An Exploratory Study into Open Source Platform Adoption

Research on open source software has focused mainly on the motivations of open source programmers and the organization of open source projects [17] [19]. Some researchers portray open source as an extension of the earlier open systems movement [36]. While there has been some research on open-systems software adoption by corporate MIS organizations [4] the issue of open source adoption has received little attention. We use a series of interviews with MIS managers to develop a grounded theory of open source platform adoption. We contrast this to prior academic and popular reports about the adoption of open source

Climate change adaptation in industry and business

This report delivers a best practice framework to integrate financial risk assessment, governance and disclosure with existing governance principles around climate change adaptation.AbstractThe Australian business community has long been aware of the risks and opportunities associated with greenhouse gas mitigation and climate change policies. Some businesses have taken initial steps to adapt to the expected effects of climate change; however, most enterprises are only vaguely aware of the breadth of adaptation that may be required. Associated with strategic adaptation are the principles of financial/operational risk management and governance, as well as financial impact disclosure to investors and regulators. We develop a consolidated framework in which boards and executive managers can develop a robust approach to climate change adaptation governance, climate change risk assessment and financial disclosure. The project outlines a matrix of disclosures required for investors to enable them to evaluate corporate exposure to climate change risk.The project initially comprised a set of workshops with members of the Australian business community, industry representatives, regulatory authorities and academics with expertise in business risk and disclosure effects. Each workshop focused on a separate theme that built upon the work of previous workshops. A set of follow-up discussions was held with some of the key members who contributed to the project, including the Australian Stock Exchange (ASX) Investor Group on Climate Change (IGCC), the Australian Accounting Standards Board (AASB) and the Australian Institute of Company Directors. This discussion permitted each body to comment on the final report, advise on the mechanics of the costing, reporting and disclosure approaches of climate change adaptation, and lend their expertise to the formulation of an appropriate framework.The scope of the research is constrained to firm behaviour and the requirements for investor disclosure and governance of adaptation activities. The project therefore focuses on financial analyses – including real options – undertaken by firms with regard to investing in climate change adaptation activities and projects. While the economic costs and benefits are important to organisational adaptation activities, they represent a secondary level of analysis that may need to be carried out on either an independent or cumulative scale by governments or other bodies to measure the wider effects.As the degree of sophistication in climate change adaptation activities, modelling and cost estimation increases, along with the anticipated growth in interest of both company boards and managers, it is expected that accounting standards, ASX listing rules and disclosures required under the Corporations Act would need to explicitly reflect these corporate actions. The asset allocation of banks, mutual funds, superannuation funds and other investments is also likely to adapt as companies quantify their exposure to climate change. The makeup of assets in investment portfolios may therefore markedly shift, and thus indirectly adjust to the climate change adaptation activities of companies in the broader market

Hollow Core, Whispering Gallery Resonator Sensors

A review of hollow core whispering gallery resonators (WGRs)is given. After a short introduction to the topic of whispering gallery resonators we provide a description of whispering gallery modes in hollow or liquid core WGRs. Next, whispering gallery mode (WGM) sensing mechanisms are outlined and some fabrication methods for microbubbles, microcapillaries and other tubular WGM devices are discussed. We then focus on the most common applications of hollow core WGRs, namely refractive index and temperature sensing, gas sensing, force sensing, biosensing, and lasing. The review highlights some of the key papers in this field and gives the reader a general overview of the current state-of-the-art

Leveraging Citation Networks to Visualize Scholarly Influence Over Time

Assessing the influence of a scholar's work is an important task for funding organizations, academic departments, and researchers. Common methods, such as measures of citation counts, can ignore much of the nuance and multidimensionality of scholarly influence. We present an approach for generating dynamic visualizations of scholars' careers. This approach uses an animated node-link diagram showing the citation network accumulated around the researcher over the course of the career in concert with key indicators, highlighting influence both within and across fields. We developed our design in collaboration with one funding organization---the Pew Biomedical Scholars program---but the methods are generalizable to visualizations of scholarly influence. We applied the design method to the Microsoft Academic Graph, which includes more than 120 million publications. We validate our abstractions throughout the process through collaboration with the Pew Biomedical Scholars program officers and summative evaluations with their scholars

An Intraday Empirical Analysis of Electricity Price Behaviour

This paper proposes an approach to the intraday analysis of the dynamics of electricity prices. The Growth Optimal Portfolio (GOP) is used as a reference unit in a continuous financial electricity price model. A diversified global portfolio in the form of a market capitalisation weighted index approximates the GOP. The GOP, measured in units of electricity, is normalised and then modeled as a time transformed square root process of dimension four. The dynamics of the resulting process is empirically verified. Intraday spot electricity prices from the US and Australian markets are used for this analysis. The empirical findings identify a simple but realistic model for examining the volatile behaviours of electricity prices. The proposed model reflects the historical price evolution reasonably well by using a only a few robust but readily observable parameters. The evolution of the tranformed times is modeled via a rapidly evolving market activity. A periodic, ergodic process with deterministic volatility is used to model market activity.intraday analysis; electricity price model; growth optimal portfolio; market activity

