Real estate 2.0 : towards a value ecology?

This paper anatomises emerging developments in online community engagement in a major global industry: real estate. Economists argue that we are entering a ‘social network economy’ in which ‘complex social networks’ govern consumer choice and product value. In the light of this, organisations are shifting from thinking and behaving in the conventional ‘value chain’ model--in which exchanges between firms and customers are one-way only, from the firm to the consumer--to the ‘value ecology’ model, in which consumers and their networks become co-creators of the value of the product. This paper studies the way in which the global real estate industry is responding to this environment. This paper identifies three key areas in which online real estate ‘value ecology’ work is occurring: real estate social networks, games, and locative media / augmented reality applications. Uptake of real estate applications is, of course, user-driven: the paper not only highlights emerging innovations; it also identifies which of these innovations are actually being taken up by users, and the content contributed as a result. The paper thus provides a case study of one major industry’s shift into a web 2.0 communication model, focusing on emerging trends and issues.

Analysis of acoustic emission data for structural health monitoring applications

Acoustic emission (AE) is the phenomenon where high frequency stress waves are generated by rapid release of energy within a material by sources such as crack initiation or growth. AE technique involves recording these stress waves by means of sensors placed on the surface and subsequent analysis of the recorded signals to gather information such as the nature and location of the source. It is one of the several diagnostic techniques currently used for structural health monitoring (SHM) of civil infrastructure such as bridges. Some of its advantages include ability to provide continuous in-situ monitoring and high sensitivity to crack activity. But several challenges still exist. Due to high sampling rate required for data capture, large amount of data is generated during AE testing. This is further complicated by the presence of a number of spurious sources that can produce AE signals which can then mask desired signals. Hence, an effective data analysis strategy is needed to achieve source discrimination. This also becomes important for long term monitoring applications in order to avoid massive date overload. Analysis of frequency contents of recorded AE signals together with the use of pattern recognition algorithms are some of the advanced and promising data analysis approaches for source discrimination. This paper explores the use of various signal processing tools for analysis of experimental data, with an overall aim of finding an improved method for source identification and discrimination, with particular focus on monitoring of steel bridges.

Application of acoustic emission technology in monitoring structure integrity of bridges

Bridges are an important part of a nation’s infrastructure and reliable monitoring methods are necessary to ensure their safety and efficiency. Most bridges in use today were built decades ago and are now subjected to changes in load patterns that can cause localized distress, which can result in bridge failure if not corrected. Early detection of damage helps in prolonging lives of bridges and preventing catastrophic failures. This paper briefly reviews the various technologies currently used in health monitoring of bridge structures and in particular discusses the application and challenges of acoustic emission (AE) technology. Some of the results from laboratory experiments on a bridge model are also presented. The main objectives of these experiments are source localisation and assessment. The findings of the study can be expected to enhance the knowledge of acoustic emission process and thereby aid in the development of an effective bridge structure diagnostics system.

The ecology of Bactrocera tryoni (Froggatt) (Diptera: Tephritdae) : what do we know to assist pest management?

The distribution, systematics and ecology of Bactrocera tryoni, the Queensland fruit fly are reviewed. Bactrocera tryoni is a member of the B. tryoni complex of species, which currently includes four named species, viz. B. tryoni s.s., B. neohumeralis, B. melas and B. aquilonis. The species status of B. melas and B. aquilonis are unclear (they may be junior synonyms of B. tryoni) and their validity, or otherwise, needs to be confirmed as a matter of urgency. While Queensland fruit fly is regarded as a tropical species, it cannot be assumed that its distribution will spread further south under climate change scenarios. Increasing aridity and hot dry summers, as well as more complex, indirect interactions resulting from elevated CO2, make predicting the future distribution and abundance of B. tryoni difficult. The ecology of B. tryoni is reviewed with respect to current control approaches (with the exception of Sterile Insect Technique which is covered in a companion paper). We conclude that there are major gaps in the knowledge required to implement most non-insecticide based management approaches. Priority areas for future research include host plant interactions, protein and cue-lure foraging and use, spatial dynamics, development of new monitoring tools, investigating the use of natural enemies and better integration of fruit flies into general horticultural IPM systems.

Ecology and behavior of first instar larval Lepidoptera

Neonate Lepidoptera are confronted with the daunting task of establishing themselves on a food plant. The factors relevant to this process need to be considered at spatial and temporal scales relevant to the larva and not the investigator. Neonates have to cope with an array of plant surface characters as well as internal characters once the integument is ruptured. These characters, as well as microclimatic conditions, vary within and between plant modules and interact with larval feeding requirements, strongly affecting movement behavior, which may be extensive even for such small organisms. In addition to these factors, there is an array of predators, pathogens, and parasitoids with which first instars must contend. Not surprisingly, mortality in neonates is high but can vary widely. Experimental and manipulative studies, as well as detailed observations of the animal, are vital if the subtle interaction of factors responsible for this high and variable mortality are to be understood. These studies are essential for an understanding of theories linking female oviposition behavior with larval survival, plant defense theory, and population dynamics, as well as modern crop resistance breeding programs.

