Detecting abnormal events on binary sensors in smart home environments

With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies.PostprintPeer reviewe

The Roman Catholic Diocesan Boundary and American Madawaska, 1842-1870

The Webster-Ashburton Treaty of 1842, which established the Maine-New Brunswick boundary along the St. John River, divided the Acadian settlements in the valley. Among the questions this posed for residents and for church officials was the location of the diocesan boundary: would it follow national, or ethnic lines? The ultimate resolution - the parishes south of the river were transferred to the Diocese of Portland - depended not only on established Roman Catholic practice in matters of changing national boundaries, but also upon the personalities involved, including the bishops of Portland and Saint John and the parishioners on both sides of the river in Madawaska

Towards a middleware for generalised context management

It is widely accepted in the Pervasive Computing community that contextual interactions are the key to the delivery of truly calm technology. However, there is currently no easy way to incorporate contextual data into an application. If contextual data is used, it is generally in an ad hoc manner, which means that developers have to spend time on low-level details. There have been many projects investigating this area, however as yet none of them provide support for all of the key issues of dynamic composition and flexible representation of contextual information as well as the problems of scalability and adaptability to environmental changes. In this paper we present the Strathclyde Context Infrastructure (SCI), a middleware infrastructure for discovery, aggregation, and delivery of context information

Decentralised discovery of mobile objects

The partially connected nature of mobile and ubiquitous computing environments presents software developers with hard challenges. Mobile code has been suggested as a natural fit for simplifying software development for these environments. However, existing strategies for discovering mobile code assume an underlying fixed, stable network. An alternative approach is required for mobile environments, where network size may be unknown and reliability cannot be guaranteed. This paper introduces AMOS, a mobile object platform augmented with a structure overlay network that provides a fully decentralised approach to the discovery of mobile objects. We demonstrate how this technique has better reliability and scalability properties than existing strategies, with minimal communication overhead. Building upon this novel discovery strategy, we show how load balancing of mobile objects in an AMOS network can be achieved through probabilistic means

Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries

Disease managers face many challenges when deciding on the most effective control strategy to manage an outbreak of foot and mouth disease (FMD). Decisions have to be made under conditions of uncertainty and where the situation is continually evolving. In addition, resources for control are often limited. A modelling study was carried out to identify characteristics measurable during the early phase of a FMD outbreak that might be useful as predictors of the total number of infected places, outbreak duration and the total area under control. The study involved two modelling platforms in two countries (Australia and New Zealand) and encompassed a large number of incursion scenarios. Linear regression, classification and regression tree and boosted regression tree analyses were used to quantify the predictive value of a set of parameters on three outcome variables of interest: the total number of infected places, outbreak duration and the total area under control. The number of infected premises, number of pending culls, area under control, estimated dissemination ratio, and cattle density around the index herd at days 7, 14 and 21 following first detection were associated with each of the outcome variables. Regression models for the size of the area under control had the highest predictive value (R2 = 0.51-0.9) followed by the number of infected premises (R2 = 0.3-0.75) and outbreak duration (R2 = 0.28-0.57). Predictability improved at later time points in the outbreak. Predictive regression models using various cut-points at day 14 to define small and large outbreaks had positive predictive values of 0.85‒0.98 and negative predictive values of 0.52‒0.91, with 79‒97% of outbreaks correctly classified. On the strict assumption that each of the simulation models used in this study provide a realistic indication of the spread of FMD in animal populations our conclusion is that relatively simple metrics available early in a control program can be used to indicate the likely magnitude of an FMD outbreak under Australian and New Zealand conditions

Long-term drivers of vegetation turnover in Southern Hemisphere temperate ecosystems

