Stretching the life of Twitter classifiers with time-stamped semantic graphs

Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

Life expectancy for community-based patients with heart failure from time of diagnosis

AIMS: Heart failure has been demonstrated in previous studies to have a dismal prognosis. However, the modern-day prognosis of patients with new onset heart failure diagnosed in the community managed within a disease management programme is not known. The purpose of this study is to report on prognosis of patients presenting with new onset heart failure in the community who are subsequently followed in a disease management program.

METHODS AND RESULTS: A review of patients referred to a rapid access heart failure diagnostic clinic between 2002 and 2012 was undertaken. Details of diagnosis, demographics, medical history, medications, investigations and mortality data were analysed. A total of 733 patients were seen in Rapid Access Clinic for potential new diagnosis of incident of heart failure. 38.9% (n=285) were diagnosed with heart failure, 40.7% (n=116) with HF-REF and 59.3% (n=169) with HF-PEF. There were 84 (29.5%) deaths in the group of patients diagnosed with heart failure; 41 deaths (35.3%) occurred in patients with HF-REF and 43 deaths (25.4%) occurred in patients with HF-PEF. In patients with heart failure, 52.4% (n=44) died from cardiovascular causes. 63.8% of HF patients were alive after 5 years resulting on average in a month per year loss of life expectancy over that period compared with aged matched simulated population.

CONCLUSIONS: In this community-based cohort, the prognosis of heart failure was better than reported in previous studies. This is likely due to the impact of prompt diagnosis, the improvement in therapies and care within a disease management structure.

Anförande: Pressed for time : The digital transformation of everyday life

Sociologisk Forsknings digitala arkiv

About time : life-cycle and cohort effects on support for Quebec sovereignty

L’appui à la souveraineté du Québec diminue-t-il avec l’âge, ou est-il le reflet de préférences générationnelles ? Cette recherche se base sur les théories du changement générationnel et de la socialisation politique pour répondre à cette question. À l’aide de données de sondages de 1985 à 2014, nous mesurons l’impact de l’âge et de la génération sur l’appui à cette option constitutionnelle chez les Québécois francophones. Nos deux hypothèses de recherche sont confirmées dans une certaine mesure. Premièrement, les Québécois ont moins tendance à appuyer la souveraineté en vieillissant. La relation négative entre ces variables devient par contre plus faible au début des années 2000. Deuxièmement, les Baby boomers (nés entre 1945 et 1964) ont une probabilité plus élevée d’être souverainistes que les autres générations, et ce peu importe leur âge. Ils sont suivis, dans l’ordre, par les Aînés (nés en 1944 et moins), la Génération X (nés entre 1965 et 1979) et les Milléniaux (nés en 1980 ou plus).

Teachers – Shaping One Life at a Time

A Montana Public Radio Commentary by Evan Barrett. Published newspaper columns written by Evan Barrett on this topic, which vary somewhat in content from this commentary, appeared in the following publications: Missoulian, June 16, 2015 Ravalli Republic, June 16, 2015 Montana Public Radio, June 17, 2015 Montana Standard, June 19, 2015 Great Falls Tribune, June 22, 2015

About time : life-cycle and cohort effects on support for Quebec sovereignty

L’appui à la souveraineté du Québec diminue-t-il avec l’âge, ou est-il le reflet de préférences générationnelles ? Cette recherche se base sur les théories du changement générationnel et de la socialisation politique pour répondre à cette question. À l’aide de données de sondages de 1985 à 2014, nous mesurons l’impact de l’âge et de la génération sur l’appui à cette option constitutionnelle chez les Québécois francophones. Nos deux hypothèses de recherche sont confirmées dans une certaine mesure. Premièrement, les Québécois ont moins tendance à appuyer la souveraineté en vieillissant. La relation négative entre ces variables devient par contre plus faible au début des années 2000. Deuxièmement, les Baby boomers (nés entre 1945 et 1964) ont une probabilité plus élevée d’être souverainistes que les autres générations, et ce peu importe leur âge. Ils sont suivis, dans l’ordre, par les Aînés (nés en 1944 et moins), la Génération X (nés entre 1965 et 1979) et les Milléniaux (nés en 1980 ou plus).

Determination of preventive maintenance lead time using hybrid analysis

The time for conducting Preventive Maintenance (PM) on an asset is often determined using a predefined alarm limit based on trends of a hazard function. In this paper, the authors propose using both hazard and reliability functions to improve the accuracy of the prediction particularly when the failure characteristic of the asset whole life is modelled using different failure distributions for the different stages of the life of the asset. The proposed method is validated using simulations and case studies.

