An RCT to evaluate the utility of a clinical protocol for staff in the management of behavioral and psychological symptoms of dementia in residential aged-care settings

Objectives: Behavioral and psychological symptoms of dementia (BPSD) cause significant stress and distress to both aged-care residents and staff. This study evaluated a training program to assist staff to manage BPSD in residential care. Method: A randomised controlled trial (RCT) was employed. The study was included in the Australian and New Zealand Clinical Trial Register residential care facilities. Staff (n = 204) and residents (n = 187) were from 16 residential care facilities. Facilities were recruited and randomly assigned to four staff training conditions: (1) training in the use of a BPSD-structured clinical protocol, plus external clinical support, (2) a workshop on BPSD, plus external clinical support, (3) training in the use of the structured clinical protocol alone, and (4) care as usual. Staff and resident outcome measures were obtained pre-intervention, three months and six months post-intervention. The primary outcome was changes in BPSD, measured using the Cohen-Mansfield Agitation Inventory (CMAI) as well as frequency and duration of challenging behaviors. Secondary outcomes were changes in staff adjustment. Results: There were improvements in challenging behaviors for both intervention conditions that included training in the BPSD instrument, but these were not maintained in the condition without clinical support. The training/support condition resulted in sustained improvements in both staff and resident variables, whereas the other conditions only led to improvement in some of the measured variables. Conclusion: These results demonstrate the effectiveness of the BPSD protocol in reducing BPSD and improving staff self-efficacy and stress.

Changes in quality of life associated with fragility fractures: Australian arm of the International Cost and Utility Related to Osteoporotic Fractures Study (AusICUROS).

We investigated change in health-related quality of life due to fracture in Australian adults aged over 50 years. Fractures reduce quality of life with the loss sustained at least over 12 months. At a population level, the loss was equivalent to 65 days in full health per fracture.

Tree-based iterated local search for Markov random fields with applications in image analysis

The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain.

Ordinal random fields for recommender systems

Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Markov Random Fields (MRF) to investigate the localised interactions, and proposes a unified model called Ordinal Random Fields (ORF) to take advantages of both the representational power of the MRF and the ease of modelling ordinal preferences by the OMF. Experimental result on public datasets demonstrates that the proposed ORF model can capture both types of interactions, resulting in improved recommendation accuracy.

Exploring the utility of collaborative governance in a National Sport Organization running head: Collaborative sport governance

This paper presents the outcomes of an 18-month developmental action research study to enhance the governance capability of a national sport organization. Bowls Australia, the national governing body for lawn bowls
in Australia, includes nine independent state and territory member-associations. An intervention was designed and implemented with the Bowls Australia Board. The purpose of the intervention was to enact collaborative governance to overcome a perceived cultural malaise in the governance of the sport. This study is one of the first to examine collaborative governance in a federal sport structure. Results demonstrate the utility of collaborative governance to overcome adversarial national, member-state relations for the purpose of establishing a common and unifying vision for bowls, while also enhancing governance capability. This study identified the importance of collective board leadership in governance decision-making throughout the sport. It also highlights future research directions in relation to collective board leadership in federal governance structures.

Prosecutors’ perceptions of the utility of video-evidence for adult complainants of sexual assault

Playing an adult sexual complainant’s video-recorded police interview as the basis for his or her evidence-in-chief is a reform Australia could adopt to help improve criminal justice responses to these cases. This article presents a qualitative evaluation of prosecutor’s support for this reform and their views about what conditions would determine its utility. Focus groups were held with 13 prosecutors from across New Zealand (which already has this reform) and Australia. Collectively, prosecutors supported the availability of video-evidence for adult complainants. They perceived the utility of this reform depends on the following conditions: (1) the quality of the police interview; (2) how credibly the complainant presents on video; (3) contextual factors that influence the complainant’s ability to give live evidence; and (4) the degree of stakeholder support. These findings suggest that Australia should extend video-evidence to adult complainants of sexual assault guided by careful planning aroundthese four areas.

