A customisable dashboard display for environmental performance visualisations

We conducted an exploratory study of a mobile energy monitoring tool: The Dashboard. Our point of departure from prior work was the emphasis of end-user customisation and social sharing. Applying extensive feedback, we deployed the Dashboard in real-world conditions to socially linked research participants for a period of five weeks. Participants were encouraged to devise, construct, place, and view various data feeds. The aim of our study was to test the assumption that participants, having control over their Dashboard configuration, would engage, and remain engaged, with their energy feedback throughout the trial. Our research points to a set of design issues surrounding the adoption and continued use of such tools. A novel finding of our study is the impact of social links between participants and their continued engagement with the Dashboard. Our results also illustrate the emergence of energy-voyeurism, a form of social energy monitoring by peers.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Underground power cable environment on-line monitoring and analysis

Knowledge of cable parameters has been well established but a better knowledge of the environment in which the cables are buried lags behind. Research in Queensland University of Technology has been aimed at obtaining and analysing actual daily field values of thermal resistivity and diffusivity of the soil around power cables. On-line monitoring systems have been developed and installed with a data logger system and buried spheres that use an improved technique to measure thermal resistivity and diffusivity over a short period. Results based on long term continuous field data are given. A probabilistic approach is developed to establish the correlation between the measured field thermal resistivity values and rainfall data from weather bureau records. This data from field studies can reduce the risk in cable rating decisions and provide a basis for reliable prediction of “hot spot” of an existing cable circuit

Monitoring smartphones for anomaly detection

In this paper we demonstrate how to monitor a smartphone running Symbian operating system and Windows Mobile in order to extract features for anomaly detection. These features are sent to a remote server because running a complex intrusion detection system on this kind of mobile device still is not feasible due to capability and hardware limitations. We give examples on how to compute relevant features and introduce the top ten applications used by mobile phone users based on a study in 2005. The usage of these applications is recorded by a monitoring client and visualized. Additionally, monitoring results of public and self-written malwares are shown. For improving monitoring client performance, Principal Component Analysis was applied which lead to a decrease of about 80 of the amount of monitored features.

Sustainable, safe, smart—three key elements of Singapore’s evolving transport policies

Sustainability, safety and smartness are three key elements of a modern transportation system. This study illustrates various policy directions and initiatives of Singapore to address how its transportation system is progressing in light of these three components. Sustainability targets economical efficiency, environmental justice and social equity by including policies for integrating land use and transport planning, ensuring adequate transport supply measures, managing travel demand efficiently, and incorporating environment-friendly strategies. Safety initiatives of its transportation system aim to minimize injuries and incidents of all users including motorists, public transport commuters, pedestrians, and bicyclists. Smartness incorporates qualities like real time sensing, fast processing and decision making, and automated action-taking into its control, monitoring, information management and revenue collection systems. Various policy implications and technology applications along these three directions reveal that smart technologies facilitate implementation of policies promoting sustainability and safety. The Singapore experience could serve as a good reference for other cities in promoting a transportation system that is sustainable, safe and smart.

Introducing sustainability assessment to civil infrastructure projects & assets

Civil infrastructure plays a key role in supporting and improving current way of life. However, the assets can have a large impact on the region around them, which are both positive (usually for the purpose they are built) and negative (consequences and unintended effects). There is an increasing trend for society to place an importance on the role of sustainability to ensure that there is a world suitable for future generations. In order to ensure that the world for future generations is in the best possible condition it is increasingly important to look at integrating sustainability outcomes into the way industry operates, including the infrastructure industry. It is therefore important to undertake sustainability assessment of civil infrastructure projects. By having organisations take on sustainability assessments of civil infrastructure assets both during construction and in operation, the industry can assist to drive outcomes and results that will benefit society and future generations and make their own operations more efficient.