Use of acoustic emission technique for structural health monitoring of bridges

Bridges are valuable assets of every nation. They deteriorate with age and often are subjected to additional loads or different load patterns than originally designed for. These changes in loads can cause localized distress and may result in bridge failure if not corrected in time. Early detection of damage and appropriate retrofitting will aid in preventing bridge failures. Large amounts of money are spent in bridge maintenance all around the world. A need exists for a reliable technology capable of monitoring the structural health of bridges, thereby ensuring they operate safely and efficiently during the whole intended lives. Monitoring of bridges has been traditionally done by means of visual inspection. Visual inspection alone is not capable of locating and identifying all signs of damage, hence a variety of structural health monitoring (SHM) techniques is used regularly nowadays to monitor performance and to assess condition of bridges for early damage detection. Acoustic emission (AE) is one technique that is finding an increasing use in SHM applications of bridges all around the world. The chapter starts with a brief introduction to structural health monitoring and techniques commonly used for monitoring purposes. Acoustic emission technique, wave nature of AE phenomenon, previous applications and limitations and challenges in the use as a SHM technique are also discussed. Scope of the project and work carried out will be explained, followed by some recommendations of work planned in future.

Scaling acoustic data analysis through collaboration and automation

Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; collaboration, manual, automatic and human-in-the loop analysis.

Review: acoustic emission technique - opportunities, challenges and current work at QUT

Acoustic emission (AE) is the phenomenon where high frequency stress waves are generated by rapid release of energy within a material by sources such as crack initiation or growth. AE technique involves recording these stress waves by means of sensors placed on the surface and subsequent analysis of the recorded signals to gather information such as the nature and location of the source. AE is one of the several non-destructive testing (NDT) techniques currently used for structural health monitoring (SHM) of civil, mechanical and aerospace structures. Some of its advantages include ability to provide continuous in-situ monitoring and high sensitivity to crack activity. Despite these advantages, several challenges still exist in successful application of AE monitoring. Accurate localization of AE sources, discrimination between genuine AE sources and spurious noise sources and damage quantification for severity assessment are some of the important issues in AE testing and will be discussed in this paper. Various data analysis and processing approaches will be applied to manage those issues.

The Bayesian conditional independence model for measurement error: applications in ecology

The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.

Characterizing the signal pattern of a four-cylinder diesel engine using acoustic emission and vibration analysis

Condition monitoring of diesel engines can prevent unpredicted engine failures and the associated consequence. This paper presents an experimental study of the signal characteristics of a 4-cylinder diesel engine under various loading conditions. Acoustic emission, vibration and in-cylinder pressure signals were employed to study the effectiveness of these techniques for condition monitoring and identifying symptoms of incipient failures. An event driven synchronous averaging technique was employed to average the quasi-periodic diesel engine signal in the time domain to eliminate or minimize the effect of engine speed and amplitude variations on the analysis of condition monitoring signal. It was shown that acoustic emission (AE) is a better technique than vibration method for condition monitor of diesel engines due to its ability to produce high quality signals (i.e., excellent signal to noise ratio) in a noisy diesel engine environment. It was found that the peak amplitude of AE RMS signals correlating to the impact-like combustion related events decreases in general due to a more stable mechanical process of the engine as the loading increases. A small shift in the exhaust valve closing time was observed as the engine load increases which indicates a prolong combustion process in the cylinder (to produce more power). On the contrary, peak amplitudes of the AE RMS attributing to fuel injection increase as the loading increases. This can be explained by the increase fuel friction caused by the increase volume flow rate during the injection. Multiple AE pulses during the combustion process were identified in the study, which were generated by the piston rocking motion and the interaction between the piston and the cylinder wall. The piston rocking motion is caused by the non-uniform pressure distribution acting on the piston head as a result of the non-linear combustion process of the engine. The rocking motion ceased when the pressure in the cylinder chamber stabilized.

Graphene-like nano-sheets for surface acoustic wave gas sensor applications

The gas sensing properties of graphene-like nano-sheets deposited on 36° YX lithium tantalate (LiTaO3) surface acoustic wave (SAW) transducers are reported. The thin graphene-like nano-sheets were produced via the reduction of graphite oxide which was deposited on SAW interdigitated transducers (IDTs). Their sensing performance was assessed towards hydrogen (H2) and carbon monoxide (CO) in a synthetic air carrier gas at room temperature (25 °C) and 40 °C. Raman and X-ray photoelectron spectroscopy (XPS) revealed that the deposited graphite oxide (GO) was not completely reduced creating small, graphitic nanocrystals ∼2.7 nm in size. © 2008 Elsevier B.V.

Large scale participatory acoustic sensor data analysis : tools and reputation models to enhance effectiveness

Acoustic sensors play an important role in augmenting the traditional biodiversity monitoring activities carried out by ecologists and conservation biologists. With this ability however comes the burden of analysing large volumes of complex acoustic data. Given the complexity of acoustic sensor data, fully automated analysis for a wide range of species is still a significant challenge. This research investigates the use of citizen scientists to analyse large volumes of environmental acoustic data in order to identify bird species. Specifically, it investigates ways in which the efficiency of a user can be improved through the use of species identification tools and the use of reputation models to predict the accuracy of users with unidentified skill levels. Initial experimental results are reported.