Prioritization of responsive maintenance tasks via machine learning-based inference

Maintenance task prioritization is essential for allocating resources. It is estimated that almost 1/3 of the maintenance cost is wasted to unnecessary activities. Task prioritization is based on risk assessment that takes into account the probability of failure and the criticality of an asset. The criticality analysis is defined by the asset owner based on several parameters, among them safety, downtime cost, productivity, whilst the probability of failure is determined based on deterioration models, regular manual inspections, or installed sensors. Currently, the latter is an extremely complicated and labour intensive procedure, when multiple and different types of assets need to be managed. This paper proposes an innovative method that exploits the advances in mobile communications, social networking, Internet of Things and machine learning to address this shortcoming. This approach brings building elements and assets online using asset tags with an online ‘asset profile’ linked to it. Users of assets are able to scan these tags using a mobile phone app to not only see the information about those assets, but also enter ‘comments’ describing issues and problems on the profiles. These comments are processed through machine learning-based inference methods to estimate the probability that a failure has occurred. This paper validates the proposed method using historical data collected from the Estate Management, of the University of CambridgeInnovate U

The Changing Epidemiology of Murray Valley Encephalitis in Australia: The 2011 Outbreak and a Review of the Literature

Murray Valley encephalitis virus (MVEV) is the most serious of the endemic arboviruses in Australia. It was responsible for six known large outbreaks of encephalitis in south-eastern Australia in the 1900s, with the last comprising 58 cases in 1974. Since then MVEV clinical cases have been largely confined to the western and central parts of northern Australia. In 2011, high-level MVEV activity occurred in south-eastern Australia for the first time since 1974, accompanied by unusually heavy seasonal MVEV activity in northern Australia. This resulted in 17 confirmed cases of MVEV disease across Australia. Record wet season rainfall was recorded in many areas of Australia in the summer and autumn of 2011. This was associated with significant flooding and increased numbers of the mosquito vector and subsequent MVEV activity. This paper documents the outbreak and adds to our knowledge about disease outcomes, epidemiology of disease and the link between the MVEV activity and environmental factors. Clinical and demographic information from the 17 reported cases was obtained. Cases or family members were interviewed about their activities and location during the incubation period. In contrast to outbreaks prior to 2000, the majority of cases were non-Aboriginal adults, and almost half (40%) of the cases acquired MVEV outside their area of residence. All but two cases occurred in areas of known MVEV activity.This outbreak continues to reflect a change in the demographic pattern of human cases of encephalitic MVEV over the last 20 years. In northern Australia, this is associated with the increasing numbers of non-Aboriginal workers and tourists living and travelling in endemic and epidemic areas, and also identifies an association with activities that lead to high mosquito exposure. This outbreak demonstrates that there is an ongoing risk of MVEV encephalitis to the heavily populated areas of south-eastern Australia

Rainfall and sentinel chicken seroconversions predict human cases of Murray Valley encephalitis in the north of Western Australia

Background Murray Valley encephalitis virus (MVEV) is a flavivirus that occurs in Australia and New Guinea. While clinical cases are uncommon, MVEV can cause severe encephalitis with high mortality. Sentinel chicken surveillance is used at many sites around Australia to provide an early warning system for risk of human infection in areas that have low population density and geographical remoteness. MVEV in Western Australia occurs in areas of low population density and geographical remoteness, resulting in logistical challenges with surveillance systems and few human cases. While epidemiological data has suggested an association between rainfall and MVEV activity in outbreak years, it has not been quantified, and the association between rainfall and sporadic cases is less clear. In this study we analysed 22 years of sentinel chicken and human case data from Western Australia in order to evaluate the effectiveness of sentinel chicken surveillance for MVEV and assess the association between rainfall and MVEV activity. Methods Sentinel chicken seroconversion, human case and rainfall data from the Kimberley and Pilbara regions of Western Australia from 1990 to 2011 were analysed using negative binomial regression. Sentinel chicken seroconversion and human cases were used as dependent variables in the model. The model was then tested against sentinel chicken and rainfall data from 2012 and 2013.Results Sentinel chicken seroconversion preceded all human cases except two in March 1993. Rainfall in the prior three months was significantly associated with both sentinel chicken seroconversion and human cases across the regions of interest. Sentinel chicken seroconversion was also predictive of human cases in the models. The model predicted sentinel chicken seroconversion in the Kimberley but not in the Pilbara, where seroconversions early in 2012 were not predicted. The latter may be due to localised MVEV activity in isolated foci at dams, which do not reflect broader virus activity in the region. Conclusions We showed that rainfall and sentinel chickens provide a useful early warning of MVEV risk to humans across endemic and epidemic areas, and that a combination of the two indicators improves the ability to assess MVEV risk and inform risk management measures

