driven business model, with entrepreneurship at the core, is developed with institutional, financing and pricing mechanisms for diffusion of energy services. This approach, termed as EMPOWERS (entrepreneurship model for provision of wholesome energy-related basic services), if adopted, can facilitate large-scale dissemination of energy-efficient and renewable technologies like small-scale biogas/biofuel plants, and distributed power generation technologies to provide clean, safe, reliable and sustainable energy to rural households and urban poor. It is expected to integrate the processes of market transformation and entrepreneurship development involving government, NGOs, financial institutions and community groups as stakeholders. (C) 2009 Elsevier Ltd. All rights reserved.

A real-time algorithm for simulation of flexible objects and hyper-redundant manipulators

Flexible objects such as a rope or snake move in a way such that their axial length remains almost constant. To simulate the motion of such an object, one strategy is to discretize the object into large number of small rigid links connected by joints. However, the resulting discretised system is highly redundant and the joint rotations for a desired Cartesian motion of any point on the object cannot be solved uniquely. In this paper, we revisit an algorithm, based on the classical tractrix curve, to resolve the redundancy in such hyper-redundant systems. For a desired motion of the `head' of a link, the `tail' is moved along a tractrix, and recursively all links of the discretised objects are moved along different tractrix curves. The algorithm is illustrated by simulations of a moving snake, tying of knots with a rope and a solution of the inverse kinematics of a planar hyper-redundant manipulator. The simulations show that the tractrix based algorithm leads to a more `natural' motion since the motion is distributed uniformly along the entire object with the displacements diminishing from the `head' to the `tail'.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Simulation and modelling tools for quantitative safety assessments of unmanned aircraft systems and operations

This paper presents two simple simulation and modelling tools designed to aid in the safety assessment required for unmanned aircraft operations within unsegregated airspace. First, a fast pair-wise encounter generator is derived to simulate the See and Avoid environment. The utility of the encounter generator is demonstrated through the development of a hybrid database and a statistical performance evaluation of an autonomous See and Avoid decision and control strategy. Second, an unmanned aircraft mission generator is derived to help visualise the impact of multiple persistent unmanned operations on existing air traffic. The utility of the mission generator is demonstrated through an example analysis of a mixed airspace environment using real traffic data in Australia. These simulation and modelling approaches constitute a useful and extensible set of analysis tools, that can be leveraged to help explore some of the more fundamental and challenging problems facing civilian unmanned aircraft system integration.

The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model

We investigate the ability of a global atmospheric general circulation model (AGCM) to reproduce observed 20 year return values of the annual maximum daily precipitation totals over the continental United States as a function of horizontal resolution. We find that at the high resolutions enabled by contemporary supercomputers, the AGCM can produce values of comparable magnitude to high quality observations. However, at the resolutions typical of the coupled general circulation models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, the precipitation return values are severely underestimated.

Playing the policy game: A review of the barriers and enablers of nutrition policy change

- Objective To progress nutrition policy change and develop more effective advocates, it is useful to consider real-world factors and practical experiences of past advocacy efforts to determine the key barriers and enablers to nutrition policy change. This review aimed to identify and synthesize the enablers and barriers to public policy change within the field of nutrition. - Design Electronic databases were searched systematically for studies examining policymaking in public health nutrition. An interpretive synthesis was undertaken. Setting: International, national, state and local government jurisdictions within high-income, democratic countries. - Results Sixty-three studies were selected for inclusion. Numerous themes were identified explaining the barriers and enablers to policy change, all of which fell under the overarching category, ‘political will’, underpinned by a second major category, ‘public will’. Sub-themes, including pressure from industry; neoliberal ideology; use of emotions and values, and being visible were prevalent in describing links between public will, political will and policy change. - Conclusions The frustration around lack of public policy change in nutrition frequently stems from a belief that policymaking is a rational process in which evidence is used to assess the relative costs and benefits of options. The findings from this review confirm that evidence is only one component of influencing policy change. For policy change to occur there needs to be the political will, and often the public will, for the proposed policy problem and solution. This review presents a suite of enablers which can assist health professionals to influence political and public will in future advocacy efforts.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

An efficient algorithm for scheduling in bluetooth piconets and scatternets

Bluetooth is a short-range radio technology operating in the unlicensed industrial-scientific-medical (ISM) band at 2.45 GHz. A piconet is basically a collection of slaves controlled by a master. A scatternet, on the other hand, is established by linking several piconets together in an ad hoc fashion to yield a global wireless ad hoc network. This paper proposes a scheduling policy that aims to achieve increased system throughput and reduced packet delays while providing reasonably good fairness among all traffic flows in bluetooth piconets and scatternets. We propose a novel algorithm for scheduling slots to slaves for both piconets and scatternets using multi-layered parameterized policies. Our scheduling scheme works with real data and obtains an optimal feedback policy within prescribed parameterized classes of these by using an efficient two-timescale simultaneous perturbation stochastic approximation (SPSA) algorithm. We show the convergence of our algorithm to an optimal multi-layered policy. We also propose novel polling schemes for intra- and inter-piconet scheduling that are seen to perform well. We present an extensive set of simulation results and performance comparisons with existing scheduling algorithms. Our results indicate that our proposed scheduling algorithm performs better overall on a wide range of experiments over the existing algorithms for both piconets (Das et al. in INFOCOM, pp. 591–600, 2001; Lapeyrie and Turletti in INFOCOM conference proceedings, San Francisco, US, 2003; Shreedhar and Varghese in SIGCOMM, pp. 231–242, 1995) and scatternets (Har-Shai et al. in OPNETWORK, 2002; Saha and Matsumot in AICT/ICIW, 2006; Tan and Guttag in The 27th annual IEEE conference on local computer networks(LCN). Tampa, 2002). Our studies also confirm that our proposed scheme achieves a high throughput and low packet delays with reasonable fairness among all the connections.

An efficient and optimized bluetooth scheduling algorithm for piconets

Bluetooth is an emerging standard in short range, low cost and low power wireless networks. MAC is a generic polling based protocol, where a central Bluetooth unit (master) determines channel access to all other nodes (slaves) in the network (piconet). An important problem in Bluetooth is the design of efficient scheduling protocols. This paper proposes a polling policy that aims to achieve increased system throughput and reduced packet delays while providing reasonably good fairness among all traffic flows in a Bluetooth Piconet. We present an extensive set of simulation results and performance comparisons with two important existing algorithms. Our results indicate that our proposed scheduling algorithm outperforms the Round Robin scheduling algorithm by more than 40% in all cases tried. Our study also confirms that our proposed policy achieves higher throughput and lower packet delays with reasonable fairness among all the connections.

Understanding who cares: Creating the evidence to address the long-standing policy problem of staff shortages in early childhood education and care

Evidence-based policy is a means of ensuring that policy is informed by more than ideology or expedience. However, what constitutes robust evidence is highly contested. In this paper, we argue policy must draw on quantitative and qualitative data. We do this in relation to a long entrenched problem in Australian early childhood education and care (ECEC) workforce policy. A critical shortage of qualified staff threatens the attainment of broader child and family policy objectives linked to the provision of ECEC and has not been successfully addressed by initiatives to date. We establish some of the limitations of existing quantitative data sets and consider the potential of qualitative studies to inform ECEC workforce policy. The adoption of both quantitative and qualitative methods is needed to illuminate the complex nature of the work undertaken by early childhood educators, as well as the environmental factors that sustain job satisfaction in a demanding and poorly understood working environment.