Decision support for the efficient annotation of bioacoustic events

Acoustic sensors allow scientists to scale environmental monitoring over large spatiotemporal scales. The faunal vocalisations captured by these sensors can answer ecological questions, however, identifying these vocalisations within recorded audio is difficult: automatic recognition is currently intractable and manual recognition is slow and error prone. In this paper, a semi-automated approach to call recognition is presented. An automated decision support tool is tested that assists users in the manual annotation process. The respective strengths of human and computer analysis are used to complement one another. The tool recommends the species of an unknown vocalisation and thereby minimises the need for the memorization of a large corpus of vocalisations. In the case of a folksonomic tagging system, recommending species tags also minimises the proliferation of redundant tag categories. We describe two algorithms: (1) a “naïve” decision support tool (16%–64% sensitivity) with efficiency of O(n) but which becomes unscalable as more data is added and (2) a scalable alternative with 48% sensitivity and an efficiency ofO(log n). The improved algorithm was also tested in a HTML-based annotation prototype. The result of this work is a decision support tool for annotating faunal acoustic events that may be utilised by other bioacoustics projects.

Decision support system for infrastructure sustainability in operations

There is an increasing awareness of sustainability and climate change and its impact on infrastructure and engineering asset management in design, construction, and operations. Sustainability rating tools have been proposed and/or developed that provide ratings of infrastructure projects in differing phases of their life cycle on sustainability. This paper provides an overview of decision support systems using sustainability rating framework that can be used to prioritize or select tasks and activities within projects to enhance levels of sustainability outcomes. These systems can also be used to prioritize projects within an organization to optimize sustainability outcomes within an allocated budget.

Data driven and practice-based evidence : design and development of efficient and effective clinical decision support system

Decision-making is such an integral aspect in health care routine that the ability to make the right decisions at crucial moments can lead to patient health improvements. Evidence-based practice, the paradigm used to make those informed decisions, relies on the use of current best evidence from systematic research such as randomized controlled trials. Limitations of the outcomes from randomized controlled trials (RCT), such as “quantity” and “quality” of evidence generated, has lowered healthcare professionals’ confidence in using EBP. An alternate paradigm of Practice-Based Evidence has evolved with the key being evidence drawn from practice settings. Through the use of health information technology, electronic health records (EHR) capture relevant clinical practice “evidence”. A data-driven approach is proposed to capitalize on the benefits of EHR. The issues of data privacy, security and integrity are diminished by an information accountability concept. Data warehouse architecture completes the data-driven approach by integrating health data from multi-source systems, unique within the healthcare environment.

A decision support framework for prioritization of engineering asset management activities under uncertainty

Engineers and asset managers must often make decisions on how to best allocate limited resources amongst different interrelated activities, including repair, renewal, inspection, and procurement of new assets. The presence of project interdependencies and the lack of sufficient information on the true value of an activity often produce complex problems and leave the decision maker guessing about the quality and robustness of their decision. In this paper, a decision support framework for uncertain interrelated activities is presented. The framework employs a methodology for multi-criteria ranking in the presence of uncertainty, detailing the effect that uncertain valuations may have on the priority of a particular activity. The framework employs employing semi-quantitative risk measures that can be tailored to an organisation and enable a transparent and simple-to-use uncertainty specification by the decision maker. The framework is then demonstrated on a real world project set from a major Australian utility provider.

Formative evaluation to benchmark and improve climate-based decision support for graziers in Western Queelsland

Researchers developing climate-based forecasts, workshops, software tools and information to aid grazier decisions undertook an evaluation study to enhance planning and benchmark impact. One hundred graziers in Western Queensland were randomly selected from 7 shires and surveyed by mail and telephone (43 respondents) to explore levels of knowledge and use of climate information, practices and information needs. We found 36% of respondents apply the Southern Oscillation Index to property decisions but 92% were unaware El Niño Southern Oscillation’s predictive signal in the region is greater for pasture growth than rainfall, suggesting they may not recognise the potential of pasture growth forecasts. Almost 75% of graziers consider they are conservative or risk averse in their attitude to managing their enterprise. Mail respondents (n= 20) if given a 68%, on average, probability of exceeding median rainfall forecast may change a decision; almost two-thirds vary stocking rate based on forage available, last year’s pasture growth or the Southern Oscillation Index; the balance maintain a constant stocking rate strategy; 90% have access to a computer; 75% to the internet and 95% have a fax. This paper presents findings of the study and draws comparisons with a similar study of 174 irrigators in the Northern Murray-Darling Basin (Aust. J. Exp. Ag. 44, 247-257). New insights and information gained are helping the team better understand client needs and plan, design and extend tools and information tailored to grazier knowledge, practice, information needs and preferences. Results have also provided a benchmark against which to measure project impact and have influenced the team to make important changes to their project planning, activities and methods for transferring technology tailored to grazier preferences.