The limitations of employment as a tool for social inclusion

<p>Abstract</p> <p>Background</p> <p>One important component of social inclusion is the improvement of well-being through encouraging participation in employment and work life. However, the ways that employment contributes to wellbeing are complex. This study investigates how poor health status might act as a barrier to gaining good quality work, and how good quality work is an important pre-requisite for positive health outcomes.</p> <p>Methods</p> <p>This study uses data from the PATH Through Life Project, analysing baseline and follow-up data on employment status, psychosocial job quality, and mental and physical health status from 4261 people in the Canberra and Queanbeyan region of south-eastern Australia. Longitudinal analyses conducted across the two time points investigated patterns of change in employment circumstances and associated changes in physical and mental health status.</p> <p>Results</p> <p>Those who were unemployed and those in poor quality jobs (characterised by insecurity, low marketability and job strain) were more likely to remain in these circumstances than to move to better working conditions. Poor quality jobs were associated with poorer physical and mental health status than better quality work, with the health of those in the poorest quality jobs comparable to that of the unemployed. For those who were unemployed at baseline, pre-existing health status predicted employment transition. Those respondents who moved from unemployment into poor quality work experienced an increase in depressive symptoms compared to those who moved into good quality work.</p> <p>Conclusions</p> <p>This evidence underlines the difficulty of moving from unemployment into good quality work and highlights the need for social inclusion policies to consider people's pre-existing health conditions and promote job quality.</p

Selection for environmental variance of litter size in rabbits

[EN] Background: In recent years, there has been an increasing interest in the genetic determination of environmental variance. In the case of litter size, environmental variance can be related to the capacity of animals to adapt to new environmental conditions, which can improve animal welfare. Results: We developed a ten-generation divergent selection experiment on environmental variance. We selected one line of rabbits for litter size homogeneity and one line for litter size heterogeneity by measuring intra-doe phenotypic variance. We proved that environmental variance of litter size is genetically determined and can be modified by selection. Response to selection was 4.5% of the original environmental variance per generation. Litter size was consistently higher in the Low line than in the High line during the entire experiment. Conclusions: We conclude that environmental variance of litter size is genetically determined based on the results of our divergent selection experiment. 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Assessing learning and memory in pigs

In recent years, there has been a surge of interest in (mini) pigs (Sus scrofa) as species for cognitive research. A major reason for this is their physiological and anatomical similarity with humans. For example, pigs possess a well-developed, large brain. Assessment of the learning and memory functions of pigs is not only relevant to human research but also to animal welfare, given the nature of current farming practices and the demands they make on animal health and behavior. In this article, we review studies of pig cognition, focusing on the underlying processes and mechanisms, with a view to identifying. Our goal is to aid the selection of appropriate cognitive tasks for research into pig cognition. To this end, we formulated several basic criteria for pig cognition tests and then applied these criteria and knowledge about pig-specific sensorimotor abilities and behavior to evaluate the merits, drawbacks, and limitations of the different types of tests used to date. While behavioral studies using (mini) pigs have shown that this species can perform learning and memory tasks, and much has been learned about pig cognition, results have not been replicated or proven replicable because of the lack of validated, translational behavioral paradigms that are specially suited to tap specific aspects of pig cognition. We identified several promising types of tasks for use in studies of pig cognition, such as versatile spatial free-choice type tasks that allow the simultaneous measurement of several behavioral domains. The use of appropriate tasks will facilitate the collection of reliable and valid data on pig cognition