A prototype system dynamic model for assessing the sustainability of construction projects

There is a worldwide demand for an increasingly sustainable built environment. This has resulted in the need for a more accurate evaluation of the level of sustainability of construction projects. To do this it involves the development of better measurement and benchmarking methods. One approach is to use a theoretical model to assess construction projects in terms of their sustainable development value (SDV) and sustainable development ability (SDA) for implementation in the project life cycle, where SDA measures the contribution of a project to development sustainability and as a major criterion for assessing its feasibility. This paper develops an improved SDA prototype model that incorporates the effects of dynamical factors on project sustainability. This involves the introduction of two major factors concerning technological advancement and changes in people's perceptions. A case study is used to demonstrate the procedures involved in simulation and modeling, one outcome of which is to demonstrate the greater influence of technological advancement on project sustainability than changes in perception.

Embedding environmental sustainability in the undergraduate chemistry curriculum : a case study

In spite of increasing attention devoted to the importance of embedding sustainability in university curricula, few Australian universities include specific green chemistry units, and there is no mention of green or sustainable chemistry concepts in the majority of units. In this paper, an argument is posited that all universities should embed sustainable chemistry within all Chemistry courses because it is the morally correct stance to minimise the harm of climate change. Attitudes of chemistry lecturers towards integrating sustainability into their teaching have been probed and it was found, using an established model, that personal environmental perspectives are critical to their attitude. Importantly, academic staff whose research has an environmental component were more likely to incorporate sustainability into their teaching while others struggled to find ways to do so even when they believed it to be important. This paper will recommend that resources are required to assist academic staff without a green chemistry research program to incorporate sustainability into their teaching and several suggestions are provided.

Real-time risk monitoring in business processes : a sensor-based approach

This article proposes an approach for real-time monitoring of risks in executable business process models. The approach considers risks in all phases of the business process management lifecycle, from process design, where risks are defined on top of process models, through to process diagnosis, where risks are detected during process execution. The approach has been realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of negative process states (faults) to eventuate. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a business process management system to prompt the results to process administrators who may take remedial actions. The proposed architecture has been implemented on top of the YAWL system, and evaluated through performance measurements and usability tests with students. The results show that risk conditions can be computed efficiently and that the approach is perceived as useful by the participants in the tests.

Performance characterization of VGCF/epoxy nanocomposite sensors under static load cycles and in static structural health monitoring

Compared to conventional metal-foil strain gauges, nanocomposite piezoresistive strain sensors have demonstrated high strain sensitivity and have been attracting increasing attention in recent years. To fulfil their ultimate success, the performance of vapor growth carbon fiber (VGCF)/epoxy nanocomposite strain sensors subjected to static cyclic loads was evaluated in this work. A strain-equivalent quantity (resistance change ratio) in cantilever beams with intentionally induced notches in bending was evaluated using the conventional metal-foil strain gauges and the VGCF/epoxy nanocomposite sensors. Compared to the metal-foil strain gauges, the nanocomposite sensors are much more sensitive to even slight structural damage. Therefore, it was confirmed that the signal stability, reproducibility, and durability of these nanocomposite sensors are very promising, leading to the present endeavor to apply them for static structural health monitoring.

Sustainability in post disaster road infrastructure recovery projects in Queensland, Australia

between mid 2010 and early 2011, Queensland road related infrastructures were devastated by flood and cyclone related natural disasters. Responding to these recent events and in preparing for more regular and intense climate-change induced events in future, the Queensland Government is now reviewing how post-disaster road infrastructure recovery projects are planned and delivered. In particular, there is awareness that rebuilding such infrastructure need sustainable strategies across economic, environmental and social dimensions. A comprehensive sustainability assessment framework for pre and post disaster situations can minimize negative impact on our communities, economy and environment. This research is underway to develop a comprehensive sustainability element frame work for post disaster management in road infrastructures in Queensland, Australia. Analyzing the implications of disruption to transport network and associated services is an important part of preparing local and regional responses to the impacts of natural disasters. This research can contribute to strategic planning, management leading to safe, efficient and integrated transport system that supports sustainable economic, social and environmental outcomes in Queensland. Within this context, this paper provides an overview of the qualitative mixed-method research approach involving literature reviews and case studies to explore and evaluate a number of sustainability elements with a view to develop operational strategies for disaster recovery road projects.

