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Today's information systems log vast amounts of data. These collections of data (implicitly) describe events (e.g. placing an order or taking a blood test) and, hence, provide information on the actual execution of business processes. The analysis of such data provides an excellent starting point for business process improvement. This is the realm of process mining, an area which has provided a repertoire of many analysis techniques. Despite the impressive capabilities of existing process mining algorithms, dealing with the abundance of data recorded by contemporary systems and devices remains a challenge. Of particular importance is the capability to guide the meaningful interpretation of 'oceans of data' by process analysts. To this end, insights from the field of visual analytics can be leveraged. This article proposes an approach where process states are reconstructed from event logs and visualised in succession, leading to an animated history of a process. This approach is customisable in how a process state, partially defined through a collection of activity instances, is visualised: one can select a map and specify a projection of events on this map based on the properties of the events. This paper describes a comprehensive implementation of the proposal. It was realised using the open-source process mining framework ProM. Moreover, this paper also reports on an evaluation of the approach conducted with Suncorp, one of Australia's largest insurance companies. [ABSTRACT FROM AUTHOR]

This paper proposes a rule-based power management algorithm to enable the dispatch of a utility-scale PV power plant consisting of a hybrid energy storage system, in accordance with the Australian national electricity rules. This algorithm is purposely designed to regulate the instantaneous power of a PV plant with the same level of dispatchability as conventional power plants. Additionally, the proposed algorithm is robust under large forecasting errors (up to 60%) of solar irradiance and it can be easily implemented in practice. Specifically, this algorithm can be executed within seconds even when considering detailed market operation and model nonlinearities. A 30 MW PV plant, including a 7.5 MW/1.25 MWh vanadium redox battery and a 1.5 MW/0.25 MWh suppercapacitors bank, has been modeled using MATLAB/Simulink and PLECS software environment. The simulation results were based on various scenarios and confirmed the effectiveness of the proposed power management algorithm. It has been shown that even in the worst-case scenario of solar forecasting error of 60%, the proposed algorithm still managed to deliver over 94% of the available energy for a given solar irradiance profile of one month. [ABSTRACT FROM AUTHOR]

Previous work has been put into the creation of a comprehensive soil classification system (CSCS) using a harmonised dataset of 23 soil properties at 18 depth intervals. The classification consists of selected soil taxa from the US Soil Taxonomy, World Reference Base for Soil Resources, the Australian Soil Classification, and the New Zealand Soil Classification. In this paper, the CSCS was tested for allocation using data for from 44 soil profiles collected in Iran. A distance-based algorithm was used to allocate and name the soil profiles according to the CSCS. It was found that 36 Iranian soil profiles are close to the existing taxa of the CSCS in the taxonomic space. Three Iranian profiles with distances between 25 and 30 taxonomic units to the closest CSCS taxa were added to the CSCS and assigned with new systematic names. Allocating the remaining 5 Iranian taxa would require regenerating the nomenclature system. The CSCS has shown advantages for allocating soil profiles from other regions of the world other than the USA, Australia and New Zealand. It also enables cross-referencing with existing soil classification systems. In the future, the CSCS can be further improved by adding taxa from other global or regional soil classification systems. [ABSTRACT FROM AUTHOR]

Regulating how digital platforms use algorithms to determine and control content displayed to their users is both a controversial topic and an important societal challenge. Existing research acknowledges institutional tussles around regulating how digital platforms use algorithms to determine and control content. However, we lack research showing how the development of regulation unfolds. We build on data from a longitudinal discourse analysis of 410 media articles and 483 policy and industry documents to study two cases of algorithmic control regulation in Australia. The first involves algorithmic control for content display, the second for content moderation. We develop a process model of institutional work towards regulation of algorithmic control. It captures the institutional tussles between governments, digital platforms and third parties as each expresses their perspective on legitimate forms of algorithmic control and shapes the process and outcome of regulation. Building on our model, we discuss the dynamics of regulation development in light of the constellation of actors and their power position in the process. We further consider the regulatory outcome and highlight future research questions that build on our findings. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the “miniphone,” an instrument to measure aeolian saltation. This instrument is a modified electret microphone that detects the impacts of individual grains. The unidirectional miniphone is inexpensive (approximately US$10), small, and poses minimal disruption to the wind field. It can be sampled at rates up to 44,100 Hz using commonly available sound card technology or it can be interfaced with a data acquisition system. Data from deployments on beaches on Marco Island, FL, USA and near Shoalhaven Heads, NSW, Australia using sample rates of 44,100 Hz and 6000 Hz, are presented. An algorithm for identifying discrete impacts of grains is described. The number of saltation impacts was not reduced when sub-sampling a record from 44,100 Hz to 6000 Hz. The most immediate use for the miniphone is for short-term deployments to detect unsteadiness in the saltation field. [Copyright &y& Elsevier]

Study Objective: Continuous Positive Airway Pressure (CPAP) is considered to be the gold standard treatment for obstructive sleep apnoea (OSA). CPAP monitoring systems allow tracking of patient CPAP adherence and treatment efficacy, by measuring residual sleep-disordered breathing, hours of CPAP use, and mask leak etc. The American Thoracic Society (ATS) published a position paper in 2013 highlighting issues of interpreting CPAP data such as a lack of consistency between CPAP manufacturers data algorithms, legal implications of CPAP data and implications for CPAP adherence. This paper extends on this work by investigating these issues in an Australasian context.Method: A review of current literature on CPAP monitoring systems, privacy and security of CPAP data for major Australasian CPAP providers, and CPAP adherence was undertaken. A legal review was also commissioned for issues related to privacy and security of CPAP data.Results: CPAP manufacturers' utilize different algorithms for respiratory event detection and clinicians need to be aware the implications for interpreting CPAP data. Australasian CPAP manufacturers have created security/privacy policies with the intent to follow relevant legislation to protect patients' CPAP data, however they do need to be constantly reviewed and updated to avoid data breaches and changes to agreements. No guarantees can be provided by the Australasian Sleep Association on CPAP manufacturers' compliance with these policies and there is the potential for some degree of liability for physicians and CPAP providers associated with CPAP data. Lastly, providing patients with feedback on their CPAP usage and OSA management appears to have positive influence CPAP adherence.Conclusions: CPAP data provides many opportunities to increase OSA patient care and to help patients self-manage this chronic condition. However, issues relating to lack of standardization of CPAP parameters, privacy, security, and legal implications will need to be managed in this changing technologic and clinical environment. [ABSTRACT FROM AUTHOR]

In Australia, the Grassland Fire Danger Index is determined by several inputs including an essential component, the degree of grassland curing, defined as the proportion of senescent material. In the state of Victoria (south-eastern Australia), techniques used for curing assessment have included the use of ground-based observations and the use of satellite imagery. Both techniques alone have inherent limitations. An improved technique has been developed for estimating the degree of curing that entails the use of satellite observations adjusted by observations from the ground. First, a satellite model was developed, named MapVictoria, based on historical satellite and ground-based observations. Second, with use of the new (MapVictoria) satellite model, an integrated model was developed, named the Victorian Improved Satellite Curing Algorithm, combining near-real-time satellite data with weekly observations of curing from the ground. This integrated model was deployed in operations supporting accurate fire danger calculations for grasslands in Victoria in 2013. [ABSTRACT FROM AUTHOR]