Utility of biological sensor tags in animal conservation

Electronic tags (both biotelemetry and biologging platforms) have informed conservation and resource management policy and practice by providing vital information on the spatial ecology of animals and their environments. However, the extent of the contribution of biological sensors (within electronic tags) that measure an animal's state (e.g., heart rate, body temperature, and details of locomotion and energetics) is less clear. A literature review revealed that, despite a growing number of commercially available state sensor tags and enormous application potential for such devices in animal biology, there are relatively few examples of their application to conservation. Existing applications fell under 4 main themes: quantifying disturbance (e.g., ecotourism, vehicular and aircraft traffic), examining the effects of environmental change (e.g., climate change), understanding the consequences of habitat use and selection, and estimating energy expenditure. We also identified several other ways in which sensor tags could benefit conservation, such as determining the potential efficacy of management interventions. With increasing sensor diversity of commercially available platforms, less invasive attachment techniques, smaller device sizes, and more researchers embracing such technology, we suggest that biological sensor tags be considered a part of the necessary toolbox for conservation. This approach can measure (in real time) the state of free-ranging animals and thus provide managers with objective, timely, relevant, and accurate data to inform policy and decision making.

A random coefficient approach to the predictability of stock returns in panels

© The Author, 2014. Most studies of the predictability of returns are based on time series data, and whenever panel data are used, the testing is almost always conducted in an unrestricted unit-by-unit fashion, which makes for a very heavy parametrization of the model. On the other hand, the few panel tests that exist are too restrictive in the sense that they are based on homogeneity assumptions that might not be true. As a response to this, the current study proposes new predictability tests in the context of a random coefficient panel data model, in which the null of no predictability corresponds to the joint restriction that the predictive slope has zero mean and variance. The tests are applied to a large panel of stocks listed at the New York Stock Exchange. The results suggest that while the predictive slopes tend to average to zero, in case of book-to-market and cash flow-to-price the variance of the slopes is positive, which we take as evidence of predictability.

Preference relation-based markov random fields

A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings fi rst, and then inferring the item rankings, based on the assumption of availability of explicit feed-backs such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and signifi cantly improved Top-N recommendation performance has been achieved.

Privacy aware K-means clustering with high utility

Privacy-preserving data mining aims to keep data safe, yet useful. But algorithms providing strong guarantees often end up with low utility. We propose a novel privacy preserving framework that thwarts an adversary from inferring an unknown data point by ensuring that the estimation error is almost invariant to the inclusion/exclusion of the data point. By focusing directly on the estimation error of the data point, our framework is able to significantly lower the perturbation required. We use this framework to propose a new privacy aware K-means clustering algorithm. Using both synthetic and real datasets, we demonstrate that the utility of this algorithm is almost equal to that of the unperturbed K-means, and at strict privacy levels, almost twice as good as compared to the differential privacy counterpart.

Labeled random finite sets and the bayes multi-target tracking filter

An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate Priors,” IEEE Trans. Signal Process., vol. 61, no. 13, pp. 3460-3475, 2014]. As a sequel to that paper, the present paper details efficient implementations of the δ-GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the δ-GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the L1-error in the multi-target density arising from the truncation is presented.

Effective Data Management Enables Intelligent Utility Management

Instrumentation and automation plays a vital role to managing the water industry. These systems generate vast amounts of data that must be effectively managed in order to enable intelligent decision making. Time series data management software, commonly known as data historians are used for collecting and managing real-time (time series) information. More advanced software solutions provide a data infrastructure or utility wide Operations Data Management System (ODMS) that stores, manages, calculates, displays, shares, and integrates data from multiple disparate automation and business systems that are used daily in water utilities. These ODMS solutions are proven and have the ability to manage data from smart water meters to the collaboration of data across third party corporations. This paper focuses on practical, utility successes in the water industry where utility managers are leveraging instantaneous access to data from proven, commercial off-the-shelf ODMS solutions to enable better real-time decision making. Successes include saving $650,000 / year in water loss control, safeguarding water quality, saving millions of dollars in energy management and asset management. Immediate opportunities exist to integrate the research being done in academia with these ODMS solutions in the field and to leverage these successes to utilities around the world.