Monitoring android for collaborative anomaly detection : a first architectural draft

Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways, e.g. for payment systems or assisting the lives of elderly or disabled people. Security threats for these devices become more and more dangerous since there is still a lack of proper security tools for protection. Android emerges as an open smartphone platform which allows modification even on operating system level and where third-party developers first time have the opportunity to develop kernel-based low-level security tools. Android quickly gained its popularity among smartphone developers and even beyond since it bases on Java on top of "open" Linux in comparison to former proprietary platforms which have very restrictive SDKs and corresponding APIs. Symbian OS, holding the greatest market share among all smartphone OSs, was even closing critical APIs to common developers and introduced application certification. This was done since this OS was the main target for smartphone malwares in the past. In fact, more than 290 malwares designed for Symbian OS appeared from July 2004 to July 2008. Android, in turn, promises to be completely open source. Together with the Linux-based smartphone OS OpenMoko, open smartphone platforms may attract malware writers for creating malicious applications endangering the critical smartphone applications and owners privacy. Since signature-based approaches mainly detect known malwares, anomaly-based approaches can be a valuable addition to these systems. They base on mathematical algorithms processing data that describe the state of a certain device. For gaining this data, a monitoring client is needed that has to extract usable information (features) from the monitored system. Our approach follows a dual system for analyzing these features. On the one hand, functionality for on-device light-weight detection is provided. But since most algorithms are resource exhaustive, remote feature analysis is provided on the other hand. Having this dual system enables event-based detection that can react to the current detection need. In our ongoing research we aim to investigates the feasibility of light-weight on-device detection for certain occasions. On other occasions, whenever significant changes are detected on the device, the system can trigger remote detection with heavy-weight algorithms for better detection results. In the absence of the server respectively as a supplementary approach, we also consider a collaborative scenario. Here, mobile devices sharing a common objective are enabled by a collaboration module to share information, such as intrusion detection data and results. This is based on an ad-hoc network mode that can be provided by a WiFi or Bluetooth adapter nearly every smartphone possesses.

Corporate commitment to sustainability : is it all hot air? An Australian review of the linkage between executive pay and sustainable performance

Across time, companies have increasingly made public commitments to sustainable development and to reducing their impacts on climate change. Management remuneration plans (MRPs) are a key mechanism to motivate managers to achieve corporate goals. We review the MRPs negotiated with key management personnel in a sample of large Australian carbon-intensive companies. Our results show that, as in past decades, the companies in our sample have MRPs in place that continue to fixate on financial performance. We argue that this provides evidence of a disconnection, or ‘decoupling’, between the sustainability-related rhetoric of the sample companies, and their ‘real’ organisational practices and priorities.

Sustainability initiatives in early years settings

Children bring much knowledge about sustainability issues into the early childhood classroom. In recent times, I have overheard children as young as three years of age discuss events such as the BP Oil Spill in American waters and extreme weather patterns. While aspects of these events can be overwhelming, responding to children's existing knowledge allows for an educative approach to sustainability issues, and a focus on the multitude of ways individuals and communities are working to create positive change.

Social signal processing for pain monitoring using a hidden conditional random field

utomatic pain monitoring has the potential to greatly improve patient diagnosis and outcomes by providing a continuous objective measure. One of the most promising methods is to do this via automatically detecting facial expressions. However, current approaches have failed due to their inability to: 1) integrate the rigid and non-rigid head motion into a single feature representation, and 2) incorporate the salient temporal patterns into the classification stage. In this paper, we tackle the first problem by developing a “histogram of facial action units” representation using Active Appearance Model (AAM) face features, and then utilize a Hidden Conditional Random Field (HCRF) to overcome the second issue. We show that both of these methods improve the performance on the task of pain detection in sequence level compared to current state-of-the-art-methods on the UNBC-McMaster Shoulder Pain Archive.