Predicting wood value in Queensland Caribbean pine plantations using a decision support system

A decision support system has been developed in Queensland to evaluate how changes in silvicultural regimes affect wood quality, and specifically the graded recovery of structural timber. Models of tree growth, branch architecture and wood properties were developed from data collected in routine Caribbean pine plantations and specific silvicultural experiments. These models were incorporated in software that simulates the conversion of standing trees into logs, and the logs into boards, and generates detailed data on knot location and basic density distribution. The structural grade of each board was determined by simulating the machine stress-grading process, and the predicted graded recovery provided an indicator of wood value. The decision support system improves the basis of decision-making by simulating the performance of elite genetic material under specified silvicultural regimes and by predicting links between wood quality and general stand attributes such as stocking and length of rotation.

Choosing a model of maternity care: decision support needs of Australian women

- BACKGROUND Access to information on the features and outcomes associated with the various models of maternity care available in Australia is vital for women's informed decision-making. This study sought to identify women's preferences for information access and decision-making involvement, as well as their priority information needs, for model of care decision-making. - METHODS A convenience sample of adult women of childbearing age in Queensland, Australia were recruited to complete an online survey assessing their model of care decision support needs. Knowledge on models of care and socio-demographic characteristics were also assessed. - RESULTS Altogether, 641 women provided usable survey data. Of these women, 26.7 percent had heard of all available models of care before starting the survey. Most women wanted access to information on models of care (90.4%) and an active role in decision-making (99.0%). Nine priority information needs were identified: cost, access to choice of mode of birth and care provider, after hours provider contact, continuity of carer in labor/birth, mobility during labor, discussion of the pros/cons of medical procedures, rates of skin-to-skin contact after birth, and availability at a preferred birth location. This information encompassed the priority needs of women across age, birth history, and insurance status subgroups. - CONCLUSIONS This study demonstrates Australian women's unmet needs for information that supports them to effectively compare available options for model of maternity care. Findings provide clear direction on what information should be prioritized and ideal channels for information access to support quality decision-making in practice.

A Practitioner-Oriented Decision Support Process for Forestry Pest Management

The article discusses a new decision support process for forestry pest management. Over the past few years, DSS have been introduced for forestry pest management, providing forest growers with advice in areas such as selecting the most suitable pesticide and relevant treatment. Most of the initiatives process knowledge from various domains for providing support for specific decision making problems. However, very few studies have identified the requirements of developing a combined process model in which all relevant practitioners can contribute and share knowledge for effective decision making; such an approach would need to include the decision makers’ perspective along with other relevant attributes such as the problem context and relevant policies. We outline a decision support process for forestry pest management, based on the design science research paradigm, in which a focus group technique has application to acquire both expert and practical knowledge in order to construct the DSS solution.

Owner-driven decision support in holding-specific forest planning

Socio-economic and demographic changes among family forest owners and demands for versatile forestry decision aid motivated this study, which sought grounds for owner-driven forest planning. Finnish family forest owners’ forest-related decision making was analyzed in two interview-based qualitative studies, the main findings of which were surveyed quantitatively. Thereafter, a scheme for adaptively mixing methods in individually tailored decision support processes was constructed. The first study assessed owners’ decision-making strategies by examining varying levels of the sharing of decision-making power and the desire to learn. Five decision-making modes – trusting, learning, managing, pondering, and decisive – were discerned and discussed against conformable decision-aid approaches. The second study conceptualized smooth communication and assessed emotional, practical, and institutional boosters of and barriers to such smoothness in communicative decision support. The results emphasize the roles of trust, comprehension, and contextual services in owners’ communicative decision making. In the third study, a questionnaire tool to measure owners’ attitudes towards communicative planning was constructed by using trusting, learning, and decisive dimensions. Through a multivariate analysis of survey data, three owner groups were identified as fusions of the original decision-making modes: trusting learners (53%), decisive learners (27%), and decisive managers (20%). Differently weighted communicative services are recommended for these compound wishes. The findings of the studies above were synthesized in a form of adaptive decision analysis (ADA), which allows and encourages the decision-maker (owner) to make deliberate choices concerning the phases of a decision aid (planning) process. The ADA model relies on adaptability and feedback management, which foster smooth communication with the owner and (inter-)organizational learning of the planning institution(s). The summarized results indicate that recognizing the communication-related amenity values of family forest owners may be crucial in developing planning and extension services. It is therefore recommended that owners, root-level planners, consultation professionals, and pragmatic researchers collaboratively continue to seek stable change.