Self-tuning diagnosis of routine alarms in rotating plant items

Condition monitoring of rotating plant items in the energy generation industry is often achieved through examination of vibration signals. Engineers use this data to monitor the operation of turbine generators, gas circulators and other key plant assets. A common approach in such monitoring is to trigger an alarm when a vibration deviates from a predefined envelope of normal operation. This limit-based approach, however, generates a large volume of alarms not indicative of system damage or concern, such as operational transients that result in temporary increases in vibration. In the nuclear generation context, all alarms on rotating plant assets must be analysed and subjected to auditable review. The analysis of these alarms is often undertaken manually, on a case- by-case basis, but recent developments in monitoring research have brought forward the use of intelligent systems techniques to automate parts of this process. A knowledge- based system (KBS) has been developed to automatically analyse routine alarms, where the underlying cause can be attributed to observable operational changes. The initialisation and ongoing calibration of such systems, however, is a problem, as normal machine state is not uniform throughout asset life due to maintenance procedures and the wear of components. In addition, different machines will exhibit differing vibro- acoustic dynamics. This paper proposes a self-tuning knowledge-driven analysis system for routine alarm diagnosis across the key rotating plant items within the nuclear context common to the UK. Such a system has the ability to automatically infer the causes of routine alarms, and provide auditable reports to the engineering staff

Matched filter stochastic background characterization for hyperspectral target detection

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters, which may be derived in many different scientific fields, can be used to locate spectral targets by modeling scene background as either structured geometric) with a set of endmembers (basis vectors) or as unstructured stochastic) with a covariance matrix. In unstructured background research, various methods of calculating the background covariance matrix have been developed, each involving either the removal of target signatures from the background model or the segmenting of image data into spatial or spectral subsets. The objective of these methods is to derive a background which matches the source of mixture interference for the detection of sub pixel targets, or matches the source of false alarms in the scene for the detection of fully resolved targets. In addition, these techniques increase the multivariate normality of the data from which the background is characterized, thus increasing adherence to the normality assumption inherent in the matched filter and ultimately improving target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This thesis will establish a strong theoretical foundation, describing the necessary preprocessing of hyperspectral imagery, deriving the spectral matched filter, and capturing current methods of unstructured background characterization. The extensive experimentation will allow for a comparative evaluation of several current unstructured background characterization methods as well as some new methods which improve stochastic modeling of the background. The results will show that consistent improvements over the scene-wide statistics can be achieved through spatial or spectral subsetting, and analysis of the results provides insight into the tradespaces of matching the interference, background multivariate normality and target exclusion for these techniques

Self-tuning routine alarm analysis of vibration signals in steam turbine generators

This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques

Investigation of gas circulator response to load transients in nuclear power plant operation

Gas circulator units are a critical component of the Advanced Gas-cooled Reactor (AGR), one of the nuclear power plant (NPP) designs in current use within the UK. The condition monitoring of these assets is central to the safe and economic operation of the AGRs and is achieved through analysis of vibration data. Due to the dynamic nature of reactor operation, each plant item is subject to a variety of system transients of which engineers are required to identify and reason about with regards to asset health. The AGR design enables low power refueling (LPR) which results in a change in operational state for the gas circulators, with the vibration profile of each unit reacting accordingly. The changing conditions subject to these items during LPR and other such events may impact on the assets. From these assumptions, it is proposed that useful information on gas circulator condition can be determined from the analysis of vibration response to the LPR event. This paper presents an investigation into asset vibration during an LPR. A machine learning classification approach is used in order to define each transient instance and its behavioral features statistically. Classification and reasoning about the regular transients such as the LPR represents the primary stage in modeling higher complexity events for advanced event driven diagnostics, which may provide an enhancement to the current methodology, which uses alarm boundary limits

The Role of Oceanic Processes in the Initiation of Boreal Winter Intraseasonal Oscillations over the Indian Ocean

Observational analyses and a hierarchy of ocean general circulation model (OGCM) experiments were performed to understand the influence of oceanic processes on the warm sea surface temperature anomalies (SSTAs) prior to the convection initiation of boreal winter intraseasonal oscillations (ISOs), including the Madden-Julian Oscillation (MJO), in the equatorial Indian Ocean. We found 39 strong ISOs that passed over the Indian Ocean Warm Pool region during the November-April season of the 2001-2012 period. 17/39 ISO events initiated in the Seychelles-Chagos Thermocline Ridge (SCTR) before propagating eastward; the remaining events initiated in the southern Arabian Sea (6) or Warm Pool (16) regions. The SCTR event set was notable in that it contained more global-scale MJOs (71-76%), as defined by the RMM and OMI indices, than the WP events (25-44%). Additionally, ~24% (44%) of the SCTR (Warm Pool) events were preceded by strong oceanic process-induced SSTAs of similar magnitude to those of shortwave radiative and turbulent heat fluxes. The Arabian Sea events, however, were not associated with statistically significant SSTA signals prior to convection. Based on a mixed layer heat budget analysis, entrainment and upwelling reduction were the dominant oceanic processes contributing to the warming, in contrast with boreal summer, when horizontal advection dominated. We examined several case studies, including primary MJO events, where oceanic Rossby waves were associated with the entrainment and upwelling reduction. Two simple atmospheric boundary layer convergence models revealed that the SSTAs contributed at least half of the total convergence and suggested that the ocean dynamical effect was responsible for the majority of SSTA-forced convergence for those case studies. These results underscore the need for climate prediction models to accurately represent the ocean structure and processes to include the effects of oceanic predictors