© 2020 John Wiley & Sons Ltd Aim: Knowledge of the drivers of ecosystem changes in the past is key to understanding present ecosystem responses to changes in climate, fire regimes and anthropogenic impacts. Northern Hemisphere-focussed studies suggest that climate and human activities drove turnover during the Holocene in temperate ecosystems. Various drivers have been invoked to explain changes in Southern Hemisphere temperate vegetation, but the region lacks a quantitative assessment of these drivers. To better understand the regional drivers of past diversity, we present a quantitative meta-analysis study of turnover and richness during the lateglacial and Holocene in Australian temperate ecosystems. Location: South-east Australia (Tasmania, Bass Strait, SE mainland). Methods: We conducted a meta-analysis study of 24 fossil pollen records across south-east Australian temperate ecosystems, applying an empirical turnover threshold to fossil records to identify periods of major turnover for the first time in Australia. We tested pollen richness as a proxy for vegetation richness to estimate past richness and applied this to fossil pollen data. The resulting reconstructions were compared to independent records of climate, sea-level change and fire through generalized linear modelling. Results and conclusion: Our results show changes in available moisture and sea level drove turnover and richness in most parts of SE Australia in the past, explaining up to c.97% deviance. However, fire mainly drove turnover in Bass Strait. Our richness reconstructions also support the intermediate disturbance hypothesis, suggesting that high biodiversity was partially maintained by anthropogenic-managed fire regimes. While temperature change is considered key to Northern Hemisphere palaeodiversity, past turnover and richness in Southern Hemisphere temperate ecosystems responded mainly to moisture availability and sea-level change (considering its role in modulating regional oceanic climate)

Evaluation of a commercially developed semiautomated PCR-surface-enhanced Raman scattering assay for diagnosis of invasive fungal disease

Nonculture-based tests are gaining popularity in the diagnosis of invasive fungal disease (IFD), but PCR is excluded from disease-defining criteria because of limited standardization and a lack of commercial assays. Commercial PCR assays may have a standardized methodology while providing quality assurance. The detection of PCR products by a surface-enhanced Raman scattering (SERS) assay potentially provides superior analytical sensitivity and multiplexing capacity compared to that of real-time PCR. Using this approach, the RenDx Fungiplex assay was developed to detect Candida and Aspergillus. Analytical and clinical evaluations of the assay were undertaken using extraction methods according to European Aspergillus PCR Initiative (EAPCRI) recommendations. A total of 195 previously extracted samples (133 plasma, 49 serum, and 13 whole blood) from 112 patients (29 with proven/probable IFD) were tested. The 95% limit of detection of Candida and Aspergillus was 200 copies per reaction, with an overall reproducibility of 92.1% for detecting 20 input copies per PCR, and 89.8% for the nucleic acid extraction–PCR-SERS process for detecting fungal burdens of <20 genome equivalents per sample. A clinical evaluation showed that assay positivity significantly correlated with IFD (P < 0.0001). The sensitivity of the assay was 82.8% and was similar for both Candida (80.0%) and Aspergillus (85.7%). The specificity was 87.5% and was increased (97.5%) by using a multiple (≥2 samples) PCR-positive threshold. In summary, the RenDx Fungiplex assay is a PCR-SERS assay for diagnosing IFD and demonstrates promising clinical performance on a variety of samples. This was a retrospective clinical evaluation, and performance is likely to be enhanced through a prospective analysis of clinical validity and by determining clinical utility

Ensemble modelling and structured decision-making to support Emergency Disease Management

Epidemiological models in animal health are commonly used as decision-support tools to understand the impact of various control actions on infection spread in susceptible populations. Different models contain different assumptions and parameterizations, and policy decisions might be improved by considering outputs from multiple models. However, a transparent decision-support framework to integrate outputs from multiple models is nascent in epidemiology. Ensemble modelling and structured decision-making integrate the outputs of multiple models, compare policy actions and support policy decision-making. We briefly review the epidemiological application of ensemble modelling and structured decision-making and illustrate the potential of these methods using foot and mouth disease (FMD) models. In case study one, we apply structured decision-making to compare five possible control actions across three FMD models and show which control actions and outbreak costs are robustly supported and which are impacted by model uncertainty. In case study two, we develop a methodology for weighting the outputs of different models and show how different weighting schemes may impact the choice of control action. Using these case studies, we broadly illustrate the potential of ensemble modelling and structured decision-making in epidemiology to provide better information for decision-making and outline necessary development of these methods for their further application