AQUAMAN: a web-based decision support system for irrigation scheduling in peanuts

Peanut (Arachis hypogaea L.) is an economically important legume crop in irrigated production areas of northern Australia. Although the potential pod yield of the crop in these areas is about 8 t ha(-1), most growers generally obtain around 5 t ha(-1), partly due to poor irrigation management. Better information and tools that are easy to use, accurate, and cost-effective are therefore needed to help local peanut growers improve irrigation management. This paper introduces a new web-based decision support system called AQUAMAN that was developed to assist Australian peanut growers schedule irrigations. It simulates the timing and depth of future irrigations by combining procedures from the food and agriculture organization (FAO) guidelines for irrigation scheduling (FAO-56) with those of the agricultural production systems simulator (APSIM) modeling framework. Here, we present a description of AQUAMAN and results of a series of activities (i.e., extension activities, case studies, and a survey) that were conducted to assess its level of acceptance among Australian peanut growers, obtain feedback for future improvements, and evaluate its performance. Application of the tool for scheduling irrigations of commercial peanut farms since its release in 2004-2005 has shown good acceptance by local peanuts growers and potential for significantly improving yield. Limited comparison with the farmer practice of matching the pan evaporation demand during rain-free periods in 2006-2007 and 2008-2009 suggested that AQUAMAN enabled irrigation water savings of up to 50% and the realization of enhanced water and irrigation use efficiencies.

Exploring the Potential Impacts of Climate Variability on Spring Wheat Yield with the APSIM Decision Support Tool

Assessing the impacts of climate variability on agricultural productivity at regional, national or global scale is essential for defining adaptation and mitigation strategies. We explore in this study the potential changes in spring wheat yields at Swift Current and Melfort, Canada, for different sowing windows under projected climate scenarios (i.e., the representative concentration pathways, RCP4.5 and RCP8.5). First, the APSIM model was calibrated and evaluated at the study sites using data from long term experimental field plots. Then, the impacts of change in sowing dates on final yield were assessed over the 2030-2099 period with a 1990-2009 baseline period of observed yield data, assuming that other crop management practices remained unchanged. Results showed that the performance of APSIM was quite satisfactory with an index of agreement of 0.80, R2 of 0.54, and mean absolute error (MAE) and root mean square error (RMSE) of 529 kg/ha and 1023 kg/ha, respectively (MAE = 476 kg/ha and RMSE = 684 kg/ha in calibration phase). Under the projected climate conditions, a general trend in yield loss was observed regardless of the sowing window, with a range from -24 to -94 depending on the site and the RCP, and noticeable losses during the 2060s and beyond (increasing CO2 effects being excluded). Smallest yield losses obtained through earlier possible sowing date (i.e., mid-April) under the projected future climate suggested that this option might be explored for mitigating possible adverse impacts of climate variability. Our findings could therefore serve as a basis for using APSIM as a decision support tool for adaptation/mitigation options under potential climate variability within Western Canada.

A randomized controlled trial protocol testing a decision support intervention for older patients with advanced kidney disease

Aim To assess the effectiveness of a decision support intervention using a pragmatic single blind Randomized Controlled Trial. Background Worldwide the proportion of older people (aged 65 years and over) is rising. This population is known to have a higher prevalence of chronic diseases including chronic kidney disease. The resultant effect of the changing health landscape is seen in the increase in older patients (aged ≥65 years) commencing on dialysis. Emerging evidence suggests that for some older patients dialysis may provide minimal benefit. In a majority of renal units non-dialysis management is offered as an alternative to undertaking dialysis. Research regarding decision-making support that is required to assist this population in choosing between dialysis or non-dialysis management is limited. Design. A multisite single blinded pragmatic randomized controlled trial is proposed. Methods Patients will be recruited from four Queensland public hospitals and randomizd into either the control or intervention group. The decision support intervention is multimodal and includes counselling provided by a trained nurse. The comparator is standard decision-making support. The primary outcomes are decisional regret and decisional conflict. Secondary outcomes are improved knowledge and quality of life. Ethics approval obtained November 2014. Conclusion This is one of the first randomized controlled trials assessing a decision support intervention in older people with advance chronic kidney disease. The results may provide guidance for clinicians in future approaches to assist this population in decision-making to ensure reduced decisional regret and decisional conflict.