The Second Life of urban planning? Using neogeography tools for community engagement

The majority of the world’s citizens now live in cities. Although urban planning can thus be thought of as a field with significant ramifications on the human condition, many practitioners feel that it has reached the crossroads in thought leadership between traditional practice and a new, more participatory and open approach. Conventional ways to engage people in participatory planning exercises are limited in reach and scope. At the same time, socio-cultural trends and technology innovation offer opportunities to re-think the status quo in urban planning. Neogeography introduces tools and services that allow non-geographers to use advanced geographical information systems. Similarly, is there potential for the emergence of a neo-planning paradigm in which urban planning is carried out through active civic engagement aided by Web 2.0 and new media technologies thus redefining the role of practicing planners? This paper traces a number of evolving links between urban planning, neogeography and information and communication technology. Two significant trends – participation and visualisation – with direct implications for urban planning are discussed. Combining advanced participation and visualisation features, the popular virtual reality environment Second Life is then introduced as a test bed to explore a planning workshop and an integrated software event framework to assist narrative generation. We discuss an approach to harness and analyse narratives using virtual reality logging to make transparent how users understand and interpret proposed urban designs.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

The impact of perceived stage of cancer on carers’ anxiety and depression during the patients’ final year of life

Objective: This paper explores the effects of perceived stage of cancer (PSOC) on carers' anxiety and depression during the patients' final year. Methods: A consecutive sample of patients and carers (N=98) were surveyed at regular intervals regarding PSOC, and anxiety and depression using the Hospital Anxiety and Depression Scale. Means were compared by gender using the Mann-Whitney U-test. The chi-square was used to analyse categorical data. Agreement between carers' and patients' PSOC was estimated using kappa statistics. Correlations between carers' PSOC and their anxiety and depression were calculated using the Spearman's rank correlation. Results: Over time, an increasing proportion of carers reported that the cancer was advanced, culminating at 43% near death. Agreement regarding PSOC was fair (kappa=0.29-0.34) until near death (kappa=0.21). Carers' anxiety increased over the year; depression increased in the final 6 months. Females were more anxious (p=0.049, 6 months; p=0.009, 3 months) than males, and more depressed until 1 month to death. The proportion of carers reporting moderate-severe anxiety almost doubled over the year to 27%, with more females in this category at 6 months (p=0.05). Carers with moderate-severe depression increased from 6 to 15% over the year. Increased PSOC was weakly correlated with increased anxiety and depression. Conclusions: Carers' anxiety exceeded depression in severity during advanced cancer. Females generally experienced greater anxiety and depression. Carers were more realistic than patients regarding the ultimate outcome, which was reflected in their declining mental health, particularly near the end.

Risk assessment in life-cycle costing for road asset management

Queensland Department of Main Roads, Australia, spends approximately A$ 1 billion annually for road infrastructure asset management. To effectively manage road infrastructure, firstly road agencies not only need to optimise the expenditure for data collection, but at the same time, not jeopardise the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates could be accurately estimated. And finally, the prediction of budgets for maintenance and rehabilitation must provide a certain degree of reliability. This paper presents the results of case studies in using the probability-based method for an integrated approach (i.e. assessing optimal costs of pavement strength data collection; calibrating deterioration prediction models that suit local condition and assessing risk-adjusted budget estimates for road maintenance and rehabilitation for assessing life-cycle budget estimates). The probability concept is opening the path to having the means to predict life-cycle maintenance and rehabilitation budget estimates that have a known probability of success (e.g. produce budget estimates for a project life-cycle cost with 5% probability of exceeding). The paper also presents a conceptual decision-making framework in the form of risk mapping in which the life-cycle budget/cost investment could be considered in conjunction with social, environmental and political issues.

Exercise for health : a breast cancer recovery program - quality of life benefits [Conference Abstract]

Introduction: Five-year survival from breast cancer in Australia is 87%. Hence, ensuring a good quality of life (QOL) has become a focal point of cancer research and clinical interest. Exercise during and after treatment has been identified as a potential strategy to optimise QOL of women diagnosed with breast cancer.----- Methods: Exercise for Health is a randomised controlled trial of an eight-month, exercise intervention delivered by Exercise Physiologists. An objective of this study was to assess the impact of the exercise program during and following treatment on QOL. Queensland women diagnosed with unilateral breast cancer in 2006/07 were eligible to participate. Those living in urban-Brisbane (n=194) were allocated to either the face-to-face exercise group, the telephone exercise group, or the usual-care group, and those living in rural Queensland (n=143) were allocated to the telephone exercise group or the usual-care group. QOL, as assessed by the Functional Assessment of Cancer Therapy-Breast (FACT-B+4) questionnaire, was measured at 4-6 weeks (pre-intervention), 6 months (mid-intervention) and 12 months (three months post-intervention) post-surgery.----- Results: Significant (P<0.01) increases in QOL were observed between pre-intervention and three months post-intervention 12 months post-surgery for all women. Women in the exercise groups experienced greater mean positive changes in QOL during this time (+10 points) compared with the usual-care groups (+5 to +7 points) after adjusting for baseline QOL. Although all groups experienced an overall increase in QOL, approximately 20% of urban and rural women in the usual-care groups reported a decline in QOL, compared with 10% of women in the exercise groups.----- Conclusions: This work highlights the potential importance of participating in physical activity to optimise QOL following a diagnosis of breast cancer. Results suggest that the telephone may be an effective medium for delivering exercise counselling to newly diagnosed breast cancer patients.